General circulation model

A general circulation model (GCM) is a type of climate model. It employs a mathematical model of the general circulation of a planetary atmosphere or ocean. It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for computer programs used to simulate the Earth's atmosphere or oceans. Atmospheric and oceanic GCMs (AGCM and OGCM) are key components along with sea ice and land-surface components.

GCMs and global climate models are used for weather forecasting, understanding the climate and forecasting climate change.

Versions designed for decade to century time scale climate applications were originally created by Syukuro Manabe and Kirk Bryan at the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey.[1] These models are based on the integration of a variety of fluid dynamical, chemical and sometimes biological equations.

AtmosphericModelSchematic
Climate models are systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. To "run" a model, scientists divide the planet into a 3-dimensional grid, apply the basic equations, and evaluate the results. Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points.[1]
This visualization shows early test renderings of a global computational model of Earth's atmosphere based on data from NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5).

Terminology

The acronym GCM originally stood for General Circulation Model. Recently, a second meaning came into use, namely Global Climate Model. While these do not refer to the same thing, General Circulation Models are typically the tools used for modelling climate, and hence the two terms are sometimes used interchangeably. However, the term "global climate model" is ambiguous and may refer to an integrated framework that incorporates multiple components including a general circulation model, or may refer to the general class of climate models that use a variety of means to represent the climate mathematically.

History

In 1956, Norman Phillips developed a mathematical model that could realistically depict monthly and seasonal patterns in the troposphere. It became the first successful climate model.[2][3] Following Phillips's work, several groups began working to create GCMs.[4] The first to combine both oceanic and atmospheric processes was developed in the late 1960s at the NOAA Geophysical Fluid Dynamics Laboratory.[1] By the early 1980s, the United States' National Center for Atmospheric Research had developed the Community Atmosphere Model; this model has been continuously refined.[5] In 1996, efforts began to model soil and vegetation types.[6] Later the Hadley Centre for Climate Prediction and Research's HadCM3 model coupled ocean-atmosphere elements.[4] The role of gravity waves was added in the mid-1980s. Gravity waves are required to simulate regional and global scale circulations accurately.[7]

Atmospheric and oceanic models

Atmospheric (AGCMs) and oceanic GCMs (OGCMs) can be coupled to form an atmosphere-ocean coupled general circulation model (CGCM or AOGCM). With the addition of submodels such as a sea ice model or a model for evapotranspiration over land, AOGCMs become the basis for a full climate model.[8]

Structure

Three-dimensional (more properly four-dimensional) GCMs apply discrete equations for fluid motion and integrate these forward in time. They contain parameterisations for processes such as convection that occur on scales too small to be resolved directly.

A simple general circulation model (SGCM) consists of a dynamic core that relates properties such as temperature to others such as pressure and velocity. Examples are programs that solve the primitive equations, given energy input and energy dissipation in the form of scale-dependent friction, so that atmospheric waves with the highest wavenumbers are most attenuated. Such models may be used to study atmospheric processes, but are not suitable for climate projections.

Atmospheric GCMs (AGCMs) model the atmosphere (and typically contain a land-surface model as well) using imposed sea surface temperatures (SSTs).[9] They may include atmospheric chemistry.

AGCMs consist of a dynamical core which integrates the equations of fluid motion, typically for:

  • surface pressure
  • horizontal components of velocity in layers
  • temperature and water vapor in layers
  • radiation, split into solar/short wave and terrestrial/infrared/long wave
  • parameters for:

A GCM contains prognostic equations that are a function of time (typically winds, temperature, moisture, and surface pressure) together with diagnostic equations that are evaluated from them for a specific time period. As an example, pressure at any height can be diagnosed by applying the hydrostatic equation to the predicted surface pressure and the predicted values of temperature between the surface and the height of interest. Pressure is used to compute the pressure gradient force in the time-dependent equation for the winds.

OGCMs model the ocean (with fluxes from the atmosphere imposed) and may contain a sea ice model. For example, the standard resolution of HadOM3 is 1.25 degrees in latitude and longitude, with 20 vertical levels, leading to approximately 1,500,000 variables.

