B43J-03:
Terrestrial ecosystem model performance for net primary productivity and its vulnerability to climate change in permafrost regions
Thursday, 18 December 2014: 2:10 PM
Jianyang Xia1, Anthony David McGuire2, David M Lawrence3, Eleanor Burke4, Xiaodong Chen5, Christine L Delire6, Charles D Koven7, Andrew H MacDougall8, Shushi Peng9, Annette Rinke10, Kazuyuki Saito11, Wenxin Zhang12, Ramdane Alkama6, Theodore J Bohn13, Philippe Ciais9, Bertrand Decharme6, Isabelle Gouttevin14, Tomohiro Hajima11, Duoying Ji15, Gerhard Krinner16, Dennis P Lettenmaier17, Paul A Miller12, John C Moore15, Ben Smith18, Tetsuo Sueyoshi19, Zheng Shi1, Liming Yan20, Junyi Liang1, Lifen Jiang1 and Yiqi Luo1, (1)University of Oklahoma Norman Campus, Norman, OK, United States, (2)Univ Alaska Fairbanks, Fairbanks, AK, United States, (3)National Center for Atmospheric Research, Boulder, CO, United States, (4)Met Office Hadley Centre, Exeter, United Kingdom, (5)University of Washington Seattle Campus, Seattle, WA, United States, (6)Unité mixte de recherche CNRS/Meteo-France, Toulouse cedex, France, (7)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (8)University of Victoria, Victoria, BC, Canada, (9)CEA Saclay DSM / LSCE, Gif sur Yvette, France, (10)Alfred Wegener Institute Helmholtz-Center for Polar and Marine Research Potsdam, Potsdam, Germany, (11)JAMSTEC Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan, (12)Lund University, Physical Geography and Ecosystem Science, Lund, Sweden, (13)Arizona State University, Tempe, AZ, United States, (14)UJF–Grenoble 1/CNRS, LGGE, Grenoble, France, (15)Beijing Normal University, Beijing, China, (16)LGGE Laboratoire de Glaciologie et Géophysique de l’Environnement, Saint Martin d'Hères, France, (17)University of California Los Angeles, Los Angeles, CA, United States, (18)Lund University, Department of Physical Geography and Ecosystem Science, Lund, Sweden, (19)NIPR National Institute of Polar Research, Tokyo, Japan, (20)Fudan University, School of Biology, Shanghai, China
Abstract:
A more accurate prediction of future climate-carbon (C) cycle feedbacks requires better understanding and improved representation of the carbon cycle in permafrost regions within current earth system models. Here, we evaluated 10 terrestrial ecosystem models for their estimated net primary productivity (NPP) and its vulnerability to climate change in permafrost regions in the Northern Hemisphere. Those models were run retrospectively between 1960 and 2009. In comparison with MODIS satellite estimates, most models produce higher NPP (310 ± 12 g C m-2 yr-1) than MODIS (240 ± 20 g C m-2 yr-1) over the permafrost regions during 2000‒2009. The modeled NPP was then decomposed into gross primary productivity (GPP) and the NPP/GPP ratio (i.e., C use efficiency; CUE). By comparing the simulated GPP with a flux-tower-based database [Jung et al. Journal of Geophysical Research 116 (2011) G00J07] (JU11), we found although models only produce 10.6% higher mean GPP than JU11 over 1982‒2009, there was a two-fold disparity among models (397 to 830 g C m-2 yr-1). The model-to-model variation in GPP mainly resulted from the seasonal peak GPP and in low-latitudinal permafrost regions such as the Tibetan Plateau. Most models overestimate the CUE in permafrost regions in comparison to calculated CUE from the MODIS NPP and JU11 GPP products and observation-based estimates at 8 forest sites. The models vary in their sensitivities of NPP, GPP and CUE to historical changes in air temperature, atmospheric CO2 concentration and precipitation. For example, climate warming enhanced NPP in four models via increasing GPP but reduced NPP in two other models by decreasing both GPP and CUE. The results indicate that the model predictability of C cycle in permafrost regions can be improved by better representation of those processes controlling the seasonal maximum GPP and the CUE as well as their sensitivity to climate change.
