Meteorological impacts of a novel debris‐covered glacier category in a regional climate model across a Himalayan catchment

Abstract

Many of the glaciers in the Nepalese Himalaya are partially covered in a layer of loose rock known as debris cover. In the Dudh Koshi River Basin, Nepal, approximately 25% of glaciers are debris‐covered. Debris‐covered glaciers have been shown to have a substantial impact on near‐surface meteorological variables and the surface energy balance, in comparison to clean‐ice glaciers. The Weather Research and Forecasting (WRF) model is often used for high‐resolution weather and climate modelling, however representation of debris‐covered glaciers is not included in the standard land cover and soil categories. Here we include a simple representation of thick debris‐covered glaciers in the WRF model, and investigate the impact on the near‐surface atmosphere over the Dudh Koshi River Basin for July 2013. Inclusion of this new category is found to improve the model representation of near‐surface temperature and relative humidity, in comparison with a simulation using the default category of clean‐ice glaciers, when compared to observations. The addition of the new debris‐cover category in the model warms the near‐surface air over the debris‐covered portion of the glacier, and the wind continues further up the valley, compared to the simulation using clean‐ice. This has consequent effects on water vapour and column‐integrated total water path, over both the portions of the glacier with and without debris cover. Correctly simulating meteorological variables such as these is vital for accurate precipitation forecasts over glacierized regions, and therefore estimating future glacier melt and river runoff in the Himalaya. These results highlight the need for debris cover to be included in high‐resolution regional climate models over debris‐covered glaciers.

Daytime along-valley winds in the Himalayas as simulated by the Weather Research and Forecasting (WRF) model

“…Overall, the model manages to simulate the diurnal cycle of the winds that was evident in the observations. The along-valley wind characteristics in Khumbu Valley were found to be similar to what previous research has shown, with respect to both observational (Inoue, 1976;Ueno and Kayastha, 2001;Bollasina et al, 2002;Ueno et al, 2008;Bonasoni et al, 2010;Shea et al, 2015) and model-based studies (Potter et al, 2018(Potter et al, , 2021.…”

“…This study concentrates on four valleys located in the Nepal Himalayas during a 4 d period in December 2014. The local wind patterns in Khumbu Valley (one of the valleys that is investigated in this study) have been studied in the past by means of meteorological observations (Inoue, 1976;Ueno and Kayastha, 2001;Bollasina et al, 2002;Ueno et al, 2008;Bonasoni et al, 2010;Shea et al, 2015;Yang et al, 2018) and high-resolution meteorological modelling (Karki et al, 2017;Potter et al, 2018Potter et al, , 2021. Overall, the daily cycle of the along-valley winds in Khumbu Valley are simi-lar among existing studies, with well-defined daytime upvalley winds and weaker night-time winds flowing either in the up-or down-valley direction.…”

Quantifying Supraglacial Debris‐Related Melt‐Altering Effects on the Djankuat Glacier, Caucasus, Russian Federation

“…Spatially distributed wind modeling is more challenging and involves complex relationships with respect to topography and thermal/dynamic atmospheric processes (Ayala et al, 2017;Potter et al, 2020). In this study, the wind pattern is spatialized using equations from the MicroMet model (Liston & Elder, 2006;Liston & Sturm, 1998):…”

“…Spatially distributed wind modeling is more challenging and involves complex relationships with respect to topography and thermal/dynamic atmospheric processes (Ayala et al., 2017; Potter et al., 2020). In this study, the wind pattern is spatialized using equations from the MicroMet model (Liston & Elder, 2006; Liston & Sturm, 1998): $$$u={u}_{AWS}\ast \frac{\left(1+{\gamma }_{s}{{\Omega }}_{s}+{\gamma }_{c}{{\Omega }}_{c}\right)}{\left(1+{\gamma }_{s}{{\Omega }}_{s(AWS)}+{\gamma }_{c}{{\Omega }}_{c(AWS)}\right)}$$$ where u is the topographically modified wind speed, u AWS is the measured wind speed at the AWS site, Ω c the terrain curvature, and Ω s is the slope in the direction of the wind: $$${{\Omega }}_{s}={S}_{t}\left({\theta }_{u}-{A}_{t}\right)$$$ …”

Understanding monsoon controls on the energy and mass balance of Himalayan glaciers

“…Studies employing distributed energy balance models could, for example, test the melt-equalizing effect of debris at the glacier scale and its overall effect on the catchment runoff. This could be combined with generating spatially consistent forcing data using high-resolution weather modelling as in Bonekamp et al (2019) or Potter et al (2020), and expanded to larger domains. Realistic representations of the glacier surface, including distributed debris thickness, and supraglacial features, such as ice cliffs and surface ponds, should also be established to provide well-constrained water budgets and improved representations of glacierised environments in land-surface models under present and future environmental conditions.…”