Constraining climate model properties using optimal fingerprint detection methods
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 fo...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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Springer
2001
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| _version_ | 1826262946467020800 |
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| author | Forest, C Allen, M Sokolov, A Stone, P |
| author_facet | Forest, C Allen, M Sokolov, A Stone, P |
| author_sort | Forest, C |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T19:43:52Z |
| format | Journal article |
| id | oxford-uuid:219649ab-9b5b-4f5f-b23e-244b6bc1feee |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:43:52Z |
| publishDate | 2001 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:219649ab-9b5b-4f5f-b23e-244b6bc1feee2022-03-26T11:34:18ZConstraining climate model properties using optimal fingerprint detection methodsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:219649ab-9b5b-4f5f-b23e-244b6bc1feeePhysicsClimate systems and policyEnvironmentAtmospheric,Oceanic,and Planetary physicsEnglishOxford University Research Archive - ValetSpringer2001Forest, CAllen, MSokolov, AStone, PWe 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. |
| spellingShingle | Physics Climate systems and policy Environment Atmospheric,Oceanic,and Planetary physics Forest, C Allen, M Sokolov, A Stone, P Constraining climate model properties using optimal fingerprint detection methods |
| title | Constraining climate model properties using optimal fingerprint detection methods |
| title_full | Constraining climate model properties using optimal fingerprint detection methods |
| title_fullStr | Constraining climate model properties using optimal fingerprint detection methods |
| title_full_unstemmed | Constraining climate model properties using optimal fingerprint detection methods |
| title_short | Constraining climate model properties using optimal fingerprint detection methods |
| title_sort | constraining climate model properties using optimal fingerprint detection methods |
| topic | Physics Climate systems and policy Environment Atmospheric,Oceanic,and Planetary physics |
| work_keys_str_mv | AT forestc constrainingclimatemodelpropertiesusingoptimalfingerprintdetectionmethods AT allenm constrainingclimatemodelpropertiesusingoptimalfingerprintdetectionmethods AT sokolova constrainingclimatemodelpropertiesusingoptimalfingerprintdetectionmethods AT stonep constrainingclimatemodelpropertiesusingoptimalfingerprintdetectionmethods |