A method is proposed for removing the drift of coupled atmosphere-ocean models, which in the past has often hindered the application of coupled models in climate response and sensitivity experiments. The ocean-atmosphere flux fields exhibit inconsistencies when evaluated separately for the individual subsystems in independent, uncoupled mode equilibrium climate computations. In order to balance these inconsistencies a constant ocean-atmosphere flux correction field is introduced in the boundary conditions coupling the two subsystems together. The method ensures that the coupled model operates at the reference climate state for which the individual model subsystems were designed without affecting the dynamical response of the coupled system in climate variability experiments. The method is illustrated for a simple two component box model and an ocean general circulation model coupled to a two layer diagnostic atmospheric model.
Abstract:The seasonal snow cover of the Tibetan Plateau exerts a profound environmental influence both regionally and globally. Daily observations of snow depth at 37 meteorological stations in Tibet and MODIS eight-day snow products (MOD10A2) during the period 2001-2015 are analyzed with respect to the frequency and spatial distribution of snow cover for each season and for various altitude ranges. The results show that the average snow cover percentage was 16%. Snow cover frequency was less than 21% for 70% of the Tibetan area, while it was more than 40% in eastern Tibet and in the Himalayas. We also estimated the variations in the starting times of snow accumulation and ablation. During the 15 years, both datasets revealed a significant trend of earlier onset of ablation, but no evident trend for the start of accumulation. The two datasets differed slightly with respect to the seasonal variation of snow cover. MODIS data showed more snow in winter than in other seasons, but the ground data showed most snow in early spring. For the station locations, the correlation between ground and MODIS snow cover percentage (number of snow-covered stations/number of cloud-free stations) is 0.77. Combining the advantages of remote sensing data and ground observation data is the best way to investigate snow in Tibet.
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