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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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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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