Termonia, Piet

Van Schaeybroeck, Bert

Vannitsem, Stéphane

Ghent University Academic Bibliography

Geophysical Research AbstractsVol. 19, EGU2017-6519, 2017EGU General Assembly 2017© Author(s) 2017. CC Attribution 3.0 License.Using bias correction to achieve reliable near-term climate projectionsBert Van Schaeybroeck, Stéphane Vannitsem, and Piet TermoniaRoyal Meteorological Institute of Belgium, Research and development, Brussels, Belgium (bertvanschaeybroeck@gmail.com)Internationally coordinated climate initiatives (e.g. CORDEX or CMIP5) collect large multi-model ensemblesof climate simulations to provide a sample of potential outcomes. Due to their large model biases, the climatemodels are used for sensitivity experiments in the context of climate change, rather than for reproducing exactclimatological conditions. In other words, the main model output from model simulations under a certaingreenhouse gas scenario are the climate changes or trends rather than the absolute values.The ensemble simulations are perfectly reliable when the reality (observations) can be considered as amember from the ensemble. The reliability of climate predictions was already investigated on different timescales, from monthly and seasonal up to decadal ones (Räisänen, 2007; Weisheimer, 2011; Corti, 212). However,it has been shown that global models cannot reliably reproduce climate change trends of the past decades (VanOldenborgh et al. 2013). More specifically, for both precipitation and temperature, the observations are morefrequently an outlier to the CMIP5 ensemble than expected. Such underdispersive ensembles are common tomedium-range ensemble weather forecasting. However, in numerical weather predictions, the lack of reliability isovercome by the use of advanced bias-correction methods (Van Schaeybroeck and Vannitsem, 2015).We present an application of such post-processing (also called Model Output Statistics, MOS) techniquesto climate predictions, with the aim of increasing the reliability of climate trends from the CORDEX ensemble.After a validation of the method on a historical period, we apply the calibration to different future near-termscenarios. The applied technique allows to correct each ensemble member in such a way that spatio-temporalcorrelations are preserved.ReferencesS. Corti, A. Weisheimer, T. N. Palmer, F. J. Doblas-Reyes, and L. Magnusson, Reliability of decadal pre-dictions, Geophys. Res. Lett., 39, L21712, 2012.A. Weisheimer, T. N. Palmer, and F. J. Doblas-Reyes, Assessment of representations of model uncertaintyin monthly and seasonal forecast ensembles, 38, L16703, 2011.Räisänen, J. How reliable are climate models? Tellus A, 59, 2-29, 2007.Van Oldenborgh, G., Doblas Reyes, F., Drijfhout, S., Hawkins, E. Reliability of regional climate modeltrends, Environmental Research Letters, 8, 014055, 2013.Van Schaeybroeck, B. and Vannitsem, S., Ensemble post-processing using member-by-member approaches:theoretical aspects. Q.J.R. Meteorol. Soc., 141: 807-818, 2015.

Using bias correction to achieve reliable near-term climate projections

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Using bias correction to achieve reliable near-term climate projections

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