Global Circulation Model (GCM)

Purpose:	Simulate general circulation of planetary atmosphere or oceans, thereby model global climate system
Developer:	Academic and research fraternities
Key Features:	Planet earth represented in a three-dimensional grid; Processes of atmosphere, oceans, and several other components and their interdisciplinary interactions are being modeled.
Latest Release:	NA
OS Platform:	NA
Cost:	Free

Introduction

Global Circulation Model (GCM) is a model that simulates general circulation of planetary atmosphere or oceans. The term general circulation is used to indicate large-scale atmospheric or oceanic motions with its persistent as well as transient features on various scales. GCM employs a combination of mathematical expressions that represent governing physics of circulations processes and empirical calculations which replicate processes based on data. Circulation related processes for planetary atmosphere or ocean are complex and interdisciplinary, therefore, modeling of these processes involves many assumptions as it is difficult to represent all processes in the model, huge number of calculations because of number of processes to be modeled, and uncertainty due to lack of complete knowledge of all processes. GCMs that model planetary atmospheric processes are known as atmospheric GCMs, and similarly oceanic GCMS and land surface GCMs are known for modeling of oceanic and land surface processes, respectively. Earth’s climate is driven by interactions between processes of atmosphere, oceans, and several other components such as biosphere, hydrosphere, and cryosphere. Therefore, modeling of a climate system requires coupling of various GCMs including components to model sea ice and other key planetary processes. Coupled GCMs referred as Global Climate Models, which also have similar abbreviation as Global Circulation Models, i.e., GCM.

GCM’s spatial resolution varies from 100km to 500 km, and consists of significant number of vertical layers to represent atmosphere as well as oceans. Number of factors including spatial- and temporal- resolution and level of representation of processes play a key role in GCM’s ability to represent entire planetary system and consequently model’s accuracy and its performance in various aspects. GCMs, by design, estimates numerous variables, for example, surface radiation, humidity, temperature, and precipitation at original spatial and temporal resolutions, and application of post-processing techniques, which popularly known as downscaling techniques, allow estimation of variables at customized spatial scales and time step intervals.

GCMs have many applications including understanding, simulating, and predicting earth’s climate system. A well-known application is to understand changes in earth’s climate system over lengthy time periods for future plausible scenarios which are either due to natural changes in various components of earth system or increase in greenhouse gas emissions that are chiefly responsible for global warming or combination of both. As part of the Coupled Model Intercomparison Project (CMIP), major climate modeling groups worldwide ran conventional atmospheric ocean global climate models (AOGCMs) and earth system models, and produced future climate projections for twenty-first century and beyond. Ensemble of GCMs that constitute phase 3 and phase 5 of the CMIP are known as CMIP3 and CMIP5, respectively. Relative to CMIP3 data, CMIP5 data derived from more number of GCMs and that are more advanced. In addition, both datasets differ in how they define emission scenarios, which reflects future emissions and consequent wide variety of effects. Therefore, direct comparison between two datasets is not possible, however, similarities among a few scenarios allow comparison of model outputs from CMIP3 and CMIP5. Analysis of model outputs assists in understanding climate variability and climate change. CMIP3 and CMIP5 model outputs provided input for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and Fifth Assessment Report (FA5), respectively.

Advantages and Limitations

Advantages	Limitations
Estimates variety of variables, e.g., precipitation, radiation, sea levels, etc., at customized grid locations for future time periods GCMs’ have wide range of applications, e.g., weather forecasting, understanding climate for different scenarios Gridded output allows to analyze any part of the planet in various aspects	Poorly performs, i.e., lack of accuracy and significant uncertainty, for regions that lack data and of which processes can’t be represented realistically Many assumptions Huge number of calculations
Illustrative Screens

Additional Information

Climate Modeling from GFDL, NOAA
Basic details of GCM, Penn State University

Sample Applications

Africa	East Asia and the Pacific	Europe & Central Asia	Latin America & the Caribbean	Middle East and North Africa	South Asia