Climate model

Short description: Quantitative methods used to simulate climate

This page is about the theories and mathematics of climate modeling. For computer-driven prediction of Earth's climate, see General circulation model.

For broader coverage of Oceanic model, see Atmospheric model.

Climate models apply knowledge in various sciences to process extensive sets of input climate data, executing differential equations among grid elements in a three-dimensional model of Earth's climate system. The models produce simulated climates having an array of climatic and weather elements, such as heatwaves and storms. Each element may be described by several attributes, including intensity, frequency, and impacts. Climate models support adaptation to projected climate change, and enable extreme event attribution to explain specific weather events.

Numerical climate models (or climate system models) are mathematical models that can simulate the interactions of important drivers of climate. These drivers are the atmosphere, oceans, land surface and ice. Scientists use climate models to study the dynamics of the climate system and to make projections of future climate and of climate change. Climate models can also be qualitative (i.e. not numerical) models and contain narratives, largely descriptive, of possible futures.[1]

Climate models take account of incoming energy from the Sun as well as outgoing energy from Earth. An imbalance results in a change in temperature. The incoming energy from the Sun is in the form of short wave electromagnetic radiation, chiefly visible and short-wave (near) infrared. The outgoing energy is in the form of long wave (far) infrared electromagnetic energy. These processes are part of the greenhouse effect.

Climate models vary in complexity. For example, a simple radiant heat transfer model treats the Earth as a single point and averages outgoing energy. This can be expanded vertically (radiative-convective models) and horizontally. More complex models are the coupled atmosphere–ocean–sea ice global climate models. These types of models solve the full equations for mass transfer, energy transfer and radiant exchange. In addition, other types of models can be interlinked. For example Earth System Models include also land use as well as land use changes. This allows researchers to predict the interactions between climate and ecosystems.

Climate models are systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. Scientists divide the planet into a 3-dimensional grid and apply the basic equations to those grids. Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points. These are coupled with oceanic models to simulate climate variability and change that occurs on different timescales due to shifting ocean currents and the much larger heat storage capacity of the global ocean. External drivers of change may also be applied. Including an ice-sheet model better accounts for long term effects such as sea level rise.

## Contents

* 1 Uses
* 2 General circulation models (GCMs)
* 3 Energy balance models (EBMs)
* 3.1 Zero-dimensional models
* 3.1.1 Model with combined surface and atmosphere
* 3.1.2 Models with separated surface and atmospheric layers
* 3.2 Radiative-convective models
* 3.3 Higher-dimension models
* 4 Earth systems models of intermediate complexity (EMICs)
* 5 Box models
* 6 Networked data models
* 7 History
* 7.1 Increase of forecasts confidence over time
* 8 Coordination of research
* 9 Issues
* 9.1 Electricity consumption
* 9.2 Parametrization
* 10 See also
* 11 References
* 12 External links

## Uses

Climate models divide the planet into a 3-dimensional grid and apply differential equations to each grid. Climate model equations are based on the basic laws of physics, fluid motion, and chemistry.

Complex climate models enable extreme event attribution, which is the science of identifying and quantifying the role that human-caused climate change plays in the frequency, intensity and impacts of extreme weather events.[2][3] Attribution science aims to determine the degree to which such events can be explained by or linked to human-caused global warming, and are not simply due to random climate variability[4] or natural weather patterns.[5]

There are three major types of institution where climate models are developed, implemented and used:

* National meteorological services: Most national weather services have a climatology section.
* 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, US), the Geophysical Fluid Dynamics Laboratory (GFDL, in Princeton, New Jersey, US), 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.

