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Increasing impact of warm droughts on northern ecosystem productivity over recent decades

Nature Climate Change volume 11, pages 772–779 (2021)Cite this article

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A Publisher Correction to this article was published on 20 June 2023

This article has been updated

Abstract

Climate extremes such as droughts and heatwaves have a large impact on terrestrial carbon uptake by reducing gross primary production (GPP). While the evidence for increasing frequency and intensity of climate extremes over the last decades is growing, potential systematic adverse shifts in GPP have not been assessed. Using observationally-constrained and process-based model data, we estimate that particularly northern midlatitude ecosystems experienced a +10.6% increase in negative GPP extremes in the period 2000–2016 compared to 1982–1998. We attribute this increase predominantly to a greater impact of warm droughts, in particular over northern temperate grasslands (+95.0% corresponding mean increase) and croplands (+84.0%), in and after the peak growing season. These results highlight the growing vulnerability of ecosystem productivity to warm droughts, implying increased adverse impacts of these climate extremes on terrestrial carbon sinks as well as a rising pressure on global food security.

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Fig. 1: Regional changes in ecosystem productivity linked to negative GPP extreme events between the 2000–2016 and 1982–1998 study periods over the IPCC regions.
Fig. 2: Changes in negative GPP extremes over the northern midlatitudes between the 2000–2016 and 1982–1998 study periods.
Fig. 3: Changes in negative GPP extremes attributed to significant climate drivers between the 2000–2016 and 1982–1998 periods.
Fig. 4: Regional changes in the composition (%) of negative GPP extremes attributed to climate drivers between the 2000–2016 and 1982–1998 study periods over the IPCC regions.
Fig. 5: Changes in negative GPP extremes for specific land covers over the northern midlatitudes between the two study periods (2000–2016 compared to 1982–1998).

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Data availability

The data to reproduce and further interpret the main results presented are publicly available at figshare80. The TRENDY v.6 datasets applied in this study have been preprocessed by M.O. and are available from the University of Exeter (https://doi.org/10.24378/exe.2883) and on request. The original TRENDY v.6 datasets can be requested from S. Sitch (s.a.sitch@exeter.ac.uk) and P. Friedlingstein (p.friedlingstein@exeter.ac.uk). The FLUXCOM dataset is publicly available through the FLUXCOM data portal (https://www.bgc-jena.mpg.de/geodb/projects/FileDetails.php). The LUE datasets are provided by W.K.S. and publicly available at https://wkolby.org/data-code/. The CRUNCEP reanalysis data are available through the Climatic Research Unit data portal (https://crudata.uea.ac.uk/cru/data/ncep/#dataset_access).

Code availability

All relevant codes to reproduce the figures presented in this study are publicly available at figshare (https://doi.org/10.6084/m9.figshare.14845005)80. Further codes and materials are available from D.G. on request.

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References

  1. Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X. & Bronaugh, D. Climate extremes indices in the CMIP5 multimodel ensemble. Part 2. Future climate projections. J. Geophys. Res. Atm. 118, 2473–2493 (2013).

    Article  Google Scholar 

  2. Lehmann, J., Coumou, D. & Frieler, K. Increased record-breaking precipitation events under global warming. Climatic Change 132, 501–515 (2015).

    Article  Google Scholar 

  3. Yin, J. et al. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat. Commun. 9, 4389 (2018).

    Article  CAS  Google Scholar 

  4. Fischer, E. M., Beyerle, U. & Knutti, R. Robust spatially aggregated projections of climate extremes. Nat. Clim. Change 3, 1033–1038 (2013).

    Article  Google Scholar 

  5. Spinoni, J. et al. Future global meteorological drought hotspots: a study based on CORDEX data. J. Clim. 33, 3635–3659 (2020).

    Article  Google Scholar 

  6. Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).

    Article  Google Scholar 

  7. Alexander, L. V. Global observed long-term changes in temperature and precipitation extremes: a review of progress and limitations in IPCC assessments and beyond. Weather Clim. Extremes 11, 4–16 (2016).

