Climate Sensitivity and Feedbacks of a New Coupled Model CAMS-CSM to Idealized CO_2 Forcing: A Comparison with CMIP5 Models
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摘要
Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences(CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5(CMIP5). Based on two idealized CO_2 forcing scenarios, i.e.,abruptly quadrupled CO_2 and CO_2 increasing 1% per year, the equilibrium climate sensitivity(ECS) and transient climate response(TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean(MME) of CMIP5 due to compensation of a relatively low ocean heat uptake(OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback(λSWCL) over the tropical Indo-Pacific Ocean.The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level(925–700 hPa)cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.
Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences(CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5(CMIP5). Based on two idealized CO_2 forcing scenarios, i.e.,abruptly quadrupled CO_2 and CO_2 increasing 1% per year, the equilibrium climate sensitivity(ECS) and transient climate response(TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean(MME) of CMIP5 due to compensation of a relatively low ocean heat uptake(OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback(λSWCL) over the tropical Indo-Pacific Ocean.The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level(925–700 hPa)cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.
Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences(CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5(CMIP5). Based on two idealized CO_2 forcing scenarios, i.e.,abruptly quadrupled CO_2 and CO_2 increasing 1% per year, the equilibrium climate sensitivity(ECS) and transient climate response(TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean(MME) of CMIP5 due to compensation of a relatively low ocean heat uptake(OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback(λSWCL) over the tropical Indo-Pacific Ocean.The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level(925–700 hPa)cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.
引文
Andrews,T.,J.M.Gregory,M.J.Webb,et al.,2012:Forcing,feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models.Geophys.Res.Lett.,39,L09712,doi:10.1029/2012GL051607.
Boucher,O.,D.Randall,P.Artaxo,et al.,2013:Clouds and aerosols.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,T.F.Stocker,D.H.Qin,G.K.Plattner,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,USA,1535 pp.
Bretherton,C.S.,2015:Insights into low-latitude cloud feedbacks from high-resolution models.Philos.Trans.Roy.Soc.A:Math.,Phys.Eng.Sci.,373,20140415,doi:10.1098/rsta.2014.0415.
Ceppi,P.,D.L.Hartmann,and M.J.Webb,2016:Mechanisms of the negative shortwave cloud feedback in middle to high latitudes.J.Climate,29,139-157,doi:10.1175/JCLI-D-15-0327.1.
Ceppi,P.,F.Brient,M.D.Zelinka,et al.,2017:Cloud feedback mechanisms and their representation in global climate models.WIREs Climate Change,8,e465,doi:10.1002/wcc.465.
Charney,J.G.,A.Arakawa,D.J.Baker,et al.,1979:Carbon Dioxide and Climate:A Scientific Assessment.Report of an Ad Hoc Study Group on Carbon Dioxide and Climate.National Academy of Sciences Press,Washington D.C.,22 pp,doi:10.17226/12181.
Chen,X.L.,T.J.Zhou,and Z.Guo,2014:Climate sensitivities of two versions of FGOALS model to idealized radiative forcing.Sci.China Earth Sci.,57,1363-1373,doi:10.1007/s11430-013-4692-4.
Cox,P.M.,C.Huntingford,and M.S.Williamson,2018:Emergent constraint on equilibrium climate sensitivity from global temperature variability.Nature,553,319-322,doi:10.1038/nature25450.
Dai,Y.J.,X.B.Zeng,R.E.Dickinson,et al.,2003:The common land model.Bull.Amer.Meteor.Soc.,84,1013-1024,doi:10.1175/BAMS-84-8-1013.
Flato,G.,J.Marotzke,B.Abiodun,et al.,2013:Evaluation of climate models.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,T.F.Stocker,D.H.Qin,G.K.Plattner,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,USA,1535 pp.
Gregory,J.,and M.Webb,2008:Tropospheric adjustment induces a cloud component in CO2 forcing.J.Climate,21,58-71,doi:10.1175/2007JCLI1834.1.
