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气候模式 (查看源代码)

2022年3月15日 (二) 21:51的版本

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* σ is the Stefan–Boltzmann constant—approximately 5.67×10−8 J·K−4·m−2·s−1
 
* σ is the Stefan–Boltzmann constant—approximately 5.67×10−8 J·K−4·m−2·s−1
 
* ε is the effective emissivity of earth, about 0.612
 
* ε is the effective emissivity of earth, about 0.612
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* 一个非常简单的地球辐射平衡模型是：
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(1-a)S \pi r^2 = 4 \pi r^2 \epsilon \sigma T^4
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* 左边代表来自太阳的能量。
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* 右边代表来自地球的能量，根据 Stefan-Boltzmann 定律计算，假设模型假设温度 t，有时称为“地球的平衡温度”。
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* s 是太阳常数，即单位面积内的入射太阳辐射ー约1367 w m ー2
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* a 是地球的平均反照率，测量值为0.3。
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* r 为地球半径ー大约6.371 × 106m
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* π 为数学常数(3.141...)
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* σ 为斯蒂芬-玻尔兹曼常数ー大约5.67 × 10<sup>-8</sup>jk<sup>-4</sup>m<sup>-2</sup>s<sup>-1</sup>
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* ε 为地球的有效发射率，大约0.612
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The constant ''πr''<sup>2</sup> can be factored out, giving
 
The constant ''πr''<sup>2</sup> can be factored out, giving
    
:(1-a)S = 4 \epsilon \sigma T^4
 
:(1-a)S = 4 \epsilon \sigma T^4
 
Solving for the temperature,
 
Solving for the temperature,
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:<nowiki>T = \sqrt[4]{ \frac{(1-a)S}{4 \epsilon \sigma}}</nowiki> 常数 πr2可以分解出来，给出
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:<nowiki>T = \sqrt[4]{ \frac{(1-a)S}{4 \epsilon \sigma}}</nowiki>
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:This yields an apparent effective average earth temperature of Convert.<ref>[http://eospso.gsfc.nasa.gov/ftp_docs/lithographs/CERES_litho.pdf]  {{webarchive |url=https://web.archive.org/web/20130218204711/http://eospso.gsfc.nasa.gov/ftp_docs/lithographs/CERES_litho.pdf |date=18 February 2013 }}</ref> This is because the above equation represents the effective ''radiative'' temperature of the Earth (including the clouds and atmosphere).
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求解温度 ,
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:* 一个非常简单的地球辐射平衡模型是： (1-a)S \pi r^2 = 4 \pi r^2 \epsilon \sigma T^4
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:* 左边代表来自太阳的能量。
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<nowiki>T = sqrt [4]{ frac {(1-a) s }{4 epsilon sigma }}</nowiki>
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:* 右边代表来自地球的能量，根据 Stefan-Boltzmann 定律计算，假设模型假设温度T，有时称为“地球的平衡温度”。
−
 
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:* S是太阳常数，即单位面积内的入射太阳辐射约1367 W·m<sup>−2</sup>
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This yields an apparent effective average earth temperature of Convert.<ref>[http://eospso.gsfc.nasa.gov/ftp_docs/lithographs/CERES_litho.pdf ]  {{webarchive |url=https://web.archive.org/web/20130218204711/http://eospso.gsfc.nasa.gov/ftp_docs/lithographs/CERES_litho.pdf |date=18 February 2013 }}</ref> This is because the above equation represents the effective ''radiative'' temperature of the Earth (including the clouds and atmosphere).  
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:* a 是地球的平均反照率，测量值为0.3。
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:* r 为地球半径ー大约6.371 × 106m
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这样得到转换的表观有效地球平均温度。这是因为上面的方程代表了地球的有效辐射温度(包括云和大气)。
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:* π 为数学常数(3.141...)
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:* σ 为斯蒂芬-玻尔兹曼常数ー大约5.67 × 10<sup>-8</sup>jk<sup>-4</sup>m<sup>-2</sup>s<sup>-1</sup>
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This very simple model is quite instructive. For example, it easily determines the effect on average earth temperature of changes in solar constant or change of albedo or effective earth emissivity.
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:* ε 为地球的有效发射率，大约0.612
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::常数 πr2可以分解出来，给出
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::求解温度 , <nowiki>T = sqrt [4]{ frac {(1-a) s }{4 epsilon sigma }}</nowiki>  这样得到转换的表观有效地球平均温度。这是因为上面的方程代表了地球的有效辐射温度(包括云和大气)。
    
This very simple model is quite instructive. For example, it easily determines the effect on average earth temperature of changes in solar constant or change of albedo or effective earth emissivity.
 
