Technical Documentation
Seasonality and Climate Change: A Review of
Observed Evidence in the United States
Environmental Protection Agency
Office of Air and Radiation
Climate Change Division
EPA 430-R-21-002
www.epa.gov/climate-indicators/seasonality-and-climate-change
December 2021

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Technical Documentation
This document provides technical documentation for the set of EPA's climate change indicators
used in Seasonality and Climate Change: A Review of Observed Evidence in the United States,
including criteria for their evaluation and selection for inclusion in the report, and information
regarding their derivation and underlying datasets. This technical documentation also discusses
limitations and sources of uncertainty associated with the indicators.
Contents
Introduction	1
Evaluation of Indicators	1
Indicator Selection	2
Technical Information	3
Literature Cited	14
Introduction
The Earth's climate is changing, which is driving changes related to seasonality in the United
States. The U.S. Environmental Protection Agency (EPA) developed a set of key climate
change indicators based on long-term observational datasets to understand these changes.
This report uses indicators as well as an extensive review of relevant scientific literature to
document historical changes in seasonality and help readers better understand their
implications for ecology and human communities. Scientific literature provides support for
conclusions drawn from indicators, as well as corresponding consequences and attribution to
climate change. For more information on EPA's indicators, see Climate Change Indicators in the
United States at: https://www.epa.gov/climate-indicators.
The climate change indicators and conclusions drawn from them are illustrative. The selection
of indicators in the report is not intended to be comprehensive of all observed changes related
to climate; it provides a review of some of the changes that are currently occurring in the United
States and around the world. The report is not meant to serve as a comprehensive state of the
science related to changes in seasonality, but rather a summary of key historical trends
elucidated through indicator datasets and other existing sources of information. While the report
aims to highlight examples of the ways in which changes to one seasonal process can drive
changes in another (e.g., changes to snowpack affect water availability and therefore
agricultural output), the real stories of changes related to seasonality are broader and more
complex than what could be captured in this report.
The sections below provide information related to the climate change indicators used in this
report, including criteria for their selection, data sources and derivation, and a discussion of
limitations and uncertainty.
Evaluation of Indicators
EPA uses a set of 10 criteria to select climate change indicators. These criteria include:
•	Indicator data are available to show trends over time.
•	Indicator data consist of actual measurements, observations, and derivations thereof.
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•	Indicator data have broad national coverage or significance.
•	Indicator data are peer-reviewed and published.
•	Information regarding uncertainty associated with indicator data is available.
•	The indicator informs issues of national importance associated with human or natural
systems.
•	The relationship between the indicator and climate change is supported by published,
peer-reviewed science and data.
•	Indicator data, methods, and analysis are scientifically objective and transparent.
•	Indicator data and analysis are understandable to the public.
•	The indicator is feasible to construct within a reasonable timeframe.
To ensure each indicator is fully transparent and peer reviewed, EPA follows an established
framework to identify datasets, select indicators, obtain independent expert review, and publish
indicators in reports and online. EPA uses the following approach to develop indicators: 1)
identify and develop a list of candidate indicators; 2) conduct initial research and screen against
a subset of indicator criteria; 3) conduct detailed research and screen against the full set of
indicator criteria; 4) select indicators for development; 5) develop draft indicators; 6) facilitate
expert review of draft indicators; and 7) periodically re-evaluate indicators. The approach for
technical documentation is published online here:
https://www.epa.gov/sites/production/files/2016-08/documents/technical-documentation-
overview-2016.pdf
All of EPA's climate change indicators address either causes or effects of climate change. While
each indicator has a scientifically based relationship to climate change, EPA acknowledges that
some indicators more closely correspond to climate than others. Indicators generally consider
large space and time scales (e.g., regional observations spanning decades) in order to resolve
trends relevant to climate change. In addition, EPA develops a handful of indicators associated
with "Community Connection" and "A Closer Look" (e.g., Cherry Blossom Bloom Dates in
Washington, DC) in order to address topics of interest specific to particular regions or locations.
Indicator Selection
A subset of EPA's climate change indicators was selected for inclusion in this report. The
criteria for choosing which indicators to discuss included whether they exhibited distinct
seasonal patterns in the United States, their relevance to the four illustrative themes in the
report, and data quality.
The selected indicators are those determined to exhibit important aspects of seasonality such
as seasonal processes, shifts in timing or magnitude of seasonal events, or seasonal responses
with direct or indirect implications to ecological and social systems or human health. For the
indicators featured and for the observational evidence mentioned in the report, the connections
to seasonality and climate are consistent with and reference the scientific literature. Some
indicators represent seasonality better than others. In all cases, the connection between
indicators and other observational evidence to climate change is defined by the referenced
scientific literature in the report.