Gregory,J.M.,R.J.Stouffer,S.C.B.Raper,et al.,2002:An observationally based estimate of the climate sensitivity.J.Climate,15,3117-3121,doi:10.1175/1520-0442(2002)015<3117:AOBEOT>2.0.CO;2.
Gregory,J.M.,W.J.Ingram,M.A.Palmer,et al.,2004:A new method for diagnosing radiative forcing and climate sensitivity.Geophys.Res.Lett.,31,L03205,doi:10.1029/2003GL018747.
Hansen,J.,A.Lacis,D.Rind,et al.,1984:Climate sensitivity:Analysis of feedback mechanisms.Climate Processes and Climate Sensitivity,J.E.Hansen,and T.Takahashi,Eds.,American Geophysical Union,Washington D.C.,130-163.
Held,I.M.,and B.J.Soden,2000:Water vapor feedback and global warming.Annu.Rev.Energy Environ.,25,441-475,doi:10.1146/annurev.energy.25.1.441.
Knutti,R.,M.A.A.Rugenstein,and G.C.Hegerl,2017:Beyond equilibrium climate sensitivity.Nat.Geosci.,10,727-736,doi:10.1038/ngeo3017.
Li,C.,J.S.Von Storch,and J.Marotzke,2013:Deep-ocean heat uptake and equilibrium climate response.Climate Dyn.,40,1071-1086,doi:10.1007/s00382-012-1350-z.
Li,J.,H.M.Chen,X.Y.Rong,et al.,2018:How well can a climate model simulate an extreme precipitation event:A case study using the Transpose-AMIP experiment.J.Climate,31,6543-6556,doi:10.1175/JCLI-D-17-0801.1.
Meraner,K.,T.Mauritsen,and A.Voigt,2013:Robust increase in equilibrium climate sensitivity under global warming.Geophys.Res.Lett.,40,5944-5948,doi:10.1002/2013GL058118.
Murray,R.J.,1996:Explicit generation of orthogonal grids for ocean models.J.Comput.Phys.,126,251-273,doi:10.1006/jcph.1996.0136.
Myhre,G.,E.J.Highwood,K.P.Shine,et al.,1998:New estimates of radiative forcing due to well mixed greenhouse gases.Geophys.Res.Lett.,25,2715-2718,doi:10.1029/98GL01908.
Qu,X.,A.Hall,S.A.Klein,et al.,2014:On the spread of changes in marine low cloud cover in climate model simulations of the 21st century.Climate Dyn.,42,2603-2626,doi:10.1007/s00382-013-1945-z.
Randall,D.A.,R.A.Wood,S.Bony,et al.,2007:Climate models and their evaluation.Climate Change 2007:The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,S.Solomon,D.H.Qin,M.Manning,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,USA,996 pp.
Rieck,M.,L.Nuijens,and B.Stevens,2012:Marine boundary layer cloud feedbacks in a constant relative humidity atmosphere.J.Atmos.Sci.,69,2538-2550,doi:10.1175/JAS-D-11-0203.1.
Roe,G.,2009:Feedbacks,timescales,and seeing red.Annu.Rev.Earth Planet Sci.,37,93-115,doi:10.1146/annurev.earth.061008.134734.
Roeckner,E.,U.Schlese,J.Biercamp,et al.,1987:Cloud optical depth feedbacks and climate modelling.Nature,329,138-140,doi:10.1038/329138a0.
Roeckner,E.,G.B?uml,L.Bonaventura,et al.,2003:The Atmospheric General Circulation Model ECHAM5.Part I:Model Description.Report No.349,Max Planck Institute for Meteorology,Hamburg,Germany,127 pp.
Rong,X.Y.,J.Li,H.M.Chen,et al.,2018:The CAMS Climate System Model and a basic evaluation of its climatology and climate variability simulation.J.Meteor.Res.,32,839-861,doi:10.1007/s13351-018-8058-x.