This very simple model is quite instructive. For example, it easily determines the effect on average earth temperature of changes in solar constant or change of albedo or effective earth emissivity.
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The average emissivity of the earth is readily estimated from available data. The emissivities of terrestrial surfaces are all in the range of 0.96 to 0.99<ref>{{cite web|url=http://www.icess.ucsb.edu/modis/EMIS/html/seawater.html|title=Seawater Samples - Emissivities|work=ucsb.edu}}</ref><ref>{{cite journal |doi=10.1175/JCLI3720.1 |vauthors=Jin M, Liang S |title=An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations |journal=J. Climate |volume=19 |issue=12 |pages=2867–81 |date=15 June 2006 |url=http://www.glue.umd.edu/~sliang/papers/Jin2006.emissivity.pdf|bibcode = 2006JCli...19.2867J }}</ref> (except for some small desert areas which may be as low as 0.7). Clouds, however, which cover about half of the earth's surface, have an average emissivity of about 0.5<ref>{{cite conference |author1=T.R. Shippert |author2=S.A. Clough |author3=P.D. Brown |author4=W.L. Smith |author5=R.O. Knuteson |author6=S.A. Ackerman |title=Spectral Cloud Emissivities from LBLRTM/AERI QME  |book-title=Proceedings of the Eighth Atmospheric Radiation Measurement (ARM) Science Team Meeting March 1998 Tucson, Arizona |url=http://www.arm.gov/publications/proceedings/conf08/extended_abs/shippert_tr.pdf }}</ref> (which must be reduced by the fourth power of the ratio of cloud absolute temperature to average earth absolute temperature) and an average cloud temperature of about {{convert|258|K|abbr=on}}.<ref>{{cite conference |author1=A.G. Gorelik |author2=V. Sterljadkin |author3=E. Kadygrov |author4=A. Koldaev |title=Microwave and IR Radiometry for Estimation of Atmospheric Radiation Balance and Sea Ice Formation |book-title=Proceedings of the Eleventh Atmospheric Radiation Measurement (ARM) Science Team Meeting March 2001 Atlanta, Georgia |url=http://www.arm.gov/publications/proceedings/conf11/extended_abs/gorelik_ag.pdf }}</ref> Taking all this properly into account results in an effective earth emissivity of about 0.64 (earth average temperature {{convert|285|K|abbr=on}}).
 
The average emissivity of the earth is readily estimated from available data. The emissivities of terrestrial surfaces are all in the range of 0.96 to 0.99<ref>{{cite web|url=http://www.icess.ucsb.edu/modis/EMIS/html/seawater.html|title=Seawater Samples - Emissivities|work=ucsb.edu}}</ref><ref>{{cite journal |doi=10.1175/JCLI3720.1 |vauthors=Jin M, Liang S |title=An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations |journal=J. Climate |volume=19 |issue=12 |pages=2867–81 |date=15 June 2006 |url=http://www.glue.umd.edu/~sliang/papers/Jin2006.emissivity.pdf|bibcode = 2006JCli...19.2867J }}</ref> (except for some small desert areas which may be as low as 0.7). Clouds, however, which cover about half of the earth's surface, have an average emissivity of about 0.5<ref>{{cite conference |author1=T.R. Shippert |author2=S.A. Clough |author3=P.D. Brown |author4=W.L. Smith |author5=R.O. Knuteson |author6=S.A. Ackerman |title=Spectral Cloud Emissivities from LBLRTM/AERI QME  |book-title=Proceedings of the Eighth Atmospheric Radiation Measurement (ARM) Science Team Meeting March 1998 Tucson, Arizona |url=http://www.arm.gov/publications/proceedings/conf08/extended_abs/shippert_tr.pdf }}</ref> (which must be reduced by the fourth power of the ratio of cloud absolute temperature to average earth absolute temperature) and an average cloud temperature of about {{convert|258|K|abbr=on}}.<ref>{{cite conference |author1=A.G. Gorelik |author2=V. Sterljadkin |author3=E. Kadygrov |author4=A. Koldaev |title=Microwave and IR Radiometry for Estimation of Atmospheric Radiation Balance and Sea Ice Formation |book-title=Proceedings of the Eleventh Atmospheric Radiation Measurement (ARM) Science Team Meeting March 2001 Atlanta, Georgia |url=http://www.arm.gov/publications/proceedings/conf11/extended_abs/gorelik_ag.pdf }}</ref> Taking all this properly into account results in an effective earth emissivity of about 0.64 (earth average temperature {{convert|285|K|abbr=on}}).
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The average emissivity of the earth is readily estimated from available data. The emissivities of terrestrial surfaces are all in the range of 0.96 to 0.99 (except for some small desert areas which may be as low as 0.7). Clouds, however, which cover about half of the earth's surface, have an average emissivity of about 0.5 (which must be reduced by the fourth power of the ratio of cloud absolute temperature to average earth absolute temperature) and an average cloud temperature of about . Taking all this properly into account results in an effective earth emissivity of about 0.64 (earth average temperature ).
      
