POTENTIAL EFFECTS OF FUTURE CLIMATE CHANGES^,
ON FORESTS AND VEGETATION, AGRICULTURE, WATER
RESOURCES, AND HUMAN HEALTH
APPENDIX B
Edited by
Dennis A. Tirpak
October 1986
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PREFACE
This report was prepared as part of the United States Environmental
Protection Agency's review of information on Stratospheric Ozone
Modification. The review was conducted in conjunction with the United Nations
Environment Programme as partial fulfillment of requirements of the Vienna
Convention on the Protection of the Ozone Layer. It is a summary,
integration, and interpretation of the current scientific understanding of the
effects of potential global climate change in the areas of forest and
vegetation, agriculture, water resources, and human health.
This appendix is a multi-authored review of the scientific literature on
the effects of global climate change. Direct effects of C02 are generally not
included because they have been recently reviewed elsewhere. The authors have
attempted to consciously write for the informed lay person and to draw
together interpretive evidence from the literature. The document was not
designed to set standards or to suggest regulatory policies or recommendations.
It was designed to provide supplementary information for use by the
Environmental Protection Agency (EPA) as it assesses the impact of chemicals
on the stratospheric ozone layer.
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ACKNOWLEDGEMENTS
The editor is indebted to the members of the EPA staff and the scientific
community who have reviewed this document and provided many suggestions to
improve its contents. In particular, Holly Stallworth, David Bennett, and
Susan Farris are to be acknowledged for their editorial assistance. Finally,
this report could not have been written without the dedicated and constructive
contributions of each of the authors.
This draft will be revised after review by EPA's Scientific Advisory
Committee and other scientists. The final version of this report will be
published as part of the agency's Stratospheric Ozone Assessment Review
Document.
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TABLE OF CONTENTS
Section
I. SUMMARY 1
II. EFFECTS ON FORESTS 'AND VEGETATION 7
A. Findings 7
B. Introduction 9
C. Climate-Induced Vegetation Changes of the
Past 18,000 Years 12
D. Potential Limits to Vegetation Growth 20
E. Predicting the Future Effects of Climate Change of
Natural Vegetation 28
F. Conclusions and Directions for Future Research 41
III. EFFECTS ON AGRICULTURE 55
A. Findings 55
B. Introduction 57
C. Potential Effects of Climate Change on Crops 62
D. Assessing Climate Impacts on Agriculture: Methods and
Recent Results 70
E. Future Directions for Research and Climate Impact Analysis .. 86
F. Summary 87
IV. EFFECTS ON WATER RESOURCES- 95
A. Findings 95
B. Introduction 96
C. General Circulation Models and Hydrology 100
D. Potential Effects of Climate Change on Water Resources 101
E. Criteria for Using Regional Models to Evaluate
Climate Changes 112
F. Future Research Directions 116
V. EFFECTS ON HUMAN HEALTH. 122
A. Findings 122
B. Introduction 124
C. Temperature Effects 125
D. Humidity and Precipitation Effects 140
E. Frontal Passage, Sunshine, and Cloud Cover Impacts 142
F. Potential Effects of Global Climate Change on
Future Human Mortality 142
G. Summary 143
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I. SUMMARY
The greenhouse effect resulting from increased levels of C02,
chloroflurocarbons, methane, N20, and other trace gases in the atmosphere has
been recognized by the scientific community for several decades as a potential
cause of future climate change. In the last few years, estimates of the rate
of change of these gases in the atmosphere has heightened concern about global
warming and associated climate and environmental change. Chapter 6--Global
Warming--presents a review of recent chemical and physical evidence supporting
the greenhouse phenomenon. From this evidence it is generally concluded that
in the relatively short period of time of the next 50-100 years the earth's
climate will undergo important changes. These include: potential increases
in temperatures, changes in precipitation, humidity, windfields, ocean
currents, and the frequency of extreme events such as hurricanes.
Furthermore, these climate parameters will induce still other shifts in sea
levels, ice margins, the hydrologic cycle, air pollution episodes and other
phenomenon.
Recently the World Meterological Organization (WHO), the United Nations
Environment Programme (UNEP) and the International Council of Scientific Union
(ICSU) summarized current scientific data on global climate change. These
findings are presented in Exhibit 1-1. Similar findings have been reported by
NAS (1979 and 1982).
EXHIBIT 1-1
Summary of Findings from the WMO/UNEP/ICSU Conference
on Global Climate Held in Villach, Austria, October 1985
• Many important economic and social decisions are being
made today on long-term projects -- major water resource
management activities such as irrigation and
hydro-power, drought relief, agricultural land use,
structural designs and coastal engineering projects, and
energy planning -- all based on the assumption that past
climatic data, without modification, are a reliable
guide to the future climate conditions. This is no
longer a valid assumption, since the increasing
concentrations of greenhouse gases are expected to cause
a significant warming of the global climate in the next
century. It is a matter of urgency to refine estimates
of future climate conditions to improve these decisions.
• The amounts of some trace gases in the troposhere,
notably carbon dioxide (C02), nitrous oxide (N20),
methane (CH4), ozone (03), and chlorofluorocarbons
(CFCs), are increasing. These gases are essentially
transparent to incoming short-wave solar radiation, but
absorb and emit long-wave radiation and are thus able to
influence the earth's climate.
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The role of greenhouse gases other than C02 in
changing the climate is already about as important as
that of C02. If present trends continue, the combined
concentrations of atmospheric C02 and other greenhouse
gases would be (radiatively) equivalent to a doubling of
C02 from pre-industrial levels, possibly as early as the
2030's.
The most advanced experiments with general circulation
models of the climatic system show increases of the
global mean equilibrium surface temperature of between
1.5°C and 4.5°C for a doubling of the atmospheric C02
concentration or equivalent. Because of the complexity
of the climatic system and the imperfections of the
models, particularly with respect to ocean-atmosphere
interactions and clouds, values outside of this range
cannot be excluded. The realization of such changes
will be slowed by the inertia of the oceans; the delay
in reaching the mean equilibrium temperatures
corresponding to doubled greenhouse gas concentrations
is expected to be a matter of decades.
While other factors such as aerosol concentrations,
changes in solar energy input, and changes in vegetation
may also influence climate, the increased amounts of
greenhouse gases are likely to be the most important
cause of climate change over the next century.
Regional scale changes in climate have not yet been
modelled with confidence. However, regional differences
from the global averages show that warming may be
greater in high latitudes during late autumn and winter
than in the tropics; annual mean runoff may increase in
high latitudes; and summer dryness may become more
frequent over the continents at middle latitude in the
Northern Hemisphere. In tropical regions, temperature
increases are expected to be'smaller than the average
global rise, but the effects on ecosystems and humans
could have far-reaching consequences. Potential
evapotranspiration probably will increase throughout the
tropics, whereas in moist, tropical regions, convective
rainfall could increase.
Based on the observed changes since the beginning of
this century, it is estimated that global warming of
1.5°C to 4.5°C would lead to a sea-level rise of 20 to
140 cm. A sea-level rise in the upper portion of this
range would have major direct effects on coastal areas
and estuaries. A significant melting of the West
Antarctic ice sheet leading to a much larger rise in sea
level, although possible at some future date, is not
expected during the next century.
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• Based on analyses of observational data, the estimated
increase in global mean temperature of between 0.3°C and
0.7°C during the last 100 years is consistent with the
projected temperature increase attributable to the
observed increase in C02 and other greenhouse gases,
although it cannot be ascribed in a scientifically
vigorous manner to these factors alone.
• Based on evidence of the effects of past climate
changes, there is little doubt that a future change in
climate of the magnitude obtained from climate models
could have a profound effect on global ecosystems,
agriculture, water resources, and sea ice.
As noted in the WMO/UNEP/ICSU report the projected changes in climate will
have important impacts on all aspects of society. Agriculture, forests, human
health, water resources, energy planning, and recreation are among the sectors
likely to be affected. Moreover, all these sectors are likely to be affected
simultaneously throughout the world, but to different extents. Today we know
a great deal from paleoclimatic records about how past shifts in climate
affected the growth and development of forest systems, the location of lakes,
and development of agriculture. But the changes that occurred to these
systems in the past took 18 to 20 thousand years to unfold as the earth warmed
approximately 4°C-5°C. During that time, forest composition shifted, some
lake systems were lost and new ones were formed. Most importantly, the
changes took place during a period when the earth's population was small and
civilizations were in formative stages.
Today modern society is much more complex, but still vulnerable to
climatic changes. Our industrial society relies on a sustained climate to
replenish natural resources as a source of raw materials, for transport of
goods, and for the food we eat. We assume that the climate that supports our
society, while variable and difficult to predict over short periods, will not
shift appreciably. Indeed, most decisions made by farmers, forest managers,
state and local water management officials, utility executives, and government
policy makers assume that climate will be constant. However, if current
predictions from global climate models prove to be correct, some increase in
global temperatures may be inevitable simply because of the presence of trace
gases which have already been emitted to the atmosphere.
Our current understanding of the effects of global climate change on the
environment is incomplete. Moreover, several features of the greenhouse
phenomenon make it unique, different from other environmental issues, and
difficult to analyze. Among these features are the following:
• The effects will not take place immediately, but over
several decades.
• The effects will be virtually irreversible over
several centuries.
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• All nations of the world will experience the effects
at the same time.
• There is no historical analog for the amount of global
warming likely to occur in the relatively short period
of the next 50 to 100 years.
Scientists have only begun to analyze the potential impacts from global
warming and changes in other climate variables. Insights are available from
historical data and from the application of predictive models. In most cases,
however, our understanding of the consequences, both beneficial and
detrimental, is in a formative stage. Historical analogs provide qualitative
information about likely effects, but they cannot predict the future because
the anticipated increase in temperature and the rate of that increase are
beyond the range of previous warm periods. Prediction models of both the
climate system and potential effects often do not include complete
parameterizations of important system variables. Thus, more advanced global
climate models capable of providing regional predictions are needed, and more
comprehensive and sophisticated analyses of environmental effects are
necessary to understand fully the implications .throughout the world.
Recognizing these limitations, the following section summarizes what is known
about climate impacts on the environment with emphasis on forests,
agriculture, water resources, and human health. Perhaps of equal importance
are potential impacts which have not been reported or analyzed, including
potential impacts on national parks, ports, electricity demand and supply,
population shifts, work-place absentee rates, hurricane frequency, air
pollution emissions, wildlife management, and our national security. These
potential impact areas and many others represent the challenges to be
investigated as the science supporting predictions of global climate change
improves.
The greenhouse phenomenon challenges the scientific community in an
unprecedented manner. In almost all areas, substantial additional research
remains to be done before the effects of climate change can be predicted with
certainty. However, the body of literature on future climate effects is
growing, and this summary attempts to provide an accurate and impartial
evaluation of what is known about these effects. The following are general
findings that are common to several areas reviewed in this report. Other more
detailed findings are to be found at the beginning of each section.
1. Paleoecological and paleoclimatological records indicate
that, for a global warming which is nearly the
equivalent of the predicted future temperature of
1.5°-4.5°C, substantial changes have occurred to forest,
agriculture, and water resource systems throughout the
globe.
2. Available paleoclimatic data do not provide an analog
for the high rate of climate change that is predicted to
occur over the next 50 to 100 years. Previous
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environmental changes, since the last ice age, took
thousands of years to evolve rather than the predicted
decadal changes.
3. Limited analyses and experiments with models of forest,
agriculture, and water resource systems suggest that
major changes are likely to occur over the next 50-100
years as a result of global warming. For example,
current analyses suggest an intensified hydrologic cycle
will accompany predicted global warming.
4. Even relatively small changes in global temperatures
could affect agriculture and forest systems in regions
that are near the maximum tolerance limits for
sensitive species.
5. Accommodating to small changes in climate may be
possible, but the costs, which are not currently
estimated, are likely to be large. For example,
agriculture may need to shift to new lands and crops, to
improve or develop new irrigation systems, to develop
improved soil management and pest control programs, and
to design heat/drought-tolerant species. Forest
managers may need to adjust forest technology, planning,
and tree breeding programs. Many large-scale water
management projects may need to be analyzed with
consideration given to potential climate shifts.
6. Much research remains to be done in order to improve
predictions of the effects of climate change on the
environment. For example, improved estimates of future
regional climates are needed in order to develop
estimates of effects on the environment; vegetation
models must be improved and developed for all kinds of
vegetation and locations around the world, and
agricultural research and analyses must be expanded and
integrated to include a wide range of crops/locations
and potential responses.
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REFERENCES CITED IN SECTION
National Academy of Sciences (1979), Carbon Dioxide and Climate: A
Scientific Assessment, National Academy Press, Washington, D.C.
National Academy of Sciences (1982), Carbon Dioxide and Climats:
Assessment, National Academy Press, Washington, D.C.
World Meteorological Organization, United Nations Environment ?
International Council of Scientific Unions (1986), Report of j'
Conference on the Assessment of the Role of Carbon Dioxide an
Greenhouse Gases in Climate Variations and Associated Impacts,
Austria, 9-16 October, 1986 WMO No. 661.
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EFFECTS ON FORESTS AND VEGETATION
Prepared by:
Jonathan T. Overpeck
A. FINDINGS
1. Climate models predict that a global warming of approximately
1.5°-4.5°C will be induced by a doubling of atmospheric C02 and
other trace gases during the next 50-100 years. The period
18,000 to 0 yr B.P. is the only general analog for a global
climate change of this magnitude. The geological record from
this glacial to interglacial interval is a key to understanding
how vegetation may change in response to large climate change.
2. The paleovegetational record shows that climate change as large
as that expected to occur in response to the equivalent of a C02
doubling is likely to induce significant changes in the
composition and patterns of the world's biomes. Changes of
2°-4°C have been significant enough to alter the composition of
biomes, to cause new biomes to appear and others to disappear.
At 18,000 B.P., eastern North American vegetation was quite
distinct from that of the present day. The cold/dry climate of
that time seems to have precluded the widespread growth of birch,
hemlock, beech, alder, hornbeam, ash, elm, and chestnut -- all of
which are fairly abundant in present-day forests. Southern pines
grew alongside oak and hickory and were limited to Florida.
3. Available paleoecological and paleoclimatological records do not
provide an analog for the high rate of climate change that will
probably occur over the next century and the unprecedented
global warming that is predicted to occur. Previous changes in
vegetation have been associated with climates that were nearly
5°-7°C cooler and took thousands of years to evolve rather than
decades--the timeframe in which changes resulting from the
greenhouse effect are predicted to occur. Insufficient temporal
resolution (e.g., via radiocarbon dates) limits our ability to
analyze the decadal-scale rates of change that occurred prior to
the present millennium.
4. Limited experiments conducted with dynamic vegetation models for
North America suggest that decreases in net biomass may occur
and that significant changes in species composition are
likely. Experiments with one model suggest that eastern North
American biomass may be reduced by 11 megagrams per hectare (10%
of live biomass) given the equivalent of a doubled C02
environment. Plant taxa will respond individualistically to
regional changes in climate variables rather than as whole
communities.
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5. Future forest management decisions in major timber-growing
regions may be affected by changes in natural growing
conditions. For example, one study suggests that loblolly pine
populations are likely to move to the north and northeast into
Pennsylvania and New Jersey while its range shrinks in the west.
The total geographic range of the species may increase, but a net
loss in productivity may result because of shifts to less
accessible and productive sites. While the extent of such
changes is unclear, adjustments will be needed in forest
technology, resource allocation, planning, tree breeding
programs, and decision-making to maintain and increase
productivity.
6. Dynamic vegetation models based on theoretical descriptions of
plant growth must be improved and/or developed for all major
kinds of vegetation. In order to make more accurate future
predictions, these models must be validated using the geologic
record and empirical ecological response surfaces. In
particular, the geologic record can be used to test the ability
of vegetation models to simulate vegetation that grew under
climate conditions unlike any of the modern day.
7. Dynamic vegetation models should eventually incorporate direct
effects of atmospheric C02 increases on plant growth and other
air pollution effects. Improved estimates of future, regional
climates are also required in order to make accurate predictions
of future vegetation changes.
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B. INTRODUCTION
1. Importance of Forests
Forests, the most abundant and important vegetation type on land, serve an
integral role in the world's ecological and climatological system, covering
35%-40% of the earth's surface, producing 65% of the annual carbon fixation
(net primary productivity), and storing over 80% of the world's organic carbon
(Solomon and West, 1985). Ecologically and economically, forests serve a
number of purposes: they help protect the quality of streams and groundwater
supplies, prevent soil erosion, provide fuelwood, timber, paper, chemicals and
other forest products, constitute a home for wildlife, and serve as a
recreation area for millions of persons.
Globally, the areal extent of forests have been shrinking since
pre-agricultural times as deforestation rates have exceeded rates of
reforestation. This reduced supply, coupled with expected increases in global
demand for wood, may result in critical wood shortages by the end of this
century. In addition, deforestation poses enormous ecological and
climatological consequences. Loss of forests usually implies some
hydrological damage as well as increased soil erosion. Climatologically,
deforestation, which causes a net release of carbon into the atmosphere, is
estimated to contribute as much as 30% to global warming. A well-known
example of this is in the Brazilian Amazon. During the period 1973-1980,
efforts to develop the Amazon basin resulted in a 44% decrease in one forest
area (Fearnside, 1986). Shown in Figure II-l is a world distribution of
natural vegetation regions.
2. Scope of Information in this Section
The overall purpose of this section is to describe how large changes in
climate predicted by global climate models discussed elsewhere in this report
may affect natural vegetation. It will focus on the changes in vegetational
composition and patterns that have accompanied long-term (decades to
millennia) changes in climate (Table II-l). It does not discuss the predicted
growth enhancement and changes in competitive balance that may result from
higher concentrations of C02 (Bazzaz, 1986; Bazzaz et al., 1985; Oechel and
Strain, 1985; Strain, 1985), nor potential reductions in aboveground biomass
and C02-induced stress (Fried et al., 1986; Surano et al., 1986), nor does it
discuss the short-term (annual to decade) changes in growth rate induced by
short-term changes in climate (Fritts, 1976; Jacoby, 1986). These
physiological and short-term responses are adequately discussed elsewhere.
Additional information about the nature and possible effects of increased
atmospheric aerosols and trace gases can be found elsewhere (MacCracken and
Luther, 1985a, 1985b; Strain and Cure, 1985; Trabalka, 1985).
The close association between human populations and the terrestrial
biosphere requires that we understand how climate change affects natural
vegetation and how vegetation change, in turn, influences global climate. The
best estimates of future trace-gas-induced climate change are derived from
computer models that are sensitive, by way of realistic feedback mechanisms,
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FIGURE 11-1
Natural Vegetation Regions of the World
Source: Adapted from: Strahler, Arthur N. 1969. Physical Geography,
3rd ed. J. Wiley & Sons, Inc., N.Y.
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TABLE 11-1
Important Aspects of CO2-Induced Vegetation Change
NATURE Of CHANGE VEGETATION RESPONSE TIME SCALE DATA SOURCE MODELS REFERENCES
([ flgyrM. 1988
Increase CO ^ •) Growth response of ^ 2 to 10 years Growth chambers Plant IJ BBMM M ^ iggj
different tpecles X Physiology "fi Q^^ tnll Str-r1i 1985
H \ d Strain, 1985
Vj b) Growth response of J 2 to 10 years Tree rings
if i^ different species \ /
^^ / /
Ornate Change ^- c) Abundance and rang* '^^S 10 lo 500 y,wl p0*en
changes In vegetation '^
i
Response
Surfaces
cimate
Response
surfaces
Oynamk;
vegetalkm
models >
|Fr«ts et al.. 1971
. FrWs, 1978
Graumach and Brubaker. 1988
vljacoby. 1988
• THIS REPORT
Source: Overpeck, 1986.
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to the directions, magnitudes, patterns, and rates of vegetation change that
may result from the climate change itself (Manabe and Wetherald, 1980;
Washington and Meehl, 1984; Dickinson, 1986; Hansen et al., 1984; Rind,
1984). Models with and without these feedbacks predict a general global
warming of approximately 1.5° to 4.5°C over the next 100 to 200 years
(National Research Council, 1983; MacCracken and Luther, 1985a), a magnitude
of change unlike any in the past 10,000 years. Only by looking at the
vegetation change of the past 18,000 years can we find a past analog for the
vegetation change that can accompany a global temperature change as large as
that anticipated by greenhouse warming. The geological record of the
vegetation change during this glacial to interglacial interval (18,000 to 0 yr
B.P.) is sufficient in some continental regions to characterize the rates,
patterns, and magnitudes of past vegetation change (Huntley, 1986; Huntley and
Birks, 1983; Webb, 1986a, 1986b). This past behavior of the vegetation may
serve as a general analog for the types of changes that may accompany
trace-gas-induced climate warming.
This section describes this vegetation change and the methods by which
fossil pollen data from lakes and mires can be used to obtain quantitative
estimates of past climates. Application of pollen-climate calibration
functions and empirical ecological response functions will be highlighted.
Also discussed are the limitations of the geological perspective and how we
might overcome these limitations. Particular emphasis is placed on the need
to quantify the rates and nature of response of vegetation to trace-gas-
induced climate changes unlike any that have been observed in records of the
past. This section does not explore potential shifts in forest pests and
pathogens which may also be affected by climate. Finally, because research is
just beginning to examine changes on methane emissions which may result from
changes in forests, this intrinsic feedback relationship to climate is also
not included.
C. CLIMATE-INDUCED VEGEGATION CHANGES OF THE PAST 18,000 YEARS
1. The Nature of Climate Change 18,000 Yr B.P. To Present
Both geological data and climate simulations suggest that the past 18,000
years, spanning a glacial to interglacial period, were characterized by global
mean temperature changes of 5°-7°C (CLIMAP, 1976; Manabe and Hahn, 1977;
Kutzbach and Guetter, 1986). This change and associated changes in the
regional patterns of climate were driven primarily by variations in seasonal
radiation that resulted from variations in the earth's orbit (Figure II-2).
Northern hemisphere solar radiation was approximately 8% greater in the summer
(and, consequently, 8% weaker in the winter) at 9000 yr B.P. than at 18,000 or
0 yr B.P. Dramatically cooler ocean surface temperatures also existed at mid-
to high latitudes prior to 10,000 yr B.P., and high-latitude ice sheets
persisted as late as 6,000 yr B.P. Numerous studies, including the
international efforts by CLIMAP (Climate: Long-range Investigation, Mapping,
and Prediction) and COHMAP (Cooperative Holocene Mapping Project) (Webb et
al., 1985), have used global networks of paleoclimatic data and climate models
to describe the regional patterns of climate change over the past 18,000
years. These studies have highlighted the need to consider how several
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FIGURE 11-2
Climatic Changes 18,000 B.P. to the Present
TIME (YEARS BEFORE PRESENT X 1000)
Schematic of major changes of external forcing (northern hemisphere solar
radiation for June to August and December to February, in percent difference
from present, right-hand scale) and internal climatic boundary conditions not
explicitly simulated by general circulation models (land ice, ocean
temperature, C02, aerosol -- arbitrary scale of plus or minus departure from
present conditions). Question marks indictate uncertainty concerning the
exact magnitudes, timing, and, where appropriate, location of the boundary
condition changes.
Source: Diagram from J.E. Kutzbach, modified from version in
Webb et al., 1985.
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climate variables (e.g., seasonal and annual means of temperature,
precipitation, etc.) interact to produce vegetation change. The large
(5°-7°C) variations of climate that characterized the last 18,000 years
contrast markedly with the small variations (less than 1°-2°C) that occurred
during the past 9000 years. A mid-Holocene (8000 to 4000 yr B.P.) period of
increased mean global temperatures has been cited as a possible analog for a
C02 trace-gas-warmed world (Butzer, 1980; Kellogg, 1978; Webb, 1985), but
Kutzbach and Guetter (1986) and Webb and Wigley (1985) have recently raised
doubt that this period was actually characterized by significantly higher mean
global temperatures. The "Medieval warm period" (ca. 800 to 1200 A.D.) and
the "Little Ice Age" (ca. 1450 to 1850 A.D.) were also characterized by small
climate variations (Williams and Wigley, 1983).
C02 trace-gas-induced temperature increases are expected to exceed any
hemispheric or global temperature change recorded in instrumental records of
the past 100 years (Clark, 1982; Webb and Wigley, 1985; Wigley et al., 1985).
An examination of how vegetation changed over the past 18,000 years can thus
provide unique clues about the kinds of vegetation change that could be
induced by future greenhouse warming. The paleocliraatic data reveal the
long-term equilibrium response of vegetation to large climate changes, but
they lack the time resolution to reveal the spatial patterns of vegetation
change on the time scales of decades and centuries over which future climate
change may occur. Models for the short-term disequilibrium response of
forests will be aided by knowledge of what the long-term response might be.
2. Continental-Scale Vegetation Change: 18,000 to 500 yr B.P.
Sufficient pollen data from radiocarbon-dated lake and mire sediments are
now available to reconstruct major patterns of the vegetation change that took
place over the past 18,000 years. The percentages of different pollen types
from large networks of sites can be mapped and contoured to document changes
of different plant populations spatially and numerically in response to past
climate change (Webb 1986a, 1986b). Drawn at a continental scale, these
isopoll maps demonstrate that climate-induced vegetation change can be
significant enough to alter the composition of biomes and can actually cause
new biomes to appear and others to disappear. To illustrate the types of
vegetation change that can be induced by mean global temperature changes in
excess of 2°-4°C, isopoll maps are described and interpreted by Webb (1986a,
1986b, 1986c). Webb's inferences regarding the impact of large-scale climate
change on natural vegetation are supported by complementary maps produced for
part of Eurasia (Huntley and Birks, 1983; Huntley, 1986; Peterson, 1983).
Maps for other continental-scale regions are not yet available but are being
developed by COHMAP. .
A comparison between the spatial distribution of modern biomes (Figure
II-3) and the mapped abundances of major pollen types (Figure II-4)
demonstrates that mapped pollen percentages are a valuable tool for tracing
vegetation change. High values of forb (Artemisia, Ambrosia, other
Compositae, Chenopodiaceae, and Amaranthaceae) and sedge (Cyperaceae) pollen
coincide with sites in the prairie; spruce (Picea), fir (Abies), and birch
(Betula) pollen occur in high abundances at sites in the boreal forest; high
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FIGURE 11-3
Generalized Vegetation Map for Eastern North America
Source: Webb, 1986b.