    Article  Google Scholar 

  8. Seneviratne, S. I., Donat, M. G., Mueller, B. & Alexander, L. V. No pause in the increase of hot temperature extremes. Nat. Clim. Change 4, 161–163 (2014).

    Article  Google Scholar 

  9. Barriopedro, D., Sousa, P. M., Trigo, R. M., García-Herrera, R. & Ramos, A. M. The exceptional Iberian heatwave of summer 2018. Bull. Am. Meteor. Soc. 101, 29–34 (2020).

    Article  Google Scholar 

  10. Alizadeh, M. R. et al. A century of observations reveals increasing likelihood of continental- scale compound dry-hot extremes. Sci. Adv. 6, eaaz4571 (2020).

    Article  Google Scholar 

  11. Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die–off from hotter drought in the Anthropocene. Ecosphere 6, 8 (2015).

    Article  Google Scholar 

  12. Chiang, F., Mazdiyasni, O. & AghaKouchak, A. Amplified warming of droughts in southern United States in observations and model simulations. Sci. Adv. 4, eaat2380 (2018).

    Article  Google Scholar 

  13. Padrón, R. S. et al. Observed changes in dry-season water availability attributed to human- induced climate change. Nat. Geosci. 13, 477–481 (2020).

    Article  Google Scholar 

  14. Markonis, Y. et al. The rise of compound warm-season droughts in Europe. Sci. Adv. 7, eabb9668 (2021).

    Article  Google Scholar 

  15. Le Quéré, C. et al. Global carbon budget 2017. Earth Syst. Sci. Data 10, 405–448 (2017).

    Article  Google Scholar 

  16. Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).

    Article  CAS  Google Scholar 

  17. Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).

    Article  CAS  Google Scholar 

  18. Anderegg, W. R. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).

    Article  CAS  Google Scholar 

  19. Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).

    Article  CAS  Google Scholar 

  20. Jia, G. E. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) 131–247 (IPCC, 2019).

  21. Ryu, Y., Berry, J. A. & Baldocchi, D. D. What is global photosynthesis? History, uncertainties and opportunities. Remote Sens. Environ. 223, 95–114 (2019).

    Article  Google Scholar 

  22. Tramontana, G. et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosci. 13, 4291–4313 (2016).

    Article  CAS  Google Scholar 

  23. Jung, M. et al. The FLUXCOM ensemble of global land–atmosphere energy fluxes. Sci. Data 6, 74 (2019).

  24. Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).

    Article  Google Scholar 

  25. IPCC. Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012).

  26. Alexander, L. V. et al. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. Atm. 111, D05109 (2006).

    Article  Google Scholar 

  27. Orlowsky, B. & Seneviratne, S. I. Global changes in extreme events: regional and seasonal dimension. Climatic Change 110, 669–696 (2012).

    Article  Google Scholar 

  28. Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeoch. Cyc. 21, GB3018 (2007).

    Google Scholar 

  29. O’Sullivan, M. et al. Climate-driven variability and trends in plant productivity over recent decades based on three global products. Glob. Biogeochem. Cyc. 34, e2020GB006613 (2020).

    Google Scholar 

  30. Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. 229, 2413–2445 (2021).

  31. McKee, T. B., Doesken, N. J. & Kleist, J. The relationship of drought frequency and duration to time scales. Proc. 8th Conf. Appl. Clim. 17, 179–183 (1993).

    Google Scholar 

  32. Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).

    Article  Google Scholar 

  33. Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).

    Article  Google Scholar 

  34. Choi, W. & Kim, K. Y. Physical mechanism of spring and early summer drought over North America associated with the boreal warming. Sci. Rep. 8, 7533 (2018).

  35. Zhang, Y., Parazoo, N. C., Williams, A. P., Zhou, S. & Gentine, P. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. USA 117, 9216–9222 (2020).

    Article  CAS  Google Scholar 

  36. Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).

    Article  CAS  Google Scholar 

  37. Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).

    Article  CAS  Google Scholar 

  38. McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739 (2008).

    Article  Google Scholar 

  39. Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).

