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Title: Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

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). Here, 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 × 10 4km 2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 ×10 4km 2). However the uncertainty (1 to 128 × 10 4km 2) 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 definitionmore » 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.« less

Authors:
 [1];  [2];  [1];  [1];  [1];  [3];  [3]; ORCiD logo [4];  [5];  [6];  [7]; ORCiD logo [8];  [9]; ORCiD logo [10];  [11];  [12]; ORCiD logo [13];  [9]
  1. Beijing Normal Univ. (China)
  2. Beijing Normal Univ. (China); Alfred Wegener Inst. Helmholtz Centre for Polar and Marine Research, Potsdam (Germany)
  3. Chinese Academy of Sciences (CAS), Beijing (China)
  4. Lanzhou Univ. (China)
  5. Northwest Univ., Xi'an (China)
  6. National Center for Atmospheric Research, Boulder, CO (United States)
  7. Univ. of Alaska, Fairbanks, AK (United States)
  8. Lund Univ. (Sweden)
  9. Centre National de la Recherche Scientifique (CNRS), Toulouse Cedex (France)
  10. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  11. Japan Agency for Marine-Earth Science and Technology, Yokohama (Japan)
  12. Univ. of Victoria, BC (Canada)
  13. Met Office Hadley Centre, Exeter (United Kingdom)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1471007
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
The Cryosphere (Online)
Additional Journal Information:
Journal Name: The Cryosphere (Online); Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 1994-0424
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

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. United States: N. p., 2016. Web. doi:10.5194/tc-10-287-2016.
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.. Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area. United States. doi:10.5194/tc-10-287-2016.
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.. Fri . "Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area". United States. doi:10.5194/tc-10-287-2016. https://www.osti.gov/servlets/purl/1471007.
@article{osti_1471007,
title = {Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area},
author = {Wang, W. and Rinke, A. and Moore, J. C. and Cui, X. and Ji, D. and Li, Q. and Zhang, N. and Wang, C. and Zhang, S. and Lawrence, D. M. and McGuire, A. D. and Zhang, W. and Delire, C. and Koven, C. and Saito, K. and MacDougall, A. and Burke, E. and Decharme, B.},
abstractNote = {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). Here, 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.},
doi = {10.5194/tc-10-287-2016},
journal = {The Cryosphere (Online)},
number = 1,
volume = 10,
place = {United States},
year = {2016},
month = {2}
}

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