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FIGURE 11-4
Mapped Abundances of Major Pollen Types
Maps with isopolls (contours of equal pollen percentage) for 500 yr B.P. for
forb (1,5,10%), sedge (1,5,10%), spruce (1,5,20%), alder (1,5,20%), birch
(1,10,20%), fir (1,2,6%), oak (1,5,20%), hemlock (1,5,10%), and pine (20,40%)
pollen. Numbers in parentheses give percentages for isopolls. Stippling
highlights regions with intermediate (white with black dots) and high (black
with white circles) percentages. Forb pollen is the sum of Ambrosia,
Artemisia, other Compositae, Chenopodiaceae, and Amaranthacae pollen. The
differential scaling of pollen types depends upon the overall abundance of the
type in the pollen record. 500-year-old samples were mapped to avoid the
biases associated with recent land clearance and settlement.
Source: Webb, 1986b.
* * * DRAFT FINAL * *
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-17-
values of sedge, birch, and alder (Alnus) characterize the tundra and
forest-tundra biomes; sites in the mixed conifer-hardwood forest contain large
values of birch and hemlock (Tsuga) pollen in the east and of pine (Pinus)
pollen in the west; oak (Quercus) pollen distinguishes the deciduous forest;
and the southern pine forests are marked by abundant oak and pine pollen
(Webb, 1986a, 1986b). The maps (Figures II-3 and II-4) show that steep
gradients in pollen abundances delimit the major ecotones between biomes
(Webb, 1986a, 1986b). Maps of other pollen types show similar correspondence
with patterns in the vegetation (Bernabo and Webb, 1977).
The abundances of five major pollen types were mapped at 2000-year
intervals by Webb (1986a) for the period 18,000 to 500 yr B.P. (Figure II-5).
Most of the broad-scale vegetational change indicated by these maps was
induced by regional macroclimatic change associated with the global climate
change that accompanied the shift from glacial to interglacial conditions
(Figure II-2). At time scales of 103 to 10k years, the response time of
the vegetation is sufficiently short, when compared with the time scale of
climatic forcing, for the vegetation to be in dynamic equilibrium with climate
(Gaudreau, 1986; Webb, 1986c). This condition implies that, at the time scale
of glacial to interglacial change, vegetation can track changing environmental
conditions and produce assemblages of coexisting plant taxa unlike any modern
communities. The historical perspective afforded by fossil pollen data thus
yields insight into the potential equilibrium response of vegetation to
C02-induced climate change.
The maps of changing pollen abundances from 18,000 to 500 yr B.P. (Figure
II-5) clearly show that the equilibrium response of natural vegetation to
large global climatic change can be dramatic in terms of magnitude and pattern
of change (Webb, 1986a, 1986b). At 18,000 yr B.P., eastern North America was
characterized by vegetation patterns and composition quite distinct from those
of the present day. South of the Laurentide ice sheet, an open spruce
woodland biome quite different from the modern boreal forest was marked by
high abundances of forb, sedge, and spruce pollen. This vegetational
community apparently contained little birch or alder. Northern pines grew far
south of their modern ranges, and southern pines were limited to growing with
oak and hickory in Florida (Watts, 1983). Just as the pollen evidence
suggests that a modern-like boreal forest did not exist at 18,000 yr B.P.,
there is little evidence of a modern-like deciduous forest before 12,000 yr
B.P. (Davis, 1983; Webb, 1986a). The cold/dry climate of 18,000 yr B.P. in
eastern North America seems to have also precluded the widespread growth of
beech (Fagus), hemlock, birch, alder, hornbeam (Ostrya/Carpinus), ash
(Fraxinus), elm (Ulmus), and chestnut (Castanea), all fairly abundant in
present-day forests (Kutzbach and Wright, 1986; Webb, 1986a, 1986b).
After 18,000 yr B.P., the individualistic response of different plant taxa
to climate change produced significant changes in the vegetation of eastern
North America. The late-Pleistocene boreal woodland shifted northward and
into a more east-west orientation. By 10,000 yr B.P., this biome became a
closed forest with the movement of pine populations into the region south of
the receding ice sheet, and by 7000 yr B.P., this biome all but disappeared,
only to be replaced after 6000 yr B.P. by a boreal forest like that of the
* * * DRAFT FINAL * * *
-------
-18-
FIGURE 11-5
Changing Pollen Abundances from 18,000 B.P. to 500 B.P.
CYPEMACCAE
oufftcus
Maps with isopolls for 18,000 yr B.P. to 500 yr B.P. (in 103 yr B.P.) for
prairie forbs, sedge, spruce, pine, and oak pollen. See Figure II-3 for
labeling of isopolls and stippled regions for each pollen type.
Source: Webb, 1986b.
* * * DRAFT FINAL * * *
-------
-19-
FIGURE 11-5 (Continued)
\ PRAIRIE
"a. FORBS
0
-------
-20-
present day. Another currently extinct vegetational assemblage produced high
abundances of spruce and ash pollen and became widespread by 13,000 yr B.P.
It disappeared by 9000 yr B.P. (Overpeck et al., 1985; Wright et al., 1963;
Webb et al., 1983). By 10,000 yr B.P., the modern prairie became established
in the northern plains, and the southern conifer (pine) forests first became
widespread after 6000 yr B.P. This dramatic reshuffling of plant taxa across
eastern North America is detailed by Webb (1986a, 1986b, 1986c). Similar data
from regions outside North America support the possibility that the magnitude
of the equilibrium response of vegetation to a mean global warming of
1.5°-4.5°C could be large, with the likelihood of significant compositional
changes in existing biomes and considerable movement of ecotones (Huntley and
Birks, 1983; Huntley, 1986; Peterson, 1983; Peteet, 1986).
3. Regional-Scale Vegetation Change: 6000 TO 500 YR B.P.
Before 6000 yr B.P., the climates of the northern hemisphere were
influenced to a large degree by the presence of a glacial ice sheet in North
America. After this time, the Laurentide ice sheet disappeared and global
climate change became largely a function of changing solar radiation at the
top of the atmosphere. Variations in the earth's orbit since 6000 yr B.P.
have decreased July insolation in the northern Hemisphere by 5% and have
increased January insolation by 5% (see Figure II-2; Berger, 1981; Kutzbach
and Guetter, 1984; Webb, 1986c). In the upper midwestern United States, this
decrease in summer insolation resulted in a southward readvance of the
spruce-rich boreal forest, whereas an associated increase in regional
precipitation in the Midwest caused forests to advance westward into areas
formerly occupied by prairie (Figure II-6; Bartlein et al., 1984). The same
orbital forcing induced beech populations to move northward in southern Quebec
over distances of 200 km in response to milder winter temperatures at the same
time that populations of spruce were able to move 200 km southward in response
to cooler summer temperatures (Figure II-7; Webb 1986c). These examples of
vegetation change reemphasize the tendency of plant taxa to respond
individualistically and to different climate variables. Thus, it seems safe
to anticipate that this type of response will typify vegetation change in a
future trace-gas-warmed world. It remains to be seen if we can predict
quantitatively the rates at which vegetation change will respond to rapid
trace-gas-induced climate change or how vegetation will change'given future
climates unlike any yet recorded.
D. POTENTIAL LIMITS TO VEGETATION GROWTH
1. Methodologies for Identifying Vegetation Limits
Fossil pollen data can provide key insights into the manner in which
vegetation responds to climate change, and pollen data can also be used to
reconstruct past climates. Many paleoclimatic interpretations based on fossil
pollen data implicitly or explicitly search for samples of modern pollen that
resemble the fossil sample of interest and then reason by analogy that the
vegetation and climate associated with the fossil sample were probably similar
to those associated with the modern samples. The quantitative nature of
pollen data lends itself to going one step further, however, by formulating
* * * DRAFT FINAL * * *
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-21-
FIGURE 11-6
Forest Movement in Upper Midwestern U.S.,
10,000 B.P. to 500 B.P.
WCC4IS
ttOCHHONES « 10
Isochrone maps for 10,000 to 500 yr B.P. (in 103 yr B.P.) from
radiocarbon-dated pollen diagrams in the Midwest. The 5% spruce isochrones
depict the movement of the approximate southern edge of the boreal forest,
whereas the 20% prairie forbs isochrones map the approximate location of the
prairie (to the west) -- forest boundary through time.
Source: Webb et al., 1983.
* * * DRAFT FINAL * * *
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-22-
FIGURE 11-7
Forest Movement in Southern Quebec: 10,000 B.P.
t>IC£A 3% tochronts
FAG US 3% Itochronts
Isochrone map in 103 yr B.P. fox the southward extension of the 5% isopoll
for spruce pollen and for the northward extension of the 3% isopoll for beech
pollen.
Source: from Webb, 1986c.
* * * DRAFT FINAL * * *
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-23-
the functional relationships between the abundances of different pollen types
and various climatic variables. Quantitative estimates of past climates are
thus possible, as are numerical pollen-climate relationships that can be
forward modeled to obtain quantitative estimates of the magnitudes and
directions of future vegetation change. The methods described here for
pollen-climate calibration are similar to those used in reconstructions of
paleoclimate that are based on tree rings (Fritts, 1976; Fritts et al., 1971;
Graumlich and Brubaker, 1986).
A robust and straightforward model for calibrating pollen data in climatic
terras is the multiple regression model:
c. P B1 . e1
n 1 = n m + n
where n is the number of samples,
m is the number of pollen types,
Q
n 1 is an n vector of values for a particular climate variable,
P
n m is an n X m matrix of pollen percentages,
gi
m is an m vector of regression coefficients, and
e1
n is an n vector of errors.
This approach takes advantage of the strong quantitative relationships that
exist between the distribution of plant taxa, as reflected by their pollen,
and different climate variables. For example, suitably transformed values of
oak pollen are linearly related to mean July temperatures in the Midwest
United States, whereas the abundances of prairie forb pollen are related to
the amount of mean annual precipitation (Figure II-8; Bartlein et al., 1984).
If used with proper statistical considerations (Bartlein and Webb, 1986;
Bartlein et al., 1984), this linear regression model allows reliable estimates
of past climate values to be generated using regression coefficients (mBl)
obtained by regressing modern samples of pollen on the climate values of
interest. Statistical calibration procedures of this type are an extension of
the transfer function model developed for reconstructing past sea surface
conditions (Imbrie and Kipp, 1971; Imbrie and Webb, 1981; Webb and Clark,
1977) and have been used successfully to derive estimates of terrestrial
paleoclimate for portions of North America (Bartlein et al., 1984; Bartlein
and Webb, 1985) and Eurasia (Huntley, 1986; Peterson, 1983).
Another powerful approach to quantifying the relationship between pollen
(vegetation) and climate is the construction of empirical ecological response
surfaces. Rather than relating the abundances of a particular group of plant
taxa to single climatic variables via a linear model, empirical response
surfaces are "non-linear functions describing the way in which the abundances
of taxa depend on the joint effects of two or more environmental variables"
(Bartlein et al., 1986, p.35). Again, the extensive pollen and climate
database available for eastern North America has provided the first
* * * DRAFT FINAL * * *
-------
-24-
FIGURE 11-8
Pollen Versus Precipitation
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Scatter diagrams for (A) July mean temperature vs the percentages of oak
pollen, (B) July mean temperature vs the percentages of oak pollen raised to
the 0.25 power, (C) annual precipitation vs the percentage of prairie-forb
pollen, and (D) annual precipitation vs the percentages of prairie-forb poller
raised to the 0.50 power.
Source: Bartlein and Webb et al., 1985.
* * * DRAFT FINAL * * *
-------
-25-
large-scale test and application for empirical response surfaces (Bartlein et
al., 1986). Empirical response surfaces describe the individualistic relation
between plant taxa and climate by recasting the geographic distribution of
taxon abundance into a surface in climate space. For example, the percentages
of spruce and pine pollen exhibit spatial correlation with both mean July
temperature and annual precipitation in eastern North America (Figure II-9).
When the same percentages are plotted directly against the two climate
variables and contoured using appropriate methods, the resulting "surfaces"
yield a quantitative description of the optima and range limits of a given
taxon, as well as the relative sensitivity (gradient) of the taxon to
variation in the two climate variables (Figure 11-10; Bartlein et al., 1986).
2. Examples of How Temperature and Precipitation Influence
Vegetation Growth
Results for the major pollen types of eastern North America support the
hypothesis that plant taxa exhibit individualistic responses to different
climate variables. The response surface for spruce (Figure II-10a) shows that
spruce is most abundant when precipitation exceeds 800 mm/yr and mean July
temperatures are less than 15°C. The conditions for the growth of spruce
become less favorable with increasing temperatures and decreasing
precipitation. The response surface also indicates under what conditions
spruce populations are not found (e.g., the range boundaries for spruce) and
the portions of climate space, marked by closely spaced contours, in which the
abundances of spruce are most sensitive to climatic change. The response
surface for pine is more complex than that for spruce, primarily due to the
existence of two distinct sets of pine species, one in northeast North America
and one in the southeast (Figure II-9a). The southeast pine characterized by
optimum abundance at high mean July temperatures (25°-30°C) and high
precipitation values (1250-1750 mm/yr), are most sensitive to changes in July
temperature, whereas in slightly drier climates (750-1000 mra/yr), pine
populations are more sensitive to precipitation. In the driest (250-750
mm/yr) parts of the range for pine, summer temperature again seems to have the
most influence on the abundance of pine.
Plants typical of the prairie are highly responsive to precipitation
values, although their joint dependence on temperature suggests that annual
evapotranspiration may be the best predictor variable for prairie forbs
(Figure II-10c; Bartlein et al., 1986). The response surfaces for oak (Figure
II-10d) and other major pollen types all reinforce the hypothesis that
individual plant taxa have differing sensitivities to different climate
variables (Bartlein et al., 1986). Ecological response surface methodology
has already begun to show great promise in reconstructing past climates and in
helping to validate computer simulations of climate (Bartlein et al., 1986;
Graumlich and Brubaker, 1986). Efforts to assess the potential sensitivities
of vegetation in response to future climate change should also benefit
significantly from the use of response surfaces.
* * * DRAFT FINAL * * *
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-26-
FIGURE 11-9
Response Surfaces for Pine and Spruce
B
Maps of the distribution of pollen types (spruce and pine) and climate
variables (mean July temperature and annual precipitation).
Source: Bartlein et al., 1986.
* * * DRAFT FINAL * * *
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-27-
FIGURE 11-10
Ecological Response Surfaces
l"
i
ANNUAL MCCJHTATION
ANNUAL
S"
I.
I
%OUCftCUS
ate fe Hi
Empirical ecological response surfaces for spruce, pinus, prairie-forb, and
oak pollen showing how the abundances of these taxa vary as a function of mean
July temperature and annual precipitation.
Source: Bartlein et al., 1986.
* DRAFT FINAL * * *
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-28-
E. PREDICTING THE FUTURE EFFECTS OF CLIMATE CHANGE
ON NATURAL VEGETATION
1. Introduction
Analysis of the geological record adds a necessary time dimension to the
understanding of vegetation and climate dynamics. Only by examining the
geologic record can empirical information be obtained about the magnitude,
pattern, and rate of vegetation change that can result from climate change as
large (1.5° to 4.5°C) as that anticipated for a trace-gas-warmed world. The
period 18,000 yr B.P. to present is most appropriate for study because of the
magnitude of change that has occurred over this interval. More recent
intervals of time were characterized by smaller magnitudes of climate change,
but still form a useful source of information about the nature of vegetation
change that can be induced by climate change.
The geologic record of vegetation change is not, however, a perfect analog
for use in predicting the changes in natural vegetation that will result from
greenhouse warming (Schneider, 1984; Kutzbach and Guetter, 1986). The future
configurations of climatic boundary conditions will be distinct from the
boundary conditions that gave rise to past patterns of vegetation change. The
future dominance of climatic forcing by greenhouse gases and the unique
time-dependent response of the climate system to this forcing should lead to
patterns and rates of climate and vegetation change that are without
precedence in the available geologic record (Webb and Wigley, 1985). This
section discusses how we might build on our geological perspective to gain a
clearer picture of what lies ahead in terms of climate-induced vegetation
change.
2. Uncertainties in Predicting the Regional Macroclimates
of the Future
Current knowledge is sufficient to make some generalizations about how
vegetation will be affected by trace-gas-induced climate change. Empirical
climate-vegetation classification, empirical ecological response surfaces, and
dynamic process models of vegetation also make it possible to begin estimating
the sensitivity of different vegetation types to various elements of climatic
forcing. Before discussing these aspects of predicted vegetation change,
however, it is worth considering what we know and need to know about
anticipated climate change. The study of past and present climate-vegetation
interaction suggests that plant taxa respond individualistically to prevailing
regional macroclimatic changes in multiple climate variables. In order to map
the actual patterns, magnitudes, and rates of future vegetation change,
climatic variables must first be accurately described. Vegetation estimates
require reliable climate estimates for the mean and seasonal values for
temperature, precipitation, evapotranspiration, and other variables.
A number of steps are required to predict how regional macroclimate will
change in the next 50-100 years (Figure 11-11). The rates of C02 and trace
gas emissions must first be estimated. Deforestation, another cause of
increasing C02 concentrations, at least until 1950-1960 (Houghton et al.,
* * * DRAFT FINAL * * *
-------
-29-
FIGURE 11-11
7?
f MMNB CO. |
PREDICTING FUTURE VEGETATION CHANGE
Flow diagram outlining the steps that go into predicting future vegetation
change. Considerable uncertainty is associated with each step. See text for
details.
* * * DRAFT FINAL * *
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-30-
1985; Woodwell et al., 1983), must also be determined (Emanuel et al., 1985a;
Olson, 1982). Finally, the nature of global carbon cycling and the
interactions between the atmosphere and other carbon reservoirs (e.g., the
oceans and terrestrial biosphere) must be modeled more successfully.
These projections of global trace gases must be translated by mathematical
models of the climate system. Temperature estimates from these models seem to
be least uncertain. A mean global warming of 1.5°-4.5°C is the most cited
estimate for atmospheric C02 doubling (National Research Council, 1983;
MacCracken and Luther, 1985a; Hansen et al., 1981), but recent experiments
with independent atmospheric general circulation models (GCMs) suggest that
global greenhouse warming will be in the 3.5°-4.2°C range (Schlesinger and
Mitchell, 1985). These recent experiments also "seem to agree on many features
of the global-scale change that will be induced by C02 doubling, including a
predicted mean global precipitation increase of 7.1%-10.0% (Schlesinger and
Mitchell, 1985). In contrast, the most recent experiments (Manabe and
Wetherald, 1986) suggest an even greater mid-latitude warming coupled with
extended drought. The issue is obviously in doubt.
There is little doubt that the climate changes predicted by recent GCM
experiments will cause significant changes in the natural vegetation, but
accurate predictions of future vegetation change require better estimates of
anticipated regional macroclimate. Unfortunately, more uncertainty must be
attached to present GCM predictions of regional climate change than to
predictions of global change. Considerable new research must be done before
the regional climate patterns can be worked out with confidence (Hoffert and
Flannery, 1985; MacCracken and Luther, 1985b; Schlesinger and Mitchell,
1985). In particular, reliable time-dependent estimates of regional climate
change as begun by Hansen et al. (1986) are required before future rates of
vegetation change can be estimated. Accurate estimates of future vegetation
change are needed, in turn, to improve the ability of climate models to
incorporate the effects of vegetation-climate feedbacks (Dickinson, 1986;
Rind, 1984). Lastly, the ability of GCMs to simulate modern and past climates
can serve as a test of the GCM's ability to simulate future climates. Climate
estimates derived from fossil pollen data are necessary for this model
validation (Webb et al., 1985).
3. Assessing the Sensitivity of Vegetation to Future Climate Change
Analysis of the geologic record suggests that climate change as large as
that predicted for a trace-gas-warmed world is sufficient to cause potentially
large changes in the composition and patterns of natural vegetation. A major
goal in the effort to predict specific regional vegetation change, however,
must be to reduce the uncertainties (Figure 11-11) associated with predictions
of future regional climate change. Methods to transform regional climate
predictions into regional vegetation change also need to be perfected and
tested. This section will discuss the utility and limitations of three
principal methods for converting climate change into vegetation change (Figure
11-11): 1) vegetation-climate classification, 2) empirical ecological
response surfaces, and 3) dynamic vegetation models. Although accurate
predictions of vegetation change await better estimates of future climate
* * * DRAFT FINAL * * *
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-31-
change, the application of these three methods can already begin to delimit
what portions of the natural vegetation may be most sensitive to future
climate change.
a. Vegetation-Climate Classification
The most widely used method of equating climate change with changes in the
vegetation is vegetation-climate classification (Emanuel et al., 1985a; Hansen.
et al., 1984; Solomon, 1986a, b). These classification schemes, based on
Koppen (1936) and Holdridge (1947), use mapped coincidences between major
present-day vegetation regions and climatic zones to define a small number
(less than 40) of unique global "climate-vegetation zones" (see Figure
11-12). Two climate variables, usually related to temperature and
precipitation, are typically employed to define the boundaries of the
climate-vegetation zones. Predicted variations in these two climate variables
can then be inserted into a vegetation-climate classification to get predicted
vegetation. This approach assumes that the vegetation-climate relationship
captured by the classification is invariant in time and that all possible
vegetation-climate combinations are represented on the present surface of the
globe.
Solomon (1986b) outlined some of the advantages and disadvantages of the
Koppen and Holdridge schemes. Vegetation-climate classification has been
widely used primarily because it is simple to implement, global in scale
(i.e., compatible with GCMs), and able to reproduce the modern patterns in the
vegetation. Emanuel et al. (1985b, 1985c) used simulated climate and the
Holdridge classification to predict that climate change induced by C02
doubling could cause over 30% of the earth's vegetation to shift from one
vegetation type to another. The largest vegetational changes were predicted
to occur at high latitudes where the simulated temperature increase is largest
and the temperature intervals defining vegetation zones are smallest (Emanuel
et al., 1985b). In the absence of projected precipitation changes, tropical
vegetation would change least because of small C02-induced climate changes,
whereas the distribution of subtropical vegetation was expected to change by
moderate amounts. In a recent paper, Mather (1986) compared the results from
several GCMs to develop global water budgets and estimates of vegetation
change (see Figure 11-13).
There are several substantial limitations to the use of vegetation-climate
classification. Most important, the Koppen and Holdridge schemes ignore much
of what is known about vegetation dynamics. The geologic record and empirical
ecological response surfaces show that: 1) plant taxa within the vegetation
respond individualistically to climate change; 2) plant taxa tend to respond
to different mean and seasonal climate variables; and 3) the modern vegetation
regions are ephemeral with regard to large climate change. Only the avail-
ability of meteorological records limit the types of climate variables that can
be used in vegetation-climate classification schemes, but mapped coincidence
of climate and vegetation cannot, by itself, imply cause and effect (Solomon,
1986b). All plant taxa do not respond to the same two or three climate
variables. It may therefore be difficult to incorporate realistic
vegetation-climate relationships into vegetation-climate classification.
* * * DRAFT FINAL * * *
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-32-
FIGURE 11-12
Holdridge Life Zones Mapped
r-:':':':'j TROPICAL DRY FOREST
r-r—n SUBTROPICAL MOIST
E'--' "3 FOREST
f—-jWARM TEMPERATE
I.. • < FOREST
•SSSIORY WARM TEMPERATE
•S*" FOREST
I COOL TEMPERATE
•FOREST
JWET BOREAL FOREST
I MOIST BOREAL FOREST
t-~ i-l TUNDRA
Source: Emanual, W.R., H.H. Shugart and M.P. Stevenson, 1985: Climatic
change and the broad-scale distribution of terrestrial ecosystem
complexes. Climatic Change, 7, 29-43.
* * * DRAFT FINAL * * *
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-33-
FIGURE 11-13
Predicted Changes in Natural Vegetation in Selected Regions
as a Result of Increased Carbon Dioxide -- GISS Model
300-
1800
.100
MoMura IndBi. Im
1 - North/central Siberia
2 - South/central Canada
3 - Opper midwest (OSA)
4 - Pacific northwest
5 - Ukraine (OSSR)
6 - Southeast China
7 - Texas and N. Mexico
8 - West/central Africa
9 - Northeast Brazil
10 - Southeast Australia
11 - Southern Africa
12 - Argentina (Paapas)
* DRAFT FINAL * * *
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-34-
The individualistic behavior of plants and the ephemeral nature of biomes
are ignored by vegetation-climate classification. The vegetation regions of
the modern world cannot always serve as analogs for past or future assemblages
of plant taxa. Vegetation-climate classifications based on the modern world
also lose some credibility when they are used to predict the vegetation change
that will occur under climatic conditions unlike any of the present day. This
problem of extrapolation, and an equally serious inability to predict the
time-dependent rates of vegetation change, are two key issues that must be
solved before any method yields accurate estimates of future climate-induced
vegetation change.
b. Empirical Ecological Response Surfaces
Another approach to estimating the equilibrium response of vegetation to
climate change centers on the application of empirical ecological response
surfaces. Unlike vegetation-climate classification, response surface
methodology explicitly accounts for the individualistic response of plant taxa
to different climate variables (Bartlein et al., 1986). Given suitable plant
or pollen and climate data, response surfaces can be used to explore for and
to represent the correct combination of climate variables (e.g., mean and
seasonal values of temperature, precipitation, evapotranspiration, and other
variables) that jointly control the numerical and spatial abundances of a
particular taxon. Predicted values can then be used with individual response
surfaces to estimate the magnitude and direction of anticipated changes in
taxon abundance. For example, the abundances of spruce in North America
(Figure II-9a) are likely to be quite sensitive to changes in mean July
temperatures where annual precipitation values exceed 800 mm/yr. In addition,
a small trace-gas-induced increase in annual precipitation over the midwest
U.S. could result in forests expanding into regions now occupied by prairie
(Figure II-10c). Steep slopes on response surfaces indicate areas in "climate
space" where vegetation is most sensitive to environmental change. Response
surface methodology could also be used to estimate future changes in
short-term tree growth that might result from traced-gas-induced climate
change (Graumlich and Brubaker, 1986).
Empirical ecological response surfaces are designed to model the
individualistic response of plant taxa to climate. Unfortunately, it is not
practical to construct response surfaces for all of the world's taxa. Even in
North America, where much work has been done (Bartlein et al., 1986), it is
difficult to estimate response surfaces for more than 10-20 of the dominant
plant taxa. For this reason, maps of predicted large-scale vegetation change
may be difficult to produce using response surfaces alone. Response surfaces
can provide clues about the likely direction of anticipated vegetation change
under no-analog climate, but this type of extrapolation must be done with
caution. As with vegetation-climate classification, response surfaces are not
suited to estimate rates of vegetation change. Empirical ecological response
surfaces are still the best way to characterize the individualistic behavior
of plant taxa, and as such they deserve further attention and application.
