Lake Ice
Identification
1. Indicator Description
This indicator tracks when a set of lakes in the United States froze and thawed each year between
approximately 1850 and 2015. The formation of ice cover on lakes in the winter and its disappearance
the following spring depends on climate factors such as air temperature, cloud cover, and wind.
Conditions such as heavy rains or snowmelt in locations upstream or elsewhere in the watershed also
affect the length of time a lake is frozen. Thus, ice formation and breakup dates are relevant indicators
of climate change. If lakes remain frozen for longer periods, it can signify that the climate is cooling.
Conversely, shorter periods of ice cover suggest a warming climate.
Components of this indicator include:
• First freeze dates of selected U.S. lakes since 1850 (Figure 1).
• Ice breakup dates of selected U.S. lakes since 1850 (Figure 2).
• Trends in ice breakup dates of selected U.S. lakes since 1905 (Figure 3).
2. Revision History
April 2010:
June 2015:
August 2016:
Indicator published.
Updated indicator on EPA's website with data through winter 2014-2015. Added seven
lakes and removed one due to discontinued data (Lake Michigan at Traverse City).
Removed the original Figure 1, which showed duration, and added Figure 3, a map
showing long-term rates of change in thaw dates.
Updated indicator with data through winter 2015-2016.
Data Sources
3. Data Sources
This indicator is mainly based 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 (LIAG). The database is hosted on the web by the National Snow and Ice Data Center
(NSIDC), and it currently contains ice cover data for 750 lakes and rivers throughout the world, some
with records as long as 150 years.
Data for many of the selected lakes have not been submitted to the Global Lake and River Ice Phenology
Database since 2005. Thus, the most recent data points were obtained from the organizations that
originally collected or compiled the observations.
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4. Data Availability
Most of the lake ice observations used for this indicator are publicly available from the sources listed
below. All of the years listed below and elsewhere in this indicator are presented as base years. Base
year 2004 (for example) refers to the winter that begins in 2004, even though the freeze date
sometimes occurs in the following year (2005) and the thaw date always occurs in the following year.
The NSIDC's Global Lake and River Ice Phenology Database provides data through 2004 for most lakes,
2005 for Mirror Lake, 2011 for Lake Superior at Bayfield, and 2012 for Lakes Mendota and Monona.
Users can access the NSIDC database at: http://nsidc.org/data/lake river ice. Database documentation
can be found at: http://nsidc.org/data/docs/noaa/g01377 lake river ice.
Users can also view descriptive information about each lake or river in the Global Lake and River Ice
Phenology Database. This database contains the following fields, although many records are incomplete:
• Lake or river name
• Lake or river code
• Whether it is a lake or a river
• Continent
• Country
• State
• Latitude (decimal degrees)
• Longitude (decimal degrees)
• Elevation (meters)
• Mean depth (meters)
• Maximum depth (meters)
• Median depth (meters)
• Surface area (square kilometers)
• Shoreline length (kilometers)
• Largest city population
• Power plant discharge (yes or no)
• Area drained (square kilometers)
• Land use code (urban, agriculture, forest, grassland, other)
• Conductivity (microsiemens per centimeter)
• Secchi depth (Secchi disk depth in meters)
• Contributor
Access to the Global Lake and River Ice Phenology Database is unrestricted, but users are encouraged to
register so they can receive notification of changes to the database in the future.
Data for years beyond those included in the Global Lake and River Ice Phenology Database come from
the following sources:
• Cobbosseecontee Lake, Damariscotta Lake, Moosehead Lake, and Sebago Lake: U.S. Geological
Survey (USGS). Data through 2008 come from Hodgkins (2010). Post-2008 data were provided
by USGS staff.
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• Detroit Lake and Lake Osakis: Minnesota Department of Natural Resources at:
www.dnr.state.mn.us/ice out.
• Geneva Lake: Geneva Lake Environmental Agency Newsletters at:
www.genevaonline.com/~glea/newsletters.php.
• Lake George: published by the Lake George Association and collected by the Darrin Freshwater
Institute. These data are available online at: www.lakegeorgeassociation.org/who-we-
are/documents/lce-ln-lce-Out2Q15.pdf.
• Lake Mendota and Lake Monona: North Temperate Lakes Long Term Ecological Research site at:
https://lter.limnology.wisc.edu/lakeinfo/ice-data?lakeid=ME and:
https://lter.limnology.wisc.edu/lakeinfo/ice-data?lakeid=MO.
• Mirror Lake: thaw dates available from the Lake Placid Ice Out Benefit contest. The winning
dates are published in the Adirondack Daily Enterprise Newspaper and the Lake Placid News.
The most recent freeze date is available from the Lake Placid News.
• Otsego Lake: available in the Annual Reports from the State University of New York (SUNY)
Oneonta Biological Field Station at: www.oneonta.edu/academics/biofld/publications.asp.
