High-resolution interactive modelling of the mountain glacier–atmosphere interface: an application over the Karakoram

E Collier,D Scherer,C Mayer,A B G Bush,T Mölg,F Maussion

doi:10.5194/tc-7-779-2013

Abstract

Abstract. The traditional approach to simulations of alpine glacier mass balance (MB) has been one-way, or offline, thus precluding feedbacks from changing glacier surface conditions on the atmospheric forcing. In addition, alpine glaciers have been only simply, if at all, represented in atmospheric models to date. Here, we extend a recently presented, novel technique for simulating glacier–atmosphere interactions without the need for statistical downscaling, through the use of a coupled high-resolution mesoscale atmospheric and physically-based climatic mass balance (CMB) modelling system that includes glacier CMB feedbacks to the atmosphere. We compare the model results over the Karakoram region of the northwestern Himalaya with remote sensing data for the ablation season of 2004 as well as with in situ glaciological and meteorological measurements from the Baltoro glacier. We find that interactive coupling has a localized but appreciable impact on the near-surface meteorological forcing data and that incorporation of CMB processes improves the simulation of variables such as land surface temperature and snow albedo. Furthermore, including feedbacks from the glacier model has a non-negligible effect on simulated CMB, reducing modelled ablation, on average, by 0.1 m w.e. (−6.0%) to a total of −1.5 m w.e. between 25 June–31 August 2004. The interactively coupled model shows promise as a new, multi-scale tool for explicitly resolving atmospheric-CMB processes of mountain glaciers at the basin scale.

Highlights

Spatially-distributed simulations of glacier surface and climatic mass balance require distributed meteorological forcing; obtaining these data is complicated both by the spatial and temporal scarcity of in situ observations and by the “scale mismatch” between the spatial scales represented in atmospheric models and those relevant for surface and climatic MB calculations (e.g. Machguth et al, 2009; Molg and Kaser, 2011)
The elevational profile of land surface temperature (LST) averaged over the simulation period produced by the climatic mass balance (CMB) model is in good agreement with the MODIS data above ∼ 5200 m and is an improvement on the Noah land surface model (LSM) values at all resolved elevations (Fig. 2a)
The CMB model gives an improved performance over the LSM alone, LST is generally under-predicted, with mean biases of −1.0, −1.3, and −6.1 K in the INT, OFF, and Noah LSM simulations, respectively

Summary

Introduction

Spatially-distributed simulations of glacier surface and climatic mass balance (where the latter term denotes surface plus near-subsurface mass balance following; Cogley et al, 2011) require distributed meteorological forcing; obtaining these data is complicated both by the spatial and temporal scarcity of in situ observations and by the “scale mismatch” between the spatial scales represented in atmospheric models and those relevant for surface and climatic MB calculations (e.g. Machguth et al, 2009; Molg and Kaser, 2011). Machguth et al, 2009; Molg and Kaser, 2011) To overcome these issues, forcing data can be obtained by extrapolation from point measurements by automated weather stations, where available, or interpolation from climate reanalyses and atmospheric model output, using surface- and free-air lapse rates. Surface lapse rates exhibit significant spatial and temporal variability, leading to uncertainty in temperature downscaling from altitude changes (Marshall et al, 2007; Gardner et al, 2009; Petersen and Pellicciotti, 2011). The assumption of linear lapse rates over glacier surfaces may be inappropriate (Petersen and Pellicciotti, 2011) and may under-predict nearsurface temperature over debris-covered regions (Reid et al, 2012). Additional corrections are often required for the poor representation of the strength and spatial variability of processes relevant to mass balance, such as orographic precipitation, in coarse spatial-resolution atmospheric models (e.g. Paul and Kotlarski, 2010; Radicand Hock, 2011).

Methods

Results

Conclusion