* * DRAFT FINAL * * *
-------
-35-
c. Dynamic Vegetation Models
Neither vegetation-climate classification or empirical ecological response
surfaces are ideal for describing climate-vegetation interactions under
equilibrium conditions. Vegetation-climate classification is flawed
theoretically, and it is impractical to construct response surfaces for all
the world's plant taxa. In addition, both of these methods fail to deal
adequately with extrapolation beyond modern climate space and with
time-dependent rates of change. The use of dynamic vegetation models (Figure
11-10) may be one way to overcome these inadequacies and eventually to obtain
accurate estimates of the magnitude, pattern, and rates of anticipated
regional climate change.
Time-dependent estimates of vegetation composition and structure can now
be simulated by models that represent forest dynamics (birth, death, growth,
disturbance, competition, etc.) by a series of stochastic and deterministic
equations. These models have been most widely developed to simulate the
dynamics of very small (less than 1 ha) forest patches (Davis and Botkin,
1985; Shugart, 1984; Solomon, 1986a). More recently, new efforts have been
initiated to model larger scales of vegetation and to incorporate better
representations of landscape heterogeneity into the model (Prentice et al.,
1986).
Plant growth in dynamic vegetation models is usually determined by climate
response functions for each taxon being simulated. These response functions
are keyed heavily to empirical growth data, but the current trend is to move
away from an empirically based representation of climate-vegetation
interaction to a more process-oriented theoretical basis. Models built on
this theoretical basis will be better suited to simulate vegetation change
under climate conditions unlike any of the present day. Just as climate
models (e.g., GCMs) can be validated, dynamic vegetation models can be tested
against the record of past vegetation change (Solomon and Shugart, 1984;
Solomon and Webb, 1985). In particular, it is important that dynamic
vegetation models be able to simulate the individualistic behavior of taxa
through time. Models that can reproduce the magnitudes, patterns, and rates
of change observed in the geological record can then be used to infer
vegetation change in a trace-gas-warmed world. Dynamic vegetation models
should also be able to reproduce the vegetation patterns that are on the
modern landscape.
Several experiments with GCM output coupled with dynamic vegetation models
have suggested that future climate-induced vegetation change will be
significant in North America (Solomon, 1986a, 1986b; Solomon et al., 1984).
As shown in Figure 11-14, Solomon's simulation of 21 sites in eastern North
America resulted in a loss of live, aboveground stand biomass of 11 megagrams
per hectare (10% of live biomass) given a doubled C02 scenario. Shown in
Figure 11-15 are results of simulated dynamics at three sites: a boreal
forest in west central Ontario, a coniferous-deciduous transition forest in
northwest Michigan, and a deciduous forest in east central Tennessee (Solomon
and West, 1986). At the boreal site, when C02 doubling is reached, biomass
declines for 50-75 years as warming kills off large boreal forest species.
* * * DRAFT FINAL * * *
-------
-36:
FIGURE 11-14
Carbon Storage Dynamics
2«C02-I*C02 BIOMASS
NETCHANGE = -l1mt/ha
BIOMASS
NETCHANGE = -17mt/ha
200 YEARS AFTER
4xC02-1*C02
NETCHANGE = -l3mt/ha
Carbon storage dynamics (in metric tons per hectare [mt/ha]) simulated at 21
sites in eastern North America. Maps show differences between carbon storage
simulated with contemporary climate and that simulated with 2 x C02 climate
(A), 4 x C02 climate (B), and 200 years after 4 x C02 climate stabilizes (C).
Source: Solomon, in press.
* * * DRAFT FINAL * * *
-------
-37-
FIGURE 11-15
Simulated Dynamics at Sites in Boreal, Transition, and Deciduous Forests
0200400600000 0 200 400 WO »CO 0 200 400 SOO WO WOO
CD sow**.
F?3 MOTTHC^N MA*O*OOO FORESTS
EZ3 MCSC 9COOUOU5 I
MIXED
* * * DRAFT FINAL * * *
-------
-38-
This loss of biomass is eventually recovered as new northern hardwoods begin
to grow in the plot. Similarly, in the transition forest of Michigan, biomass
losses occur with C02 doubling and are recovered later with the growth of
northern hardwoods, then decline again, then rise again with the onset of
other species more adapted to a warmer climate (quadrupled C02). Unlike the
first two simulations, the deciduous forests of Tennessee show a permanent
loss of dense forest.
For a number of reasons, these results must be considered preliminary.
Solomon (1986b) argues that response to climatic variance incorporated into
dynamic stand models, must also be incorporated into the GCMS. Empirical
response functions based on pollen, tree-ring, and forest inventory data may
also lead to better climate response functions and more realistic
simulations. In several experiments (those of Solomon, et al., 1982; Davis
and Botkin, 1985; Solomon et al., 1984) the rates at which vegetation can
respond to climate change have begun to be explored. These experiments
suggest that a vegetational response to trace-gas-induced climate change could
be observed in less than 100 years. Improved dynamic vegetation models
coupled with time-dependent climate simulations of future trace-gas-induced
change should provide a clearer picture of how vegetation will change in the
future.
d. Potential Climate Change and Forest Management
In June 1984, forest scientists, climatologists, forest industry execu-
tives, and others, met in Boulder, Colorado, at a conference sponsored by the
National Forest Products Association, the Society of American Foresters, The
Conservation Foundation, and the U.S. Environmental Protection Agency. The
objective was to exchange information on C02 accumulation, possible changes in
climate, potential effects on trees and forest ecosystems, and to explore
forest management options and identify research needs. In a forthcoming book
edited by Shands (1986), a number of authors review the effects on the forest
product industry. They point out that while climate change could disrupt
ecosystems and alter growing conditions, whether forest productivity decreases
or increases will depend to a substantial degree on how forest managers respond
to environmental changes. They suggest that if atmospheric concentrations of
C02 and the greenhouse effect change natural growing conditions, "adjustments
will be needed in forest technology, resource allocation, planning, and
decision-making to maintain or increase productivity."
Just how climate change might affect important commercial species in two
of the nation's major timber-growing regions is explored in papers by Leverenz
and Lev (1986) and by Miller (1986) in Shand's book.
Leverenz and Lev project distribution under a scenario of doubled
atmospheric C02 for six western U.S. tree species -- ponderosa pine,
Douglas-fir, western hemlock, western larch, lodgepole pine, and Englemann
spruce. Their analysis is derived from information on physiological changes
in plants, induced by accumulating C02, combined with rising temperature and
water stress. These projections are sensitive to assumed responses to the
balance between precipitation and evaporation on a site and the assumed
chilling requirements of different species.
* * * DRAFT FINAL * * *
-------
-39-
In brief, Leverenz and Lev make the following predictions: Ponderosa pine
will increase in area and importance in California and in the Oregon Cascade
Mountains, but decrease in range on the east slope of the Rocky Mountains from
Canada to Mexico, Douglas-fir either maintains or expands its importance
throughout most of it commercially significant range, while decreasing in
importance on southern and coastal sites and on the east slope of the Rocky
Mountains. Western hemlock's range decreases in northern Idaho and east of
the Willamette Valley in Oregon but increases in importance along Oregon's
coast. Western larch generally increases in importance but decreases in area
in Washington and Oregon. Lodgepole pine is not affected significantly, while
Engleman spruce declines in acreage over its entire present range.
Miller found that loblolly pine would be affected significantly in terms
of both range and productivity (Figure 11-16). Its range is likely to move to
the north and northeast, into eastern Pennsylvania and New Jersey, and shrink
in the west, withdrawing into central Arkansas. The total range of the
species is likely to increase from the present 344,000 square miles (139,271
hectares) to 385,000 square miles (155,870 hectares). However, they project a
net loss of commercial loblolly lands because a large proportion of the new
range will be in highland sites less accessible and less productive than
present loblolly sites in the Coastal Plain.
In this same volume, papers by Woodman and by Rose et al. look at the
situation from an industry perspective. Under the doubled C02 climate
scenario, Rose and colleagues conclude that industry strategies are unlikely
to change significantly. In the Lake states, shortfalls of aspen can be
covered by other presently underutilized hardwoods and aspen stand residues.
In the Southeast, however, climate change could have a major impact on supply,
since shortfalls are currently predicted, irrespective of climate change, in
both softwoods and hardwoods by the year 2030. However, companies now are
reevaluating their land ownerships and management programs, and climate change
scenarios are not likely to affect current strategies to deal with the
expected supply shortfall.
As for the management of Douglas-fir and western hemlock on the coast
ranges and west slope of the Cascade Mountains in Washington and Oregon,
Woodman too foresees no major changes in management as a result of climate
changes attributable to a doubling of atmospheric C02. He notes that the
transition from harvesting of old growth natural forests to the tending of
man-established stands will be complete in the not-too-distant future. The
trend now underway toward prescribing management practices on the basis of
site productivity--with intensified management of stands on highly productive
sites--should allow forest managers to adjust to gradual change in a timely
fashion. Nonetheless, Woodman expects management problems in forest
regeneration at an affordable cost in areas where water stress occurs.
In the final paper of Shand's book, Lee and Kramer argue that by
anticipating changes that might occur because of rising C02, "the forestry
community can avoid surprises, identify alternatives and seek to temper
decisions with technological information." Lee and Kramer argue that with or
without C02-induced climate change, research on problems of C02 accumulation
* * * DRAFT FINAL * * *
-------
-40-
FIGURE 11-16
The Projected Changes in Loblolly Range Asjumia, Doubled Atmospheric CO2
LEGEND
LOBLOLLY PINE RANGE
- 1 CO • In)
- 2 (No Chang*)
- 3 (Lo»«)
Source: Shands, 1986.
* * * DRAFT FINAL * * *
-------
-41-
and climate change will help answer a number of contemporary questions about
how trees respond to environmental stress and how adverse effects can be
mitigated. They argue that increasing atmospheric C02 is affecting tree
growth now; that the forest products industries should not consider it a
distant problem to be addressed sometime in the future; that the direct
effects of C02 buildup should be separated, for research purposes, from
indirect, climate change effects; and that plant physiologists and forest
scientists should concentrate on understanding how current weather extremes
affect the physiological processes that control tree growth.
F. CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH
Table II-2 lists the primary.knowns, uncertainties, and unknowns that must
be resolved so that responses of forest ecosystems to climate changes induced
by C02 (Solomon and West, 1985) can be evaluated. Despite these large
uncertainties, a number of inferences can be drawn from the research to date.
Although no single global climate change of comparable magnitude and rate is
apparent in the geological record, the period 18,000 yr B.P. to the present
can serve as a basis for understanding how vegetation can respond to a climate
change as large as that expected in the next 100-150 years. Well-dated
paleovegetation data spanning 18,000 to 0 yr B.P. from a global network of
sites are presently being assembled by COHMAP. Available pollen data from
North America and Eurasia show that the overall vegetation change associated
with this glacial to interglacial interval was significant and that plant taxa
responded individualistically to changes in multiple climate variables. The
patterns and composition of biomes are not necessarily constant in time.
These observations must be incorporated into any model that is expected to
yield accurate estimates of future vegetation change.
The geological record holds additional promise as a baseline for
understanding how vegetation responds to climate change. Temporal resolution
is not presently sufficient to study the past response of vegetation to rapid
(within 10-100 years) climatic change. Improved temporal resolution,
independent paleoclimatic and paleovegetation data, and improved methods for
the quantitative analysis of time series could make it possible to study the
time-dependent magnitude, pattern, and rate of vegetation change following
rapid climate change (Overpeck, 1986). These empirical studies of vegetation
change would provide an additional basis for the validation of models that are
used to predict vegetation change. The geological record thus offers a wide
range of experience on which to build and test models of future vegetation
change.
The time-dependent nature of the anticipated climate-induced vegetation
change must be studied using dynamic vegetation models. Current versions of
these models need improvement and models remain to be developed for most of
the world's vegetation. At the present time, little can be said about the
potential climate response of low-latitude forests. We know that vegetation
change occurred over the glacial to interglacial interval (Peteet, 1986), but
too few basic ecological data exist for these low-latitude vegetation
regions. Empirical ecological response surfaces remain to be exploited to
their full potential. Dynamic vegetation models should be tested against
response surfaces as well as against records of past vegetation change.
* * * DRAFT FINAL * * *
-------
TABLE 11-2
PRIMARY KNOWS, UNCERTAINTIES AND UNKNOWNS REQUIRING RESOLUTION IN ORDER TO APPRAISE
FOREST ECOSYSTEM REPSONSES TO CLIMATE CHANGE INDUCED BY 002
ISSUES
KNOWNS
UNCERTAINTIES
UNKNOWNS
A. ISSUES DETAILED IN OTHER SOA
REPORTS
1. Carbon Cycle Issues
a. Atmospheric 002
Accumulation
*
*
*
b. Atmospheric 002
o Depletion
> 2. Climate Effects Issues
*
*
*
a. Geography of Climate
Change
h. Variance from Mean Climate
Changes
c. Temporal Chronology
d. Seasonal Features of
Climate Changes
Atmospheric 002 concentration is
rising
Seasonal 002 amplitude is
increasing
Land use (forest clearance) con-
tributes to atmospheric 002
Oceans are the primary global
atmosplteric 002
Terrestrial biota is a secondary
global atmospheric 002 s^J
Greater warming at poles than at
equator
Changes will occur in geography
of precipitation
Variance may or may not change
Climate will change over time
Greater wanning in winter than in
summer
Quicker warming in simmer than in
winter
preindustrial OOj value
Source of increasing amplitude
Rates at which land-use generates
atmospheric C02 increases
Rates at which 002 cr°sses the
ocean-atmospheric interface by
geographic region
Current areas and amounts of carbon
stored in terrestrial biosphere
Intensity of latitudinal tempera-
ture differences
Geography of precipitation
Presence or absence of variance
changes from current geography
Rate and nature (step function.
climate continuou slinear, etc)
of climate change
Amount of seasonal warming
Time difference between summer
and winter rates of wanning
Time when doubled and maximum 002
occur. Maximum future 002
concentration.
Significance of amplitude changes to
carbon cycle, vegetation, climate effects.
Future contribution to 002 ^y Land use.
Internal circulation of oceans by geo-
graphy that controls up and down welling
of 002 rich and CC^-poor water masses.
Future carbon source, sink, and storage
properties of terrestrail biosphere
Local and regional temperature shifts
Local and regional precipitation shifts
Amount of variance change, if any,
locally, regionally, globally
Temporal chronology of climate change
Local and regional change in seasonal
warming
Local and regional time difference
between summer and winter warming
I
4^
NO
I
-------
TAB1E 11-2
(continued)
PRIMARY KNOWS, UNCERTAINTIES AND UNKNOWNS REQUIRING RESOLUTION IN ORDER TO APPRAISE
FOREST ECOSYSTEM REPSONSES TO CLIMATE CHANGE INDUCED BY 002
ISSUES
KNOWNS
UNCERTAINTIES
UNKNOWNS
3. Vegetation Effects Issues
a. Applicability of Single
Factor Greenhouse Experi-
ments to Multifactor
Field Situations
b. Applicability of Short
Duration (hour, day)
Fumigations to Long-
Term Tree Growth
c. Applicability of Experi-
ments on Herbaceous
Annuals, Leaves, Branches,
and Tree Seedlings to
Mature Trees
d. Applicability of Experi-
ments in 3a, 3b. 3c, to
Regional or Global Vege-
tation
B. ISSUES DETAILED IN THIS REPORT
I. Seedling Survival
Published reports on single-factor
experiments show increase photo-
synthesis, decreased water use,
increased leaf area, increased dry
weight, increased photosynthesis
in shade
Published reports on longer term
(months, seasons) experiments show
short-duration effects decline
and/or disappear
Effects in 3A are found in these
plants or plant parts
The few trees and fewer species
examined to date have declined in
growth, while 002 nas increased
in concentration
Temperature, precipitation vari-
ance control initial seedling sur-
vival
002 enchancement, below current
forest floor concentrations,
increases vigor, productions, bio-
mass, leaf area, and drought-
tolerance differentially By species
Pathogens and predators destroy
seeds and seedlings
No publislied reports on multiple
factor experiments that realisti-
cally simulate field conditions;
whether mature tress and ecosys-
tems will response similarly
Will field-grown trees cease
responding to 002 increases
Will mature trees respond as do
plants or plant parts tested; if
so what is the nature of forcing
function (linear, logaritmic,
etc.)
Has the current 30% 002
increase affected tree growth: im-
portance of climate change ana
other pollutants during same
period in depressing tree growth.
Will temperature and precipita-
tion variance change in the future
Will 002 enhancement above
current forest floor concentrations
have the same effects
Will pathogen and predator effects
be different in the future
Amount, if any of response in multi-
factor situationsj'quantitative
importance of offsetting (canceling)
factors to tree growth
How much, if at all, will field-grown
trees acclimatize to enhanced 002
Amount, if any, by tree species or by
other functional unit (shade tolerance,
growth rate, etc.) that trees will
respond as do other plants or plant parts
Will future changes in climate and atmos-
pheric pollutants negatively affect tree
growth: if so, how much compared with
possible 002 effects
By how much, if any, will variance in
temperature and precipitation change
Will 002 increase have long-term
effects on massive mortality and, if so,
which species will be favored
Which species will be favored and by how
much if predator and pathogen effects
occur
-------
TABLE H-2
(continued)
PRIMARY KNOWS, UNCERTAINTIES AND UNKNOWNS REQUIRING RESOLUTION IN ORDER TO APPRAISE
FOREST ECOSYSTEM REPSONSES TO CLIMATE CHANGE INDUCED BY C02
ISSUES
KNQWNS
UNCERTAINTIES
UNKNOWNS
2. Sapling and Tree Growth
3. Death and Reproduction
a. Senescence
b. Catastrophis Age-
Independent Mortality
Climate variance controls geo-
graphic ranges of mature species
Climate variance controls annual
growth rates
Nutrients for growth depend on
decomposition rates
Root respiration depends on soil
temperature, water, and oxygen
availability
Senescent trees die from stresses
that younger trees can survive
Wildfire frequency and intensity
depends on climate, fuel, and
time since last fire
Windstorm and flood damage depends
on frequency of rare climate ex-
tremes, topography, land-use
Pathogen, insect epidemics occur
rarely
Specific climate variables and
their variance values controlling
the cold, warm, and dry parts of
each species' geographic range
Specific climate variables and
their variance values
Will decomposer populations change
nutrients available because of
warming of soils
Will root respiration increase
of climate change
If climate change continuously,
will trees become prematurely
senescent because of the addi-
tional stress of this continuous
change
Geographic change in wildfire
frequency and intensity due to
future climate change
Geographic change in land use due
to climate change
Climate dependency of pathogen,
insect epidemics
Future local and regional climate vari-
ances; future effects of 002 on
responses of species to climate variance;
future geographic range of species
Riture local and regional climate
variances; resultant shifts in competi-
tive advantages among species
FViture amount of nutrients available
because of changes in decomposer
activities due to warming
If root respiration changes, will it
counteract increased production from
CC>2 if such increases occur
Will CCH induce vigor and prolong tree
life and, if so, which species will be
most affected and which will be least
affected
local, regional changes in the tempera-
ture and precipitation extremes that
control fire frequency and intensity
local, regional distribution of future
rare precipitation, wind extremes
Will climate control pathogen and insect
epidemics and, if so, how
-------
TABLE H-2
(continued)
PRIMARY KNOWS, UNCERTAINTIES AND UNKNOWNS REQUIRING RESOLUTION IN ORDER TO APPRAISE
FOREST ECOSYSTEM REPSONSES TO CLIMATE CHANGE EDUCED BY C02
ISSUES
KNGWNS
UNCERTAINTIES
UNKNOWNS
c. Chronic Age-Independent
Mortality
>
tr<
d. Plant Succession
e. Plant Migration
Occurs when production is too
slow to provide enough fixed car-
bon for metabolism, slowing growth,
reducing resistance to stress
Chronic insect predation on trees
is a normal stress that vigorous
trees survive
Air pollutant damage (gaseous,
acidic precipitation) is an
increasing stress from which
vigorous trees previously survived
Rates, outcomes of succession under
current constraints by climate
variance, seed sources, disturb-
ance frequencies, OC>2 concen-
trations
Trees migrated 100-400m/y in the
past 10,000-15,000 years
Quantification of "too slow" by
species of tree
Will chronic insect attacks change
because of changing climate
variance, changing forest composi-
tion
Future air pollutant levels; loss
of tree vigor by species to future
pollutant levels
Will rates slow because of stress
of possible continuous climate
change; presence, intensity of
forest-dieback and recovery
F\iture migration rates on a land-
use dissected landscape, survival
of seedlings of species ill-
adapted to new conditions
Will (X>2 experiments with seedlings
apply to mature trees, reducing chronic
age-independent mortality; future local
and regional distribution, frequency of
climate variance that slows growth
Climate variance that controls insect
population sizes, species composition;
forest composition that controls same
Future combined effect of unknown air
pollutant levels, climate extremes,
insect, pathogen predation, and 002
effects on forest growth
Will CO? enhance rates by relieving
stress;future local and regional
distribution of climate constraints
(variance); future disturbance by wind,
fire, flood
Availability of seed sources with time;
availability of empty niches; tree
planting
-------
-46-
The primary focus of this section has been to evaluate what is known about
the potential long-term response of plant abundances and range limits to
large-scale climate change. Needed steps to reduce uncertainty and obtain
accurate predictions of future vegetation change are also outlined. No
accurate predictions can be made at this time, although there seems to be a
weak consensus that vegetation change will be greater at high latitudes than
at lower latitudes. Coastal vegetation is likely to be affected by an
anticipated sea-level rise of approximately 1 meter (National Research
Council, 1984). Uncertainty about the response of other vegetation to
C02-induced change is compounded by the even greater uncertainty that
surrounds the possible effects of climate forcing on short-term plant growth
and the possible direct effects of C02 on the growth and the competitive
balance of vegetation (Table II-l). For example, the actual shapes of
ecological response functions could change with increasing concentrations of
atmospheric trace gases. Considerable research remains to be done on all
aspects of the vegetation-climate issue before accurate predictions can be
made.
* * * DRAFT FINAL * *
-------
-47-
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* * * DRAFT FINAL * * *
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II. EFFECTS ON AGRICULTURE
Prepared by:
Cynthia Rosenzweig
A. Findings
1. Climate has had a significant impact on farm productivity and
geographic distribution of crops. Examples include the 1983
drought, which contributed to a near 30 percent reduction in corn
yields in the U.S., the persistent Great Plains drought between
1932 and 1937, which contributed to nearly 200,000 farm
bankruptcies, and the climate shift of the Little Ice Age
(1500-1800), which led to the abandonment of agricultural
settlements in Scotland and Norway.
2. World agriculture is likely to undergo significant shifts if
trace-gas-induced climate warming in the range of 1.5° to 4.5°C
occurs over the next 50-100 years. Climate effects on
agriculture will extend from local to regional and international
levels. However, modern agriculture is very dynamic and is
constantly responding to changes in production, marketing, and
government programs.
3. The main effects likely to occur at the field level will be
physical impacts of changes in thermal regimes, water conditions,
and pest infestations. High temperatures have caused direct
damage to crops such as wheat and corn; moisture stress, often
associated with elevated temperatures, is harmful to corn,
soybean, and wheat during flowering, and grain fill and increased
pest infestation are often associated with higher, more favorable
temperatures.
4. Even relatively small increases in the mean temperature can
increase the probability of harmful effects in some regions.
Analysis of historical data has shown that an increase of 1.7°C
(3°F) in mean temperature for a city like Des Moines increases by
about a factor of three the likelihood of a five-consecutive-day
maximum temperature of at least 35°C (95°F). In regions where
crops are grown close to their maximum tolerance limits, changes
in extreme temperature events may have significant harmful effects
on crop growth and yield.
5. Limited experiments using climate scenarios and agricultural
productivity models have demonstrated the sensitivity of
agricultural systems to climate change. Future farm yields are
likely to be affected by climate because of changes in the length
of growing season, heating units, extreme winter temperatures,
precipitation, and evaporative demand. In addition, experiments
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show that total productivity is a function of the availability of
land, the location of soil and water resources, the ability of
farmers to shift to different crops, and agricultural trade
regulations.
6. The transition costs associated with adjusting to global climate
change are not easily calculated, but are likely to be
substantial. Accommodating to climate change may require
shifting to new lands and crops, creating support services and
industries, improving and relocating irrigation systems,
developing new soil management and pest control programs, and
breeding and introducing new heat- or drought-tolerant species.
The consequences of these decisions on the total quantity,
quality, and cost of food are difficult to predict.
7. Current projections of the effects of climate change on
agriculture are limited because of uncertainties in global
climate models' prediction of local temperature and precipitation
patterns and because of the need for improved research studies
using integrated modeling approaches.
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B. INTRODUCTION
1. Importance of Agriculture
Recent famine in Africa and decline in U.S. trade shares for many
commodities demonstrate the importance of agriculture both worldwide and in
the national economy. Agricultural products must provide sustenance for the
world's growing population, now estimated at 5 billion. Agriculture is also a
critical American industry, contributing export products for the nation's
trade balance.
Both production and consumption of grain have grown steadily worldwide
since 1960, although fluctuations in production have occurred due to
variations in climate and socioeconomic factors (Figure Ill-la). Despite
adequate world food supplies, production and distribution in Africa have
deteriorated in the 1980's because of drought, low grain stocks, economic
difficulties and lack of support systems. These problems have taken place
within the context of normal climatic variability.
Total U.S. agricultural exports were valued at $38 billion for the
1983-1984 fiscal year, down from a peak of $42.6 billion in 1980-81 (Figure
III-2). Grains and oilseeds account for approximately two-thirds of these
agricultural exports in recent years. Although technological advances such as
irrigation systems have reduced the dependence of crop yields on the natural
environment, there is still a major response of yield to climate.
Parry (1986) provides several examples of the impact of previous climate
fluctuations on agriculture. For example, midsummer 1983 saw a pronounced
drought in the U.S. Corn Belt and the Southeastern United States. U.S. corn
yields fell by about a third, from over 7,000 kg/ha to about 5,000 kg/ha. In
the same year, however, the Payment in Kind Program (PIK) had encouraged large
numbers of farmers not to plant corn (as part of an effort by the USDA to
reduce the national grain surplus). As a result, the U.S. area planted to
corn also fell by about a third, from 30 to 21 million ha. The combined
effect of decreased yield and reduced area was a fall in U.S. corn production
by almost one-half (from 210 million metric tons (mmt) in 1982 to 110 mmt in
1983). The effects were felt not only nationally, but also globally because
U.S. corn accounts for about one-eighth of the world's total market cereal
production. In 1983, world total grain production fell by 3%, harvested area
by 1.4%, and yield by 2%, these reductions being almost fully accounted for by
the U.S. figures alone.
