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Volume 9, issue 1
Earth Syst. Sci. Data, 9, 281–292, 2017
https://doi.org/10.5194/essd-9-281-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Earth Syst. Sci. Data, 9, 281–292, 2017
https://doi.org/10.5194/essd-9-281-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Review article 15 May 2017

Review article | 15 May 2017

An open-access CMIP5 pattern library for temperature and precipitation: description and methodology

Cary Lynch et al.

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Barnes, E. A. and Barnes, R. J.: Estimating Linear Trends: Simple Linear Regression versus Epoch Differences, J. Climate, 28, 9969–9976, https://doi.org/10.1175/JCLI-D-15-0032.1, 2015.
Castruccio, S., McInerney, D. J., Stein, M. L., Liu Crouch, F., Jacob, R. L., and Moyer, E. J.: Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs, J. Climate, 27, 1829–1844, https://doi.org/10.1175/JCLI-D-13-00099.1, 2014.
Christensen, J., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R., Kwon, W.-T., Laprise, R., Rueda, V. M., Mearns, L., Menéndez, C., Räisänen, J., Rinke, A., Sarr, A., and Whetton, P.: Regional Climate Projections, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K., Tignor, M., and Miller, H., chap. 11, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA., 2007.
Dessai, S., Lu, X., and Hulme, M.: Limited sensitivity analysis of regional climate change probabilities for the 21st Century, J. Geophys. Res.-Atmos., 110, D19, https://doi.org/10.1029/2005JD005919, 2005.
Fowler, H. J., Blenkinsop, S., and Tebaldi, C.: Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, 1547–1578, https://doi.org/10.1002/joc.1556, 2007.
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Short summary
Pattern scaling climate model output is a computationally efficient way to produce a large amount of data for purposes of uncertainty quantification. Using a multi-model ensemble we explore pattern scaling methodologies across two future forcing scenarios. We find that the simple least squares approach to pattern scaling produces a close approximation of actual model output, and we use this as a justification for the creation of an open-access pattern library at multiple time increments.
Pattern scaling climate model output is a computationally efficient way to produce a large...
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