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Lake heatwaves under climate change

Nature volume 589, pages 402–407 (2021)Cite this article

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Abstract

Lake ecosystems, and the organisms that live within them, are vulnerable to temperature change1,2,3,4,5, including the increased occurrence of thermal extremes6. However, very little is known about lake heatwaves—periods of extreme warm lake surface water temperature—and how they may change under global warming. Here we use satellite observations and a numerical model to investigate changes in lake heatwaves for hundreds of lakes worldwide from 1901 to 2099. We show that lake heatwaves will become hotter and longer by the end of the twenty-first century. For the high-greenhouse-gas-emission scenario (Representative Concentration Pathway (RCP) 8.5), the average intensity of lake heatwaves, defined relative to the historical period (1970 to 1999), will increase from 3.7 ± 0.1 to 5.4 ± 0.8 degrees Celsius and their average duration will increase dramatically from 7.7 ± 0.4 to 95.5 ± 35.3 days. In the low-greenhouse-gas-emission RCP 2.6 scenario, heatwave intensity and duration will increase to 4.0 ± 0.2 degrees Celsius and 27.0 ± 7.6 days, respectively. Surface heatwaves are longer-lasting but less intense in deeper lakes (up to 60 metres deep) than in shallower lakes during both historic and future periods. As lakes warm during the twenty-first century7,8, their heatwaves will begin to extend across multiple seasons, with some lakes reaching a permanent heatwave state. Lake heatwaves are likely to exacerbate the adverse effects of long-term warming in lakes and exert widespread influence on their physical structure and chemical properties. Lake heatwaves could alter species composition by pushing aquatic species and ecosystems to the limits of their resilience. This in turn could threaten lake biodiversity9 and the key ecological and economic benefits that lakes provide to society.

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Fig. 1: Historical and future projections of the intensity and duration of lake heatwaves.
Fig. 2: Historical and future projections of global lake heatwave strength.
Fig. 3: Seasonal variations in lake heatwave occurrence under historical and future climate change.
Fig. 4: Total heatwave duration and the emergence of a permanent heatwave state in lakes globally.

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Data availability

The lake model source code is available to download from http://www.flake.igb-berlin.de/. Climate model projections (ISIMIP2b; date accessed: August 01, 2020) are available at https://www.isimip.org/protocol/#isimip2b. Satellite-derived lake surface temperatures used in this study are available from https://catalogue.ceda.ac.uk/uuid/76a29c5b55204b66a40308fc2ba9cdb3 (Globolakes; accessed 1 August 2020) and from https://catalogue.ceda.ac.uk/uuid/3c324bb4ee394d0d876fe2e1db217378 (ESA CCI; accessed 1 August 2020). Data for the light extinction coefficient used in this study are from the United States Environmental Protection Agency National Lakes Assessment (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B668F7BE3-50D1-465C-A73D-B21625689159%7D) and the World Lake Database (http://wldb.ilec.or.jp/). All lake heatwave simulations, as well as a table of lake-specific information, are available at https://doi.org/10.5281/zenodo.4081165.

Code availability

The MATLAB code used to produce the figures in this paper are available at https://doi.org/10.5281/zenodo.4081165.

References

  1. O’Reilly, C. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 10773–10781 (2015).

    ADS  Google Scholar 

  2. Sharma, S., Jackson, D. A., Minns, C. K. & Shuter, B. J. Will northern fish populations be in hot water because of climate change? Glob. Change Biol. 13, 2052–2064 (2007).

    Article  ADS  Google Scholar 

  3. Farmer, T. M., Marschall, E. A., Dabrowski, K. & Ludsin, S. A. Short winters threaten temperate fish populations. Nat. Commun. 6, 7724 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Hansen, G. J., Read, J. S., Hansen, J. F. & Winslow, L. A. Projected shifts in fish species dominance in Wisconsin lakes under climate change. Glob. Change Biol. 23, 1463–1476 (2017).

    Article  ADS  Google Scholar 

  5. Jonsson, T. & Setzer, M. A freshwater predator hit twice by the effects of warming across trophic levels. Nat. Commun. 6, 5992 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Till, A. et al. Fish die-offs are concurrent with thermal extremes in north temperate lakes. Nat. Clim. Change 9, 637–641 (2019).

