Abstract

Due to their high sensitivity to changes in climate, glaciers are one of the best natural indicators of climate change. Despite this, many underlying processes that control glacier response to climate change are poorly understood. One potentially important set of such processes are feedback mechanisms that can amplify or dampen glacier melt in response to a change in climate. Though feedbacks are recognized as important processes affecting glacier mass balances, little has been done to systematically quantify their effects. This study develops a surface energy and mass balance model to quantify the contribution of the albedo-feedback to glacier mass balance. Specifically, we quantify the roles of three trigger processes that initiate the albedo-feedback: snowpack thickness, snowfall event frequency, and heat flux supplied by precipitation. The model follows common energy balance methods but includes “switches” to turn these trigger processes off. The model is applied to Chhota Shigri Glacier using meteorological inputs from three different climate regions in High Mountain Asia (HMA). The results show that up to 80% of the average glacier melt increase from a +1°C temperature change can be attributed to the albedo-feedback. Furthermore, the system gain due to the albedo-feedback depends most on snowfall event frequency and the availability of incoming shortwave radiation during the melt season, and are thus generally largest in summer accumulation settings of HMA. This sensitivity to snowfall timing and frequency results in system gains being highest near the equilibrium line altitude, where a small change in temperature can shift precipitation phase from snow to rain. Regional analysis using climatological estimates suggests that many glaciers in the monsoonal Himalayas and southern Tibetan Plateau are likely to exhibit particularly strong albedo feedbacks. These results contribute to a growing body of literature suggesting that the mass balance of summer-accumulation type glaciers is strongly controlled by summer snowfall amount and frequency, which is closely linked with changes in air temperature. It also highlights the significance of the albedo feedback on glacier mass balance and the need to further explore feedbacks associated with glacier surface processes.

Highlights

  • Because of their high sensitivity to changes in climate, glaciers are one of the best natural indicators of climate change (Oerlemans, 1994; IPCC Report, 2001; Roe et al, 2016)

  • These results highlight the potential importance of feedbacks on glacier mass balance and its modeling, as well as the conditions under which the albedo-feedback is most important to glacier mass balance. They provide a first-order estimate of the magnitude of the albedo-feedback contribution, as well as the trigger processes that initiate it, for four very different climate settings. These results demonstrate that the potential impact of the albedo-feedback on glacier mass balance can be significant

  • This study develops a surface energy and mass balance model to quantify the contribution of the albedo-feedback to glacier mass balances in different climatic regions of High Mountain Asia (HMA)

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Summary

Introduction

Because of their high sensitivity to changes in climate, glaciers are one of the best natural indicators of climate change (Oerlemans, 1994; IPCC Report, 2001; Roe et al, 2016). Attributing glacier length or mass balance changes to changes in climate is often not straightforward (Roe and Baker, 2016; Sakai and Fujita, 2017). The majority have noted the significance of complex glacier-climate feedbacks, related to surface albedo and precipitation seasonality. While these feedbacks are often recognized as important factors in determining glacier mass balance (e.g., Arnold et al, 2006; Pepin et al, 2015), their influence has yet to be quantified in a systematic way

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