Exploring objective climate classification

A three-step climate classification was applied to a spatial domain covering the Hi-malayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal compo-5 nents analysis (PCA) of those variables and k means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight sub-regional climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential both for enhancing assessment of climatic influences on water resources and food security as well as for characterising the 10 skill and bias of gridded datasets, both meteorological reanalyses and climate models, for reproducing sub-regional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for 15 regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.