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Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region

Xia, Jianyang; McGuire, A. David; Lawrence, David; Burke, Eleanor; Chen, Guangsheng; Chen, Xiaodong; Delire, Christine; Koven, Charles; MacDougall, Andrew H. and Peng, Shushi, et al. (2017) In Journal of Geophysical Research - Biogeosciences
Abstract

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246±6gCm-2yr-1), most models produced higher NPP (309±12gCm-2yr-1) over the permafrost region during 2000-2009. By comparing the simulated gross primary productivity (GPP) with a flux... (More)

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246±6gCm-2yr-1), most models produced higher NPP (309±12gCm-2yr-1) over the permafrost region during 2000-2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982-2009, there was a twofold discrepancy among models (380 to 800gCm-2yr-1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

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@article{0358b19a-d681-4b9e-8b96-35b8f00e106e,
  abstract     = {<p>Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246±6gCm<sup>-2</sup>yr<sup>-1</sup>), most models produced higher NPP (309±12gCm<sup>-2</sup>yr<sup>-1</sup>) over the permafrost region during 2000-2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982-2009, there was a twofold discrepancy among models (380 to 800gCm<sup>-2</sup>yr<sup>-1</sup>), which mainly resulted from differences in simulated maximum monthly GPP (GPP<sub>max</sub>). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (V<sub>cmax_25</sub>), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO<sub>2</sub> concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPP<sub>max</sub> as well as their sensitivity to climate change.</p>},
  author       = {Xia, Jianyang and McGuire, A. David and Lawrence, David and Burke, Eleanor and Chen, Guangsheng and Chen, Xiaodong and Delire, Christine and Koven, Charles and MacDougall, Andrew H. and Peng, Shushi and Rinke, Annette and Saito, Kazuyuki and Zhang, Wenxin and Alkama, Ramdane and Bohn, Theodore J. and Ciais, Philippe and Decharme, Bertrand and Gouttevin, Isabelle and Hajima, Tomohiro and Hayes, Daniel J. and Huang, Kun and Ji, Duoying and Krinner, Gerhard and Lettenmaier, Dennis P. and Miller, Paul A. and Moore, John C. and Smith, Benjamin and Sueyoshi, Tetsuo and Shi, Zheng and Yan, Liming and Liang, Junyi and Jiang, Lifen and Zhang, Qian and Luo, Yiqi},
  issn         = {2169-8953},
  keyword      = {Arctic,Carbon use efficiency,Climate warming,CO elevation,High latitudes,Model intercomparison},
  language     = {eng},
  month        = {02},
  publisher    = {American Geophysical Union},
  series       = {Journal of Geophysical Research - Biogeosciences},
  title        = {Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region},
  url          = {http://dx.doi.org/10.1002/2016JG003384},
  year         = {2017},
}