Abstract
Abstract. We describe a methodology for ensemble member's weighting of operational seasonal forecasting systems (SFS) based on an enhanced prediction of a climate driver strongly affecting meteorological parameters over a certain region. We have applied it to the North Atlantic Oscillation (NAO) influence on the Iberian Peninsula winter precipitation. The first step in the proposed approach is to find the best estimation of winter NAO. Skill and error characteristics of forecasted winter NAO index by different Copernicus SFS are analysed in this study. Based on these results, a bias correction scheme is proposed and implemented for the ECMWF System 5 ensemble mean of NAO index, and then a modified NAO index pdf based on Gaussian errors is formulated. Finally, we apply the statistical estimation theory to achieve the Best linear unbiased estimate of winter NAO index and its uncertainty. For this purpose, two a priori estimates are used: the bias corrected NAO index Gaussian pdf from ECMWF System 5, and a skilful winter NAO index prediction based on teleconnection with snow cover advance with normal distributed errors. The second step of the proposed methodology is to employ the enhanced NAO index pdf estimates for ensemble member's weighting of a SFS based on a single dynamical model. The new NAO pdfs obtained in this work have been used to improve the skill of the ECMWF System 5 to predict both NAO index and precipitation over the Iberian Peninsula. We show the improvement of NAO prediction, and of winter precipitation forecasts over our region of interest, when members are weighted with the bias corrected NAO index Gaussian pdf based on ECMWF System 5 compared with the usual approach based on equiprobability of ensemble members. Forecast skill is further enhanced if the Best NAO index pdf based on an optimal combination of the two a priori NAO index estimates is used for ensemble member's weighting.
Highlights
In this study we present a methodology for ensemble member’s weighting of operational seasonal forecasting systems (SFSs) based on the extent that certain climate variables are captured by a driver of climate variability, as e.g., the North Atlantic Oscillation (NAO)
We show the improvement of NAO prediction, and of winter precipitation forecasts over our region of interest, when members are weighted with the bias corrected NAO index Gaussian pdf based on ECMWF System 5 compared with the usual approach based on equiprobability of ensemble members
It makes use of the predictability associated to this climate variability pattern by assigning a different weight to each ensemble member of a particular operational SFS according to its forecasted winter NAO index
Summary
In this study we present a methodology for ensemble member’s weighting of operational seasonal forecasting systems (SFSs) based on the extent that certain climate variables (e.g. temperature, precipitation) are captured by a driver of climate variability, as e.g., the North Atlantic Oscillation (NAO). We describe and apply a new developed methodology aiming at improving NAO and total precipitation seasonal forecasts for the extended winter (November to March, NDJFM) over this geographical area It makes use of the predictability associated to this climate variability pattern by assigning a different weight to each ensemble member of a particular operational SFS according to its forecasted winter NAO index. The second step of the methodology proposed in this paper relies on the knowledge of an enhanced winter NAO forecast pdf to weight members of a SFS based on a single dynamical model in order to achieve improved seasonal forecasts. 3. The enhancement of NAO index prediction and precipitation forecast skill of a single SFS using weighted ensemble members based on two different NAO pdf estimates is shown in Sect.
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