Verification and Bias Adjustment of ECMWF SEAS5 Seasonal Forecasts over Europe for Climate Service Applications

Alice Crespi,Paola Marson,Marcello Petitta,Lucas Grigis,Christian Viel

doi:10.3390/cli9120181

Abstract

This work discusses the ability of a bias-adjustment method using empirical quantile mapping to improve the skills of seasonal forecasts over Europe for three key climate variables, i.e., temperature, precipitation and wind speed. In particular, the suitability of the approach to be integrated in climate services and to provide tailored predictions for local applications was evaluated. The workflow was defined in order to allow a flexible implementation and applicability while providing accurate results. The scheme adjusted monthly quantities from the seasonal forecasting system SEAS5 of the European Centre for Medium-Range Forecasts (ECMWF) by using ERA5 reanalysis as reference. Raw and adjusted forecasts were verified through several metrics analyzing different aspects of forecast skills. The applied method reduced model biases for all variables and seasons even though more limited improvements were obtained for precipitation. In order to further assess the benefits and limitations of the procedure, the results were compared with those obtained by the ADAMONT method, which calibrates daily quantities by empirical quantile mapping conditioned by weather regimes. The comparable performances demonstrated the overall suitability of the proposed method to provide end users with calibrated predictions of monthly and seasonal quantities.

Highlights

Seasonal climate forecasting systems are primary tools to derive predictions of the seasonal climatic conditions several months in advance and, due to recent improvements in forecasting, they are gaining relevance as support to decision-making processes in a wide range of sectors, such as energy, agriculture, water and risk management [1,2].Several centers worldwide, such as the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF), provide seasonal climate predictions using fully coupled ocean-atmosphere general circulation models (GCMs)
ERA5 global fields were cropped over an extended European area (26.5◦ N–72.5◦ N, 22◦ W–45.5◦ E), which was used as target domain for the assessment of both raw and calibrated ECMWF SEAS5 seasonal predictions
The spread of forecast errors was larger and mostly negative for Alpine regions and southern areas, while more pronounced seasonal differences and sharper Mean error (ME) distributions were reported for the other sub-regions

Summary

Introduction

Seasonal climate forecasting systems are primary tools to derive predictions of the seasonal climatic conditions several months in advance and, due to recent improvements in forecasting, they are gaining relevance as support to decision-making processes in a wide range of sectors, such as energy, agriculture, water and risk management [1,2].Several centers worldwide, such as the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF), provide seasonal climate predictions using fully coupled ocean-atmosphere general circulation models (GCMs). Statistical downscaling includes a large range of techniques of different complexities that are based on the relationship between large-scale climate predictors and local-scale observed predictands [8] It is beneficial in a wide range of applications since it is less computationally demanding than dynamical methods and has been found to perform comparably in most cases [2,3]. Bias-adjustment methods are post-processing techniques that compare coarse model predictions with reference fields over a calibration period and derive the proper corrections in order to match the statistical properties of model outputs with those of local climatological values [10] They include corrections of the mean and more complex adjustments of the distribution. Most inter-comparison and evaluation studies to date have focused only on temperature or precipitation, while fewer works have discussed the bias adjustment of seasonal predictions for other climate variables, such as wind speed [17,18]

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Conclusion