Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
The Cryosphere, 10, 287-306, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
05 Feb 2016
Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area
W. Wang1, A. Rinke1,2, J. C. Moore1, X. Cui3, D. Ji1, Q. Li4, N. Zhang4, C. Wang5, S. Zhang6, D. M. Lawrence7, A. D. McGuire8, W. Zhang9, C. Delire10, C. Koven11, K. Saito12, A. MacDougall13, E. Burke14, and B. Decharme10 1College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
2Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
3School of System Science, Beijing Normal University, Beijing, 100875, China
4Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
5School of Atmospheric Sciences, Lanzhou University, Lanzhou, China
6College of Urban and Environmental Sciences, Northwest University, Xi'an, China
7NCAR, Boulder, USA
8US Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska, Fairbanks, USA
9Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
10GAME, Unité mixte de recherche CNRS/Meteo-France, Toulouse Cedex, France
11Lawrence Berkeley National Laboratory, Berkeley, CA, USA
12Department of Integrated Climate Change Projection Research, Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa, Japan
13School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada
14Met Office Hadley Centre, Exeter, UK
Abstract. 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  ×  104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101  × 104 km2). However the uncertainty (1 to 128  ×  104 km2) 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.

Citation: 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., and Decharme, B.: Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area, The Cryosphere, 10, 287-306,, 2016.
Publications Copernicus
Short summary
We use a model-ensemble approach for simulating permafrost on the Tibetan Plateau. We identify the uncertainties across models (state-of-the-art land surface models) and across methods (most commonly used methods to define permafrost).

We differentiate between uncertainties stemming from climatic driving data or from physical process parameterization, and show how these uncertainties vary seasonally and inter-annually, and how estimates are subject to the definition of permafrost used.
We use a model-ensemble approach for simulating permafrost on the Tibetan Plateau. We identify...