AOGCMs (e.g. HadCM3, GFDL CM2.X) combine the two submodels. They remove the need to specify fluxes across the interface of the ocean surface. These models are the basis for model predictions of future climate, such as are discussed by the IPCC. AOGCMs internalise as many processes as possible. They have been used to provide predictions at a regional scale. While the simpler models are generally susceptible to analysis and their results are easier to understand, AOGCMs may be nearly as hard to analyse as the climate itself.

Grid

The fluid equations for AGCMs are made discrete using either the finite difference method or the spectral method. For finite differences, a grid is imposed on the atmosphere. The simplest grid uses constant angular grid spacing (i.e., a latitude / longitude grid). However, non-rectangular grids (e.g., icosahedral) and grids of variable resolution[10] are more often used.[11] The LMDz model can be arranged to give high resolution over any given section of the planet. HadGEM1 (and other ocean models) use an ocean grid with higher resolution in the tropics to help resolve processes believed to be important for the El Niño Southern Oscillation (ENSO). Spectral models generally use a gaussian grid, because of the mathematics of transformation between spectral and grid-point space. Typical AGCM resolutions are between 1 and 5 degrees in latitude or longitude: HadCM3, for example, uses 3.75 in longitude and 2.5 degrees in latitude, giving a grid of 96 by 73 points (96 x 72 for some variables); and has 19 vertical levels. This results in approximately 500,000 "basic" variables, since each grid point has four variables (u,v, T, Q), though a full count would give more (clouds; soil levels). HadGEM1 uses a grid of 1.875 degrees in longitude and 1.25 in latitude in the atmosphere; HiGEM, a high-resolution variant, uses 1.25 x 0.83 degrees respectively.[12] These resolutions are lower than is typically used for weather forecasting.[13] Ocean resolutions tend to be higher, for example HadCM3 has 6 ocean grid points per atmospheric grid point in the horizontal.

For a standard finite difference model, uniform gridlines converge towards the poles. This would lead to computational instabilities (see CFL condition) and so the model variables must be filtered along lines of latitude close to the poles. Ocean models suffer from this problem too, unless a rotated grid is used in which the North Pole is shifted onto a nearby landmass. Spectral models do not suffer from this problem. Some experiments use geodesic grids[14] and icosahedral grids, which (being more uniform) do not have pole-problems. Another approach to solving the grid spacing problem is to deform a Cartesian cube such that it covers the surface of a sphere.[15]

Flux buffering

Some early versions of AOGCMs required an ad hoc process of "flux correction" to achieve a stable climate. This resulted from separately prepared ocean and atmospheric models that each used an implicit flux from the other component different than that component could produce. Such a model failed to match observations. However, if the fluxes were 'corrected', the factors that led to these unrealistic fluxes might be unrecognised, which could affect model sensitivity. As a result, the vast majority of models used in the current round of IPCC reports do not use them. The model improvements that now make flux corrections unnecessary include improved ocean physics, improved resolution in both atmosphere and ocean, and more physically consistent coupling between atmosphere and ocean submodels. Improved models now maintain stable, multi-century simulations of surface climate that are considered to be of sufficient quality to allow their use for climate projections.[16]

Convection

Moist convection releases latent heat and is important to the Earth's energy budget. Convection occurs on too small a scale to be resolved by climate models, and hence it must be handled via parameters. This has been done since the 1950s. Akio Arakawa did much of the early work, and variants of his scheme are still used,[17] although a variety of different schemes are now in use.[18][19][20] Clouds are also typically handled with a parameter, for a similar lack of scale. Limited understanding of clouds has limited the success of this strategy, but not due to some inherent shortcoming of the method.[21]

Software

Most models include software to diagnose a wide range of variables for comparison with observations or study of atmospheric processes. An example is the 2-metre temperature, which is the standard height for near-surface observations of air temperature. This temperature is not directly predicted from the model but is deduced from surface and lowest-model-layer temperatures. Other software is used for creating plots and animations.

Projections

Projected annual mean surface air temperature from 1970-2100, based on SRES emissions scenario A1B, using the NOAA GFDL CM2.1 climate model (credit: NOAA Geophysical Fluid Dynamics Laboratory).[22]

Coupled AOGCMs use transient climate simulations to project/predict climate changes under various scenarios. These can be idealised scenarios (most commonly, CO2 emissions increasing at 1%/yr) or based on recent history (usually the "IS92a" or more recently the SRES scenarios). Which scenarios are most realistic remains uncertain.

