Analytic comparison of temperature lapse rates and precipitation gradients in a Himalayan treeline environment:implications for statistical downscaling
High mountain regions have been identified as a major hotspot of climate change during recent decades, resulting in a rapid change of local geo- and ecosystems. The ecosystem response to changes of near-surface temperatures and precipitation is often analyzed and simulated by means of statistical or process-based modeling applications. However, these models require high-quality climate input data. Based on the assumption that freely available gridded climate data sets are often not suitable for climate change impact investigation due to their low spatial resolution and a lack of accuracy, this paper aims to suggest adequate statistical downscaling routines in order to facilitate the cooperation of climate and climate impact research. We firstly summarize the requirements of ecological climate impact studies and identify the deficiencies of freely available climate reanalysis and regionalization products. Based on a network of seven recently installed weather stations in the highly structured target area, the seasonal, diurnal, and spatial heterogeneity of near-surface temperatures and precipitation amounts is analyzed, and the major large-scale atmospheric and local-scale topographic forcing are specified. The analysis of observations highly suggests that local-scale climatic conditions are influenced by both large-scale atmospheric parameters and topographic characteristics. Based on related studies in similar environments, we eventually suggest a statistical downscaling approach integrating large-scale atmospheric fields (derived from reanalysis products or large-scale climate models) and GIS-based terrain parameterization in order to generate fully distributed fields of ecologically relevant climate parameters with high spatial resolution.