    Article  ADS  Google Scholar 

  7. Woolway, R. I. & Merchant, C. J. Worldwide alteration of lake mixing regimes in response to climate change. Nat. Geosci. 12, 271–276 (2019).

    Article  ADS  CAS  Google Scholar 

  8. Maberly, S. C. et al. Global lake thermal regions shift under climate change. Nat. Commun. 11, 1232 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Abell, R. et al. Freshwater ecoregions of the world: a new map of biogeographic units for freshwater biodiversity conservation. Bioscience 58, 403–414 (2008).

    Article  Google Scholar 

  10. Fischer, E. M. & Schär, C. Consistent geographical patterns of changes in high-impact European heatwaves. Nat. Geosci. 3, 398–403 (2010).

    Article  ADS  CAS  Google Scholar 

  11. Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (Cambridge Univ. Press, 2012).

  12. Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).

    Article  ADS  PubMed  Google Scholar 

  13. Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  14. Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 734 (2019).

    Article  Google Scholar 

  15. Jacox, M. G., Alexander, M. A., Bograd, S. J. & Scott, J. D. Thermal displacement by marine heatwaves. Nature 584, 82–86 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Holbrook, N. J. et al. A global assessment of marine heatwaves and their drivers. Nat. Commun. 10, 2624 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  17. Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).

    Article  ADS  Google Scholar 

  18. Rasconi, S., Winter, K. & Kainz, M. J. Temperature increase and fluctuation induce phytoplankton biodiversity loss. Evidence from a multi-seasonal mesocosm experiment. Ecol. Evol. 7, 2936–2946 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).

    Article  ADS  Google Scholar 

  20. Hobday, A. J. et al. Categorizing and naming marine heatwaves. Oceanography 31, 162–173 (2018).

    Article  Google Scholar 

  21. Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Woolway, R. I. & Merchant, C. J. Amplified surface temperature response of cold, deep lakes to inter-annual air temperature variability. Sci. Rep. 7, 4130 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  23. Toffolon, M. et al. Prediction of surface temperature in lakes with different morphology using air temperature. Limnol. Oceanogr. 59, 2185–2202 (2014).

    Article  ADS  Google Scholar 

  24. Wang, W. et al. Global lake evaporation accelerated by changes in surface energy allocation in a warmer climate. Nat. Geosci. 11, 410–414 (2018).

    Article  ADS  CAS  Google Scholar 

  25. Weyhenmeyer, G. A., Westöö, A. K. & Willén, E. in European Large Lakes: Ecosystem Changes And Their Ecological And Socioeconomic Impacts (eds Nõges T. et al.) 111–118 (Springer, 2007).

  26. Oliver, E. C. J. Mean warming not variability drives marine heatwave trends. Clim. Dyn. 53, 1653–1659 (2019).

    Article  Google Scholar 

  27. Welbergen, J. A., Klose, S. M., Markus, N. & Eby, P. Climate change and the effects of temperature extremes on Australian flying-foxes. Proc. R. Soc. Lond. B 275, 419–425 (2008).

    Google Scholar 

  28. Saunders, D. A., Mawson, P. & Dawson, R. The impact of two extreme weather events and other causes of death on Carnaby’s Black Cockatoo: a promise of things to come for a threatened species? Pacif. Conserv. Biol. 17, 141–148 (2011).

    Article  Google Scholar 

  29. Mitchell, D. et al. Attributing human mortality during extreme heat waves to anthropogenic climate change. Environ. Res. Lett. 11, 074006 (2016).

    Article  ADS  Google Scholar 

  30. Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  31. Winslow, L. A. et al. Small lakes show muted climate change signal in deepwater temperatures. Geophys. Res. Lett. 42, 355–361 (2015).

    Article  ADS  Google Scholar 

  32. Thackeray, S. J. et al. Food web de-synchronization in England’s largest lake: an assessment based on multiple phenological metrics. Glob. Change Biol. 19, 3568–3580 (2013).

    Article  ADS  Google Scholar 

  33. Comte, L. & Grenouillet, G. Do stream fish track climate change? Assessing distribution shifts in recent decades. Ecography 36, 1236–1246 (2013).

