The detection and attribution of climate change using an ensemble of opportunity
The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) s...
| প্রধান লেখক: | , , , , |
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| বিন্যাস: | Journal article |
| ভাষা: | English |
| প্রকাশিত: |
2007
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| _version_ | 1826276132653105152 |
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| author | Stone, D Allen, M Selten, F Kliphuis, M Stott, P |
| author_facet | Stone, D Allen, M Selten, F Kliphuis, M Stott, P |
| author_sort | Stone, D |
| collection | OXFORD |
| description | The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM, as well as some simulations from other GCMs. Considerable uncertainty exists in the estimates of the parameters in fitted EBMs. Nevertheless, temporal response patterns from these EBMs are more reliable and the combined EBM time series closely mimics the GCM in the context of transient forcing. In particular, detection and attribution results from this technique appear self-consistent and consistent with results from other methods provided that all major forcings are included in the analysis. Using this technique on the Challenge ensemble, the estimated responses to changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric volcanic aerosols are all detected in the observed record, and the responses to the greenhouse gases and tropospheric sulfate aerosols are both consistent with the observed record without a scaling of the amplitude being required. The result is that the temperature difference of the 1996-2005 decade relative to the 1940-49 decade can be attributed to greenhouse gas emissions, with a partially offsetting cooling from sulfate emissions and little contribution from natural sources. The results support the viability of the new methodology as an extension to current analysis tools for the detection and attribution of climate change, which will allow the inclusion of many more GCMs. Shortcomings remain, however, and so it should not be considered a replacement to traditional techniques. © 2007 American Meteorological Society. |
| first_indexed | 2024-03-06T23:09:24Z |
| format | Journal article |
| id | oxford-uuid:64f27055-2e5f-4fbe-835c-70d8a6b16d9d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:09:24Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | oxford-uuid:64f27055-2e5f-4fbe-835c-70d8a6b16d9d2022-03-26T18:22:14ZThe detection and attribution of climate change using an ensemble of opportunityJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:64f27055-2e5f-4fbe-835c-70d8a6b16d9dEnglishSymplectic Elements at Oxford2007Stone, DAllen, MSelten, FKliphuis, MStott, PThe detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM, as well as some simulations from other GCMs. Considerable uncertainty exists in the estimates of the parameters in fitted EBMs. Nevertheless, temporal response patterns from these EBMs are more reliable and the combined EBM time series closely mimics the GCM in the context of transient forcing. In particular, detection and attribution results from this technique appear self-consistent and consistent with results from other methods provided that all major forcings are included in the analysis. Using this technique on the Challenge ensemble, the estimated responses to changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric volcanic aerosols are all detected in the observed record, and the responses to the greenhouse gases and tropospheric sulfate aerosols are both consistent with the observed record without a scaling of the amplitude being required. The result is that the temperature difference of the 1996-2005 decade relative to the 1940-49 decade can be attributed to greenhouse gas emissions, with a partially offsetting cooling from sulfate emissions and little contribution from natural sources. The results support the viability of the new methodology as an extension to current analysis tools for the detection and attribution of climate change, which will allow the inclusion of many more GCMs. Shortcomings remain, however, and so it should not be considered a replacement to traditional techniques. © 2007 American Meteorological Society. |
| spellingShingle | Stone, D Allen, M Selten, F Kliphuis, M Stott, P The detection and attribution of climate change using an ensemble of opportunity |
| title | The detection and attribution of climate change using an ensemble of opportunity |
| title_full | The detection and attribution of climate change using an ensemble of opportunity |
| title_fullStr | The detection and attribution of climate change using an ensemble of opportunity |
| title_full_unstemmed | The detection and attribution of climate change using an ensemble of opportunity |
| title_short | The detection and attribution of climate change using an ensemble of opportunity |
| title_sort | detection and attribution of climate change using an ensemble of opportunity |
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