    Article  CAS  Google Scholar 

  40. Flach, M. et al. Vegetation modulates the impact of climate extremes on gross primary production. Biogeosci. 18, 39–53 (2021).

    Article  Google Scholar 

  41. He, W. et al. Large-scale droughts responsible for dramatic reductions of terrestrial net carbon uptake over North America in 2011 and 2012. J. Geophys. Res. Biogeosci. 123, 2053–2071 (2018).

    Article  CAS  Google Scholar 

  42. Sippel, S. et al. Drought, heat, and the carbon cycle: a review. Curr. Clim. Change Rep. 4, 266–286 (2018).

    Article  Google Scholar 

  43. Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).

  44. Schwalm, C. R. et al. Assimilation exceeds respiration sensitivity to drought: a FLUXNET synthesis. Glob. Change Biol. 16, 657–670 (2010).

    Article  Google Scholar 

  45. van der Velde, M., Wriedt, G. & Bouraoui, F. Estimating irrigation use and effects on maize yield during the 2003 heatwave in France. Agric. Ecosys. Environ. 135, 90–97 (2010).

    Article  Google Scholar 

  46. Bondeau, A. et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679–706 (2007).

    Article  Google Scholar 

  47. Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).

    Article  CAS  Google Scholar 

  48. Zscheischler, J. et al. Carbon cycle extremes during the 21st century in CMIP5 models: future evolution and attribution to climatic drivers. Geophys. Res. Lett. 41, 8853–8861 (2014).

    Article  CAS  Google Scholar 

  49. Xu, C. et al. Increasing impacts of extreme droughts on vegetation productivity under climate change. Nat. Clim. Change 9, 948–953 (2019).

    Article  CAS  Google Scholar 

  50. Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).

    Article  CAS  Google Scholar 

  51. Mueller, N. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

    Article  CAS  Google Scholar 

  52. Meza, I. et al. Global-scale drought risk assessment for agricultural systems. Nat. Hazard Earth Syst. Sci. 20, 695–712 (2020).

    Article  Google Scholar 

  53. Martignago, D., Rico-Medina, A., Blasco-Escaméz, D., Fontanet-Manzaneque, J. B. & Caño- Delgado, A. I. Drought resistance by engineering plant tissue-specific responses. Front. Plant Sci. 10, 1676 (2019).

    Article  Google Scholar 

  54. Gupta, A., Rico-Medina, A. & Caño-Delgado, A. I. The physiology of plant responses to drought. Science 368, 266–269 (2020).

    Article  CAS  Google Scholar 

  55. Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005).

    Article  Google Scholar 

  56. Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI) 3 g and fraction of photosynthetically active radiation (FPAR) 3 g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 2, 927–948 (2013).

    Article  Google Scholar 

  57. Smith, W. K., Fox, A. M., MacBean, N., Moore, D. J. & Parazoo, N. C. Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation. New Phytol. 225, 105–112 (2019).

    Article  Google Scholar 

  58. Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosci. 12, 653–679 (2015).

    Article  Google Scholar 

  59. Haverd, V. et al. A new version of the CABLE land surface model (subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).

    Article  CAS  Google Scholar 

  60. Melton, J. R. & Arora, V. K. Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0. Geosci. Model Dev. 9, 323–361 (2016).

    Article  CAS  Google Scholar 

  61. Oleson, K. W. et al. Technical Description of version 4.5 of the Community Land Model (CLM) (NCAR, 2013).

  62. Tian, H. et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Climatic Change 129, 413–426 (2015).

    Article  CAS  Google Scholar 

  63. Jain, A. K., Meiyappan, P., Song, P. & House, J. I. CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Glob. Change Biol. 19, 2893–2906 (2013).

    Article  Google Scholar 

  64. Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosci. 11, 2027–2054 (2014).

    Article  Google Scholar 

  65. Reick, C. H., Raddatz, T., Brovkin, V. & Gayler, V. Representation of natural and anthropogenic land cover change in MPI-ESM. J. Adv. Model. Earth Syst. 5, 459–482 (2013).

