Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area
Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area
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DOI：
10.5194/tc-10-287-2016
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Wenli Wang;A. Rinke;J. Moore;X. Cui;DuoyingJi;Qian Li;Ningning Zhang;Chenghai Wang;
中科院分区：
文献类型：
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作者：
Wenli Wang;A. Rinke;J. Moore;X. Cui;DuoyingJi;Qian Li;Ningning Zhang;Chenghai Wang;
文摘。5,CoLM， ISBA， JULES, LPJ-GUESS, UVic)。我们还研究了五种不同的多年冻土诊断方法（从模拟的月地温、年平均地温和气温、空气和地面霜冻指数）所引入的模拟多年冻土面积和分布的变化。两种基于气温的诊断方法具有较好的一致性（99 ~ 135 × 104 km2），也与基于观测的实际冻土区估算值（101 × 104 km2）一致。然而，三种需要模拟地温的方法的不确定性（1 ~ 128 × 104 km2）要大得多。此外，这三种方法（从月、年平均地温和地表霜冻指数诊断多年冻土）在青藏高原上的模拟多年冻土分布通常是差等的，而基于气温的方法的多年冻土分布通常是好的。在现场进行的模型评估突出了过程模拟中可能与土壤质地规格、植被类型和积雪有关的具体问题。使用土壤温度连续24个月保持在0 °C或以下的定义，模型在模拟永久冻土分布方面尤其差，这需要可靠地模拟年平均地面温度和季节周期，因此要求相对较高。虽然模型可以利用年平均地温和地表霜冻指数绘制出更好的永久冻土图，但对模拟土壤温度剖面的分析显示出实质性的偏差。
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 × 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 × 104 km2). However the uncertainty (1 to 128 × 104 km2) 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.