Constraining climate model properties using optimal fingerprint detection methods |
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Authors: | C E Forest M R Allen A P Sokolov P H Stone |
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Institution: | (1) Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA02139, USA E-mail: ceforest@mit.edu, US;(2) Space Science Department, Rutherford Appleton Laboratory, Chilton, Didcot, OX11 0QX, UK and Department of Physics, University of Oxford, UK, GB |
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Abstract: | We present a method for constraining key properties of the climate system that are important for climate prediction (climate
sensitivity and rate of heat penetration into the deep ocean) by comparing a model's response to known forcings over the twentieth
century against climate observations for that period. We use the MIT 2D climate model in conjunction with results from the
Hadley Centre's coupled atmosphere–ocean general circulation model (AOGCM) to determine these constraints. The MIT 2D model,
which is a zonally averaged version of a 3D GCM, can accurately reproduce the global-mean transient response of coupled AOGCMs
through appropriate choices of the climate sensitivity and the effective rate of diffusion of heat anomalies into the deep
ocean. Vertical patterns of zonal mean temperature change through the troposphere and lower stratosphere also compare favorably
with those generated by 3-D GCMs. We compare the height–latitude pattern of temperature changes as simulated by the MIT 2D
model with observed changes, using optimal fingerprint detection statistics. Using a linear regression model as in Allen and
Tett this approach yields an objective measure of model-observation goodness-of-fit (via the residual sum of squares weighted
by differences expected due to internal variability). The MIT model permits one to systematically vary the model's climate
sensitivity (by varying the strength of the cloud feedback) and rate of mixing of heat into the deep ocean and determine how
the goodness-of-fit with observations depends on these factors. This provides an efficient framework for interpreting detection
and attribution results in physical terms. With aerosol forcing set in the middle of the IPCC range, two sets of model parameters
are rejected as being implausible when the model response is compared with observations. The first set corresponds to high
climate sensitivity and slow heat uptake by the deep ocean. The second set corresponds to low sensitivities for all magnitudes
of heat uptake. These results demonstrate that fingerprint patterns must be carefully chosen, if their detection is to reduce
the uncertainty of physically important model parameters which affect projections of climate change.
Received: 19 April 2000 / Accepted: 13 April 2001 |
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