地球的平均比辐射率很容易从现有数据中估计出来。陆地表面的放射系数均在0.96ー0.99之间(除少数小沙漠地区可能低至0.7)。然而，覆盖地球表面大约一半的云层，其平均发射率约为0.5(必须用云的绝对温度与地球平均绝对温度之比的四次方减少) ，而云的平均温度约为0.5。适当地考虑这些因素，得到的有效地球发射率约为0.64(地球平均温度)。
 
地球的平均比辐射率很容易从现有数据中估计出来。陆地表面的放射系数均在0.96ー0.99之间(除少数小沙漠地区可能低至0.7)。然而，覆盖地球表面大约一半的云层，其平均发射率约为0.5(必须用云的绝对温度与地球平均绝对温度之比的四次方减少) ，而云的平均温度约为0.5。适当地考虑这些因素，得到的有效地球发射率约为0.64(地球平均温度)。
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This simple model readily determines the effect of changes in solar output or change of earth albedo or effective earth emissivity on average earth temperature. It says nothing, however about what might cause these things to change. Zero-dimensional models do not address the temperature distribution on the earth or the factors that move energy about the earth.
      
This simple model readily determines the effect of changes in solar output or change of earth albedo or effective earth emissivity on average earth temperature. It says nothing, however about what might cause these things to change. Zero-dimensional models do not address the temperature distribution on the earth or the factors that move energy about the earth.
 
This simple model readily determines the effect of changes in solar output or change of earth albedo or effective earth emissivity on average earth temperature. It says nothing, however about what might cause these things to change. Zero-dimensional models do not address the temperature distribution on the earth or the factors that move energy about the earth.
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== Radiative-convective models ==
 
== Radiative-convective models ==
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== Radiative-convective models ==
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== = = 辐射-对流模式 = ==
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= = = 辐射-对流模式 = =  
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The zero-dimensional model above, using the solar constant and given average earth temperature, determines the effective earth emissivity of long wave radiation emitted to space. This can be refined in the vertical to a one-dimensional radiative-convective model, which considers two processes of energy transport:
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The zero-dimensional model above, using the solar constant and given average earth temperature, determines the effective earth emissivity of long wave radiation emitted to space. This can be refined in the vertical to a one-dimensional radiative-convective model, which considers two processes of energy transport:
 
The zero-dimensional model above, using the solar constant and given average earth temperature, determines the effective earth emissivity of long wave radiation emitted to space. This can be refined in the vertical to a one-dimensional radiative-convective model, which considers two processes of energy transport:
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上述零维模型利用太阳常数和给定的地球平均温度，确定了向空间发射的长波辐射的有效地球发射率。这可以在垂直于一维辐射对流模式的方向上加以改进，该模式考虑两个能量输送过程:
      
* upwelling and downwelling radiative transfer through atmospheric layers that both absorb and emit infrared radiation
 
* upwelling and downwelling radiative transfer through atmospheric layers that both absorb and emit infrared radiation
 
* upward transport of heat by convection (especially important in the lower [[troposphere]]).
 
* upward transport of heat by convection (especially important in the lower [[troposphere]]).
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* upwelling and downwelling radiative transfer through atmospheric layers that both absorb and emit infrared radiation
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* upward transport of heat by convection (especially important in the lower troposphere).
      +
上述零维模型利用太阳常数和给定的地球平均温度，确定了向空间发射的长波辐射的有效地球发射率。这可以在垂直于一维辐射对流模式的方向上加以改进，该模式考虑两个能量输送过程:
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* 通过大气层的上升流和下降辐射转移，这些大气层通过对流层吸收和释放红外线  
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* 通过大气层的上升流和下降辐射转移，这些大气层通过对流层吸收和释放红外线
 
* 向上传送热量(在对流层下部尤其重要)。
 
* 向上传送热量(在对流层下部尤其重要)。
    
The radiative-convective models have advantages over the simple model: they can determine the effects of varying [[greenhouse gas]] concentrations on effective emissivity and therefore the surface temperature. But added parameters are needed to determine local emissivity and albedo and address the factors that move energy about the earth.
 
