Extreme climate events can have significant consequences on ecosystems and by extension human populations. Over 50 million of the world's lakes typically freeze each winter, and the absence of winter ice cover, in lakes where ice has historically been present, can be characterized as an extreme event. We quantified the effects of extreme climate events on lake ice cover using 78-year ice records from 122 lakes to show that (1) extreme ice-free years are becoming more frequent and severe, (2) winter air temperature is a significant predictor of ice cover that was driven by large-scale climate oscillations, (3) extremes in temperature are closely related to extremes in ice cover, and (4) ice-free years are forecasted to result in significant loss of ice-cover in the future. Without drastic reductions in carbon emissions, we can expect the widespread loss of lake ice cover could have significant socioeconomic and biological implications.

Key Points

Observed recent increases in ice-free years for 122 lakes since 1939
Extremes in winter air temperature were observed to closely relate to extremes in ice-free cover for lakes
Using climate change scenarios, ice-free years for lakes are predicted to increase significantly in the next decades

Plain Language Summary

Climate change is expected to have a large impact on humans and our natural systems. Freshwater lakes are an important resource and each year, millions around the world freeze during the winter months. However, climate change is expected to threaten the freezing of some of these lakes. We explored 122 lakes that typically freeze over winter and that had records of freeze/thaw since 1939. We asked, do extremes in winter air temperature affect extremes in lake ice cover and what would that mean for the future? Winter air temperature was found to closely predict ice-free years for lakes. Years with abnormally hot winters were also years where many lakes remained ice-free. Projecting into the future, we predicted that lakes will continue to experience more ice-free years. With a reduction in carbon emissions, there will be a modest increase in ice-free years, but if carbon emissions continue as they are currently, there could be a large increase in ice-free lakes. This study highlights lake ice as another victim of climate change. The loss of lake ice will have impacts to natural systems and also socioeconomic implications for human populations that are dependent on it.

1 Introduction

Predicting extreme climatic events and understanding their ecological impacts is a challenge facing scientists in an era of global change. Climate variability is predicted to increase in the future, resulting in extreme events becoming greater in duration, magnitude, and frequency (IPCC, 2014). Extreme climatic events are expected to disproportionately impact ecosystems relative to gradual changes in climate (Jentsch et al., 2007; Smith, 2011) because climate conditions can surpass tipping points that inhibit recovery (Garrabou et al., 2009). For instance, an extreme precipitation event in Sweden resulted in a large influx of nutrients into a lake, generating an abnormally large algal bloom that threatened the quality of drinking water for Stockholm (Weyhenmeyer et al., 2004). However, despite their potential impact, our understanding of these extreme events on ecological systems remains relatively limited.

Quantifying the effects of extreme events requires data sets that are global and long-term, incorporating measurements of both climate and ecological responses. Lake ice meets this standard as records have been collected for decades to centuries for hundreds of lakes around the Northern Hemisphere because of its importance to humans for transportation, refrigeration, food provisioning, and recreation (Knoll et al., 2019; Magnuson et al., 2000). One of the longest traditions of recording lake ice cover dates back to 1443 as Shinto priests celebrate the formation of ice cover and the appearance of the omiwatari on Lake Suwa, Japan (Sharma et al., 2016). For over half of the world's lakes, coverage by winter ice has been typical, and absence of ice can be characterized as an extreme event. Lake ice is a sensitive indicator of climate as lake ice formation and breakup is highly influenced by the presence of the 0°C isotherm (Brown & Duguay, 2010). Long-term records collected since the Industrial Revolution reveal that lakes are experiencing earlier ice breakup, later ice freeze, and shorter ice duration (Benson et al., 2012; Magnuson et al., 2000). A recent study estimated that approximately 15,000 lakes around the Northern Hemisphere may be experiencing extreme ice-free years since the 1970s (Sharma et al., 2019). In the warmest winters, lakes may not freeze at all (Sharma et al., 2016).

