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Ecosystem functioning is enveloped by hydrometeorological variability


Terrestrial ecosystem processes, and the associated vegetation carbon dynamics, respond differently to hydrometeorological variability across timescales, and so does our scientific understanding of the underlying mechanisms. Long-term variability of the terrestrial carbon cycle is not yet well constrained and the resulting climate–biosphere feedbacks are highly uncertain. Here we present a comprehensive overview of hydrometeorological and ecosystem variability from hourly to decadal timescales integrating multiple in situ and remote-sensing datasets characterizing extra-tropical forest sites. We find that ecosystem variability at all sites is confined within a hydrometeorological envelope across sites and timescales. Furthermore, ecosystem variability demonstrates long-term persistence, highlighting ecological memory and slow ecosystem recovery rates after disturbances. However, simulation results with state-of-the-art process-based models do not reflect this long-term persistent behaviour in ecosystem functioning. Accordingly, we develop a cross-time-scale stochastic framework that captures hydrometeorological and ecosystem variability. Our analysis offers a perspective for terrestrial ecosystem modelling and paves the way for new model–data integration opportunities in Earth system sciences.

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Fig. 1: Spatial distribution of the analysed forest sites.
Fig. 2: Ecosystem and hydrometeorological variability based on eddy covariance and micrometeorological data, respectively.
Fig. 3: Composite ecosystem and hydrometeorological variability continua.
Fig. 4: The hydrometeorological envelope of ecosystem variability continuum.
Fig. 5: Empirical versus simulated continua of ecosystem variability.
Fig. 6: A parsimonious stochastic framework for modelling ecosystem and hydrometeorological variability across timescales.


  1. 1.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  2. 2.

    Mitchell, J. M. Jr An overview of climate variability and its causal mechanisms. Quat. Res. 6, 481–493 (1976).

    Article  Google Scholar 

  3. 3.

    Franke, J., Frank, D., Raible, C. C., Esper, J. & Brönnimann, S. Spectral biases in tree-ring climate proxies. Nat. Clim. Change 3, 360–364 (2013).

    Article  Google Scholar 

  4. 4.

    Markonis, Y. & Koutsoyiannis, D. Scale-dependence of persistence in precipitation records. Nat. Clim. Change 6, 399–401 (2016).

    Article  Google Scholar 

  5. 5.

    Baldocchi, D., Falge, E. & Wilson, K. A spectral analysis of biosphere–atmosphere trace gas flux and meteorologiacal variables across hour to multi-year time scales. Agric. For. Meteorol. 107, 1–27 (2001).

    Article  Google Scholar 

  6. 6.

    Katul, G. et al. Multiscale analysis of vegetation surface fluxes: from seconds to years. Adv. Water Resour. 24, 1119–1132 (2001).

    Article  Google Scholar 

  7. 7.

    Mahecha, M. D. et al. Characterizing ecosystem–atmosphere interactions from short to interannual time scales. Biogeosciences 4, 743–758 (2007).

    Article  CAS  Google Scholar 

  8. 8.

    Stoy, P. C. et al. Biosphere–atmosphere exchange of CO2 in relation to climate: a cross-biome analysis across multiple time scales. Biogeosciences 6, 2297–2312 (2009).

    Article  CAS  Google Scholar 

  9. 9.

    Mueller, K. L., Yadav, V., Curtis, P. S., Vogel, C. & Michalak, A. M. Attributing the variability of eddy-covariance CO2 flux measurements across temporal scales using geostatistical regression for a mixed northern hardwood forest. Global Biogeochem. Cycles 24, GB3023 (2010).

    Article  CAS  Google Scholar 

  10. 10.

    Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    Article  PubMed  CAS  Google Scholar 

  11. 11.

    Franklin, O. et al. Modeling carbon allocation in trees: a search for principles. Tree Physiol. 32, 648–666 (2012).

    Article  PubMed  CAS  Google Scholar 

  12. 12.

    Hartmann, H. & Trumbore, S. Understanding the roles of nonstructural carbohydrates in forest trees—from what we can measure to what we want to know. New Phytol. 211, 386–403 (2016).

    Article  PubMed  CAS  Google Scholar 

  13. 13.

    Koutsoyiannis, D. Generic and parsimonious stochastic modelling for hydrology and beyond. Hydrol. Sci. J. 61, 225–244 (2016).

    Article  Google Scholar 

  14. 14.

    Dimitriadis, P. & Koutsoyiannis, D. Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst–Kolmogorov processes. Stoch. Environ. Res. Risk Assess. 29, 1649–1669 (2015).

    Article  Google Scholar 

  15. 15.

    Baldocchi, D. et al. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434 (2001).

    Article  Google Scholar 

  16. 16.

    Hilker, T., Coops, N. C., Wulder, M. A., Black, T. A. & Guy, R. D. The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements. Sci. Total Environ. 404, 411–423 (2008).

    Article  PubMed  CAS  Google Scholar 

  17. 17.

