New publication: weather@home – development and validation of a very large ensemble modelling system for probabilistic event attribution
N. Massey, R. Jones, F. E. L. Otto, T. Aina, S. Wilson, J. M. Murphy, D. Hassell, Y. H. Yamazaki and M. R. Allen (2014) weather@home – development and validation of a very large ensemble modelling system for probabilistic event attribution Quarterly Journal of the Royal Meteorological Society DOI: 10.1002/qj.2455
Demonstrating the effect that climate change is having on regional weather is a subject which occupies climate scientists, government policy makers and the media. After an extreme weather event occurs, the question is often posed, “Was the event caused by anthropogenic climate change?” Recently, a new branch of climate science (known as attribution) has sought to quantify how much the risk of extreme events occurring has increased or decreased due to climate change. One method of attribution uses very large ensembles of climate models computed via volunteer distributed computing. A recent advancement is the ability to run both a global climate model and a higher resolution regional climate model on a volunteer’s home computer. Such a setup allows the simulation of weather on a scale that is of most use to studies of the attribution of extreme events.
This paper introduces a global climate model that has been developed to simulate the climatology of all major land regions with reasonable accuracy. This then provides the boundary conditions to a regional climate model (which uses the same formulation but at higher resolution) to ensure that it can produce realistic climate and weather over any region of choice. The development process is documented and a comparison to previous coupled climate models and atmosphere only climate models is made. The system (known as weather@home) by which the global model is coupled to a regional climate model and run on volunteer’s home computers is then detailed. Finally, a validation of the whole system is performed, with a particular emphasis on how accurately the distributions of daily mean temperature and daily mean precipitation are modelled in a particular application over Europe. This builds confidence in the applicability of the weather@home system for event attribution studies.