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Compensatory water effects link yearly global land CO2 sink changes to temperature

Nature volume 541, pages 516–520 (2017)Cite this article

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Abstract

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems1,2,3. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales3,4,5,6,7,8,9,10,11,12,13,14. Here we use empirical models based on eddy covariance data15 and process-based models16,17 to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance3,4,5,6,9,11,12,14. Our study indicates that spatial climate covariation drives the global carbon cycle response.

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Figure 1: Climatic controls on NEE IAV at global and local scales for the period 1980–2013 derived from machine-learning-based (FLUXCOM) and process-based (TRENDY) models.
Figure 2: Effects of spatial covariation and scale on temperature versus water control of NEE IAV for FLUXCOM and TRENDY models.
Figure 3: Latitudinal patterns of water and temperature driven IAV of gross carbon fluxes (GPP and TER) and NEE for FLUXCOM and TRENDY models.
Figure 4: Spatial patterns of covariance and correlation of WAI- and TEMP-driven GPP and TER IAV for FLUXCOM models.

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Acknowledgements

We thank P. Peylin for providing RECCAP inversion results. We also thank P. Bodesheim for help with the mathematical notations, J. Nelson for proofreading the Supplementary Information, S. Schott for help with artwork, and G. Boenisch, L. Maack and P. Koch for help archiving the FLUXCOM data. M.J., M.R. and D.P. acknowledge funding from the European Union (EU) FP7 project GEOCARBON (grant number 283080) and the EU H2020 BACI project (grant number 640176). F.G. and M.R. acknowledge the European Space Agency for funding the ‘Coupled Biosphere–Atmosphere virtual LABoratory’ (CAB-LAB). S.Z. acknowledges support from the European Research Council (ERC) under the EU’s Horizon 2020 research and innovation programme (QUINCY; grant number 647204). A. Arneth acknowledges support from the EU FP7 project LUC4C (grant number 603542). C.R.S. was supported by National Aeronautics and Space Administration (NASA) grants NNX12AK12G, NNX12AP74G, NNX10AG01A and NNX11AO08A. P.C. acknowledges support from the ERC Synergy grant ERC-2013-SyG-610028 IMBALANCE-P. K.I. acknowledges support from the Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan. S.S. acknowledges the support of the Natural Environment Research Council (NERC) South AMerican Biomass Burning Analysis (SAMBBA) project (grant code NE/J010057/1). C.H. is grateful for support from the NERC CEH National Capability fund. A. Ahlström acknowledges support from The Royal Physiographic Society in Lund (Birgit and Hellmuth Hertz’ Foundation) and the Swedish Research Council (637-2014-6895). G.C.-V. was supported by the EU under ERC consolidator grant SEDAL-647423.

Author information

Authors and Affiliations

  1. Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, 07745, Germany

    Martin Jung, Markus Reichstein, Fabian Gans, Ulrich Weber & Sönke Zaehle

  2. Michael-Stifel-Center Jena for Data-driven and Simulation Science, Friedrich-Schiller-Universität Jena, Jena, 07743, Germany

    Markus Reichstein & Sönke Zaehle

  3. Woods Hole Research Center, 149 Woods Hole Road, Falmouth, 02540, Massachusetts, USA

    Christopher R. Schwalm

  4. Centre for Ecology and Hydrology, Wallingford, OX10 8BB, Oxfordshire, UK

    Chris Huntingford

  5. College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4QF, UK

    Stephen Sitch

  6. Department of Earth System Science, School of Earth, Energy and Environmental Sciences, Stanford University, Stanford, 94305, California, USA

    Anders Ahlström

  7. Department of Physical Geography and Ecosystem Science, Lund University, Lund, 223 62, Sweden

    Anders Ahlström

  8. Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Garmisch-Partenkirchen, 82467, Germany