Sherwood,S.C.,S.Bony,and J.L.Dufresne,2014:Spread in model climate sensitivity traced to atmospheric convective mixing.Nature,505,37-42,doi:10.1038/nature12829.
Slingo,J.M.,1987:The development and verification of a cloud prediction scheme for the ECMWF model.Quart.J.Roy.Meteor.Soc.,113,899-927,doi:10.1002/qj.49711347710.
Soden,B.J.,A.J.Broccoli,and R.S.Hemler,2004:On the use of cloud forcing to estimate cloud feedback.J.Climate,17,3661-3665,doi:10.1175/1520-0442(2004)017<3661:OTUOCF>2.0.CO;2.
Stephens,G.L.,2005:Cloud feedbacks in the climate system:Acritical review.J.Climate,18,237-273,doi:10.1175/JCLI-3243.1.
Stocker,T.F.,D.H.Qin,G.K.Plattner,et al.,2013:Technical summary.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,T.F.Stocker,D.H.Qin,G.K.Plattner,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,USA,1535 pp.
Taylor,K.E.,R.J.Stouffer,and G.A.Meehl,2012:An overview of CMIP5 and the experiment design.Bull.Amer.Meteor.Soc.,93,485-498,doi:10.1175/BAMS-D-11-00094.1.
Vial,J.,J.L.Dufresne,and S.Bony,2013:On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates.Climate Dyn.,41,3339-3362,doi:10.1007/s00382-013-1725-9.
Winton,M.,2000:A reformulated three-layer sea ice model.J.Atmos.Oceanic Technol.,17,525-531,doi:10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2.
Yu,R.C.,1994:A two-step shape-preserving advection scheme.Adv.Atmos.Sci.,11,479-490,doi:10.1007/BF02658169.
Zelinka,M.D.,S.A.Klein,and D.L.Hartmann,2012:Computing and partitioning cloud feedbacks using cloud property histograms.Part II:Attribution to changes in cloud amount,altitude,and optical depth.J.Climate,25,3736-3754,doi:10.1175/JCLI-D-11-00249.1.
Zhang,H.,G.Y.Shi,T.Nakajima,et al,2006a:The effects of the choice of the k-interval number on radiative calculations.J.Quant.Spectros.Radiat.Trans.,98,31-43,doi:10.1016/j.jqsrt.2005.05.090.
Zhang,H.,T.Suzuki,T.Nakajima,et al.,2006b:Effects of band division on radiative calculations.Opt.Eng.,45,016002,doi:10.1117/1.2160521.
Zhou,T.J.,and X.L.Chen,2015:Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback.J.Meteor.Res.,29,884-895,doi:10.1007/s13351-015-5036-4.
Boucher,O.,D.Randall,P.Artaxo,et al.,2013:Clouds and aerosols.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,T.F.Stocker,D.H.Qin,G.K.Plattner,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,USA,1535 pp.
Bretherton,C.S.,2015:Insights into low-latitude cloud feedbacks from high-resolution models.Philos.Trans.Roy.Soc.A:Math.,Phys.Eng.Sci.,373,20140415,doi:10.1098/rsta.2014.0415.
Ceppi,P.,D.L.Hartmann,and M.J.Webb,2016:Mechanisms of the negative shortwave cloud feedback in middle to high latitudes.J.Climate,29,139-157,doi:10.1175/JCLI-D-15-0327.1.
Ceppi,P.,F.Brient,M.D.Zelinka,et al.,2017:Cloud feedback mechanisms and their representation in global climate models.WIREs Climate Change,8,e465,doi:10.1002/wcc.465.
Charney,J.G.,A.Arakawa,D.J.Baker,et al.,1979:Carbon Dioxide and Climate:A Scientific Assessment.Report of an Ad Hoc Study Group on Carbon Dioxide and Climate.National Academy of Sciences Press,Washington D.C.,22 pp,doi:10.17226/12181.
