Modelling snowpack on ice surfaces with the ORCHIDEE land surface model: application to the Greenland ice sheet

Abstract

Current climate warming is accelerating mass loss from glaciers and ice sheets. In Greenland, the rates of mass changes are now dominated by changes in surface mass balance (SMB) due to increased surface melting. To improve the future sea-level rise projections, it is therefore critical to have an accurate estimate of the SMB, which depends on the representation of the processes occurring within the snowpack. The Explicit Snow (ES) scheme implemented in the land surface model Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) has not yet been adapted to ice-covered areas. Here, we present the preliminary developments we made to apply the ES model to glaciers and ice sheets. Our analysis mainly concerns the model's ability to represent ablation-related processes. At the regional scale, our results are compared to the MAR regional atmospheric model outputs and to MODIS albedo retrievals. Using different albedo parameterizations, we performed offline ES simulations forced by the MAR model over the 2000–2019 period. Our results reveal a strong sensitivity of the modelled SMB components to the albedo parameterization. Results inferred with albedo parameters obtained using a manual tuning approach present very good agreement with the MAR outputs. Conversely, with the albedo parameterization used in the standard ORCHIDEE version, runoff and sublimation were underestimated. We also tested parameters found in a previous data assimilation experiment, calibrating the ablation processes using MODIS snow albedo. While these parameters greatly improve the modelled albedo over the entire ice sheet, they degrade the other model outputs compared to those obtained with the manually tuned approach. This is likely due to the model overfitting to the calibration albedo dataset without any constraint applied to the other processes controlling the state of the snowpack. This underlines the need to perform a “multi-objective” optimization using auxiliary observations related to internal snowpack processes. Although there is still room for further improvements, the developments reported in the present study constitute an important advance in assessing the Greenland SMB with possible extension to mountain glaciers or the Antarctic ice sheet.