Big climate models are essential but they are not perfect. Attention still needs to be given to the real world (what is happening and why). The global models are essential to assimilate all the observations, especially from space (satellites) and produce comprehensive analyses of what is happening, and then they can be used to make predictions/projections. Simple models have a role to play that is widely abused and fails to recognize the simplifications such as not including a water cycle.[6][7]

## General circulation models (GCMs)

This section is an excerpt from General circulation model[ edit ]

## Energy balance models (EBMs)

Simulation of the climate system in full 3-D space and time was impractical prior to the establishment of large computational facilities starting in the 1960s. In order to begin to understand which factors may have changed Earth's paleoclimate states, the constituent and dimensional complexities of the system needed to be reduced. A simple quantitative model that balanced incoming/outgoing energy was first developed for the atmosphere in the late 19th century.[8] Other EBMs similarly seek an economical description of surface temperatures by applying the conservation of energy constraint to individual columns of the Earth-atmosphere system.[9]

Essential features of EBMs include their relative conceptual simplicity and their ability to sometimes produce analytical solutions.[10]: 19 Some models account for effects of ocean, land, or ice features on the surface budget. Others include interactions with parts of the water cycle or carbon cycle. A variety of these and other reduced system models can be useful for specialized tasks that supplement GCMs, particularly to bridge gaps between simulation and understanding.[11][12]

### Zero-dimensional models

Zero-dimensional models consider Earth as a point in space, analogous to the pale blue dot viewed by Voyager 1 or an astronomer's view of very distant objects. This dimensionless view while highly limited is still useful in that the laws of physics are applicable in a bulk fashion to unknown objects, or in an appropriate lumped manner if some major properties of the object are known. For example, astronomers know that most planets in our own solar system feature some kind of solid/liquid surface surrounded by a gaseous atmosphere.

#### Model with combined surface and atmosphere

A very simple model of the radiative equilibrium of the Earth is

(1−a)Sπr2=4πr2ϵσT4

where

* the left hand side represents the total incoming shortwave power (in Watts) from the Sun
* the right hand side represents the total outgoing longwave power (in Watts) from Earth, calculated from the Stefan–Boltzmann law.

The constant parameters include

* S is the solar constant – the incoming solar radiation per unit area—about 1367 W·m−2
* r is Earth's radius—approximately 6.371 million meters (m)
* π is the mathematical constant (3.141...)
* σ is the Stefan–Boltzmann constant—approximately 5.67×10−8 J·K−4·m−2·s−1

The constant πr2 can be factored out, giving a nildimensional equation for the equilibrium

(1−a)S=4ϵσT4

where

* the left hand side represents the incoming shortwave energy flux from the Sun in W·m−2
* the right hand side represents the outgoing longwave energy flux from Earth in W·m−2.

The remaining variable parameters which are specific to the planet include

* a is Earth's average albedo, measured to be 0.3.[13][14]
* T is Earth's average surface temperature, measured as about 288 Kelvin (K) as of year 2020[15]
* ϵ is the effective emissivity of Earth's combined surface and atmosphere (including clouds). It is a quantity between 0 and 1 that is calculated from the equilibrium to be about 0.61. For the zero-dimensional treatment it is equivalent to an average value over all viewing angles.

This very simple model is quite instructive. For example, it shows the temperature sensitivity to changes in the solar constant, Earth albedo, or effective Earth emissivity. The effective emissivity also gauges the strength of the atmospheric greenhouse effect, since it is the ratio of the thermal emissions escaping to space versus those emanating from the surface.[16]

The calculated emissivity can be compared to available data. Terrestrial surface emissivities are all in the range of 0.96 to 0.99[17][18] (except for some small desert areas which may be as low as 0.7). Clouds, however, which cover about half of the planet's surface, have an average emissivity of about 0.5[19] (which must be reduced by the fourth power of the ratio of cloud absolute temperature to average surface absolute temperature) and an average cloud temperature of about 258 K (−15 °C; 5 °F).[20] Taking all this properly into account results in an effective earth emissivity of about 0.64 (earth average temperature 285 K (12 °C; 53 °F)).[citation needed]

#### Models with separated surface and atmospheric layers

One-layer EBM with blackbody surface

Dimensionless models have also been constructed with functionally distinct atmospheric layers from the surface. The simplest of these is the zero-dimensional, one-layer model,[21] which may be readily extended to an arbitrary number of atmospheric layers. The surface and atmospheric layer(s) are each characterized by a corresponding temperature and emissivity value, but no thickness. Applying radiative equilibrium (i.e conservation of energy) at the idealized interfaces between layers produces a set of coupled equations which are solvable.[22]

