Effects of local advection on the spatial sensible heat flux variation on a mountain glacier

Abstract

Abstract. Distributed mass balance models, which translate micrometeorological conditions into local melt rates, have proven deficient to reflect the energy flux variability on mountain glaciers. This deficiency is predominantly related to shortcomings in the representation of local processes in the forcing data. We found by means of idealized large-eddy simulations that heat advection, associated with local wind systems, causes small-scale sensible heat flux variations by up to 100 W m −2 during clear sky conditions. Here we show that process understanding at a few observation sites is insufficient to infer the wind and temperature distributions across the glacier. The glacier-wide hourly averaged sensible heat fluxes are both over-and underestimated by up to 16 W m −2 when using extrapolated temperature and wind fields. The sign and magnitude of the differences depend on the site selection, which is used for extrapolation as well as on the large-scale flow direction. Our results demonstrate how the shortcomings in the local sensible heat flux estimates are related to topographic effects and the insufficient characterization of the temperature advection process.

Robust uncertainty assessment of the spatio-temporal transferability of glacier mass and energy balance models

“…Previous studies (e.g. Klok and Oerlemans, 2004;Sauter and Obleitner, 2015) have focused on the accumulation and ablation area separately or exclusively, but without a distinct mathematical comparison. Therefore, the approach of the split MAD was chosen.…”

“…1). We start by applying a global sensitivity analysis to reduce the parameter space extending the local sensitivity analysis used by Gurgiser et al (2013) to a global variance-based method (Saltelli et al, 2006), a procedure which has recently been applied in snow pack modelling (Sauter and Obleitner, 2015). Subsequently, we use the multi-objective optimization applied by Rye et al (2012) based on this calibration procedure.…”

Melt and surface sublimation across a glacier in a dry environment: distributed energy-balance modelling of Juncal Norte Glacier, Chile

“…On the contrary, surface sublimation shows different spatial patterns on both simulations, suggesting that the topographic exposure to large-scale winds shapes its spatial distribution (Figs 11d-f). Importantly, the spatially uniform wind fields decrease the coefficient of variation (CV) of surface sublimation (from 0.53 to 0.43), which is characterized by large spatial variations (Gascoin and others, 2013;Groot Zwaaftink and others, 2013;Musselman and others, 2015;Sauter and Galos, 2016). We ) and 120% (−0.0078°C m −1 ) of the air temperature ELR.…”

Reanalysis of a 10-year record (2004–2013) of seasonal mass balances at Langenferner/Vedretta Lunga, Ortler Alps, Italy

“…In this configuration, the model performance can be validated directly using data from available stake measurements. A spatially distributed model setup would introduce larger errors regarding the mass balance at selected points, while the point model allows for a spatially flexible model tuning and strongly reduces errors due to shortcomings in the spatial extrapolation of meteorological variables (e.g., Carturan et al, 2015;Sauter and Galos, 2016;Shaw et al, 2016) and the choice of the optimal parameter setting (e.g., MacDougall and Flowers, 2011;Gurgiser et al, 2013). The application of a relatively complex physical model is justified by the dominant influence of local topographic factors on micro-meteorological variability and the resultant large spatial variability of the surface mass balance, which can only be resolved in a sufficient way by a process-based model.…”

“…Although our study does not aim to explicitly resolve individual energy fluxes, we extrapolated meteorological variables using techniques which have also been applied in processoriented studies since insufficient extrapolation of meteorological input data is often a dominant error source in the application of physical glacier mass balance models (e.g., Gurgiser et al, 2013;Shaw et al, 2016;Sauter and Galos, 2016). The extrapolation techniques used here were optimized using data from the on-glacier weather station in the upper part of the glacier for the period July 2013 to August 2015, a period when measurements at all AWS were available.…”