Modelling glacier mass balance and climate sensitivity in the context of sparse observations: application to Saskatchewan Glacier, western Canada

Abstract

Abstract. Glacier mass balance models are needed at sites with scarce long-term observations to reconstruct past glacier mass balance and assess its sensitivity to future climate change. In this study, North American Regional Reanalysis (NARR) data were used to force a physically based, distributed glacier mass balance model of Saskatchewan Glacier for the historical period 1979–2016 and assess its sensitivity to climate change. A 2-year record (2014–2016) from an on-glacier automatic weather station (AWS) and historical precipitation records from nearby permanent weather stations were used to downscale air temperature, relative humidity, wind speed, incoming solar radiation and precipitation from the NARR to the station sites. The model was run with fixed (1979, 2010) and time-varying (dynamic) geometry using a multitemporal digital elevation model dataset. The model showed a good performance against recent (2012–2016) direct glaciological mass balance observations as well as with cumulative geodetic mass balance estimates. The simulated mass balance was not very sensitive to the NARR spatial interpolation method, as long as station data were used for bias correction. The simulated mass balance was however sensitive to the biases in NARR precipitation and air temperature, as well as to the prescribed precipitation lapse rate and ice aerodynamic roughness lengths, showing the importance of constraining these two parameters with ancillary data. The glacier-wide simulated energy balance regime showed a large contribution (57 %) of turbulent (sensible and latent) heat fluxes to melting in summer, higher than typical mid-latitude glaciers in continental climates, which reflects the local humid “icefield weather” of the Columbia Icefield. The static mass balance sensitivity to climate was assessed for prescribed changes in regional mean air temperature between 0 and 7 ∘C and precipitation between −20 % and +20 %, which comprise the spread of ensemble Representative Concentration Pathway (RCP) climate scenarios for the mid (2041–2070) and late (2071–2100) 21st century. The climate sensitivity experiments showed that future changes in precipitation would have a small impact on glacier mass balance, while the temperature sensitivity increases with warming, from −0.65 to −0.93 m w.e. a−1 ∘C−1. The mass balance response to warming was driven by a positive albedo feedback (44 %), followed by direct atmospheric warming impacts (24 %), a positive air humidity feedback (22 %) and a positive precipitation phase feedback (10 %). Our study underlines the key role of albedo and air humidity in modulating the response of winter-accumulation type mountain glaciers and upland icefield-outlet glacier settings to climate.

Evaluation of reanalysis data and dynamical downscaling for surface energy balance modeling at mountain glaciers in western Canada

“…However, they require a larger number of input variables, including incoming shortwave and longwave radiation, temperature, relative humidity, wind speed, and precipitation. SEB models of various complexity have been applied to individual glaciers worldwide, including several glaciers in western Canada (e.g., Ebrahimi and Marshall, 2016;Fitzpatrick et al, 2017;Marshall and Miller, 2020;Kinnard et al, 2022), showing good resemblance between modeled and observed melt, as long as the SEB models are forced by on-glacier meteorological observations. The caveat with these models, however, is that the on-glacier measurements of all SEB components are sparse in space (fewer than 100 sites worldwide and only a handful in western Canada) and of short duration (over one or two melt seasons on average).…”

Applying a distributed mass-balance model to identify uncertainties in glaciological mass balance on Brewster Glacier, New Zealand

“…Furthermore, while the glaciological method provides the seasonal and annual surface mass balance, the time period associated with these mass-balance quantities are important – for example, any amount of melt after the end of summer survey is not included in the glaciological mass balance (Huss and others, 2009). Since the geodetic method does not provide estimates of interannual variability, numerical models (including simple temperature index models and physically based surface energy and mass-balance models) are valuable tools that are often used to simulate the seasonal and annual glacier mass balances and to test the sensitivity of glaciers to climate change (Oerlemans and Hoogendoorn, 1989; Oerlemans, 1997; Anderson and others, 2010; Arndt and others, 2021; Kinnard and others, 2022). However, numerical models rely on high-quality climate data, which can be challenging to obtain in some glaciated regions.…”

“…However, numerical models rely on high-quality climate data, which can be challenging to obtain in some glaciated regions. The uncertainties and limitations inherent to each method suggest that comprehensive monitoring strategies include a combination of methods, such as glaciological observations, periodic geodetic surveys and modelled mass-balance estimates (Machguth and others, 2006; Mölg and others, 2008; Huss and others, 2009; Thibert and Vincent, 2009; Elagina and others, 2021; Kinnard and others, 2022).…”

Modern air, englacial and permafrost temperatures at high altitude on Mt Ortles (3905 m a.s.l.), in the eastern European Alps

“…Even though the physical processes that govern the energy and mass balances and climatic response of mountain permafrost and glaciers are known, model-based projections of their future behaviour are subject to large uncertainty. This is because the observational datasets required for model calibration and validation are particularly scarce for these summit areas, where model inputs and results are often poorly constrained and extrapolated, in the absence of direct observations (Charbonneau et al, 1981;Machguth et al, 2008;Carturan et al, 2012a;Zolles et al, 2019;Kinnard et al, 2022).…”