@article {KNZ002014, title = {Rainfall-manipulation experiments as simulated by terrestrial biosphere models: where do we stand?}, journal = {Global Change Biology}, volume = {26}, year = {2020}, pages = {3336{\textendash}3355}, 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 ten terrestrial biosphere models to reproduce observed sensitivity of ecosystem productivity to rainfall changes at ten sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed.

The key results are:
(a) Inter-model variation is generally large and model agreement varies with time scales. In severely water limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent gross primary productivity. In more mesic sites model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) time scales 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.

}, keywords = {LTER-KNZ}, doi = {10.1111/gcb.15024}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15024}, author = {Paschalis, Athanasios and Fatichi, Simone and Zscheischler, Jakob and Ciais, Philippe and Michael Bahn and Lena R. Boysen and Chang, Jinfeng and De Kauwe, Martin and Estiarte, Marc and Goll, Daniel and Hanson, Paul J. and Harper, Anna B. and Hou, Enqing and Kigel, Jaime and Alan K. Knapp and Larsen, Klaus Steenberg and Li, Wei and Lienert, Sebastian and Luo, Yiqi and Meir, Patrick and Nabel, Julia E.M.S. and Ogaya, Rom{\`a} and Parolari, Anthony J and Peng, Changhui and Pe{\~n}uelas, Josep and Pongratz, Julia and Rambal, Serge and Schmidt, Inger Kappel and Shi, Hao and Sternberg, Marcelo and Tian, Hanqin and Tschumi, Elisabeth and Ukkola, Anna and Vicca, Sara and Viovy, Nicolas and Wang, -Ping and Wang, Zhuonan and Williams, Karina and Wu, Donghai and Zhu, Qiuan} } @article {5802, title = {Soil structure~is an important omission in Earth System Models}, journal = {Nature Communications}, volume = {11}, year = {2020}, abstract = {

Most soil hydraulic information used in Earth System Models (ESMs) is derived from pedo-transfer functions that use easy-to-measure soil attributes to estimate hydraulic parameters. This parameterization relies heavily on soil texture, but overlooks the critical role of soil structure originated by soil biophysical activity. Soil structure omission is pervasive also in sampling and measurement methods used to train pedotransfer functions. Here we show how systematic inclusion of salient soil structural features of biophysical origin affect local and global hydrologic and climatic responses. Locally, including soil structure in models significantly alters infiltration-runoff partitioning and recharge in wet and vegetated regions. Globally, the coarse spatial resolution of ESMs and their inability to simulate intense and short rainfall events mask effects of soil structure on surface fluxes and climate. Results suggest that although soil structure affects local hydrologic response, its implications on global-scale climate remains elusive in current ESMs.

}, keywords = {LTER-KNZ}, doi = {10.1038/s41467-020-14411-z}, url = {http://www.nature.com/articles/s41467-020-14411-z}, author = {Fatichi, Simone and Or, Dani and Walko, Robert and Vereecken, Harry and Young, Michael H. and Ghezzehei, Teamrat A. and Hengl, Tomislav and Kollet, Stefan and Agam, Nurit and Avissar, Roni} } @article {KNZ001863, title = {Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites}, journal = {Biogeosciences}, volume = {15}, year = {2018}, pages = {3421 - 3437}, abstract = {

Field measurements of aboveground net primary productivity (ANPP) in temperate grasslands suggest that both positive and negative asymmetric responses to changes in precipitation (P) may occur. Under normal range of precipitation variability, wet years typically result in ANPP gains being larger than ANPP declines in dry years (positive asymmetry), whereas increases in ANPP are lower in magnitude in extreme wet years compared to reductions during extreme drought (negative asymmetry). Whether the current generation of ecosystem models with a coupled carbon\–water system in grasslands are capable of simulating these asymmetric ANPP responses is an unresolved question. In this study, we evaluated the simulated responses of temperate grassland primary productivity to scenarios of altered precipitation with 14 ecosystem models at three sites: Shortgrass steppe (SGS), Konza Prairie (KNZ) and Stubai Valley meadow (STU), spanning a rainfall gradient from dry to moist. We found that (1)\ the spatial slopes derived from modeled primary productivity and precipitation across sites were steeper than the temporal slopes obtained from inter-annual variations, which was consistent with empirical data; (2)\ the asymmetry of the responses of modeled primary productivity under normal inter-annual precipitation variability differed among models, and the mean of the model ensemble suggested a negative asymmetry across the three sites, which was contrary to empirical evidence based on filed observations; (3)\ the mean sensitivity of modeled productivity to rainfall suggested greater negative response with reduced precipitation than positive response to an increased precipitation under extreme conditions at the three sites; and (4)\ gross primary productivity (GPP), net primary productivity (NPP), aboveground NPP (ANPP) and belowground NPP (BNPP) all showed concave-down nonlinear responses to altered precipitation in all the models, but with different curvatures and mean values. Our results indicated that most models overestimate the negative drought effects and/or underestimate the positive effects of increased precipitation on primary productivity under normal climate conditions, highlighting the need for improving eco-hydrological processes in those models in the future.

}, keywords = {LTER-KNZ}, doi = {10.5194/bg-15-3421-2018}, url = {https://www.biogeosciences.net/15/3421/2018/}, author = {Wu, Donghai and Ciais, Philippe and Viovy, Nicolas and Alan K. Knapp and K.R. Wilcox and Michael Bahn and M.D. Smith and Vicca, Sara and Fatichi, Simone and Zscheischler, Jakob and He, Yue and Li, Xiangyi and Ito, Akihiko and Arneth, Almut and Harper, Anna and Ukkola, Anna and Paschalis, Athanasios and Poulter, Benjamin and Peng, Changhui and Ricciuto, Daniel and Reinthaler, David and Chen, Guangsheng and Tian, Hanqin and Genet, {\'e}l{\`e}ne and Mao, Jiafu and Ingrisch, Johannes and Nabel, Julia E. S. M. and Pongratz, Julia and Lena R. Boysen and Kautz, Markus and Schmitt, Michael and Meir, Patrick and Zhu, Qiuan and R. Hasibeder and Sippel, Sebastian and Dangal, Shree R. S. and Sitch, Stephen and Shi, Xiaoying and Wang, Yingping and Luo, Yiqi and Liu, Yongwen and Piao, Shilong} }