The radiative-convective models have advantages over the simple model: they can determine the effects of varying [[greenhouse gas]] concentrations on effective emissivity and therefore the surface temperature. But added parameters are needed to determine local emissivity and albedo and address the factors that move energy about the earth.
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The radiative-convective models have advantages over the simple model: they can determine the effects of varying greenhouse gas concentrations on effective emissivity and therefore the surface temperature. But added parameters are needed to determine local emissivity and albedo and address the factors that move energy about the earth.
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辐射-对流模式比简单模式有优点: 它们可以确定不同温室气体浓度对有效发射率的影响，因此也可以确定地表温度。但是需要增加参数来确定局部辐射系数和反照率，并解决地球周围能量移动的因素。
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辐射-对流模式比简单模式有优点: 它们可以确定不同温室气体浓度对有效发射率的影响，因此也可以确定地表温度。但是需要增加参数来确定局部发射率和反照率，并解决地球周围能量移动的因素。
      
Effect of ice-albedo feedback on global sensitivity in a one-dimensional radiative-convective climate model.<ref>{{cite web|url=http://pubs.giss.nasa.gov/cgi-bin/abstract.cgi?id=wa03100m|archive-url=https://archive.today/20120730021359/http://pubs.giss.nasa.gov/cgi-bin/abstract.cgi?id=wa03100m|url-status=dead|archive-date=2012-07-30|title=Pubs.GISS: Wang and Stone 1980: Effect of ice-albedo feedback on global sensitivity in a one-dimensional...|work=nasa.gov}}</ref><ref>{{Cite journal
 
Effect of ice-albedo feedback on global sensitivity in a one-dimensional radiative-convective climate model.<ref>{{cite web|url=http://pubs.giss.nasa.gov/cgi-bin/abstract.cgi?id=wa03100m|archive-url=https://archive.today/20120730021359/http://pubs.giss.nasa.gov/cgi-bin/abstract.cgi?id=wa03100m|url-status=dead|archive-date=2012-07-30|title=Pubs.GISS: Wang and Stone 1980: Effect of ice-albedo feedback on global sensitivity in a one-dimensional...|work=nasa.gov}}</ref><ref>{{Cite journal
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   | doi-access = free
 
   | doi-access = free
 
   }}</ref><ref>{{cite web|url=http://www.grida.no/climate/ipcc_tar/wg1/258.htm|title=Climate Change 2001: The Scientific Basis|work=grida.no|url-status=dead|archive-url=https://web.archive.org/web/20030325024912/http://www.grida.no/climate/ipcc_tar/wg1/258.htm|archive-date=25 March 2003}}</ref>
 
   }}</ref><ref>{{cite web|url=http://www.grida.no/climate/ipcc_tar/wg1/258.htm|title=Climate Change 2001: The Scientific Basis|work=grida.no|url-status=dead|archive-url=https://web.archive.org/web/20030325024912/http://www.grida.no/climate/ipcc_tar/wg1/258.htm|archive-date=25 March 2003}}</ref>
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Effect of ice-albedo feedback on global sensitivity in a one-dimensional radiative-convective climate model.
      
冰反照率反馈对一维辐射对流气候模式全球敏感性的影响。
 
冰反照率反馈对一维辐射对流气候模式全球敏感性的影响。
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== Higher-dimension models ==
 
== Higher-dimension models ==
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== Higher-dimension models ==
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== = = 高维模型 = = ==
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= = = 高维模型 = = =  
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The zero-dimensional model may be expanded to consider the energy transported horizontally in the atmosphere. This kind of model may well be [[Zonal and meridional|zonally]] averaged. This model has the advantage of allowing a rational dependence of local albedo and emissivity on temperature – the poles can be allowed to be icy and the equator warm – but the lack of true dynamics means that horizontal transports have to be specified.<ref>{{cite web|url=http://www.shodor.org/master/environmental/general/energy/application.html|title=Energy Balance Models|work=shodor.org}}</ref>
 
