Joint Program on the Science and Policy of Global Change Constraining Climate Model Properties Using Optimal Fingerprint Detection Methods

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.

[1]  S. Manabe,et al.  The Role of Water Vapor Feedback in Unperturbed Climate Variability and Global Warming , 1999 .

[2]  Peter H. Stone,et al.  Efficient Three-Dimensional Global Models for Climate Studies: Models I and II , 1983 .

[3]  N. Gillett,et al.  Modelled and observed variability in atmospheric vertical temperature structure , 2000 .

[4]  Robert J. Charlson,et al.  Perturbation of the northern hemisphere radiative balance by backscattering from anthropogenic sulfate aerosols , 1991 .

[5]  K. Hasselmann.  On the signal-to-noise problem in atmospheric response studies , 1979 .

[6]  G. Meehl,et al.  The Coupled Model Intercomparison Project (CMIP) , 2000 .

[7]  John F. B. Mitchell,et al.  On Surface Temperature, Greenhouse Gases, and Aerosols: Models and Observations , 1995 .

[8]  John F. B. Mitchell,et al.  Carbon Dioxide and Climate. The Impact of Cloud Parameterization , 1993 .

[9]  Henning Rodhe,et al.  A global three-dimensional model of the tropospheric sulfur cycle , 1991 .

[10]  David M. H. Sexton,et al.  A new global gridded radiosonde temperature data base and recent temperature trends , 1997 .

[11]  Inez Y. Fung,et al.  Global climate changes as forecast by Goddard Institute for Space Studies three‐dimensional model , 1988 .

[12]  Andrei P. Sokolov,et al.  A Methodology for Quantifying Uncertainty in Climate Projections , 2000 .

[13]  Patrick Minnis,et al.  Forcings and chaos in interannual to decadal climate change , 1997 .

[14]  Andrei P. Sokolov,et al.  A Global Interactive Chemistry and Climate Model , 1997 .

[15]  John F. B. Mitchell,et al.  Quantifying the uncertainty in forecasts of anthropogenic climate change , 2000, Nature.

[16]  Gerald R. North,et al.  Comparison of statistically optimal approaches to detecting anthropogenic climate change , 1997 .

[17]  A. P. Sokolov,et al.  Description and validation of the MIT version of GISS 2-D model , 1995 .

[18]  Lennart Bengtsson,et al.  Why is the global warming proceeding much slower than expected , 1999 .

[19]  B. Santer,et al.  Detecting greenhouse-gas-induced climate change with an optimal fingerprint method , 1996 .

[20]  M. R. Allen,et al.  Checking for model consistency in optimal fingerprinting , 1999 .

[21]  Tom M. L. Wigley,et al.  Towards the detection and attribution of an anthropogenic effect on climate , 1995 .

[22]  Andrei P. Sokolov,et al.  Constraining uncertainties in climate models using climate change detection techniques , 2000 .

[23]  J. Hansen,et al.  Climate Response Times: Dependence on Climate Sensitivity and Ocean Mixing , 1985, Science.

[24]  K. Hasselmann.  Optimal Fingerprints for the Detection of Time-dependent Climate Change , 1993 .