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 standalone 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 4 km 2 ) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 × 10 4 km 2 ). However the uncertainty (1 to 128 × 10 4 km 2 ) 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 airtemperature-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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Abstract. Non-Photosynthetic Vegetation (NPV) coverage is an important parameter for wildfire danger rating, prevention of soil erosion and carbon sequestration estimations. A group of spectral indices have been developed to map NPV distribution based on hyperspectral data or particular application scenarios. However, the NPV coverage estimated by those indices are not stable because they are sensitive to soil moisture or snow cover. This paper aims to develop a spectral linear transformation named as Non-photosynthetic Vegetation Index (NPVI) based on Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate non-photosynthetic vegetation (NPV) coverage in north Asian steppe. The validation result of field spectral experiment and field survey shows the NPVI has good potential to estimate NPV coverage and are robust to soil moisture and snowmelt. Furthermore, the seasonal variation of NPVI offers the possibility to monitor the wildfire risk and grazing intensity.

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