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Technical Information
EPA uses a standard format to document the technical information for each climate change indicator. In doing so, EPA complies with
the requirements of the Information Quality Act (also referred to as the Data Quality Act) and EPA's Guidelines for Ensuring and
Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by the Environmental Protection Agency.
Technical documentation accompanies each indicator to record the source(s) of the data, how each indicator was calculated, how
accurately each indicator represents the intended environmental condition, and provides documentation of the history of all revisions
and updates to the indicator. EPA's technical documentation addresses 13 elements for each indicator:
1.	Indicator description
2.	Revision history
3.	Data sources
4.	Data availability
5.	Data collection (methods)
6.	Indicator derivation (calculation steps)
7.	Quality assurance and quality control (QA/QC)
8.	Comparability overtime and space
9.	Data limitations
10.	Sources of uncertainty (and quantitative estimates, if available)
11.	Sources of variability (and quantitative estimates, if available)
12.	Statistical/trend analysis (if any has been conducted)
13.	References
Table 2 below focuses on the Description, Data Sources, Data Collection, and Indicator Derivation elements for the indicators
featured in the main report. These elements are included to provide a foundational understanding of what each indicator represents
and how EPA collected and used the data. This appendix does not cover all 13 elements for each indicator. For complete technical
documentation, look to EPA's published technical documentation, available here: https: //www, e pa. gov/cl i m ate- i n d i cators/d own I oad s-
indicators-technical-documentation.
Table 2. Summarized Technical Documentation for Indicators included in this Report.
Indicator
Description, Scope,
Period of Record
Data Sources and Collection
Indicator Derivation
Relationship to
Seasonality
Length of Growing
Season
Length of the growing
season as defined by
frost-free days in the
contiguous 48 states;
1895 to 2019.
EPA obtained the data for this
indicator from NOAA-National
Centers for Environmental
Information (NCEI). Temperature
measurements come from weather
For this indicator, the length
of the growing season is
defined as the period of time
between the last frost of
spring and the first frost of
Increasing seasonal
temperatures are driving
longer growing seasons
characterized by
changes in days per
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stations in NOAA's Cooperative
Observer Program (COOP).
Analysis of frost timing and
growing season length was
provided by Kunkel (2020)1
fall, when the air
temperature drops below the
freezing point of 32°F.
year. The growing
season is influenced by
air temperatures, frost
days, and rainfall, which
are associated with
climate.2
Timing of Last
Spring Frost and
First Fall Frost
Timing of the last spring
frost and the first fall
frost in the contiguous
48 states; 1895 to 2016.
EPA obtained the data for this
indicator from NOAA-NCEI.
Temperature measurements come
from weather stations in NOAA's
COOP. Analysis of frost timing and
growing season length was
provided by Kunkel (2020).1
For this indicator, minimum
daily temperature data from
the COOP data set were
used to determine the dates
of last spring frost and first
fall frost using an inclusive
threshold of 32°F.
Increasing seasonal
temperatures are driving
longer growing seasons
characterized by
changes in days per
year. The growing
season is influenced by
air temperatures, frost
days, and rainfall, which
are associated with
climate.2
Snowpack
Changes in springtime
mountain snowpack in
the western United
States; 1955 to 2020.
This indicator is based on data
compiled by the U.S. Department
of Agriculture's (USDA's) Natural
Resources Conservation Service
(NRCS). NRCS compiles
snowpack measurements
collected by USDA staff as well as
other agencies and organizations
(for example, many measurements
in California come from the
California Department of Water
Resources). The trend analysis
was constructed using methods
consistent with Mote et al. (2005).3
This indicator uses snow
water equivalent (SWE)
measurements to assess
trends in snowpack from
1955 through 2019. SWE is
the amount of water
contained within the
snowpack at a particular
location. EPA narrowed the
data set to stations with
sufficient data, then
calculated linear trends in
April 1 SWE measurements
from 1955 through 2020 for
each site. These trends
were then converted to
percent change since 1955.
Snowpack is a seasonal
phenomenon—it exhibits
an annual cycle—and is
subject to large year-to-
year variations. The
overall amount and the
timing of its onset and
duration are strongly
associated with changes
in seasonal temperature
and precipitation
patterns.3"6
Wildfire Activity:
Burned Area
Annual average burned
acreage by State; 1984
to 2018.
