To help understand how the climate works and how people are affecting it, we can use computers to model the different systems.

The models can range in complexity from very simple to very complex depending on what you are investigating.

CPDN (short for climateprediction.net) uses state-of-the-art climate models to investigate its different projects.

We use two main types of models: Global Climate Models (GCMs) and Regional Climate Models (RCMs). All of our Weather@Home experiments use Regional Climate Models.

In all of our experiments, we run large ensembles of models.

 

Global Climate Models

Climate models are numerical representations of various parts of the Earth’s climate system. There are two ways of looking at this. In some ways, scientists are trying to reduce the complex behaviour of the climate down to a set of mathematical equations, in the hope that they can then begin to understand the processes that are going on. This is true especially of fairly simple models.

For state-of-the-art General Circulation Models/Global Climate Models (GCMs) such as the one used in the CPDN experiment, it is more a case of trying to represent everything, even if things then get so complicated that we can’t always understand what’s going on. The equations can then be tweaked so that the model does as well as possible at producing past and current climates (compared to archived observations). If the model accurately reproduces past and current climates it can then be used to try to predict what the climate could do in the future.

GCMs try to simulate as much as possible about the climate system: the incoming and outgoing radiation, the way the air moves, the way clouds form and precipitation falls, the way the ice sheets grow or shrink, etc. They are frequently (as in the model we use) coupled to a representation of the ocean. They may take into account how the vegetation on the Earth’s surface changes. Critically, they try to calculate how all these different parts of the climate system interact, and how the feedback processes work.

Climate models divide the surface of the Earth into a horizontal grid, the atmosphere into vertical levels, and time into discrete timesteps.

Processes that are smaller than the size of one of these cubes, such as cloud formation, are difficult to model and so need to be parameterized.

 

Regional climate models

A key limitation of Global Climate Models (GCMs) is the fairly coarse horizontal resolution. For the practical planning of local issues such as water resources or flood defences, countries require information on a much more local scale than GCMs are able to provide. Regional models provide one solution to this problem.

There are three possible solutions to the problem of course resolution in GCMs:

• Run the full GCM at a finer resolution. As the model would then take much longer to complete a simulation, you will either need a very powerful computer or to run the simulation for a much shorter period (e.g. 5 years).
• Use statistical techniques to ‘downscale’ the coarse, GCM results to local detail. These techniques assume that the relationship between large scale climate variables (e.g. grid box rainfall and pressure) and the actual rainfall measured at one particular rain-gauge will always be the same. So, if that relationship is known for current climate, the GCM projections of future climate can be used to predict how the rainfall measured at that rain-gauge will change in the future.
• Embed a Regional Climate Model (RCM) in the GCM.

Regional Climate Models (RCMs) work by increasing the resolution of the GCM in a small, limited area of interest. An RCM might cover an area the size of western Europe, or southern Africa – typically 5000km x 5000km. The full GCM determines the very large scale effects of changing greenhouse gas concentrations and volcanic eruptions on global climate. The climate calculated by the GCM is used as input at the edges of the RCM for factors such as temperature and wind. RCMs can then resolve the local impacts given small scale information about orography (land height) and land use, giving weather and climate information at resolutions as fine as 50 or 25km.

The UK as represented in a Global Climate Model (GCM), a Regional Climate Model (RCM) and actual observations. Source: Met Office

In regions where the land surface is flat for thousands of kilometers, and there is no ocean anywhere near, the coarse resolution of a GCM may be enough to accurately simulate weather changes. However, most land areas have mountains, coastlines and changing vegetation characteristics on much smaller scales, and RCMs can represent the effects of these on the weather much better than GCMs.

Weather@Home

CPDN has worked together with the PRECIS group to develop a series of distributed regional climate modelling experiments, called weather@home. These experiments use the same numerical models as the PRECIS project.

PRECIS is the Met Office Hadley Centre project ‘Providing REgional Climates for Impacts Studies’. In the past, regional models have only been run independently of GCMs. A GCM would be run, saving all information generated for the region of interest. This information would then be the input to a subsequent RCM simulation. The CPDN experiment runs the GCM and RCM together, with information being passed between the models as they move forward in time. This means that nowhere near as much output needs to be saved – making the experiment possible on a home PC. More information about RCMs in general and the PRECIS project in particular can be found here.