Articles | Volume 12, issue 7
Geosci. Model Dev., 12, 3017–3043, 2019
Geosci. Model Dev., 12, 3017–3043, 2019

Development and technical paper 17 Jul 2019

Development and technical paper | 17 Jul 2019

Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation

Sihan Li et al.

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Cited articles

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P. P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-0Present), J. Hydrometeor., 4, 1147–1167,<1147:TVGPCP>2.0.CO;2, 2003. 
Allen, M.: Do-it-yourself climate prediction, Nature, 401, 642,, 1999. 
Annan, J. D. and Hargreaves, J. C.: Efficient estimation and ensemble generation in climate modelling, Philos. T. Roy. Soc. A, 365, 2077–2088, 2007. 
Annan, J. D., Lunt, D. J., Hargreaves, J. C., and Valdes, P. J.: Parameter estimation in an atmospheric GCM using the Ensemble Kalman Filter, Nonlin. Processes Geophys., 12, 363–371,, 2005. 
Bellprat, O., Kotlarski, S., Lüthi, D., and Schär, C.: Exploring perturbed physics ensembles in a regional climate model, J. Climate, 25, 4582–4599,, 2012a. 
Short summary
Understanding the unfolding challenges of climate change relies on climate models, many of which have regional biases larger than the expected climate signal over the next half-century. This work shows the potential for improving climate model simulations through a multiphased parameter refinement approach. Regional warm biases are substantially reduced, suggesting this iterative approach is one path to improving climate models and simulations of present and future climate.