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您的检索式：作者名="Daniel SGoll"
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| 1 | Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration显示文摘Resolving regional carbon budgets is critical for informing land-based mitigation policy.For nine regions covering nearly the whole globe,we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing.The net land–atmospheric carbon exchange(NEE)was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade,and riverine-carbon export to the ocean.Summing up NEE from all regions,we obtained a global‘bottom-up'NEE for net land anthropogenic CO_(2)uptake of–2.2±0.6 Pg C yr^(-1)consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000–2009.This estimate is so far the most comprehensive global bottom-up carbon budget accounting,which set up an important milestone for global carbon-cycle studies.By decomposing NEE into component fluxes,we found that global soil heterotrophic respiration amounts to a source of CO_(2)of 39 Pg C yr^(-1)with an interquartile of 33–46 Pg C yr^(-1)—a much smaller portion of net primary productivity than previously reported. | Philippe Ciais Yitong Yao Thomas Gasser Alessandro Baccini Yilong Wang Ronny Lauerwald Shushi Peng Ana Bastos Wei Li Peter A.Raymond Josep G.Canadell Glen P.Peters Rob J.Andres Jinfeng Chang Chao Yue A.Johannes Dolman Vanessa Haverd Jens Hartmann Goulven Laruelle Alexandra G.Konings Anthony W.King Yi Liu Sebastiaan Luyssaert Fabienne Maignan Prabir K.Patra Anna Peregon Pierre Regnier Julia Pongratz Benjamin Poulter Anatoly Shvidenko Riccardo Valentini Rong Wang Grégoire Broquet Yi Yin Jakob Zscheischler Bertrand Guenet Daniel SGoll Ashley-P.Ballantyne Hui Yang Chunjing Qiu Dan Zhu | 2021 | National Science Review2021，8，2： | 3 |
| 2 | Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)显示文摘Background:Large uncertainty in modeling land carbon(C)uptake heavily impedes the accurate prediction of the global C budget.Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models(ESMs).Here we present a Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)under a unified model traceability framework to evaluate multiple soil organic carbon(SOC)models.Using the MEMIP,we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter(SOM)models.By comparing the model outputs from the C-only and CN modes,the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled.Results:Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation(1900–2000).The SOC difference between the multi-layer models was remarkably higher than between the single-layer models.Traceability analysis indicated that over 80%of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes,while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation.Conclusions:The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction,especially between models with similar process representation.Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences.We stressed the importance of analyzing ensemble outputs from the fundamental model structures,and holding a holistic view in understanding the ensemble uncertainty. | Cuijuan Liao Yizhao Chen Jingmeng Wang Yishuang Liang Yansong Huang Zhongyi Lin Xingjie Lu Yuanyuan Huang Feng Tao Danica Lombardozzi Almut Arneth Daniel SGoll Atul Jain Stephen Sitch Yanluan Lin Wei Xue Xiaomeng Huang Yiqi Luo | 2022 | Ecological Processes2022，11，1： | 0 |
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