• Shell Lake: provided by the Washburn County Clerk.
This indicator has been updated through base year 2015 for as many lakes as possible. Some lakes,
however, have continued to lag behind in publication of freeze dates, thaw dates, or both.
Methodology
5. Data Collection
This indicator examines two parameters related to ice cover on lakes:
• The annual "ice-on" or freeze date, defined as the first date on which the water body was
observed to be completely covered by ice.
• The annual "ice-off," "ice-out," thaw, or breakup date, defined as the date of the last breakup
observed before the summer open water phase.
Observers have gathered data on lake ice throughout the United States for many years—in some cases,
more than 150 years. The types of observers can vary from one location to another. For example, some
observations might have been gathered and published by a local newspaper editor; others compiled by
a local resident. Some lakes have benefited from multiple observers, such as residents on both sides of
the lake who can compare notes to determine when the lake is completely frozen or thawed. At some
locations, observers have kept records of both parameters of interest ("ice-on" and "ice-off"); others
might have tracked only one of these parameters.
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To ensure sound spatial and temporal coverage, EPA limited this indicator to U.S. water bodies with the
longest and most complete historical records. After downloading data for all lakes and rivers within the
United States, EPA sorted the data and analyzed each water body to determine data availability for the
two parameters of interest. As a result of this analysis, EPA identified 14 water bodies—all lakes—with
particularly long and rich records. Special emphasis was placed on identifying water bodies with many
consecutive years of data, which can support moving averages and other trend analyses. EPA selected
the following 14 lakes for trend analysis:
• Cobbosseecontee Lake, Maine
• Damariscotta Lake, Maine
• Detroit Lake, Minnesota
• Geneva Lake, Wisconsin
• Lake George, New York
• Lake Mendota, Wisconsin
• Lake Monona, Wisconsin
• Lake Osakis, Minnesota
• Lake Superior at Bayfield, Wisconsin
• Mirror Lake, New York
• Moosehead Lake, Maine
• Otsego Lake, New York
• Sebago Lake, Maine
• Shell Lake, Wisconsin
Together, these lakes span parts of the Upper Midwest and the Northeast. The four Maine lakes and
Lake Osakis have data for only ice-off, not ice-on, so they do not appear in Figure 1 (first freeze date).
6. Indicator Derivation
Figures 1 and 2. Dates of First Freeze and Ice Thaw for Selected U.S. Lakes, 1850-2015
To smooth out some of the variability in the annual data and to make it easier to see long-term trends in
the display, EPA did not plot annual time series but instead calculated nine-year moving averages
(arithmetic means) for each of the parameters. EPA chose a nine-year period because it is consistent
with other indicators and comparable to the 10-year moving averages used in a similar analysis by
Magnuson et al. (2000). Average values are plotted at the center of each nine-year window. For
example, the average from 1990 to 1998 is plotted at year 1994. EPA did calculate averages over periods
that were missing a few data points. Early years sometimes had sparse data, and the earliest averages
were calculated only around the time when many consecutive records started to appear in the record
for a given lake.
EPA used endpoint padding to extend the nine-year smoothed lines all the way to the ends of the
analysis period for each lake. For example, if annual data were available through 2015, EPA calculated
smoothed values centered at 2012, 2013, 2014, and 2015 by inserting the 2011-2015 average into the
equation in place of the as-yet-unreported annual data points for 2016 and beyond. EPA used an
equivalent approach at the beginning of each time series.
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As discussed in Section 4, all data points in Figures 1 and 2 are plotted at the base year, which is the year
the winter season began. For the winter of 2014 to 2015, for example, the base year would be 2014,
even if a particular lake did not freeze until early 2015.
EPA did not interpolate missing data points. This indicator also does not attempt to portray data beyond
the time periods of observation—other than the endpoint padding for the 9-year moving averages—or
extrapolate beyond the specific lakes that were selected for the analysis.
Magnuson et al. (2000) and Jensen et al. (2007) describe methods of processing lake ice observations for
use in calculating long-term trends.
Figure 3. Change in Ice Thaw Dates for Selected U.S. Lakes, 1905-2015
Long-term trends in ice-off (thaw date) over time were calculated using the Sen slope method as
described in Hodgkins (2013). For this calculation, years in which a lake did not freeze were given a thaw
date one day earlier than the earliest on record, to avoid biasing the trend by treating the year as
missing data. Five lakes had years in which they did not freeze: Geneva, George, Otsego, Sebago, and
Superior. Figure 3 shows the total change, which was found by multiplying the slope of the trend line by
the total number of years in the period of record.
EPA chose to focus this map on thaw dates, not freeze dates, because several of the target lakes have
data for only ice-off, not ice-on. EPA started the Sen slope analysis at 1905 to achieve maximum
coverage over a consistent period of record. Choosing an earlier start date would have limited the map
to a smaller number of lakes, as several lakes do not have data prior to 1905.