Burn severity data and state-by-
state acreage totals in this map
come from a multi-agency project
called Monitoring Trends in Burn
Severity, which maintains a
database of wildfire events across
EPA calculated the average
annual acreage burned each
month across the United
States for each half of the
period of record. The date of
fire occurrence used in this
The timing, extent, and
severity of wildfire in the
western United States is
strongly influenced by
climate. Wldfire activity
including onset and
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the United States. These data are
publicly available at:
httDs://www.mtbs.aov/direct-
download.
analysis corresponds to the
ignition date of the fire as
defined in the Monitoring
Trends in Burn Severity
(MTBS) data set. For
purposes of this analysis,
fires were aggregated by
month.
season length are
influenced by several
climate variables
(temperature,
precipitation, drought).7 8
Wildfire Activity:
Season Length
Comparison of monthly
burned area due to
wildfires in the United
States; between 1984 to
2000 and 2001 to 2017.
Wildfire burn acreage was
obtained from the MTBS project,
sponsored by the Wildland Fire
Leadership Council. The project
provides data on individual wildfire
incidents that meet certain size
criteria (> 1,000 acres in the
western United States or > 500
acres in the eastern United
States). These data were available
from 1984 to 2017. These data are
publicly available at:
www.mtbs.aov.
EPA filtered MTBS's
database output to remove
any fires not meeting
MTBS's size criteria and
removed fires classified as
"prescribed," "wildland fire
use," or "unknown." EPA
then divided the period of
record into two equal halves
(1984-2000 and 2001-
2017) to support analysis of
change overtime. EPA
calculated the difference in
days between the onset of
the "historical" wildfire
season and the recent onset
of the wildfire season. EPA
defined the start of the
wildfire season as the time
of year when 10 percent of
the average annual burn
acreage was reached, using
the cumulative total burned
acreage for each of the two
timeframes. Changes were
characterized using this
method rather than
measuring a slope overtime
(e.g., a linear regression)
because of the year-to-year
variability in the data set.
Wildfire activity is
strongly associated with
warming and earlier
spring snowmelt.8-10
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Freeze-Thaw
Season (formerly
Unfrozen Days)
Number of unfrozen
days in the contiguous
48 States; 1979 to 2019.
This indicator is based on data
collected by National Aeronautics
and Space Administration (NASA)
satellites and analyzed by the
University of Montana. EPA's
indicator is an updated version of
an analysis originally published in
the scientific literature in 2017.11
The indicator is based on
the freeze-thaw Earth
system data record (FT-
ESDR) and was developed
for land areas where the
average number of days
with freezing temperatures
exceeds five days per year
based on surface air
temperature (SAT) daily
minima over a 36-year
record (1979-2014). This
indicator is also restricted to
land areas with at least
some vegetation, as defined
from a MODIS land cover
map, and limited to areas
that are not permanently
frozen. Thus, it excludes
large water bodies and
permanent ice/snow
features and focus on areas
(covering most of the
country) where freeze-thaw
cycles influence vegetative
growth.
The length of the
unfrozen season can be
an important factor in
determining impacts to
surface hydrology,
including
evapotranspiration and
the timing and extent of
seasonal snowmelt77
and the potential
growing season for
vegetation—for
anticipating landscape
phenological shifts and
important impacts on
agriculture and natural
resource sectors.12
Ragweed Pollen
Season
Change in ragweed
pollen season; 1995 to
2015.
Data for this indicator come from
the National Allergy Bureau, which
is part of the American Academy
of Allergy, Asthma, and
Immunology's Aeroallergen
Network. Data were compiled and
analyzed by a team of researchers
that published a more detailed
version of this analysis in a
scientific journal with data through
2009.13
This indicator established
start and end dates for the
ragweed pollen season
using daily ragweed pollen
counts. The start date is the
point at which 1 percent of
the cumulative pollen count
for the season has been
observed, meaning 99
percent of all ragweed
pollen appears after this
day. Similarly, the end date
is the point at which 99
percent of the cumulative
Seasonal changes in
phenological events
from plants such as
flowering—especially
their timing and
relationship to with
weather and climate—
are among the most
sensitive biological
responses.1315
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pollen count for the season
has been observed.

Heat Waves:
Frequency and
Length
Heat wave
characteristics in 50
large U.S. cities; 1961 to
2019.
This indicator is based on
temperature and humidity
measurements from weather
stations managed by NOAA's
National Weather Service. NOAA
calculates daily apparent
temperatures for metropolitan
areas and publishes the results at
www.ncdc.noaa.aov/societal-
impacts/heat-stress/data.
