Recommendations for diagnosing effective radiative forcing from climate models for CMIP6
The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than tradi...
Ausführliche Beschreibung
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| Autor*in: |
Forster, Piers M [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: © 2016. The Authors. |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of geophysical research / D - Washington, DC : Union, 1984, 121(2016), 20 |
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| Übergeordnetes Werk: |
volume:121 ; year:2016 ; number:20 |
| Links: |
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| DOI / URN: |
10.1002/2016JD025320 |
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| 520 | |a The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) | ||
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| 650 | 4 | |a CMIP6 | |
| 650 | 4 | |a climate models | |
| 650 | 4 | |a effective radiative forcing | |
| 650 | 4 | |a Confidence intervals | |
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| 700 | 1 | |a Richardson, Thomas |4 oth | |
| 700 | 1 | |a Maycock, Amanda C |4 oth | |
| 700 | 1 | |a Smith, Christopher J |4 oth | |
| 700 | 1 | |a Samset, Bjorn H |4 oth | |
| 700 | 1 | |a Myhre, Gunnar |4 oth | |
| 700 | 1 | |a Andrews, Timothy |4 oth | |
| 700 | 1 | |a Pincus, Robert |4 oth | |
| 700 | 1 | |a Schulz, Michael |4 oth | |
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10.1002/2016JD025320 doi PQ20170206 (DE-627)OLC198877389X (DE-599)GBVOLC198877389X (PRQ)p1370-bdc2441decc7499f14550115d50d477caf6514b79cb93b00d12614a6a2222da80 (KEY)0137985220160000121002000000recommendationsfordiagnosingeffectiveradiativeforc DE-627 ger DE-627 rakwb eng 550 DNB Forster, Piers M verfasserin aut Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) Nutzungsrecht: © 2016. The Authors. RFMIP AerChemMIP radiative forcing CMIP6 climate models effective radiative forcing Confidence intervals Climate Richardson, Thomas oth Maycock, Amanda C oth Smith, Christopher J oth Samset, Bjorn H oth Myhre, Gunnar oth Andrews, Timothy oth Pincus, Robert oth Schulz, Michael oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 121(2016), 20 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:121 year:2016 number:20 http://dx.doi.org/10.1002/2016JD025320 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD025320/abstract http://search.proquest.com/docview/1845021130 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 121 2016 20 |
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10.1002/2016JD025320 doi PQ20170206 (DE-627)OLC198877389X (DE-599)GBVOLC198877389X (PRQ)p1370-bdc2441decc7499f14550115d50d477caf6514b79cb93b00d12614a6a2222da80 (KEY)0137985220160000121002000000recommendationsfordiagnosingeffectiveradiativeforc DE-627 ger DE-627 rakwb eng 550 DNB Forster, Piers M verfasserin aut Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) Nutzungsrecht: © 2016. The Authors. RFMIP AerChemMIP radiative forcing CMIP6 climate models effective radiative forcing Confidence intervals Climate Richardson, Thomas oth Maycock, Amanda C oth Smith, Christopher J oth Samset, Bjorn H oth Myhre, Gunnar oth Andrews, Timothy oth Pincus, Robert oth Schulz, Michael oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 121(2016), 20 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:121 year:2016 number:20 http://dx.doi.org/10.1002/2016JD025320 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD025320/abstract http://search.proquest.com/docview/1845021130 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 121 2016 20 |
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10.1002/2016JD025320 doi PQ20170206 (DE-627)OLC198877389X (DE-599)GBVOLC198877389X (PRQ)p1370-bdc2441decc7499f14550115d50d477caf6514b79cb93b00d12614a6a2222da80 (KEY)0137985220160000121002000000recommendationsfordiagnosingeffectiveradiativeforc DE-627 ger DE-627 rakwb eng 550 DNB Forster, Piers M verfasserin aut Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) Nutzungsrecht: © 2016. The Authors. RFMIP AerChemMIP radiative forcing CMIP6 climate models effective radiative forcing Confidence intervals Climate Richardson, Thomas oth Maycock, Amanda C oth Smith, Christopher J oth Samset, Bjorn H oth Myhre, Gunnar oth Andrews, Timothy oth Pincus, Robert oth Schulz, Michael oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 121(2016), 20 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:121 year:2016 number:20 http://dx.doi.org/10.1002/2016JD025320 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD025320/abstract http://search.proquest.com/docview/1845021130 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 121 2016 20 |
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10.1002/2016JD025320 doi PQ20170206 (DE-627)OLC198877389X (DE-599)GBVOLC198877389X (PRQ)p1370-bdc2441decc7499f14550115d50d477caf6514b79cb93b00d12614a6a2222da80 (KEY)0137985220160000121002000000recommendationsfordiagnosingeffectiveradiativeforc DE-627 ger DE-627 rakwb eng 550 DNB Forster, Piers M verfasserin aut Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) Nutzungsrecht: © 2016. The Authors. RFMIP AerChemMIP radiative forcing CMIP6 climate models effective radiative forcing Confidence intervals Climate Richardson, Thomas oth Maycock, Amanda C oth Smith, Christopher J oth Samset, Bjorn H oth Myhre, Gunnar oth Andrews, Timothy oth Pincus, Robert oth Schulz, Michael oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 121(2016), 20 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:121 year:2016 number:20 http://dx.doi.org/10.1002/2016JD025320 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD025320/abstract http://search.proquest.com/docview/1845021130 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 121 2016 20 |
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10.1002/2016JD025320 doi PQ20170206 (DE-627)OLC198877389X (DE-599)GBVOLC198877389X (PRQ)p1370-bdc2441decc7499f14550115d50d477caf6514b79cb93b00d12614a6a2222da80 (KEY)0137985220160000121002000000recommendationsfordiagnosingeffectiveradiativeforc DE-627 ger DE-627 rakwb eng 550 DNB Forster, Piers M verfasserin aut Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) Nutzungsrecht: © 2016. The Authors. RFMIP AerChemMIP radiative forcing CMIP6 climate models effective radiative forcing Confidence intervals Climate Richardson, Thomas oth Maycock, Amanda C oth Smith, Christopher J oth Samset, Bjorn H oth Myhre, Gunnar oth Andrews, Timothy oth Pincus, Robert oth Schulz, Michael oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 121(2016), 20 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:121 year:2016 number:20 http://dx.doi.org/10.1002/2016JD025320 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD025320/abstract http://search.proquest.com/docview/1845021130 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 121 2016 20 |
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Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 |
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The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) |
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The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) |
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The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m −2 . For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons. We recommend a protocol for estimating ERF in GCMs Error characteristics of ERF make diagnosing small forcings hard Some CMIP6 protocols may not work (AerCHemMIP in particular) |
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