Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.
Bibliographical noteFunding Information:
We thank Prof. Gil Bohrer and three anonymous reviewers for their constructive comments that help us improve the manuscript. A.P. acknowledges financial support from NERC (grant no. NE/S003495/1). J.Z. acknowledges the Swiss National Science Foundation (Ambizione Grant 179876). D.S.G., P.C., W.L., M.E., R.O. and J.P. are funded by the ?IMBALANCE-P? project of the European Research Council (ERC-2013-SyG-610028). C.P. acknowledges the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) Discover Grant. Y.P.W. acknowledges the financial support from the National Environmental Science Program for Earth System and Climate Change from the Australian Federal Government. I.K.S. and K.S.L. acknowledge the financial support to the CLIMAITE project at Brandbjerg from the Villum Foundation. J.Po. was supported by the German Research Foundation's (DFG) Emmy Noether Program (PO 1751/1-1). L.B. was supported by the DFG's CE-LAND project. Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the German Federal Ministry of Education and Research (BMBF). M.B. acknowledges the support of the Austrian Science Fund (FWF; P22214-B17), and the European Community's Seventh Framework Programme (FP7/2007-2013, project ?CARBO-Extreme', grant agreement no. 226701), the Austrian Academy of Sciences (OeAW; ClimLUC) and the Austrian Research Promotion Agency (FFG; LTER-CWN). We thank all site operators, MODIS and FLUXNET2015 for providing the data for this study.
© 2020 John Wiley & Sons Ltd
- rainfall manipulation experiment
- terrestrial biosphere models