For consistency across the
country, this indicator
defines a heat wave as a
period of two or more
consecutive days where the
daily minimum apparent
temperature in a particular
city is higher than the 85th
percentile of historical July
and August temperatures for
that city. Historical July and
August baseline
temperatures are analyzed
for a base period of 1981—
2010, which was chosen for
consistency with other
climatology metrics. The
analysis is based on the
methodology used by
Habeebetal. (2015)16 to
define and analyze heat
waves.
Robust evidence that
climate change is
affecting the frequency,
intensity, and duration of
heatwaves.1619 Heat
waves (summer months)
show evidence of
increasing while cold
waves (winter months)
appear to be decreasing
in frequency and
intensity.20
Ice Breakup in
Three Alaskan
Rivers
Ice Breakup Dates for
Three Alaskan Rivers,
1896-2020.
Ice breakup dates for the Tanana
River at Nenana, the Yukon River
at Dawson City, and the
Kuskokwim River at Bethel have
been recorded and made publicly
available as part of three long-
running community competitions:
the Nenana Ice Classic, the Yukon
This indicator considers
annual ice breakup dates for
each river. No annual data
points were missing in the
periods of record for these
two rivers, and EPA
converted all breakup dates
to Julian days.
Lake and river ice
phenology are a part of
the hydrological cycle,
and current trends
reflect the shrinkage of
the Earth's cryosphere,
a widely recognized
effect of ongoing
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River Breakup, and the
Kuskokwim Ice Classic. The data
shown here and other information
can be found online at:
www.nenanaakiceclassic.com,
www.vukonriverbreakuD.com/statis
tics, httD://iceclassic.ora/historical-
data, and additional sources linked
from these websites. Ice breakup
dates for these rivers are also
archived by the National Snow and
Ice Data Center at:
htto://nsidc.ora/data/lake river ice
and the National Weather Service
at:
www.weather.aov/aorfc/breakuDD
B.

climate change.21-23
Lake Ice Freeze
and Thaw Dates
Timing of lake freeze
and thaw for a subset of
lakes in the northern
United States; 1850 to
2015.
This indicator is based mainly on
data from the Global Lake and
River Ice Phenology Database,
which was compiled by the North
Temperate Lakes Long Term
Ecological Research program at
the Center for Limnology at the
University of Wisconsin-Madison
from data submitted by
participants in the Lake Ice
Analysis Group. The database is
hosted by the National Snow and
Ice Data Center (NSIDC) at
httD://nsidc.ora/data/lake river ice.
This indicator considered
nine-year moving averages
for each of the parameters
in order to smooth out some
of the variability in the
annual data and to make it
easier to see long-term
trends in the display. Long-
term trends in thaw date
overtime were calculated
using the Sen slope method.
This indicator focuses on
thaw dates, not freeze
dates, because several of
the target lakes have data
for only ice-off, not ice-on.
Lake and river ice
phenology are a part of
the hydrological cycle,
and current trends
reflect the shrinkage of
the Earth's cryosphere,
a widely recognized
effect of ongoing
climate change.21-23
Timing of Spring
Snowmelt
Magnitude and timing of
streamflow in rivers and
streams across the
United States; 1940 to
2014.
The indicator is based on
streamflow data from a set of
reference stream gauges specified
in the Geospatial Attributes of
Gages for Evaluating Streamflow
(GAGES-II) database. Daily mean
streamflow data are stored in the
The timing of snowmelt
runoff is defined using the
winter-spring center-of-
volume date, which is the
date when half of the total
streamflow between January
1 and July 31 for sites in the
Warming temperatures
promote snowmelt. The
timing of snowmelt and
subsequent streamflow
runoff is significantly tied
to seasonal changes.624-
26
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USGS National Water Information
System (NWIS) at
httD://waterdata.usas.aov/nwis/sw.
western United States, or
half of the total streamflow
between January 1 and May
31 for sites in the eastern
United States, has passed
through the gauging station.
Rates of change from 1940
to 2014 were computed
using the Sen slope, which
was then multiplied by the
number of years in the study
period to estimate total
change overtime.

Seasonal
Temperatures
Changes in average
seasonal air
temperature for the
United States; 1896 to
2019.
