Constraining palaeotemperature–precipitation regimes in monsoonal Eastern Himalayas using glacier geomorphology and melt modelling

Reconstructing palaeoclimate in steep, data-sparse terrain remains a fundamental challenge in mountain palaeoclimatology. In this study, we present a process-informed framework that combines geomorphological glacier proxies with an enhanced degree-day melt model to constrain past temperature–precipitation regimes for the first time in the 900 km long Arunachal Himalayas. This is an understudied region critical for understanding the sensitivity of monsoonal glaciers.

Using geomorphological evidence and geochronological data from the Dri Valley, we reconstruct palaeo-glacier surfaces during the Late Pleistocene. For smaller advances during the Late-glacial, we apply a flowline-based GIS model. For the Last Glacial Maximum and early MIS 3 extents, where geomorphology suggests catchment breaching, we adopt an empirical surface reconstruction method. Resulting ELAs show some of the deepest depressions recorded across High Mountain Asia.

These reconstructions are paired with an enhanced degree-day model that integrates realistic seasonal variability through daily mean temperatures, lapse-rates and degree-day factors generated via a cubic spline fit to monthly mean values based on local climatology. The model is calibrated using nearby meteorological data and validated against observed mass balance at two glaciers in the region, with melt predictions within ± 25% of observed values.

To estimate total precipitation from the reconstructed melt, we implement logistic snow–rain partitioning functions tied to seasonal temperature thresholds and monsoonal timing. We derive palaeoclimatic states that are physically consistent with sustaining palaeoglaciation at the reconstructed ELAs by iteratively depressing mean monthly temperatures. We then compare our results against other temperature and precipitation reconstructions in the region.

This study demonstrates how simple physics-informed models, integrated with geomorphological data and modern climatology, can constrain estimates of past climate. Our approach is particularly suited to regions with summer-accumulation glaciers and sparse observational records and offers a structured approach to translating palaeoglacier extent into quantitative paleoclimate signals, contributing to regional and global climate reconstructions.