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David M. W. Pritchard
Nathan Forsythe
Hayley J. Fowler
Greg M. O’Donnell
, and
Xiao-Feng Li


Data paucity is a severe barrier to the characterization of Himalayan near-surface climates. Regional climate modeling can help to fill this gap, but the resulting data products need critical evaluation before use. This study aims to extend the appraisal of one such dataset, the High Asia Refined Analysis (HAR). Focusing on the upper Indus basin (UIB), the climatologies of variables needed for process-based hydrological and cryospheric modeling are evaluated, leading to three main conclusions. First, precipitation in the 10-km HAR product shows reasonable correspondence with most in situ measurements. It is also generally consistent with observed runoff, while additionally reproducing the UIB’s strong vertical precipitation gradients. Second, the HAR shows seasonally varying bias patterns. A cold bias in temperature peaks in spring but reduces in summer, at which time the high bias in relative humidity diminishes. These patterns are concurrent with summer overestimation (underestimation) of incoming shortwave (longwave) radiation. Finally, these seasonally varying biases are partly related to deficiencies in cloud, snow, and albedo representations. In particular, insufficient cloud cover in summer leads to the overestimation of incoming shortwave radiation. This contributes to the reduced cold bias in summer by enhancing surface warming. A persistent high bias in albedo also plays a critical role, particularly by suppressing surface heating in spring. Improving representations of cloud, snow cover, and albedo, and thus their coupling with seasonal climate transitions, would therefore help build upon the considerable potential shown by the HAR to fill a vital data gap in this immensely important basin.

Open access