Chen,X.L.,T.J.Zhou,and Z.Guo,2014:Climate sensitivities of two versions of FGOALS model to idealized radiative forcing.Sci.China Earth Sci.,57,1363-1373,doi:10.1007/s11430-013-4692-4.
Cox,P.M.,C.Huntingford,and M.S.Williamson,2018:Emergent constraint on equilibrium climate sensitivity from global temperature variability.Nature,553,319-322,doi:10.1038/nature25450.
Dai,Y.J.,X.B.Zeng,R.E.Dickinson,et al.,2003:The common land model.Bull.Amer.Meteor.Soc.,84,1013-1024,doi:10.1175/BAMS-84-8-1013.
Flato,G.,J.Marotzke,B.Abiodun,et al.,2013:Evaluation of climate models.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,T.F.Stocker,D.H.Qin,G.K.Plattner,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,USA,1535 pp.
Gregory,J.,and M.Webb,2008:Tropospheric adjustment induces a cloud component in CO2 forcing.J.Climate,21,58-71,doi:10.1175/2007JCLI1834.1.
Gregory,J.M.,R.J.Stouffer,S.C.B.Raper,et al.,2002:An observationally based estimate of the climate sensitivity.J.Climate,15,3117-3121,doi:10.1175/1520-0442(2002)015<3117:AOBEOT>2.0.CO;2.
Gregory,J.M.,W.J.Ingram,M.A.Palmer,et al.,2004:A new method for diagnosing radiative forcing and climate sensitivity.Geophys.Res.Lett.,31,L03205,doi:10.1029/2003GL018747.
Hansen,J.,A.Lacis,D.Rind,et al.,1984:Climate sensitivity:Analysis of feedback mechanisms.Climate Processes and Climate Sensitivity,J.E.Hansen,and T.Takahashi,Eds.,American Geophysical Union,Washington D.C.,130-163.
Held,I.M.,and B.J.Soden,2000:Water vapor feedback and global warming.Annu.Rev.Energy Environ.,25,441-475,doi:10.1146/annurev.energy.25.1.441.
Knutti,R.,M.A.A.Rugenstein,and G.C.Hegerl,2017:Beyond equilibrium climate sensitivity.Nat.Geosci.,10,727-736,doi:10.1038/ngeo3017.
Li,C.,J.S.Von Storch,and J.Marotzke,2013:Deep-ocean heat uptake and equilibrium climate response.Climate Dyn.,40,1071-1086,doi:10.1007/s00382-012-1350-z.
Li,J.,H.M.Chen,X.Y.Rong,et al.,2018:How well can a climate model simulate an extreme precipitation event:A case study using the Transpose-AMIP experiment.J.Climate,31,6543-6556,doi:10.1175/JCLI-D-17-0801.1.
Meraner,K.,T.Mauritsen,and A.Voigt,2013:Robust increase in equilibrium climate sensitivity under global warming.Geophys.Res.Lett.,40,5944-5948,doi:10.1002/2013GL058118.
Murray,R.J.,1996:Explicit generation of orthogonal grids for ocean models.J.Comput.Phys.,126,251-273,doi:10.1006/jcph.1996.0136.
Myhre,G.,E.J.Highwood,K.P.Shine,et al.,1998:New estimates of radiative forcing due to well mixed greenhouse gases.Geophys.Res.Lett.,25,2715-2718,doi:10.1029/98GL01908.
Qu,X.,A.Hall,S.A.Klein,et al.,2014:On the spread of changes in marine low cloud cover in climate model simulations of the 21st century.Climate Dyn.,42,2603-2626,doi:10.1007/s00382-013-1945-z.
Randall,D.A.,R.A.Wood,S.Bony,et al.,2007:Climate models and their evaluation.Climate Change 2007:The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,S.Solomon,D.H.Qin,M.Manning,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,USA,996 pp.