The 2001 IPCC Third Assessment Report F igure 9.3 shows the global mean response of 19 different coupled models to an idealised experiment in which emissions increased at 1% per year.[23] Figure 9.5 shows the response of a smaller number of models to more recent trends. For the 7 climate models shown there, the temperature change to 2100 varies from 2 to 4.5 °C with a median of about 3 °C.

Future scenarios do not include unknown events – for example, volcanic eruptions or changes in solar forcing. These effects are believed to be small in comparison to greenhouse gas (GHG) forcing in the long term, but large volcanic eruptions, for example, can exert a substantial temporary cooling effect.

Human GHG emissions are a model input, although it is possible to include an economic/technological submodel to provide these as well. Atmospheric GHG levels are usually supplied as an input, though it is possible to include a carbon cycle model that reflects vegetation and oceanic processes to calculate such levels.

Emissions scenarios

Projected change in annual mean surface air temperature from the late 20th century to the middle 21st century, based on SRES emissions scenario A1B
Projected change in annual mean surface air temperature from the late 20th century to the middle 21st century, based on SRES emissions scenario A1B (credit: NOAA Geophysical Fluid Dynamics Laboratory).[22]

For the six SRES marker scenarios, IPCC (2007:7–8) gave a "best estimate" of global mean temperature increase (2090–2099 relative to the period 1980–1999) of 1.8 °C to 4.0 °C.[24] Over the same time period, the "likely" range (greater than 66% probability, based on expert judgement) for these scenarios was for a global mean temperature increase of 1.1 to 6.4 °C.[24]

In 2008 a study made climate projections using several emission scenarios.[25] In a scenario where global emissions start to decrease by 2010 and then declined at a sustained rate of 3% per year, the likely global average temperature increase was predicted to be 1.7 °C above pre-industrial levels by 2050, rising to around 2 °C by 2100. In a projection designed to simulate a future where no efforts are made to reduce global emissions, the likely rise in global average temperature was predicted to be 5.5 °C by 2100. A rise as high as 7 °C was thought possible, although less likely.

Another no-reduction scenario resulted in a median warming over land (2090–99 relative to the period 1980–99) of 5.1 °C. Under the same emissions scenario but with a different model, the predicted median warming was 4.1 °C.[26]

Model accuracy

Hadcm3-era-sst-annual
SST errors in HadCM3
Climate model NA annual precipitation 2002
North American precipitation from various models
Global Warming Predictions
Temperature predictions from some climate models assuming the SRES A2 emissions scenario

AOGCMs internalise as many processes as are sufficiently understood. However, they are still under development and significant uncertainties remain. They may be coupled to models of other processes, such as the carbon cycle, so as to better model feedbacks. Most recent simulations show "plausible" agreement with the measured temperature anomalies over the past 150 years, when driven by observed changes in greenhouse gases and aerosols. Agreement improves by including both natural and anthropogenic forcings.[27][28][29]

Imperfect models may nevertheless produce useful results. GCMs are capable of reproducing the general features of the observed global temperature over the past century.[27]

A debate over how to reconcile climate model predictions that upper air (tropospheric) warming should be greater than observed surface warming, some of which appeared to show otherwise,[30] was resolved in favour of the models, following data revisions.

Cloud effects are a significant area of uncertainty in climate models. Clouds have competing effects on climate. They cool the surface by reflecting sunlight into space; they warm it by increasing the amount of infrared radiation transmitted from the atmosphere to the surface.[31] In the 2001 IPCC report possible changes in cloud cover were highlighted as a major uncertainty in predicting climate.[32][33]

Climate researchers around the world use climate models to understand the climate system. Thousands of papers have been published about model-based studies. Part of this research is to improve the models.