    Myneni, R. B. et al. A large carbon sink in the woody biomass of Northern forests. Proc. Natl Acad. Sci. USA 98, 14784–14789 (2001).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. 18.

    Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).

    Article  Google Scholar 

  19. 19.

    Rocha, A. V., Goulden, M. L., Dunn, A. L. & Wofsy, S. C. On linking interannual tree ring variability with observations of whole-forest CO2 flux. Glob. Change Biol. 12, 1378–1389 (2006).

    Article  Google Scholar 

  20. 20.

    Babst, F. et al. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. New Phytol. 201, 1289–1303 (2014).

    Article  PubMed  CAS  Google Scholar 

  21. 21.

    Campioli, M. et al. Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of forests. Nat. Commun. 7, 13717 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. 22.

    Delpierre, N., Berveiller, D., Granda, E. & Dufrene, E. Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest. New Phytol. 210, 459–470 (2016).

    Article  PubMed  CAS  Google Scholar 

  23. 23.

    Cavanaugh, N. R. & Shen, S. S. P. The effects of gridding algorithms on the statistical moments and their trends of daily surface air temperature. J. Clim. 28, 9188–9205 (2015).

    Article  Google Scholar 

  24. 24.

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

    Article  CAS  Google Scholar 

  25. 25.

    Scheffer, M., Carpenter, S. R., Dakos, V. & Van Nes, E. H. Generic indicators of ecological resilience: inferring the chance of a critical transition. Annu. Rev. Ecol. Evol. Syst. 46, 145–167 (2015).

    Article  Google Scholar 

  26. 26.

    Ogle, K. et al. Quantifying ecological memory in plant and ecosystem processes. Ecol. Lett. 18, 221–235 (2015).

    Article  PubMed  Google Scholar 

  27. 27.

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

    Article  PubMed  CAS  Google Scholar 

  28. 28.

    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 

  29. 29.

    Reyer, C. P. O. et al. A plant’s perspective of extremes: terrestrial plant responses to changing climatic variability. Glob. Change Biol. 19, 75–89 (2013).

    Article  Google Scholar 

  30. 30.

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

    Article  PubMed  CAS  Google Scholar 

  31. 31.

    Luo, Y., Keenan, T. F. & Smith, M. J. Predictability of the terrestrial carbon cycle. Glob. Change Biol. 21, 1737–1751 (2015).

    Article  Google Scholar 

  32. 32.

    Fisher, R. et al. Assessing uncertainties in a second-generation dynamic vegetation model caused by ecological scale limitations. New Phytol. 187, 666–681 (2010).

    Article  PubMed  Google Scholar 

  33. 33.

    Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y. & Moorcroft, P. R. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. J. Geophys. Res. 114, G01002 (2009).

    Article  Google Scholar 

  34. 34.

    Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic global vegetation models: learning from community ecology. New Phytol. 198, 957–969 (2013).

    Article  PubMed  Google Scholar 

  35. 35.

    Fatichi, S., Leuzinger, S. & Körner, C. Moving beyond photosynthesis: from carbon source to sink-driven vegetation modeling. New Phytol. 201, 1086–1095 (2014).

    Article  PubMed  CAS  Google Scholar 

  36. 36.

    Körner, C. Paradigm shift in plant growth control. Curr. Opin. Plant Biol. 25, 107–114 (2015).

    Article  PubMed  CAS  Google Scholar 

  37. 37.

    Pappas, C., Fatichi, S., Leuzinger, S., Wolf, A. & Burlando, P. Sensitivity analysis of a process-based ecosystem model: pinpointing parameterization and structural issues. J. Geophys. Res. Biogeosci. 118, 505–528 (2013).

    Article  Google Scholar 

  38. 38.

    Fatichi, S., Pappas, C. & Ivanov, V. Y. Modeling plant–water interactions: an ecohydrological overview from the cell to the global scale. Wiley Interdiscip. Rev. Water 3, 327–368 (2016).

    Article  Google Scholar 

  39. 39.

    Pugh, T. A. M., Müller, C., Arneth, A., Haverd, V. & Smith, B. Key knowledge and data gaps in modelling the influence of CO2 concentration on the terrestrial carbon sink. J. Plant Physiol. 203, 3–15 (2016).

    Article  PubMed  CAS  Google Scholar 

  40. 40.

    Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Change 4, 471–476 (2014).

    Article  CAS  Google Scholar 

  41. 41.

    Aubin, I. et al. Traits to stay, traits to move: a review of functional traits to assess sensitivity and adaptive capacity of temperate and boreal trees to climate change. Environ. Rev. 24, 164–186 (2016).

    Article  Google Scholar 

  42. 42.

    Lombardozzi, D. L., Bonan, G. B., Smith, N. G., Dukes, J. S. & Fisher, R. A. Temperature acclimation of photosynthesis and respiration: a key uncertainty in the carbon cycle-climate feedback. Geophys. Res. Lett. 42, 8624–8631 (2015).

