Best practices for post-processing ensemble climate forecasts, part I: selecting appropriate recalibration methods
Journal of Climate
American Meteorological Society
Reason for embargo
This study describes a systematic approach to selecting optimal statistical recalibration methods and hindcast designs for producing reliable probability forecasts on seasonal-to-decadal time scales. A new recalibration method is introduced that includes adjustments for both unconditional and conditional biases in the mean and variance of the forecast distribution, and linear time-dependent bias in the mean. The complexity of the recalibration can be systematically varied by restricting the parameters. Simple recalibration methods may outperform more complex ones given limited training data. A new cross-validation methodology is proposed that allows the comparison of multiple recalibration methods and varying training periods using limited data. Part I considers the effect on forecast skill of varying the recalibration complexity and training period length. The interaction between these factors is analysed for grid box forecasts of annual mean near-surface temperature from the CanCM4 model. Recalibration methods that include conditional adjustment of the ensemble mean outperform simple bias correction by issuing climatological forecasts where the model has limited skill. Trend-adjusted forecasts outperform forecasts without trend adjustment at almost 75% of grid boxes. The optimal training period is around 30 years for trend-adjusted forecasts, and around 15 years otherwise. The optimal training period is strongly related to the length of the optimal climatology. Longer training periods may increase overall performance, but at the expense of very poor forecasts where skill is limited.
This is the final version of the article. Available from the publisher via the DOI in this record.
Vol. 29, pp.7247 - 7264