These multi-layered EBMs are examples of multi-compartment models. They can estimate average temperatures closer to those observed for Earth's surface and troposphere.[23] They likewise further illustrate the radiative heat transfer processes which underlie the greenhouse effect. Quantification of this phenomenon using a version of the one-layer model was first published by Svante Arrhenius in year 1896.[8]

### Radiative-convective models

See also: Physics:Radiative-convective equilibrium

Water vapor is a main determinant of the emissivity of Earth's atmosphere. It both influences the flows of radiation and is influenced by convective flows of heat in a manner that is consistent with its equilibrium concentration and temperature as a function of elevation (i.e. relative humidity distribution). This has been shown by refining the zero dimension model in the vertical to a one-dimensional radiative-convective model which considers two processes of energy transport:[24]

* upwelling and downwelling radiative transfer through atmospheric layers that both absorb and emit infrared radiation
* upward transport of heat by air and vapor convection, which is especially important in the lower troposphere.

Radiative-convective models typically use a distributed model of the atmosphere versus elevation. This has advantages over the lumped models and also lays a foundation for more complex models.[25] They can estimate both surface temperature and the temperature variation with elevation in a more realistic manner. In particular, they properly simulate the observed decline in upper atmospheric temperature and the rise in surface temperature when trace amounts of other non-condensible greenhouse gases such as carbon dioxide are included.[24]

Other parameters are sometimes included to simulate localized effects in other dimensions and to address the factors that move energy about Earth. For example, the effect of ice-albedo feedback on global climate sensitivity has been investigated using a one-dimensional radiative-convective climate model.[26][27]

### Higher-dimension models

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.[28]

Early examples include research of Mikhail Budyko and William D. Sellers who worked on the Budyko-Sellers model.[29][30] This work also showed the role of positive feedback in the climate system and has been considered foundational for the energy balance models since its publication in 1969.[9][31]

## Earth systems models of intermediate complexity (EMICs)

Main article: Earth: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 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.[32]

## Box models

See also: Multi-compartment model

Schematic of a simple box model used to illustrate fluxes in geochemical cycles, showing a source (Q), sink (S) and reservoir (M)

Box models are simplified versions of complex systems, reducing them to boxes linked by fluxes. The boxes contain reservoirs (i.e. inventories) of species of matter and energy that are assumed to be mixed homogeneously. The concentration of any species is therefore uniform at any time within a box. However, the abundance of a species within a given box may vary as a function of time due to input flows or output flows; and may also vary due to the production, consumption or transformation of a species within the box.[33][34]

Simple box models, i.e. box model with a small number of boxes whose properties (e.g. their volume) do not change with time, are often useful to derive analytical formulas describing the dynamical and steady-state abundances of a species. The formulae are called governing equations and are derived from conservation laws (e.g. conservation of energy, conservation of mass, etc.). Larger sets of interacting species and equations are evaluated with numerical techniques to describe behavior of the system.[35]

Box models are used extensively to simulate environmental systems and ecosystems. In 1961 Henry Stommel was the first to use a simple 2-box model to study the stability of large-scale ocean circulation.[36] A more complex model has examined interactions between ocean circulation and the carbon cycle.[37]

## Networked data models

See also: Data model

This section is an excerpt from Climate as complex networks[ edit ]

## History

See also: Earth:History of climate change science

This section is an excerpt from Atmospheric model#Climate modeling[ edit ]

### Increase of forecasts confidence over time

The pattern correlation between global climate models and observations has improved over sequential CMIP phases 3, 5, and 6.[38]

The Coupled Model Intercomparison Project (CMIP) has been a leading effort to foster improvements in GCMs and climate change understanding since 1995.[39][40]

The IPCC stated in 2010 it has increased confidence in forecasts coming from climate models:

> "There is considerable confidence that climate models provide credible quantitative estimates of future climate change, particularly at continental scales and above. This confidence comes from the foundation of the models in accepted physical principles and from their ability to reproduce observed features of current climate and past climate changes. Confidence in model estimates is higher for some climate variables (e.g., temperature) than for others (e.g., precipitation). Over several decades of development, models have consistently provided a robust and unambiguous picture of significant climate warming in response to increasing greenhouse gases."[41]

## Coordination of research

The World Climate Research Programme (WCRP), hosted by the World Meteorological Organization (WMO), coordinates research activities on climate modelling worldwide.

