中等复杂程度地球系统模型

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Earth systems models of intermediate complexity (EMICs) form an important class of climate models, primarily used to investigate the earth's systems on long timescales or at reduced computational cost. This is mostly achieved through operation at lower temporal and spatial resolution than more comprehensive general circulation models (GCMs). Due to the nonlinear relationship between spatial resolution and model run-speed, modest reductions in resolution can lead to large improvements in model run-speed.[1] This has historically allowed the inclusion of previously unincorporated earth-systems such as ice sheets and carbon cycle feedbacks. These benefits are conventionally understood to come at the cost of some model accuracy. However, the degree to which higher resolution models improve accuracy rather than simply precision is contested.[2][3]


Earth systems models of intermediate complexity (EMICs) form an important class of climate models, primarily used to investigate the earth's systems on long timescales or at reduced computational cost. This is mostly achieved through operation at lower temporal and spatial resolution than more comprehensive general circulation models (GCMs). Due to the nonlinear relationship between spatial resolution and model run-speed, modest reductions in resolution can lead to large improvements in model run-speed. This has historically allowed the inclusion of previously unincorporated earth-systems such as ice sheets and carbon cycle feedbacks. These benefits are conventionally understood to come at the cost of some model accuracy. However, the degree to which higher resolution models improve accuracy rather than simply precision is contested.Jakob, C. (2014). Going back to basics. Nature Climate Change, 4:1042–1045.Lovejoy, S. (2015). A voyage through scales, a missing quadrillion and why the climate is not what you expect. Climate Dynamics, 44(11):3187–3210.

中等复杂度的地球系统模型(emic)是一类重要的气候模型，主要用于研究地球系统的长时间尺度或减少计算成本。这主要是通过在较低的时间和空间分辨率操作比更全面的大气环流模式(GCMs)。由于空间分辨率和模型运行速度之间的非线性关系，分辨率的适度降低可以导致模型运行速度的大幅度提高。这历史上允许包括以前未合并的地球系统，如冰原和碳循环反馈。这些好处通常被认为是以牺牲一些模型的准确性为代价的。然而，高分辨率模型提高精度而不是简单精度的程度是有争议的。返璞归真。4:1042-1045. Lovejoy，s. (2015) .一次通过天平的航行，一个失踪的千万亿，以及为什么气候不是你所期望的。Climate Dynamics, 44(11):3187–3210.

History

History

= 历史 =

Computing power had become sufficiently powerful by the middle of the 20th century to allow mass and energy flow models on a vertical and horizontally resolved grid.[4] By 1955 these advances had produced what is recognisable now as a primitive GCM (Phillips prototype [5]). Even at this early stage, a lack of computing power formed a significant barrier to entry and limitation on model-time.

Computing power had become sufficiently powerful by the middle of the 20th century to allow mass and energy flow models on a vertical and horizontally resolved grid.Lynch, P. (2008). The origins of computer weather prediction and climate modeling. Journal of Computational Physics, 227(7):3431–3444 By 1955 these advances had produced what is recognisable now as a primitive GCM (Phillips prototype Phillips, N. A. (1956). The general circulation of the atmosphere: A numerical experiment. Quarterly Journal of the Royal Meteorological Society, 82(352):123–164). Even at this early stage, a lack of computing power formed a significant barrier to entry and limitation on model-time.

到20世纪中叶，计算能力已经足够强大，可以在垂直和水平分辨的网格上建立质量和能量流模型。林奇(2008)。计算机天气预报和气候模拟的起源。到1955年，这些进步已经产生了现在可以确认为原始 GCM 的东西(菲利普斯原型，n. a.)。(1956).大气环流: 一个数值实验。皇家气象学会季刊，82(352) : 123-164)。即使在这个早期阶段，计算能力的缺乏也对进入和模型时间的限制形成了重要的障碍。

The next half century saw rapid improvement and exponentially increasing computational demands.[6] Modelling on ever smaller length scales required smaller time steps due to the Courant–Friedrichs–Lewy condition.[7] For example, doubling the spatial resolution increases the computational cost by a factor of 16 (factors of 2 for each spatial dimension and time).[1] As well as working on smaller scales, GCMs began to solve more accurate versions of the Navier–Stokes equations.[8] GCMs also began to incorporate more earth systems and feedback mechanisms, transforming themselves into coupled Earth Systems Models. The inclusion of elements from the cryosphere, carbon cycle and cloud feedbacks was both facilitated and constrained by growth in computing power.[1]

The next half century saw rapid improvement and exponentially increasing computational demands.McGuffie, K. and Henderson-Sellers, A. (2001). Forty years of numerical climate modelling. International Journal of Climatology, 21(9):1067–1109. Modelling on ever smaller length scales required smaller time steps due to the Courant–Friedrichs–Lewy condition.Courant, R., Friedrichs, K., and Lewy, H. (1967). On the partial difference equations of mathematical physics. IBM journal of Research and Development, 11(2):215–234. For example, doubling the spatial resolution increases the computational cost by a factor of 16 (factors of 2 for each spatial dimension and time).Flato, G. M. (2011). Earth system models: an overview. Wiley Interdisciplinary Reviews: Climate Change, 2(6):783– 800. As well as working on smaller scales, GCMs began to solve more accurate versions of the Navier–Stokes equations.White, A. A. and Bromley, R. A. (1995). Dynamically consistent, quasi-hydrostatic equations for global models with a complete representation of the coriolis force. Quarterly Journal of the Royal Meteorological Society, 121(522):399– 418. GCMs also began to incorporate more earth systems and feedback mechanisms, transforming themselves into coupled Earth Systems Models. The inclusion of elements from the cryosphere, carbon cycle and cloud feedbacks was both facilitated and constrained by growth in computing power.