In 2000, a comparison between measurements and dozens of GCM simulations of ENSO-driven tropical precipitation, water vapor, temperature, and outgoing longwave radiation found similarity between measurements and simulation of most factors. However the simulated change in precipitation was about one-fourth less than what was observed. Errors in simulated precipitation imply errors in other processes, such as errors in the evaporation rate that provides moisture to create precipitation. The other possibility is that the satellite-based measurements are in error. Either indicates progress is required in order to monitor and predict such changes.[34]

A more complete discussion of climate models is provided in the IPCC's Third Assessment Report.[35]

  • The model mean exhibits good agreement with observations.
  • The individual models often exhibit worse agreement with observations.
  • Many of the non-flux adjusted models suffered from unrealistic climate drift up to about 1 °C/century in global mean surface temperature.
  • The errors in model-mean surface air temperature rarely exceed 1 °C over the oceans and 5 °C over the continents; precipitation and sea level pressure errors are relatively greater but the magnitudes and patterns of these quantities are recognisably similar to observations.
  • Surface air temperature is particularly well simulated, with nearly all models closely matching the observed magnitude of variance and exhibiting a correlation > 0.95 with the observations.
  • Simulated variance of sea level pressure and precipitation is within ±25% of observed.
  • All models have shortcomings in their simulations of the present day climate of the stratosphere, which might limit the accuracy of predictions of future climate change.
    • There is a tendency for the models to show a global mean cold bias at all levels.
    • There is a large scatter in the tropical temperatures.
    • The polar night jets in most models are inclined poleward with height, in noticeable contrast to an equatorward inclination of the observed jet.
    • There is a differing degree of separation in the models between the winter sub-tropical jet and the polar night jet.
  • For nearly all models the r.m.s. error in zonal- and annual-mean surface air temperature is small compared with its natural variability.
    • There are problems in simulating natural seasonal variability. ( 2000)
      • In flux-adjusted models, seasonal variations are simulated to within 2 K of observed values over the oceans. The corresponding average over non-flux-adjusted models shows errors up to about 6 K in extensive ocean areas.
      • Near-surface land temperature errors are substantial in the average over flux-adjusted models, which systematically underestimates (by about 5 K) temperature in areas of elevated terrain. The corresponding average over non-flux-adjusted models forms a similar error pattern (with somewhat increased amplitude) over land.
      • In Southern Ocean mid-latitudes, the non-flux-adjusted models overestimate the magnitude of January-minus-July temperature differences by ~5 K due to an overestimate of summer (January) near-surface temperature. This error is common to five of the eight non-flux-adjusted models.
      • Over Northern Hemisphere mid-latitude land areas, zonal mean differences between July and January temperatures simulated by the non-flux-adjusted models show a greater spread (positive and negative) about observed values than results from the flux-adjusted models.
      • The ability of coupled GCMs to simulate a reasonable seasonal cycle is a necessary condition for confidence in their prediction of long-term climatic changes (such as global warming), but it is not a sufficient condition unless the seasonal cycle and long-term changes involve similar climatic processes.
  • Coupled climate models do not simulate with reasonable accuracy clouds and some related hydrological processes (in particular those involving upper tropospheric humidity). Problems in the simulation of clouds and upper tropospheric humidity, remain worrisome because the associated processes account for most of the uncertainty in climate model simulations of anthropogenic change.

The precise magnitude of future changes in climate is still uncertain;[36] for the end of the 21st century (2071 to 2100), for SRES scenario A2, the change of global average SAT change from AOGCMs compared with 1961 to 1990 is +3.0 °C (5.4 °F) and the range is +1.3 to +4.5 °C (+2.3 to 8.1 °F).

The IPCC's Fifth Assessment Report asserted "very high confidence that models reproduce the general features of the global-scale annual mean surface temperature increase over the historical period". However, the report also observed that the rate of warming over the period 1998–2012 was lower than that predicted by 111 out of 114 Coupled Model Intercomparison Project climate models.[37]

Relation to weather forecasting

The global climate models used for climate projections are similar in structure to (and often share computer code with) numerical models for weather prediction, but are nonetheless logically distinct.

Most weather forecasting is done on the basis of interpreting numerical model results. Since forecasts are short – typically a few days or a week – such models do not usually contain an ocean model but rely on imposed SSTs. They also require accurate initial conditions to begin the forecast – typically these are taken from the output of a previous forecast, blended with observations. Predictions must require only a few hours; but because they only cover a one-week the models can be run at higher resolution than in climate mode. Currently the ECMWF runs at 9 km (5.6 mi) resolution[38] as opposed to the 100-to-200 km (62-to-124 mi) scale used by typical climate model runs. Often local models are run using global model results for boundary conditions, to achieve higher local resolution: for example, the Met Office runs a mesoscale model with an 11 km (6.8 mi) resolution[39] covering the UK, and various agencies in the US employ models such as the NGM and NAM models. Like most global numerical weather prediction models such as the GFS, global climate models are often spectral models[40] instead of grid models. Spectral models are often used for global models because some computations in modeling can be performed faster, thus reducing run times.