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.[42] 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.[43]

## Issues

### Electricity consumption

Cloud-resolving climate models are nowadays run on high intensity super-computers which have a high power consumption and thus cause CO2 emissions.[44] They require exascale computing (billion billion – i.e., a quintillion – calculations per second). For example, the Frontier exascale supercomputer consumes 29 MW.[45] It can simulate a year’s worth of climate at cloud resolving scales in a day.[46]

Techniques that could lead to energy savings, include for example: "reducing floating point precision computation; developing machine learning algorithms to avoid unnecessary computations; and creating a new generation of scalable numerical algorithms that would enable higher throughput in terms of simulated years per wall clock day."[44]

### Parametrization

This section is an excerpt from Parametrization (climate modeling)[ edit ]

## See also

* Atmospheric reanalysis
* Chemical transport model
* Atmospheric Radiation Measurement (ARM) (in the US)
* Climate Data Exchange
* Climateprediction.net
* Numerical Weather Prediction
* Static atmospheric model
* Tropical cyclone prediction model
* Verification and validation of computer simulation models
* CICE sea ice model

## References

1. ↑ IPCC (2014), AR5 Synthesis Report - Climate Change 2014. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, p. 58, https://www.ipcc.ch/site/assets/uploads/2018/02/SYR_AR5_FINAL_full.pdf#page=74, "Box 2.3. ‘Models’ are typically numerical simulations of real-world systems, calibrated and validated using observations from experiments or analogies, and then run using input data representing future climate. Models can also include largely descriptive narratives of possible futures, such as those used in scenario construction. Quantitative and descriptive models are often used together."
2. ↑ "The Science Connecting Extreme Weather to Climate Change". Union of Concerned Scientists. 4 June 2018. https://www.ucs.org/sites/default/files/attach/2018/06/The-Science-Connecting-Extreme-Weather-to-Climate-Change.pdf.
3. ↑ McSweeney, Robert; Tandon, Ayesha (18 November 2024). "Mapped: How climate change affects extreme weather around the world". Climate Central. https://interactive.carbonbrief.org/attribution-studies/index.html.
4. ↑ Zeng, Zubin (25 August 2021). "Is climate change to blame for extreme weather events? Attribution science says yes, for some – here's how it works". https://theconversation.com/is-climate-change-to-blame-for-extreme-weather-events-attribution-science-says-yes-for-some-heres-how-it-works-164941.
5. ↑ Lindsey, Rebecca; Herring, Stephanie; Kapnick, Sarah; van der Wahl, Karin (15 December 2016). "Extreme event attribution: the climate versus weather blame game". National Oceanic and Atmospheric Administration (NOAA). https://www.climate.gov/news-features/understanding-climate/extreme-event-attribution-climate-versus-weather-blame-game.
6. ↑ Giardina, Francesco; Padrón, Ryan S.; Stocker, Benjamin D.; Schumacher, Dominik L.; Seneviratne, Sonia I. (2025-06-16). "Large biases in the frequency of water limitation across Earth system models" (in en). Communications Earth & Environment 6 (1): 1–9. doi:10.1038/s43247-025-02426-7. ISSN 2662-4435. https://www.nature.com/articles/s43247-025-02426-7.
7. ↑ Trenberth, Kevin E. (2022). "Chapter 1: Earth and Climate System". The Changing Flow of Energy Through the Climate System (1 ed.). Cambridge University Press. doi:10.1017/9781108979030. ISBN 978-1-108-97903-0. https://www.cambridge.org/core/product/identifier/9781108979030/type/book.
