It is a computer-based model of the climate system to understand and predict its behavior. Climate models incorporate the physics and chemistry of the atmosphere and the oceans and aim to answer questions such as when the next El Niño might occur, and what might happen if greenhouse gas concentrations double. The challenge for climate models is to run forward in time much faster than the real atmosphere and oceans. To do this, they must make a large number of simplifying assumptions and perform huge numbers of calculations. Various types of models are used to analyze different aspects of the climate. They can be relatively simple one, two- or three dimensional, and can be applied to a single physical feature of climatic relevance, or they may contain fully interactive, three-dimensional processes in all three domains: atmosphere, ocean and land surface. Sometimes, where complicated processes vary according to a wide variety of factors, it can be best to begin to explore the processes in one dimension only. For example, when looking at chemical reactions that vary with the physical conditions through the depth of the atmosphere, one approach is to look at the reactions at each level from the ground to the top of the atmosphere. One-dimensional models were initially used for energy-related studies of the climate system. As greater confidence is obtained in the way a simple model handles a particular process, the ideas are then incorporated into more complex two three- and four (time)-dimensional representations, which incorporate the dynamics of the climate system.
3. Roles of climate models:
Many climate models have been developed to perform climate projections ie. to simulate and understand climate changes in response to the emission of greenhouse gases and aerosols. In addition, models can be formidable tools to improve our knowledge of the most important characteristics of the climate system and of the causes of climate variations. Obviously, climatologists cannot perform experiments on the real climate system to identify the role of a particular process clearly or to test a hypothesis. However, this can be done in the virtual world of climate models. For highly non-linear systems, the design of such tests, often called sensitivity experiments, has to be very carefully planned. Though, in simple experiments, neglecting a process or an element of the modeled system (for instance the influence of the increase in CO₂ concentration on the radiative properties of the atmosphere) can often provide a first estimate of the role of this process or this element in the system.
4. Different types of climate models
Modelers have first to decide the variables or processes to be taken into account and those that will be taken as constants. This provides a method of classifying models as function of the components that are represented interactively. In the majority of climate studies, at least the physical behavior of the atmosphere, ocean and sea ice must be represented. In addition, the terrestrial and marine carbon cycles, the dynamic vegetation and the ice sheet components are more and more regularly included, leading to what are called Earth-system models.
A second way of differentiating between models is related to the complexity of the processes that are included. At one end of the spectrum, General Circulation Models (GCMs) try to account for all the important properties of the system at the highest affordable resolution. The term GCM was introduced because one of the first goals of these models is to simulate the three dimensional structure of winds and currents of atmosphere and ocean realistically. They have classically been divided into Atmospheric General Circulation Models (AGCMs) and Ocean General Circulation Models (OGCMS). For climate studies using interactive atmospheric and oceanic components, the acronyms AOGCM (Atmosphere Ocean General Circulation Model) and the broader CGCM (Coupled General Circulation Model) are generally chosen.
At the other end of the spectrum, simple climate models (such as the Energy pectrum, or EBMs) propose a highly simplified version of the dynamic of the climate system. The variables are averaged over large regions, sometimes over the whole Earth, and many processes are not represented or accounted I for by the parameterizations. EBMs thus include a relatively small number of degree of freedom.
EMICS (Earth Models of Intermediate Complexity) are located between those two extremes. They are based on a more complex representation of the system than EBMs but include simplifications and parameterizations for some processes that are explicitly accounted for in GCMs. Actually, the EMICs form the broader category of models. Some of them are relatively close to simple models, while others could be considered as slightly degraded GCMs.
What are GCMs?
Global Climate Models or General Circulation Models (GCMs) are comprised of fundamental concepts (laws) and parameterisations of physical, biological, and chemical components of the climate system. These concepts and parameterisations are expressed as mathematical equations, averaged over time and grid volumes. The equations describe the evolution of many variables (e.g. temperature, wind speed, humidity and pressure) and together define the state of the atmosphere. These equations are then converted to a programming language, defining among other things their possible interactionswith other formulations, so that they can be be solved on a computer and integrated forward in discrete time steps.