    Article  Google Scholar 

  34. Woolway, R. I. & Maberly, S. C. Climate velocity in inland standing waters. Nat. Clim. Change 10, 1124–1129 (2020).

    Article  ADS  Google Scholar 

  35. Zarfl, C. et al. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2015).

    Article  Google Scholar 

  36. Muhlfeld, C. C. et al. Invasive hybridization in a threatened species is accelerated by climate change. Nat. Clim. Change 4, 620–624 (2014).

    Article  ADS  Google Scholar 

  37. Jankowski, T. et al. Consequences of the 2003 European heat wave for lake temperature profiles, thermal stability, and hypolimnetic oxygen depletion: implications for a warmer world. Limnol. Oceanogr. 51, 815–819 (2006).

    Article  ADS  Google Scholar 

  38. Jöhnk, K. et al. Summer heatwaves promote blooms of harmful cyanobacteria. Glob. Change Biol. 14, 495–512 (2008).

    Article  ADS  Google Scholar 

  39. Bertani, I., Primicerio, R. & Rossetti, G. Extreme climatic event triggers a lake regime shift that propagates across multiple trophic levels. Ecosystems 19, 16–31 (2016).

    Article  Google Scholar 

  40. Stockwell, J. D. et al. Storm impacts on phytoplankton community dynamics in lakes. Glob. Change Biol. 26, 2756–2784 (2020).

    Article  ADS  Google Scholar 

  41. Woolway, R. I. et al. Physical and chemical impacts of a major storm on a temperate lake: a taste of things to come? Clim. Change 151, 333–347 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  42. Zhou, Y., Michalak, A. M., Beletsky, D., Rao, Y. R. & Richards, R. P. Record-breaking Lake Erie hypoxia during 2012 drought. Environ. Sci. Technol. 49, 800–807 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  43. Lake, P. S. Disturbance, patchiness, and diversity in streams. J. N. Am. Benthol. Soc. 14, 573–592 (2000).

    Article  Google Scholar 

  44. Schewe, J. et al. State-of-the-art global models underestimate impacts from climate extremes. Nat. Commun. 10, 1005 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  45. Carrea, L. & Merchant, C. J. GloboLakes: Lake Surface Water Temperature (LSWT) version 4.0, https://catalogue.ceda.ac.uk/uuid/76a29c5b55204b66a40308fc2ba9cdb3 (Centre for Environmental Data Analysis, 2019).

  46. MacCallum, S. N. & Merchant, C. J. Surface water temperature observations of large lakes by optimal estimation. Can. J. Remote Sens. 38, 25–45 (2012).

    Article  ADS  Google Scholar 

  47. Carrea, L., Embury, O. & Merchant, C. J. Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates. Geosci. Data J. 2, 83–97 (2015).

    Article  ADS  PubMed  Google Scholar 

  48. Woolway, R. I. & Merchant, C. J. Intra-lake heterogeneity of lake thermal responses to climate change: a study of large Northern Hemisphere lakes. J. Geophys. Res. Atmos. 123, 3087–3098 (2018).

    Article  ADS  Google Scholar 

  49. Fichot, C. et al. Assessing change in the overturning behaviour of the Laurentian Great Lakes using remotely sensed lake surface water temperatures. Remote Sens. Environ. 235, 111427 (2019).

    Article  ADS  Google Scholar 

  50. Mironov, D. Parameterization of Lakes in Numerical Weather Prediction: Part 1. Description of a Lake Model. COSMO Technical Report No. 11, http://www.cosmo-model.org (Deutscher Wetterdienst, 2008).

  51. Mironov, D. et al. Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO. Boreal Environ. Res. 15, 218–230 (2010).

    Google Scholar 

  52. Rooney, G. & Jones, I. D. Coupling the 1-D lake model FLake to the community land-surface model JULES. Boreal Environ. Res. 15, 501–512 (2010).

    Google Scholar 

  53. Samuelsson, P., Kourzeneva, E. & Mironov, D. The impact of lakes on the European climate as simulated by a regional climate model. Boreal Environ. Res. 15, 113–129 (2010).

