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Disproportionate impact of atmospheric heat events on lake surface water temperature increases

Nature Climate Change (2024)Cite this article

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Abstract

Hot temperature extremes (HTEs) in the atmosphere can also affect lake surface water temperature, but how this impact changes with global warming is not well understood. Here we use numerical modelling and satellite observations to quantify the contribution of HTEs to variations in summer lake surface water temperature and lake heatwaves in 1,260 water bodies worldwide between 1979 and 2022. Over this time period, HTE duration and cumulative intensity over the studied lakes increased significantly, at average rates of 1.4 days per decade and 0.92 °C days per decade, respectively. Despite only accounting for 7% of the total summer days, HTEs are responsible for 24% of lake surface summer warming trends, with the most pronounced effect observed in Europe at 27%. Moreover, HTEs are key drivers of both the duration and cumulative intensity of lake heatwaves. Our findings underscore the pivotal role played by short-term climatic extreme events in shaping long-term lake surface water temperature dynamics.

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Fig. 1: Changes in LSWT during 1979–2022.
Fig. 2: HTEs in ERA5-Land during 1979–2022.
Fig. 3: Contribution of HTE metrics to the variation of summer mean LSWT.
Fig. 4: Contribution of HTEs to the trend in summer mean LSWT.

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Data availability

ERA5-Land reanalysis is available to download from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview. Lakes_cci is available to download from https://data.ceda.ac.uk/neodc/esacci/lakes/data/lake_products/L3S/v2.0. GloboLakes is available to download from https://catalogue.ceda.ac.uk/uuid/76a29c5b55204b66a40308fc2ba9cdb3. GLTC is available to download from https://doi.org/10.6073/pasta/89bacfc9dfcabce545ae11b353a8e5fd. Daily LSWT in the CTL and CFT experiments and a table of lake characteristics are provided at https://doi.org/10.11888/Terre.tpdc.301309 (ref. 51). Data used to create figures are available via Zenodo at https://doi.org/10.5281/zenodo.13189351 (ref. 52). Source data are provided with this paper.

Code availability

The source code of the FLake model can be found at http://www.flake.igb-berlin.de. Codes used to generate figures in the manuscript are available via Zenodo at https://doi.org/10.5281/zenodo.13189351 (ref. 52).

Change history

  • 25 September 2024

    In the version of the article initially published, the reporting summary was missing and has now been added to the online version of the article.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (U22A20561 and 42425102), the Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0202), the NIGLAS foundation (E1SL002) and the Water Resource Science and Technology Project in Jiangsu Province (2020057). R.I.W. was supported by a UKRI Natural Environment Research Council (NERC) Independent Research Fellowship (grant number NE/T011246/1) and NERC grant reference number NE/X019071/1, ‘UK EO Climate Information Service’.

Author information

Authors and Affiliations

  1. Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China

    Xiwen Wang, Kun Shi, Boqiang Qin & Yunlin Zhang

  2. School of Geography and Ocean Science, Nanjing University, Nanjing, China

    Xiwen Wang & Boqiang Qin

  3. University of Chinese Academy of Sciences, Beijing, China

    Kun Shi & Yunlin Zhang

  4. College of Nanjing, University of Chinese Academy of Sciences, Nanjing, China

    Yunlin Zhang

  5. School of Ocean Sciences, Bangor University, Menai Bridge, UK

    R. Iestyn Woolway

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Contributions

X.W. conceived the work, performed the numerical modelling, completed the data analysis and wrote the manuscript. K.S. conceived the work and revised the manuscript. B.Q., Y.Z. and R.I.W. revised the manuscript.

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Correspondence to Kun Shi.

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Extended data

Extended Data Fig. 1 Characteristics and distribution of the 1260 studied lakes.

a-c Histogram of the average depth (log10[m], a), surface area (log10[km2], b), and elevation (m, c). d Spatial distribution of studied lakes.

Source data

Extended Data Fig. 2 Modelled vs. Lakes_cci LSWT during 2000–2020.

a-b Number of available Lakes_cci data from 2000 to 2020 across all seasons (a) and in summer (b). Each range of the amount of validation data is presented on the x-axis, while the number of lakes falling into each category is depicted on the y-axis. c-f Correlation coefficients between FLake, CSFLake, and Lakes_cci throughout the year (c, e) and summer (d, f). g-j RMSEs (°C) between FLake, CSFLake, and Lakes_cci throughout the year (g, i) and summer (h, j). Note that there are four lakes not shown in FLake results (c, d) because they stayed frozen throughout the year.

Source data

Extended Data Fig. 3 Modelled vs. observed LSWT.

a CSFLake vs. GloboLakes. b CSFLake vs. GLTC satellite data. c CSFLake vs. GLTC in situ data. d CSFLake vs. in situ data for seven Chinese lakes. The paired modelled and observed LSWT are daily means in a and d, and summer means in b and c. Each point represents the values of a paired CSFLake-observation data. The density of data is the normalized kernel density and is represented by the colour of points.

Source data

Extended Data Fig. 4 Validation of simulated LHW metrics from 2000 to 2020.

a-b The mean absolute errors (MAEs) between LHW cumulative intensity calculated from Lakes_cci and modelled LSWT. c-d MAEs between LHW duration calculated from Lakes_cci and modelled LSWT. All the metrics were averaged over 2000–2020. In b and d, each point represents a lake; the density of data is the normalized kernel density and is represented by the colour of points.

Source data

Extended Data Fig. 5 The impact of HTEs on the intra-annual variability of LSWT.

a Interannual variations of the intra-annual variability in CTL and CFT averaged over all studied lakes. The p values of trends were calculated using a two-tailed test. b-c Intra-annual variability trends (decade−1) for each lake in CTL (b) and CFT (c).

Source data

Extended Data Fig. 6 Changes of LHW metrics from 1979 to 2022.

a-b Annual trends in LHW duration (a; days decade−1) and LHW cumulative intensity (b; °C days decade−1). c-d Summer trends in LHW duration (c; days decade−1) and LHW cumulative intensity (d; °C days decade−1).

Source data

Extended Data Fig. 7 Relationships between air temperature, HTE metrics, and LHW metrics in summer.

LHW duration trend vs. air temperature trend (a). LHW duration trend vs. HTE cumulative intensity trend (b). LHW duration trend vs. HTE duration trend (c). LHW cumulative intensity trend vs. air temperature trend (d). LHW cumulative intensity trend vs. HTE cumulative intensity trend (e). LHW cumulative intensity trend vs. HTE duration trend (f). Only lakes with non-zero trends in LHW metrics are shown in a-f. Mean LHW duration vs. mean air temperature (g). Mean LHW duration vs. mean HTE cumulative intensity (h). Mean LHW cumulative intensity vs. mean air temperature (i). Mean LHW cumulative intensity vs. mean HTE cumulative intensity (j). Pearson correlation coefficients (r) are shown in the upper corner.

Source data

Extended Data Fig. 8 HTEs contributions to trends in summer LHW metrics for all studied lakes.

a Interannual variations of LHW duration (days) averaged across all studied lakes in CTL and CFT from 1979 to 2022. b Same as a but for LHW cumulative intensity (°C days). The p values were calculated using a two-tailed test.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Table 1.

Reporting Summary

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Wang, X., Shi, K., Qin, B. et al. Disproportionate impact of atmospheric heat events on lake surface water temperature increases. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-02122-y

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