8. ↑ 8.0 8.1 Svante Arrhenius (1896). "On the influence of carbonic acid in the air upon the temperature of the ground" (in en). Philosophical Magazine and Journal of Science 41 (251): 237–276. doi:10.1080/14786449608620846. https://zenodo.org/record/1431217.
9. ↑ 9.0 9.1 North, Gerald R.; Stevens, Mark J. (2006), "Energy-balance climate models", in Kiehl, J. T., Frontiers in Climate Modelling, Cambridge University, p. 52, doi:10.1017/CBO9780511535857.004, ISBN 9780511535857
10. ↑ North, Gerald R.; Kwang-Yul, Kim (2017), Energy Balance Climate Models, Wiley Series in Atmospheric Physics and Remote Sensing, Wiley-VCH, ISBN 978-3-527-41132-0
11. ↑ Held, Isaac M. (2005). "The gap between simulation and understanding in climate modelling". Bulletin of the American Meteorological Society 86 (11): 1609–1614. doi:10.1175/BAMS-86-11-1609.
12. ↑ Polvani, L. M.; Clement, A. C.; Medeiros, B.; Benedict, J. J.; Simpson, I. R. (2017). "When less is more: opening the door to simpler climate models". Eos (98). doi:10.1029/2017EO079417.
13. ↑ Goode, P. R. (2001). "Earthshine Observations of the Earth's Reflectance". Geophys. Res. Lett. 28 (9): 1671–4. doi:10.1029/2000GL012580. Bibcode: 2001GeoRL..28.1671G. https://authors.library.caltech.edu/50838/1/grl14388.pdf.
14. ↑ "Scientists Watch Dark Side of the Moon to Monitor Earth's Climate". American Geophysical Union. 17 April 2001. http://www.agu.org/sci_soc/prrl/prrl0113.html.
15. ↑ "Climate Change: Global Temperature". NOAA. https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature. Retrieved 6 July 2023.
16. ↑ "Clouds and the Earth's Radiant Energy System". NASA. 2013. http://eospso.gsfc.nasa.gov/ftp_docs/lithographs/CERES_litho.pdf.
17. ↑ "Seawater Samples - Emissivities". ucsb.edu. http://www.icess.ucsb.edu/modis/EMIS/html/seawater.html.
18. ↑ "An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations". J. Climate 19 (12): 2867–81. 15 June 2006. doi:10.1175/JCLI3720.1. Bibcode: 2006JCli...19.2867J. http://www.glue.umd.edu/~sliang/papers/Jin2006.emissivity.pdf.
19. ↑ T.R. Shippert; S.A. Clough; P.D. Brown; W.L. Smith; R.O. Knuteson; S.A. Ackerman. "Spectral Cloud Emissivities from LBLRTM/AERI QME". http://www.arm.gov/publications/proceedings/conf08/extended_abs/shippert_tr.pdf.
20. ↑ A.G. Gorelik; V. Sterljadkin; E. Kadygrov; A. Koldaev. "Microwave and IR Radiometry for Estimation of Atmospheric Radiation Balance and Sea Ice Formation". http://www.arm.gov/publications/proceedings/conf11/extended_abs/gorelik_ag.pdf.
21. ↑ "ACS Climate Science Toolkit - Atmospheric Warming - A Single-Layer Atmosphere Model". American Chemical Society. https://www.acs.org/content/acs/en/climatescience/atmosphericwarming/singlelayermodel.html. Retrieved 2 October 2022.
22. ↑ "ACS Climate Science Toolkit - Atmospheric Warming - A Multi-Layer Atmosphere Model". American Chemical Society. https://www.acs.org/content/acs/en/climatescience/atmosphericwarming/multilayermodel.html. Retrieved 2 October 2022.
23. ↑ "METEO 469: From Meteorology to Mitigation - Understanding Global Warming - Lesson 5 - Modelling of the Climate System - One-Layer Energy Balance Model". Pennsylvania State University College of Mineral and Earth Sciences - Department of Meteorology and Atmospheric Sciences. https://www.e-education.psu.edu/meteo469/node/198. Retrieved 2 October 2022.
24. ↑ 24.0 24.1 Manabe, Syukuro; Wetherald, Richard T. (1 May 1967). "Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity". Journal of the Atmospheric Sciences 24 (3): 241–259. doi:10.1175/1520-0469(1967)024<0241:TEOTAW>2.0.CO;2. Bibcode: 1967JAtS...24..241M.
25. ↑ "Syukuro Manabe Facts". https://www.nobelprize.org/prizes/physics/2021/manabe/facts/.