    Article  Google Scholar 

  66. Clark, D. B. et al. The Joint UK Land Environment Simulator (JULES), model description—Part 2: carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).

    Article  Google Scholar 

  67. Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere–biosphere system. Glob. Biogeoch. Cyc. 19, GB1015 (2005).

    Google Scholar 

  68. Guimberteau, M. et al. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation. Geosci. Model Dev. 11, 121–163 (2018).

    Article  CAS  Google Scholar 

  69. Zeng, N., Mariotti, A. & Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Glob. Biogeoch. Cyc. 9, 23 (2005).

    Google Scholar 

  70. Kato, E., Kinoshita, T., Ito, A., Kawamiya, M. & Yamagata, Y. Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model. J. Land Use Sci. 8, 104–122 (2013).

    Article  Google Scholar 

  71. Mahecha, M. D., Fürst, L. M., Gobron, N. & Lange, H. Identifying multiple spatiotemporal patterns: a refined view on terrestrial photosynthetic activity. Patt. Recogn. Lett. 31, 2309–2317 (2010).

    Article  Google Scholar 

  72. Zscheischler, J., Mahecha, M. D., Harmeling, S. & Reichstein, M. Detection and attribution of large spatiotemporal extreme events in Earth observation data. Ecol. Inform. 15, 66–73 (2013).

    Article  Google Scholar 

  73. Seneviratne, S. et al. in Special Report on 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).

  74. Mahecha, M. D. et al. Detecting impacts of extreme events with ecological in situ monitoring networks. Biogeosci. 14, 4255–4277 (2017).

    Article  Google Scholar 

  75. Zscheischler, J. et al. Extreme events in gross primary production: a characterization across continents. Biogeosci. 11, 2909–2924 (2014).

    Article  Google Scholar 

  76. Zscheischler, J. et al. A few extreme events dominate global interannual variability in gross primary production. Environ. Res. Lett. 9, 035001 (2014).

    Article  Google Scholar 

  77. Thornthwaite, C. W. An approach toward a rational classification of climate. Geogr. Rev. 38, 55–94 (1948).

    Article  Google Scholar 

  78. Friedl, M. & Sulla-Menashe, D. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006 (NASA EOSDIS Land Processes DAAC, 2015); https://doi.org/10.5067/MODIS/MCD12C1.006

  79. Yin, Z. et al. Improvement of the irrigation scheme in the ORCHIDEE land surface model and impacts of irrigation on regional water budgets over China. J. Adv. Model. Earth Syst. 12, e2019MS001770 (2020).

    Article  CAS  Google Scholar 

  80. Gampe, D. Increasing impact of warm droughts on northern ecosystem productivity over recent decades (figshare, 2021); https://doi.org/10.6084/m9.figshare.14845005

  81. Moore, D. J. et al. Persistent reduced ecosystem respiration after insect disturbance in high elevation forests. Ecol. Lett. 16, 731–737 (2013).

    Article  Google Scholar 

  82. Kurz, W. A. et al. Mountain pine beetle and forest carbon feedback to climate change. Nature 452, 987–990 (2008).

    Article  CAS  Google Scholar 

  83. Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).

    Article  Google Scholar 

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Acknowledgements

J.Z. has been supported by the Swiss National Science Foundation grant no. 179876 and the Helmholtz Initiative and Networking Fund (Young Investigator Group COMPOUNDX, grant no. VH-NG-1537). S.S. has been supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil. W.K.S. acknowledges funding from NASA Terrestrial Ecosystems grant no. 80NSSC19M0103.

Author information

Authors and Affiliations

  1. Institute of Geography, Augsburg University, Augsburg, Germany

    David Gampe & Wolfgang Buermann

  2. Department of Geography, Ludwig Maximilian University of Munich, Munich, Germany

    David Gampe

  3. Climate and Environmental Physics, University of Bern, Bern, Switzerland

    Jakob Zscheischler

  4. Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    Jakob Zscheischler

  5. Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

    Jakob Zscheischler

  6. Max Planck Institute for Biogeochemistry, Jena, Germany

    Markus Reichstein

  7. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    Michael O’Sullivan

  8. School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA

    William K. Smith

  9. College of Life and Environmental Sciences, University of Exeter, Exeter, UK

    Stephen Sitch

  10. Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA

    Wolfgang Buermann

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Contributions

D.G. and W.B. developed the conceptual framework of this research project. D.G. carried out all data analysis with M.O. and W.K.S. providing the preprocessed GPP and climate datasets. D.G. drafted the initial version of the manuscript and all authors contributed to writing the final paper and the interpretation of the results.