在接下来的半个世纪里，计算能力得到了迅速提高，计算能力的需求呈指数级增长。(2001).四十年的数值气候模拟。国际气候学杂志，21(9) : 1067-1109。由于 Courant-Friedrichs-Lewy 条件，在更小的长度尺度上建模需要更小的时间步骤。库兰特，r。 ，弗里德里希斯，k。和刘易斯，h。(1967)。关于数学物理中的偏差分方程。IBM 研究与发展杂志，11(2) : 215-234。例如，空间分辨率增加一倍，计算成本增加了16倍(每个空间维度和时间的因子为2)。弗拉托，通用汽车(2011)。地球系统模型: 概述。威利跨学科评论: 气候变化，2(6) : 783-800。除了在更小的尺度上工作，GCMs 开始解决更精确版本的纳维尔-斯托克斯方程。白色，a。和 Bromley，R.a。(1995).完全代表科里奥利力的全球模型的动力一致的准静力方程。皇家气象学会季刊，121(522) : 399-418。大气环流模型也开始结合更多的地球系统和反馈机制，将自身转化为耦合的地球系统模型。来自冰冻圈、碳循环和云反馈的元素被计算能力的增长所促进和限制。

The powerful computers and high cost required to run these "comprehensive" models limited accessibility to many university research groups. This helped drive the development of EMICs. Through judicious parametrisation of key variables, researchers could run climate simulations on less powerful computers, or alternatively much faster on comparable computers. A modern example of this difference in speed can be seen between the EMIC JUMP-LCM and the GCM MIROC4h; the former runs 63,000 times faster than the latter.[9] The decrease in required computing power allowed EMICs to run over longer model times, and thus include earth systems occupying the "slow domain".

The powerful computers and high cost required to run these "comprehensive" models limited accessibility to many university research groups. This helped drive the development of EMICs. Through judicious parametrisation of key variables, researchers could run climate simulations on less powerful computers, or alternatively much faster on comparable computers. A modern example of this difference in speed can be seen between the EMIC JUMP-LCM and the GCM MIROC4h; the former runs 63,000 times faster than the latter.Hajima, T., Kawamiya, M., Watanabe, M., Kato, E., Tachiiri, K., Sugiyama, M., Watanabe, S., Okajima, H., and Ito, A. (2014). Modeling in earth system science up to and beyond ipcc ar5. Progress in Earth and Planetary Science, 1(1):29. The decrease in required computing power allowed EMICs to run over longer model times, and thus include earth systems occupying the "slow domain".

运行这些“全面”模型所需的强大的计算机和高昂的成本限制了许多大学研究小组的访问。这有助于推动电子集成电路的发展。通过明智地对关键变量进行参数化处理，研究人员可以在性能较差的计算机上运行气候模拟，或者在类似的计算机上运行得更快。A modern example of this difference in speed can be seen between the EMIC JUMP-LCM and the GCM MIROC4h; the former runs 63,000 times faster than the latter.Hajima, T., Kawamiya, M., Watanabe, M., Kato, E., Tachiiri, K., Sugiyama, M., Watanabe, S., Okajima, H., and Ito, A.(2014).地球系统科学的建模直至 ipcc ar5及以后。地球与行星科学进展，1(1) : 29。所需计算能力的下降使得电子集成电路可以运行更长的模型时间，因此包括占据“慢域”的地球系统。

Petoukhov's 1980 statistical dynamical model[10] has been cited as the first modern EMIC,[9] but despite development throughout the 1980s, their specific value only achieved wider recognition in the late 1990s with inclusion in IPCC AR2 under the moniker of "Simple Climate Models". It was shortly afterwards at the IGBP congress in Shonnan Village, Japan, in May 1999, where the acronym EMICs was publicly coined by Claussen. The first simplified model to adopt the nomenclature of "intermediate complexity" is now one of the best known: CLIMBER 2. The Potsdam conference under the guidance of Claussen identified 10 EMICs, a list updated to 13 in 2005.[11] Eight models contributed to IPCC AR4, and 15 to AR5.[12][13]

Petoukhov's 1980 statistical dynamical modelPetoukhov, V. (1980). A zonal climate model of heat and moisture exchange in the atmosphere over the underlying layers of ocean and land in: Golitsyn gs, yaglom am (eds) physics of the atmosphere and the problem of climate. has been cited as the first modern EMIC, but despite development throughout the 1980s, their specific value only achieved wider recognition in the late 1990s with inclusion in IPCC AR2 under the moniker of "Simple Climate Models". It was shortly afterwards at the IGBP congress in Shonnan Village, Japan, in May 1999, where the acronym EMICs was publicly coined by Claussen. The first simplified model to adopt the nomenclature of "intermediate complexity" is now one of the best known: CLIMBER 2. The Potsdam conference under the guidance of Claussen identified 10 EMICs, a list updated to 13 in 2005. Eight models contributed to IPCC AR4, and 15 to AR5.Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., et al. (2007). Climate models and their evaluation. In Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR), pages 589–662. Cambridge University Press.Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M. (2013). Evaluation of Climate Models, book section 9, page 741866. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Petoukhov 的1980年统计动力学模型 Petoukhov，v. (1980)。海洋和陆地下层大气热量和湿度交换的纬向气候模式: Golitsyn gs，yaglom am (eds)大气物理学和气候问题。尽管在整个1980年代取得了进展，但它们的具体价值直到1990年代后期才得到更广泛的承认，被列入政府间气候变化专门委员会第二次评估报告，题为”简单气候模型”。不久之后，1999年5月在日本首南村举行的 IGBP 大会上，克劳森公开创造了缩写 EMICs。采用“中等复杂性”命名法的第一个简化模型现在是最著名的模型之一: climb2。在 Claussen 的指导下，美国波茨坦会议协会确定了10个 EMICs，2005年更新到了13个。8个模型贡献给 IPCC AR4,15个贡献给 ar5. randall，d. ，Wood，R.a. ，Bony，s. ，Colman，r. ，Fichefet，t. ，Fyfe，j. ，Kattsov，v. ，Pitman，a. ，Shukla，j. ，Srinivasan，j. ，等等。(2007).气候模型及其评价。2007年气候变化: 物理科学基础。第一工作组对警监会第四次评估报告的贡献，第589-662页。Cambridge University Press.Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M. (2013).气候模型的评估，第9部分，741866页。剑桥大学出版社，英国剑桥和美国纽约。