Examining ice-free years in the context of extreme climate events could improve predictions of climate change effects in freshwater lakes. We assembled a lake ice cover database for 122 lakes with records extending 78 years from 1939–2018. These lakes are those that typically freeze every winter and an ice-free event for any of the lakes would have been considered uncommon. Of the 122 lakes in our data set, 14 lakes (11%) were ice-free at least once in the last 78 years. A local extreme event happens in a year where an individual lake is ice-free. A global extreme event happens in a year where a significant proportion of the lakes were concurrently ice-free relative to the long-term mean for all lakes. Specifically, we asked the following: (1) Are these local and global extreme events becoming more frequent and severe? (2) Do extremes in climate relate to extremes in global ice-free events? and (3) How will climate change influence the severity of local and global extreme ice-free events? Our study is the first to forecast how often lakes are predicted to experience extreme ice-free winters depending upon the extent of greenhouse gas emissions. Understanding the drivers, frequency, and severity of ice-free winters will be essential to ultimately forecast the ecological and socioeconomic consequences associated with lakes losing ice.

2 Data Acquisition

We obtained lake ice cover records (did the lake freeze or not) for 122 lakes with time-series extending from 1939–2016 (Figure 1) from the National Snow and Ice Data Center and the data portal from the Long-term Ecological Research Network (Benson et al., 2000; Sharma et al., 2019). Within the 122 lakes, we included Grand Traverse Bay at Lake Michigan and Bayfield Bay at Lake Superior. Freezing of these bays may not result in the freezing of the entire lake, and thus we treated them separately. Fourteen of these lakes (11%) had a minimum of one ice-free year in the 78-year record. These lakes are distributed across North America (10 lakes), Europe (three lakes), and Asia (one lake) (supporting information Table S1). A lake was considered frozen if it was completely covered in ice (i.e., 100%) for at least 1 day and considered ice-free in all other cases (i.e., open water or partial freeze). There were some exceptions where an ice-on event may be recorded as less than 100%, such as the lakes in Sweden. While the methods between lakes may not be perfectly standardized, the methodology for each lake was consistent within the time series. Ice cover was monitored by local residents familiar to the lake and in some cases passed down for generations within the same family. The ice-off period of lakes can be more subjective among observers, such as different thresholds of open water defining an ice-free lake. However, we used a binary annual comparison (frozen vs. ice-free) to better standardize records across observers. Typically, records of ice cover were maintained in these lakes because it provided information for the commercial shipping or fishing industries, recreation, and religious reasons. Many of the lakes within this data set began prior to 1939 (e.g., Lake Suwa 1443, Lake Simcoe 1852, Lake Baikal 1896). However, we selected 1939 as the start of the time series because this window had the largest number of lakes consistently measured.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

A distribution of 122 lakes where ice cover was surveyed since 1939. Lakes with intermittent ice-cover are identified as white squares (n = 14) whereas blue circles represent lakes that freeze annually (n = 108).

We obtained a range of weather variables for each lake. We acquired monthly mean air temperature, mean cloud cover, and total precipitation from 1939–2017, available for all grids on the globe from the University of East Anglia's Climatic Research Unit (CRU TS4.03; http://www.cru.uea.ac.uk/). These climate data were derived from meteorological station measurements that were interpolated to a 0.5° latitude/longitude grid. We calculated winter (December, January, February) and spring (March, April, and May) means (or totals in the case of precipitation) for each weather variable. The winter and spring variables correspond with the ice-cover season for that lake, i.e., the 2001 ice-cover year would include values from December 2001 to May 2002.

We obtained a range of teleconnection index data, reflecting both Sea Surface Temperatures (SST) in the Atlantic and the Pacific Oceans, and Sea Level Pressure (SLP) in the North Atlantic. The NINO3 index we used is the anomaly of the area averaged SST from 5°S–5°N and 150–90°W (Rayner et al., 2003), and converted to an annual mean. The Atlantic Multidecadal Oscillation (AMO) index is the area weighted average of the North Atlantic SST, unsmoothed and linearly detrended (Enfield et al., 2001), again converted to an annual mean. The Pacific Decadal Oscillation (PDO) index we acquired is the leading principle component of monthly SST anomalies in the North Pacific Ocean (Zhang & Delworth, 2007). The North Atlantic Oscillation winter index (NAO_DJFM) is based on the difference in normalized sea level pressure between two stations in Portugal and Iceland (Hurrell, 2020). The Arctic Oscillation (AO) index is a measure of the northern midlatitude to high-latitude circulation patterns (Thompson & Wallace, 2000). We obtained the monthly means for each index and calculated annual means, except for the NAO for which we calculated a winter mean.