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Correspondence to David Gampe.

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Peer review information Nature Climate Change thanks Weizhe Chen, Ainong Li and Chonggang Xu for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Regional changes in ecosystem productivity linked to negative GPP extreme events between the 2000–2016 and 1982–1998 study periods over the IPCC regions based on the globally largest 100 events.

The cumulative GPP anomalies associated with negative GPP extremes were calculated for each study period separately, and then subtracted from one another (2000–2016 minus 1982–1998) to yield the changes in negative GPP extremes (ΔGPP (PgC)). Regions that experienced a consistent increase in ΔGPP in all three datasets are highlighted (pink regions). Associated increased ΔGPP (expressed as negative values; barplots) were derived from the median of the individual datasets (combined bar represents mean of these three medians). The presented error bars were estimated from the minimum and maximum ΔGPP of the individual datasets (error bars of the combined ΔGPP were estimated as the minimum and maximum ΔGPP of the means of the three GPP datasets). Numbers above the barplots correspond to the change in the number of events where a positive (negative) value indicates an increased (decreased) number of events presented as mean of the three datasets (the information in brackets refers to the corresponding absolute number of events in the first period).

Extended Data Fig. 2 Regional changes in the composition (%) of negative GPP extremes attributed to climate drivers between the 2000–2016 and 1982–1998 study periods over the IPCC regions based on the globally largest 100 events.

First, the relative contribution of GPP anomalies attributed to each of the climate drivers to the overall negative GPP extremes was calculated for each period to yield the composition of attributed negative GPP extremes. The associated change in the composition was then expressed as the difference between the two study periods (2000–2016 minus 1982–1998). The corresponding changes between the two periods (barplots) were derived from the median of the individual datasets. The presented error bars were estimated through the corresponding minimum and maximum of the three datasets. Regions that experienced a consistent increase in ΔGPP in all three datasets are highlighted (pink regions).

Extended Data Fig. 3 Regional changes in the impact of climate drivers on negative GPP extremes between the 2000–2016 and 1982–1998 study periods based on the globally largest 100 events.

The change in negative GPP extremes was calculated as the difference in cumulative GPP anomalies linked to negative GPP extremes attributed to each for the significant climate drivers and per study period (2000–2016 minus 1982–1998; ΔGPP). Absolute changes in ΔGPP (PgC) attributed to each of the considered climate drivers between the two periods (barplots) were derived from the medians of the three individual datasets. The presented error bars were estimated as the minimum and maximum of these three medians. Here, negative values indicate increased ΔGPP attributed to the corresponding climate driver in the later period. Regions that experienced a consistent increase in ΔGPP in all three datasets are highlighted (pink regions).

Extended Data Fig. 4 Cumulative GPP changes and their modulation through negative GPP extremes between the 2000–2016 and 1982–1998 study periods.

a, c, Cumulative changes in GPP (ΔGPP_c (PgC); expressed as the differences in cumulative GPP between the study periods) driven by a, Climate only (all datasets) and c, Climate + CO2 fertilization (TRENDY models only; see Methods) between the two periods over regions with consistent increased GPP extremes. b, d, Corresponding modulation (%) of b, Climate only and d, Climate + CO2 fertilization ΔGPP_c through GPP extremes calculated as (Δ Extremes)/(Δ GPP_c) where ΔExtremes is the difference of cumulative negative GPP extremes between the periods. Grey areas mark regions where the datasets differ in the direction of GPP modulation.

