globalchange  > 影响、适应和脆弱性
DOI: 10.5194/tc-10-287-2016
Scopus记录号: 2-s2.0-84957596169
论文题名:
Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area
作者: Wang W; , Rinke A; , Moore J; C; , Cui X; , Ji D; , Li Q; , Zhang N; , Wang C; , Zhang S; , Lawrence D; M; , McGuire A; D; , Zhang W; , Delire C; , Koven C; , Saito K; , MacDougall A; , Burke E; , Decharme B
刊名: Cryosphere
ISSN: 19940416
出版年: 2016
卷: 10, 期:1
起始页码: 287
结束页码: 306
语种: 英语
英文关键词: air temperature ; environmental modeling ; frost ; land surface ; model test ; permafrost ; snow cover ; soil temperature ; soil texture ; uncertainty analysis ; vegetation type ; China ; Qinghai-Xizang Plateau
英文摘要: We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135 × 104km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 ×104km2). However the uncertainty (1 to 128 × 104km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0°C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau. © Author(s) 2016.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75192
Appears in Collections:影响、适应和脆弱性
气候变化与战略

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作者单位: College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, Germany; School of System Science, Beijing Normal University, Beijing, China; Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; School of Atmospheric Sciences, Lanzhou University, Lanzhou, China; College of Urban and Environmental Sciences, Northwest University, Xi'an, China; NCAR, Boulder, United States; US Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska, Fairbanks, United States; Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; GAME, Unité Mixte de Recherche CNRS/Meteo-France, Toulouse Cedex, France; Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Department of Integrated Climate Change Projection Research, Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa, Japan; School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada; Met Office Hadley Centre, Exeter, United Kingdom

Recommended Citation:
Wang W,, Rinke A,, Moore J,et al. Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area[J]. Cryosphere,2016-01-01,10(1)
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