Articles | Volume 10, issue 5
https://doi.org/10.5194/gmd-10-1849-2017
https://doi.org/10.5194/gmd-10-1849-2017
Model evaluation paper
 | 
05 May 2017
Model evaluation paper |  | 05 May 2017

weather@home 2: validation of an improved global–regional climate modelling system

Benoit P. Guillod, Richard G. Jones, Andy Bowery, Karsten Haustein, Neil R. Massey, Daniel M. Mitchell, Friederike E. L. Otto, Sarah N. Sparrow, Peter Uhe, David C. H. Wallom, Simon Wilson, and Myles R. Allen

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Cited articles

Allen, M.: Do-it-yourself climate prediction, Nature, 401, 642–642, https://doi.org/10.1038/44266, 1999.
Anderson, D. P.: Boinc: A system for public-resource computing and storage, in: Fifth IEEE/ACM International Workshop on Grid Computing, IEEE, 4–10, 2004.
Anstey, J. A., Davini, P., Gray, L. J., Woollings, T. J., Butchart, N., Cagnazzo, C., Christiansen, B., Hardiman, S. C., Osprey, S. M., and Yang, S.: Multi-model analysis of Northern Hemisphere winter blocking: Model biases and the role of resolution, J. Geophys. Res., 118, 3956–3971, https://doi.org/10.1002/jgrd.50231, 2013.
Berckmans, J., Woollings, T., Demory, M.-E., Vidale, P.-L., and Roberts, M.: Atmospheric blocking in a high resolution climate model: influences of mean state, orography and eddy forcing, Atmos. Sci. Lett., 14, 34–40, https://doi.org/10.1002/asl2.412, 2013.
Black, M. T., Karoly, D. J., Rosier, S. M., Dean, S. M., King, A. D., Massey, N. R., Sparrow, S. N., Bowery, A., Wallom, D., Jones, R. G., Otto, F. E. L., and Allen, M. R.: The weather@home regional climate modelling project for Australia and New Zealand, Geosci. Model Dev., 9, 3161–3176, https://doi.org/10.5194/gmd-9-3161-2016, 2016.
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Short summary
The weather@home climate modelling system uses the computing power of volunteers around the world to generate a very large number of climate model simulations. This is particularly useful when investigating extreme weather events, notably for the attribution of these events to anthropogenic climate change. A new version of weather@home is presented and evaluated, which includes an improved representation of the land surface and increased horizontal resolution over Europe.
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