Lastly, we acquired bias-corrected annual forecasted (2010–2099) air temperatures for four general circulation models (GCMs: GFDL-ESM 2M, IPSL-CM5A-LR, HadGEM2-ES, and MIROC5) and three greenhouse gas emissions scenarios (representative concentration pathways, RCP, of 2.6, 6.0, and 8.5) at a spatial resolution of 0.5° from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b).

3 Material and Methods

3.1 Are Local and Global Extreme Ice-Free Events Becoming More Frequent and Severe?

We examined all 122 lakes and calculated the percentage of ice-free lakes each year (ν_t). We calculated the standard deviation of ice-free percentage across the entire time series (Equation 1). The ice-free percentage was considered extreme for a year when it surpassed the standard deviation of the mean for the entire time series. We plotted ice-free percentages each year to observe trends over time and defined years that exceeded the standard deviation as extreme. The larger the percentage above this threshold, the more severe the event.

$urn:x-wiley:00948276:media:grl61105:grl61105-math-0001$ (1)

To determine if the severity of global ice-free events has been increasing within our time series, we divided our 78-year time series in half with a historic window (1939–1977) and a recent window (1978–2016). We computed the average and kernel density estimate for the percentage of ice-free lakes in each of the two time windows. The severity of ice-free events would be increasing if the average percentage of ice-free lakes increased between these two frames. The severity would also be considered increasing if the variability in the percentage of ice-free events increased.

To determine if the frequency of extreme ice-free events has been increasing, we examined the change in probability over the timeseries. We calculated the cumulative average percentage of lakes that were ice-free on a yearly basis. The value calculated for each year represents the average percentage of lakes that were ice-free for that current year and all the years previous (Equation 2). We fitted a generalized additive model (GAM) with year as the predictor variable and probability_t as the response variable (function gam, package mgcv; Wood, 2017) in R Version 3.5.0 (R Development Core Team, 2019). The GAM was fitted to a Gaussian distribution using an identity link function and we specified a smoothing penalty to not exceed a third order polynomial. We compared the model output from a third order polynomial to second order and linear fits by contrasting R² values and conducting an ANOVA on the residual sum of squares. Our GAM identified a significant increase in the average percentage of the 122 lakes that are ice-free in a year (F_2.98 = 233.5, p < 0.001, R² = 0.90; Figure 1c). The third order polynomial explained significantly greater variation relative to second order (R² = 0.80) and linear (R² = 0.79) fits when the residual sum of squares were compared using an ANOVA (p < 0.001).

$urn:x-wiley:00948276:media:grl61105:grl61105-math-0002$ (2)

We further investigated the question of whether local extreme events are becoming more frequent by exploring each of the lakes individually and recording the numbers of years between ice-free events. Fourteen of the lakes have experienced at least one ice-free winter in the last 78 years. For these lakes, we calculated the timing between extreme ice-free events by calculating the time since the lake was last ice-free, for every year when there is an extreme ice-free event. If there were no previous ice-free events for that lake, we used the first year in the lake's ice record. We developed Poisson and negative binomial regression models using statsmodels (Seabold & Perktold, 2010) to calculate the trend in the frequency of extreme ice-free years for each lake. A Poisson regression was appropriate for cases where the ratio of the Pearson χ² coefficient and the degrees of freedom was close to one, whereas a negative binomial regression was used when this ratio is above one (overdispersed cases).