An example of a prolonged climate event with cumulative impacts occurred
in the Great Plains between 1932 and 1937. A persistent drought helped bring
about 200,000 farm bankruptcies or involuntary transfers and the migration of
more than 300,000 people from the region. If the same weather were to occur
today, assuming 1975 technology and a 1976 crop area, the impact would still
be considerable. For a recurrence of the worst weather-year, 1936, simulated
wheat production for the region shows a drop of 25%, reducing national wheat
production by about 15% (assuming average production elsewhere in the U.S.).
The cumulative effect of a prolonged drought in the Great Plains could be
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FIGURE Ill-l
World Grain Production and Consumption
1700-
1500-
1300-
1 100-
900-
Milhon Metric Tons
and % of Utilization
^ *•»
20-
15-
10-
5 -
0-
^
g
*\ e
^
i
«
CZ3 Cirryovcr ttock>. % of utilizttion
World gram consumption
WO_Md jjrain production
64-65
68-69
72-73
76-77
80-81
Source: U.S. Department of Agriculture. Foreign Agricultural Service. Foreign Agriculture Circular.
•Grains. FG 7-85 (Washington, O.C.). May 1985 and various otner issues.
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S Billion
-59-
FIGURE 111-2
U.S. Agricultural Exports
E2 Others
O Fruits, nuts and vegetables
S3 Canon
Q Livestock and products
Z3 Oilseeds and products
•I Grains and preparations
70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
Source: U.S. Department at Agriculture. Economic Research Service. Foreign Agricultural Trade o< tne
United States (Washington. D.C.), January-FeDruary 1985 and various other issues. Livestock excluding
poultry and dairy products.
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-60-
substantial: yearly yields simulated for the weather over the period
1932-1940 average about 9% to 14% below normal and amount to a cumulative loss
over the decade equal to about a full year's production in the Great Plains
(Warrick, 1984).
A final example of the importance of climate to agriculture can be found
in the experience of western Europe during the Little Ice Age (1500-1800 AD).
During that time, mean annual temperatures were about 1.5°C below the present
norm and also of the preceding warm epoch of 800-1200 AD. At the nadir of
this cold episode the accumulated temperature of the growing season in
southern Scotland was 10% less than at present (Parry, 1978). There was a
permanent snowfield in the Scottish highlands, where icebergs were reported to
have drifted ashore carrying polar bears from the Arctic (Lamb, 1982).
Widespread desertion occurred at the more marginal upland settlements in
Scotland, and in Norway about half of the farms were abandoned in the late
Middle Ages (Parry, 1978). In Iceland the cultivation of cereal grains died
out before 1600AD. The period was clearly one of considerable difficulty for
those farming near the northern limit of agriculture. Even under these
unusual climatic conditions, however, there existed a range of adjustments
which farmers could employ to respond and survive. Abandoning settlements and
moving southward was one form of response.
Future changes in climate will affect agriculture because light, water,
and temperature regimes are important forces that govern plant growth and
reproduction. As climatic factors change, a cascade of effects will occur
throughout the agricultural system, general economy, and society (Figure
III-3). Physical effects of climate change on the field level, such as
changes in thermal regimes, water conditions, levels of pest infestations,
and, most important, yields, may lead to changes in farm management decisions
based on altered risk assessments. Consequences of the combined management
decisions of many farmers could result in changes in farming systems, land
use, and food quality. Ultimately, impacts of climate change on agriculture
may reverberate throughout the global food economy and thus through society as
a whole.
2. Description of this Study
This section will address three important .questions concerning climate
change and agriculture:
1. What are the major physical effects of climate change on
agricultural crops and what are the potential
consequences of these effects?
2. What methods are available and most appropriate for
assessing the impact of climate on agriculture?
3. What are the future directions for research on impacts
of climate change on agriculture?
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-61-
FIGURE 111-3
The Hypothetical Pathways of Drought Impacts on Society
Suit AGRICULTURAL
ECONOMIC
SOCIAL
/
I
GLOBAL /
/
GLOBAL
FOOD
SUPPLY
'7'
/
STRESS
/ . i ECONOMIES / cond.ct /
/_ _£ £ A f_ '
/ / / ^ FOOD PRICES. 'u STRESS /
NATinNAi / US FOOD I S DISTRIBUTION S t~ * " • /
NATIONAL , SUPPLY ~^C « /' U'd' b|l»nc»-/
/ / J^NATIQNAL s f inflHion '
/ A I ECONOMY ' I
! I ' I STRESS /
RFr.lOMAi / REGIONAL -J - ECONOMIC I * <» . '
W W C / PRODUCTION / "~ PRODUCTIVITY '^ mormon. '
I A ; A ! — /
t.™/. — .JL — ../ — T' /
/ YIELDS b-^. ^MF .V $TRESS '
/ j . . INCOME f— • • . /
LOCAL
*
/ / rrunjgtiTMnt t
L-/-V-
RESOURCE
BASE
ptretotiofl
- -- , r
T'
Prictl
Gov't policy
,
Ftfm
wiln«f»biltty
DROUGHT
Source: Warrick and Bowden, 1981.
* * DRAFT FINAL * *
-------
-62-
Current concern about, climate change is due to the observed increase of
atmospheric trace gases, particularly of C02 (Keeling et al., 1982). Since
C02 and the other trace gases absorb infrared radiation from the earth's
surface, global warming and other climatic effects have been predicted with
general circulation models (GCMs) (see Figure III-4) (Manabe and Stouffer,
1980; Hansen et al., 1981; Washington and Meehl, 1984). At the same time, C02
is a necessary component of photosynthesis and affects stomatal opening with
possible lowering of the rate of transpiration; therefore it has direct
physiological effects on agricultural crops (Lemon, 1983). Research on the
direct physiological effects of increased C02 on vegetation is described in
Strain and Cure (1985).
This review concentrates on the climatic rather than on the physiological
effects of increased trace gases. However, it is difficult to separate these
effects, since in some cases they are interactive; for example, transpiration
rate is related to both temperature and C02 level. Some studies have examined
both climatic and physiological effects simultaneously; more studies of this
kind should help to clarify their interactions. This review is also limited
to the effects of climate change as projected by GCM studies of increased
atmospheric trace gases. All GCM studies predict warmer surface temperatures;
some predict lower growing season soil moisture. However, climate change
effects on the hydrologic cycle are not well understood due to the lack of
ocean dynamics and physically based ground hydrology in GCMs. Regional
precipitation estimates must be viewed with caution. Finally, research
reported in this paper concentrates on the effects of climate change on crops
rather than on animal or forage production. For a review of animal and forage
production see Decker et al. (1986).
C. POTENTIAL EFFECTS OF CLIMATE CHANGE ON CROPS
The major effects of climate change on physical aspects of crop growth are
changes in (1) thermal regimes, (2) water conditions, and (3) pest
infestations. These are all important determinants of yield. Other aspects
of agriculture that may be affected by climate change are soil fertility,
erosion, and plant breeding. Potential consequences of these physical effects
are shifts in cropping patterns with ensuing changes in land use,
environmental impacts and food quality. Eventually, economic effects may
extend from the farm all the way to global food trade.
1. Thermal Regimes
One consequence of trace-gas-induced climate change important to crops is
a lengthening of the growing season, usually defined as the period from the
last frost in the spring to the first frost in the fall. Longer growing
seasons may allow earlier planting dates, earlier maturity and harvesting, and
multiple cropping (growing more than one crop in a season). Another
consequence of warmer climate for crops is an accelerated accumulation of
thermal units. By summing temperatures over time and relating the summations
to crop phenology, agronomists have developed indices that quantify the
thermal requirements for various crops (Newman, 1980). These thermal units,
often called growing degree days (GDDs), are based on daily or monthly minimum
* * * DRAFT FINAL * * *
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-63-
FIGURE 111-4
Geographical Distribution of Annual Mean Surface Air Warming
for Doubled Atmospheric C02 in the GISS GCM
A Strfoc* Air Ttmptrolurt (*C)
-(•0
•120
-to o *o
Longitude (degrees)
120
Source: Hansen et al., 1984.
* * * DRAFT FINAL * * *
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-64-
and maximum temperatures. Newman (1980), Biasing and Solomon (1984), and
Rosenzweig (1985) all used GDDs in their analyses of potential
climatically-induced changes in crop locations and found that increased
accumulation of GDDs would contribute to geographical shifts in U.S. corn and
Canadian wheat belts (Figure III-5).
High temperatures can cause damage to crops. Ramirez and Bauer (1973)
found that the number of days that temperatures exceeded 32°C has a
significant effect on final yield of wheat. Daily maximum temperatures above
32.2°C have significant negative effects on corn yields (Thompson, 1975) and
the number of days with maximum temperatures above 37.8°C has been related to
decreased corn yields (McQuigg, 1981). Developmental stage is related to
vulnerability of crops to damage caused by high temperatures. For example,
extreme high temperatures are particularly damaging to wheat at the time of
grain filling: high temperatures with daily maxima of 30°C or greater can
terminate grain filling (Evans et al., 1975). Results from GCMs can provide
results that estimate the changes in number of days above, for example, 32.2°C
for a given location (Hansen, 1986) and hence can be used to develop insights
about the impacts on wheat-growing regions and crop yields. These can then be
related to decreases in crop yields. Heat-tolerant varieties of major crops
may become an important plant-breeding objective.
2. Water Conditions
Summer dryness and decreased soil moisture in the interiors of continents
have been predicted from some climate models (Manabe et al., 1981; Manabe and
Wetherald, 1986); however, all climate models do not show these effects (Rind,
1986). But even if summer dryness in continental areas is not ubiquitous,
shifts in rainfall patterns are predicted by GCMs for some areas. Changes in
water regimes for agriculture in any given region may occur because of changes
in total and/or seasonal precipitation, seasonal distribution of
precipitation, and interannual variability. In general, global precipitation,
especially at high latitudes, is expected to increase due to the capacity of
warmer air to hold more water vapor. However, the same increase in capacity
will be responsible for an increase in evaporation and this suggests that
drier conditions (in terms of soil moisture) should accompany climatic warming
even if rainfall patterns are unaffected.
•
If dry periods occur during critical development stages of a crop, yields
may decrease (Figure III-6). For example, moisture stress during flowering
(pollination) and grain fill are harmful to corn, soybeans, wheat and sorghum
(Decker et al., 1986). Farmers may respond to shifts in rainfall regimes by
changing irrigation schedules or by utilizing soil moisture conservation
techniques such as minimum- or no-tillage, and mulches. More conservative
irrigation techniques, such as trickle or drip systems, would probably
increase in drier summer conditions (see Withers and Vipond, 1980). Breeding
crop varieties with greater drought-tolerance may become important.
Farmers may respond to summer dry periods simply by increasing demand for
irrigation water. Studies such as one by Callaway and Decker (reported in
Decker et al., 1986) have simulated irrigation demand using prescriptive
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-65-
FIGURE 111-5
Heat and Moisture in the North American Corn Belt
PRECIPITATION
cm in.
125 50
tOO 40
75 30
50 20
25 10
a o
DAIRY
SPRING.
WHEAT *
IRRIGATED
CORN
WINTER
WHEAT
L L I I t t I t t 1
0 1000 2000 3000 4000 5000 6OOO *F
i i I i I i I
K500 2000
GROW1NG-OEGREE-DAYS
3000
Heat and moisture characteristics of the North American corn belt. Numbers 1-5
correspond to geographical locations in or near the corn belt. Dots outside
the lines correspond to central Wisconsin (dairy), central North Dakota
(spring wheat), and west-central Kansas (winter wheat).
Source: Biasing and Solomon, 1984.
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-66-
FIGURE 111-6
Effect of Water Stress at Various Growth Stages
on Grain Yield Reduction in Wheat
100
Emergence
10 20 30 40 50 60 70 BO 90 100
Days After Emergence
Source: Ramirez et al., 1975.
* * * DRAFT FINAL * * *
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-67-
climate scenarios. Where water supplies are already diminishing, extra demand
could require some land to be removed from irrigation. For example, in the
region supplied with water by the Ogallala aquifer, it may become uneconomical
to irrigate cropland as energy costs and well depths increase, even without
climatic warming (High Plains Associates, 1982). Studies that examine
potential irrigation needs for climate change should take availability of
water resources into account (Glantz and Ausubel, 1984).
If rainfall patterns change and irrigated agriculture moves to new
locations in response, previous investments may be lost and new investments
needed for the new locations. The economic costs of shifting the location of
irrigated agriculture could be considerable. Construction of irrigation
systems consisting of reservoirs, ditches, pumps, sprinklers, and wells
requires extensive capital investment (estimated by Postel (1986) to be
between $1500 and $5000 per hectare).
3. Pest Infestations -- Insects, Diseases, and Weeds
In a warmer climate, as predicted for increased atmospheric trace gases,
insect pests may increase due to higher, more favorable temperatures (see
Hatfield and Thomason, 1982) . Longer growing seasons may increase the number
of insect reproductive cycles during crop growth, and warmer winter
temperatures may allow over-wintering of larvae, now limited by cold, thereby
increasing infestations. Ranges of insects could extend to higher latitudes,
particularly since warming is predicted to be greater in those regions.
Altered atmospheric circulation patterns may affect the spread of
wind-dispersed disease pathogens. Finally, shifts in timing of development
stages may change plant/pest interactions since crops, insects, diseases, and
weeds may respond differently and at different rates to changing climate
(Decker et al., 1986).
Since agronomists, entomologists, plant pathologists, and weed specialists
are used to responding to changing pest situations, their adaptations to
changing climate will most probably be extensions of their current techniques:
climate and pest monitoring, crop breeding for new resistances, changes in
planting dates, continued study of pest behavior, and improving integrated
pest management techniques (Hatfield and Thomason, 1982).
•
4. Soil Fertility and Erosion
It is difficult to estimate the effects of trace-gas-induced climate
change on soil fertility and erosion. Warmer temperatures could alter the
microbial decomposition of organic matter thereby adversely affecting soil
fertility. Increases in root biomass due to increased photosynthetic activity
might offset potential decreases in fertility. Cycling of nutrients may be
accelerated and nitrogen fixation increased. Biomass accumulation and
decomposition of organic matter might be decreased if summers are drier (as
suggested by Manabe and Wetherald, 1986). Fertilizer applications would be
likely to change in response to these effects.
* * * DRAFT FINAL * * *
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-68-
Summer dryness might also increase both water and wind erosion due to
reduced vegetation cover and drier soil particles. This could generate a
"Dust Bowl" effect. In areas where soil moisture is reduced, minimum- or
no-tillage management systems can reduce these types of erosion.
5. Consequences to Agricultural Systems
a. Cropping Patterns
Crop regions will begin to shift as soon as comparative economic advantage
shifts. Eventually, as climate zones change, environmental requirements for
different crops may not be fulfilled and substitutions will become necessary.
Support services and industries will need to be created, as well as new
markets in new locations. The costs of changing farming systems can thus be
considerable, especially when land-use planning and environmental impacts are
taken into account. Another effect of climate change may be the loss of
agricultural land due to sea level rise, if expansion of the oceans and
melting of high-latitude ice occurs as predicted.
If milder winters and longer growing seasons occur, agriculture may extend
north into sensitive areas such as northern woodland and forest ecosystems.
This would introduce agricultural chemicals into the land and water systems
and expose soils to erosion. Many of the soils in these areas are relatively
infertile and require careful management if they are to be used for
agriculture (Furuseth and Pierce, 1982). Substantial investment would be
needed .to prepare these soils for agriculture and erosivity may be higher.
Another aspect of shifts in cropping patterns may be changes in food and
grain quality. For example, several studies have shown that winter wheat may
replace spring wheat in northern areas (Figure III-7) (Stewart, 1986 and
Rosenzweig, 1985). However, the protein content of winter wheat is not quite
as high as that of spring wheat (Martin et al., 1976). In other areas, wheat
types grown for animal consumption may replace wheat grown for human
consumption.
b. Plant Breeding
Plant breeding for the predicted climate change will most likely be
focused on development of heat- and drought-tolerant varieties of major
crops. In general, the agricultural research and plant-breeding communities
are confident about their ability to respond to the climate changes predicted
by GCMs. If the change is gradual, this attitude may be justified, since new
varieties of major crops (corn, soybean, wheat, and sorghum) can be developed
in 10 years or less (Decker et al., 1986). On the other hand, recent research
reports show projected warming may begin to be observable in the next decade
and may be quite rapid (Hansen, 1986). Since the projected temperature trends
are all in one direction, new varieties may always be about a decade behind in
levels of heat tolerance. Genetic resources may be strained if mean surface
temperature changes reach or exceed the high values (i.e., 4.5°C) predicted by
GCMs for doubled C02.
* * * DRAFT FINAL * * *
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-69-
FIGURE 111-7
Wheat Regions of North America
(A)
WHEAT REGIONS OF NORTH AMCRICA
ACTUAL WHEAT REGIONS
ITf*
MODEL GRID
WHEAT REGIONS
CONTROL RUN
Q HARD WINTER £B HARD SPRING
BSOFT WINTER QHARO FALL'-SOWM SPRING
WHEAT REGIONS
OOU8LE0 COz
a HARD *INTE» a HARD FALL-SOWN SPRING
B SOFT WWTCR a SOFT FALL-SOWN SPWNO
fflMARO SPOINO »r«M-J»-«
QMARO WINTER OMARO FALL-SOWN SPRING
Q SOFT WINTER Q SOFT FALL-SOWN SPflttO
* Fill-Saxi
(A) Major wheat-growing areas of North America. Source: U.S. Wheat Associates
and Foreign Agricultural Service, USDA; (B) Actual wheat-growing regions of
North America on the GISS GCM grid; (C) Simulated North American wheat regions
using the GISS GCM control run; (D) Simulated wheat regions using the GISS GCM
doubled C02 run.
Source: Rosenzweig, 1985.
* * * DRAFT FINAL * * *
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-70-
D. ASSESSING CLIMATE IMPACTS ON AGRICULTURE:
METHODS AND RECENT RESULTS
The variety of research methods used to study the effects of climate
change on agriculture includes:
• Climate scenarios are developed to guide both
modeling and chamber studies in the specification of
temperature and water regimes. These scenarios may be
specified either as simple prescriptive changes (e.g.,
+2°C in July) or from historical ranges based on
meteorological observations, or may be derived from
global climate modeling results.
• Controlled atmosphere studies (conducted in
phytotrons, greenhouses, and field chambers) are used to
determine the direct physical consequences of specified
climatic regimes, often combined with increased
atmospheric C02, on agricultural crops.
• Statistical regression models are used to relate
historical crop yields in a specific location to
concurrent or antecedent climatic factors.
• Dynamic process crop growth models employ functional
relationships of the transfer of mass and energy in crop
canopies and soil and climatic factors to determine crop
growth and yield.
• Probability analyses are used to estimate changes in
extreme climatic events, such as drought or high
temperature, and their effects on crop yield.
• Integrated agricultural and economic models combine
results from all of the types of studies above to
develop economic estimates of the cascading effects of
climate change throughout economic sectors of society.
1. Climate Scenarios
Climate scenarios must be developed for chamber studies, statistical
regression analyses, and for climate-driven dynamic crop growth models. These
may be simple prescriptive changes such as a 2°C increase in July temperatures
or more complex sets of changes derived from recorded or modeled climate.
a. Simple Prescriptive Changes
The simplest approach to scenario development is the application of
prescriptive changes (such as a 2°C increase in temperature or a 10% decrease
in precipitation over the whole or part of a year) to observed climate. Tests
with these simple changes are in the nature of sensitivity studies. Waggoner
* * * DRAFT FINAL * * *
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-71-
(1983) used this approach to study yields for the major cropping regions of
the United States using regression and crop growth models. For a 1°C warming
and a 10% decrease in rain, the decrease in yields ranged from 0.04 to 0.18
T/ha, a decrease of between 2% and 12%.
Newman (1980) used growing season thermal units to measure the sensitivity
of the corn belt to +1°C daily temperature changes. He found that the corn
belt would shift 175 km per degree centigrade in a SSW or NNE direction.
Newman did not consider precipitation changes per se, but did include the
impact of a +5°C temperature change on potential evapotranspiration in the
simulation.
b. Historical Analogs
Studies of historical periods are useful for providing insight into the
responses of farmers and farming systems to prolonged climatic extremes such
as .the Dust Bowl Years (1930's) in the Southern Great Plains (see Wigley et
al., 1981). The use of specific historical analog scenarios for modeling
studies is based on the assumption that patterns of climate warmings are
similar regardless of atmospheric forcing mechanism. Manabe and Wetherald
(1980) have shown that climate model variables reacted similarly to two
different types of forcing, increased solar constant and higher atmospheric
C02. This argument has been used to justify the development of scenarios from
20th century records (Lough et al., 1983).
Lough et al. (1983) constructed climate scenarios for the Northern
Hemisphere based on the instrumental observations of the warmest five-year and
twenty-year periods in this century. The warmest period occurred from 1934 to
1953 (Figure III-8). The effects of these climate scenarios on crop yields in
England and Wales were calculated by means of regression models that were
developed from a principal components regression technique. Results showed a
decrease in yield for most crops due to warmer summers, drier springs, and
wetter autumns.
The climate forcing argument used to justify the analog approach may not
hold for previous 20th century warm periods, since it is not obvious that the
solar constant has been higher, and increased C02 effects have probably not
yet been evident during this century. Also, the temperature rise of the
warmest period in the 20th century is only 0.4°C (warm minus cold temperature
change for the Northern Hemisphere) compared to the predicted rise for doubled
C02 which is 1.5°-4.5°C (doubled C02 run minus control run for global mean
surface temperature) (National Research Council, 1982). Therefore, neither
the forcing mechanism nor the magnitude of the 20th century warm period
analogs appear to be appropriate for climate change studies due to increased
trace gas concentrations.
c. Scenarios Developed from Climate Models
GCMs simulate climate by solving the fundamental equations for
conservation of mass, momentum, energy and water. Output from GCM experiments
with specified climate forcing mechanisms (e.g., doubled or quadrupled
* * * DRAFT FINAL * * *
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-72-
FIGURE 111-8
Northern Hemisphere Temperature Variations
a. 5
a. a
-0.5
-l.i
1920
1940
1960
I960
Northern Hemisphere mean surface air temperature variations (°C) showing the
chosen warm and cool 20-year periods. The curve shows 20-year filtered values
using a 15-term Gaussian filter padded at each end with the mean of the first
or last seven values.
Source: Lough et al., 1983.
* * * DRAFT FINAL * * *
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-73-
atmospheric C02 levels) have been used in agricultural impact studies. The
advantages of this type of scenario are their internal consistency and global
extent. Disadvantages are the present lack of realism of the GCMs at regional
scales (particularly for precipitation) and their coarse spatial resolution.
Even though GCMs reproduce global climate realistically, they are not yet
reliable for regional scale studies. Gridbox sizes in various GCMs range from
330 km x 330 km (approximately 100,000 km2) to 8° latitude x 10° longitude
(about 600,000 km2) at 45° latitude.
Another problem with most GCM scenarios is the use of a step change in the
atmospheric C02 content. Results from these model experiments show the
equilibrium response of climate to increased trace gases, whereas the nature
of the transient climate should be the real concern. To date, only one GCM
study has used gradually increasing trace gas levels (Figure III-9) (Hansen et
al., 1986). Results show global warming of 1°C by the mid-1990's and of 2°C
by 2020.
d. Studies with GCM Climate Scenarios
Output from GCMs has been used to study impacts of C02-induced climatic
change on West European agriculture (Santer, 1985), potential shift in the
U.S. corn belt (Biasing and Solomon, 1984), potential impacts on North
American wheat-producing regions (Rosenzweig, 1985), yields of spring wheat in
Saskatchewan, Canada (Stewart, 1986), and implications for Ontario's
agriculture sector (Smit, 1986).
Santer (1985) examined assumptions and methodologies for study of climate
change impact on agriculture. He used output from two GCMs, that of the
Goddard Institute for Space Studies (GISS) and the British Meteorological
Office (BMO), to run a linear regression model for winter wheat yield and a
simple biomass production simulation model. The simulation model appeared to
provide more physically realistic results than did the linear regression model
for various European regions.
Biasing and Solomon (1984) mapped potential shifts in the corn belt using
selected temperature and precipitation simulations obtained by halving values
from a quadrupled C02 run of a climate model (Manabe and Stouffer, 1980;
Manabe et al., 1981) in relation to thermal unit and precipitation
requirements for corn. The study indicated that projected warmer and drier
conditions favored the replacement of corn with other more adapted crops, such
as winter wheat in the southwestern corn belt, and that regional climatic
effects of the Great Lakes prevented the corn belt from extending appreciably
northward.
In order to generate wheat regions for a potential C02-induced climate,
Rosenzweig (1985) specified environmental requirements for growth of different
wheat types in North America and compared them to temperature and
precipitation results from the control and doubled C02 runs of the GISS GCM.
Changes in modeled precipitation due to doubled C02 were applied to observed
precipitation. In the simulation, areas of production increased in North
America, particularly in Canada, due to increased growing degree units. Major
* * * DRAFT FINAL * * *
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H.flO
-74-
FIGURE 111-9
Calculated Temperature Trends for the Southern Great Plains
Gridbox for the Transient Run of the GISS GCM
13 X
IJ.OD
O
O
1101
cc
UJ
cr
UJ
o
»»
10.1
ing
Mil IB4 IW JOB
YEAR
Source: From experiment described in Hansen et al., 1986,
* * * DRAFT FINAL * * *
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-75-
wheat regions in the U.S. could still support wheat production, although types
of wheat grown changed in some locations. Wheat regions in Mexico were
identified as vulnerable due to high temperature stress.
Stewart (1986) used GISS GCM results from a doubled C02 experiment to run
a generalized crop growth model for Saskatchewan spring wheat by modifying- the
temperature and precipitation data in relation to the 1951-1980 normals.
Without any direct effects of C02 (i.e., increased photosynthesis and
water-use efficiency), modeled wheat production was reduced by 16-26%. Even
with a 15% increase in photosynthetic capacity, wheat production was reduced
(Figure 111-10). An implication of these results is that mid-summer drought
would be likely to cause farmers to shift to fall-sown crops, since the life
cycle of winter wheat allows better utilization of fall and spring
precipitation.