Computations

Climate models use quantitative methods to simulate the interactions of the atmosphere, oceans, land surface and ice.

All climate models take account of incoming energy as short wave electromagnetic radiation, chiefly visible and short-wave (near) infrared, as well as outgoing energy as long wave (far) infrared electromagnetic radiation from the earth. Any imbalance results in a change in temperature.

The most talked-about models of recent years relate temperature to emissions of greenhouse gases. These models project an upward trend in the surface temperature record, as well as a more rapid increase in temperature at higher altitudes.[41]

Three (or more properly, four since time is also considered) dimensional GCM's discretise the equations for fluid motion and energy transfer and integrate these over time. They also contain parametrisations for processes such as convection that occur on scales too small to be resolved directly.

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[42]) combine the two models.

Models range in complexity:

  • 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), or horizontally
  • Finally, (coupled) atmosphere–ocean–sea ice global climate models discretise and solve the full equations for mass and energy transfer and radiant exchange.
  • Box models treat flows across and within ocean basins.

Other submodels can be interlinked, such as land use, allowing researchers to predict the interaction between climate and ecosystems.

Other climate models

Earth-system models of intermediate complexity (EMICs)

The Climber-3 model uses a 2.5-dimensional statistical-dynamical model with 7.5° × 22.5° resolution and time step of 1/2 a day. An oceanic submodel is MOM-3 (Modular Ocean Model) with a 3.75° × 3.75° grid and 24 vertical levels.[43]

Radiative-convective models (RCM)

One-dimensional, radiative-convective models were used to verify basic climate assumptions in the 1980s and 1990s.[44]

Earth system models

GCMs can form part of Earth system models, e.g. by coupling ice sheet models for the dynamics of the Greenland and Antarctic ice sheets, and one or more chemical transport models (CTMs) for species important to climate. Thus a carbon chemistry transport model may allow a GCM to better predict anthropogenic changes in carbon dioxide concentrations. In addition, this approach allows accounting for inter-system feedback: e.g. chemistry-climate models allow the effects of climate change on ozone hole to be studied.[45]

See also

References

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Further reading

External links

Atsumu Ohmura

Atsumu Ohmura (大村 纂, Ōmura Atsumu, born 1942) is a Japanese climatologist, known for his discovery and contributions to the theory of global dimming.Ohmura was born in the Bunkyō ward of Tokyo in 1942. In 1965 he graduated with a B.Sc from the University of Tokyo and in 1969 received an M.Sc from McGill University. He later received a Dr.sc.nat from the ETH Zurich.Ohmura is a professor emeritus of the Institute for Atmospheric and Climate Science at ETH Zurich, where he was the leader of the institute's climate research group. The group has strong interests in the planetary boundary layer and the cryosphere including its interaction with the atmosphere and ocean. The group maintains a general circulation model. Ohmura also initiated the Baseline Surface Radiation Network (BSRN).

Climate of Mars

The climate of the planet Mars has been a topic of scientific curiosity for centuries, in part because it is the only terrestrial planet whose surface can be directly observed in detail from the Earth with help from a telescope.

Although Mars is smaller than the Earth, 11% of Earth's mass, and 50% farther from the Sun than the Earth, its climate has important similarities, such as the presence of polar ice caps, seasonal changes and observable weather patterns. It has attracted sustained study from planetologists and climatologists. While Mars' climate has similarities to Earth's, including periodic ice ages, there are also important differences, such as much lower thermal inertia. Mars' atmosphere has a scale height of approximately 11 km (36,000 ft), 60% greater than that on Earth. The climate is of considerable relevance to the question of whether life is or was present on the planet. The climate briefly received more interest in the news due to NASA measurements indicating increased sublimation of one near-polar region leading to some popular press speculation that Mars was undergoing a parallel bout of global warming, although Mars' average temperature has actually cooled in recent decades, and the polar caps themselves are growing.

