General circulation model

It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat).

Coupled atmosphere-ocean GCMs (AOGCMs, e.g. HadCM3, EdGCM, GFDL CM2.X, ARPEGE-Climat)[2] combine the two models.

The first general circulation climate model that combined both oceanic and atmospheric processes was developed in the late 1960s at the NOAA Geophysical Fluid Dynamics Laboratory[3] AOGCMs represent the pinnacle of complexity in climate models and internalise as many processes as possible.

Versions designed for decade to century time scale climate applications were created by Syukuro Manabe and Kirk Bryan at the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey.

[4] General Circulation Models (GCMs) discretise the equations for fluid motion and energy transfer and integrate these over time.

Unlike simpler models, GCMs divide the atmosphere and/or oceans into grids of discrete "cells", which represent computational units.

Three-dimensional (more properly four-dimensional) GCMs apply discrete equations for fluid motion and integrate these forward in time.

A simple general circulation model (SGCM) consists of a dynamic core that relates properties such as temperature to others such as pressure and velocity.

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.

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.

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.

Another approach to solving the grid spacing problem is to deform a Cartesian cube such that it covers the surface of a sphere.

[11] Some early versions of AOGCMs required an ad hoc process of "flux correction" to achieve a stable climate.

[17] Most models include software to diagnose a wide range of variables for comparison with observations or study of atmospheric processes.

The 2001 IPCC Third Assessment Report Figure 9.3 shows the global mean response of 19 different coupled models to an idealised experiment in which emissions increased at 1% per year.

Future scenarios do not include unknown events – for example, volcanic eruptions or changes in solar forcing.

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.

[20] 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.

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.

GCMs are capable of reproducing the general features of the observed global temperature over the past century.

[27] In the 2001 IPCC report possible changes in cloud cover were highlighted as a major uncertainty in predicting climate.

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, because weather forecasts only cover around 10 days the models can also be run at higher vertical and horizontal resolutions than climate mode.

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.

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

[40] In 1956, Norman Phillips developed a mathematical model that could realistically depict monthly and seasonal patterns in the troposphere.

[43] The first to combine both oceanic and atmospheric processes was developed in the late 1960s at the NOAA Geophysical Fluid Dynamics Laboratory.

[45] Later the Hadley Centre for Climate Prediction and Research's HadCM3 model coupled ocean-atmosphere elements.

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 ]
Projected annual mean surface air temperature from 1970 to 2100, based on SRES emissions scenario A1B, using the NOAA GFDL CM2.1 climate model (credit: NOAA Geophysical Fluid Dynamics Laboratory ) [ 18 ]
In the 21st century, changes in global mean temperature are projected to vary across the world
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 ) [ 18 ]
SST errors in HadCM3
North American precipitation from various models
Temperature predictions from some climate models assuming the SRES A2 emissions scenario
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).