3.2 What are the Climatic Factors That are Driving Ice-Free Events?

We developed multiple linear regression models, using statsmodels (Seabold & Perktold, 2010), to identify the significant local weather variables and large-scale climate oscillations driving the percentage of ice-free lakes in a given year. We used best-subset models and chose the most parsimonious model based on the lowest AIC of all the models. Winter air temperature was the most important predictor variable explaining over 47% of the variation in the severity of ice-free years and had the lowest AIC.

3.3 Do Extremes in Climate Relate to Extremes in Global Ice-Free Events?

To determine if extremes in climate relate to extremes in global ice-free events, we calculated the average winter air temperature and percentage of lakes that were ice-free for each year. We calculated these annual averages only for the 14 lakes that are currently experiencing intermittent ice cover. We fit a binomial regression model with the percentage of ice-free lakes as the response variable and winter air temperature as the predictor. For both winter air temperature and percentage of ice-free lakes we identified the 68th and 95th percentiles using the first and second standard deviations from the mean.

3.4 How Will Climate Change Influence the Severity of Local and Global Extreme Ice-Free Events?

We created a set of logistic regression models for each individual lake with intermittent ice cover, exploring the effects of all combinations of local seasonal weather (temperature, cloud cover, precipitation) on ice-free events, selecting the model with the lowest AIC for each lake. Next, we introduced a set of regularization terms that keep the model from overfitting the historical data, using logit.fit_regularized from the Python module statsmodels (Seabold & Perktold, 2010). From these, we selected the model for each lake which minimized the number of false positives (i.e., predicts ice-free when the lake was frozen) to prevent the overprediction of the number of ice-free winters in the future. Of the 14 lakes, only eight lakes had models that significantly predicted ice-free events (Appendix S4). Winter air temperature was again the most important variable for each of these eight lakes (Appendix S4). The mean winter air temperature threshold for when these lakes did not freeze varied between −6 and −2°C, with one outlier (Balaton) at 3°C (Appendix S4). In many warmer winters, Balaton only freezes for a few days, but that is still categorized as a freeze event for this lake.

Models that had significant climate variables that predicted ice-free events were used to project forward until the end of winter 2099. We projected modeled ice-free events using different RCP (RCP 2.6, 6.0, and 8.5) of climate change scenarios and across four General Circulation Models (GFDL-ESM 2M, IPSL-CM5A-LR, HadGEM2-ES, and MIROC5). In total, we predicted ice-free events under 12 different climate change scenarios.

4 Results

4.1 Extreme Ice-Free Events Becoming more Frequent and Severe

We observed an increase in the percentage of lakes that are ice-free in more recent years. In the first half of the time-series (1939–1977), there were a total of 31 ice-free events and only 2 years when the percentage of ice-free lakes exceeded our threshold for a global extreme event (Figure 1a). In the more recent half of the time-series (1978–2016), there were 108 ice-free events and more than a third (38%) of the years exceeded our threshold for extreme (Figure 2a). The greatest number of ice-free lakes in a year occurred in 2001 (2001–2002 winter) (n = 10), followed by 2011/2015 (each n = 7) and 1997/2016 (each n = 6). Over the past 78 years, Lake Champlain (United States) and Grand Traverse Bay in Lake Michigan (United States) had the highest number of ice-free years with over 40% of the years with no ice cover. In 25% of the years, Lake Suwa, Japan, did not freeze. Seven of our lakes were ice-free in more than 5% of the years (Table S1).

We also found an increase in the severity (i.e., mean and variability) of global ice-free events between the historic (1939–1977) and recent (1978–2016) windows of our time series. In the historic window, there was a mean of 0.8% lakes out of 122 lakes that experienced an ice-free winter with a maximum of four lakes experiencing an ice-free winter in a season. Comparatively, during the more recent period (1978–2016), the mean number of ice-free lakes in a winter has increased fivefold (i.e., average ~2.5% per year) with a maximum of 10 lakes experiencing an ice-free winter within a season (Figure 2b). The probability of an ice-free event occurring in one of our 122 lakes also appears to be increasing since 1939 (Figure 2c).

