Constraining cloud responses to CO2 and warming in climate models: physical and statistical approaches

Thursday, 18 December 2014: 4:30 PM
Steven C Sherwood1, David Fuchs2, Sandrine Bony3 and Dufresne Jean-Louis3, (1)University of New South Wales, Climate Change Research Centre, Sydney, NSW, Australia, (2)University of New South Wales, Climate Change Research Centre, Sydney, Australia, (3)Laboratoire de Météorologie Dynamique UPMC, Paris, France
We describe two avenues for constraining the sensitivity of the climate system to external perturbations, using present-day observations. The first is physically motivated, based on recently published work showing that differences in the simulated strength of convective mixing between the lower and middle tropical troposphere explain about half of the variance in climate sensitivity estimated by 43 climate models. The apparent mechanism is that such mixing dehydrates the low-cloud layer at a rate that increases as the climate warms, and this rate of increase depends on the initial mixing strength, linking the mixing to cloud feedback. The mixing inferred from observations appears to be sufficiently strong to imply a climate sensitivity of more than 3 degrees for a doubling of carbon dioxide. This is significantly higher than the currently accepted lower bound of 1.5 degrees, thereby constraining model projections towards relatively severe future warming. However, this result would be wrong if there were an important feedback in the real world that was missing from all the models.

The second approach is based on application of the fluctuation-dissipation theorem to climate models, to predict the three-dimensional equilibrium response to heating perturbations via a statistical model of the system fitted to data from a control run. We expand on previous applications of this technique for such problems by considering multivariate state vectors, showing that this improves skill and makes it possible to train skillful operators on data records of comparable length to what is available from satellite observations. We also present a new methodology for treating non-stationary processes, in particular the existence of a seasonal cycle, and show that we can obtain similar results with a realistic seasonal cycle as with an idealised non-seasonally-varying case. We focus specifically on the ability to predict how clouds in the model will respond to a forced climate change. Results indicate that the fluctuation-dissipation method may prove to be more useful than previously thought for predicting global forces responses from observed variability, although the skill varies significantly depending on the nature of the forcing perturbation.