26. ↑ "Pubs.GISS: Wang and Stone 1980: Effect of ice-albedo feedback on global sensitivity in a one-dimensional...". nasa.gov. http://pubs.giss.nasa.gov/cgi-bin/abstract.cgi?id=wa03100m.
27. ↑ Wang, W.C.; P.H. Stone (1980). "Effect of ice-albedo feedback on global sensitivity in a one-dimensional radiative-convective climate model". J. Atmos. Sci. 37 (3): 545–52. doi:10.1175/1520-0469(1980)037<0545:EOIAFO>2.0.CO;2. Bibcode: 1980JAtS...37..545W.
28. ↑ "Energy Balance Models". shodor.org. http://www.shodor.org/master/environmental/general/energy/application.html.
29. ↑ M.I. Budyko (1969). "The effect of solar radiation variations on the climate of the Earth". Tellus 21 (5): 611–619. doi:10.3402/tellusa.v21i5.10109.
30. ↑ William D. Sellers (1969). "A Global Climatic Model Based on the Energy Balance of the Earth-Atmosphere System". Journal of Applied Meteorology 8 (3): 392–400. doi:10.1175/1520-0450(1969)008<0392:AGCMBO>2.0.CO;2. Bibcode: 1969JApMe...8..392S.
31. ↑ J. Graham Cogley (1990). "Twenty-five years of physical climatology". Global and Planetary Change 2 (3–4): 213–216. doi:10.1016/0921-8181(90)90001-S.
32. ↑ "emics1". pik-potsdam.de. http://www.pik-potsdam.de/emics/.
33. ↑ Mark Bishop. "Box Models: Stocks, Flows, and Residence Times". https://institutebishop.org/Enviro_6.pdf.
34. ↑ NOAA GFDL. "Mathematical models". https://www.gfdl.noaa.gov/wp-content/uploads/files/user_files/pag/lecture2008/lecture13.pdf.
35. ↑ Calvetti, Daniela; Somersalo, Erkki (2012). "Compartment models". Computational Mathematical Modeling: An Integrated Approach Across Scales. Society for Industrial and Applied Mathematics. doi:10.1137/1.9781611972481. ISBN 978-1-61197-247-4. https://epubs.siam.org/doi/pdf/10.1137/1.9781611972481.ch2.
36. ↑ Stommel, Henry (May 1961). "Thermohaline Convection with Two Stable Regimes of Flow". Tellus 13 (2): 224–230. doi:10.3402/tellusa.v13i2.9491. ISSN 0040-2826.
37. ↑ Sarmiento, J.L.; Toggweiler, J.R. (1984). "A new model for the role of the oceans in determining atmospheric P CO 2". Nature 308 (5960): 621–24. doi:10.1038/308621a0. Bibcode: 1984Natur.308..621S.
38. ↑ Bock, L.; Lauer, A.; Schlund, M.; Barreiro, M; Bellouin, N.; Jones, C.; Meehl, G.A.; Predoi, V. et al. (2020). "Quantifying Progress Across Different CMIP Phases With the ESMValTool". Journal of Geophysical Research: Atmospheres 125 (21). doi:10.1029/2019JD032321.
39. ↑ "Coupled Model Intercomparison Project". World Climate Research Program. https://wcrp-cmip.org.
40. ↑ Meehl, Gerald A.; Boer, George J.; Covey, Curt; Latif, Mojib; Stouffer, Ronald J. (14 October 1997). "Intercomparison makes for a better climate model". Eos, Transactions American Geophysical Union 78 (41). doi:10.1029/97EO00276.
41. ↑ "Climate Models and Their Evaluation". http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter8.pdf.
42. ↑ "U.S. National Research Council Report, A National Strategy for Advancing Climate Modeling". http://dels.nas.edu/Report/National-Strategy-Advancing-Climate/13430.
43. ↑ "U.S. National Research Council Report-in-Brief, A National Strategy for Advancing Climate Modeling". http://dels.nas.edu/Materials/Report-In-Brief/4291-Climate-Modeling.
44. ↑ 44.0 44.1 Loft, Richard (2020). "Earth System Modeling Must Become More Energy Efficient". Eos (101). doi:10.1029/2020EO147051. ISSN 2324-9250. https://eos.org/opinions/earth-system-modeling-must-become-more-energy-efficient.
45. ↑ Trader, Tiffany (2021). "Frontier to Meet 20MW Exascale Power Target Set by DARPA in 2008" (in en-US). https://www.hpcwire.com/2021/07/14/frontier-to-meet-20mw-exascale-power-target-set-by-darpa-in-2008/.
46. ↑ "Cloud-resolving climate model meets world's fastest supercomputer" (in en-US). https://www.sandia.gov/labnews/2023/04/06/cloud-resolving-climate-model-meets-worlds-fastest-supercomputer/.