    Google Scholar 

  54. Balsamo, G. et al. On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model. Tellus A 64, 15829 (2012).

    Article  ADS  Google Scholar 

  55. Le Moigne, P., Colin, J. & Decharme, B. Impact of lake surface temperatures simulated by the FLake scheme in the CNRM-CM5 climate model. Tellus A 68, 31274 (2016).

    Article  ADS  Google Scholar 

  56. Woolway, R. I. et al. Impact of the 2018 European heatwave on lake surface water temperature. Inland Waters (2020).

  57. Stepanenko, V. M. et al. First steps of a Lake Model Intercomparison Project: LakeMIP. Boreal Environ. Res. 15, 191–202 (2010).

    Google Scholar 

  58. Thiery, W. et al. Understanding the performance of the FLake model over two African Great Lakes. Geosci. Model Dev. 7, 317–337 (2014).

    Article  ADS  Google Scholar 

  59. Thiery, W. V. et al. LakeMIP Kivu: evaluating the representation of a large, deep tropical lake by a set of one-dimensional lake models. Tellus A 66, 21390 (2014).

    Article  ADS  Google Scholar 

  60. Le Moigne, P. et al. Evaluation of the lake model FLake over a coastal lagoon during the THAUMEX field campaign. Tellus A 65, 20951 (2013).

    Article  ADS  Google Scholar 

  61. Su, D. et al. Numerical study on the response of the largest lake in China to climate change. Hydrol. Earth Syst. Sci. 23, 2093–2109 (2019).

    Article  ADS  Google Scholar 

  62. Frieler, K. et al. Assessing the impacts of 1.5 °C global warming—simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10, 4321–4345 (2017).

    Article  ADS  Google Scholar 

  63. Lange, S. EartH2Observe, WFDEI and ERA-Interim Data Merged and Bias-corrected for ISIMIP (EWEMBI) Version 1.1. https://doi.org/10.5880/pik.2019.004 (GFZ Data Services, 2019).

  64. Bruce, L. C. A multi-lake comparative analysis of the General Lake Model (GLM): stress-testing across a global observatory network. Environ. Model. Softw. 102, 274–291 (2018).

    Article  Google Scholar 

  65. Ayala, A. I., Moras, S. & Pierson, D. C. Simulations of future changes in thermal structure of Lake Erken: proof of concept for ISIMIP2b lake sector local simulation strategy. Hydrol. Earth Syst. Sci. 24, 3311–3330 (2020).

    Article  ADS  Google Scholar 

  66. Shatwell, T. et al. Future projections of temperature and mixing regime of European temperate lakes. Hydrol. Earth Syst. Sci. 23, 1533–1551 (2019).

    Article  ADS  Google Scholar 

  67. Hersbach, H. et al. The ERA5 global reanalysis. Quat. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  ADS  Google Scholar 

  68. Layden, A., MacCallum, S. N. & Merchant, C. J. Determining lake surface water temperatures worldwide using a tuned one-dimensional lake model (Flake, v1). Geosci. Model Dev. 9, 2167–2189 (2016).

    Article  ADS  Google Scholar 

  69. United States Environmental Protection Agency (USEPA). National Lakes Assessment: A Collaborative Survey Of The Nation’s Lakes. https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B668F7BE3-50D1-465C-A73D-B21625689159%7D (USEPA, 2009).

  70. Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems 3rd edn, 649 (Cambridge Univ. Press, 2011).

  71. Helfrich, S. R., McNamara, D., Ramsay, B. H., Baldwin, T. & Kasheta, T. Enhancements to, and forthcoming developments in the Interactive Multisensor Snow and Ice Mapping System (IMS). Hydrol. Process. 21, 1576–1586 (2007).

    Article  ADS  Google Scholar 

  72. Schlegel, R. W. & Smit, A. J. heatwaveR: a central algorithm for the detection of heatwaves and cols-spells. J. Open Source Softw. 3, 821 (2018).

    Article  ADS  Google Scholar 

  73. Jennings, E. et al. The NETLAKE Metadatabase—a tool to support automatic monitoring on lakes in Europe and beyond. Limnol. Oceanogr. Bull 26, 95–100 (2017).

    Article  Google Scholar 

  74. Schneider, P. & Hook, S. J. Space observations of inland water bodies show rapid surface warming since 1985. Geophys. Res. Lett. 37, L22405 (2010).

