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. These equations are the basis for computer programs used to simulate the Earth's atmosphere or oceans. Atmospheric and oceanic GCMs are key components along with sea ice and land-surface components.
The Tropical Ocean Global Atmosphere program (TOGA) was a ten-year study (1985–1994) of the World Climate Research Programme (WCRP) aimed specifically at the prediction of climate phenomena on time scales of months to years.
Jason-1 was a satellite altimeter oceanography mission. It sought to monitor global ocean circulation, study the ties between the ocean and the atmosphere, improve global climate forecasts and predictions, and monitor events such as El Niño and ocean eddies. Jason-1 was launched in 2001 and it was followed by OSTM/Jason-2 in 2008, and Jason-3 in 2016 – the Jason satellite series. Jason-1 was launched alongside the TIMED spacecraft.
The Fleet Numerical Meteorology and Oceanography Center (FNMOC) is an echelon IV component of the Naval Meteorology and Oceanography Command (NMOC), which provides worldwide meteorological and oceanographic data and analysis for the United States Navy and strategic allies of the United States. The center is based out of Monterey, California. FNMOC provides Global and Regional Weather Prediction Charts (WXMAP) and Global Ensemble Weather Prediction Charts (EFS). WxMAP depictions of NAVGEM predictions for side-by-side comparison with NCEP global NWS models (GFS) are also available. FNMOC provides Global and Regional Ocean Wave Prediction Charts (WW3), Global Ensemble Ocean Wave Prediction Charts, and Global Sea Surface Temperature and Sea Surface Anomaly Charts (NCODA). FNMOC provides links to satellite imagery of tropical cyclones (TCWEB) and current tropical storm forecast tracks.
Numerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs.
Parameterization in a weather or climate model is a method of replacing processes that are too small-scale or complex to be physically represented in the model by a simplified process. This can be contrasted with other processes—e.g., large-scale flow of the atmosphere—that are explicitly resolved within the models. Associated with these parameterizations are various parameters used in the simplified processes. Examples include the descent rate of raindrops, convective clouds, simplifications of the atmospheric radiative transfer on the basis of atmospheric radiative transfer codes, and cloud microphysics. Radiative parameterizations are important to both atmospheric and oceanic modeling alike. Atmospheric emissions from different sources within individual grid boxes also need to be parameterized to determine their impact on air quality.
Data assimilation is a mathematical discipline that seeks to optimally combine theory with observations. There may be a number of different goals sought – for example, to determine the optimal state estimate of a system, to determine initial conditions for a numerical forecast model, to interpolate sparse observation data using knowledge of the system being observed, to set numerical parameters based on training a model from observed data. Depending on the goal, different solution methods may be used. Data assimilation is distinguished from other forms of machine learning, image analysis, and statistical methods in that it utilizes a dynamical model of the system being analyzed.
A tropical cyclone forecast model is a computer program that uses meteorological data to forecast aspects of the future state of tropical cyclones. There are three types of models: statistical, dynamical, or combined statistical-dynamic. Dynamical models utilize powerful supercomputers with sophisticated mathematical modeling software and meteorological data to calculate future weather conditions. Statistical models forecast the evolution of a tropical cyclone in a simpler manner, by extrapolating from historical datasets, and thus can be run quickly on platforms such as personal computers. Statistical-dynamical models use aspects of both types of forecasting. Four primary types of forecasts exist for tropical cyclones: track, intensity, storm surge, and rainfall. Dynamical models were not developed until the 1970s and the 1980s, with earlier efforts focused on the storm surge problem.
In atmospheric science, an atmospheric model is a mathematical model constructed around the full set of primitive, dynamical equations which govern atmospheric motions. It can supplement these equations with parameterizations for turbulent diffusion, radiation, moist processes, heat exchange, soil, vegetation, surface water, the kinematic effects of terrain, and convection. Most atmospheric models are numerical, i.e. they discretize equations of motion. They can predict microscale phenomena such as tornadoes and boundary layer eddies, sub-microscale turbulent flow over buildings, as well as synoptic and global flows. The horizontal domain of a model is either global, covering the entire Earth, or regional (limited-area), covering only part of the Earth. The different types of models run are thermotropic, barotropic, hydrostatic, and nonhydrostatic. Some of the model types make assumptions about the atmosphere which lengthens the time steps used and increases computational speed.
Tropical cyclogenesis is the development and strengthening of a tropical cyclone in the atmosphere. The mechanisms through which tropical cyclogenesis occurs are distinctly different from those through which temperate cyclogenesis occurs. Tropical cyclogenesis involves the development of a warm-core cyclone, due to significant convection in a favorable atmospheric environment.