Classification

Classification

= 分类 =

As well as "complexity", climate models have been classified by their resolution, parametrisation and "integration".[14] Integration expresses the level of interaction of different components of the earth system. This is influenced by the number of different links in the web (interactivity of coordinates), as well as the frequency of interaction. Because of their speed, EMICs offer the opportunity for highly integrated simulations when compared with more comprehensive ESMs. Four EMIC categorisations have been suggested based on the mode of atmospheric simplification:[9] statistical-dynamical models, energy moisture balance models, quasi-geostrophic models, and primitive equation models. Of the 15 models in the community contribution to the IPCC's fifth assessment report, four were statistical-dynamic, seven energy moisture balance, two quasi-geostrophic and two primitive equations models.[15] To illustrate these categories, a case study for each is given.

As well as "complexity", climate models have been classified by their resolution, parametrisation and "integration".Claussen, M., Mysak, L., Weaver, A., Crucifix, M., Fichefet, T., Loutre, M.-F., Weber, S., Alcamo, J., Alexeev, V., Berger, A., Calov, R., Ganopolski, A., Goosse, H., Lohmann, G., Lunkeit, F., Mokhov, I., Petoukhov, V., Stone, P., and Wang, Z. (2002). Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models. Climate Dynamics, 18(7):579–586. Integration expresses the level of interaction of different components of the earth system. This is influenced by the number of different links in the web (interactivity of coordinates), as well as the frequency of interaction. Because of their speed, EMICs offer the opportunity for highly integrated simulations when compared with more comprehensive ESMs. Four EMIC categorisations have been suggested based on the mode of atmospheric simplification: statistical-dynamical models, energy moisture balance models, quasi-geostrophic models, and primitive equation models. Of the 15 models in the community contribution to the IPCC's fifth assessment report, four were statistical-dynamic, seven energy moisture balance, two quasi-geostrophic and two primitive equations models.Eby, M., Weaver, A. J., Alexander, K., Zickfeld, K., Abe-Ouchi, A., Cimatoribus, A. A., Crespin, E., Drijfhout, S. S., Edwards, N. R., Eliseev, A. V., Feulner, G., Fichefet, T., Forest, C. E., Goosse, H., Holden, P. B., Joos, F., Kawamiya, M., Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov, I. I., Monier, E., Olsen, S. M., Pedersen, J. O. P., Perrette, M., Philippon-Berthier, G., Ridgwell, A., Schlosser, A., Schneider von Deimling, T., Shaffer, G., Smith, R. S., Spahni, R., Sokolov, A. P., Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., Zeng, N., and Zhao, F. (2013). Historical and idealized climate model experiments: an intercomparison of earth system models of intermediate complexity. Climate of the Past, 9(3):1111–1140. To illustrate these categories, a case study for each is given.

As well as "complexity", climate models have been classified by their resolution, parametrisation and "integration".Claussen, M., Mysak, L., Weaver, A., Crucifix, M., Fichefet, T., Loutre, M.-F., Weber, S., Alcamo, J., Alexeev, V., Berger, A., Calov, R., Ganopolski, A., Goosse, H., Lohmann, G., Lunkeit, F., Mokhov, I., Petoukhov, V., Stone, P., and Wang, Z.(2002).具有中等复杂性的地球系统模型: 缩小气候系统模型谱中的差距。Climate dynamics，18(7) : 579-586.积分表示地球系统不同组成部分之间相互作用的程度。这受到网络中不同链接的数量(坐标的交互性)以及交互频率的影响。由于它们的速度，与更全面的 ESMs 相比，EMICs 为高度集成的模拟提供了机会。在大气简化模式的基础上提出了四种主位地转模式: 统计动力学模式、能量水分平衡模式、准地转模式和原始方程模式。在社区为 IPCC 第五次评估报告提供的15个模型中，4个是统计动态模型、7个能量湿度平衡模型、2个准地转模型和2个原始方程组模型。亚历山大，k，Zickfeld，k，Abe-Ouchi，a，Cimatoribus，a。A., Crespin, E., Drijfhout, S. S., Edwards, N. R., Eliseev, A. V., Feulner, G., Fichefet, T., Forest, C. E., Goosse, H., Holden, P. B., Joos, F., Kawamiya, M., Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov, I. I., Monier, E., Olsen, S. M., Pedersen, J. O. P., Perrette, M., Philippon-Berthier, G., Ridgwell, A., Schlosser, A., Schneider von Deimling, T., Shaffer, G., Smith, R. S., Spahni, R., Sokolov, A. P., Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., Zeng, N., and Zhao, F. (2013).历史性和理想化的气候模式实验: 中等复杂性地球系统模式的相互比较。过去的气候，9(3) : 1111-1140。为了说明这些类别，给出了每个类别的案例研究。

Statistical-dynamical models: CLIMBER models

Statistical-dynamical models: CLIMBER models

= = 统计动力学模型: 攀爬模型 =

CLIMBER-2 and CLIMBER-3α are successive generations of 2.5 and 3 dimensional statistical dynamical models.[16][17] Rather than continuous evolution of solutions to the Navier–Stokes or primitive equations, atmospheric dynamics are handled through statistical knowledge of the system (an approach not new to CLIMBER [18]). This approach expresses the dynamics of the atmosphere as large-scale, long term fields of velocity and temperature. Climber-3α's horizontal atmospheric resolution is substantially coarser than a typical atmospheric GCM at 7.5°x 22.5°.