    Article  ADS  Google Scholar 

  75. Wood, S. N. Generalized Additive Models: An Introduction With R (CRC Press, 2017).

  76. Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. Linear and nonlinear mixed effects models. R package ‘nlme’, https://CRAN.R-project.org/package=nlme (2007).

  77. R Core Team R: A Language And Environment For Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2019).

  78. Zuur, A. F., Ieno, E. N. & Walker, N. Mixed Effect Models and Extensions in Ecology with R (Springer, 2009).

  79. Woolway, R. I. et al. Substantial increase in minimum lake surface temperatures under climate change. Clim. Change 155, 81–94 (2019).

    Article  ADS  CAS  Google Scholar 

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Acknowledgements

R.I.W. received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 791812. We thank the GloboLakes project (NE/J021717/1) and the Hydroscape project (NE/N00597X/1), funded by the Natural Environment Research Council in the United Kingdom. We also thank the Copernicus Climate Change Service Hydrology, funded by the European Union, and the European Space Agency Climate Change Initiative project for the satellite data. We also acknowledge the International Lake Environment Committee Foundation (ILEC, http://www.ilec.or.jp/en/), which maintains the World Lake Database (http://wldb.ilec.or.jp/). The computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppmax, partially funded by the Swedish Research Council through grant agreement number 2016-07213. T.S. was partially supported by German Research Foundation grants DFG KI 853/13-1 and CDZ 1259.

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Authors and Affiliations

  1. Centre for Freshwater and Environmental Studies, Dundalk Institute of Technology, Dundalk, Ireland

    R. Iestyn Woolway & Eleanor Jennings

  2. European Space Agency Climate Office, ECSAT, Didcot, UK

    R. Iestyn Woolway

  3. Department of Lake Research, Helmholtz Centre for Environmental Research (UFZ), Magdeburg, Germany

    Tom Shatwell

  4. Department of Ecology and Genetics/Limnology, Uppsala University, Uppsala, Sweden

    Malgorzata Golub & Don C. Pierson

  5. UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, UK

    Stephen C. Maberly

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  1. R. Iestyn Woolway
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Contributions

R.I.W. conceived the work, developed the concept of the study, performed the numerical modelling, completed the data analysis, and wrote the manuscript with input from S.C.M. All authors edited and revised the manuscript. T.S. performed the three-hour FLake simulations and led the light attenuation analysis, as used in the global simulations. E.J. performed the statistical analyses. M.G. and D.C.P. assisted with the large-scale computations and data handling.

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Correspondence to R. Iestyn Woolway.

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Extended data figures and tables

Extended Data Fig. 1 Definitions and examples of lake heatwaves.

a, b, Examples of the method used to define a lake heatwave event (light orange) from lake surface temperatures (black) (a) and the categorization scheme used for defining the severity of lake heatwaves (b). Lake heatwave categories are defined according to multiples of the 90th percentile differences (1, 2, 3 and 4 times the threshold) relative to a 30-year (1970–1999) climatological mean (blue) and are described as Moderate (light orange), Strong (orange), Severe (red), or Extreme (dark red). Also shown are examples of historical lake heatwave intensity (c, d) and duration (e, f) in Lake Vättern (Sweden) (c, e) and Wörthersee (Austria) (d, f), where observational data are available from 1960 to 2017.

Extended Data Fig. 2 Specific characteristics of the studied lakes.

a–c, Histograms of log10[surface area (km2)] (a), log10[average depth (m)] (b) and elevation (m) (c) of the lakes studied. d–g, We also show, for illustration, the global distribution of lake thermal regions (d), their climatological seasonal cycle (e), a map of studied lakes categorized by thermal region (f), and the number of studied lakes (points) as well as the number of lakes globally (information from the Hydrolakes database21) situated within each lake thermal region (line) (g).

Extended Data Fig. 3 Validation of simulated lake temperatures and heatwave characteristics.

Comparison of modelled (mod) and satellite-observed (obs) (a, b) lake surface water temperatures for the studied lakes in which satellite data were available (\({R}_{{\rm{adj}}}^{2}\) = 0.97, P < 0.001); and lake heatwave duration (\({R}_{{\rm{adj}}}^{2}\) = 0.64, P < 0.001) (c, d) and intensity (\({R}_{{\rm{adj}}}^{2}\) = 0.5, P < 0.001) (e, f) for lakes with sufficient data to identify lake heatwaves from 2000 to 2005 (see Methods). Simulated results are based on the average simulations from the lake model driven by the four climate models. Abs, absolute; MAE, mean absolute error.

