The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Paul Miller. Photo.

Paul Miller

Senior lecturer

Paul Miller. Photo.

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


  • Jianyang Xia
  • A. David McGuire
  • David Lawrence
  • Eleanor Burke
  • Guangsheng Chen
  • Xiaodong Chen
  • Christine Delire
  • Charles Koven
  • Andrew H. MacDougall
  • Shushi Peng
  • Annette Rinke
  • Kazuyuki Saito
  • Wenxin Zhang
  • Ramdane Alkama
  • Theodore J. Bohn
  • Philippe Ciais
  • Bertrand Decharme
  • Isabelle Gouttevin
  • Tomohiro Hajima
  • Daniel J. Hayes
  • Kun Huang
  • Duoying Ji
  • Gerhard Krinner
  • Dennis P. Lettenmaier
  • Paul A. Miller
  • John C. Moore
  • Benjamin Smith
  • Tetsuo Sueyoshi
  • Zheng Shi
  • Liming Yan
  • Junyi Liang
  • Lifen Jiang
  • Qian Zhang
  • Yiqi Luo

Summary, in English

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.


  • Dept of Physical Geography and Ecosystem Science
  • MERGE: ModElling the Regional and Global Earth system
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year







Journal of Geophysical Research - Biogeosciences





Document type

Journal article




  • Ecology
  • Climate Research


  • Arctic
  • Carbon use efficiency
  • Climate warming
  • CO elevation
  • High latitudes
  • Model intercomparison




  • ISSN: 2169-8953