The zero-dimensional model may be expanded to consider the energy transported horizontally in the atmosphere. This kind of model may well be [[Zonal and meridional|zonally]] averaged. This model has the advantage of allowing a rational dependence of local albedo and emissivity on temperature – the poles can be allowed to be icy and the equator warm – but the lack of true dynamics means that horizontal transports have to be specified.<ref>{{cite web|url=http://www.shodor.org/master/environmental/general/energy/application.html|title=Energy Balance Models|work=shodor.org}}</ref>
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The zero-dimensional model may be expanded to consider the energy transported horizontally in the atmosphere. This kind of model may well be zonally averaged. This model has the advantage of allowing a rational dependence of local albedo and emissivity on temperature – the poles can be allowed to be icy and the equator warm – but the lack of true dynamics means that horizontal transports have to be specified.
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可以扩展零维模型以考虑大气中水平输送的能量。这种模型很可能是纬向平均的。这个模型的优点是允许局部反照率和辐射系数与温度有合理的依赖关系——两极可以是冰的，赤道可以是暖的——但缺乏真正的动力学，这意味着必须具体说明水平输送。
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可以扩展零维模型以考虑大气中水平输送的能量。这种模型很可能是纬向平均的。这个模型的优点是允许局部反照率和发射率与温度有合理的依赖关系——两极可以是冰的，赤道可以是暖的——但缺乏真正的动力学，这意味着必须具体说明水平输送。
      
== EMICs (Earth-system models of intermediate complexity) ==
 
== EMICs (Earth-system models of intermediate complexity) ==
 
{{Main|Earth systems model of intermediate complexity}}
 
{{Main|Earth systems model of intermediate complexity}}
 
Depending on the nature of questions asked and the pertinent time scales, there are, on the one extreme,  conceptual, more inductive models, and, on the other extreme, [[general circulation model]]s operating at the highest spatial and temporal resolution currently feasible. Models of intermediate complexity bridge the gap. One example is the Climber-3 model. Its atmosphere is a 2.5-dimensional statistical-dynamical model with 7.5° × 22.5° resolution and time step of half a day;  the ocean is MOM-3 ([[Modular Ocean Model]]) with a 3.75° × 3.75° grid and 24 vertical levels.<ref>{{cite web|url=http://www.pik-potsdam.de/emics/|title=emics1|work=pik-potsdam.de}}</ref>
 
Depending on the nature of questions asked and the pertinent time scales, there are, on the one extreme,  conceptual, more inductive models, and, on the other extreme, [[general circulation model]]s operating at the highest spatial and temporal resolution currently feasible. Models of intermediate complexity bridge the gap. One example is the Climber-3 model. Its atmosphere is a 2.5-dimensional statistical-dynamical model with 7.5° × 22.5° resolution and time step of half a day;  the ocean is MOM-3 ([[Modular Ocean Model]]) with a 3.75° × 3.75° grid and 24 vertical levels.<ref>{{cite web|url=http://www.pik-potsdam.de/emics/|title=emics1|work=pik-potsdam.de}}</ref>
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Depending on the nature of questions asked and the pertinent time scales, there are, on the one extreme,  conceptual, more inductive models, and, on the other extreme, general circulation models operating at the highest spatial and temporal resolution currently feasible. Models of intermediate complexity bridge the gap. One example is the Climber-3 model. Its atmosphere is a 2.5-dimensional statistical-dynamical model with 7.5° × 22.5° resolution and time step of half a day;  the ocean is MOM-3 (Modular Ocean Model) with a 3.75° × 3.75° grid and 24 vertical levels.
      
根据所提问题的性质和相关的时间尺度，在一个极端，概念上有更多的归纳模型，而在另一个极端，大气环流模型在最高的空间和时间解析度上运行，目前是可行的。中等复杂性模型弥补了这一差距。其中一个例子是 climber-3模型。其大气为2.5维统计动力学模式，分辨率为7.5 ° × 22.5 ° ，时间步长为半天，海洋为模块化海洋模式 MOM-3，网格为3.75 ° × 3.75 ° ，垂直高度为24 ° 。
 
根据所提问题的性质和相关的时间尺度，在一个极端，概念上有更多的归纳模型，而在另一个极端，大气环流模型在最高的空间和时间解析度上运行，目前是可行的。中等复杂性模型弥补了这一差距。其中一个例子是 climber-3模型。其大气为2.5维统计动力学模式，分辨率为7.5 ° × 22.5 ° ，时间步长为半天，海洋为模块化海洋模式 MOM-3，网格为3.75 ° × 3.75 ° ，垂直高度为24 ° 。
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== GCMs (global climate models or general circulation models) ==
 
== GCMs (global climate models or general circulation models) ==
 
{{Main|General circulation model}}
 
{{Main|General circulation model}}
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General Circulation Models (GCMs) discretise the equations for fluid motion and energy transfer and integrate these over time. Unlike simpler models, GCMs divide the atmosphere and/or oceans into grids of discrete "cells", which represent computational units. Unlike simpler models which make mixing assumptions, processes internal to a cell—such as convection—that occur on scales too small to be resolved directly are parameterised at the cell level, while other functions govern the interface between cells.
      