Extended Data Fig. 4 Relationship between average lake depth and average heatwave intensity.

For each lake thermal region, the relationship between log10[lake depth (m)] and the average intensity of lake heatwave events is shown for the historic period (averaged over all years from 1970 to 1999) and for the end of the twenty-first century (averaged over all years from 2070 to 2099) under RCP 2.6, RCP 6.0 and RCP 8.5. The relationships between lake depth and the heatwave metrics (square, not significant: P > 0.001; circle, significant: P < 0.001) were calculated with a generalized additive model (see Methods).

Extended Data Fig. 5 Relationship between average lake depth and average heatwave duration from 1970 to 1999.

For each lake thermal region, the relationship between log10[lake depth (m)] and the average duration of lake heatwave events is shown for the historic period (averaged over all years from 1970 to 1999). The relationships between lake depth and the heatwave metrics (square, not significant: P > 0.001; circle, significant: P < 0.001) were calculated with a generalized additive model (see Methods).

Extended Data Fig. 6 Relationship between average lake depth and average heatwave duration from 2070 to 2099.

For each lake thermal region, the relationship between log10[mean lake depth (m)] and the average duration of lake heatwave events is shown for the end of the twenty-first century (averaged over all years from 2070 to 2099) under RCP 2.6, RCP 6.0 and RCP 8.5. The relationships between lake depth and the heatwave metrics (square, not significant: P > 0.001; circle, significant: P < 0.001) were calculated with a generalized additive model (see Methods).

Extended Data Fig. 7 Relationship between average lake depth and total heatwave duration from 1970 to 1999.

For each lake thermal region, the relationship between log10[mean lake depth (m)] and the total duration of lake heatwave events per year is shown for the historic period (averaged over all years from 1970 to 1999). The relationships between lake depth and the heatwave metrics (square, not significant: P > 0.001; circle, significant: P < 0.001) were calculated with a generalized additive model (see Methods).

Extended Data Fig. 8 Relationship between average lake depth and total heatwave duration from 2070 to 2099.

For each lake thermal region, the relationship between log10[mean lake depth (m)] and the total duration of lake heatwave events per year is shown for the end of the twenty-first century (averaged over all years from 2070 to 2099) under RCP 2.6, RCP 6.0 and RCP 8.5. The relationships between lake depth and the heatwave metrics (square, not significant: P > 0.001; circle, significant: P < 0.001) were calculated with a generalized additive model (see Methods).

Extended Data Fig. 9 Lake thermal responses to climate change.

Here we show the percentage of studied lakes which are projected to experience annual ice cover (a), and experience a permanent heatwave state (b) during the twenty-first century (RCP 8.5). In b, percentages are calculated relative to the number of lakes studied that are projected not to experience annual ice cover by 2070–2099. In c we show a temporally varying (1-year shifting window) 30-year climatological mean, with temperatures plotted as anomalies relative to the historical climatological mean (1970 to 1999). We also demonstrate the future projections of lake heatwave annual average intensity (d), annual average duration (e) and total duration (f) during the twenty-first century (RCP 8.5) calculated after linearly detrending the lake surface temperature anomalies. All results are based on the average simulations from the lake model driven by the four climate models; the shaded regions represent the standard deviation and the dashed lines represent the range across the lake-climate model ensembles.

Extended Data Fig. 10 Comparison of simulated lake heatwaves from two models of different temporal resolution.

Here we compare the simulated lake heatwave intensity (a) and duration (b) by the end of the twenty-first century (averaged over all years from 2070 to 2099) from the FLake model50,51 driven at a temporal resolution of 3 h and 24 h for three case study lakes. All results are based on the average simulations from the FLake model, driven by the four climate models.

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Woolway, R.I., Jennings, E., Shatwell, T. et al. Lake heatwaves under climate change. Nature 589, 402–407 (2021). https://doi.org/10.1038/s41586-020-03119-1

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