The basic equations:
The basic equations for atmospheric flows are from the principle of the conservation of mass, momentum and energy. The conservation laws are derived considering the rate of change in mass, momentum and energy per unit volume. The basic equations that govern the atmosphere can be formulated as a set of seven equations with seven unknowns: the three components of the wind velocity (u, v and w), the pressure p, the temperature T, the specific humidity q and the density. The seven equations, written for the atmosphere, are:
Newton's second law of motion (momentum equation, i.e., force equals mass times acceleration)
neglected compared to the other terms of the continuity equation, filtering the sound waves. However, supplementary equations for the liquid water content of atmospheric parcels or other variables related to clouds are often added to this set of equations.
Representation of GCMs through grid boxes:
➤ In a GCM, grid boxes cover the entire planet (ocean and atmosphere)
➤ Typical size is 100-200 km in the horizontal
➤ 40-60 layers in the vertical both in atmosphere and ocean
➤ Typical time step can be 30 min
Inputs for climate models
Any climate model requires some input data to run it. These are given below ➤ Earth's properties (Earth's radius and period of rotation, land topography, coastline and bathymetry of the ocean, properties of land/soils (shape of the land, background albedo, surface roughness, some dynamic properties of soil like organic matter, soil structure, bulk density, water and nutrient holding capacity)
Boundary conditions for all sub-systems not explicitly included in the model (distribution of vegetation, complex topography of ice sheets, coast-lines and land cover inhomogeneity)
Solar forcing (monthly or annual solar irradiance)
➤ Emissions (monthly or annual gridded CO2 emissions) and concentrations of GHGs and aerosols (global mean time series of BC (black carbon), organic carbon(OC), CH4, Sulfur)
➤ Volcanic forcing (dust, H₂O and SO2 emissions)
➤ Ozone (time-evolving 3-D concentrations for forcing in models that do not include interactive chemistry)
➤ Land-use [emissions (Gt C) from land cover changes]
In general, information needed to run a GCM are: i) Initial state of all variables in all boxes; ii) A description of the land surface (topography and land use); iii) Solar radiation; iv) Gas and aerosol composition of the atmosphere. While the resources needed to run a GCM (atmosphere)
Model initialization and spin-up
In order to start a model run, the values of certain variable, which is actually going to predict like temperature, salinity, density, sea level, and velocity need to be specified over various model parameters like domains, grids, and bathymetry.
NWP (Numerical Weather Prediction) integrations started from very similar initial conditions (input of the models provided at the beginning of the integration) may result in quite different forecasts. Simulations with climate models can never be directly compared to observations: It is not advisableto compare a single month or year from model simulations to the corresponding month or year of observation, rather comparing statistics of a longer period (decades or more) is justified.
Model parameterization:
There are certain physical processes that act at a scale much the characteristic grid interval of the climate models or are too complex to clearly understand and hence to physically represent them in the model. Some such examples include turbulence, the phase change of water, descent rate of raindrops, convective clouds, simplifications of the atmospheric radiative transfer on the basis of atmospheric radiative transfer codes and cloud microphysics. If the complete physics of these processes, for example, clouds, were to be computed explicitly at each time step and at every grid-point, the huge amount of data produced would swamp the computer. However, these processes cannot be eliminated; so simplifying equations are developed to represent the gross effect of the many small-scale processes within a grid cell as accurately as possible. This approach is called parameterization i.e. it is the method of replacing very small-scale or complex physical process in the model by a simplified process. There is a lot of research going on to devise better and more efficient ways for incorporating these small-scale processes into climate models.
Major steps to do parameterization in the numerical models are: i) ignore some processes ii) simplifications of some processes based on some assumptions iii) statistical/empirical relationships
and approximations based on observations, and iv) Nested models and super-parameterization (Embed a cloud model as a parameterization into climate models)
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