General Circulation Models (GCMs) discretise the equations for fluid motion and energy transfer and integrate these over time. Unlike simpler models, GCMs divide the atmosphere and/or oceans into grids of discrete "cells", which represent computational units. Unlike simpler models which make mixing assumptions, processes internal to a cell—such as convection—that occur on scales too small to be resolved directly are parameterised at the cell level, while other functions govern the interface between cells.
 
General Circulation Models (GCMs) discretise the equations for fluid motion and energy transfer and integrate these over time. Unlike simpler models, GCMs divide the atmosphere and/or oceans into grids of discrete "cells", which represent computational units. Unlike simpler models which make mixing assumptions, processes internal to a cell—such as convection—that occur on scales too small to be resolved directly are parameterised at the cell level, while other functions govern the interface between cells.
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Atmospheric GCMs (AGCMs) model the atmosphere and impose [[sea surface temperature]]s as boundary conditions. Coupled atmosphere-ocean GCMs (AOGCMs, e.g. [[HadCM3]], [[EdGCM]], [[GFDL CM2.X]], ARPEGE-Climat)<ref>[http://www.cnrm.meteo.fr/gmgec/site_engl/arpege/arpege_en.html ]  {{webarchive |url=https://web.archive.org/web/20070927215849/http://www.cnrm.meteo.fr/gmgec/site_engl/arpege/arpege_en.html |date=27 September 2007 }}</ref> combine the two models. The first general circulation climate model that combined both oceanic and atmospheric processes was developed in the late 1960s at the [[NOAA]] [[Geophysical Fluid Dynamics Laboratory]]<ref>{{cite web|url=http://celebrating200years.noaa.gov/breakthroughs/climate_model/welcome.html|title=NOAA 200th Top Tens: Breakthroughs: The First Climate Model|work=noaa.gov}}</ref> AOGCMs represent the pinnacle of complexity in climate models and internalise as many processes as possible. However, they are still under development and uncertainties remain.  They may be coupled to models of other processes, such as the [[carbon cycle]], so as to better model feedback effects. Such integrated multi-system models are sometimes referred to as either "earth system models" or "global climate models."
 
Atmospheric GCMs (AGCMs) model the atmosphere and impose [[sea surface temperature]]s as boundary conditions. Coupled atmosphere-ocean GCMs (AOGCMs, e.g. [[HadCM3]], [[EdGCM]], [[GFDL CM2.X]], ARPEGE-Climat)<ref>[http://www.cnrm.meteo.fr/gmgec/site_engl/arpege/arpege_en.html ]  {{webarchive |url=https://web.archive.org/web/20070927215849/http://www.cnrm.meteo.fr/gmgec/site_engl/arpege/arpege_en.html |date=27 September 2007 }}</ref> combine the two models. The first general circulation climate model that combined both oceanic and atmospheric processes was developed in the late 1960s at the [[NOAA]] [[Geophysical Fluid Dynamics Laboratory]]<ref>{{cite web|url=http://celebrating200years.noaa.gov/breakthroughs/climate_model/welcome.html|title=NOAA 200th Top Tens: Breakthroughs: The First Climate Model|work=noaa.gov}}</ref> AOGCMs represent the pinnacle of complexity in climate models and internalise as many processes as possible. However, they are still under development and uncertainties remain.  They may be coupled to models of other processes, such as the [[carbon cycle]], so as to better model feedback effects. Such integrated multi-system models are sometimes referred to as either "earth system models" or "global climate models."
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Atmospheric GCMs (AGCMs) model the atmosphere and impose sea surface temperatures as boundary conditions. Coupled atmosphere-ocean GCMs (AOGCMs, e.g. HadCM3, EdGCM, GFDL CM2.X, ARPEGE-Climat)  combine the two models. The first general circulation climate model that combined both oceanic and atmospheric processes was developed in the late 1960s at the NOAA Geophysical Fluid Dynamics Laboratory AOGCMs represent the pinnacle of complexity in climate models and internalise as many processes as possible. However, they are still under development and uncertainties remain.  They may be coupled to models of other processes, such as the carbon cycle, so as to better model feedback effects. Such integrated multi-system models are sometimes referred to as either "earth system models" or "global climate models."
      
