MSc in Geography
Modelling summer ablation on the Brewster Glacier, New Zealand
Dr. Nicolas Cullen
To calculate a full energy balance from micrometeorological measurements
To identify the most important climatic processes that control ablation
To determine degree-day factors for snow and ice
To compare the relative accuracy of the energy balance and degree-day models and determine if the degree-day method can be used as a reliable tool to estimate glacier ablation in New Zealand.
Micrometeorological data collected over a total of 85 days during the summer of 2007/08 in the ablation zone of the Brewster Glacier, New Zealand were used to determine the main atmospheric processes controlling ablation. Understanding the main processes controlling ablation is necessary to develop robust models to compute both present and future ablation. During the field season, ablation was measured and modelled using two models of differing complexity, an energy balance model (EBM) and a degree-day model (DDM). Calculation of the energy balance over the glacier using the EBM revealed that net radiation provided the largest source of energy for ablation (54%). The turbulent sensible heat flux was the next largest energy source (24%), followed by the turbulent latent heat flux (19%) and the rain heat flux (3%). During large ablation events (> 60 mm water equivalent (w.e.) per day) the turbulent heat fluxes dominated the energy source for ablation (>50%). Total ablation at the measurement site was 3441.6 ± 273.5 mm w.e. The EBM overestimated ablation by 7.2% and the DDM underestimated ablation by 10.0%. A comparison between daily total ablation modelled by the EBM and DDM showed the DDM performed reasonably well because of a good correlation between net radiation and air temperature. The large differences in ablation predicted by the two models (>30 mm w.e.) occurred when the latent heat flux was the largest energy source (>40%), which has a poor correlation with air temperature resulting in a large underestimation in ablation modelled by the DDM. Applying a ‘super degree-day factor’ of 9.1 mm d-1 ºC-1 to days affected notably improved model prediction.