Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

• Main Authors: Wang, W, Rinke, A, Moore, JC, Cui, X, Ji, D, Li, Q, Zhang, N, Wang, C, Zhang, S, Lawrence, DM, McGuire, AD, Zhang, W, Delire, C, Koven, C, Saito, K, MacDougall, A, Burke, E, Decharme, B
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2016
• Subjects: Meteorology & Atmospheric Sciences; Oceanography; Physical Geography and Environmental Geoscience; Jules; permafrost
• Online Access: https://escholarship.org/uc/item/1mc506vg

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.