Mars has been studied by Earth-based instruments since the 17th century, but it is only since the exploration of Mars began in the mid-1960s that close-range observation has been possible. Flyby and orbital spacecraft have provided data from above, while landers and rovers have measured atmospheric conditions directly. Advanced Earth-orbital instruments today continue to provide some useful "big picture" observations of relatively large weather phenomena.

The first Martian flyby mission was Mariner 4, which arrived in 1965. That quick two-day pass (July 14–15, 1965) with crude instruments contributed little to the state of knowledge of Martian climate. Later Mariner missions (Mariner 6, and Mariner 7) filled in some of the gaps in basic climate information. Data-based climate studies started in earnest with the Viking program landers in 1975 and continue with such probes as the Mars Reconnaissance Orbiter.

This observational work has been complemented by a type of scientific computer simulation called the Mars general circulation model. Several different iterations of MGCM have led to an increased understanding of Mars as well as the limits of such models.

Common modeling infrastructure

Common modeling infrastructure refers to software libraries that can be shared across multiple institutions in order to increase software reuse and interoperability in complex modeling systems. Early initiatives were in the climate and weather domain, where software components representing distinct physical domains (for example, ocean or atmosphere) tended to be developed by domain specialists, often at different organizations. In order to create complete applications, these needed to be combined together, using for instance a general circulation model, that transfers data between different components. An additional challenge is that these models generally require supercomputers to run, to account for the collected data and for data analyses. Thus, it was important to provide an efficient massively parallel computer system, and the processing hardware and software, to account for all the different workloads and communication channels.

ECHAM

ECHAM is a general circulation model (GCM) developed by the Max Planck Institute for Meteorology, one of the research organisations of the Max Planck Society. It was created by modifying global forecast models developed by ECMWF to be used for climate research. The model was given its name as a combination of its origin (the 'EC' being short for 'ECMWF') and the place of development of its parameterisation package, Hamburg. The default configuration of the model resolves the atmosphere up to 10 hPa (primarily used to study the lower atmosphere), but it can be reconfigured to 0.01 hPa for use in studying the stratosphere and lower mesosphere.Different versions of ECHAM, primarily different configurations of ECHAM5, have been the basis of many publications, listed on the ECHAM5 website [1].

FESOM

FESOM (Finite-Element/volumE Sea ice-Ocean Model) is a

multi-resolution ocean general circulation model that solves the equations

of motion describing the ocean and sea ice using finite-element and

finite-volume methods on unstructured computational grids. The model

is developed and supported by researchers at the Alfred Wegener

Institute, Helmholtz Centre for Polar and Marine Research (AWI), in Bremerhaven,

Germany.

Geophysical Fluid Dynamics Laboratory Coupled Model

Geophysical Fluid Dynamics Laboratory Coupled Model (GFDL CM2.5) is a coupled atmosphere–ocean general circulation model (AOGCM) developed at the NOAA Geophysical Fluid Dynamics Laboratory in the United States. It is one of the leading climate models used in the Fourth Assessment Report of the IPCC, along with models developed at the Max Planck Institute for Climate Research, the Hadley Centre and the National Center for Atmospheric Research.

HadCM3

HadCM3 (abbreviation for Hadley Centre Coupled Model, version 3) is a coupled atmosphere-ocean general circulation model (AOGCM) developed at the Hadley Centre in the United Kingdom. It was one of the major models used in the IPCC Third Assessment Report in 2001.

Unlike earlier AOGCMs at the Hadley Centre and elsewhere (including its predecessor HadCM2), HadCM3 does not need flux adjustment (additional "artificial" heat and freshwater fluxes at the ocean surface) to produce a good simulation. The higher ocean resolution of HadCM3 is a major factor in this; other factors include a good match between the atmospheric and oceanic components; and an improved ocean mixing scheme (Gent and McWilliams). HadCM3 has been run to produce simulations for periods of over a thousand years, showing little drift in its surface climate.

HadCM3 is composed of two components: the atmospheric model HadAM3 and the ocean model HadOM3 (which includes a sea ice model). Simulations use a 360-day calendar, where each month is 30 days.

