Compensatory water effects link yearly global land CO2 sink changes to temperature (original) (raw)

References

  1. Bousquet, P. et al. Regional changes in carbon dioxide fluxes of land and oceans since 1980. Science 290, 1342–1346 (2000)
    Article ADS CAS Google Scholar
  2. Keeling, C. D., Piper, S. C. & Heimann, M. in Aspects of Climate Variability in the Pacific and Western Americas (ed. Peterson, D. H. ) 305–363 (Washington, D.C., 1989)
  3. Kindermann, J., Wurth, G., Kohlmaier, G. H. & Badeck, F. W. Interannual variation of carbon exchange fluxes in terrestrial ecosystems. Glob. Biogeochem. Cycles 10, 737–755 (1996)
    Article ADS CAS Google Scholar
  4. Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015)
    Article ADS Google Scholar
  5. Anderegg, W. R. L. et al. Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proc. Natl Acad. Sci. USA 112, 15591–15596 (2015)
    ADS CAS PubMed Google Scholar
  6. Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013)
    Article ADS CAS Google Scholar
  7. Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003)
    Article ADS CAS Google Scholar
  8. Peylin, P. et al. Multiple constraints on regional CO2 flux variations over land and oceans. Glob. Biogeochem. Cycles 19, http://dx.doi.org/10.1029/2003GB002214 (2005)
  9. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014)
    Article ADS CAS Google Scholar
  10. Tian, H. Q. et al. Effect of interannual climate variability on carbon storage in Amazonian ecosystems. Nature 396, 664–667 (1998)
    Article ADS CAS Google Scholar
  11. Wang, J., Zeng, N. & Wang, M. Interannual variability of the atmospheric CO2 growth rate: roles of precipitation and temperature. Biogeosciences 13, 2339–2352 (2016)
    Article ADS CAS Google Scholar
  12. Wang, W. et al. Variations in atmospheric CO2 growth rates coupled with tropical temperature. Proc. Natl Acad. Sci. USA 110, 13061–13066 (2013)
    Article ADS CAS Google Scholar
  13. Wang, X. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014)
    Article ADS CAS Google Scholar
  14. Zeng, N., Mariotti, A. & Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Glob. Biogeochem. Cycles 19, http://dx.doi.org/10.1029/2004GB002273 (2005)
  15. Tramontana, G. et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13, 4291–4313 (2016)
    Article ADS CAS Google Scholar
  16. Le Quéré, C. et al. Global carbon budget 2014. Earth Syst. Sci. Data Discuss. 7, 521–610 (2014)
    Article ADS Google Scholar
  17. Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015)
    Article ADS CAS Google Scholar
  18. Gurney, K. R., Baker, D., Rayner, P. & Denning, S. Interannual variations in continental-scale net carbon exchange and sensitivity to observing networks estimated from atmospheric CO2 inversions for the period 1980 to 2005. Glob. Biogeochem. Cycles 22, http://dx.doi.org/10.1029/2007gb003082 (2008)
  19. Peylin, P. et al. Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions. Biogeosciences 10, 6699–6720 (2013)
    Article ADS CAS Google Scholar
  20. Baldocchi, D. Turner Review No. 15. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26 (2008)
    Article CAS Google Scholar
  21. Lasslop, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol. 16, 187–208 (2010)
    Article ADS Google Scholar
  22. 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 ADS Google Scholar
  23. Schimel, D. et al. Observing terrestrial ecosystems and the carbon cycle from space. Glob. Change Biol. 21, 1762–1776 (2015)
    Article ADS Google Scholar
  24. Rödenbeck, C., Houweling, S., Gloor, M. & Heimann, M. CO2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport. Atmos. Chem. Phys. 3, 1919–1964 (2003)
    Article ADS Google Scholar
  25. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. D 108, http://dx.doi.org/10.1029/2002JD002670 (2003)
  26. Biederman, J. A. et al. Terrestrial carbon balance in a drier world: the effects of water availability in southwestern North America. Glob. Change Biol. 22, 1867–1879 (2016)
    Article ADS Google Scholar
  27. Bonal, D., Burban, B., Stahl, C., Wagner, F. & Hérault, B. The response of tropical rainforests to drought—lessons from recent research and future prospects. Ann. For. Sci. 73, 27–44 (2016)
    Article Google Scholar
  28. Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005)
    Article ADS CAS Google Scholar
  29. Schwalm, C. R. et al. Assimilation exceeds respiration sensitivity to drought: a FLUXNET synthesis. Glob. Change Biol. 16, 657–670 (2010)
    Article ADS Google Scholar
  30. van der Molen, M. K. et al. Drought and ecosystem carbon cycling. Agric. For. Meteorol. 151, 765–773 (2011)
    Article ADS Google Scholar
  31. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001)
    Article Google Scholar
  32. Haykin, S. Neural Networks: A Comprehensive Foundation (Prentice Hall, 1998)
  33. Friedman, J. H. Multivariate adaptive regression splines. Ann. Stat. 19, 1–67 (1991)
    Article MathSciNet Google Scholar
  34. Jung, M. & Zscheischler, J. A guided hybrid genetic algorithm for feature selection with expensive cost functions. Procedia Comput. Sci. 18, 2337–2346 (2013)
    Article Google Scholar
  35. Jung, M. et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. Biogeosci. 116, G00J07 (2011)
    Article Google Scholar
  36. Jung, M. & FLUXCOM team FLUXCOM (RS+METEO) global land carbon fluxes using CRUNCEP climate data. FLUXCOM Data Portalhttp://dx.doi.org/10.17871/FLUXCOM_RS_METEO_CRUNCEPv6_1980_2013_v1 (2016)
  37. Friedl, M. A. et al. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010)
    Article ADS Google Scholar
  38. New, M., Hulme, M. & Jones, P. Representing twentieth-century space-time climate variability. Part II: Development of 1901-96 monthly grids of terrestrial surface climate. J. Clim. 13, 2217–2238 (2000)
    Article ADS Google Scholar
  39. Wang, Y. P., Law, R. M. & Pak, B. A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences 7, 2261–2282 (2010)
    Article ADS CAS Google Scholar
  40. Jain, A. K., Meiyappan, P., Song, Y. & 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 ADS Google Scholar
  41. Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003)
    Article ADS Google Scholar
  42. Smith, B., Prentice, I. C. & Sykes, M. T. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob. Ecol. Biogeogr. 10, 621–637 (2001)
    Article Google Scholar
  43. Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, GB1015, http://dx.doi.org/1010.1029/2003GB002199 (2005)
    Article ADS Google Scholar
  44. 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

Download references

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
  2. Markus Reichstein
  3. Christopher R. Schwalm
  4. Chris Huntingford
  5. Stephen Sitch
  6. Anders Ahlström
  7. Almut Arneth
  8. Gustau Camps-Valls
  9. Philippe Ciais
  10. Pierre Friedlingstein
  11. Fabian Gans
  12. Kazuhito Ichii
  13. Atul K. Jain
  14. Etsushi Kato
  15. Dario Papale
  16. Ben Poulter
  17. Botond Raduly
  18. Christian Rödenbeck
  19. Gianluca Tramontana
  20. Nicolas Viovy
  21. Ying-Ping Wang
  22. Ulrich Weber
  23. Sönke Zaehle
  24. Ning Zeng

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.

Corresponding author

Correspondence toMartin Jung.

Ethics declarations

Competing interests

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).

Supplementary information

PowerPoint slides

Source data

Rights and permissions

About this article

Cite this article

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

Download citation