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Publication Date
1 August 2013

Diagnosing Present and Future Permafrost from Climate Models



Permafrost is a characteristic aspect of the terrestrial Arctic and the fate of near-surface permafrost over the next century is likely to exert strong controls on Arctic hydrology and biogeochemistry. Using output from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the authors assess its ability to simulate present-day and future permafrost. Permafrost extent diagnosed directly from each climate model's soil temperature is a function of the modeled surface climate as well as the ability of the land surface model to represent permafrost physics. For each CMIP5 model these two effects are separated by using indirect estimators of permafrost driven by climatic indices and compared to permafrost extent directly diagnosed via soil temperatures. Several robust conclusions can be drawn from this analysis. Significant air temperature and snow depth biases exist in some model's climates, which degrade both directly and indirectly diagnosed permafrost conditions. The range of directly calculated present-day (1986–2005) permafrost area is extremely large (~4–25 × 106 km2). Several land models contain structural weaknesses that limit their skill in simulating cold region subsurface processes. The sensitivity of future permafrost extent to temperature change over the present-day observed permafrost region averages (1.67 ± 0.7) × 106 km2 °C−1 but is a function of the spatial and temporal distribution of climate change. Because of sizable differences in future climates for the representative concentration pathway (RCP) emission scenarios, a wide variety of future permafrost states is predicted by 2100. Conservatively, the models suggest that for RCP4.5, permafrost will retreat from the present-day discontinuous zone. Under RCP8.5, sustainable permafrost will be most probable only in the Canadian Archipelago, Russian Arctic coast, and east Siberian uplands.

“Diagnosing Present And Future Permafrost From Climate Models”. 2013. Journal Of Climate 26: 5608–5623. doi:10.1175/JCLI-D-12-00341.1.
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