CLIMBER-2 and CLIMBER-3α are successive generations of 2.5 and 3 dimensional statistical dynamical models.Petoukhov, V., Ganopolski, A., Brovkin, V., Claussen, M., Eliseev, A., Kubatzki, C., and Rahmstorf, S. (2000). Climber-2: a climate system model of intermediate complexity. part i: model description and performance for present climate. Climate Dynamics, 16(1):1–17.Montoya, M., Griesel, A., Levermann, A., Mignot, J., Hofmann, M., Ganopolski, A., and Rahmstorf, S. (2005). The earth system model of intermediate complexity climber-3. part i: description and performance for present-day conditions. 25:237–263. Rather than continuous evolution of solutions to the Navier–Stokes or primitive equations, atmospheric dynamics are handled through statistical knowledge of the system (an approach not new to CLIMBER Saltzman, B. (1978). A survey of statistical-dynamical models of the terrestrial climate. volume 20 of Advances in Geophysics, pages 183 – 304. Elsevier.). This approach expresses the dynamics of the atmosphere as large-scale, long term fields of velocity and temperature. Climber-3α's horizontal atmospheric resolution is substantially coarser than a typical atmospheric GCM at 7.5°x 22.5°.

Climber-2和 CLIMBER-3α 是2.5维和3维统计动力学模型的继代。佩托霍夫，v. ，加诺波尔斯基，a. ，Brovkin，v. ，克劳森，m. ，Eliseev，a. ，库巴茨基，c. ，和拉姆斯托夫，s. (2000)。攀援植物2: 一个具有中等复杂性的气候系统模型。第一部分: 当前气候的模式描述和性能。Climate dynamics，16(1) : 1-17. Montoya，m. ，Griesel，a. ，Levermann，a. ，Mignot，j. ，Hofmann，m. ，Ganopolski，a. ，and Rahmstorf，s. (2005).中等复杂度的攀登者 -3的地球系统模型。第一部分: 现有条件下的描述和性能。25:237–263.大气动力学是通过系统的统计学知识来处理的，而不是连续演化的纳维-斯托克斯或原始方程组的解，(这种方法对攀援萨尔茨曼来说并不新鲜。(1978).陆地气候统计动力学模式综述。《地球物理进展》第20卷，第183-304页。爱思唯尔)。这种方法将大气的动力学表示为大尺度的、长期的速度和温度场。攀缘者 -3α 的水平大气分辨率比7.5 ° x 22.5 ° 的典型大气 GCM 要低得多。

With a characteristic spatial scale of 1000km, this simplification prohibits resolution of synoptic level features. Climber-3α incorporates comprehensive ocean, sea ice and biogeochemistry models. Despite these full descriptions, simplification of the atmosphere allows it to operate two orders of magnitude faster than comparable GCMs.[17] Both CLIMBER models offer performances comparable to that of contemporary GCMs in simulating present climates. This is clearly of interest due to the significantly lower computational costs. Both models have been principally used to investigate paleoclimates, particularly ice sheet nucleation.[19]

With a characteristic spatial scale of 1000km, this simplification prohibits resolution of synoptic level features. Climber-3α incorporates comprehensive ocean, sea ice and biogeochemistry models. Despite these full descriptions, simplification of the atmosphere allows it to operate two orders of magnitude faster than comparable GCMs. Both CLIMBER models offer performances comparable to that of contemporary GCMs in simulating present climates. This is clearly of interest due to the significantly lower computational costs. Both models have been principally used to investigate paleoclimates, particularly ice sheet nucleation.Ganopolski, A., Rahmstorf, S., Petoukhov, V., and Claussen, M. (1998). Simulation of modern and glacial climates with a coupled global model of intermediate complexity. Nature, 391(6665):351–356.

这种简化具有1000公里的特征空间尺度，不利于天气层特征的分辨率。Climb-3α 综合了海洋、海冰和生物地球化学的综合模型。尽管有这些完整的描述，但是对大气的简化使得它比同类的 GCMs 运行速度快了2个数量级。在模拟当前气候条件下，两种攀援模型的性能都可与当代 GCMs 相媲美。这显然是有趣的，因为显着降低了计算成本。这两种模型都主要用于研究古气候，特别是冰盖成核。加诺波尔斯基，a. ，拉姆斯托夫，s. ，佩托霍夫，v. ，和克劳森，m. (1998)。中等复杂性全球耦合模式对现代和冰川气候的模拟。《自然》 ，391(6665) : 351-356。

Energy and moisture balance models: UVic ESCM

Energy and moisture balance models: UVic ESCM

= = 能量和水分平衡模型: UVic ESCM = =

The thermodynamic approach of the UVic model involves simplification of mass transport (with Fickian diffusion) and precipitation conditions.[20] This model can be seen as a direct descendant of earlier energy balance models.[21][22][23] These reductions reduce the atmosphere to three state variables, surface air temperature, sea surface temperature and specific humidity.引用错误：没有找到与</ref>对应的<ref>标签 By parametrising heat and moisture transport with diffusion, timescales are limited to greater than annual and length scales to greater than 1000km. A key result of the thermodynamic rather than fluid dynamic approach is that the simulated climate exhibits no internal variability.[20] Like CLIMBER-3α, it is coupled to a state of the art, 3D ocean model and includes other cutting edge models for sea-ice and land-ice. Unlike CLIMBER, the UVic model does not have significantly coarser resolution than contemporary AOGCMs (3.6°x 1.8°). As such, all computational advantage is from the simplification of atmospheric dynamics.

