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Global lakes are warming slower than surface air temperature due to accelerated evaporation

Nature Water volume 1, pages 929–940 (2023)Cite this article

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Abstract

Widespread increases in lake surface water temperature have been documented in recent decades. Yet our understanding of global lake warming is mainly based on summertime measurements and includes relatively few observations from high latitudes (>60° N) where half of the world’s lakes are located. Here we provide temporally and spatially detailed high-resolution lake surface water temperatures for 92,245 lakes (36% are located within the Arctic) based on satellite remote sensing and numerical modelling. The global lake surface water temperature data suggested a mean increase of +0.24 °C decade−1 (uncertainty 0.02 °C decade−1) from 1981 to 2020, which is significantly (P < 0.05) slower than the change in surface air temperature (mean rate +0.29 °C decade−1) during the same period. We show that climatic forces (long- and short-wave radiation and specific humidity) other than surface air temperature contribute more than half of the lake warming, and energy loss, through accelerated evaporation rate, is mainly responsible for the slower warming rate. Lake warming is likely to continue from 2021 to 2099 unless a low greenhouse gas emission scenario is followed. Our dataset provides important baseline information to further evaluate the physical and biological responses of lakes to past and future warming.

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Fig. 1: Global patterns of lake warming from 1981 to 2020.
Fig. 2: Attribution of global lake warming over the past four decades.
Fig. 3: Global patterns of lake surface heat fluxes and their trends.
Fig. 4: Long-term changes in LSWT, SAT and heat fluxes from 1981 to 2099.

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Data availability

The developed GLAST dataset can be accessed through https://zenodo.org/record/8322038.

Code availability

The source code for the FLake model is accessible at http://www.flake.igb-berlin.de/.

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Acknowledgements

We thank the United States Geological Survey for providing global Landsat satellite images and the GEE for providing the GSW dataset and global data processing capability. We acknowledge the organizations and individuals (listed in Supplementary Tables 1 and 2) who made substantial efforts to collect global in situ LSWT and surface heat flux datasets. L.F. was supported by the National Key Research and Development Program of China (2022YFC3201802), the National Natural Science Foundation of China (nos. 41971304 and 42271322), Guangdong Provincial Higher Education Key Technology Innovation Project (2020ZDZX3006) and by the Support Plan Program of Shenzhen Natural Science Fund under grant no. 20200925155151006, R.I.W. was supported by a UKRI Natural Environment Research Council (NERC) Independent Research Fellowship award [grant number NE/T011246/1].

Author information

Authors and Affiliations

  1. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yan Tong, Lian Feng, Xinchi Wang & Xuehui Pi

  2. Shenzhen Ecological and Environmental Monitoring Center of Guangdong Province, Shenzhen, China

    Wang Xu

  3. School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey, UK

    R. Iestyn Woolway

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  1. Yan Tong
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Contributions

Y.T. was involved in methodology, data processing and analyses and writing. L.F. undertook conceptualization, methodology, funding acquisition, supervision and writing. X.W. and X.P. did the data processing. W.X. and R.I.W. participated in interpreting the results and refining the paper.

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Correspondence to Lian Feng.

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Extended data

Extended Data Fig. 1 Flowchart for developing the GLAST dataset.

The workflow to produce the GLAST dataset can be divided into four steps. Step 1) Determination of lake centers: Lake centers were determined for further retrieving and simulations of global lake surface water temperature. For each lake, the lake center is defined as the point with the largest distance to the shoreline of the permanent water, which was delineated by HydroLAKES polygons and the GSWO map. Step 2) Landsat retrieving: Landsat-retrieved LSWTs were generated to calibrate the FLake model for individual lakes. Landsat images, GSW images and TCWV datasets are the primary dataset in this step. Three candidate algorithms (GSC, PSC and SMW) were applied and compared over various lakes using in situ LSWT dataset. The retrieving algorithm with the best performance was selected and then used to retrieve the LSWT of global lakes. Step 3) FLake model calibration: lake-specific FLake models were calibrated using Landsat retrievals to determine the optimal model settings for individual lakes. For each lake, 2,880 times FLake simulations were conducted based on hourly ERA5-Land dataset and 2,880 combinations of model settings. The optimal model settings for individual lakes were then selected. The simulated hourly LSWTs over the past four decades (that is, 1981–2020) were used to produce the GLAST dataset. Step 4) FLake validation and future simulation: GLAST historical dataset (1981–2020) was validated using independent in situ LSWT, lake surface heat fluxes, lake evaporation rate and ice phenology. The calibrated and validated lake-specific FLake models were then used to project future GLAST dataset (2021–2099) under different greenhouse gas-emission scenarios (RCP 2.6, 6.0 and 8.5).