    Almut Arneth

  9. Image Processing Laboratory, Universitat de València, Catedrático José Beltrán, Paterna, 46980, València, Spain

    Gustau Camps-Valls

  10. Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, 91191, France

    Philippe Ciais & Nicolas Viovy

  11. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QE, UK

    Pierre Friedlingstein

  12. Department of Environment Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, 236-0001, Yokohama, Japan

    Kazuhito Ichii

  13. Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, 305-8506, Japan

    Kazuhito Ichii

  14. Department of Atmospheric Sciences, University of Illinois, Urbana, 61801, Illinois, USA

    Atul K. Jain

  15. Global Environment Program, The Institute of Applied Energy, Tokyo, 105-0003, Japan

    Etsushi Kato

  16. Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Viterbo, 01100, Italy

    Dario Papale, Botond Raduly & Gianluca Tramontana

  17. NASA Goddard Space Flight Center, Biospheric Science Laboratory, Greenbelt, 20771, Maryland, USA

    Ben Poulter

  18. Department of Bioengineering, Sapientia Hungarian University of Transylvania, M-Ciuc, 530104, Romania

    Botond Raduly

  19. Department of Biogeochemical Systems, Max Planck Institute for Biogeochemistry, Jena, 07745, Germany

    Christian Rödenbeck

  20. CSIRO Oceans and Atmosphere, PMB #1, Aspendale, 3195, Victoria, Australia

    Ying-Ping Wang

  21. Institute of Atmospheric Physics, Chinese Academy of Science, 100029, Beijing, China

    Ning Zeng

  22. Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, 20742, USA

    Ning Zeng

Authors
  1. Martin Jung
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Contributions

M.J. and M.R. designed the analysis. M.J. carried out the analysis and wrote the manuscript with contributions from all authors. M.J., C.R.S., G.C.-V., F.G., K.I., D.P., B.R., G.T. and U.W. contributed to FLUXCOM results. S.S., P.F., C.H., A. Ahlström, A. Arneth, P.C., A.K.J., E.K., B.P., N.V., Y.-P.W. and N.Z. contributed to TRENDY results.

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Correspondence to Martin Jung.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks C. Funk and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Global patterns of NEE IAV for FLUXCOM (left) and TRENDY (right).

Maps of NEE IAV magnitude (mean of ensemble members; a, b) defined as standard deviation of annual NEE normalized by the mean standard deviation (values above 1 indicate above-average IAV). Dashed lines separate areas north and south of 30° N. Time series of integrated NEE over broad latitudinal bands (c–f) or global (g, h) for 1980–2013 normalized by the standard deviation (s.d.) of globally integrated NEE. Black lines show the mean of FLUXCOM or TRENDY ensemble members and the shaded area refers to the ensemble spread (1 s.d.). Independent estimates from the GCP, the Jena and the RECCAP inversions (see Methods) are presented with coloured lines (see key); correlation coefficients with those are given in the same colour. See Supplementary Information section 1 for further cross-consistency analysis.

Extended Data Figure 2 Local versus global dominance of NEETEMP versus NEEWAI for FLUXCOM and TRENDY ensemble members.

Dots show individual ensemble members and the crosses show ensemble means with one standard deviation. Plotted is the difference of local NEEWAI and NEETEMP dominance (the difference of the leftmost blue and green data points in Fig. 2e and f) against the difference of global NEEWAI and NEETEMP dominance (the difference of the rightmost blue and green data points in Fig. 2e and f). The majority of ensemble members as well as ensemble means fall in the lower right quadrant, meaning an overall agreement that NEEWAI dominates at individual grid cells (local) but NEETEMP dominates the globally integrated flux anomaly (global).

Extended Data Figure 3 Spatial patterns of covariance and correlation of WAI- and TEMP-driven GPP and TER IAV for TRENDY models.

Maps of the covariance of annual anomalies (see equation (8) in Methods) of GPP and TER climatic components show large compensation effects (positive covariance) for WAI (a) but nearly no covariance for TEMP (c). Correlations between GPPWAI and TERWAI are large and everywhere positive (b) while correlations among GPPTEMP and TERTEMP are weaker with a distinct spatial pattern of negative correlations in hot regions (d). All results refer to the mean of all TRENDY ensemble members. See Fig. 4 for equivalent FLUXCOM results, and Extended Data Fig. 4 for uncertainties.

Extended Data Figure 4 Ensemble spread of covariation between TEMP and WAI components of GPP and TER for FLUXCOM and TRENDY.

Plots show mean covariance (left) and correlation (right) between GPPTEMP and TERTEMP and GPPWAI and TERWAI for latitudinal bins of 5° for individual ensemble members (thin dotted lines) and ensemble mean (thick solid line with shaded area for 1 s.d.). Despite uncertain magnitudes of GPPTEMP and TERTEMP correlation (large green-shaded area in right panels, b and d) their covariance is negligible (small green-shaded area in left panels, a and c). In comparison, there is large positive covariance of GPPWAI and TERWAI but its magnitude differs substantially among ensemble members (large blue-shaded area in left panels, a and c).

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Jung, M., Reichstein, M., Schwalm, C. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516–520 (2017). https://doi.org/10.1038/nature20780

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