Atmospheres, 101(D10):15111–15128.</ref> By parametrising heat and moisture transport with diffusion, timescales are limited to greater than annual and length scales to greater than 1000km. A key result of the thermodynamic rather than fluid dynamic approach is that the simulated climate exhibits no internal variability. Like CLIMBER-3α, it is coupled to a state of the art, 3D ocean model and includes other cutting edge models for sea-ice and land-ice. Unlike CLIMBER, the UVic model does not have significantly coarser resolution than contemporary AOGCMs (3.6°x 1.8°). As such, all computational advantage is from the simplification of atmospheric dynamics.

大气，101(D10) : 15111-15128。通过参数化热量和水分的扩散输送，时间尺度被限制在大于年度尺度和长度尺度大于1000公里。热力学而非流体动力学方法的一个关键结果是，模拟的气候没有内部变化。就像 CLIMBER-3α 一样，它与最先进的3d 海洋模型相结合，还包括其他海冰和陆冰的尖端模型。与攀援模式不同，UVic 模式的分辨率并不比当代的 AOGCMs (3.6 ° x 1.8 °)显著地低。因此，所有的计算优势都来自于大气动力学的简化。

Quasi-geostrophic models: LOVECLIM

Quasi-geostrophic models: LOVECLIM

= = 准地转模式: LOVECLIM = =

The quasi-geostrophic equations are a reduction of the primitive equations first written down by Charney.[24] These equations are valid in the case of low Rossby number, signifying only a small contribution from inertial forces. Assumed dominance of the Coriolis and pressure-gradient forces facilitates the reduction of the primitive equations to a single equation for potential vorticity in five variables.[25] LOVECLIM features a horizontal resolution of 5.6° and uses the quasi geostrophic atmosphere model ECBilt. It includes a vegetation feedback module by Brovkin et al. (1997).[26] The model exhibits some significant limitations that are fundamentally linked to its design. The model predicts an Equilibrium Climate Sensitivity of 1.9°C, at the lower end of the range of GCM predictions. The model's surface temperature distribution is overly-symmetric, and does not represent the northern bias in location of the Intertropical Convergence Zone. The model generally shows lower skill at low latitudes. Other examples of quasi-geostrophic models are PUMA.

The quasi-geostrophic equations are a reduction of the primitive equations first written down by Charney.Majda, A. and Wang, X. (2006). Nonlinear Dynamics and Statistical Theories for Basic Geophysical Flows. Cambridge University Press. These equations are valid in the case of low Rossby number, signifying only a small contribution from inertial forces. Assumed dominance of the Coriolis and pressure-gradient forces facilitates the reduction of the primitive equations to a single equation for potential vorticity in five variables.Marshall, J. and Molteni, F. (1993). Toward a dynamical understanding of planetary-scale flow regimes. Journal of the Atmospheric Sciences, 50(12):1792–1818. LOVECLIM features a horizontal resolution of 5.6° and uses the quasi geostrophic atmosphere model ECBilt. It includes a vegetation feedback module by Brovkin et al. (1997).Brovkin, V., Claussen, M., Driesschaert, E., Fichefet, T., Kicklighter, D., Loutre, M. F., Matthews, H. D., Ramankutty, N., Schaeffer, M., and Sokolov, A. (2006). Biogeophysical effects of historical land cover changes simulated by six earth system models of intermediate complexity. Climate Dynamics, 26(6):587–600. The model exhibits some significant limitations that are fundamentally linked to its design. The model predicts an Equilibrium Climate Sensitivity of 1.9°C, at the lower end of the range of GCM predictions. The model's surface temperature distribution is overly-symmetric, and does not represent the northern bias in location of the Intertropical Convergence Zone. The model generally shows lower skill at low latitudes. Other examples of quasi-geostrophic models are PUMA.

准地转方程是 Charney，a. 和 Wang，x. 首先写下的原始方程组的缩减。(2006).基本地球物理流的非线性动力学和统计理论。剑桥大学出版社。这些方程式在罗斯比数较低的情况下是有效的，这表示惯性力的贡献很小。假设科里奥利力和压力梯度力占优势，有利于将原始方程组降低到5个变量的位涡方程。和 Molteni，f. (1993)。对行星尺度流动状态的动力学理解。大气科学杂志，50(12) : 1792-1818。LOVECLIM 的水平分辨率为5.6 ° ，使用了准地转大气模式 ECBilt。它包括了 Brovkin 等人设计的植被反馈模块。(1997).Brovkin, V., Claussen, M., Driesschaert, E., Fichefet, T., Kicklighter, D., Loutre, M. F., Matthews, H. D., Ramankutty, N., Schaeffer, M., and Sokolov, A.(2006).六种中等复杂地球系统模型模拟历史土地覆盖变化的生物地球物理效应。Climate dynamics，26(6) : 587-600.这个模型展示了一些重要的限制，这些限制与它的设计有着根本的联系。该模式预测平衡气候敏感性为1.9 ° c，位于 GCM 预测范围的下端。该模型的表面温度分布是过度对称的，不能代表热带辐合带位置的北方偏差。模型通常显示低纬度地区的低技能。类地转模式的其他例子是 PUMA。

