Background
Before any effective and decisive moves toward adaptation (by implementing measures designed to alleviate the adverse effects of climate change) can be taken, scientists require information about how the climate will change in the future. This requires a climate model, which accurately simulates the current climate system using a set of mathematical equations that best represent the principal laws that govern the way the atmosphere behaves. Because these models include many components of the climate system in detail, they require significant computing resources.
The current basis for all climate projections is a General Circulation Model (GCM) of the earth's atmosphere-land-ocean system. There are many GCMs; however, the typical length of a GCM grid cell is of the order of 300km, and some useful information can be typically extracted at this resolution. Thus, while this resolution allows broad statements about climate change at the regional to continental scale, it is usually too coarse for determining the impacts of climate change on water resources and agriculture for specific countries or regions. As an example, the GCM's low resolution fails to accurately represent topography (mountains), which has a direct effect on climate and hence any localized effects, e.g. rainfall caused by steep topography.
To develop projections at scales appropriate for understanding climate change in specific regions of Africa, downscaling of the GCM projection is necessary. This can be achieved via regionalized versions of a GCM, known as Regional Climate Models (RCMs), which, with typical grid cell sizes of 30km, simulate the atmosphere at a higher spatial resolution than the encompassing GCM atmosphere. This allows better resolution of topography and small-scale weather systems.
Even so, an RCM, with its higher resolution, needs to represent processes in the atmosphere that occur on scales less than 30km. It does this by using algorithms that have been derived from experiments or from observing the current climate. These algorithms use parameters or fixed variables, which partly determine how well the RCM simulates the climate of a given region. There are often several realistic values that these parameters can take, and often the most appropriate value depends on the simulated region.
World Community Grid and AfricanClimate@Home
Researchers in the Climate Systems Analysis Group (CSAG) at the University of Cape Town intend to reduce the uncertainty surrounding which values to use for these parameters when simulating the African climate. Using the power of World Community Grid, they intend to test possible combinations of multiple parameters and values for different regions and time periods. This is computationally intensive, though necessary, as each parameter functions differently at different times and locations, which is important to quantify if climate change projections are to be interpreted correctly.
Such experiments are ideally suited to a distributed computational grid, as exemplified by the climateprediction.net experiment undertaken by the Hadley Centre in the United Kingdom. Whereas their experiment seeks to span the largest range of parameterizations on a global scale, this experiment design on World Community Grid seeks to reduce the range of parameterizations for smaller domains over Africa.
This experiment fills a critical gap in the computational climate studies for Africa, and complements the range of other modeling projects to strengthen the overall understanding and value to the continent.
World Community Grid's server will send each volunteer's computer a dataset representing the large-scale atmosphere over a particular region of Africa, as well as a suite of RCM formulations (each representing a different combination of parameters) that it will use to simulate the local climate. The results will be checked against observations to identify the model formulations that best simulate the observed climate.
The results of this experiment will enable researchers to constrain the range of parameters used within an RCM. Narrowing the range of these parameters will, in turn, reduce uncertainty in modeling atmospheric processes and rainfall over the region and hence projections of climate change using RCMs. Furthermore, reduced uncertainty in modeling the regional atmosphere will enable more accurate modeling of the effect of changing land use practices (e.g. crop farming and rangeland grazing) on the regional climate. Currently this is an aspect of human-induced uncertainty in scenarios of climate change that has so far received little attention. Taken together, these reductions in uncertainty will enable regions, which are particularly vulnerable to future change, to be identified with greater confidence. This will then provide foundational research to those studying the impacts of climate change across a wide range of disciplines, including but not exclusively agriculture and water resources and HIV/health.
