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Understanding the atmospheric circulation response to climate change (ACRCC)
Date du début: 1 mars 2014, Date de fin: 28 févr. 2019 PROJET  TERMINÉ 

"Computer models based on known physical laws are our primary tool for predicting climate change. Yet the state-of-the-art models exhibit a disturbingly wide range of predictions of future climate change, especially when examined at the regional scale, which has not decreased as the models have become more comprehensive. The reasons for this are not understood. This represents a basic challenge to our fundamental understanding of climate.The divergence of model projections is presumably related to systematic model errors in the large-scale fluxes of heat, moisture and momentum that control regional aspects of climate. That these errors stubbornly persist in spite of increases in the spatial resolution of the models suggests that they are associated with errors in the representation of unresolved processes, whose effects must be parameterised.Most attention in climate science has hitherto focused on the thermodynamic aspects of climate. Dynamical aspects, which involve the atmospheric circulation, have received much less attention. However regional climate, including persistent climate regimes and extremes, is strongly controlled by atmospheric circulation patterns, which exhibit chaotic variability and whose representation in climate models depends sensitively on parameterised processes. Moreover the dynamical aspects of model projections are much less robust than the thermodynamic ones. There are good reasons to believe that model bias, the divergence of model projections, and chaotic variability are somehow related, although the relationships are not well understood. This calls for studying them together.My proposed research will focus on this problem, addressing these three aspects of the atmospheric circulation response to climate change in parallel: (i) diagnosing the sources of model error; (ii) elucidating the relationship between model error and the spread in model projections; (iii) understanding the physical mechanisms of atmospheric variability."