Experimental strategy

The climateprediction.net project comprises three separate experiments.

The climateprediction.net project consists of several projects described at the projects pages, with weather@home being by far the largest project. Each project comprises separate experiments with different aims. The standard experiments conducted for all projects is an initial condition ensemble over a historical period. Below some common experiments are described most of which are carried out by some projects but not for all.

Initial condition ensembles

Initial condition ensembles involve the same model, with the same forcings, run from a variety of different start dates. Because the climate system is chaotic, tiny changes in things such as temperatures, winds, and humidity in one place can lead to very different paths for the system as a whole. We can work around this by setting off several runs started with slightly different starting conditions, and then look at the evolution of the group as a whole. This is similar to what they do in weather forecasting. Initial condition ensembles allow us to investigate the internal variability of the climate system, such ensembles, driven with present day forcing conditions, are therefore the best basis for model validation with observed data. This process is called a hindcast: it’s like a forecast, only you know the outcome. We know what happened in the historic period we simulate, but it’s still a challenge for the model to do a good job of simulating it. We can use the models’ performance in simulating the past to see how good they are simulating recent and present climate. In a good model we expect initial condition ensembles to represent weather events with a similar frequency of occurrence than in observed records. Only good models will be used for predicting the future. Which does not mean they are necessarily good for that job too, but for models that are bad in simulating the present we don’t have any reasons to think they will produce reliable future projections.

Initial condition ensembles let us also investigate how sensitive certain process in the climate system are to changing initial conditions. Every single model run by participants will be unique.

Perturbed physics ensembles

Perturbed physics ensembles form a major scientific focus of the whole project, especially using the coupled model. Modern climate models do a good job of simulating many large-scale features of present-day climate. However, these models contain large numbers of adjustable parameters which are known, individually, to have a significant impact on simulated climate. While many of these are well constrained by observations, there are many which do not directly relate to observed quantities and are subject to considerable uncertainty as they are obtained in a trial-and-error way, trying to best match observations. We do not know the extent to which different choices of parameter-settings or schemes may provide equally realistic simulations of 20th century climate but different forecast for the 21st century. The most thorough way to investigate this uncertainty is to run a massive ensemble experiment in which each relevant parameter combination is investigated. Thus the perturbed physics ensemble is a central feature of the climateprediction.net project. You can read more about the rationale for this experiment by following the other links in the menu bar on the left.

The knowledge we gain from this experiment about model sensitivity will enable the scientific community to design better models in the future. By perturbing parameters which control the models physical processes (such as cloud formation) it is also possible to see different realisations of climate change. As in the initial condition experiment everybody’s model will be unique because each will have a different combinations of parameters.

Forcings

Forcings are the things which drive the climate system. The chaotic variability we target in the initial condition ensemble is due to factors internal to the climate system, while things such as solar variability, sulphate (volcanic, etc) forcing and greenhouse gases are treated as external to the climate system. We call them forcings because they force the system from the outside: if these things change, we expect the climate system to respond.(If the sun puts out more energy, we would expect the Earth to heat up, for instance). We normally distinguish between natural forcings, e.g. volcanoes, sun activity, etc. and human-induced forcings as, for example, greenhouse gases, aerosol forcing, or landuse change. The first experiment we normally run in a new project is one with present day forcings to investigate whether or not the model responds to the measured forcings in a similar way to the real climate system.
In most projects we want to analyse the sensitivity of the model to different forcing conditions. In the geoengineering projects we changed the amount of sulphate aerosols in the aerosol forcing to a higher concentration, aiming to test how the climate system would react to such a measure. In the projects that aim to identify the influence of anthropogenic forcing, mainly greenhouse gas forcing, on present day climate, we change the greenhouse gas forcing files to simulate a world that might have been without anthropogenic climate change. Most weather@home projects comprise this experiment.

All projects aiming at future projections need to include forcings for the future, which we obviously cannot measure but have to assume. Therefore we need a future forcings ensemble with variations of different forcings because we don’t know what the sun or the volcanoes are going to do over the next fifty years. We also don’t know how levels of greenhouse gases are going to change over that period. So we’re going to run a large number of different futures which seem to be plausible, in which we vary solar, sulphate and greenhouse forcing, to span what we hope will be the likely range. In a lot of future experiments, however, we are interested in isolating the effect of a certain forcing, e.g. a doubling of the amount of carbon dioxide in the atmosphere. In such an experiment we keep solar and volcanic forcings constant, i.e. we assume there will be no major volcanic eruption in the future period of interest, and only change the greenhouse gas forcing. Applying this strategy it is possible to identify the influence of a single type of forcing if comparing the future simulations with present day simulations.