Extended Data Fig. 2 Validation of FLake-simulated LSWT using in situ measurements.

Density plots of simulated and in situ LSWT (unit: °C) at (a) hourly, (b) daily, (c) seasonal and (d) annual scales, where the ‘Low’ and ‘High’ labels in the colorbar denote the density of the matched pairs. (e) Locations of the in situ measured data (the data sources refer to Supplementary Table 1).

Source data

Extended Data Fig. 3 Validation of FLake-simulated lake surface heat fluxes and evaporation rate using observed data.

(a) Net radiation (Rn, in W/m2), (b) Latent heat flux (LE, in W/m2), (c) Sensible heat flux (H, in W/m2), (d) Heat storage change (ΔG, in W/m2), (e) Evaporation rate (E, in mm/day). Red density points indicate monthly matchups and hollow points indicate seasonal or annual matchups. The ‘Low’ and ‘High’ labels in the colorbar denote the density of the matched pairs. (f) Spatial distributions of the in situ data used for the validations and the data sources refer to Supplementary Table 2.

Source data

Extended Data Fig. 4 Validation of ERA5-Land LSWT using in situ data.

The in situ data used here are the same as in Extended Data Fig. 2. Density scatter plots of simulated and in situ LSWT (unit: °C) at (a) hourly, (b) daily, (c) seasonal and (d) annual scales, where the ‘Low’ and ‘High’ labels in the colorbar denote the density of the matched pairs.

Source data

Extended Data Fig. 5 Warming trends of LSWT for different lake groups.

(a) Lake area, (b) Ice duration, (c) Changes in ice duration. The blue curves within the panels show the number of lakes for different lake bins. On each box, the centre line represents the median value, the lower and upper bounds indicate the first and third quartiles and the whiskers extend to the maximum and minimum values within the non-outlier range.

Source data

Extended Data Fig. 6 Global patterns of the lake-to-air temperature difference.

(a) Mean and (b) trend of lake-to-air temperature differences during the period of 1981–2020. The data are aggregated into 1°×1° grid cells. (c) Time series of global mean lake-to-air temperature difference from 1981 to 2099. The linear slopes for historical (1981–2020) and future (2021–2099) periods under three emissions scenarios are annotated (the font colours correspond to curves) and statistically significant trends are indicated by ‘*’. The shadings associated with the future data represent standard deviations of four climate models.

Source data

Extended Data Fig. 7 Global patterns of lake warming from 1981 to 2020 for different seasons.

(a) Spring. (b) Summer, (c) Autumn and (d) Winter. The right panels show latitudinal profiles of the trends for LSWT and SAT.

Source data

Extended Data Fig. 8 Sensitivity of lake–air temperature warming difference on surface latent heat flux.

The elasticity (e) represents the difference of changes in the lake and air temperature (dLSWT–dSAT) in response to changes in latent heat flux (dLE). The grey colour represent the 95% confidence interval.

Source data

Extended Data Fig. 9 Long-term changes of future (2021–2099) LSWT, SAT and heat fluxes under different emissions scenarios.

(a) LSWT, (b) SAT, (c) Net radiation flux (Rn), (d) Latent heat flux (LE), (e) Sensible heat flux (H), (f) Heat storage change (ΔG). The data are presented as the anomalies relative to the 1981–2020 mean. The linear decadal slopes for historical (1981–2020) and future (2021–2099) periods are annotated (the font colours correspond to the respective curves) and statistically significant trends are indicated by ‘*’. The shadings represent the standard deviations of four climate models. The time series for the historical period (1981–2020) are also plotted within each panel.

Source data

Extended Data Fig. 10 Potential impacts of lake surface warming.

(a) Latitudinal profiles of the mean evaporation rate for the past (2006–2015), future (2090–2099) and their differences, the results for our study and Wang et al. (2018) are compared. (b) Increases in evaporation rate across global coverage and different climatic regions from 2006–2015 to 2090–2099, estimated using simulated data in our study (solid bars). The relative changes (%) are annotated. The hatched bars show the changes estimated by Wang et al. (2018), between 2006–2015 and 2090–2099. The spatial distributions of different climatic zones are shown within the panel.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–16 and Tables 1–6.

Reporting Summary

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Tong, Y., Feng, L., Wang, X. et al. Global lakes are warming slower than surface air temperature due to accelerated evaporation. Nat Water 1, 929–940 (2023). https://doi.org/10.1038/s44221-023-00148-8

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