General Circulation Models

General Circulation Models
NOAA · Public domain · source
Name	General Circulation Models
Discipline	Climate Science
Developed in	United States, United Kingdom, Soviet Union
First appeared	1950s
Notable	Syukuro Manabe, Knut Ångström, Jule Charney

General Circulation Models General Circulation Models are comprehensive numerical frameworks used to simulate the three-dimensional state and evolution of the Earth's atmosphere, oceans, cryosphere and land surface. They combine physical laws, parameterizations and numerical techniques developed across institutions such as Princeton University, NASA, NOAA, Met Office, and Max Planck Institute for Meteorology to produce projections and reanalyses that inform policy and scientific assessments by bodies like the Intergovernmental Panel on Climate Change and World Meteorological Organization.

Overview

GCMs integrate conservation equations derived from Isaac Newton, James Clerk Maxwell, and Joseph Fourier to represent momentum, mass and energy within discretized representations of the atmosphere and oceans; major modeling centers include Hadley Centre, GFDL, ECMWF, NCAR, and MPI-M. These systems couple atmospheric components to oceanic models such as those developed at Scripps Institution of Oceanography, Woods Hole Oceanographic Institution, and Geophysical Fluid Dynamics Laboratory while linking to modules for sea ice, vegetation and biogeochemistry created at Oak Ridge National Laboratory and Lawrence Berkeley National Laboratory. The outputs support assessments by IPCC, national agencies like EPA, and interagency programs including U.S. Global Change Research Program.

History and Development

Early theoretical foundations trace to work by Vilhelm Bjerknes and the Bergen School, and numerical experiments by Lewis Fry Richardson; practical GCMs emerged after World War II with pioneers such as Syukuro Manabe and Jule Charney producing atmospheric and coupled models at Princeton, GFDL, and Georgetown University. Developments in computing at IBM, Cray Research, Los Alamos National Laboratory, and Argonne National Laboratory enabled higher resolution simulations, while international collaboration through projects like CMIP and observatories such as TOGA and CLIVAR expanded validation datasets. The progression from early baroclinic models to present Earth system models involved incorporation of radiative transfer schemes influenced by work at Harvard University, Columbia University, and California Institute of Technology.

Model Components and Physics

Atmospheric modules implement primitive or full Navier–Stokes equations coupled to radiative transfer schemes rooted in spectroscopy traditions from Max Planck Institute for Solar System Research and parameterizations informed by field campaigns like ARM, GCSS and GATE. Ocean modules solve hydrostatic or nonhydrostatic equations with turbulence closures developed at Scripps Institution of Oceanography and WHOI. Sea-ice components draw on formulations from Norwegian Polar Institute and Scott Polar Research Institute, while land-surface schemes incorporate vegetation and hydrology concepts advanced at USDA Forest Service and CSIRO. Chemistry and aerosol routines build on mechanisms studied at NOAA Aeronomy Laboratory and JPL, and carbon cycle representations reflect work by Lawrence Livermore National Laboratory and MPI-BGC. Coupling frameworks produced by ESMF and OASIS coordinate exchange among these subsystems.

Numerical Methods and Resolution

Temporal integration uses schemes derived from numerical analysis traditions associated with Courant, Friedrichs, and Kutta, with spatial discretization options including spectral methods pioneered at Meteorological Office, finite-volume approaches from Los Alamos, and finite-element techniques developed at Stanford University. Grid choices span latitude–longitude meshes used by Met Office to cubed-sphere and unstructured grids advanced at Princeton and NCAR for polar treatment and scalability. Parameterized subgrid physics address convective processes, turbulence and cloud microphysics based on studies by NOAA and NASA; horizontal resolutions range from O(100 km) in early models to O(1 km) in convection-permitting regional runs by groups like CMA and ECMWF.

Applications and Uses

GCM outputs inform climate projections used by IPCC Working Groups, impacts assessments for agencies such as EPA and European Environment Agency, and adaptation planning in municipalities like New York City and London. They underpin attribution studies credited in reports by National Academy of Sciences and legal proceedings involving climate liability in jurisdictions including California and European Union courts. Sectors relying on GCMs include agriculture analyses at FAO, energy assessments by IEA, and biodiversity studies coordinated with IUCN and CBD.

Evaluation and Uncertainty

Model evaluation uses observations from satellites operated by NASA, ESA, and JAXA, in situ networks like Argo, GRACE, GCOS, and paleoclimate proxies curated by PAGES and NOAA Paleoclimatology. Intercomparison projects such as CMIP6 and CORDEX quantify intermodel spread; uncertainty arises from scenario choices exemplified by RCPs and SSPs, structural differences among models developed at GFDL, Hadley Centre, and MPI-M, and internal variability tied to phenomena like El Niño–Southern Oscillation and Atlantic Meridional Overturning Circulation. Detection and attribution methods developed at Oxford University and Harvard parse signal from noise.

Computational Challenges and Future Directions

Scaling to exascale architectures at facilities like Oak Ridge Leadership Computing Facility and Argonne Leadership Computing Facility pushes groups including NERSC and PRACE to optimize codebases such as those from CESM, HadGEM, and ICON. Future work emphasizes seamless prediction across timescales promoted by initiatives at WMO and improved process representation via machine learning efforts from DeepMind collaborations and university labs at MIT and ETH Zurich. Integration with observation systems like GEOS and community codes supported by GitHub ecosystems will shape ensemble strategies, with an eye to informing international assessments by IPCC and national policy decisions.

Category:Climate models