Smit (1986) used estimates of the potential impacts of a doubling in
atmospheric concentration of C02 on monthly normals for mean temperature and
total precipitation derived from a general circulation model developed by the
Atmospheric Environment Service (AES), Environment Canada. These data were
used to estimate agroclimatic resources (e.g., length of growing season, heat
units, evapotranspiration) available for crop production in each of six
regions in Ontario. Estimates of the current range in precipitation levels
for each defined region in Ontario were derived using monthly precipitation
levels recorded between 1951 and 1981. In the absence of information on the
degree to which the equivalent of a doubled C02 environment might affect
variability in annual levels of precipitation, it was assumed that the
relative range in precipitation about the norm for the growing season would
not change.
Smit's results suggest that risks to agricultural production in Ontario
would increase substantially given the specified changes in climate,
especially in those years with low precipitation levels. Changes in long-term
normals would contribute to a modest decline in production potential (Table
III-l), but these losses could be recouped in those years with higher than
average precipitation during the growing season. The largest is associated
with drier than average years. Relatively dry years currently impinge upon
provincial opportunities for food production. Low levels of precipitation,
combined with the estimated increases in long-term temperature normals, would
impose severe constraints on crop production opportunities and could threaten
the security of Ontario's food supply.
Smit's results demonstrate the complexity of potential impacts of climate
change on regional agriculture. For example, in Northern Ontario the array of
crops that could be grown under the altered climatic regime would be expanded
to include corn, wheat, and soybean. Furthermore, yield for the forages and
cereal grains that are currently grown in Northern Ontario would increase
substantially. In drier years, production on lands with a lower tolerance to
drought would be limited, but these losses would be countered by enhanced
prospects on lands with relatively high moisture reserves. The opposite would
be expected in years with above average levels of precipitation.
* * * DRAFT FINAL * * *
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-76-
FIGURE 111-10
Variation in Spring Wheat Yields (% of Normal) in Saskatchewan
for Two Scenarios Derived from GISS GCM Doubled CO2 Results
a) GISS3 - T.P
•J-J. - 15% f Photosynthesis
»
108
100
102
b) GISS4 - T
5-f - 15* I Photosynthesis -\-
'I \
10SW
Source: Stewart, 1986.
* * * DRAFT FINAL * *
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TABLE 111-1
Ontario's Potential for Food Production Given
Changes in Long-Term Climatic Normals
(thousands of metric tons)
PRODUCTION POTENTIAL GIVEN:
Current Norms Future Norms
CROP (Scenario 1) (Scenario 2)
Grain Corn 7804 7485
Barley ' 1761 1689
Oats 1104 1059
Winter Wheat 1063 1019
Soybeans 1176 1127
Hay 8590 8238
Fodder Corn 11678 11200
Potatoes 614 589
Improved Pasture 7865 7543
Unimproved Pasture 550 528
Source: Adapted from Smit, 1986.
* * DRAFT FINAL *
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-78-
In the southern parts of Ontario, moisture stress associated with the longer
and considerably warmer summers would impinge upon the production
opportunities for most field crops, despite the longer growing season. The
greatest impacts would occur on well-drained -- to excessively well-drained --
lands during the drier years.
2. Controlled Atmosphere Studies
Results from field experiment studies may relate the current range of
variability in climatic factors to crop yield. When the effects of specific
climate regimes are studied, controlled atmosphere experiments in phytotrons,
greenhouses, or field chambers are conducted. These have often been used to
combine the effects of climate and increased C02 on crop growth. However,
these controlled atmosphere experiments rarely use climate regimes that are
based on climate change projections.
Studies combining both controlled climatic factors (e.g., light,
temperature, and/or moisture) and physiological effects of increased C02 have
been done on corn (Goudriaan and de Ruiter, 1983), cotton (Mauney et al.,
1978), potato (Collins, 1976), sorghum (Marc and Gifford, 1984), soybean
(Sionit, 1983), and wheat (Chaudhuri et al., 1986, see Figure III-ll).
3. Statistical Regression Models
Multivariate regression models have been developed from the relationships
between historical crop yields in a specific location and climatic variables.
These relationships have been used to predict future yields under varying
climatic scenarios. Among the many models of this type are those of Thompson
for corn (1969a) and wheat (1969b) for parts of the central United States;
Haigh (1977) for corn, soybean, and wheat for certain states in the mid-west;
Baier (1973) and Williams (1975) for wheat in Canada; and Ramirez et al.
(1975) for wheat in the Great Plains.
The use of regression models for prediction of crop yields, particularly
in association with climate change, has been criticized (Biswas, 1980; Katz,
1977; Hayes et al., 1982; and Rosenberg, 1982). Major problems of this type
of model include their "black-box" nature, their reliance on statistical
coefficients rather than on physical relationships, and the difficulty of
separating changes in yield due to climate from changes due to differences in
management or technology. For the most part, regression models assume linear
relationships between crop yields and environmental variables and static
states of crop varieties and production technology. Despite these problems,
statistical regression models have been used quite extensively for climate
impact studies, usually with simple prescriptive scenarios (e.g., Waggoner,
1983; Couter and Haug, 1986; see Table III-2).
* * * DRAFT FINAL * * *
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-79-
FIGURE 111-11
Observed and Predicted Stem Dry Weight for Winter Wheat
as Affected by Enriched CO2 at Two Moisture Regimes
1888—
888-
« eee-J
u
488-
LJ
i-
co
288
WHEAT C 1984-1985
UEU.-UATBED
HOZSTURC-f
i I i > I i
288 388
i i I i i i i j i i I I I I I i r j IT i i I i i i i 1
488 588 688 788 688 988
CONCENTRATION C U. LL"1
Source: Chaudhuri et al., 1986.
* * DRAFT FINAL * * *
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-80-
TABLE 111-2
_2
Effect of Weather on Yields (in quintals ha ) of Corn in Three States
Crop Region, State
Iowa
Illinois
Indiana
Variable
Yield, Average 1978-1980 72.7 68.8 65.3
Temperature, °C
July -- -1.56
Aug -- -0.64
Oct -- 0.57
July to Aug Average -- -- -2.34
Precipitation, mm
May -- -- -0.017
Sept to June 0.013
b/
Sept to June SDFN -0.0001 -0.00006
July. -- -- 0.045
Combined Variables
c/-
Apr and May PET -0.12
May Prec/PET . -- -1.49
July Prec minus PET 0.076 0.025
d/
(June ET/ET + July ET/ET)/2 -- -- 2.27
Calculated Estimated Change
Yield, quintals/ha -2.36 -1.72 -2.80
Change from 1978-1980 average -3% -3% -4%
a/ The effects are given as b coefficients in quintals/ha/unit of
variable, i.e., mm of precipitation, °C of temperature or fraction of a
ratio (after Leduc, 1980). B. Estimates of the change in yield with a
1°C increase in temperature and a 10% decrease in precipiation from the
historic average temperature and precipitation recorded for the regions.
b/ SDFN, departure from normal precipitation, squared.
c/ PET, potential evapotranspiration in millimeters, a measure of the
demand for water.
d/ ET/ET, evapotranspiration divided by average evapotranspiration.
Source: Waggoner, 1983.
* * DRAFT FINAL * * *
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-81-
4. Dynamic Process Crop Growth Models
Dynamic crop growth models are driven by climate variables and reflect
current understanding of the basic relationships .of crop growth and climate.
They employ knowledge of the underlying physiological and morphological plant
processes and the transfer of energy and mass within a crop canopy to provide
prediction (and explanation) of integrated plant behavior. Loomis et al.
(1979) describe this "systems approach" to crop modeling. Dynamic process
models exist for almost every major crop (e.g., Stapper and Arkin (1980) for
corn, Acock et al. (1985) for soybean, and Ritchie and Otter (1984) for
wheat). Crop growth models are useful for studying the effects of transient
climate change and for testing possible adjustments to climate change such as
modified irrigation scheduling and shifts in planting date.
Crop growth models are suitable for studying the combined effects of both
physiological and climatic changes due to increased trace gases (Figure
111-12). A meeting of experts organized by the World Meteorological
Organization (WHO) and the International Meteorological Institute in Stockholm
concluded that sensitivity studies with process-based crop models and climate
change scenarios are an appropriate first step in studying the impacts of
increasing C02 and changing climate on crop growth and yield (WMO, 1984).
Carter et al. (1984) tested the sensitivity of crop models to daily and
monthly time resolutions and found that monthly climatic variables (most GCMs
provide monthly data) are adequate for some crop modeling studies. This may
not always be the case, since some crop models need daily inputs of climate
variables. Carter et al. also used crop models to show that if interannual
variability of temperature and precipitation changes, sensitivity to
short-term climatic variation may be different from sensitivity to long-term
climate change.
Kanemasu (1980) used a wheat model to estimate the effect of increasing
C02 and temperature on wheat yields and found a 59% increase in yield under
C02 enrichment to 500 ppm. Baker et al. (1985) adapted a crop-climate model
to investigate the interactive effects of C02, leaf area index (LAI), and the
environment on midday crop water use and water-use efficiency. The
simulations showed that increased C02 in conjunction with increased LAI can
offset the lowered transpiration caused by increased stomatal resistance.
Couter and Haug (1986) used the CERES-Wheat model (Ritchie and Otter, 1984) in
their study of the effect of climate change on agriculture in Oklahoma.
5. Probability Analyses
Several authors have emphasized the need to study changes in the
probabilities of climate variables, since extreme meteorological events such
as a brief span of high temperatures can have a large detrimental effect on
crop yields. These climatic probabilities can then be related to effects on
yield. Mearns et al. (1984) found that the relationships between changes in
mean temperature and corresponding changes in the probabilities of extreme
temperature events are nonlinear, and that relatively small changes in mean
temperature can result in relatively large changes in event probabilities. In
particular, for Des Moines the likelihood of a five-consecutive-day maximum
* * DRAFT FINAL * *
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-82-
FIGURE 111-12
CERES-WHEAT YIELDS UNDER
NORMAL AND 2xC02 CONDITIONS
6000
3000
4000
3000
2000
1000
I
I
WELL-WATERED
(.NORMAL
2. 2xC02 LININT2
3. 2xC02 LININT2
4.00 DMC
0.85 EPC
LESS WELL-WATERED
4. NORMAL
5. 2xC02 LININT2
6. 2xC02LININT2
4.00 DMC
0.83 EPC
REVISED IRRIGATION*
7. 2xC02
4.00 DMC
0.85 EPC
* DAY 286,341,85,100
CERES-Wheat yields under normal and doubled C02 conditions with climate
scenario based on the GISS GCM doubled C02 run alone and combined with
physiological changes of increased C02 for Cases 1-3: well- watered; Cases
4-6: less well-watered and Case 7: irrigation revised to give adequate water
at all development stages.
Source: Rosenzweig et al., 1986.
* * * DRAFT FINAL * * *
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-83-
temperature occurrence of at least 35°C (95°F) is about three times greater
under a relatively small 1.7°C (3°F) increase in mean temperature than under
current climate conditions. In regions where crops are grown under conditions
that are close to the crops' maximum-temperature tolerance limits, high
temperature extremes can have significantly harmful effects on crop growth and
yield.
In an analysis of rainfall probabilities, Waggoner (1986) showed that the
relative increase in probability of drought caused by a decrease in rain will
be greater than the absolute change in probability and much greater than the
relative change in rain, especially for abnormal drought or for consecutive
events like two dry months. When Waggoner analyzed the relative changes in
the probabilities of low yields when rain decreases, he found that the
relative change in the probability of low yield may be much more than the
relative change in rain.
Parry and Carter (1985) also show that a change in the frequency of
extreme events is an important indicator for analysis of climate impacts.
This has implications for scenario development: the statistical
characteristics of predicted future climates from GCMs should be included in
scenarios rather than means alone. Parry and Carter assessed the risk of crop
failure resulting from low levels of accumulated temperature for oats farming
in southern Scotland and concluded that minor climatic variations in the
United Kingdom have induced substantial changes in magnitudes of agricultural
risks (Figure 111-13).
6. Integrated Agricultural and Economic Studies
The scales of integrated studies range from individual farms to regional
agricultural economies, the national agricultural sector, and finally to the
global food trade. These studies seek to describe how the effects of climate
change may reverberate throughout some or all of these scales.
Callaway et al. (1982) analyzed methods and models for assessing the
economic impacts of C02-induced climate change on the U.S. agricultural
economy. Their principal finding was that there would be widespread impacts
throughout both the agricultural sector and the economy in general. They
emphasize that model studies snould be used to analyze the sensitivity of
various sectors of the economy to a range of climate change impacts. These
analyses should then be useful when considering possible long-term adjustments,
Couter and Haug (1986) examined the impacts of a climate change scenario
on the agricultural sector of the Oklahoma economy. They used a climate
scenario with an average 10% decrease in April through September
precipitation, but specified that it would derive from rainfall events of
0.01-0.50 inches occurring in a 24-hour period. Using both statistical
regression and crop growth models and an input/output economic model, they
showed that the climate scenario they postulated would cause a loss of $86
million in winter wheat income and a total decrease of $327 million in state
economic output (Table III-3).
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-84-
FIGURE 111-13
Crop Failure Risks
St. Abb's
Head
COLDINGHAM
MOOR
Ijoplethsof 1-in-10
and 1-in-50 frequency
—1 in 50—' of crop failure, 1661 — 1710
—1 in 10-- liopleths of 1-in-10
-4 in50~- and 1-in-50 frequency
of crop failure, 1931-80
UPPER MERSE
6 8 10 km
Locational shift of a high-risk zone between the cool (1661-1710) and warm
(1931-80) periods. Risk is expressed as frequency of oats crop failure.
Source: Parry and Carter, 1985.
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-85-
TABLE 111-3
Average Annual Impacts Resulting from Climate Change
Hypothesis for Oklahoma
Impacted Sector (Activity)
Average Annual State
Level Impacts
Precipitation (area weighted)
Winter Wheat
Corn
Sorghum
Cotton
Hay
Pest and Pathogen Damage Control
Rate of Maturity
Field Work Days
Irrigation Costs
Irrigation Applications
Irrigation Water Demand
State Output
Final Demand
Personal Income
Taxation
Gross State Product
-2.21 inches
-$86,240,000
-$1,380,000
-$1,090,000
-$480,000
-$1,270,000
+3 to +5 days
+§45,441**
+0.40**
+0.41 ac-in**
-$327,700,000
-$176,900,000
-$113,400,000
-$20,800,000
-$171,500,000
* Qualitative analysis only.
** Analysis of small region.
Source: Couter and Haug, 1986.
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-86-
Parry et al. (1986) reviewed results of integrated studies for several
crops and areas of the world including Canada, Iceland, Finland, USSR and
Japan. Doubled C02 climate changes specified for Canada from the GISS GCM
(Table III-3) resulted in decreases in total provincial wheat production, of
20%, 22%, and 33%, depending on soil type, and reductions in total provincial
farm income, employment, and household purchasing power. Results of other
experiments in Japan (rice), USSR (oats, rye, barley), Finland (barley), and
Iceland (hay) further demonstrate the complexity of effects of climate change
on agricultural systems, including regional variations and potential impacts
of shifts to new varieties.
E. FUTURE DIRECTIONS FOR RESEARCH AND CLIMATE IMPACT ANALYSIS
The goals of climate change studies for agriculture are (1) to determine
the nature and range of the possible impacts; (2) to understand the potential
interactions among the impacts; and (3) to provide a basis for consideration
of possible means for preventing, adjusting to, or alleviating any negative
consequences of the changes. An additional goal should be to frame
alternatives for national and international agricultural strategies and
environmental policies. An initial objective should be to increase societal
flexibility in creating potential responses.
1. Global Studies
Existing global agricultural assessments, while providing us with valuable
information on the potential effects of climate change on agriculture, suffer
from a number of limitations, as described by Liverman (1986) in a paper
prepared for the International Institute for Applied Systems Analysis.
Several of Liverman's conclusions about current agricultural assessments are
appropriate here. They:
• lack good meterological and agricultural data,
particularly on subsistence production;
• fail to consider climate change as a change in climate
variability (hence affecting agricultural risk) rather
than just changes in the mean climate;
• fail to provide estimates of the uncertainty
associated with assessments; and
• fail to incorporate the possibility of technological
change or governmental intervention, which may alter the
vulnerability of global agriculture.
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-87-
2. Research Directions
Important future directions of studies on how climate change affects
agriculture should include:
i
• Focusing on transient climate change scenarios rather
than predicted future equilibrium climates in
agricultural studies. Analyses of potential annual or
10-year agricultural shifts offer an opportunity to
build on previous static analyses based on a step-change
to a doubled C02. Collaborating studies between GCM
modelers, the agricultural research community, and
climate change impact analysts are needed so that GCM
output of key variables is compatible with users' needs;
for example, daily climate output is necessary for some
crop growth models, and probabilities of extreme
climatic events are often critical to economic
decisions. On the other hand, impact analysts can
benefit from the understanding of changing climate
provided by the GCM modelers.
• Continuing development of climate, crop, water-use,
and economic models. Previous studies should be
repeated with the improved models, and the results
compared and contrasted with previous results.
• Integrating climate, crop, water-use, and economic
models in order to study the cascade of climate change
effects through the entire range of interest, from
environmental impacts to shifts in agricultural land
use, and to global food supply and demand. Integrated
models should also be used to study the combined
climatic and physiological effects of trace gases on
crop growth.
• Developing policy analyses to study alternative
adjustment strategies aimed at increasing the
flexibility of society's responses to climatic changes.
F. SUMMARY
Given the caveats that must accompany any predictions of an uncertain
future, the following conclusions about the effects of atmospheric trace-gas-
induced climate change on agriculture may be drawn: at the field level, the
major physical processes affected will most probably be the thermal and water
regimes and the levels and timing of pest infestations. Stresses caused by
changes in these processes can all have detrimental effects on crop yields.
For plant breeders, there will be demand for more heat- and drought-tolerant
crops. If climate change occurs to the extent predicted by the GCMs, shifts
in farming systems and cropping patterns will occur. These shifts will have
* * * DRAFT FINAL * * *
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-88-
impacts on local economies and environments -- sensitive ecosystems may be
threatened. Climate change effects on agriculture will eventually be felt at
regional, national, and international levels.
Methods for studying climate change and agriculture are improving as
climate, crop growth, and economic models continue to be developed. Study of
the transient climate, rather than equilibrium climate, and its effects should
be emphasized, as should the impact of changes on frequency of climate
extremes. The goals of climate change impact research are to define more
clearly the ranges of possible impacts and to engender flexibility in
society's reponses to them. As the models improve, impact studies should be
iterated, integrated, and expanded to include testing of possible responses to
the potential effects. In this way, improved information will be developed
for future policy decisions.
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-89-
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IV. EFFECTS ON WATER RESOURCES
Prepared by:
Peter H. Gleick
A. FINDINGS
1. There is evidence that climate change since the last ice age (18,000
years B.P.) has significantly altered the location of lakes, although
the extent of present-day lakes is broadly comparable with 18,000
years B.P. For example, there is evidence indicating the existence
of many tropical lakes and swamps in the Sahara, Arabian, and Thor
Deserts around 9-8,000 years B.P.
2. The inextricable linkages between the water cycle and climate ensure
that potential future climate change will significantly alter
hydrological processes throughout the world. All natural
hydrological processes—precipitation, infiltration, storage and
movement of soil moisture, surface and subsurface runoff, recharge of
groundwater and evapotranspiration--will change if climate changes.
3. As a result of changes in key hydrological variables such as
precipitation, evaporation, soil moisture, and runoff, climate change
is expected to have significant effects on water availability. Early
hydrological impact studies provide evidence that relatively small
changes in precipitation and evaporation patterns might result in
significant, perhaps critical, changes in water availability. For
many aspects of water resources--including human consumption,
agricultural water supply, flooding and drought management,
groundwater use, and recharge and reservoir design and
operation — these hydrologic changes will have serious implications.
4. Despite significant differences among climate change scenarios, a
consistent finding among hydrologic impact studies is the prediction
of a reduction in summer soil moisture and changes in the timing and
magnitude of runoff. Winter runoff is expected to increase and
summer runoff will decrease. These results are robust across a range
of climate change scenarios.
5. Future directions for research and analyses suggest that improved
estimates of climate variables are needed from large-scale climate
models, innovative techniques are needed for regional assessments,
increased numbers of assessments are necessary to broaden our
knowledge of effects on different users, and increased analyses of
the impacts of changes in water resources on the economy and society
are necessary.
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B. INTRODUCTION
The growing concern in the scientific community about the effect of global
climate changes on water availability has stimulated research into appropriate
methods for evaluating such hydrologic alterations. There have also been a
number of case studies of possible regional hydrologic changes based on
plausible future climatic changes. This work involves such diverse academic
fields as climatology, surface hydrology, atmospheric chemistry, and dynamics
of water-management systems. Because of the many implications for society of
changes in global climate and subsequent changes in water quantity and
quality, there is room for a significant expansion both in the areas and in
the methods of study of the hydrologic impacts of climatic change.
There are many possible methods for analyzing changes in water-resource
availability due to climatic changes -- methods that depend on a wide variety
of variables, including the hydrometeorological characteristics of a region,
the types and timing of existing and expected water demands, and the interests
and goals of the researcher. A review of the major works to date is an
appropriate introduction to the problems and pitfalls facing climatologists
and hydrologists, as well as to the strengths and weaknesses of a number of
possible research methods.
1. Importance of Water Resources
Water resources, essential to the survival of civilization, are
significantly affected by climate. All natural hydrologic processes
(precipitation, infiltration, storage and movement of soil moisture, surface
and subsurface runoff, recharge of groundwater and evapotranspiration) are
affected (Figure IV-1). Certainly alterations in these hydrologic cycles will
result in significant changes in water availability. Because of the intimate
linkages between the water cycle, vegetation, and climate, the full range of
water-resource issues can be expected to respond to climate change. These
water-resource issues and their sensitivity to climate change are listed in
Table IV-1, drawn from a Department of Energy report (DOE 1985). The
importance of climate change in water resources is further described in a 1984
EPA report:
As part of their jobs water planners, coastal engineers, and
agronomists necessarily make assumptions about future
water-resource supplies, temperature, droughts, and storms.
In general, they assume that in the future these conditions
will repeat those of the past -- there will be the same
amount of water available, the worst storm during the next
hundred years will be similar to the worst storm during the
past 100 years, and droughts will be of similar frequency
and duration... Unfortunately, the underlying assumption
that future climate change will essentially repeat the past
no longer appears valid (U.S. Environmental Protection
Agency, 1984, p. i).
* * * DRAFT FINAL * *
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-97-
FIGURE IV-1
Schematic Diagram of the Hydrologic System of a Drainage Basin
Evaporation
—i. — In
Evaporation
t
Evapotranspiration
t
Consumptive Use
t
Groundwater _.
Inflow ^
Clouds
Precipitatic
terception
Throughfall
>n
Evaporation
f
^ . Snow 1
Snow ^ storage
Snowmelt
Consumptive
f Uses and
bvapotranspiration
Surface *
Storage ^ Surface J L
Infiltration
Surface X
^ Channel and LaKe
_____ , . ._ Storaae
Subsurface \ I
Runoff j L 1
Surface
riiirflnvA/
Percolation Stream Channel
1 Groundwater
-Lr Interface
^
t Storage i—---__^ Groundwater
I „ , .... Outflow
Source: Callaway and Currie, 1985.
* * * DRAFT FINAL * * *
-------
-98-
Finally, there is substantial evidence, based on paleoclimatic data, that
demonstrates the important linkages between climate and hydrological systems.
Because accurate climatic records extend back only a few hundred years, most
information about earlier climatic trends must be obtained from a variety of
geophysical sources, including tree-ring analysis (Stockton and Meko 1983),
archaeological evidence (McGhee 1981), and physical .indicators of lake levels
(Street-Perrott and Harrison 1985). Nicholson (1986) reviews some of this
information and concludes that it is possible to document some significant
changes in water availability on an historical time scale, but that no
consistent regional pattern of increased or decreased rainfall can be
associated with a change in global temperatures.
Nicholson's study of climatic history -- drawing from available data such
as landscape descriptions, drought, flood and harvest information, and climate
and weather descriptions -- observes that the Altithermal period of ca. 6000
B.P. provides an historical analog to projected global warming. During this
period, rainfall was considerably higher than at present in many low-latitude
dryland regions but relatively dry in others, such as the agricultural regions
of the United States. These conditions are thus similar to model projections
for global warming, yet Nicholson cautions that firm predictions cannot be
made given that similar rainfall conditions occurred during periods of
globally reduced temperatures. Nicholson concludes that changes in rainfall
and water resources could be gradual or abrupt, involving changes of mean
conditions, variability about the mean, or seasonality. Any of these changes
might have severe impacts on populations. Webb and Wigley DOE (1985) however
have reviewed the literature and argue that there is little evidence for a
"global" altithermal period 6000 years ago. They suggest that there is
evidence for local warmth that was probably less than 1°C when averaged over
the globe. Hence, the period 6000 B.P. may not be a good analog to projected
global warming.
Kutzbach and Street-Perrott (1985) provide evidence that climate change
since the last ice age (18,000 years B.P.) has significantly altered the
location of lakes--although the extent of present-day lakes is broadly
comparable with that existing in 18,000 years B.P. At the last glacial
maximum, lake levels in the northern tropics were low and falling. This trend
continued until 12,500 years B.P. when water levels began to rise, and
eventually culminated in many lakes and swamps in the Saharan, Arabian, and
Thor Deserts around 9,000-8,000 years B.P. Approximately 6,000-5,000 years
ago, drying out set in and the current population of lakes evolved.
2. Scope of this Study
This section will address the following major questions regarding climate
change and water resources:
1. What hydrological impact studies have been conducted
thus far and what do they say about potential effects of
climate change on water resources?
* * * DRAFT FINAL * * *
-------
-99-
TABLE IV-1
Potential Sensitivity of Water Resource
Issues to CO2 Buildup
Potential Sensitivity
Issue to C02 Buildup
Inadequate Surfacewater Supply/Storage HIGH
Groundwater Mining
Conflicts in Use
Water Losses from Storage Systems
Vegetation Management
Drought
Flooding
River Sedimentation
Salinity Problems
Waterlogging of Soils
Saline Intrusion of Aquifers
Surfacewater Contamination
Effects of Land Use Change on Basin
Hydrology MODERATE
Acidification of Lakes
Waterborne Diseases
Eutrophication
Inefficient Irrigation Practices/
Management
Navigation Problems
Reservoir Sedimentation
Groundwater Contamination
Effect of Changing Lake Levels on
Aquatic Ecosystems LOW
Conveyance Losses
Availability of Potable Water
Inadequate Water Treatment Facilities
Channel Scour/Channel Erosion
Groundwater Infiltration into Municipal
Sewer Systems
* * * DRAFT FINAL * * *
-------
-100-
2. What criteria should be applied to the evaluation of
regional hydrologic models to assess their strengths and
weaknesses?