Primitive equations model: FAMOUS

Primitive equations model: FAMOUS

= = 原始方程组模型: FAMOUS = =

The UK Met-Office's FAMOUS blurs the line between more coarsely resolved comprehensive models and EMICs. Designed to run paleoclimate simulations of the Pleistocene, it has been tuned to reproduce the climate of its parent, HADCM3, by solving the primitive equations written down by Charney. These are of higher complexity than the quasi-geostrophic equations. Originally named ADTAN, preliminary runs had significant biases involving sea ice and the AMOC, which were later corrected through tuning of sea-ice parameters. The model runs at half the horizontal resolution of HADCM3. Atmospheric resolution is 7.5°x5°, and oceanic is 3.75°x 2.5°. Atmosphere-Ocean coupling is done once daily.

The UK Met-Office's FAMOUS blurs the line between more coarsely resolved comprehensive models and EMICs. Designed to run paleoclimate simulations of the Pleistocene, it has been tuned to reproduce the climate of its parent, HADCM3, by solving the primitive equations written down by Charney. These are of higher complexity than the quasi-geostrophic equations. Originally named ADTAN, preliminary runs had significant biases involving sea ice and the AMOC, which were later corrected through tuning of sea-ice parameters. The model runs at half the horizontal resolution of HADCM3. Atmospheric resolution is 7.5°x5°, and oceanic is 3.75°x 2.5°. Atmosphere-Ocean coupling is done once daily.

英国气象局(Met-Office)的 FAMOUS 模糊了更为粗糙、分辨率更高的综合型号与 EMICs 之间的界限。它被设计用来模拟更新世的古气候，通过解决 Charney 写下的原始方程组气候模型，它已经被调整到再现其母体 hadcm3的气候。这些方程比准地转方程具有更高的复杂度。最初命名为 ADTAN，初步试验有明显的偏差，涉及海冰和 AMOC，后来通过调整海冰参数得到纠正。该模型的水平分辨率是 hadcm3的一半。大气分辨率为7.5 ° x5 ° ，海洋分辨率为3.75 ° x2.5 ° 。Atmosphere-Ocean 耦合每天进行一次。

Comparisons and assessments

Comparisons and assessments

= 比较与评估 =

Systematic intercomparison of EMICs has been undertaken since 2000, most recently with a community contribution to the IPCC's fifth assessment report.[15] The equilibrium and transient climate sensitivity of EMICs broadly fell within the range of contemporary GCMs with a range of 1.9 - 4.0°C (compared to 2.1° - 4.7°C, CMIP5). Tested over the last millennium, the average response of the models was close to the real trend, however this conceals much wider variation between individual models. Models generally overestimate ocean heat uptake over the last millennium and indicate a moderate slowing. No relationship was observed in EMICs between levels of polar amplification, climate sensitivity, and initial state.[15] The above comparisons to the performance of GCMs and comprehensive ESMs do not reveal the full value of EMICs. Their ability to run as “fast ESMs” allows them to simulate much longer periods, up to many millennia. As well as running on time-scales far greater than available to GCMs, they provide fertile ground for development and integration of systems that will later join GCMs.

Systematic intercomparison of EMICs has been undertaken since 2000, most recently with a community contribution to the IPCC's fifth assessment report. The equilibrium and transient climate sensitivity of EMICs broadly fell within the range of contemporary GCMs with a range of 1.9 - 4.0°C (compared to 2.1° - 4.7°C, CMIP5). Tested over the last millennium, the average response of the models was close to the real trend, however this conceals much wider variation between individual models. Models generally overestimate ocean heat uptake over the last millennium and indicate a moderate slowing. No relationship was observed in EMICs between levels of polar amplification, climate sensitivity, and initial state. The above comparisons to the performance of GCMs and comprehensive ESMs do not reveal the full value of EMICs. Their ability to run as “fast ESMs” allows them to simulate much longer periods, up to many millennia. As well as running on time-scales far greater than available to GCMs, they provide fertile ground for development and integration of systems that will later join GCMs.

自2000年以来，对经济和社会发展综合指数进行了系统的相互比较，最近的一次是社区对气专委第五次评估报告作出贡献。经济和气候变化中心的平衡和瞬态气候敏感性大致在当代大气环流模式的范围内，范围为1.9-4.0摄氏度(相比之下，cmip5为2.1-4.7摄氏度)。在过去一千年的测试中，模型的平均反应接近真实趋势，但这掩盖了个别模型之间更广泛的差异。在过去的一千年里，模型通常高估了海洋的热量吸收，并显示出适度的减缓。极性放大水平、气候敏感性和初始状态之间没有相关性。上述与大气环流模型和综合环境管理模型的性能的比较并没有显示出环境管理模型的全部价值。它们能够以“快速 ESMs”的形式运行，这使得它们能够模拟更长的周期，最长可达数千年。除了在远远超过大气环流模式可用的时间尺度上运行之外，它们还为以后将加入大气环流模式的系统的开发和集成提供了肥沃的土壤。

Outlook

Outlook

= Outlook =

Possible future directions for EMICs are likely to be in assessment of uncertainties and as a vanguard for incorporation of new earth systems.[27] By virtue of speed they also lend themselves to the creation of ensembles with which to constrain parameters and assess earth systems.[28] EMICs have also recently led in the field of climate stabilisation research.[9] McGuffie and Henderson-Sellers argued in 2001 that in the future, EMICs would be “as important” as GCMs to the climate modelling field [6] - while this has perhaps not been true in the time since that statement, their role has not diminished. Finally, as climate science has come under increasing levels of scrutiny,[29][30] the ability of models not just to project but to explain has become important. The transparency of EMICs is attractive in this domain, as causal chains are easier to identify and communicate (as opposed to emergent properties generated by comprehensive models).

