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Towards understanding the processes that influence global mean temperature


Global mean surface temperature variability is largely determined by the global mean surface energy budget, which is driven by many natural and anthropogenic forcings. In theory, if all natural sources of global mean temperature variability could be removed from the global mean temperature time series the anthropogenic signal would be clearer. Previous studies have exploited this reasoning to remove the signature of volcanoes, the El-Niño Southern Oscillation (ENSO), and dynamic variability from the global mean temperature time series. This thesis extends previous work by 1) examining the linkages between global mean temperature and natural variability as a function of timescale; and 2) examining the two-way coupling between area-averaged surface temperatures and sea ice concentration. The results reveal a series of unique spatial structures in surface temperatures that drive intraannual, interannual, and decadal variability in global mean temperature. The results confirm the apparent role of hemispheric mean temperatures in driving sea ice variability, and also point to a possible feedback between wintertime sea ice concentration and springtime surface temperatures over the Northern Hemisphere. Linkages between sea ice concentration and surface temperature in the Southern Hemisphere are much weaker, and it can be argued that the hemispheric difference in these linkages may aid in explaining the different trends in sea ice between the two hemispheres.


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sea ice
global mean temperature


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