Intermediate General Circulation Model

The Reading Intermediate General Circulation Model (IGCM), is a simplified or "intermediate" Global climate model, which is developed by members of the Department of Meteorology at the University of Reading, and by members of the Stratospheric Dynamics and Chemistry Group of the Department of Atmospheric and Oceanic Sciences at McGill University.

The IGCM is based on the primitive-equations baroclinic model of Hoskins and Simmons, which has been converted to run on workstations. Several variations have been developed by adjusting representations of the physics.

IGCM1: Portable version of the original spectral, dry baroclinic model formulated in sigma-levels, with an option for Newtonian relaxation and Rayleigh friction, with no surface.

IGCM2: Includes simplified moist parameterisations, a cheap "radiation scheme" (i.e. constant tropospheric cooling), a bulk formulation scheme for the boundary layer, fixed surface temperatures and humidity, uniform vertical diffusion, and can advect tracers.

IGCM3x: Intermediate climate model that includes more sophisticated moisture/clouds parameterisations, a radiation scheme with various gas absorbers and a more realistic surface with an orography and land and sea surface schemes.The adiabatic version, IGCM1, is freely available. Access to IGCM2 and IGCM3 is restricted to members of the Department of Meteorology at the University of Reading and collaborating researchers.

Joseph Smagorinsky

Joseph Smagorinsky (29 January 1924 – 21 September 2005) was an American meteorologist and the first director of the National Oceanic and Atmospheric Administration (NOAA)'s Geophysical Fluid Dynamics Laboratory (GFDL).

List of ocean circulation models

This is a list of ocean circulation models, as used in physical oceanography. Ocean circulation models can also be used to study chemical oceanography, biological oceanography, geological oceanography, and climate science.

MIT General Circulation Model

The MIT General Circulation Model (MITgcm) is a numerical computer code that solves the equations of motion governing the ocean or Earth's atmosphere using the finite volume method. It was developed at the Massachusetts Institute of Technology and was one of the first non-hydrostatic models of the ocean. It has an automatically generated adjoint that allows the model to be used for data assimilation.

Mars general circulation model

The Mars general circulation model (MGCM) is the result of a research project by NASA to understand the nature of the general circulation of the atmosphere of Mars, how that circulation is driven and how it affects the climate of Mars in the long term.

Norman A. Phillips

Norman A. Phillips (July 9, 1923 – March 15, 2019) was an American meteorologist notable for his contributions to geophysical fluid dynamics. In 1956, he developed a mathematical model that could realistically depict monthly and seasonal patterns in the troposphere, which became the first successful general circulation model of climate.Phillips was born in Chicago, Illinois. His parents, Alton Elmer Anton Phillips and Linnea (Larson) Phillips, were the children of Swedish immigrants to the United States. He enrolled at the University of Chicago in 1940, intending to study chemistry, but the start of World War II and the influence of Carl-Gustaf Rossby inspired him to join the Army Air Corps in 1943.After graduating from the meteorological cadet program at Chanute Field as fourth in a class of 391, he served in the Azores and then at Westover Field until October 1946. He returned to the University of Chicago after the war, earning his bachelor's degree in 1947, his master's in 1948, and his PhD in 1951.Shortly before completing his PhD, Phillips accepted a position on the research staff of the Electronic Computer Project at the Institute for Advanced Study in Princeton, New Jersey. In 1956, he was recruited by the Department of Meteorology at the Massachusetts Institute of Technology, eventually becoming department head.In 1974, Phillips left MIT to join the National Weather Service at the National Meteorological Center, where he served as the principal scientist of the NMC Development Division. When he retired, the Nested Grid Model was popularly known as "Norm's Great Model."Phillips died at Grace House in Windham, New Hampshire on March 15, 2019. He published his last academic paper, on the Foucault pendulum, at the age of 90.

Ocean general circulation model

Ocean general circulation models (OGCMs) are a particular kind of general circulation model to describe physical and thermodynamical processes in oceans. The oceanic general circulation is defined as the horizontal space scale and time scale larger than mesoscale (of order 100 km and 6 months). They depict oceans using a three-dimensional grid that include active thermodynamics and hence are most directly applicable to climate studies. They are the most advanced tools currently available for simulating the response of the global ocean system to increasing greenhouse gas concentrations. A hierarchy of OGCMs have been developed that include varying degrees of spatial coverage, resolution, geographical realism, process detail, etc.

