ClimatePrediction.net
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| Developer(s) | Oxford University |
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| Initial release | September 12, 2003 |
| Development status | Active |
| Operating system | Cross-platform |
| Platform | BOINC |
| License | proprietary |
| Average performance | 32.6 TFLOPS |
| Active users | 20,274 |
| Total users | 252,234 |
| Active hosts | 27,527 |
| Total hosts | 507,912 |
| Website | climateprediction |
Climateprediction.net (CPDN) is a distributed computing project to investigate and reduce uncertainties in climate modelling. It aims to do this by running hundreds of thousands of different models (a large climate ensemble) using the donated idle time of ordinary personal computers, thereby leading to a better understanding of how models are affected by small changes in the many parameters known to influence the global climate.
The project relies on the volunteer computing model using the BOINC framework where voluntary participants agree to run some processes of the project at the client-side in their personal computers after receiving tasks from the server-side for treatment.
CPDN, which is run primarily by Oxford University in England, has harnessed more computing power and generated more data than any other climate modelling project. It has produced over 100 million model years of data so far. As of June 2016[update], there are more than 12,000 active participants from 223 countries with a total BOINC credit of more than 27 billion, reporting about 55 teraflops (55 trillion operations per second) of processing power.
The aim of the Climateprediction.net project is to investigate the uncertainties in various parameterizations that have to be made in state-of-the-art climate models. The model is run thousands of times with slight perturbations to various physics parameters (a 'large ensemble') and the project examines how the model output changes. These parameters are not known exactly, and the variations are within what is subjectively considered to be a plausible range. This will allow the project to improve understanding of how sensitive the models are to small changes and also to things like changes in carbon dioxide and sulphur cycle. In the past, estimates of climate change have had to be made using one or, at best, a very small ensemble (tens rather than thousands) of model runs. By using participants' computers, the project will be able to improve understanding of, and confidence in, climate change predictions more than would ever be possible using the supercomputers currently available to scientists.
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