Rieck,M.,L.Nuijens,and B.Stevens,2012:Marine boundary layer cloud feedbacks in a constant relative humidity atmosphere.J.Atmos.Sci.,69,2538-2550,doi:10.1175/JAS-D-11-0203.1.
Roe,G.,2009:Feedbacks,timescales,and seeing red.Annu.Rev.Earth Planet Sci.,37,93-115,doi:10.1146/annurev.earth.061008.134734.
Roeckner,E.,U.Schlese,J.Biercamp,et al.,1987:Cloud optical depth feedbacks and climate modelling.Nature,329,138-140,doi:10.1038/329138a0.
Roeckner,E.,G.B?uml,L.Bonaventura,et al.,2003:The Atmospheric General Circulation Model ECHAM5.Part I:Model Description.Report No.349,Max Planck Institute for Meteorology,Hamburg,Germany,127 pp.
Rong,X.Y.,J.Li,H.M.Chen,et al.,2018:The CAMS Climate System Model and a basic evaluation of its climatology and climate variability simulation.J.Meteor.Res.,32,839-861,doi:10.1007/s13351-018-8058-x.
Sherwood,S.C.,S.Bony,and J.L.Dufresne,2014:Spread in model climate sensitivity traced to atmospheric convective mixing.Nature,505,37-42,doi:10.1038/nature12829.
Slingo,J.M.,1987:The development and verification of a cloud prediction scheme for the ECMWF model.Quart.J.Roy.Meteor.Soc.,113,899-927,doi:10.1002/qj.49711347710.
Soden,B.J.,A.J.Broccoli,and R.S.Hemler,2004:On the use of cloud forcing to estimate cloud feedback.J.Climate,17,3661-3665,doi:10.1175/1520-0442(2004)017<3661:OTUOCF>2.0.CO;2.
Stephens,G.L.,2005:Cloud feedbacks in the climate system:Acritical review.J.Climate,18,237-273,doi:10.1175/JCLI-3243.1.
Stocker,T.F.,D.H.Qin,G.K.Plattner,et al.,2013:Technical summary.Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,T.F.Stocker,D.H.Qin,G.K.Plattner,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,USA,1535 pp.
Taylor,K.E.,R.J.Stouffer,and G.A.Meehl,2012:An overview of CMIP5 and the experiment design.Bull.Amer.Meteor.Soc.,93,485-498,doi:10.1175/BAMS-D-11-00094.1.
Vial,J.,J.L.Dufresne,and S.Bony,2013:On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates.Climate Dyn.,41,3339-3362,doi:10.1007/s00382-013-1725-9.
Winton,M.,2000:A reformulated three-layer sea ice model.J.Atmos.Oceanic Technol.,17,525-531,doi:10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2.
Yu,R.C.,1994:A two-step shape-preserving advection scheme.Adv.Atmos.Sci.,11,479-490,doi:10.1007/BF02658169.
Zelinka,M.D.,S.A.Klein,and D.L.Hartmann,2012:Computing and partitioning cloud feedbacks using cloud property histograms.Part II:Attribution to changes in cloud amount,altitude,and optical depth.J.Climate,25,3736-3754,doi:10.1175/JCLI-D-11-00249.1.
Zhang,H.,G.Y.Shi,T.Nakajima,et al,2006a:The effects of the choice of the k-interval number on radiative calculations.J.Quant.Spectros.Radiat.Trans.,98,31-43,doi:10.1016/j.jqsrt.2005.05.090.
Zhang,H.,T.Suzuki,T.Nakajima,et al.,2006b:Effects of band division on radiative calculations.Opt.Eng.,45,016002,doi:10.1117/1.2160521.
Zhou,T.J.,and X.L.Chen,2015:Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback.J.Meteor.Res.,29,884-895,doi:10.1007/s13351-015-5036-4.