Regional Ocean Modeling System

Regional Ocean Modeling System (ROMS) is a free-surface, terrain-following, primitive equations ocean model widely used by the scientific community for a diverse range of applications. The model is developed and supported by researchers at the Rutgers University, University of California Los Angeles and contributors worldwide.

ROMS is used to model how a given region of the ocean responds to physical forcings such as heating or wind. It can also be used to model how a given ocean system responds to inputs like sediment, freshwater, ice, or nutrients, requiring coupled models nested within the ROMS framework.

Simon Tett

Simon Tett is a climatologist working at the University of Edinburgh. He used to work at the Hadley Centre.

His most-cited paper, is Mitchell, J.F.B.; Johns, T.C.; Gregory, J.M.; Tett, S.F.B. (10 August 1995). "Climate response to increasing levels of greenhouse gases and sulphate aerosols". Nature. 376 (6540): 501–4. doi:10.1038/376501a0., and of it he says:

All attempts at detecting and attributing climate change signals need a reliable observed data set and simulations with mechanisms that drive climate change included. In a nutshell, this paper is important because it was the first study to investigate the effect of sulphate aerosols in a general circulation model of the climate system. The experiments simulate the climate back to 1860 (which is when the global records of surface temperature became reliable)... After 1970 our model with greenhouse gases alone begins to depart significantly from the observations. However, when we included sulphate aerosols, which have a cooling effect, the model agreed with the data from the 1930s and onwards. The rapid warming that has taken place since 1970 is, according to the model, attributable to a heating effect from greenhouse gases and a cooling effect from sulphate aerosols.

Syukuro Manabe

Syukuro "Suki" Manabe (真鍋 淑郎, Manabe Shukurō, born September 21, 1931 in Ehime) is a meteorologist and climatologist who pioneered the use of computers to simulate global climate change and natural climate variations.

TOMCAT/SLIMCAT

TOMCAT/SLIMCAT is an off-line chemical transport model (CTM), which models the time-dependent distribution of chemical species in the troposphere and stratosphere. It can be used to study topics such as ozone depletion and tropospheric pollution, and was one of the models used the IPCC report on Aviation and the Global Atmosphere [1]. It incorporates a choice of detailed chemistry schemes for the troposphere or stratosphere, and an optional chemical data assimilation scheme.

The original model code, called the Toulouse Off-line Model of Chemistry And Transport (TOMCAT), was written by Martyn Chipperfield at Météo France. "Off-line" in this sense describes the fact that although the meteorological data (wind components and other fields) which are used as input to the CTM typically derive from a general circulation model (GCM), the CTM is run as a separate program outside of a GCM; this is as distinct from a chemistry simulation scheme which runs within a GCM, in which the simulated chemical distributions, e.g. of ozone, can provide feedback on the meteorology via the GCM's radiation scheme.

A version called the Single Layer Isentropic Model of Chemistry And Transport (SLIMCAT) was developed in 1995. This used a level of constant potential temperature (or equivalently, of constant specific entropy, hence isentropic), exploiting the fact that due to approximate conservation of energy, atmospheric motions are approximately adiabatic and hence air parcels remain on isentropic levels on short timescales. A diabatic heating scheme was later added, to allow for multiple isentropic levels with transport between them on longer timescales, although the name "SLIMCAT" has remained despite the multiple levels.

The two programs are now maintained as a single code base, which runs in Fortran, and has been parallelised.

TOMCAT has been further extended to include a detailed treatment of aerosol. GLOMAP (Global Model of Aerosol Processes) simulates a wide range of aerosol species including black carbon, sulfate, sea spray, soil dust, and secondary organic aerosol. The primary purpose of TOMCAT/GLOMAP is to simulate aerosol radiative forcing and the impact of aerosol on climate.

Wave base

The wave base, in physical oceanography, is the maximum depth at which a water wave's passage causes significant water motion. For water depths deeper than the wave base, bottom sediments and the seafloor are no longer stirred by the wave motion above.

Atmospheric, oceanographic, cryospheric, and climate models

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