Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations |
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| Authors: | Benjamin M Sanderson C Piani W J Ingram D A Stone M R Allen |
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| Institution: | (1) AOPP, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford, OX1 3PU, UK;(2) International Center for Theoretical Physics, Trieste, Italy;(3) Meteorological Office, Exeter, UK;(4) Tyndall Centre for Climate Change Research, Oxford, UK |
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| 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. |
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