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Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

Wang, W.; Rinke, A.; Moore, J. C. LU ; Cui, X.; Ji, D.; Li, Q. LU ; Zhang, N.; Wang, C.; Zhang, S. LU and Lawrence, D. M., et al. (2016) In Cryosphere 10(1). p.287-306
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 × 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... (More)

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.

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Cryosphere
volume
10
issue
1
pages
20 pages
publisher
Copernicus Gesellschaft mbH
external identifiers
  • scopus:84957596169
ISSN
1994-0416
DOI
10.5194/tc-10-287-2016
language
English
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yes
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9cbd51f7-6a59-449a-9660-4b0e09296e6c
date added to LUP
2018-10-17 15:59:19
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2019-09-11 04:05:18
@article{9cbd51f7-6a59-449a-9660-4b0e09296e6c,
  abstract     = {<p>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 × 10<sup>4</sup>km<sup>2</sup>) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 ×10<sup>4</sup>km<sup>2</sup>). However the uncertainty (1 to 128 × 10<sup>4</sup>km<sup>2</sup>) 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.</p>},
  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.},
  issn         = {1994-0416},
  language     = {eng},
  month        = {02},
  number       = {1},
  pages        = {287--306},
  publisher    = {Copernicus Gesellschaft mbH},
  series       = {Cryosphere},
  title        = {Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area},
  url          = {http://dx.doi.org/10.5194/tc-10-287-2016},
  volume       = {10},
  year         = {2016},
}