We found that the frequency of local extreme ice-free events is significantly increasing for four lakes (Appendix S2). Lake Balaton increased in the frequency of ice-free events from one every 40 years to one every 7 years (Appendix S2). The pattern in other lakes is even more drastic, with Lake Suwa increasing from an extreme event once every 30 years to one almost annually. Two lakes (Oneida and Big Green) experienced their only ice-free winter at the turn of this century (Appendix S2). Around the same time, four other lakes (Geneva, Superior at Bayfield, Lunz, and Otsego) experienced their first ice-free event on record, and have since had at least one additional ice-free winter.

4.2 Extremes in Climate Relate to Extremes in Global Ice-Free Events

We found that winter air temperature was the most important predictor of global ice-free events (mean effect = 0.83 ± 0.27, χ2 = 124, lakes = 122, p < 0.001). The percentage of ice-free lakes began to increase substantially above −4.1°C, i.e., the 68th percentile.

When examining only the 14 lakes that are currently experiencing intermittent ice cover, years with extremes in winter air temperature were found to closely relate to extremes in winter ice-cover (mean effect = 0.75 ± 0.29, χ2 = 7.75, lakes = 14, p = 0.005). Almost all years that experienced above average winter temperatures also had an extreme ice-free year (Figure 3). For instance, the 2 years with the highest percentage of ice-free lakes coincided with mean winter air temperatures above the 95th percentile (−2.7°C; Figure 3).

Warmer winter air temperatures were consistently identified as the most important drivers of the likelihood of a lake experiencing an extreme ice-free year. We observed the greatest number of extreme ice-free years in 1997, 2001, 2011, 2015, and 2016. These years coincided with warmer winters in response to climate change and phase switches of large-scale climate oscillations in the direction of even warmer winters (Appendix S3), such as the El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO) (Lopez et al., 2019). Air temperature is the most important determinant of ice cover as generally air temperatures need to be consistently below 0°C for a lake to freeze. The 0°C isotherm is highly associated with dates of ice breakup and formation (Arp et al., 2013; Duguay et al., 2006; Shuter et al., 2013); and in Alaskan lakes, along with surface area, can explain over 80% of the variation in ice breakup dates (Arp et al., 2013). With climate change, isotherms associated with the transition from freezing annually to intermittently are likely to move northward and into higher elevations (Sharma et al., 2019). Lakes in more southern and coastal regions tend to experience the greatest rates of warming in North America and Scandinavia (Jensen et al., 2007; Korhonen, 2006; Weyhenmeyer et al., 2011), in part because they are in a transition zone where winter air temperatures hover around 0°C (Weyhenmeyer et al., 2004) and are most vulnerable to experiencing an ice-free winter (Sharma et al., 2019). Winter air temperatures best predict ice-free years, but are determined by large-scale climate oscillations.

4.3 Climate Change is Expected to Influence the Severity of Local and Global Extreme Ice-Free Events

Using the eight lakes that had models which significantly predicted ice-free events, we forecasted a higher number of extreme ice-free winters in the future, regardless of the GCM scenario (Figure 4a). For example, Lakes Champlain and Suwa are predicted to experience 67.5 (median) and 72 ice-free winters over the next 80 years. The remaining lakes may not freeze in close to half of the next 80 years: Grand Traverse Bay in Lake Michigan (median = 57), Bayfield Bay in Lake Superior (23.5), Lake Sebago (52.5), Lake George (47.5), Randsfjorden (45), and Lake Balaton (40.5) (Figure 4a). Across lakes, the percentage of lakes experiencing a concurrent extreme ice-free year has increased over the past 78 years and is predicted to be even higher under all climate change scenarios we used in our forecasts (Figure 4b). Over a 20-year rolling average, 7.6 of our eight study lakes are forecasted to be ice-free each year by 2,100 based on the highest greenhouse gas emissions scenario (RCP 8.5), compared to approximately 6.4 of these lakes based on moderate greenhouse gas emission scenarios (RCP 6.0). The lowest greenhouse gas emissions scenarios (RCP 2.6) forecasts that the proportion of these lakes experiencing extreme ice-free winters will stabilize by ~2050 with approximately four of these lakes experiencing extreme ice-free years each year, until the end of the century (Figure 4b).