Indicator Development
The version of this indicator that appeared in EPA's Climate Change Indicators in the United States, 2012
covered eight lakes, and it presented an additional graph that showed the duration of ice cover at the
same set of lakes. For the 2014 edition, EPA enhanced this indicator by adding data for seven additional
lakes and adding a map with a more rigorous analysis of trends over time. To make room for the map,
EPA removed the duration graph, as it essentially just showed the difference between the freeze and
thaw dates, which are already shown in other graphs. In fact, in many cases, the data providers
determined the duration of ice cover by simply subtracting the freeze date from the thaw date,
regardless of whether the lake might have thawed and refrozen during the interim. EPA also removed
one lake from the indicator because data are no longer routinely collected there.
7. Quality Assurance and Quality Control
The LIAG performed some basic quality control checks on data that were contributed to the database,
making corrections in some cases. Additional corrections continue to be made as a result of user
comments. For a description of some recent corrections, see the database documentation at:
http://nsidc.org/data/docs/noaa/g01377 lake river ice.
Ice observations rely on human judgment. Definitions of "ice-on" and "ice-off" vary, and the definitions
used by any given observer are not necessarily documented alongside the corresponding data. Where
possible, the scientists who developed the database have attempted to use sources that appear to be
consistent from year to year, such as a local resident with a long observation record.
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Analysis
8. Comparability Over Time and Space
Historical observations have not been made systematically or according to a standard protocol. Rather,
the Global Lake and River Ice Phenology Database—the main source of data for this indicator-
represents a systematic effort to compile data from a variety of original sources.
Both parameters were determined by human observations that incorporate some degree of personal
judgment. Definitions of these parameters can also vary over time and from one location to another.
Human observations provide an advantage, however, in that they enable trend analysis over a much
longer time period than can be afforded by more modern techniques such as satellite imagery. Overall,
human observations provide the best available record of seasonal ice formation and breakup, and the
breadth of available data allows analysis of broad spatial patterns as well as long-term temporal
patterns.
9. Data Limitations
Factors that may impact the confidence, application, or conclusions drawn from this indicator are as
follows:
1. Although the Global Lake and River Ice Phenology Database provides a lengthy historical record
of freeze and thaw dates for a much larger set of lakes and rivers, some records are incomplete,
ranging from brief lapses to large gaps in data. Thus, this indicator is limited to 14 lakes with
relatively complete historical records. Geographic coverage is limited to sites in four states
(Minnesota, Wisconsin, New York, and Maine).
2. Data used in this indicator are all based on visual observations. Records based on visual
observations by individuals are open to some interpretation and can reflect different definitions
and methods.
3. Historical observations for lakes have typically been made from the shore, which might not be
representative of lakes as a whole or comparable to satellite-based observations.
10. Sources of Uncertainty
Ice observations rely on human judgment, and definitions of "ice-on" and "ice-off" vary, which could
lead to some uncertainty in the data. For example, some observers might consider a lake to have
thawed once they can no longer walk on it, while others might wait until the ice has entirely melted.
Observations also depend on one's vantage point along the lake, particularly a larger lake—for example,
if some parts of the lake have thawed while others remain frozen. In addition, the definitions used by
any given observer are not necessarily documented alongside the corresponding data. Therefore, it is
not possible to ensure that all variables have been measured consistently from one lake to another—or
even at a single lake over time—and it is also not possible to quantify the true level of uncertainty or
correct for such inconsistencies.
Accordingly, the Global Lake and River Ice Phenology Database does not provide error estimates for
historical ice observations. Where possible, however, the scientists who developed the database have
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attempted to use sources that appear to be consistent from year to year, such as a local resident who
collects data over a long period. Overall, the Global Lake and River Ice Phenology Database represents
the best available data set for lake ice observations, and limiting the indicator to 14 lakes with the
longest and most complete records should lead to results in which users can have confidence.
Consistent patterns of change over time for multiple lakes also provide confidence in the lake ice data.
11. Sources of Variability
For a general idea of the variability inherent in these types of time series, see Magnuson et al. (2000)
and Jensen et al. (2007)—two papers that discuss variability and statistical significance for a broader set
of lakes and rivers, including some of the lakes in this indicator. Magnuson et al. (2005) discuss
variability between lakes, considering the extent to which observed variability reflects factors such as
climate patterns, lake morphometry (shape), and lake trophic status. The timing of freeze-up and break-
up of ice appears to be more sensitive to air temperature changes at lower latitudes (Livingstone et al.,
2010), but despite this, lakes at higher latitudes appear to be experiencing the most rapid reductions in
duration of ice cover (Latifovic and Pouliot, 2007).
