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

  • Author(s): Xia, J
  • McGuire, AD
  • Lawrence, D
  • Burke, E
  • Chen, G
  • Chen, X
  • Delire, C
  • Koven, C
  • MacDougall, A
  • Peng, S
  • Rinke, A
  • Saito, K
  • Zhang, W
  • Alkama, R
  • Bohn, TJ
  • Ciais, P
  • Decharme, B
  • Gouttevin, I
  • Hajima, T
  • Hayes, DJ
  • Huang, K
  • Ji, D
  • Krinner, G
  • Lettenmaier, DP
  • Miller, PA
  • Moore, JC
  • Smith, B
  • Sueyoshi, T
  • Shi, Z
  • Yan, L
  • Liang, J
  • Jiang, L
  • Zhang, Q
  • Luo, Y
  • et al.
Abstract

©2017. American Geophysical Union. All Rights Reserved. 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 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−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 800 g C m−2 yr−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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