    Article  CAS  Google Scholar 

  43. 43.

    Medlyn, B. E., Duursma, Ra & Zeppel, M. J. B. Forest productivity under climate change: a checklist for evaluating model studies. Wiley Interdiscip. Rev. Clim. Change 2, 332–355 (2011).

    Article  Google Scholar 

  44. 44.

    Potter, K. A., Arthur Woods, H. & Pincebourde, S. Microclimatic challenges in global change biology. Glob. Change Biol. 19, 2932–2939 (2013).

    Article  Google Scholar 

  45. 45.

    Pappas, C., Fatichi, S., Rimkus, S., Burlando, P. & Huber, M. The role of local-scale heterogeneities in terrestrial ecosystem modeling. J. Geophys. Res. Biogeosci. 120, 341–360 (2015).

    Article  Google Scholar 

  46. 46.

    Sun, Y. et al. Impact of mesophyll diffusion on estimated global land CO2 fertilization. Proc. Natl Acad. Sci. USA 111, 15774–15779 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. 47.

    Friend, A. D. et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc. Natl Acad. Sci. USA 111, 3280–3285 (2014).

    Article  PubMed  CAS  Google Scholar 

  48. 48.

    Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E. & Pacala, S. W. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Change 4, 1099–1102 (2014).

    Article  CAS  Google Scholar 

  49. 49.

    Friedlingstein, P. & Prentice, I. C. Carbon–climate feedbacks: a review of model and observation based estimates. Curr. Opin. Environ. Sustain. 2, 251–257 (2010).

    Article  Google Scholar 

  50. 50.

    Ault, T. R., Cole, J. E. & St. George, S. The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models. Geophys. Res. Lett. 39, L21705 (2012).

    Article  Google Scholar 

  51. 51.

    Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    Article  Google Scholar 

  52. 52.

    Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).

    Article  Google Scholar 

  53. 53.

    Kanamitsu, M. et al. NCEP-DOE AMIP-II reanalysis (R-2). Bull. Am. Meteorol. Soc. 83, 1631–1643 (2002).

    Article  Google Scholar 

  54. 54.

    Whitaker, J. S., Compo, G. P., Wei, X. & Hamill, T. M. Reanalysis without radiosondes using ensemble data assimilation. Mon. Weather Rev. 132, 1190–1200 (2004).

    Article  Google Scholar 

  55. 55.

    Compo, G. P., Whitaker, J. S. & Sardeshmukh, P. D. Feasibility of a 100-year reanalysis using only surface pressure data. Bull. Am. Meteorol. Soc. 87, 175–190 (2006).

    Article  Google Scholar 

  56. 56.

    Compo, G. P. et al. The twentieth century reanalysis project. Q. J. R. Meteorol. Soc. 137, 1–28 (2011).

    Article  Google Scholar 

  57. 57.

    Mitchell, T. D., Carter, T. R., Jones, P. D. & Hulme, M. A Comprehensive Set of High-Resolution Grids of Monthly Climate for Europe and the Globe: the Observed Record (1901–2000) and 16 Scenarios (2001–2100) Working paper no. 55 (Tyndall Centre for Climate Change Research, 2004).

  58. 58.

    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations-the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  59. 59.

    Pinty, B. et al. Exploiting the MODIS albedos with the Two-Stream Inversion Package (JRC-TIP): 1. Effective leaf area index, vegetation, and soil properties. J. Geophys. Res. 116, D09105 (2011).

    Google Scholar 

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This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. We thank the participants of the TRENDY project, namely, P. Levy (Hyland), S. Sitch and C. Huntingford (JULES/TRIFFID), B. Poulter (LPJ), A. Ahlström (LPJ-GUESS), S. Levis (NCAR-CLM4), N. Viovy, S. Zaehle (OCN), M. Lomas (SDGVM) and N. Zeng (VEGAS), who made their simulations results (TRENDY v.1, experiment S2) freely available. C.P. acknowledges the support of the Stavros Niarchos Foundation and the ETH Zurich Foundation (grant P2EZP2_162293) through a Swiss National Science Foundation (SNSF) Early Postdoc. Mobility fellowship. M.D.M., F.B. and D.C.F. acknowledge funding from the European Union via the Horizon 2020 project ‘BACI’ (grant 640176). F.B. acknowledges funding from the Swiss National Science Foundation (grant P300P2_154543).

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C.P. designed the study, conducted the analysis and wrote the manuscript with input from M.D.M. and D.K. D.C.F. provided the tree-ring width data and F.B. the above-ground biomass increment data. All authors contributed to editing the manuscript.

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Correspondence to Christoforos Pappas.

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Supplementary Tables 1–3, Supplementary Figures 1–18, Supplementary References

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Pappas, C., Mahecha, M.D., Frank, D.C. et al. Ecosystem functioning is enveloped by hydrometeorological variability. Nat Ecol Evol 1, 1263–1270 (2017).

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