Professor Myles Allen CBE FRS

Abstract:

A linear analysis is applied to a multi-thousand member "perturbed physics" GCM ensemble to identify the dominant physical processes responsible for variation in climate sensitivity across the ensemble. Model simulations are provided by the distributed computing project, climate prediction.net. A principal component analysis of model radiative response reveals two dominant independent feedback processes, each largely controlled by a single parameter change. The leading EOF was well correlated with the value of the entrainment coefficient - a parameter in the model's atmospheric convection scheme. Reducing this parameter increases high vertical level moisture causing an enhanced clear sky greenhouse effect both in the control simulation and in the response to greenhouse gas forcing. This effect is compensated by an increase in reflected solar radiation from low level cloud upon warming. A set of 'secondary' cloud formation parameters partly modulate the degree of shortwave compensation from low cloud formation. The second EOF was correlated with the scaling of ice fall speed in clouds which affects the extent of cloud cover in the control simulation. The most prominent feature in the EOF was an increase in longwave cloud forcing. The two leading EOFs account for 70% of the ensemble variance in λ - the global feedback parameter. Linear predictors of feedback strength from model climatology are applied to observational datasets to estimate real world values of the overall climate feedback parameter. The predictors are found using correlations across the ensemble. Differences between predictions are largely due to the differences in observational estimates for top of atmosphere shortwave fluxes. Our validation does not rule out all the strong tropical convective feedbacks leading to a large climate sensitivity. © Springer-Verlag 2007.

Abstract:

A methodology for constraining climate forecasts, developed for application to the multithousand member perturbed physics ensemble of simulations completed by the distributed computing project ClimatePrediction.net, is here presented in detail. The methodology is extended to produce constrained forecasts of mean surface temperature and precipitation within 21 land-based regions and is validated with climate simulations from other models available from the IPCC (AR4) data set. The mean forecasted values of temperature and precipitation largely confirm prior results for the same regions. In particular, precipitation in the Mediterranean basin is shown to decrease and temperature over northern Europe is shown to increase with comparatively little uncertainty in the forecast (i.e., with tight constraints). However, in some cases the forecasts show large uncertainty, and there are a few cases where the forecasts cannot be constrained at all. These results illustrate the effectiveness of the methodology and its applicability to regional climate variables. Copyright 2007 by the American Geophysical Union.