Possible future directions for EMICs are likely to be in assessment of uncertainties and as a vanguard for incorporation of new earth systems.Weber, S. L. (2010). The utility of earth system models of intermediate complexity (emics). Wiley Interdisciplinary Reviews: Climate Change, 1(2):243–252. By virtue of speed they also lend themselves to the creation of ensembles with which to constrain parameters and assess earth systems.Brovkin, V., Claussen, M., Driesschaert, E., Fichefet, T., Kicklighter, D., Loutre, M. F., Matthews, H. D., Ramankutty, N., Schaeffer, M., and Sokolov, A. (2006). Biogeophysical effects of historical land cover changes simulated by six earth system models of intermediate complexity. Climate Dynamics, 26(6):587–600. EMICs have also recently led in the field of climate stabilisation research. McGuffie and Henderson-Sellers argued in 2001 that in the future, EMICs would be “as important” as GCMs to the climate modelling field - while this has perhaps not been true in the time since that statement, their role has not diminished. Finally, as climate science has come under increasing levels of scrutiny,McCright, A. M., Dunlap, R. E., & Marquart-Pyatt, S. T. (2016). Political ideology and views about climate change in the European Union. Environmental Politics, 25(2), 338-358.Dunlap, R. E., McCright, A. M., & Yarosh, J. H. (2016). The political divide on climate change: Partisan polarization widens in the US. Environment: Science and Policy for Sustainable Development, 58(5), 4-23. the ability of models not just to project but to explain has become important. The transparency of EMICs is attractive in this domain, as causal chains are easier to identify and communicate (as opposed to emergent properties generated by comprehensive models).

经济和微观经济学中心今后可能的方向可能是评估不确定性，并作为纳入新的地球系统的先锋。韦伯(2010)。中等复杂性地球系统模型的应用。威利跨学科评论: 气候变化，1(2) : 243-252。由于速度的优势，它们还有助于创造集合，以限制参数和评估地球系统。布罗夫金，v. ，克劳森，m. ，德里斯查尔特，e. ，菲什菲特，t. ，Kicklighter，d. ，Loutre，m. f. ，马修斯，h. d. ，Ramankutty，n. ，舍费尔，m. ，和索科洛夫，a。(2006).六种中等复杂地球系统模型模拟历史土地覆盖变化的生物地球物理效应。Climate dynamics，26(6) : 587-600.最近，经济与工业综合体在气候稳定研究领域也处于领先地位。麦克古菲和亨德森-塞勒斯在2001年辩称，未来，在气候模型领域，电子工程学将与大气环流模型一样“重要”——尽管自那次声明发表以来，这种说法可能并不正确，但它们的作用并未减弱。最后，随着气候科学受到越来越严格的审查，McCright，A.m. ，Dunlap，R.e. ，& Marquart-Pyatt，S.t. (2016)。欧盟关于气候变化的政治意识形态和观点。《环境政治学》 ，25(2) ，338-358。邓拉普，r. e. ，麦克莱特，A.M. ，和亚罗什，j. h. (2016)。在气候变化问题上的政治分歧: 美国党派分化加剧。环境: 科学与政策促进可持续发展，58(5) ，4-23。模型的能力不仅仅是投射，而是解释已经变得很重要。EMICs 的透明性在这个领域很有吸引力，因为因果链更容易识别和通信(相对于综合模型生成的紧急属性)。