3. What are the future research directions needed to expand
our knowledge of hydrologic impacts of climate change?
C. GENERAL CIRCULATION MODELS AND HYDROLOGY
Climate models sometimes include a mathematical description of .surface
hydrological processes. Yet even state-of-the-art General Circulation Models
(GCMs) use parameterizations of surface hydrologic processes that are greatly
simplified compared with actual hydrologic processes. While advances in
parameterizations of hydrologic processes can and will no doubt be made, major
improvements in such parameterizations may be slow in developing (Dickinson
1984). In particular, the complexity of small-scale surface processes is now
only poorly represented by climate models whose surface resolutions are too
coarse to account for such small-scale phenomena. As a result, improvements
in modeling soil hydrology, the effects of vegetation, and other detailed
hydrologic factors will continue to be limited by a lack of adequate data sets
of important surface variables.
The prospect of global warming presents the potential for changes in
certain critical Hydrologic variables -- precipitation and evapotranspiration
-- and thus raises the possibility of major regional water-supply problems,
including changes in runoff and soil-moisture patterns. Budyko and Vinnakov
(1977), Manabe et al. (1981), Mitchell (1983), and Manabe and.Wetherald (1986)
have suggested that major reductions in summer soil-moisture patterns in the
middle latitudes are a possible outcome of a doubling of atmospheric carbon
dioxide. Results from Washington and Meehl (1984) show different changes in
zonal-mean soil-moisture values associated with changes in the timing of
precipitation, the timing and magnitude of spring runoff, and increased
soil-moisture supplies during the summer months. In their study, positive
feedbacks among soil moisture, precipitation, and low clouds are associated
with the persistence of soil moisture through the summer months.
Soil-moisture changes are thus possible in some of the most productive
agricultural areas of the world. Significant persistent changes in soil
moisture in these regions, whether the changes are manifested as increases or
decreases, would have serious societal consequences. In order to evaluate
these changes, more detailed evaluations of regional hydrologic effects are
needed than can be accomplished solely with existing models.
We are thus faced with a dilemma: those tools capable of providing
information on the likely effects of human activities on global climate are at
present unsuited for evaluating the nature and magnitude of important regional
effects. Yet information on regional effects is important for determining
appropriate policy responses to climatic changes. Until realistic surface
hydrology can be incorporated into general circulation models with adequate
resolution, evaluations of regional and local hydrologic effects must be
accomplished through other methods.
* * * DRAFT FINAL * * *
-------
-101-
D. POTENTIAL EFFECTS OF CLIMATE CHANGE ON WATER RESOURCES
In the last decade, there have been a number of analyses of the
relationship between water resources and global climatic change. This section
reviews the most important and informative of these analyses and discusses
approaches that (1) take advantage of the strengths of GCMs and (2) permit
more detailed regional assessments than can be accomplished with GCMs alone.
Studies that have evaluated the regional hydrologic implications of climatic
changes include those of Schwarz 197.7, Stockton and Boggess 1979, Nemec and
Schaake 1982, Revelle and Waggoner 1983, U.S. Environmental Protection Agency
1984, Flaschka 1984, Novaky et al. 1985, Gleick 1986a, 1986b, 1986c, Cohen
1986, and Mather and Feddema 1986. These works provided the first evidence
that relatively small changes in regional precipitation and evaporation
patterns might result in significant, perhaps critical, changes in regional
water availability. The following section reviews the methods, results,
strengths, and limitations of these works.
In one of the earliest comprehensive studies, Schwarz (1977) looked at
existing hydrologic conditions in the northeastern United States and attempted
to evaluate the effects on water supply of hypothetical climatic changes.
Schwarz considered three approaches: (1) a review of individual water-supply
systems and their previous responses to climatic anomalies; (2) a general
speculation about the effects of climatic changes on a series of broad
hydrologic criteria; and (3) a first attempt using synthetic streamflows to
evaluate the effect on reliable water supply from hydrologic variations that
may result from climate change. Even in the absence of estimates of likely
climatic change, Schwarz concluded that certain characteristics of water
supplies, particularly the variability of streamflow, are very sensitive to
changes in climate. He also concluded that a full understanding of the
relationships between climatic changes and water supply, not yet achieved, is
a desirable goal and that a range of likely climatic-change scenarios should
be available to water-resource planners.
Stockton and Boggess (1979) built upon the classic empirical relationships
between precipitation, temperature, and runoff developed by Langbein and
others (1949), to evaluate the hydrologic effects of hypothetical changes in
both temperature and precipitation. Stockton and Boggess analyzed four
climate scenarios, involving changes of +2°C and -2°C in temperature and
changes of +10% and -10% in total annual precipitation, in order to estimate
the resulting changes in the average annual runoff of major water basins
throughout the United States. They concluded that a change toward a warmer
and drier climate would have the greatest impacts nationwide, with the most
severe effects in western, water-limited regions. The most beneficial effects
would result from a change to cooler and wetter conditions, although there
would be negative consequences from increased flooding on most major river
systems. Although this important work had a large geographical scope and thus
excluded considerable temporal, physical, and hydrologic detail, it was one of
the first studies to raise the possibility of non-linear hydrologic responses
to climatic changes.
* * * DRAFT FINAL * * *
-------
-102-
Nemec and Schaake (1982) also used hypothetical climate-change scenarios
to evaluate the influence of climatic variations on runoff. Significant
changes in runoff in both an arid and a humid watershed were shown to result
from only moderate variations in climate -- specifically, slight changes in
temperature and precipitation. Moreover, such climate variations had major
impacts on the design and operation of reservoir storage. They concluded that
the serious impact of changes in runoff suggested by their, models
substantiates the need for further consideration of the effects of climate
change on the design and operation of water-resource systems in different
climatic regions of the world. In a recent follow-up to the Nemec and Schaake
work, Klemes (1985) reviewed the role of hydrologic models and provided an
excellent discussion of the types of studies that should provide the most
useful information on the sensitivity of water resources to variations in
climate. Klemes also discussed the types of tests that should be applied to
certain types of hydrologic models to ensure that significant results are
obtained (or that insignificant results are discounted).
Revelle and Waggoner (1983), like Stockton and Boggess (1979), used the
statistical correlations among temperature, precipitation, and runoff from
Langbein and others (1949) to evaluate the effects of hypothetical changes in
precipitation on runoff in the western United States, particularly in the
Colorado River Basin. In their study, they showed that warmer air
temperatures and slight decreases in precipitation could reduce severely both
the quantity and the quality of western U.S. water resources. Using multiple
correlations among historical precipitation, temperature, and runoff in the
Colorado River Basin, they then evaluated the consequences of a 2°C increase
in temperature and 10% increases and decreases in precipitation. From these
relationships, it appears that the mean annual runoff of the Colorado River
Basin has been very sensitive to changes in precipitation. If such effects
are valid under conditions of future climate changes, some significant
consequences for water resources may occur. Whether or not such effects are
seen when the temporal resolution is increased to a seasonal or monthly level
remains to be evaluated. This study adds further evidence to support previous
suggestions of a non-linear relationship between changes in precipitation and
changes in runoff.
In one of the first attempts to use data from large-scale general
circulation models, rather than purely hypothetical scenarios, the U.S.
Environmental Protection Agency (1984) examined aggregate measures of changes
in the hydrologic cycle under steady-state doubled atmospheric concentrations
of carbon dioxide. The EPA study used coarse-resolution output from the
Goddard Institute for Space Sciences (GISS) model to look at changes in
precipitation, evaporation, runoff, and soil moisture over large regions of
North America. Despite the acknowledged limitations of the EPA report,
including the coarse resolution of the GCM and the crude reproduction of
actual hydrologic and climatic characteristics in many areas, this study
reaffirmed earlier suggestions that climatic changes may cause substantial
changes in runoff and soil-moisture patterns in the Northern Hemisphere.
Figure IV-2, taken from the EPA report, shows the change in runoff over land,
along with the percentage change from the control run. The general pattern
shows increased runoff in the northwest and southwest, with decreases in the
* * * DRAFT FINAL * * *
-------
-103-
FIGURE IV-2
Changes in Runoff for Doubled CO2
120 <
37
HI4)
Change in runoff between the last ten years of the doubled C02 run and the
last ten years of the control run, for the annual average. The top number
indicates the actual change (mm), the bottom number in parentheses gives the
percentage change relative to the control run.
* * DRAFT FINAL * * *
-------
-104-
central and eastern regions. Figure IV-3 shows the monthly variation in
precipitation, evaporation, and runoff as the change from the control run
values for a grid box that includes all or parts of such states as Idaho,
Washington, and Oregon. The results indicate an increase in the hydrologic
cycle as increases are shown for all three parameters in all months except
September and April.
Flaschka (1984) used regional modeling techniques to evaluate the
hydrologic effects of climatic changes in the Great Basin of the United
States. This study, like that of Nemec and Schaake (1982), directly used
hydrologic-modeling techniques to evaluate hypothetical climatic changes,
although the author's modeling methods differ from those of Neraec and Schaake
in certain important respects. Flaschka applied water-balance techniques to
four watersheds in the Great Basin to evaluate the consequences of the same
types of climate-change scenarios studied by Stockton and Boggess (1979) and
Revelle and Waggoner (1983). She concluded that such changes could result in
decreases in annual-average runoff of over 50 percent for precipitation
decreases of 25 percent coupled with temperature increases of 2°C. While this
study was one of the first to use regional water-balance modeling techniques,
it suffered from some significant limitations, including problems with input
data for model verification, the lack of algorithms for handling snowfall and
snowmelt, and lack of sufficient historical unimpaired runoff values for those
rivers in the basin that are heavily regulated. Two of the consequences of
these drawbacks are the inability to model monthly or seasonal flows and a
limited ability to verify model accuracy using historical data. Despite these
problems, this study marks a major improvement over the earlier use of
statistical, non-physically-based approaches.
A review published in 1985 (Novaky et al. 1985) offers a detailed
framework into which integrated climate/water-resource/societal impact studies
might be most conveniently organized. Although this paper is a purely
qualitative study of the hydrologic impacts of future climatic change, it
offers the first good outline of the elements of a complete hydrologic impact
assessment. The authors discuss the importance of transforming physical water
availability into economic and social values and emphasize the importance of
the role of water-management techniques in reducing the social impacts of
changes in water availability. Also emphasized is the importance of
understanding the time-scales and sensitivities of different hydrologic
factors. These points are discussed further in Gleick (1986b).
A recent review of the literature (Beran 1986) provides valuable
information on various approaches for evaluating the changes in the
availability of water resources from climatic changes. Table IV-2, taken from
Beran's paper, summarizes the hydrological models used to determine
hydrological and water-resource sensitivity due to particular climate
scenarios. Beran also discusses the advantages and limitations of each type
of hydrological model, as well as research requirements and topics. No
preference is stated for any particular model because each method has its
strengths and weaknesses. Beran also stresses caution in using existing
information on future changes because improvements continue to be made in the
treatment of oceans, clouds, and soil moisture within large-scale climate
* * * DRAFT FINAL * * *
-------
-105-
FIGURE IV-3
Change in Precipitation (P), Evaporation (E) and Runoff (R)
Between the Doubled CO2 Run and the Control Run as a
Function of Month for Idaho, Washington and Oregon
1.6
1.4
1.2
1.0
.8
.6
.4
0
-4
-.6
-.8
-1.0
-1.2
-1.4
-1.6
M
M
J J
Month
0
N
D
* * * DRAFT FINAL * * *
-------
-106-
TABLE IV-2
Summary of Hydrological Models Used to Determine
Hydrological and Water Resource Sensitivity or Impact Due to
Particular Climatic Scenario
Type
of Model
Causal/
Physics
Based
Conceptual
Empirical
Name of Model
Author and Year
Schnell (1984)
based on Brigg's
biomass model
Combination
Aston (1984)
Sacramento
Nemec et al. (1982)
SHOLSIM
Aston (1984)
Regression
Beard et al. (1979)
Historic contrast
Beran (c.f.) c/
Evaporation model
Cohen (1986)
Simulation
Schwartz (1977)
Storage yield
Beran (1984)
Input
Met. a/
T.S. b/ .
Met.
T.S.
Met.
T.S.
Met.
T.S.
Met.
T.S.
Runoff
T.S.
GCM
T.S.
Synthetic
runoff
Runoff
M.V.
Output
Runoff
T.S.
Runoff
T.S.
Runoff
T.S.
Runoff
T.S.
Runoff
T.S.
Runoff
M.V.
Runoff
T.S.
Storage
M.V. d/
Storage
M.V.
Application
Average climatic
runoff over Europe
Transpiration reduc-
tion effect
Sensitivity of annual
runoff and reservoir
yield
Transpiration reduc-
tion effect
Annual runoff
sensitivity
Response to recent
warming
Great Lakes
basin runoff
Reservoir sensitivity
to runoff change
Reservoir sensitivity
to runoff change
* * * DRAFT FINAL * * *
-------
-107-
TABLE IV-2 (Continued)
Type
of Model
Water
Budget
Name of Model
Author and Year
Rain-runoff
Stockton (1979)
Idso et al. (1984)
Subtract ive model
Wigley et al. (1985)
Proportional model
Beran (c.f.)
Input
Met.
M.V.
Met.
M.V.
Met.
M.V.
Met.
M.V.
Output
Runoff
M.V.
Runoff
M.V.
Runoff
M.V.
Runoff
M.V.
Application
Annual runoff
sensitivity
Transpiration reduc-
tion effect
Average runoff
sensitivity
Average runoff
sensitivity
a/ Met. means meteorological inputs required such as precipitation,
evaporation, radiation.
b/ T.S. means time series data input or output.
c/ Beran (c.f.) means reference is to this report.
d/ M.V. means mean (and other statistics) input or output.
* * * DRAFT FINAL * *
-------
-108-
models. Finally, Beran supports the use of paleocliraatology to help produce
information on past climatic analogs. Such analogs may provide clues to
existing hydrologic sensitivities.
Cohen (1986) looked at the implications of climatic changes on the Great
Lakes region, with a particular emphasis on the long-term effect of higher
temperatures and precipitation changes on lake water levels. Using basin-wide
data from two general circulation models, Cohen used a simple water-balance
model and a lake evaporation model to evaluate changes in lake levels and "net
basin supply" -- the difference between inflow from precipitation and runoff
and outflow from lake evaporation. Preliminary results shown in Table IV-3
indicate that there would be a significant decline in mean net basin supply
for the Great Lakes which may be partially attributable to increases in lake
evaporation. These results, however, "are highly dependent on assumptions
about wind speed over the lake, and lake surface temperatures" -- two
variables about which there remains considerable uncertainty. Cohen, like
Novaky et al. (1985), emphasizes the need for assessments of the societal
impacts of C02-induced hydrologic changes, and presents an outline for
evaluating impacts on the Great Lakes region (see Figure IV-4). Cohen does
not address either the difficulties of doing consistent quantitative
comparisons across diverse areas or the need to develop reliable impact
indicators -- two significant problems that will have to be addressed in later
works.
Mather and Feddema (1986) apply large-scale water-balance techniques to
major regions of the earth's surface and calculate changes in four water
budget factors of prime interest--annual potential evapotranspiration,
precipitation, water deficits, and soil water surpluses—using precipitation
and temperature data from general circulation models. Table IV-4, taken from
Mather and Feddema, shows changes in these four factors based on modeled
changes in temperature and precipitation from both the Geophysical Fluid
Dynamics Laboratory (GFDL) and Goddard Institute for Space Sciences (GISS)
models. The results indicate a reasonable agreement between the two models,
showing an increase in potential evapotranspiration in all regions
investigated. Precipitation is found to increase in the majority of the
regions studied, but these increases are generally smaller than the increases
in potential evaportranspiration. As a result, annual water deficits (the
ratio of actual evapotranspiration to potential evapotranspiration) increase
in most of the regions.
The continued use of these approaches has both advantages and
disadvantages. The principal limitation is the lack of detail about
important, basin-specific characteristics that play major roles in determining
actual water availability: snowfall and snowmelt, groundwater storage,
vegetative effects, storm runoff, and so on. Perhaps the major advantage of
this sort of large-scale assessment technique is its ability to identify
regions of hydrologic sensitivity where only marginal changes in temperature
or precipitation may lead to more significant, non-linear hydrologic responses.
* * * DRAFT FINAL * *
-------
-109-
TABLE IV-3
Effects of Climatic Change Scenarios on Annual
Water Balance of the Great Lakes Basin
Temperature Change
Precipitation
Actual Evapotranspiration
Snowmelt
Runoff
Soil Moisture Deficit (Summer)
NBS (Present Normal Winds)
NBS -- Consumption Use (2,035 proj . )
NBS (80% Winds)
NBS (80% Winds) -- Consumption Use
(2,035 proj.)
. GISS
4-4.3 to 4-4. 8°C
4-6 . 4%
+18.1%
-45.9%
-10.9%
4-116.4%
-20.8%
-28.9%
-4.1%
-11.8%
GFDL3
4-3.1 to 4-3. 7°C
4-0.8%
+6 . 7%
-35.8%
-8.2%
+166.2%
-18.4%
-26.4%
-4 . 0%
-11.7%
GFDL = Geophysical Fluid Dynamics Laboratory
NBS = Net Basin Supply
Source: Adapted from Cohen (1986a).
* * * DRAFT FINAL * * *
-------
-110-
FIGURE IV-4
Great Lakes Impacts Study
n 1MATF
VARIATION
A r. a P.
U IrrijucnfiCY
A \C3Mtnv
Aihrtthulilt
— ^
,->>
^
k-^-
*-
Direct Effect on
\tmicr fccroiiitn
Minlff mud iiKimiciKiiiLC
winicr Moiin d.mi.i^c
Mini me r \i«irm A Ilimd d. intake
liiu*\iry. lores I Incv
u.ldhlc
Direct Effect on
• , i * , f~.
f A('. ^ujtcr h;il;incc)
Direct Effect on
Water Withdrawals
and Consumption
(all stClurs)
|A( III)
• nil (.':i\ di'tii-iiul
HID
• crop i\pc JIM! \ icltl
4
Direct F.liccl on
New Lake Levels
^ i r- «-
^ illlCl MOWS '^
(Direct and Inilirccl Tffcclsl
r ' ^
. L *-
alcr
Wiihdrawals
and Consumption
f
/
s
• road accidents
• insurance
* pipe and scwcr
maintenance
(existing)
• retail
• Hydro
Production
* Shipping ports
• Fisheries
• Summer
recreation
• Shoreline
properties
• Pipe and Sewer
services (new)
• Sewage
Treatment
services
• Irrigation pipe
and sprinklers
• Wells
• Import/export
agr.
• Import/export
elcc.
• Import oil/gas
• Electricity prod.
-*
'^V
1
CHANGES
IN
TII r .^H
l M L ^H
REGIONS
"*" ECONOMY
S
employment
I/O
External Demand for
Water
coal, oil
* * * DRAFT FINAL * * *
-------
-111-
TABLE IV-4
Annual Water Budget Factors for Selected Regions
Computed from NOAA and GISS Global Climate Models
(Water budgets computed using current T + AT,
current P + %AP - current T,P)
Annual Potential
Evapotranspiration Precipitation
Location GFDL GISS • GFDL GISS
North/Central Siberia
South/Central Canada
Upper Midwest (USA)
Pacific Northwest
Ukraine (USSR)
Southeast China
Texas and North Mexico
West/Central Africa
Northeast Brazil
Southeast Australia
Southern Africa
Argentina (Pampas)
119
138
255
171
153
143
342
347
380
248
299
191
75
106
149
122
97
298
265
426
442
303
332
363
55
208
-8
62
98
298
-53
95
237
-20
-12
134
70
54
28
92
136
103
-15
221
-164
53
66
291
Water
Deficit
GFDL
63
-68
251
142
85
14
395
253
155
267
311
57
GISS
4
52
75
61
-26
0
280
205
156
251
266
72
Water
Surplus
GFDL
0
2
-13
32
29
168
0
-1
12
0
0
0
GISS
0
0
-46
32
13
-195
0
0
-451
0
0
0
Source: Mather and Feddema, 1986.
* * * DRAFT FINAL * * *
-------
-112-
A series of papers by Gleick (1986a, 1986b, and 1986c) discuss in far
greater detail the advantages and limitations of regional hydrologic
assessment techniques, particularly water-balance techniques for
intermediate-size watersheds. The ability of such water-balance models to
incorporate detail on short-time scales and geophysical details excluded by
larger models permits more accurate evaluations of changes in both runoff and
soil moisture that would result from plausible future climatic changes.
Gleick (1986b) discusses the theoretical basis for evaluating regional
hydrologic impacts of global climatic changes and presents criteria for
choosing appropriate methods (see Table IV-5). Water-balances methods,
modified from their original formulation by Thornthwaite and Mather (1955,
1957), were then applied to a specific. watershed in California that is likely
to be sensitive to future climatic changes -- a criterion that Novaky et al.
(1985) noted was of particular importance. A wide range of climate-change
scenarios was evaluated, including both hypothetical climate changes and
changes suggested by three state-of-the-art general circulation models.
Gleick's most significant finding is that there are certain consistent
hydrologic impacts despite significant differences among the climate-change
scenarios. In particular, major decreases in available soil moisture and
runoff were observed for all the scenarios, together with increases in winter
runoff (Figure IV-5a,b). These changes — and the mechanisms that lead to the
changes--correspond well with recent GCM hydrologic results published by
Manabe and Wetherald (1986), which showed statistically significant summer
soil-moisture drying in many areas. In summary, Gleicks results are
consistent with Cohen and Mather, who used the GISS and GFDL models.
The works described above provide a solid foundation of both methodology
and case studies upon which future research can be built. In particular, the
focus on the use of regional hydrologic models (see Nemec and Schaake 1982,
Flaschka 1984, Beran 1986, Cohen 1986, and Gleick 1986a, 1986b, 1986c) is the
result of the difficulty of using large GCMs for small-scale hydrologic
assessments. Until GCMs greatly improve their surface hydrology and their
resolution, regional hydrologic assessments of future climatic changes will
continue to be done using smaller, more accurate hydrologic models.
E. CRITERIA FOR USING REGIONAL MODELS TO
EVALUATE CLIMATIC CHANGES
Because of the diverse modeling methods available, it would be valuable to
have a set of criteria for choosing techniques for assessing the regional
hydrologic effects of climatic changes. This section outlines important
factors that should affect the choice of modeling methods.
To successfully use regional-scale modeling techniques to evaluate the
hydrologic consequences of climatic changes, the strengths and weaknesses of
the regional hydrologic models used must be well understood. Gleick (1986b)
describes six important limiting technical factors that must be considered
when selecting and using a regional hydrologic model to study the impacts of
changes in climate on regional water resources. These factors are listed in
Table IV-5 and discussed below.
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TABLE IV-5
Criteria for Using Regional Hydrologic Models
for Climatic Impact Assessment
1. The accuracy of the model in reproducing existing hydrologic conditions;
2. The degree to which model parameters depend upon the climatic conditions
for which the model is calibrated;
3. The availability of the input data, including comparative historical data;
4. The accuracy of the input data;
5. Model flexibility, ease of use, and adaptability to diverse hydrologic
conditions; and
6. Compatibility with existing general circulation models.
Source: Gleick 1986b.
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c
FIGURE IV-5a
Percent Change in Average Summer Soil Moisture (June, July, and August)
Over the Base Run for All Eight GCM Scenarios
*
;• (a) Temperature onJy
(b) Temperature and Relative Precipitation
(c) Temperature and Absolute Precipitation
u
C.
^»
u
2
fe
C
cc
s.
u:
Z
Z
S
cc
-JO -r
20 4-
-40 J.
-60 J.
NCAR
(a) (c)
(a)
GFDL
(b)
(c)
(a)
GISS
(b)
(c)
FIGURE IV-5b
Percent Change in Average Winter (December, January, and February)
Runoff for All Eight GCM Scenarios
- 100 T
e
n
80 f
Gk
= 60-1
40 -j-
ae
h 20 +
-20
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1. The Inherent Accuracy of the Model. Models vary in their ability to
reproduce existing hydrologic conditions in a watershed. Obviously, models
designed to investigate one small physical characteristic of a watershed may
be less accurate and less applicable for climate impact assessment than a
model that incorporates all the important hydrologic characteristics of a
basin.
2. Initial Model Calibration and Changing Conditions. When first
calibrating a model, assumptions are made about the initial values of input
parameters, and model output is computed and compared with the actual behavior
of the hydrologic system. If necessary, input parameters are then changed and
the comparison repeated until the fit is considered to be satisfactory. The
dependence of the model on its initial calibration has particular significance
for those circumstances in which a climatic change significantly affects the
underlying pre-defined parameters of a model. An example of this situation is
the extent to which a climatic change might lead to a significant change in
either the extent of vegetative cover or the hydrologic behavior of the
existing cover. Under these conditions, the initial calibration of the model
is no guarantee that the model can be used accurately for evaluating
conditions following a climatic change. The most appropriate models,
therefore, are those that can account for changes in the initial conditions
and assumptions through the incorporation of deterministic, physically-based
components, rather than mathematically-based parameterizations.
3. The Availability of Input Data, Including Comparative Historical
Data. This factor plays a major role in verifying model capabilities.
Historical hydrologic data must be available both to calibrate any given model
and to evaluate the accuracy and .applicability of a model to any given
watershed. Unless a model can be verified using actual historical records,
the use of a regional model to evaluate changes in climate will be
questioned. Estimating the accuracy of any specific simulation is difficult
in the absence of good records of existing conditions. Given such records,
however, it is reasonably simple to compare observed data with model flows and
to evaluate model accuracy.
4. The Accuracy of the Input Data. The quality of the input data is
important for model verification and for the initial choice of model
parameters. This point may be obvious to most hydrologists familiar with the
varying quality of temperature and precipitation data, but should be kept in
mind by climatologists and climate impact analysts: hydrologic measurements
are frequently inaccurate or simply unavailable. As a result, care should be
taken in the choice and application of data sets. Perhaps the most
significant problems encountered in achieving a good fit are errors in input
data or errors in the historical data used for model calibration. In these
cases, adjusting input parameters to meet erroneous data will result, in biases
to subsequent model runs. This problem is not unique to hydrologic simulation
-- almost all forms of modeling are limited by the quality of input or
historical data. Modelers must be aware of these limitations when evaluating
model accuracy and calibrating model parameters.
