Search NASA⌕ Search

DOE OSTI · 1630510

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Abstract

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. Finally, the relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Paschalis, Athanasios, Fatichi, Simone, Zscheischler, Jakob, Ciais, Philippe, Bahn, Michael, Boysen, Lena, Chang, Jinfeng, De Kauwe, Martin, Estiarte, Marc, Goll, Daniel, Hanson, Paul J., Harper, Anna B., Hou, Enqing, Kigel, Jaime, Knapp, Alan K., Larsen, Klaus S., Li, Wei, Lienert, Sebastian, Luo, Yiqi, Meir, Patrick, Nabel, Julia E. M. S., Ogaya, Romà, Parolari, Anthony J., Peng, Changhui, Peñuelas, Josep, Pongratz, Julia, Rambal, Serge, Schmidt, Inger K., Shi, Hao, Sternberg, Marcelo, Tian, Hanqin, Tschumi, Elisabeth, Ukkola, Anna, Vicca, Sara, Viovy, Nicolas, Wang, Ying‐Ping, Wang, Zhuonan, Williams, Karina, Wu, Donghai, Zhu, Qiuan. 2020-02-03. Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?. https://doi.org/10.1111/gcb.15024

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