## External links

| The Wikibook Historical Geology has a page on the topic of: Climate models

|

* Timeline: The History of Climate Modelling CarbonBrief, 16 January 2018
* Why results from the next generation of climate models matter CarbonBrief, Guest post by Belcher, Boucher, Sutton, 21 March 2019

Climate models on the web:

* NCAR/UCAR Community Climate System Model (CCSM)
* Do it yourself climate prediction
* Primary research GCM developed by NASA/GISS (Goddard Institute for Space Studies)
* Original NASA/GISS global climate model (GCM) with a user-friendly interface for PCs and Macs
* CCCma model info and interface to retrieve model data

* v
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Climate change

Overview

* Causes of climate change
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* By country and region

Causes

| Overview|

* Climate system
* Greenhouse effect (Carbon dioxide in the atmosphere of Earth)
* Scientific consensus on climate change

|

Sources|

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Effects and issues

| Physical|

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|

Flora and fauna|

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Social and economic|

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* Economic impacts
* U.S. insurance industry
* Fisheries
* Gender
* Health
* Infectious diseases
* Mental health
* In the United Kingdom
* In the Philippines
* Human rights
* Indigenous peoples
* Migration
* Poverty
* Psychological impacts
* Security and conflict
* Urban flooding
* Water scarcity
* Water security

By country and region|

* Africa
* Americas
* Antarctica
* Arctic
* Asia
* Australia
* Caribbean
* Europe
* Middle East and North Africa
* Small island countries
* by individual country

Mitigation

| Economics and finance|

* Carbon budget
* Carbon emission trading
* Carbon offsets and credits
* Gold Standard (carbon offset standard)
* Carbon price
* Carbon tax
* Climate debt
* Climate finance
* Climate risk insurance
* Co-benefits of climate change mitigation
* Economics of climate change mitigation
* Fossil fuel divestment
* Green Climate Fund
* Low-carbon economy
* Net zero emissions

|

Energy|

* Carbon capture and storage
* Energy transition
* Fossil fuel phase-out
* Nuclear power
* Renewable energy
* Sustainable energy

Preserving and enhancing
carbon sinks|

* Blue carbon
* Carbon dioxide removal
* Carbon sequestration
* Direct air capture
* Carbon farming
* Climate-smart agriculture
* Forest management
* afforestation
* forestry for carbon sequestration
* REDD+
* reforestation
* Land use, land-use change, and forestry (LULUCF and AFOLU)
* Nature-based solutions

Other|

* Earth Strike
* Fridays for Future
* Geoengineering
* Individual action on climate change
* Positive tipping points
* Sustainable architecture
* Sustainable transport

Society and adaptation

| Society|

* Business action
* Climate action
* Climate emergency declaration
* Climate movement
* School Strike for Climate
* Denial
* Ecological grief
* Governance
* Justice
* Litigation
* Politics
* Public opinion
* Women

|

Adaptation|

* Adaptation strategies on the German coast
* Adaptive capacity
* Disaster risk reduction
* Ecosystem-based adaptation
* Flood control
* Loss and damage
* Managed retreat
* Nature-based solutions
* Resilience
* Risk
* Vulnerability
* The Adaptation Fund
* National Adaptation Programme of Action

Communication|

* Climate Change Performance Index
* Climate crisis (term)
* Climate spiral
* Education
* Media coverage
* Popular culture depictions
* art
* fiction
* video games
* Warming stripes