This indicator is based on
temperature anomaly data
provided by the National Oceanic
and Atmospheric Administration's
(NOAA's) NCEI, formerly the
National Climatic Data Center
(NCDC). Specifically, the indicator
uses data from NCEI's nClimDiv
data set, which is based on data
from the daily version of the Global
Historical Climatology Network
(GHCN-Daily). This dataset is
available through NCEI's "Climate
at a Glance" web interface
(www.ncdc.noaa.aov/caa').
Monthly temperature means
were calculated for each
station represented in the
nClimDivdata set. Data
were adjusted to remove
biases introduced by
differences in the time of
observation and shifts in
local-scale data that might
reflect changes in
instrumentation, station
moves, or urbanization
effects. Area-weighted
averages of grid-point
estimates were calculated
from station data to achieve
uniform spatial coverage.
Results were initially
averaged within each
climate division, then
aggregated by state and for
the contiguous 48 states as
a whole based on an area-
weighted average of climate
divisions. Winter data are
nominally assigned to the
year in which the winter
Seasonal temperature is
an important climatic
factor influencing
season variation and
change. Temperature is
an environmental cue for
plant and animal
processes. From a
seasonal perspective in
the United States,
warming is occurring in
all seasons and was
greatest and most
widespread in winter,
with increases of over
1,5°F in most areas.20
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ended; for example, "1896"
refers to the consecutive
three-month period of
December 1895, January
1896, and February 1896.

Snow-to-
Precipitation Ratio
Changes in the ratio of
snowfall to total winter
precipitation in the
contiguous 48 states;
1949 to 2016.
The data used for this indicator are
based on long-term weather
station records compiled by the
National Oceanic and Atmospheric
Administration's (NOAA's) NCEI
following an update published in
the scientific literature in 2007.27
For this indicator, snow-to-
precipitation ratios for each
year were calculated by
comparing the total snowfall
during the months of interest
(in terms of liquid-water
equivalent) with total
precipitation (snow plus
rain). Long-term rates of
change at each station were
determined using a
Kendall's tau slope
estimator.
Warmer temperatures
associated with climate
change can influence
snowfall by altering
weather patterns,
causing more
precipitation overall, and
causing more
precipitation to fall in the
form of rain instead of
snow.2728
Glacier Mass
Balance (U.S. and
Global)
Balance between snow
accumulation and
melting in glaciers, and
mass change of glaciers
in the United States and
globally; 1950s to 2015.
Mass balance datasets are
available for Gulkana, Wolverine,
and South Cascade glaciers on
the USGS Benchmark Glacier
website at:
www2.usas.aov/climate landuse/cl
u rd/alacierstudies/default.aso.
Mass balance data for Lemon
Creek Glacier are available on the
WGMS Reference Glacier website
at:
httDV/wams.ch/oroducts ref alacie
rs; this site also provides data for
the three USGS benchmark
glaciers. EPA obtained the most
up-to-date data directly from
USGS.
For this indicator, glacier
surface measurements were
used to determine the net
change in mass balance
from one year to the next,
referenced to the previous
year's summer surface
measurements. The
indicator documents
changes in mass and
volume rather than total
mass or volume of each
glacier because the latter is
more difficult to determine
accurately.
Over the last several
decades, climate
change has led to
widespread shrinking of
the cryosphere,
including seasonal
glacier mass loss.7 29
Leaf and Bloom
Dates
Timing of first leaf dates
and flower bloom dates
in lilacs and
honeysuckle plants in
This indicator is based on leaf and
bloom observations that are
archived by the USA National
Phenology Network (USA-NPN)
and climate data that are
This indicator was
developed by applying
phenological models to
nearly 3,000 sites in the
contiguous 48 states where
Terrestrial plants time
their reproduction based
on one or more
proximate environmental
cues, including
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the contiguous 48
states; 1900 to 2020.
maintained by the National
Oceanic and Atmospheric
Administration's (NOAA's) NCEI
(formerly the National Climatic
Data Center). Data for this
indicator were analyzed using a
method described in Schwartz et
al. (2013).30
sufficient weather data have
been collected. The exact
number of sites varies from
year to year over the period
1900-2020 depending on
data availability.
photoperiod,
temperatures, the length
of winter (vernalization),
and moisture levels or
the availability of
water.31 Phenological
changes are associated
with the seasonal timing
of biological
events.30'3233
Cherry Blossom
Peak Bloom Date
Peak bloom date (PBD)
for the most common
species of cherry tree
planted around the Tidal
Basin in Washington,
DC; 1921 to 2019
All cherry blossom PBD data, as
well as National Cherry Blossom
Festival dates, are maintained by
the NPS. PBD data back to the
1990s can be found on the
National Cherry Blossom Festival
and NPS websites at:
www.nationalcherrvblossomfestiva
l.ora/about/bloom-watch and:
www.nDs.aov/subiects/cherrvbloss
om/bloom-watch.htm. Festival
dates for 2012-2019 were
provided by the organizers of the
festival (contact information at:
www.nationalcherrvblossomfestiva
I.org).
