科研之友
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摘要
High mountain regions are characterized by a large climatic heterogeneity which is not sufficiently represented by state-of-the-art climate models or reanalysis products. With regard to the increasing demand for high-resolution temperature data for climate impact studies, a statistical approach is presented, which allows estimating high-resolution near-surface temperature fields in complex terrain. High-resolution free air temperatures are derived from climate model data by considering the current stratification of the atmosphere. The residuals compared with in situ observation of near-surface temperatures are subsequently analyzed using a regression tree approach with suitable large-scale atmospheric and local-scale terrain parameters as predictors. The model identifies the predominant synoptic and topographic controls for the local-scale distribution of residuals and can be used to regionalize residual fields with high spatial resolution. The disadvantage that a tree-structured model generates stepwise constant predictant values can be overcome by integrating a fuzzifying routine. A fuzzified regression tree model was applied to analyze and predict the spatial and temporal variability of topographically induced temperatures for a target area in the Central Himalayas. Large-scale atmospheric variables, derived from the ERA-Interim reanalysis, and local terrain parameters were used as potential predictors. The model sufficiently identified the main influencing factors for the temperature heterogeneity. The potential solar insolation was found to be the predominant predictor, but also, hydroclimatic large-scale variables were found to be crucial. During clear nights, the model showed a distinct elevation dependency of residuals which indicates the importance of nocturnal cold air drainage and accumulation for the local-scale temperature distribution in the highly structured target area.
- 出版日期2015-10