The Princeton Ocean Model (POM) is a community general numerical model for ocean circulation that can be used to simulate and predict oceanic currents, temperatures, salinities and other water properties. POM-WEB and POMusers.org
The history of numerical weather prediction considers how current weather conditions as input into mathematical models of the atmosphere and oceans to predict the weather and future sea state has changed over the years. Though first attempted manually in the 1920s, it was not until the advent of the computer and computer simulation that computation time was reduced to less than the forecast period itself. ENIAC was used to create the first forecasts via computer in 1950, and over the years more powerful computers have been used to increase the size of initial datasets as well as include more complicated versions of the equations of motion. The development of global forecasting models led to the first climate models. The development of limited area (regional) models facilitated advances in forecasting the tracks of tropical cyclone as well as air quality in the 1970s and 1980s.
The sigma coordinate system is a common coordinate system used in computational models for oceanography, meteorology and other fields where fluid dynamics are relevant. This coordinate system receives its name from the independent variable used to represent a scaled pressure level.
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.
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. 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.
The Global Drifter Program (GDP) was conceived by Prof. Peter Niiler, with the objective of collecting measurements of surface ocean currents, sea surface temperature and sea-level atmospheric pressure using drifters. It is the principal component of the Global Surface Drifting Buoy Array, a branch of NOAA's Global Ocean Observations and a scientific project of the Data Buoy Cooperation Panel (DBCP). The project originated in February 1979 as part of the TOGA/Equatorial Pacific Ocean Circulation Experiment (EPOCS) and the first large-scale deployment of drifters was in 1988 with the goal of mapping the tropical Pacific Ocean's surface circulation. The current goal of the project is to use 1250 satellite-tracked surface drifting buoys to make accurate and globally dense in-situ observations of mixed layer currents, sea surface temperature, atmospheric pressure, winds and salinity, and to create a system to process the data. Horizontal transports in the oceanic mixed layer measured by the GDP are relevant to biological and chemical processes as well as physical ones.
The Model for Prediction Across Scales (MPAS) is an Earth system modeling software that integrates atmospheric, oceanographic, and cryospheric modeling across scales from regional to planetary. It includes climate and weather modeling and simulations that were used initially by researchers in 2013. The atmospheric models were created by the Earth System Laboratory at the National Center for Atmospheric Research and the oceanographic models were created by the Climate, Ocean, and Sea Ice Modeling Group at Los Alamos National Laboratory. The software has been used to model real-time weather as well as seasonal forecasting of convection, tornadoes and tropical cyclones. The atmospheric modeling component of the software can be used with other atmospheric modeling software including the Weather Research and Forecasting Model, the Global Forecast System, and the Community Earth System Model.
CICE is a computer model that simulates the growth, melt and movement of sea ice. It has been integrated into many coupled climate system models as well as global ocean and weather forecasting models and is often used as a tool in Arctic and Southern Ocean research. CICE development began in the mid-1990s by the United States Department of Energy (DOE), and it is currently maintained and developed by a group of institutions in North America and Europe known as the CICE Consortium. Its widespread use in earth system science in part owes to the importance of sea ice in determining Earth's planetary albedo, the strength of the global thermohaline circulation in the world's oceans, and in providing surface boundary conditions for atmospheric circulation models, since sea ice occupies a significant proportion (4-6%) of earth's surface. CICE is a type of cryospheric model.
Tropical Cyclone Heat Potential (TCHP) is one of such non-conventional oceanographic parameters influencing the tropical cyclone intensity. The relationship between Sea Surface Temperature (SST) and cyclone intensity has been long studied in statistical intensity prediction schemes such as the National Hurricane Center Statistical Hurricane Intensity Prediction Scheme (SHIPS) and Statistical Typhoon Intensity Prediction Scheme (STIPS). STIPS is run at the Naval Research Laboratory in Monterey, California, and is provided to Joint Typhoon Warning Centre (JTWC) to make cyclone intensity forecasts in the western North Pacific, South Pacific, and Indian Oceans. In most of the cyclone models, SST is the only oceanographic parameter representing heat exchange. However, cyclones have long been known to interact with the deeper layers of ocean rather than sea surface alone. Using a coupled ocean atmospheric model, Mao et al., concluded that the rate of intensification and final intensity of cyclone were sensitive to the initial spatial distribution of the mixed layer rather than to SST alone. Similarly, Namias and Canyan observed patterns of lower atmospheric anomalies being more consistent with the upper ocean thermal structure variability than SST.
Helene Hewitt is a British climate scientist who is a research fellow at the Met Office. Her research considers climate and ocean models. Hewitt serves on the CLIVAR Ocean Model Development Panel. She was awarded an Order of the British Empire in the 2022 Birthday Honours.
