Preprints
https://doi.org/10.5194/npg-2021-12
https://doi.org/10.5194/npg-2021-12

  19 Mar 2021

19 Mar 2021

Review status: a revised version of this preprint is currently under review for the journal NPG.

Improving the Potential Accuracy and Usability of EURO-CORDEX Estimates of Future Rainfall Climate using Mean Squared Error Model Averaging

Stephen Jewson1, Giuliana Barbato2, Paola Mercogliano2, Jaroslav Mysiak2, and Maximiliano Sassi3 Stephen Jewson et al.
  • 1Independent Researcher, London, UK
  • 2Euro-Mediterranean Center on Climate Change (CMCC) Foundation, Via Augusto Imperatore, 16, 73100, Lecce, Italy
  • 3Risk Management Solutions Ltd, EC3R 7AG, London, UK

Abstract. Probabilities of future climate states can be estimated by fitting distributions to the members of an ensemble of climate model projections. The change in the ensemble mean can be used as an estimate of the unknown change in the mean of the distribution of the climate variable being predicted. However, the level of sampling uncertainty around the change in the ensemble mean varies from case to case and in some cases is large. We compare two model averaging methods that take the uncertainty in the change in the ensemble mean into account in the distribution fitting process. They both involve fitting distributions to the ensemble using an uncertainty-adjusted value for the ensemble mean in an attempt to increase predictive skill relative to using the unadjusted ensemble mean. We use the two methods to make projections of future rainfall based on a large dataset of high resolution EURO-CORDEX simulations for different seasons, rainfall variables, RCPs and points in time. Cross-validation within the ensemble using both point and probabilistic validation methods shows that in most cases predictions based on the adjusted ensemble means show higher potential accuracy than those based on the unadjusted ensemble mean. They also perform better than predictions based on conventional Akaike model averaging and statistical testing. The adjustments to the ensemble mean vary continuously between situations that are statistically significant and those that are not. Of the two methods we test, one is very simple, and the other is more complex and involves averaging using a Bayesian posterior. The simpler method performs nearly as well as the more complex method.

Stephen Jewson et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-12', Anonymous Referee #1, 13 Apr 2021
    • AC1: 'Reply on RC1', Stephen Jewson, 14 Apr 2021
    • AC3: 'Reply on RC1', Stephen Jewson, 26 May 2021
  • RC2: 'Comment on npg-2021-12', Anonymous Referee #2, 28 Apr 2021
  • CC1: 'Comment on npg-2021-12', Rasmus Benestad, 04 May 2021

Stephen Jewson et al.

Stephen Jewson et al.

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Short summary
Climate model simulations are uncertain. In some cases this makes it difficult to know how to use them. Significance testing is often used to deal with this issue but has various shortcomings. We describe two alternative ways to manage uncertainty in climate model simulations that avoid these shortcomings. We test them on simulations of future rainfall over Europe and show they produce more accurate projections than either using unadjusted climate model output or statistical testing.