Extended Data Fig. 5 Attribution of negative GPP events to specific climate drivers and changes in the frequency of compound events.

a, Percentage of attributed negative GPP events to each climate driver based on the entire period 1982–2016 and global scale (bars; median of each dataset) with corresponding percentage of attributed negative GPP relative to the total negative GPP extremes. The attribution allows for multiple drivers per event thus percentages add up to more than 100% (see Methods for details). In total 68.7% (72.1%) of the events (Cumulative GPP anomaly) can be associated with these climate drivers (median of the three datasets) with potential other drivers such as fire76, insects81,82, wind explaining the remainder83. b, c, Changes in the frequency of compound events of considered droughts (b, SPI and c, SPEI (% of events)) coinciding with high temperatures between the two periods. The black vertical (a) / horizontal (b,c) lines correspond to the defined threshold of 10% where lower attribution rates indicate insignificance of the corresponding driver or occurrence of analysed compound events, respectively (see methods).

Extended Data Fig. 6 Changes in negative GPP extremes attributed to their main climate driver between the 2000–2016 and 1982–1998 periods.

The cumulative GPP anomalies linked with GPP extremes attributed to the climate driver showing the highest coinciding anomalies (that is, the main driver; see Methods) were calculated for each study period and then subtracted from one another (2000–2016 minus 1982–1998; attributed ΔGPP). a–c, The corresponding attributed ΔGPP to each of the three significant climate drivers SPEI (a), SPI (b) and concurrent low precipitation (c). Here, each negative GPP extreme event was attributed only to the climate drivers that showed the largest coinciding anomaly thus the corresponding GPP anomaly contributed only to the balance of that driver (panels; in contrary to Fig. 3). Each map was derived as the mean of the three datasets (originating from the median across the ensemble members of: LUE, FLUXCOM and TRENDY).

Extended Data Fig. 7 Regional changes in the impact of climate drivers on negative GPP extremes between the 2000–2016 and 1982–1998 study periods.

The cumulative negative GPP anomalies attributed to each of the climate drivers were calculated for each period and the associated changes were then expressed as the difference between the two study periods (2000–2016 minus 1982–1998; ΔGPP). The corresponding increased (decreased) ΔGPP (expressed as negative (positive) values) between the two periods (barplots) were derived from the median of the three individual datasets. The presented error bars were estimated through the corresponding minimum and maximum of the three datasets. Regions that experienced a consistent increase in ΔGPP in all three datasets are highlighted (pink regions).

Extended Data Fig. 8 Changes in the impact of warm droughts (SPEI) on negative GPP extremes over the northern midlatitudes between the 2000–2016 and 1982–1998 study periods.

The change in negative GPP extremes was calculated as the difference in cumulative GPP anomalies linked to negative GPP extremes attributed to warm droughts (SPEI) per study period (2000–2016 minus 1982–1998; ΔGPP). a–c, Monthly anomalies in negative GPP extremes attributed to SPEI relative to the climatological mean of the first period for LUE (a), FLUXCOM (b) and TRENDY (c). Spirals start with the first entry of the time series (Jan. 1982; centre) and end in December 2016 (outside) with the year 1999 masked (grey; see Methods). Outside numbers indicate cumulative monthly GPP anomalies linked to negative GPP extremes over the two study periods 2000–2016 (first entry) and 1982–1998 (second entry). Thereby, brackets denote corresponding insignificant differences between these two periods (Mann–Whitney U-test, p-value < 0.05). d, Summarized relative changes (%) for the boreal growing season (April–September) in warm drought-driven GPP extremes, derived from panels a–c for each dataset.

Extended Data Fig. 9 Aggregated MCD12C1 land-cover map applied for the analyses related to land cover in this study.

The land cover information presented is based on the MCD12C178 land cover information and was aggregated to the 0.5° target resolution by considering the largest land cover class fraction per grid cell, scattered white areas denote regions affected by land cover changes and are masked from all analyses (see Methods). Only vegetated land cover classes were considered in this study.

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Gampe, D., Zscheischler, J., Reichstein, M. et al. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Chang. 11, 772–779 (2021). https://doi.org/10.1038/s41558-021-01112-8

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