Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area
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 dist...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-02-01
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| Series: | The Cryosphere |
| Online Access: | http://www.the-cryosphere.net/10/287/2016/tc-10-287-2016.pdf |
| _version_ | 1818141767462027264 |
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| author | W. Wang A. Rinke J. C. Moore X. Cui D. Ji Q. Li N. Zhang C. Wang S. Zhang D. M. Lawrence A. D. McGuire W. Zhang C. Delire C. Koven K. Saito A. MacDougall E. Burke B. Decharme |
| author_facet | W. Wang A. Rinke J. C. Moore X. Cui D. Ji Q. Li N. Zhang C. Wang S. Zhang D. M. Lawrence A. D. McGuire W. Zhang C. Delire C. Koven K. Saito A. MacDougall E. Burke B. Decharme |
| author_sort | W. Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T11:05:07Z |
| format | Article |
| id | doaj.art-5135119da8d643beb410d2698f3a67f0 |
| institution | Directory Open Access Journal |
| issn | 1994-0416 1994-0424 |
| language | English |
| last_indexed | 2024-12-11T11:05:07Z |
| publishDate | 2016-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj.art-5135119da8d643beb410d2698f3a67f02022-12-22T01:09:44ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242016-02-0110128730610.5194/tc-10-287-2016Diagnostic and model dependent uncertainty of simulated Tibetan permafrost areaW. Wang0A. Rinke1J. C. Moore2X. Cui3D. Ji4Q. Li5N. Zhang6C. Wang7S. Zhang8D. M. Lawrence9A. D. McGuire10W. Zhang11C. Delire12C. Koven13K. Saito14A. MacDougall15E. Burke16B. Decharme17College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaCollege of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaCollege of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaSchool of System Science, Beijing Normal University, Beijing, 100875, ChinaCollege of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaSchool of Atmospheric Sciences, Lanzhou University, Lanzhou, ChinaCollege of Urban and Environmental Sciences, Northwest University, Xi'an, ChinaNCAR, Boulder, USAUS Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska, Fairbanks, USADepartment of Physical Geography and Ecosystem Science, Lund University, Lund, SwedenGAME, Unité mixte de recherche CNRS/Meteo-France, Toulouse Cedex, FranceLawrence Berkeley National Laboratory, Berkeley, CA, USADepartment of Integrated Climate Change Projection Research, Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa, JapanSchool of Earth and Ocean Sciences, University of Victoria, Victoria, BC, CanadaMet Office Hadley Centre, Exeter, UKGAME, Unité mixte de recherche CNRS/Meteo-France, Toulouse Cedex, FranceWe 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.http://www.the-cryosphere.net/10/287/2016/tc-10-287-2016.pdf |
| spellingShingle | W. Wang A. Rinke J. C. Moore X. Cui D. Ji Q. Li N. Zhang C. Wang S. Zhang D. M. Lawrence A. D. McGuire W. Zhang C. Delire C. Koven K. Saito A. MacDougall E. Burke B. Decharme Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area The Cryosphere |
| title | Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area |
| title_full | Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area |
| title_fullStr | Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area |
| title_full_unstemmed | Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area |
| title_short | Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area |
| title_sort | diagnostic and model dependent uncertainty of simulated tibetan permafrost area |
| url | http://www.the-cryosphere.net/10/287/2016/tc-10-287-2016.pdf |
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