The impact of cloud cover on the net radiation budget of the Greenland ice sheet F. G.L. Cawkwell, J.L. Bamber Centre for Polar Observation Modelling, Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, England ABSTRACT . Energy-balance models driven by radiation and turbulent heat fluxes have been widely applied to predicting the response of the Greenland ice sheet to climate change. However, a lack of knowledge of the temporal and spatial distribution of cloud amount and type has necessitated the use of parameterizations or statistical models of cloud cover. This deficiency results in large uncertainties in both shortwave and longwave radiation fluxes. Stereo-matching of nadir and forward viewAlongTrack Scanning Radiometer-2 (ATSR-2) image pairs has been shown to be a reliable method of retrieving cloud top height, and further cloud properties can be derived from thermal imagery allowing classification into cloud type. A 1year cloud record for a transect across southern Greenland derived from stereo-matching is presented here, and comparisons are made with climate re-analysis data and ground observations. The cloud-cover data were used in a simple radiative transfer model, and the impact of clouds on the net radiation fluxes was found to be considerable. Different cloud scenarios produced up to 40 W m ^2 difference in net radiation balance. In the ablation zone, where the albedo is lower and most variable, the sensitivity to cloud-cover fraction was less marked, but the higher spatial resolution of the ATSR-2 cloud record was reflected by a much more varied trend in radiation balance. Whether the net radiation balance increases or decreases with increased cloud cover was found to be a function of the cloud amount and type and also the surface albedo. The sensitivity of the model to a §5% change in cloud amount was found to be comparable to a 1K change in temperature. This clearly demonstrates the importance of reliable, quantitative cloud data in mass-balance and other glaciological studies. INTRODUCTION The ability of physically based climate models to provide detailed estimates of future climate changes has improved significantly in recent years. However, there remain some aspects of the climate which cannot be accurately simulated, due to a lack of understanding and of observational data, one of the most influential being the interaction of clouds with radiation and aerosols (IPCC, 2001). Indeed, despite consid- erable research into understanding the role of clouds in climate change there is still uncertainty surrounding the nature of changes in both cloud fraction and type, and even the sign of overall climate change induced by altered cloud cover. Some climate theories predict that a warmer atmos- phere is capable of holding more water vapour, resulting in increased cover of low, thick clouds, which counteract warm- ing by reflecting a greater proportion of incoming radiation backto space. However, recent research by Del Genio and Wolf (2000) suggests that warmer air temperatures cause cloud bases to form at higher elevations, generating thinner clouds which are less efficient at reflecting solar radiation, thereby limiting the cooling effect of clouds. Additional uncertainty is introduced at high latitudes, where climate- model simulations predict warming above the global aver- age of 1.4^5.8C, possibly by >40%, with local warming over Greenland likely to be 1^3 times the global mean (IPCC, 2001). This is largely attributed to the ice^albedo feedback mechanism, which relates the decrease in surface albedo associated with the retreat of snow and ice cover to an increase in the amount of incoming radiation absorbed by the Earth^atmosphere system. The uncertainty in the degree of attenuation of radiation by changing cloud cover, however, means that the contribution of the ice^albedo feedback to climate change remains unknown. Furthermore, it is import- ant to realize that cloud cover over Arctic regions plays an important role not only in determining local climatological conditions but also in global atmospheric processes such as meridional heat transfer. Cloud parameterization schemes within climate models vary widely but are often empirically based, with cloud cover incorporated through simplification of physical interactions derived from variables such as relative humidity. Conse- quently, model global cloud fractions can differ by a factor of nearly 2 (e.g. Cess and others,1990), highlighting the need for an accurate global cloud climatology. Numerical models of ice-sheet surface energy and mass balance also rely on parameterizations of cloud cover, frequently derived from temporally and spatially limited surface observations that are often concentrated in coastal regions due to the inacces- sibility of ice-sheet interiors. In such areas, satellite remote- sensing techniques provide the most consistent method of obtaining regular data with a comparatively high spatial and temporal resolution. However, distinguishing cloud from snow and ice in satellite imagery is difficult due to the lack of radiance contrast, the small differences in brightness tem- perature and exaggeratedbidirectional effects at large zenith angles (Lubin and Morrow, 1998). Many automated tech- niques of cloud identification in visible and thermal imagery rely on thresholding and classification algorithms whereby a number of images are visually analyzed and the characteris- Annals of Glaciology 34 2002 # International Glaciological Society 141