5 Discussion and Conclusions

The occurrence of extreme climate events is accelerating. Under climate change, the probability of an extreme climate event occurring is expected to shift in mean and increase in variability (Jentsch et al., 2007, 2009). We observed this pattern in ice-free events with the global average percentage of ice-free lakes both increasing and becoming more variable since 1939. Attributing extreme events, that are inherently uncommon, to climate change can be difficult (Diffenbaugh et al., 2017). However, there is accumulating evidence that extreme climate events are becoming more frequent or severe and that climate change is the driving mechanism, such as for heat anomalies (Stott et al., 2016), drought (Lott et al., 2013), and intense rainfall (Westra et al., 2014). The increasing absence of winter lake ice and close relationship with winter air temperature provides further support of climate change driving increases in extreme events. In the future, we can expect additional indicators of extreme climate events to become evident as climate change pushes ecosystems beyond historic trends.

Ice-free events are ideal indicators of extreme climate events. Extremes in climate may not necessarily translate into extremes in ecological responses because of plastic response of ecosystems to climate variability. A recent study identified that the ecological response of grasslands to extreme climate events can be unequal or even nonexistent (Du et al., 2018). However, ice-free events for lakes occur very predictably with warmer winter air temperature and thus can represent a crucial biological indicator of extreme climate events. The binary response of lake ice is also a clear definition of an extreme response relative to other characterization of extreme events, such as percentile declines in productivity (e.g., Sippel et al., 2015; Smith, 2011). In addition to frequency and magnitude, duration is another metric used to evaluate the severity of extreme climate events. In evaluating local extremes in lake ice, the duration of an event can be represented as the time until a freeze event. In our study lakes, there was a relatively recent increase (after 1990) in the number of consecutive ice-free years. For example, Grand Traverse Bay, Lake Suwa, and Lake Champlain each experience multiyear (3+) periods without freezing once. In the future, we may be required to assess severity by the number of consecutive ice-free years rather than the number of ice-free lakes, especially with our projections suggesting some lakes may never freeze again. Lake ice can be a simple, yet highly effective indicator for quantifying future changes in the severity of extreme climate events under climate change.

We predict more ice-free events in the future under climate change scenarios for all of our study lakes regardless of greenhouse gas emissions. Under the climate scenario with significant reductions in greenhouse gas emissions (RCP 2.6), the percentage of ice-free lakes is expected to increase, but will stabilize after 2050. However, for the higher emissions scenarios (RCP 6.0 and 8.5), the percentage of ice-free lakes continues to increase until 2100 with no evidence of a plateau. The loss of lake ice in a culturally significant lake, such as Lake Suwa, will contribute to the loss of winter cultural traditions as 15 generations of Shinto priests have observed and celebrated the formation of lake ice each year since 1443 (Arakawa, 1954; Sharma et al., 2016). Further, over the last 78 years, Bayfield Bay in Lake Superior did not freeze in four winters and this number is projected to increase to 16 (range: 7–27) for a RCP 2.6 scenario, 21 (range: 14–33) for a RCP 6.0 scenario, and 46 (range: 24–59) for a RCP 8.5 scenario by 2100. The loss of ice cover in ecologically and socioeconomically important lakes, such as Lakes Superior, could result in warmer water temperatures (Austin & Colman, 2007; i.e., ice-albedo feedback, Curry et al., 1995), increased likelihood of algal blooms (Weyhenmeyer, Willén, & Sonesten, 2004), the loss of recreational activities, such as ice fishing and skating (Knoll et al., 2019), and the loss of winter transportation, including transporting children to attend local elementary schools (Magnuson & Lathrop, 2014).