To smooth out some of the interannual variability and to make it easier to see long-term trends in the
display, EPA did not plot annual time series but instead calculated nine-year moving averages
(arithmetic means) for each of the parameters, following an approach recommended by Magnuson et al.
(2000).
12. Statistical/Trend Analysis
Figure 1 shows data for the nine individual lakes with freeze date data. Figures 2 and 3 show data for all
14 individual lakes. No attempt was made to aggregate the data for multiple lakes.
To support some of the statements in the "Key Points" section of the indicator, EPA analyzed freeze
trends over the full period of record for each lake by ordinary least-squares regression, a common
statistical method. EPA performed this regression on the actual annual values, not the smoothed values
displayed in Figure 1. As Table TD-1 shows, eight of the nine lakes have trends toward later freezing that
are significant to a 95-percent level (p < 0.05).
EPA calculated 1905-2015 trends in thaw dates (Figures 2 and 3) by computing the Sen slope—another
type of linear regression—and the corresponding Mann-Kendall p-values. EPA performed this regression
on the actual annual values, not the smoothed values displayed in Figure 2. EPA used this approach to
be consistent with the thaw date analysis published by Hodgkins (2013) and others. Table TD-2 provides
the results of this statistical testing, which can be summarized as follows:
• Seven lakes (Cobbosseecontee, Damariscotta, Monona, Mirror, Moosehead, Sebago, and
Superior) have trends toward earlier thaw that are significant to a 95-percent level (Mann-
Kendall p-value < 0.05).
• Seven lakes (Detroit, Geneva, George, Mendota, Osakis, Otsego, and Shell) have trends toward
earlier thaw that are not significant to a 95-percent level (Mann-Kendall p-value > 0.05).
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Table TD-1. Ordinary Least-Squares Linear Regression of Freeze Dates
Lake
Period of
analysis
Slope
(days/year)
P-value
Detroit
1908-2015
0.109
0.00026
Geneva
1862-2015
0.108
0.00073
George
1911-2014
0.003
0.95
Mendota
1855-2015
0.087
<0.0001
Mirror
1903-2015
0.113
<0.0001
Monona
1855-2015
0.084
<0.0001
Otsego
1849-2014
0.069
0.0048
Shell
1905-2015
0.074
0.0044
Superior
1857-2010
0.154
<0.0001
Table TD-2. Sen Slope Linear Regression of Thaw Dates, 1905-2015
Lake
Slope
(days/year)
P-value
Cobbosseecontee
-0.08824
0.002318
Damariscotta
-0.08621
0.012152
Detroit
-0.01515
0.56257
Geneva
-0.03448
0.32114
George
-0.03922
0.13436
Mendota
-0.04651
0.13368
Mirror
-0.05882
0.042978
Monona
-0.07407
0.021956
Moosehead
-0.05
0.040222
Osakis
-0.01176
0.57626
Otsego
-0.03448
0.2797
Sebago
-0.1369
0.001061
Shell
-0.05195
0.072528
Superior
-0.2143
<0.0001
These results align with conclusions from the literature. For example, Magnuson et al. (2000) and Jensen
et al. (2007) found that long-term trends in freeze and breakup dates for many lakes were statistically
significant (p < 0.05).
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References
Hodgkins, G.A. 2010. Historical ice-out dates for 29 lakes in New England, 1807-2008. U.S. Geological
Survey Open-File Report 2010-1214.
Hodgkins, G.A. 2013. The importance of record length in estimating the magnitude of climatic changes:
an example using 175 years of lake ice-out dates in New England. Climatic Change 119:705-718.
Jensen, O.P., B.J. Benson, and J.J. Magnuson. 2007. Spatial analysis of ice phenology trends across the
Laurentian Great Lakes region during a recent warming period. Limnol. Oceanogr. 52(5):2013-2026.
Latifovic, R., and D. Pouliot. 2007. Analysis of climate change impacts on lake ice phenology in Canada
using the historical satellite data record. Remote Sens. Environ. 106:492-507.
Livingstone, D.M., R. Adrian, T. Blencker, G. George, and G.A. Weyhenmeyer. 2010. Lake ice phenology.
In: George, D.G. (ed.). The impact of climate change on European lakes. Aquatic Ecology Series 4:51-62.
Magnuson, J., D. Robertson, B. Benson, R. Wynne, D. Livingstone, T. Arai, R. Assel, R. Barry, V. Card, E.
Kuusisto, N. Granin, T. Prowse, K. Steward, and V. Vuglinski. 2000. Historical trends in lake and river ice
cover in the Northern Hemisphere. Science 289:1743-1746.
Magnuson, J.J., B.J. Benson, O.P. Jensen, T.B. Clark, V. Card, M.N. Futter, P.A. Soranno, and K.M. Stewart.
2005. Persistence of coherence of ice-off dates for inland lakes across the Laurentian Great Lakes region.
Verh. Internat. Verein. Limnol. 29:521-527.
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