EPA converted bloom and
festival dates into Julian
days to support graphing
and calculations. By this
method, January 1 = day 1,
etc. The method also
accounts for leap years,
such that March 31 = day 90
in a non-leap year and day
91 in a leap year, for
example.
Phenological changes
are associated with the
seasonal timing of
biological events.3032-34
Lyme Disease
Prevalence
Incidence of Lyme
disease in the United
States; 1991 to 2018.
This indicator is based on annual
numbers of confirmed Lyme
disease cases, nationally and by
state, compiled by the Centers for
Disease Control and Prevention's
(CDC's) Division of Vector-Borne
Diseases. Incidence was
calculated using the most recent
mid-year population estimates for
each year from the U.S. Census
Bureau. The 1996 and 2017
comparison maps also came from
CDC.
National incidence of Lyme
disease was calculated
using the total number of
confirmed Lyme disease
cases and the national
population for each year
from 1991 through 2014.
EPA calculated incidence by
dividing the number of
confirmed cases per year by
the corresponding
population on July 1 in the
same calendar year. EPA
then multiplied the per-
A warming climate can
enhance the risk of
vector-borne diseases,
such as Lyme disease,
by increasing the range
of suitable vector
habitat.15'3536
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person rate by 100,000 to
generate a normalized
incidence rate per 100,000
people. This is CDC's
standard method of
expressing the incidence of
Lyme disease.

Bird Wintering
Ranges
Changes in the winter
ranges of North
American birds from the
winter of 1966-1967 to
2013
This indicator is based on data
collected by the annual Christmas
Bird Count (CBC), managed by the
National Audubon Society. Data
used in this indicator are collected
by citizen scientists who
systematically survey certain areas
and identify and count widespread
bird species. The CBC has been in
operation since 1900, but data
used in this indicator begin in
winter 1966-1967.
This indicator is based on
the center of abundance for
each species, which is the
center of the population
distribution at any point in
time. This is a common way
to characterize the general
location of a population. This
indicator reports the position
of the center of abundance
for each year, relative to the
position of the center of
abundance in 1966 (winter
1966-1967). The change in
position is averaged across
all 305 species for changes
in latitude and across 272
species for changes in
distance from the coast.
The timing of seasonal
life-cycle events in birds
such as migration and
bird arrival is
driven by temperature,
sun angle, and other
conditions. As such,
shifts in spatial and
temporal
patterns of bird behavior
can indicate changes in
seasonal meteorological
conditions or
changes in the
availability of suitable
food and habitat.3738
Tropical Cyclone
Activity
Aggregate activity of
hurricanes and other
tropical storms in the
Atlantic Ocean,
Caribbean, and Gulf of
Mexico; 1878 to 2015.
This indicator is based on data
maintained by the National
Oceanic and Atmospheric
Administration's (NOAA's) National
Hurricane Center in a database
referred to as HURDAT
(HURricane DATa). This indicator
presents three separate analyses
of HURDAT data: a set of
hurricane counts compiled by
NOAA, NOAA's Accumulated
Cyclone Energy (ACE) Index.
All hurricane counts are
limited to cyclones in the
North Atlantic (i.e., north of
the equator) meeting the
definition of a hurricane,
which requires sustained
wind speeds of at least 74
miles per hour. Named
storms (including tropical
storms > 39mph) are also
included in the counts. For
all years prior to the onset of
complete satellite coverage
in 1966, total basin-wide
Tropical cyclones most
commonly occur during
the "hurricane season"
running from June
through November and
draw their energy from
warm tropical oceans.
Changes in sea surface
temperatures can alter
the intensity of wind and
rain associated with
tropical cyclones, as well
as the length of the
hurricane season.20
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counts have been adjusted
upward based on historical
records of ship track density.
The indicator also uses
NOAA's ACE Index to
describe the combined
frequency, strength, and
duration of tropical storms
and hurricanes each
season, as described in the
scientific literature by Bell
and Chelliah in 2006.39
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The full report, Seasonality and Climate Change: A Review of Observed Evidence in the United
States, is available at: https://www.epa.qov/climate-indicators/seasonalitv-and-climate-chanqe
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