Climate change is having profound effects on our natural systems and lake ice is no exception. We observed a considerable increase in the number of lakes experiencing extreme ice-free years that is expected to accelerate in the coming decades. We show, for the first time, that if greenhouse gas emissions are stabilized, we will likely see a direct benefit to the preservation of lake ice. Mitigating greenhouse gas emissions is essential to reducing the number of lakes that may lose ice cover and the corresponding ecological, cultural, and socioeconomic consequences of losing ice cover (Hampton et al., 2017; Knoll et al., 2019). Continued monitoring is also a necessity and one that can be accomplished relatively easily without the need for equipment. Winter air temperature can be used to accurately predict ice-free years, but coverage needs to be expanded beyond the 122 lakes in our data set to include the thousands of lakes that experience intermittent lake-ice cover or that will experience it in the future (Sharma et al., 2019). In lakes where lake-ice cover will be lost, strategies will need to be developed to mitigate the impacts on local human populations that are reliant on it (e.g., recreation, ice-fishing). There is also a need to better understand the response of ecological communities within these lakes to the loss of ice-cover. As extreme climate events continue to become more frequent, our response must also become extreme.

Acknowledgments

We thank all the researchers and data providers that help generate this data set. We thank the two anonymous reviewers for their comments which improved the manuscript. This research was funded by a NSERC Discovery grant, an Ontario Ministry of Innovation Early Researcher Award, and a York University Research Chair Program, all awarded to SS.

Open Research

Data Availability Statement

The timeseries of freeze-thaw cycles of lake ice for all lakes in the last 78 years can be found at Filazzola, Alessandro; Blagrave, Kevin; Imrit, Mohammad; Sharma, Sapna (2020): Patterns of freeze-thaw cycles in 122 lakes in the Northern Hemisphere. figshare. Data set. (https://doi.org/10.6084/m9.figshare.12585053.v1). Climate data were obtained from the Climatic Research Unit part of the University of East Anglia (CRU TS4.03; http://www.cru.uea.ac.uk/).

Supporting Information

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Citing Literature

Volume47, Issue18

28 September 2020

e2020GL089608

Climate Change Drives Increases in Extreme Events for Lake Ice in the Northern Hemisphere

Abstract

Key Points

Plain Language Summary

1 Introduction

2 Data Acquisition

3 Material and Methods

3.1 Are Local and Global Extreme Ice-Free Events Becoming More Frequent and Severe?

3.2 What are the Climatic Factors That are Driving Ice-Free Events?

3.3 Do Extremes in Climate Relate to Extremes in Global Ice-Free Events?

3.4 How Will Climate Change Influence the Severity of Local and Global Extreme Ice-Free Events?

4 Results

4.1 Extreme Ice-Free Events Becoming more Frequent and Severe

4.2 Extremes in Climate Relate to Extremes in Global Ice-Free Events

4.3 Climate Change is Expected to Influence the Severity of Local and Global Extreme Ice-Free Events

5 Discussion and Conclusions

Acknowledgments

Open Research

Data Availability Statement

Supporting Information

References

Citing Literature

Press Release—Warmer winters are keeping some lakes from freezing

Figures

References

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Climate Change Drives Increases in Extreme Events for Lake Ice in the Northern Hemisphere

Abstract

Key Points

Plain Language Summary

1 Introduction

2 Data Acquisition

3 Material and Methods

3.1 Are Local and Global Extreme Ice-Free Events Becoming More Frequent and Severe?

3.2 What are the Climatic Factors That are Driving Ice-Free Events?

3.3 Do Extremes in Climate Relate to Extremes in Global Ice-Free Events?

3.4 How Will Climate Change Influence the Severity of Local and Global Extreme Ice-Free Events?

4 Results

4.1 Extreme Ice-Free Events Becoming more Frequent and Severe

4.2 Extremes in Climate Relate to Extremes in Global Ice-Free Events

4.3 Climate Change is Expected to Influence the Severity of Local and Global Extreme Ice-Free Events

5 Discussion and Conclusions

Acknowledgments

Open Research

Data Availability Statement

Supporting Information

References

Citing Literature

Press Release—Warmer winters are keeping some lakes from freezing

Figures

References

Related

Information

RESOURCES

PUBLICATION INFO