Weile Wang


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Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region
Erqian Cui, Kun Huang, M. Altaf Arain, Joshua B. Fisher, D. N. Huntzinger, Akihiko Ito, Yiqi Luo, Atul K. Jain, Jiafu Mao, A. M. Michalak, Shuli Niu, Nicholas C. Parazoo, Changhui Peng, Shushi Peng, Benjamin Poulter, Daniel M. Ricciuto, Kevin Schaefer, Christopher R. Schwalm, Xiaoying Shi, Hanqin Tian, Weile Wang, Jinsong Wang, Yaxing Wei, En‐Rong Yan, Liming Yan, Ning Zeng, Qiuan Zhu, Jianyang Xia
Global Biogeochemical Cycles, Volume 33, Issue 6

Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems.


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Missing pieces to modeling the Arctic-Boreal puzzle
Joshua B. Fisher, D. J. Hayes, Christopher R. Schwalm, D. N. Huntzinger, Eric Stofferahn, Kevin Schaefer, Yiqi Luo, Stan D. Wullschleger, Scott J. Goetz, Charles E. Miller, P. C. Griffith, Sarah Chadburn, Abhishek Chatterjee, Philippe Ciais, Thomas A. Douglas, Hélène Genet, Akihiko Ito, C. S. R. Neigh, Benjamin Poulter, Brendan M. Rogers, Oliver Sonnentag, Hanqin Tian, Weile Wang, Yongkang Xue, Zong‐Liang Yang, Ning Zeng, Zhen Zhang
Environmental Research Letters, Volume 13, Issue 2

Author(s): Fisher, JB; Hayes, DJ; Schwalm, CR; Huntzinger, DN; Stofferahn, E; Schaefer, K; Luo, Y; Wullschleger, SD; Goetz, S; Miller, CE; Griffith, P; Chadburn, S; Chatterjee, A; Ciais, P; Douglas, TA; Genet, H; Ito, A; Neigh, CSR; Poulter, B; Rogers, BM; Sonnentag, O; Tian, H; Wang, W; Xue, Y; Yang, ZL; Zeng, N; Zhang, Z | Abstract: NASA has launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE). While the initial phases focus on field and airborne data collection, early integration with modeling activities is important to benefit future modeling syntheses. We compiled feedback from ecosystem modeling teams on key data needs, which encompass carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance dynamics. A suite of variables was identified as part of this activity with a critical requirement that they are collected concurrently and representatively over space and time. Individual projects in ABoVE may not capture all these needs, and thus there is both demand and opportunity for the augmentation of field observations, and synthesis of the observations that are collected, to ensure that science questions and integrated modeling activities are successfully implemented.