大气层大气环流模式(AGCMs)模拟大气，并把海面温度作为边界条件。大气-海洋耦合大气环流模式。结合了这两个模型。第一个将海洋和大气过程结合在一起的大气环流气候模式是在20世纪60年代末由美国国家海洋和大气管理局的地球物理流体动力学实验室气候模式发展起来的，它代表了气候模式复杂性的顶峰，并且尽可能地内化了许多过程。然而，它们仍在发展之中，不确定性仍然存在。它们可以与碳循环等其他过程的模型耦合，以便更好地模拟反馈效应。这种综合的多系统模型有时被称为“地球系统模型”或“全球气候模型”
 
大气层大气环流模式(AGCMs)模拟大气，并把海面温度作为边界条件。大气-海洋耦合大气环流模式。结合了这两个模型。第一个将海洋和大气过程结合在一起的大气环流气候模式是在20世纪60年代末由美国国家海洋和大气管理局的地球物理流体动力学实验室气候模式发展起来的，它代表了气候模式复杂性的顶峰，并且尽可能地内化了许多过程。然而，它们仍在发展之中，不确定性仍然存在。它们可以与碳循环等其他过程的模型耦合，以便更好地模拟反馈效应。这种综合的多系统模型有时被称为“地球系统模型”或“全球气候模型”
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== Research and development ==
      
== Research and development ==
 
== Research and development ==
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There are three major types of institution where climate models are developed, implemented and used:
 
There are three major types of institution where climate models are developed, implemented and used:
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There are three major types of institution where climate models are developed, implemented and used:
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开发、实施和使用气候模型的机构主要有三类:
      
* National meteorological services. Most national weather services have a [[climatology]] section.
 
* National meteorological services. Most national weather services have a [[climatology]] section.
 
* Universities. Relevant departments include atmospheric sciences, meteorology, climatology, and geography.
 
* Universities. Relevant departments include atmospheric sciences, meteorology, climatology, and geography.
 
* National and international research laboratories. Examples include the [[National Center for Atmospheric Research]] (NCAR, in [[Boulder, Colorado]], USA), the [[Geophysical Fluid Dynamics Laboratory]] (GFDL, in [[Princeton, New Jersey]], USA), [[Los Alamos National Laboratory]], the [[Hadley Centre for Climate Prediction and Research]] (in [[Exeter]], UK), the [[Max Planck Institute for Meteorology]] in Hamburg, Germany, or the [[Laboratoire des sciences du climat et de l'environnement|Laboratoire des Sciences du Climat et de l'Environnement]] (LSCE), France, to name but a few.
 
* National and international research laboratories. Examples include the [[National Center for Atmospheric Research]] (NCAR, in [[Boulder, Colorado]], USA), the [[Geophysical Fluid Dynamics Laboratory]] (GFDL, in [[Princeton, New Jersey]], USA), [[Los Alamos National Laboratory]], the [[Hadley Centre for Climate Prediction and Research]] (in [[Exeter]], UK), the [[Max Planck Institute for Meteorology]] in Hamburg, Germany, or the [[Laboratoire des sciences du climat et de l'environnement|Laboratoire des Sciences du Climat et de l'Environnement]] (LSCE), France, to name but a few.
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开发、实施和使用气候模型的机构主要有三类:
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* National meteorological services. Most national weather services have a climatology section.
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* Universities. Relevant departments include atmospheric sciences, meteorology, climatology, and geography.
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* National and international research laboratories. Examples include the National Center for Atmospheric Research (NCAR, in Boulder, Colorado, USA), the Geophysical Fluid Dynamics Laboratory (GFDL, in Princeton, New Jersey, USA), Los Alamos National Laboratory, the Hadley Centre for Climate Prediction and Research (in Exeter, UK), the Max Planck Institute for Meteorology in Hamburg, Germany, or the Laboratoire des Sciences du Climat et de l'Environnement (LSCE), France, to name but a few.
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* 国家气象部门。大多数国家气象服务机构都有气候学部分。
 
* 国家气象部门。大多数国家气象服务机构都有气候学部分。
 
* 大学。相关部门包括大气科学、气象学、气候学和地理学。
 
* 大学。相关部门包括大气科学、气象学、气候学和地理学。
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The [[World Climate Research Programme]] (WCRP), hosted by the [[World Meteorological Organization]] (WMO), coordinates research activities on climate modelling worldwide.
 
The [[World Climate Research Programme]] (WCRP), hosted by the [[World Meteorological Organization]] (WMO), coordinates research activities on climate modelling worldwide.
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The World Climate Research Programme (WCRP), hosted by the World Meteorological Organization (WMO), coordinates research activities on climate modelling worldwide.
      