International agreements|

* Glasgow Climate Pact
* Integrity Council for the Voluntary Carbon Market
* Taskforce on Scaling Voluntary Carbon Markets
* Kyoto Protocol
* Paris Agreement
* Cooperative mechanisms under Article 6 of the Paris Agreement
* Nationally determined contributions
* Sustainable Development Goal 13
* United Nations Framework Convention on Climate Change

Background and theory

| Measurements|

* Global surface temperature
* Instrumental temperature record
* Proxy
* Satellite temperature measurement

|

Theory|

* Albedo
* Carbon cycle
* atmospheric
* biologic
* oceanic
* permafrost
* Carbon sink
* Climate sensitivity
* Climate variability and change
* Cloud feedback
* Cloud forcing
* Fixed anvil temperature hypothesis
* Cryosphere
* Earth's energy budget
* Extreme event attribution
* Feedbacks
* Global warming potential
* Illustrative model of greenhouse effect on climate change
* Orbital forcing
* Radiative forcing

Research and modelling|

* Climate change scenario
* Climate model
* Coupled Model Intercomparison Project
* Intergovernmental Panel on Climate Change (IPCC)
* IPCC Sixth Assessment Report
* Paleoclimatology
* Representative Concentration Pathway
* Shared Socioeconomic Pathways

* '
* Category
* Glossary
* Index

* v
* t
* e

Atmospheric, oceanographic, cryospheric, and climate models

Model types

* Atmospheric model
* Oceanographic model
* Cryospheric model
* Climate model
* Numerical weather prediction
* Tropical cyclone forecast model
* Atmospheric dispersion modeling
* Chemical transport model
* Upper-atmospheric models
* Ensemble forecasting
* Model output statistics
* Meteorological reanalysis
* Parametrization

Specific models

| Climate|

* IGCM
* HadCM3
* HadGEM1
* GFDL CM2.X
* CGCM
* CCSM
* CESM
* CFSv2
* ECHAM

|

Global weather|

* IFS (ECMWF)
* FIM
* GEM / GDPS
* FV3 GFS
* MPAS
* NAEFS
* NAVGEM
* UM
* JMA-GSM
* GME / ICON
* ARPEGE

Regional and mesoscale atmospheric|

* NAM
* RR / RAP
* RAMS
* WRF
* RAQMS
* HIRLAM
* LAPS
* RPM
* HWRF

Regional and mesoscale oceanographic|

* HyCOM
* ROMS
* POM
* MOM
* FVCOM
* MITgcm
* FESOM
* ADCIRC

Atmospheric dispersion|

* ADMS
* AERMOD
* ATSTEP
* AUSTAL2000
* CALPUFF
* DISPERSION21
* ISC3
* MEMO
* MERCURE
* NAME
* OSPM
* PUFF-PLUME
* RIMPUFF
* SAFE AIR

Chemical transport|

* CLaMS
* MOZART
* GEOS-Chem

Land surface parametrization|

* JULES
* CLASS
* ISBA

Cryospheric models|

* CICE

Discontinued|

* AVN / MRF / GSM / GFS
* Eta
* LFM
* MM5
* NGM
* NOGAPS
* RUC

* Mathematical model
* Statistical model
* Scientific modelling
* Computer simulation

* v
* t
* e

Scientific modelling

Biological|

* Cellular model
* Chemical process modeling
* Ecosystem model
* Infectious disease model
* Metabolic network modelling
* Modelling biological systems
* Protein structure prediction

Environmental|

* Atmospheric model
* Chemical transport model
* Climate model
* Geologic modelling
* Groundwater model
* Hydrological model
* Hydrological transport model
* Modular Ocean Model
* Wildfire modeling

Sustainability|

* Energy modeling
* Integrated assessment modelling
* Population model

Social|

* Biopsychosocial model
* Business process modelling
* Catastrophe modeling
* Construction and management simulation
* Crime mapping
* Economic model
* Input-output model

Related topics|

* Data visualization
* List of computer simulation software
* Mathematical modeling
* Systems theory
* Systems thinking
* Visual analytics

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