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5. Model Flexibility, Ease of Use, and Adaptability. Modeling
techniques designed for one hydrologic regime may not be directly applicable
to different types of watersheds. Hydrologic characteristics in different
watersheds are extremely variable in the timing, nature, and magnitude of
flows. If any information that can be generalized for different types of
watersheds is to be gained by regional impact assessment, assessment
techniques must be applicable to many diverse watersheds and they must be
flexible in their application.
6. Compatibility with Existing General Circulation Models. Finally,
the need for compatibility with existing general circulation models arises
from our desire to improve our understanding of the most likely impacts of
anthropogenic climatic change. Since general circulation models (GCMs) now
provide the most detailed information on future changes in climate, the
ability to link regional hydrologic models with output from GCMs will improve
our ability to understand the regional impacts of global climatic changes.
GCMs do not yet provide sufficient detail on regional impacts to be used for
purposes of prediction, but they do provide an internally-consistent
description of plausible patterns of climatic change. By linking the
two--GCMs and regional hydrologic models—we can develop methods that enhance
the abilities of both. Furthermore, methods for assessing regional hydrologic
impacts will improve with continued improvements both in the quality of GCM
models and output data and in the flexibility and versatility of regional
models.
F. FUTURE RESEARCH DIRECTIONS
Research in the field of hydrologic impacts of climatic changes has only
begun to identify the nature of possible changes in water resources and far
more work is needed in a number of important areas, ranging from improvements
in large-scale climate models to the development of methods for evaluating the
economic and social costs of changes in water availability and water quality.
This section outlines a number of areas where substantial progress could be
made to improve our understanding of both the nature and magnitude of possible
impacts.
1. Improvements are Needed in'Large-Scale Climate Models. The best
information on the nature of plausible future climatic changes comes from a
variety of large-scale models of the climate, including general circulation
models. Unfortunately, these models have two major limitations that reduce
their value to scientists interested in the regional hydrologic impacts of
climatic changes: their parameterizations of hydrologic processes are greatly
simplified compared to actual processes, and the computer time required to
generate detailed information on regional (rather than global-averaged)
impacts is too great. Improvements are needed in both of these areas if we
are to be able to get a sense of the magnitude, and even the direction, of
some of the most important regional impacts on water resources that will
result from climatic changes over the next several decades. Furthermore, as
regional and hydrologic predictive capabilities of GCMs improve, GCM data can
be used to drive more accurate regional models in order to get better
information on smaller-scale hydrologic impacts than is presently available.
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2. Improvements are Needed in Regional Assessment Techniques. The
importance of looking at the regional hydrologic impacts of climatic changes,
rather than simple global averages, cannot be overemphasized. Quite simply,
global averages hide considerable detail that is of crucial importance. For
example, knowing that the global average increase in precipitation from a
doubling of atmospheric carbon dioxide will be on the order of 5-10 percent is
far less important than the information that soil moisture in agricultural
regions may be significantly reduced during important parts of the growing
season (Manabe and Wetherald 1986, Gleick 1986c), that the timing of runoff in
regions sensitive to winter flooding may be shifted from spring to winter
months due to changes in the extent and dynamics of snowfall and snowmelt
(Gleick 1986c), or that lake levels may decrease (or increase) due to changes
in evaporation and runoff rates (Cohen 1986). For these reasons, more work
needs to be done on developing a variety of assessment methods that are
suitable for addressing different types of regional hydrologic problems, in
different types of hydrologic basins.
3. There Need to be More Assessments Specific Region. As new methods
are developed, additional studies need to be done on specific regions whose
water resources may be sensitive to changes in climate. As Gleick (1986c)
points out for the Sacramento Basin in California, watersheds in which
existing water resources are heavily subscribed will be vulnerable to changes
in either the timing or magnitude of hydrologic changes. While arid regions
often fall in this category, many watersheds in temperate and humid climates
may also be vulnerable due to heavy industrial, commercial, or residential
water use. An important task is to identify particularly sensitive watersheds
and to begin to assess both plausible changes in water resources and the
socioeconomic areas in which such changes would have particularly severe
results.
4. Economic and Societal Impacts Changes in Water Resources. Despite
the importance of understanding how water resources will be affected by
changes in climatic conditions, the possible changes in water availability
themselves are of less interest to society than the subsequent effects on
human activities, such as changes in agricultural productivity, alterations in
flood and drought probabilities, effects on water-resource management
activities, and so on. Methods for converting hydrologic changes into
economic and social costs must be very carefully developed and reviewed.
Extreme caution must be used to do these assessments. Experience with risk
assessment in other areas, such as the environmental costs of energy
production and use (see, for example, the reviews by Holdren et al. 1979) have
highlighted the numerous difficulties of identifying appropriate indicators of
costs, the problems of "apples and oranges" comparisons, and a variety of
other pitfalls and methodological problems that must be avoided. Novaky et
al. (1985) and Cohen (1986) outline possible impact areas and methods, but
considerably more theoretical work is needed before such assessments can be
profitably attempted.
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Climate: Potential Consequences and Corrective Measures." Environment
International, Vol. 2, pp. 215-228.
Beran, M. 1986. "The Water Resource Impact of Future Climate Change and
Variability." Presented at the International Conference on Health and
Environmental Effects of Ozone Modification and Climatic Change, U.N.
Environment Programme and U.S. Environmental Protection Agency, Washington,
B.C. 16-20 June 1986.
Budyko, M.I. and K.Y. Vinnakov. 1977. "Global Warming." In W. Stumm (ed.)
Global Chemical Cycles and their Alterations by Man (Berlin).
Cohen, S.J. 1986. "impacts of C02-Induced Climatic Change on Water Resources
in the Great Lakes Basin." Climatic Change, Vol. 8, No. 2, pp. 135-154.
Dickinson, R.E. 1984. "Modeling Evapotranspiration fpr Three-Dimensional
Global Climate Models." In Climate Processes and Climate Sensitivity,
American Geophysical Union Monograph 29. Maurice Ewing Volume 5. pp. 58-72.
Flaschka, I.M. 1984. "Climatic Change and Water Supply in the Great Basin."
Master's Thesis, Department of Hydrology and Water Resources, University of
Arizona.
Gleick, P.H. 1986a. "Regional Water Availability and Global Climatic Change:
The Hydrologic Consequences of Increases in Atmospheric C02 and Other Trace
Gases." Energy and Resources Group, Ph.D. Thesis, ERG-DS-86-1. University of
California, Berkeley. 688 pp.
Gleick, P.H. 1986b. "Methods for Evaluating the Regional Hydrologic Impacts of
Global Climatic Changes." Journal of Hydrology. (In press, accepted for
publication May 26, 1986.)
Gleick, P.H. 1986c. "Regional Water Resources and Global Climatic Change."
presented at the International Conference on Health and Environmental Effects
of Ozone Modification and Climatic Change, U.N. Environment Programme and U.S.
Environmental Protection Agency, Washington, D.C. 16-20 June 1986.
Gleick, P.H. and J.P. Holdren. 1981. "Assessing Environmental Risks of
Energy." American Journal of Public Health, Vol. 71, No. 9., pp.1046-1050.
Holdren, M.W. et al. 1979. "Energy -- Calculating the Risks." Science,
204(4393), 564.
Katz, R.W. 1977. "Assessing the Impact of Climatic Change on Food
Production." Climatic Change, Vol. 1, pp. 85-96.
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Klemes, V. 1985. "Sensitivity of Water Resource Systems to Climate Variations."
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Kneese, Allen V. and Gilbert Bonem. "Hypothetical Shocks to Water Allocation
Institutions in the Colorado Basin." Reprint 225, Resources for the Future,
Washington, D.C. 1986.
Kutzbach, John E. and F. Alayne Street-Perrott. 1985. "Milankovitch forcing
of flutuations in the level of tropical lakes from 18 to 0 Kyr BP." Nature,
Vol. 317, pp. 130-134, 12 September 1985.
L'vovich, M.I. 1979. World Water Resources and Their Future. American
Geophysical Union, (English Translation edited by R.L. Nace).
Langbein, W.B. and others. 1949. "Annual Runoff in the United States."
Geological Survey Circular 52, U.S. Department of the Interior, Washington,
D.C. (June), reprinted 1959.
Manabe, S., Wetherald, R.T., .and R.J. Stouffer. 1981. "Summer Dryness Due to
an Increase of Atmospheric C02 Concentration." Climatic Change 3,
pp.347-386.
Manabe, S. and R.T. Wetherald. 1986. "Reduction in Summer Soil Wetness Induced
by an Increase in Atmospheric Carbon Dioxide." Science, Vol. 232, No 4750,
pp. 626-628.
Mather, J.R. and J. Feddema. 1986. "Hydrologic Consequences of Increases in
Trace Gases and C02 in the Atmosphere." Presented at the International
Conference on Health and Environmental Effects of Ozone Modification and
Climatic Change, U.N. Environment Programme and U.S. Environmental Protection
Agency, Washington, D.C. 16-20 June 1986.
McGhee, R. 1981. "Archaeological Evidence for Climatic Change During the Last
5000 Years." In Wigley, Ingram, and Farmer (eds.) Climate and History.
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•
Meehl, G.A. 1984. "Modeling the Earth's Climate." Climatic Change 6, pp.
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Mitchell, J.M., Jr. 1983. "An Empirical Modeling Assessment of Volcanic and
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(January 10-14).
Nemec, J. and J. Schaake. 1982. "Sensitivity of Water Resource Systems to
Climate Variation." Hydrological Sciences 27, No. 3, pp.327-343 (September).
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Nicholson, S.E. 1986. "Climatic Evolution and Variability in Dryland Regions:
Applications of History to Future Climatic Change." Presented at the
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Modification and Climatic Change, U.N. Environment Programme and U.S.
Environmental Protection Agency, Washington, B.C. 16-20 June 1986.
Novaky, B., Pachner, C., Szesztay, K., and D. Miller. 1985. "Water Resources."
In Climate Impact Assessment, Kates, Ausubel, and Berberian, (eds.). John
Wiley and Sons, New York, pp. 187-214.
Revelle, R.R. and P.E. Waggoner. 1983. "Effects of a Carbon Dioxide-Induced
Climatic Change on Water Supplies in the Western United States." In Changing
Climate, National Academy of Sciences (National Academy Press).
Rosenberg, N.J. 1981. "The Increasing C02 Concentration in the Atmosphere and
its Implication on Agricultural Productivity. I. Effects on Photosynthesis,
Transpiration and Water Use Efficiency." Climatic Change 3, pp. 265-279.
Rosenberg, N.J. 1982. "The Increasing C02 Concentration in the Atmosphere and
its Implication on Agricultural Productivity. II. Effects Through C02-Induced
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Schneider, S.H. and R. Chen. 1980. "Carbon Dioxide Warming and Coastline
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Schneider, S.H., Richard W. Katz and Linda 0. Mearus. June 1985. "Parodigm
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Schwarz, H.E. 1977. "Climatic Change and Water Supply: How Sensitive is the
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Stockton, C.W. and D.M. Meko. 1983. "Drought Recurrence in the.Great Plains
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Street-Perrott, F.A. and S. Harrison. 1985. "Lake-Level Fluctuations." In
A.D. Hecht (ed.) Paleoclimatic Analysis and Modeling. John Wiley and Sons,
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Thornthwaite, C.W. and J.R. Mather. 1955. "The Water Balance." Drexel
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Computing the Potential Evapotranspiration and the Water Balance." Drexel
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Climatology, Vol. X, No.3. 311 pages.
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D.C. Available from the National Technical Information Service, Springfield,
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Biosphere." Climatic Impact Assessment Program (CIAP) Monograph 5, Part 2.
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D.C. (October).
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Increasing Atmospheric C02 with Emphasis on Water Availability and Hydrology
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Scientific Assessment, National Academy of Sciences, Climate Research Board,
Washington, D.C.
Washington, W.M. and G.A. Meehl. 1984. "Seasonal Cycle Experiment on the
Climate Sensitivity Due to a Doubling of C02 With an Atmospheric General
Circulation Model Coupled to a Simple Mixed-Layer Ocean Model." Journal of
Geophysical Research, Vol. 89, No. D6, pp. 9475-9503 (October 20).
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V. EFFECTS ON HUMAN HEALTH
Prepared by:
Laurence S. Kalkstein
Kathleen M. Valimont
A. FINDINGS
1. Weather has a profound effect on human health and well-being. It has
been demonstrated that weather is associated with changes in birth
rates, and sperm counts, with outbreaks of pneumonia, influenza and
bronchitis, and is related to other morbidity effects linked to pollen
concentrations and high pollution levels.
2. Large increases in mortality have occurred during previous heat and
cold waves. It is estimated that 1,327 fatalities occurred in the
United States as a result of the 1980 heat wave; the number occurring
in Missouri alone accounted for over 25% of the total.
3. Hot weather extremes appear to have a more substantial impact on
mortality than cold wave episodes. Most research indicates that
mortality during extreme heat events varies with age, sex, and race.
Factors associated with increased risk from heat exposure include
alcoholism, living on higher floors of buildings, and the use of
tranquilizers. Factors associated with decreased risk are use of air
conditioning, frequent exercising, consumption of fluids, and living
in shaded residences. Acclimatization may moderate the impact of
successive heat waves over the short term.
4. Threshold temperatures for cities, which represent maximum and minimum
temperatures associated with increases in total mortality, have been
determined These threshold temperatures vary regionally; for
example, the threshold temperature for winter mortality in mild
southern cities such as Atlanta is 0°C and for more northerly cities,
such as Philadelphia, it is -5°C.
5. Humidity has an important impact on mortality since it contributes to
the body's ability to cool itself.by evaporation of perspiration. It
also has an important influence on morbidity in the winter because
cold, dry air leads to excessive dehydration of nasal passages and the
upper respiratory tract and increased chance of microbial and viral
infection.
6. Precipitation in the form of rainfall and snow is also associated
with changes in mortality. In New York City, upward trends in
mortality were noted the day after snowfalls that had accumulated 2
inches or more. In Detroit where snow is more common, the snowfall
accumulation exceeded 6 inches before mortality increases were noted.
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7. If future global warming induced by increased concentrations of trace
gases does occur, it has the potential to significantly affect human
mortality. In one study, total summertime mortality in New York City
is estimated to increase by over 3,200 deaths per year for a 7°F
trace-gas-induced warming without acclimatization. If New Yorkers
fully acclimatize, the number of additional deaths are estimated to be
no different than today. It is hypothesized that, if climate warming
occurs, some additional deaths are likely to occur because economic
conditions and the basic infrastructure of the city will prohibit full
acclimatization even if behavior changes.
8. Two areas of important future research include investigation of
morbidity impacts and the costs to society of indirect impacts (e.g.,
costs associated with modifying living and working areas, decreases in
productivity, and other climate/stress-induced impacts).
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B. INTRODUCTION
There is a large body of literature devoted to the impact of variable
climate on human well-being. Most of the research has been done by medical
scientists, and a minor amount of the work has been performed by
climatologists. This section will attempt to describe much of the relevant
research that has been published to date. Topics will be subdivided on the
basis of weather events, as many of the manuscripts evaluated employ a
regression technique to determine the impacts of one or more climatic events
on human health.
There appears to be general agreement that weather has a profound impact
on human health, but scientists do not agree on the precise mechanisms
involved. For example, some of the research suggests that extreme weather
events appear to have the greatest influence on health. Driscoll (1971a)
correlated daily mortality for 10 cities with weather conditions in January,
April, July, and October and found that large diurnal variations in
temperature, dewpoint, and pressure were associated with many high mortality
days. In addition, hot, humid weather with concomitant high pollutant
concentrations were also contributory mechanisms. Other studies do not
attribute large variations in mortality to extreme events, but rather to the
normal seasonal changes in weather (Persinger, 1980).
The importance of determining the role of weather in human health cannot
be understated. Reports of large increases in mortality during heat and cold
waves are commonplace; for example, the National Oceanic and Atmospheric
Administration (NOAA) estimated that 1,327 fatalities in the United States
were directly attributed to the 1980 heat wave; fatalities in Missouri alone
accounted for over 25% of the total excess deaths (U.S. Department of
Commerce, 1980). During a heat wave in 1963, more than 4,600 deaths above a
computed mean occurred in June and July in the eastern United States (Schuman
et al., 1964). The impact of weather on human well-being goes beyond
mortality; even birth rates and sperm counts appear to be affected by
meteorological phenomena (Calot and Blayo, 1982; Tjoa et al., 1982; White,
1985).
This report will concentrate on the effects of weather upon human
mortality. However, there are numerous other impacts of weather on the
general health of the population, including morbidity, short-term changes in
mood, emotional well-being, and aberrations from normal behavior. For
example, asthma attacks, many of which occur from inhalation of airborne
agents such as spores and molds, appear to be related to various
meteorological variables (White, 1985). Goldstein (1980) found that clusters
of attacks are preceded by the passage of a cold front followed by a high
pressure system. Morbidity attributed to pneumonia, influenza, bronchitis,
and probably many other illnesses is also weather-related (White, 1985).
In addition, several atmospheric phenomena that are indirectly related to
weather and might have an impact on mortality (the most notable being
atmospheric pollutants and pollen concentrations) are not included in this
review. A partial annotated bibliography of pollen concentration is presently
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available (Kalkstein and Robeson, 1984), but there is little research
comparing weather/pollen relationships to human health. Meteorologic
conditions exert a large influence on pollution concentrations and dispersion
and they also affect the impact of pollution on mortality and morbidity. Much
of the literature on this topic has already been summarized (Stern, 1977).
Probably the most intensively-studied weather element that affects human
mortality is air temperature, especially the impact of summer heat. A
detailed description of temperature/mortality relationships follows.
C. TEMPERATURE EFFECTS
1. General Impacts
The impact of temperature on morbidity and mortality can be assessed at
both the seasonal and daily level. The variability in occurrence of numerous
illnesses is linked to somewhat predictable seasonal trends in temperature
(Persinger, 1980), although significant year-to-year differences do occur.
Medical disorders such as bronchitis, peptic ulcer, adrenal ulcer, glaucoma,
goiter, eczema, and herpes zoster are related to seasonal variations in
temperature (Tromp, 1963). Heart failure (most often myocardial infarction)
and cerebrovascular accidents represent two general mortality categories that
have been correlated many times with ambient monthly temperatures (Persinger,
1980). Complications from these disorders can be expected at higher
temperatures since the body responds to thermal stress by forcing blood into
peripheral areas to promote heat loss through the skin. This increases
central blood pressure and encourages constriction of blood vessels near the
core of the body. However, increases in heart disease are also noted at very
cold temperatures as well. Strong negative correlations have been found
between winter temperature and deaths in certain North American, northern
Asian, and European countries (Persinger, 1980).
The degree of seasonality in the climate of a region also appears to
affect mortality rates. Katayama and Momiyama-Sakamoto (1970) reported that
countries with smaller seasonal temperature ranges exhibit steeper regression
lines in temperature-mortality correlations than do countries with greater
temperature ranges. Maximum death rates in warmer countries are found at
below normal temperatures, and in cooler countries similar temperatures will
produce no appreciable rise in mortality.
There is conflicting evidence concerning the impact of daily temperature
fluctuations on human mortality. Some studies contend that mostly long-term
(i.e., monthly and annual) fluctuations in temperature affect mortality
(Sakamoto and Katayama, 1971) and only small, irregular aberrations can be
explained by daily temperature variability (Persinger, 1980). However,
Kalkstein and Davis (1985) report that daily fluctuations in temperature can
increase mortality rates by up to 50% in certain cities. This has been
corroborated in a detailed study of New York City mortality where large
increases in total and elderly mortality occurred during the 1980 heat wave
(Figure V-l).
* * * DRAFT FINAL * *
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-126-
FIGURE V-1
Mortality During 1980 Heat Wave in New York City
100
oc
O
if
-so
s
1O 11 12 13 14 15 16 17 18 19 2O 21 22 23 24
DATE (JULY 198O)
-TOTAL MORTALITY -ELDERLY MORTALITY TEMPERATURE
Source: Kalksten, Davis and Skindlow (September 1986).
* * DRAFT FINAL *
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-127-
2. Impacts of Hot Weather
a. General Relationships
Much of the temperature-mortality research has concentrated on heat and
cold wave episodes. It appears that hot weather extremes have a more
substantial impact than cold, and many "heat stress" indices have been
developed to assess the degree of impact (Quayle and Doehring, 1981;
Kalkstein, 1982; Steadman, 1984). Driscoll (1971b) related 19 different
meteorological variables with total mortality and other more specific
mortality classes (cause of death, age) and identified high temperature as the
most important causal mechanism in summer. Many other studies support this
relationship between temperature and mortality (Ellis, 1972; Ellis et al.,
1975; Oechsli and Buechley, 1970). Interestingly, a majority of studies have
found that most of the excess deaths that occurred during periods of intense
heat were not attributed to causes traditionally considered to be
weather-related, such as heat stroke (Cover, 1938). Consequently, many
researchers continue to utilize total mortality figures in their analyses, as
deaths from a surprisingly large number of causes appear to escalate with
increasing temperature (Applegate et al., 1981; Jones et al. 1982).
Although most researchers have preferred the use of maximum temperature as
the primary predictor of mortality, others continue to utilize average daily
temperature as their primary weather statistic. While Kutschenreuter (1959)
found that maximum temperature with a 1-day lag was the single most important
predictive weather/mortality variable, Rogot (1973) worked strictly with daily
average temperature to evaluate cardiovascular diseases; others have even used
weekly averages (Lye and Kamal, 1977; Callis and LeDuc, 1985). Those who use
daily averages cite the importance of warm nights in contributing to
mortality, something that is neglected when utilizing maximum temperatures
alone (Ellis et al., 1975). However, others report that daily averages tend
to mask the effect on mortality of large daily oscillations in temperature
(MacFarlane and Waller, 1976).
A number of studies compare death rates for extreme periods with those
encountered during normal meteorological periods; this approach has met with
some success (Oechsli and Buechley, 1970; Schuman et al., 1964; Schuman,
1972). Jones et al. (1982), in summarizing the work of others, found that
high temperature, the number of days that the temperature is elevated, high
humidity, and low wind velocity are all found within the climate/mortality
models of various researchers (Figures V-2 and V-3). An earlier work by
Schuman (1972) includes smog as a related mechanism associated with
fluctuations in death rate (Figure V-4).
Rather than incorporating daily death totals, many heat wave/mortality
studies have utilized weekly mortality totals compiled by the Centers for
Disease Control for their primary input (Centers for Disease Control, 1984).
Schuman (1972) calculated expected weekly death rates based on a 5-year moving
mean, and periods of weekly excess mortality were isolated. Callis and LeDuc
(1985) compared weekly mortality rates to weather for 10 U.S. cities and
uncovered some large weather-induced fluctuations. In general, studies
* * * DRAFT FINAL * * *
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FIGURE V-2
Deaths, by Date of Occurrence, Kansas City, MO, Residents
June 1978 to July 1979 and 1980
35
30
20-
15-
10-
5-
vu r •.
24 26 28 30 2 4 6 8 10 12 '« ifl '8 20 22 24 76 ?8 30
Jure Jjiy
FIGURE V-3
Deaths, by Date of Occurrence, St. Louis, MO, Residents
June 1978 to July 1979 and 1980
60
55-
50-
45-
40-
» 35-
I 3
o
I
20-
15-
10
5 J
0
' f / /
24 26 28 30 2 4 8 8 10 12 U '6 18 20 22 24 .J6 28 30
Jun« July
Dotted line indicates mean values for 1978 to 1979 period; solid line, 1980.
Source: Jones 1980.
* * * DRAFT FINAL * * *
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-129-
FIGURE V-4
Fluctuations in Death Rate in New York Associated with Episodes of Heat
(July 2-15) and Smog (November 23-26) in 1966 --
Illustrating the Method of Excess Mortality
WEEKS
20 a« zt
4O 44
<
I
LU
Q
I I
I I
f
APRIL MAY
MONTHS
JUNE JULY
AUG. SEPT.
OCT.
NOV.
DEC.
LEGEND
EXPECTED 95% TOLERANCE ZONE
• EXPECTED. 5 YEAR MEAN
-REPORTED (MY Of REPORT)
. CORRECTED (FOR DAY Of DEATH)
JAN. FEB. MARCH
1967 »'C -OL'.O
Source: Shuman, S.H., 1972: "Patterns of Urban Heatwave Deaths and
Implications for Prevention: Data From New York and St. Louis during
July 1966." Environmental Research 5, 62.
* * DRAFT FINAL * * *
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-130-
incorporating weekly data sets are less revealing than their daily
counterparts, as extreme episodes are often dampened when time scales are
increased.
One of the most commonly reported findings in heat wave-mortality studies
involves the lag time between the temperature event and the mortality
response. A lag period of one day was most often uncovered (Ellis, 1972; Ellis
et. al., 1975; Ellis and Nelson, 1978); others, however, have observed a two-
to three-day lag (Schuman, 1972; Oechsli and Buechley, 1970), and some have
noted no lag (Kalkstein and Davis, 1985).
Temperature affects not only mortality, but also morbidity. Applegate et
al. (1981) demonstrated the relationship between temperature and morbidity.
In that study, as shown in Figures V-5 and V-6, he found that emergency room
hospital visits and admissions appear to be correlated with the 1980 heat wave
in Tennessee.
b. Responses of the Population
Kilbourne et al. (1982) conducted a case study in which a number of heat
factors associated with heat stroke were identified. Factors found to be
associated with an increased risk of heat stroke included alcoholism, living
on higher floors of buildings, and the use of tranquilizers. Factors found to
be associated with a decreased risk were use of air conditioning, frequent
exercising, consumption of fluids, and living in a well-shaded residence.
During extreme heat episodes, heat stroke risk is increased as demonstrated by
the 1980 heat wave in St. Louis, which resulted in a ten-fold increase in
total deaths (Figure V-7).
Most research indicates that mortality rates during extreme heat vary
with age, sex, and race. Oechsli and Buechley (1970) found that mortality
rates during heat waves increase with age. This is supported by the work of
others (e.g., Bridger et al., 1976, Lye and Kamal, 1977; Jones et al., 1982).
The elderly seem to suffer from impaired physiological responses and often are
unable to increase their cardiac output sufficiently during extremely hot
weather (Sprung, 1979). In addition, sweating efficiency decreases with
advancing age (Crowe and Moore, 1973), and many of the medications commonly
taken by the elderly have been reported to increase the risk of heat stroke
(Jones et al., 1982). Certain researchers have determined slight rises in
mortality rates of infants during heat waves (Bridger et al., 1976; Ellis,
1972; Foster et al. 1968), but this is not a universal finding (Schuman, 1972).
Studies relating mortality to gender also yield conflicting results.