世界气候研究计划由世界气象组织气象组织主办，负责协调全球气候建模的研究活动。
 
世界气候研究计划由世界气象组织气象组织主办，负责协调全球气候建模的研究活动。
    
A 2012 [[U.S. National Research Council]] report discussed how the large and diverse U.S. climate modeling enterprise could evolve to become more unified.<ref>{{cite web | title=U.S. National Research Council Report, ''A National Strategy for Advancing Climate Modeling'' | url=http://dels.nas.edu/Report/National-Strategy-Advancing-Climate/13430 | access-date=18 January 2021 | archive-date=3 October 2012 | archive-url=https://web.archive.org/web/20121003043232/http://dels.nas.edu/Report/National-Strategy-Advancing-Climate/13430 | url-status=dead }}</ref> Efficiencies could be gained by developing a common software infrastructure shared by all U.S. climate researchers, and holding an annual climate modeling forum, the report found.<ref>{{cite web | title=U.S. National Research Council Report-in-Brief, ''A National Strategy for Advancing Climate Modeling'' | url=http://dels.nas.edu/Materials/Report-In-Brief/4291-Climate-Modeling | access-date=3 October 2012 | archive-date=18 October 2012 | archive-url=https://web.archive.org/web/20121018071324/http://dels.nas.edu/Materials/Report-In-Brief/4291-Climate-Modeling | url-status=dead }}</ref>
 
A 2012 [[U.S. National Research Council]] report discussed how the large and diverse U.S. climate modeling enterprise could evolve to become more unified.<ref>{{cite web | title=U.S. National Research Council Report, ''A National Strategy for Advancing Climate Modeling'' | url=http://dels.nas.edu/Report/National-Strategy-Advancing-Climate/13430 | access-date=18 January 2021 | archive-date=3 October 2012 | archive-url=https://web.archive.org/web/20121003043232/http://dels.nas.edu/Report/National-Strategy-Advancing-Climate/13430 | url-status=dead }}</ref> Efficiencies could be gained by developing a common software infrastructure shared by all U.S. climate researchers, and holding an annual climate modeling forum, the report found.<ref>{{cite web | title=U.S. National Research Council Report-in-Brief, ''A National Strategy for Advancing Climate Modeling'' | url=http://dels.nas.edu/Materials/Report-In-Brief/4291-Climate-Modeling | access-date=3 October 2012 | archive-date=18 October 2012 | archive-url=https://web.archive.org/web/20121018071324/http://dels.nas.edu/Materials/Report-In-Brief/4291-Climate-Modeling | url-status=dead }}</ref>
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A 2012 U.S. National Research Council report discussed how the large and diverse U.S. climate modeling enterprise could evolve to become more unified. Efficiencies could be gained by developing a common software infrastructure shared by all U.S. climate researchers, and holding an annual climate modeling forum, the report found.
      
2012年美国国家研究委员会的一份报告讨论了美国庞大而多样化的气候模拟事业如何能够进化得更加统一。报告发现，通过开发一个由所有美国气候研究人员共享的通用软件基础设施，并举办一个年度气候模型论坛，效率可以得到提高。
 
2012年美国国家研究委员会的一份报告讨论了美国庞大而多样化的气候模拟事业如何能够进化得更加统一。报告发现，通过开发一个由所有美国气候研究人员共享的通用软件基础设施，并举办一个年度气候模型论坛，效率可以得到提高。
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*[[CICE (sea ice model)|CICE sea ice model]]
 
*[[CICE (sea ice model)|CICE sea ice model]]
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* Atmospheric reanalysis
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= = = =其他参考 =  
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* General circulation model
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* Atmospheric Radiation Measurement (ARM) (in the US)
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* Climateprediction.net
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* GFDL CM2.X
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* GO-ESSP
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* Numerical Weather Prediction
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* Static atmospheric model
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* Tropical cyclone prediction model
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* Verification and validation of computer simulation models
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*CICE sea ice model
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= = = = =  
   
* 大气重新分析  
 
* 大气重新分析  
 
* 大气环流模式  
 
* 大气环流模式  
 
* 大气辐射测量(ARM)(美国)  
 
* 大气辐射测量(ARM)(美国)  
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* climateprediction.net 
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* 气候预测网站
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* GFDL cm2. x  
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* 地球物理流体动力学实验室GFDL cm2. x Geophysical Fluid Dynamics Laboratory
 
* GO-ESSP  
 
* GO-ESSP  
 
* 数值天气预报  
 
* 数值天气预报  
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