Estimation of the Greenland ice sheet surface mass balance for the 20th and 21st centuries

• Published in: The Cryosphere
• Main Authors: Fettweis, Xavier, Hanna, Edward, Gallée, Hubert, Huybrechts, Philippe, Erpicum, Michel
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publication 2008
• Subjects: Greenland; Surface Mass Balance; Physical; chemical; mathematical & earth Sciences; Earth sciences & physical geography; Physique; chimie; mathématiques & sciences de la terre; Sciences de la terre & géographie physique; Ice Sheet; The Cryosphere
• Online Access: https://orbi.uliege.be/handle/2268/16862; https://orbi.uliege.be/bitstream/2268/16862/1/tc-2-117-2008.pdf; https://doi.org/10.5194/tc-2-117-2008

peer reviewed Results from a regional climate simulation (1970–2006) over the Greenland ice sheet (GrIS) reveals that more than 97% of the interannual variability of the modelled Surface Mass Balance (SMB) can be explained by the GrIS summer temperature anomaly and the GrIS annual precipitation anomaly. This multiple regression is then used to empirically estimate the GrIS SMB since 1900 from climatological time series. The projected SMB changes in the 21st century are investigated with the set of simulations performed with atmosphere-ocean general circulation models (AOGCMs) of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). These estimates show that the high surface mass loss rates of recent years are not unprecedented in the GrIS history of the last hundred years. The minimum SMB rate seems to have occurred earlier in the 1930s and corresponds to a zero SMB rate. The AOGCMs project that the SMB rate of the 1930s would be common at the end of 2100. The temperature would be higher than in the 1930s but the increase of accumulation in the 21st century would partly offset the acceleration of surface melt due to the temperature increase. However, these assumptions are based on an empirical multiple regression only validated for recent/current climatic conditions, and the accuracy and time homogeneity of the data sets and AOGCM results used in these estimations constitute a large uncertainty.