Studies in which increased mortality rates were found among females during hot
weather include those of Applegate et al. (1981) and Rogot and Padgett
(1976). Rotton (1983) suggests that this may be attributed to differences in
dress among the sexes. Bridger et al. (1976) and Ellis (1972) found higher
heat-induced mortality rates among men. Studies of the role of race have also
produced conflicting results. Schuraan (1972) found that blacks appear more
susceptible to heat-related deaths in St. Louis and whites are more
* * DRAFT FINAL * *
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-131-
FIGURE V-5
Daily Heat-Related Emergency Room Visits, Hospital Admissions,
and Total Deaths, June 25-July 30, 1980 -- Shelby County, Tennessee
(A
5
I a
> O
e'55
§.<2
S>6
®3
E<
(A
O
HEAT RELATED
Admits
Visits
Deaths
24 26 21 M 2 4 6 I 10 12 14 16 II 20 22 24 26 21 M
JUNf. 1»«0 JUIY. IMO
Days
FIGURE V-6
Daily Temperatures, June and July 1979 Versus June and July 1980,
and Dew Points for 1980 -- Shelby County, Tennessee
— Norm«l
-* Avg. Temp. 1979
Avg. Txnp. 1980
Max.T«mp. 1980
Avg. Om« Point 1980
WI7MMI*IO22}42&2t
JULT
Days
Source: Applegate, W.B., MD, MPH; J.W. Runyan, Jr., MD; L. Brasfield, MS;
M.L. Williams, MSW; C. Konigsberg, MD, MPH; and C. Fouche, RRA, 1981:
Analysis of the 1980 heat wave in Memphis. Journal of the American
Geriatrics Society, 29,338-29,339.
* * DRAFT FINAL * *
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-132-
FIGURE V-7
Heat-Related Illness by Date of Onset and Daily Maximum Temperatures
St. Louis, MO, July 1980
Norm* D«*y Maximum T«mpwttm
9 11 13 15 17 19 21 23 25 27 29 31
Source: Jones, T.S., MD, MPH; A.P. Liang, MD, MPH; E.M. Kilbourne, MD; M.R.
Griffin, MD; P.A. Patriarca, MD; S.G.G. Wassilak, MD; R.J. Mullan,
MD; R.F. Herrick, MS; H.D. Donnel, Jr.,.MD, MPH; K. Choi, PhD; and
S.B. Thacker, MD; 1982: Morbidity and mortality associated with the
July 1980 heat wave in St. Louis and Kansas City, MO. Journal of the
American Medical Association, 247, 3327-3330.
* * DRAFT FINAL * * *
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susceptible in New York (Table V-l). However, Ellis et al. (1975) and Bridger
et al. (1976) have discovered that white mortality rates are higher than
black's under all examined conditions. Rather than race, socioeconomic status
may have an influence on weather/mortality relationships. Large numbers of
deaths during heat waves are found among poor inner-city residents who have
little access to cooler environments (Jones et al., 1982).
Initial observations of daily standardized deaths vs. maximum temperature
suggest that weather has an impact on only the warmest 10-20% of the days;
however, the relationship on those very warm days is impressive (see Figure
V-8). During warm periods, a "threshold temperature," which is the maximum
temperature above which mortality increases, can be determined. The threshold
temperature can be calculated objectively by using a sums of squares technique
(Kalkstein, 1986). The threshold temperature for deaths in New York, above
which mortality increases dramatically, is 92°F. This procedure can be
repeated for winter, as discussed later in this section, where the threshold
temperature represents the minimum temperature below which mortality
increases.
c. Acclimatization
Several studies have evaluated acclimatization as a factor contributing to
heat-related deaths. Gover (1938) reported that excess mortality during a
second heat wave in any year will be slight in comparison to excess mortality
during the first, even if the second heat wave is unusually extreme. Two
possible explanations for this phenomenon are provided. First, the weak and
susceptible members of the population die in the early heat waves of summer,
thus lowering the population of susceptible people who would have died during
subsequent heat waves. Second, those who survive early heat waves become
physiologically acclimatized and hence deal more effectively with later heat
waves (Marmor, 1975). Rotton (1983) suggests that geographical
acclimatization is also significant, and people moving from a cool to a
subtropical climate will adapt rather quickly, often within two weeks.
However, the population must still make behavioral and cultural adjustments
(Ellis, 1972). Further support for geographical acclimatization is provided
by Kalkstein and Davis (1985), who noted that mortality increased dramatically
during heat waves in northern cities but not in southern cities.
There is some research that implies that the effect of acclimatization has
been overstated by many scientists. The use of the wind-chill index in winter
and the temperature-humidity index in summer by many meteorologists seems to
indicate that they believe acclimatization may have minimal impact on human
activities. Both indices are based on absolute values only: a temperature of
93°F with a humidity of 43% yields the .same temperature-humidity index value
whether it occurs in New Orleans or Duluth. The hot weather indices most
widely-accepted by the National Weather Service are all absolute, and they
include the temperature-humidity index, humiture, humidex, the discomfort
index, and apparent temperature (Thorn, 1959; Winterling, 1979; Steadman,
1979a; 1979b; Weiss, 1983). The only geographically relative index that has
been published, the weather stress index, is only beginning to be utilized to
evaluate a variety of the impacts that climate has on humans (e.g., mortality)
(Kalkstein and Valimont, 1986).
* * * DRAFT FINAL * *
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TABLE V-1
Comparison of Patterns of Heat-Wave Mortality in
in New York and St. Louis (July 1966)
Characteristic
New York
St. Louis
Population at risk (approximately)
Duration of heat wave
No. days over 90°F
Mortality
a/
Excess deaths (estimated number)
All ages (proportion rise)
65+ years (proportion rise)
"Rate" per million per week
Race
White
Nonwhite
.Sex
Male
Female
b/
Race-sex group at highest risk
Range of excess deaths by resi-
dence (census tract)
7.8 million
14 days
12
1181
36.3%
52.6%
75 . 7%
39%
20%
25.3%
50.4%
WF 56.2%
10% to 140%
728,000
28 days
24
618
55.8%
81.1%
197.1
41%
119%
28.0%
57.5%
NWF 140.1%
-18% to 260%
a/ Applying the method of excess mortality to an arbitrary control period
(see text): For New York--2 weeks of "average" mortality during May 7-20,
1966; for St. Louis--4 weeks of mortality during July of 1965 (7/2-7/29).
b/ WF = white female, NWF = nonwhite female.
Source: Schuman, S. H., 1972: Patterns of urban heat wave deaths and
implications for prevention: Data from New York and St. Louis during
July, 1966. Environmental Research, 5, 58-75.
* * * DRAFT FINAL * * *
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FIGURE V-8
Daily Summer-Season Standardized Mortality Versus
Maximum Temperature: New York
>. 275
25°
8 225
200
175
150
125
100
75
Q
LJLJ
M
Q
CXL
<
Q
z
to
65 75 85 95
MAXIMUM TEMPERATURE (°F)
Source: Kalkstein, L.S., R.E. Davis, J.A. Skindlov, and K.M. Valimont. The
impact of human-induced climatic warming upon human mortality: A New
York City case study. Proceedings of the International Conference on
Health and Environmental Effects of Ozone Modification and Climate
Change, in press.
* * * DRAFT FINAL * *
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One cultural adjustment that may have an impact on heat wave-related
mortality is the use of air conditioning. Kilbourne et al. (1982), in an
attempt to identify factors related to heat stroke, found a strong negative
relationship between daily hours of home air conditioning and heat-related
mortality. This finding is supported by Oechsli ajid Buechley (1970) in their
study of heat-related deaths in Los Angeles. However, Ellis and Nelson (1978)
have noted that during the past 30 years, mortality during heat waves in New
York City has not changed significantly despite the increased use of air
conditioning. Analysis by Mariner (1975) supports this finding; his study
covering a 22-year period implied that air conditioning may be decreasing
excess mortality during initial summer hot spells only.
d. Some Predictive Equations
Several general algorithms have been developed to predict mortality
changes during heat waves. Buechley et al. (1972) developed the following
algorithm for heat-related mortality at temperatures above 90°F:
n
TMR = cycle + O.lOe (1)
where TMR is the temperature-specific mortality ratio (the predicted mortality
for the day divided by the average annual daily mortality), cycle is the
expected mortality ratio for that day of the year (an attempt to account for
the impact of seasonality on mortality), and F1 is yesterday's temperature.
Cycle is computed from several years of mortality data and varies in a
sinusoidal fashion, peaking in the winter and reaching a minimum at the end of
the summer. Each day has a distinctive cycle value depending upon the mean
mortality rate for that time of year. The following example represents a
hypothetical calculation of TMR. Assume that the maximum temperature on a
given day is 100°F, and the cycle is 0.95. TMR = 0.95 4- 0. le°'2(~100~90) ,
which equals 1.70. Thus the equation predicts that mortality on the day
following the 100° maximum temperature will equal 170% of the annual mean
daily mortality. Oechsli and Buechley (1970) had previously developed a
related algorithm, the age- and temperature-specific mortality ratio model
(ATMR) :
ATMR = 98.806 + e''15'23 + '°385 ^ + "1655 F) (2)
where F is the present day's maximum temperature.
In a more recent study, Marmor (1975) attempted to develop a model that
accounted for acclimatization effects. This led to his sensitivity index,
which decreased as the population was exposed to more hot days during the
season. Sensitivity (S ) equals:
1/(1 + e(Ad - 6)/°'46) (3)
where A, is the total number of previous days with temperatures over 90°F.
* * DRAFT FINAL * * *
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This sensitivity value was added to a newer version of the TMR algorithm,
producing the following:
fF1-90')0 2
TMR = cycle + (0.05 + 0.06 sensitivity) e^ *»>»•<•
+ 0.05e(F-90)0.2 + Q Q7 e(f-75)0.2 (4)
where f is the previous day's minimum temperature, F1 is the previous day's
maximum temperature, and F is the present day's maximum temperature (Marmor
1975).
3. Impact of Cold Weather
a. General Relationships
Many studies have provided evidence that mortality rates increase during
periods of cold weather. In general, total mortality is about 15% higher on
an average winter day than on an average summer day (National Center for
Health Statistics, 1978). However, increases in mortality during exceedingly
cold periods are less dramatic than their hot weather counterparts (Kalkstein,
1984). The impact of cold on human well-being is highly variable. Not only
is cold weather responsible for direct causes of death such as hypothermia,
influenza, and pneumonia, it is also a factor in a number of indirect ways.
Death and injury from falls, accidents, carbon monoxide poisoning, and house
fires are all partially attributable to cold (U.S. Department of Commerce,
1984).
Hypothermia occurs when the core body temperature falls below 35°C
(Centers for Disease Control, 1982). Certain sectors of the population appear
more susceptible to hypothermia than others. Most victims fall in one or more
of the following categories: the elderly, newborns, the unconscious,
alcoholics, and people on medications (Fitzgerald and Jessop, 1982; Lewin et
al., 1981j Hudson and Conn, 1974; Bristow et al., 1977; Massachusetts General
Hospital, 1982). In addition, malnourishment, inadequate housing, and high
blood ethanol levels increase the incidence of hypothermia (Centers for
Disease Control, 1982).
Sex and race appear to be related to susceptibility to hypothermia.
Nonwhite elderly men generally constitute the highest risk group, while white
women comprise the lowest risk group (Rango, 1984; Centers for Disease
Control, 1982). Women possess a higher skin temperature to core temperature
gradient, suggesting that they are better able to maintain a higher body core
temperature during periods of cold stress (Cunningham et al., 1978; Hardy and
DuBois, 1940; Wyndham et al., 1964; Graham, 1983). Some studies contend that
the difference in the response of men and women to cold is related to the
amount of subcutaneous fat within the body (Hardy and DuBois, 1940; Wyndham et
al., 1964), but other studies have failed to confirm this hypothesis
(Bernstein et al., 1956; Gallow et al., 1984; Veicsteinas et al., 1982).
Although women are less susceptible to hypothermia, they appear to be more
susceptible to peripheral cold injuries such as frostbite (Graham and
Lougheed, 1985).
* * * DRAFT FINAL * * *
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Age appears to have an even greater impact upon hypothermia sensitivity
than gender, and the elderly display the highest mortality rates of all
groups. Vasoconstriction and shivering, two primary cold adaptive measures,
appear to be reduced in many elderly persons (Collins et al. 1977; Collins and
Easton et al. 1981; ; Wagner et al., 1974). In addition, many of the elderly
do not discriminate changes in temperature well and are thus less able to
adjust to them (Collins and Exton-Smith et al., 1981).
One of the first efforts to predict the impact of a severe cold wave was
published by NOAA using algorithms developed by Kalkstein. Seven cities in
the eastern and southern United States exhibited significant relationships
between winter weather and mortality, and the following regression equations
were developed for each:
Atlanta: MORT = C - .11 MT
Chicago: MORT = C - .08 MT
Cincinnati: MORT = C - .21 MT - .01 CDH + . 13 MRS
Dallas: MORT = C - . 12 MT - . 13 MIN - .02 CDH
Detroit: MORT = C - .11 MT
Oklahoma City: MORT = C - . 16 MT
Philadelphia: MORT = C + .09 MD + .01 CDH + .06 VAM - .08 WPM,
where MORT is the daily standard deviation increase in mortality above the
mean, C is a constant (different for each city), MT is daily maximum
temperature, HRS is the total hours in the day with temperatures below 32°F,
MIN is daily minimum temperature, MD is daily minimum dewpoint, WAM is 3AM
windspeed, WPM is 3PM windspeed, and CDH is a measure of the day's coldness
and is calculated as follows:
N
CDH = I (32-5), where T < 32.
T represents the hourly temperature and N represents total hours in a day with
temperatures below 32°F. A map (Figure V-9) of predicted mortality increases
during the January 1985 cold wave showed potentially significant increases in
the eastern and central United States. Data limitations have precluded these
predictions from being verified to date.
b. Adaptation
It appears that adaptation to cold temperatures can occur through repeated
exposures. Radomski and Boutelier (1982) noted that men who had bathed in
15°C water for one-half hour over nine consecutive days before a trip to the
Arctic showed less signs of cold-induced stress than non-treated men.
There appears to be a cold-adaptive mechanism influencing mortality as
well. In a study comparing winter mortality rates for 13 cities in different
climates around the U.S., a large differential response was noted. The
southern cities seemed to exhibit the greatest increases in mortality during
cold weather, while little or no response was found in northern cities
* * * DRAFT FINAL * * *
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-139-
FIGURE V-9
Predicted Impact of the January 20-21, 1985, Intense Cold on Mortality
A:
SEVEN CITY TOTAL
(151. 25%)
Ltg«nd
(Mnatir of tatto attriautod to sovoro coW,\
Poreont bicroao* In total daHy doatho
NOAA/AISC - Unlvoraity of Oolawarot Dopartmont of
i V---'
(1C ,«i*>
»' ff^
Source: Kalkstein, L.S., 1984: The impact of winter weather on human
mortality. Climate Impact Assessment: United States, December,
21-23.
* * * DRAFT FINAL * * *
-------
-140-
(Kalkstein, 1984). In a city such as Minneapolis, no increase in mortality
was noted at temperatures down to -40°C, but in Atlanta, mortality increases
were evident if the maximum temperature did not exceed 0°C (Kalkstein and
Davis, 1985). Of the 13 cities studied, 7 demonstrated a statistically
significant relationship between winter cold and mortality. The six
non-significant cities included cold weather locations (Minneapolis) and mild
West Coast locations where very cold weather is virtually unknown (Los Angeles
and San Francisco). "Threshold temperatures," which represent temperatures
below which notable increases in mortality occur, were established for the
seven cities (Kalkstein and Davis, 1985). The threshold temperatures were
comparatively mild for the more southerly cities (0°C for Atlanta; 1°C for
Dallas) and somewhat colder for the more northerly cities (-5°C for
Philadelphia). This differential geographical response seems to add credence
to the importance of relative, rather than absolute weather conditions.
There is evidence that a lag time of two to three days exists between the
offending cold weather and the ultimate mortality response (Kalkstein, 1984).
Deaths did not necessarily rise on the day of the coldest temperatures, but in
many cases, the sharpest increases were noted three days after the coldest
weather occurred. A similar lag time was not noted after extremely hot summer
days; the impact appears more immediate in summer.
D. HUMIDITY AND PRECIPITATION EFFECTS
1. Effects of Humidity
Humidity has an important impact on mortality since it influences the
body's ability to cool itself by .means of evaporation of perspiration. In
addition, humidity affects human comfort, and the perceived temperature by
humans is largely dependent upon atmospheric moisture content (Persinger,
1980).
The effects of low humidity can be especially dramatic in winter, when low
moisture content induces stress upon the nasal-pharynx and trachea. When very
cold, dry air passes through these organs, warming occurs and air temperatures
in the pharynx can reach 30°F. The ability of this warmer air to hold
moisture increases dramatically, and moisture is extracted at a prodigious
rate from the nasal passages and upper respiratory tract, leading to excessive
dehydration of these organs (Richards and Marriott, 1974). This appears to
increase the chance of microbial or viral infection since a rise in the
viscosity of bronchial mucous seems to reduce the ability of the body to fight
offending microorganisms that may enter the body from the atmosphere. This
may explain why Green (1966) found negative correlations between relative
humidity and winter absenteeism in a number of Canadian schools.
In the summer, high moisture content during hot periods can lessen the
body's ability to evaporate perspiration, possibly leading to heat stress.
Recent weather/mortality models developed for the National Oceanic and
Atmospheric Administration indicate that dewpoint temperature is directly
related to mortality in several eastern cities when temperatures are very hot
(Kalkstein, 1985). Another summer study indicated that mental well-being may
* * * DRAFT FINAL * *
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-141-
also be influenced by summer relative humidity. Persinger (1975) found
significant negative relationships between relative humidity and "mood
scores," which represent a measure of happiness. Sanders and Brizzolara
(1982) found relative humidity to be significantly related to a linear
combination of three mood variables (vigor: r = -.82; social affection:
r = -.76; elation: r = -.56).
2. Effects of Precipitation
Most of the precipitation/mortality research to date has concentrated on
the impact of snow and other forms of severe winter weather. Rogot and
Padgett (1976) found cold weather and snow to be statistically related to
deaths from stroke and heart attack--a finding that has been corroborated by
others. In a 1978 blizzard in Rhode Island, emergency room admissions for
myocardial infarction rose markedly three days after the storm, and mortality
from ischemic heart disease showed a large increase for a five-day period
after the storm (Faiche and Rose, 1979). The authors attributed this rise to
an increase in physical and psychological stress imposed by the storm. Glass
and Zack (1979) concur, suggesting that an eight-day increase in deaths from
ischemic heart disease following a number of blizzards was most likely a
function of after-storm activities (snow shoveling, car pushing, etc.).
Interestingly, these particular death increases appeared unrelated to
temperature. Males appear to be at higher risk during these storms, probably
due to the greater likelihood that they will be performing more vigorous
physical activity after the storm (Glass and Zack, 1979).
In an ongoing study on the effects of snow accumulation in five U.S.
cities, Kalkstein (1986) has determined threshold values of accumulated snow
above which mortality rates appear to rise. In New York, significant upward
trends in mortality were noted the day after snowfalls if two or more inches
of snow had accumulated. In Detroit, where snow is more common, the snowfall
accumulation exceeded six inches before mortality increases were noted. No
significant relationship between snowfall accumulation and mortality was
apparent in Chicago. Anderson and Rochard (1979) found increases in deaths
from ischemic heart disease on, and for three days after, a four-inch or
greater snowfall in Toronto. Major peaks in cardiovascular deaths in
Minneapolis-St. Paul also appeared to follow days with heavy snows, with the
rise most rapid the day after the storm (Baker Blocker, 1982).
Summer rainfall appears to have a limited impact on mortality. Kalkstein
(1986) has shown that a significant decline in mortality is experienced the
day after summer precipitation events in all of five U.S. cities studied (New
York, Philadelphia, Chicago, Atlanta, Detroit). The precipitation event
itself might have an indirect impact, as the cooler temperatures coinciding
with a summer rainfall provide relief from excessively warm weather.
However, in certain specific cases, rainfall might induce increases in
mortality. Mack (1985) found that fatal automobile accidents increased in
frequency during very light rain episodes (less than .01 inch) and heavy
rainfalls (greater than 0.1 inch per hour).
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E. FRONTAL PASSAGES, SUNSHINE, AND CLOUD COVER IMPACTS
Frontal passages may have a profound impact on well-being and mortality as
large variations in weather conditions can occur in a very short time. Rapid
changes in temperature have been shown to produce a number of physiological
changes in the body. Rapid drops may affect blood pH, blood pressure,
urination volume, and tissue permeability (Persinger, 1980). Outbreaks of
epidemics may also be .related to frontal passage. In his study of 59 years of
data, Donle (1975) noticed sudden large increases in influenza outbreaks in
Germany, Norway, and Switzerland often followed the passage of a surface
trough. In general, these outbreaks occurred simultaneously with the influx
of cold air over northern and western Europe (the passage of a surface wave is
often followed by a rapid influx of cold air). The influenza outbreaks in
Europe most frequently occurred between January and March, when cold air
masses most commonly intruded over the area.
A number of studies1have also found relationships between the numbers of
reported migraine attacks and rapid changes in barometric pressure. Cull
(1981) found fewer occurrences of attacks when barometric pressure was low.
This was partially attributed to a decrease in sunshine during low-pressure
intrusions, as solar radiation is a suspected triggering mechanism for
migraine onset. However, a Canadian Climate Center study (1981) found that
migraines were most likely to occur on days with falling pressure, rising
humidity, high winds, and rapid temperature fluctuations.
Rosen (1979) cites some startling relationships between pressure changes
and human well-being. He describes research that indicates that cancer
mortality rates seem to increase during low-pressure fluctuations, and deaths
from circulatory diseases seem to increase during high-pressure fluctuations.
He notes that rapid pressure fluctuations may penetrate buildings and
propagate wave energy from their source like ripples in a pond. Humans appear
to be quite sensitive to such changes.
The reduction of solar radiation by cloud cover may also have effects on
well-being. By increasing the brightness level, the autonomic nervous system
is affected by constriction changes in the eye pupil. According to Persinger
(1980), this increases the rate of physical activity and leads to a general
feeling of well-being. Wolfe (1981) notes that the sun's rays cause chemical
changes in neurotransmitter or hormone synthesis in the brain, perhaps
stimulating production of the hormone epinephrine, which stimulates the mind
and body. Conversely, very low light intensities are often associated with
states of relaxation, tiredness, and .sleepiness.
F. POTENTIAL EFFECTS OF GLOBAL CLIMATE CHANGE ON FUTURE
HUMAN MORTALITY
Kalkstein (1986) estimated the potential effects of global warming on New
York City. The study indicated that summer weather appears to have a
significant impact on New York's present mortality rates, and a "threshold
temperature" of 92°F was uncovered, suggesting that mortality increases quite
rapidly when the maximum temperature exceeds this value. Days with low
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relative humidities appear to increase mortality most dramatically. Five
climatic scenarios were developed to estimate New York's future weather
assuming that warming does occur, and "acclimatized" and "unacclimatized"
mortality rates were estimated for each scenario. The unacclimatized rates
were computed by utilizing New York's weather/mortality algorithm developed
from the historical analysis. Acclimatized rates were computed by selecting
present-day "analog cities" which resemble New York's predicted future
weather, and developing weather/mortality algorithms for them.
Results shown in Table V-2 indicate that the number of additional deaths
at temperatures above the threshold could increase by over tenfold if New
Yorkers do not become acclimatized to the warming. The elderly will
constitute an increasing proportion of these deaths. However, if full
acclimatization occurs, the number of additional deaths above the threshold
temperature might be no different than today. It is likely, however, that
economic conditions, as well as the basic structure of the city, will prevent
full acclimatization; therefore, actual mortality may fall somewhere in
between the estimated values. A similar procedure developed for winter
indicated that mortality is minimally affected by severe winter weather in New
York.
A preliminary precipitation/mortality analysis was also undertaken, and
summer days following a precipitation event had significantly lower mortality
rates than summer days without precipitation. In the winter, these results
were reversed, and days following rain (but not snow) had significantly higher
mortality rates than non-precipitation days.
G. SUMMARY
Although there is much literature concerned with the impact of weather on
human mortality and well-being, it appears that the contributing researchers
often disagree on the magnitude and specific nature of the impact, as well as
on the role of acclimatization. General areas of agreement include:
1. Temperature extremes (both hot and cold) appear to
increase mortality, although there is disagreement about
which sex, age group, or race seems most affected.
2. Low relative humidities in winter appear to be directly
related to frequencies of various illnesses and
mortality.
3. Winter snowfall accumulations appear to correspond with
periods of high mortality.
4. Rapid changes in the weather often induce a series of
negative physiological responses from the body.
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TABLE V-2
Average Monthly Increase in Total Mortality for the
Various Warming Scenarios in New York - ' -'
Degrees Above Present
Month 0
No Acclimatization
June 19 (45) 34 (81) 57 (136) 114 (273) 156 (373) 253 (605)
July 86 (206) 110 (263) 154 (368) 282 (674) 372 (890) 622 (1,488)
August 25 (60) 37 (88) 64 (153) 170 (407) 250 (598) 487 (1,165)
TOTAL 130 (311) 181 (432) 276 (657) 566 (1,354) 778 (1,861) 1,362 (3,258)
Full Acclimatization
June
July
August
TOTAL
19
86
25
130
(45)
(206)
(60)
(311)
33
62
29
124
(79)
(148)
(69)
(296)
32
54
55
141
(77)
(129)
(132)
(338)
5
11
4
20
(12)
(26)
(10)
(48)
0
0
0
0
0
0
0
0
a/ Numbers in parentheses represent raw, unstandardized mortality estimates.
They are calculated by multiplying the standardized values by 2.39. The
population of the New York metropolitan area in 1980 was 9,120,000, which is
2.39 times the population of the standardized city (3,811,000).
b/ These values are not adjusted for potential future population increases.
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There is a great need to quantify much of the subjective and intuitive
information that has been published on climate/mortality relationships.
Considering the enormous amount of mortality and morbidity data presently
available from the National Center for Health Statistics, the Centers for
Disease Control, and other agencies, more precise weather/health relationships
should be uncovered in the near future. Perhaps one of the greatest
challenges and areas of future research is determining the necessary cost to
society to overcome climate stress. Changes in interior environments may be
needed to overcome potential direct climate change impacts on living and
working environments. Indirect impacts (e.g., the loss of productivity
resulting from new climate conditions and increased insurance costs) have not
been estimated. It is these impacts indirectly associated with human
health/climate stress that remain important areas of research.
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