See also

  • Climate model
  • General circulation model

See also

  • Climate model
  • General circulation model

References

References

= 参考文献 =

  1. ↑ 1.0 1.1 1.2 Flato, G. M. (2011). Earth system models: an overview. Wiley Interdisciplinary Reviews: Climate Change, 2(6):783– 800.
  2. ↑ Jakob, C. (2014). Going back to basics. Nature Climate Change, 4:1042–1045.
  3. ↑ Lovejoy, S. (2015). A voyage through scales, a missing quadrillion and why the climate is not what you expect. Climate Dynamics, 44(11):3187–3210.
  4. ↑ Lynch, P. (2008). The origins of computer weather prediction and climate modeling. Journal of Computational Physics, 227(7):3431–3444
  5. ↑ Phillips, N. A. (1956). The general circulation of the atmosphere: A numerical experiment. Quarterly Journal of the Royal Meteorological Society, 82(352):123–164
  6. ↑ 6.0 6.1 McGuffie, K. and Henderson-Sellers, A. (2001). Forty years of numerical climate modelling. International Journal of Climatology, 21(9):1067–1109.
  7. ↑ Courant, R., Friedrichs, K., and Lewy, H. (1967). On the partial difference equations of mathematical physics. IBM journal of Research and Development, 11(2):215–234.
  8. ↑ White, A. A. and Bromley, R. A. (1995). Dynamically consistent, quasi-hydrostatic equations for global models with a complete representation of the coriolis force. Quarterly Journal of the Royal Meteorological Society, 121(522):399– 418.
  9. ↑ 9.0 9.1 9.2 9.3 Hajima, T., Kawamiya, M., Watanabe, M., Kato, E., Tachiiri, K., Sugiyama, M., Watanabe, S., Okajima, H., and Ito, A. (2014). Modeling in earth system science up to and beyond ipcc ar5. Progress in Earth and Planetary Science, 1(1):29.
  10. ↑ Petoukhov, V. (1980). A zonal climate model of heat and moisture exchange in the atmosphere over the underlying layers of ocean and land in: Golitsyn gs, yaglom am (eds) physics of the atmosphere and the problem of climate.
  11. ↑ Claussen, Martin (2005-05-30). "Table of EMICs (Earth System Models of Intermediate Complexity)" (PDF). Retrieved 2018-10-25.
  12. ↑ Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., et al. (2007). Climate models and their evaluation. In Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR), pages 589–662. Cambridge University Press.
  13. ↑ Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M. (2013). Evaluation of Climate Models, book section 9, page 741866. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  14. ↑ Claussen, M., Mysak, L., Weaver, A., Crucifix, M., Fichefet, T., Loutre, M.-F., Weber, S., Alcamo, J., Alexeev, V., Berger, A., Calov, R., Ganopolski, A., Goosse, H., Lohmann, G., Lunkeit, F., Mokhov, I., Petoukhov, V., Stone, P., and Wang, Z. (2002). Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models. Climate Dynamics, 18(7):579–586.
  15. ↑ 15.0 15.1 15.2 Eby, M., Weaver, A. J., Alexander, K., Zickfeld, K., Abe-Ouchi, A., Cimatoribus, A. A., Crespin, E., Drijfhout, S. S., Edwards, N. R., Eliseev, A. V., Feulner, G., Fichefet, T., Forest, C. E., Goosse, H., Holden, P. B., Joos, F., Kawamiya, M., Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov, I. I., Monier, E., Olsen, S. M., Pedersen, J. O. P., Perrette, M., Philippon-Berthier, G., Ridgwell, A., Schlosser, A., Schneider von Deimling, T., Shaffer, G., Smith, R. S., Spahni, R., Sokolov, A. P., Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., Zeng, N., and Zhao, F. (2013). Historical and idealized climate model experiments: an intercomparison of earth system models of intermediate complexity. Climate of the Past, 9(3):1111–1140.
  16. ↑ Petoukhov, V., Ganopolski, A., Brovkin, V., Claussen, M., Eliseev, A., Kubatzki, C., and Rahmstorf, S. (2000). Climber-2: a climate system model of intermediate complexity. part i: model description and performance for present climate. Climate Dynamics, 16(1):1–17.
  17. ↑ 17.0 17.1 Montoya, M., Griesel, A., Levermann, A., Mignot, J., Hofmann, M., Ganopolski, A., and Rahmstorf, S. (2005). The earth system model of intermediate complexity climber-3. part i: description and performance for present-day conditions. 25:237–263.
  18. ↑ Saltzman, B. (1978). A survey of statistical-dynamical models of the terrestrial climate. volume 20 of Advances in Geophysics, pages 183 – 304. Elsevier.
  19. ↑ Ganopolski, A., Rahmstorf, S., Petoukhov, V., and Claussen, M. (1998). Simulation of modern and glacial climates with a coupled global model of intermediate complexity. Nature, 391(6665):351–356.
  20. ↑ 20.0 20.1 Weaver, A., Eby, M., Wiebe, E., Bitz, C., Duffy, P., Ewen, T., F. Fanning, A., M. Holland, M., MacFadyen, A., Matthews, H. D., J. Meissner, K., Saenko, O., Schmittner, A., X. Wang, H., and Yoshimori, M. (2001). The uvic earth system climate model: Model description, climatology, and applications to past, present and future climates. 39:361–428.
  21. ↑ Budyko, M. I. (1969). The effect of solar radiation variations on the climate of the earth. Tellus, 21(5):611–619.
  22. ↑ Sellers, W. D. (1969). A global climatic model based on the energy balance of the earth-atmosphere system. Journal of Applied Meteorology, 8(3):392–400.
  23. ↑ North, G. R. (1975). Theory of energy-balance climate models. Journal of the Atmospheric Sciences, 32(11):2033–2043.
  24. ↑ Majda, A. and Wang, X. (2006). Nonlinear Dynamics and Statistical Theories for Basic Geophysical Flows. Cambridge University Press.
  25. ↑ Marshall, J. and Molteni, F. (1993). Toward a dynamical understanding of planetary-scale flow regimes. Journal of the Atmospheric Sciences, 50(12):1792–1818.
  26. ↑ Brovkin, V., Claussen, M., Driesschaert, E., Fichefet, T., Kicklighter, D., Loutre, M. F., Matthews, H. D., Ramankutty, N., Schaeffer, M., and Sokolov, A. (2006). Biogeophysical effects of historical land cover changes simulated by six earth system models of intermediate complexity. Climate Dynamics, 26(6):587–600.
  27. ↑ Weber, S. L. (2010). The utility of earth system models of intermediate complexity (emics). Wiley Interdisciplinary Reviews: Climate Change, 1(2):243–252.
  28. ↑ Brovkin, V., Claussen, M., Driesschaert, E., Fichefet, T., Kicklighter, D., Loutre, M. F., Matthews, H. D., Ramankutty, N., Schaeffer, M., and Sokolov, A. (2006). Biogeophysical effects of historical land cover changes simulated by six earth system models of intermediate complexity. Climate Dynamics, 26(6):587–600.
  29. ↑ McCright, A. M., Dunlap, R. E., & Marquart-Pyatt, S. T. (2016). Political ideology and views about climate change in the European Union. Environmental Politics, 25(2), 338-358.
  30. ↑ Dunlap, R. E., McCright, A. M., & Yarosh, J. H. (2016). The political divide on climate change: Partisan polarization widens in the US. Environment: Science and Policy for Sustainable Development, 58(5), 4-23.

Category:Earth system sciences Category:Numerical climate and weather models

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