Abstract
Abstract. An early warning system for drought events can provide valuable information for decision makers dealing with water resources management and international aid. However, predicting such extreme events is still a big challenge. In this study, we compare two approaches for drought predictions based on forecasted precipitation derived from the Ensemble extended forecast model (ENS) of the ECMWF, and on forecasted monthly occurrence anomalies of weather regimes (MOAWRs), also derived from the ECMWF model. Results show that the MOAWRs approach outperforms the one based on forecasted precipitation in winter in the north-eastern parts of the European continent, where more than 65 % of droughts are detected 1 month in advance. The approach based on forecasted precipitation achieves better performance in predicting drought events in central and eastern Europe in both spring and summer, when the local atmospheric forcing could be the key driver of the precipitation. Sensitivity tests also reveal the challenges in predicting small-scale droughts and drought onsets at longer lead times. Finally, the results show that the ENS model of the ECMWF successfully represents most of the observed linkages between large-scale atmospheric patterns, depicted by the weather regimes and drought events over Europe.
Highlights
Developing a robust early warning system for drought events is a key challenge for modellers and forecasters.The timescale of these events requires accurate numerical weather forecasts with long lead times
In a recent study (Lavaysse et al, 2015), it has been shown that about 40 % of the meteorological droughts, defined by an anomaly of the standardized precipitation index (SPI), can be detected 1 month in advance by using the forecasted precipitation provided by the ECMWF Ensemble extended forecast model (ENS)
The predictor assignation procedure is based on the correlation between the monthly occurrence anomalies of weather regimes (MOAWRs) and the SPI-1
Summary
Developing a robust early warning system for drought events is a key challenge for modellers and forecasters. In a recent study (Lavaysse et al, 2015), it has been shown that about 40 % of the meteorological droughts, defined by an anomaly of the standardized precipitation index (SPI), can be detected 1 month in advance by using the forecasted precipitation provided by the ECMWF Ensemble extended forecast model (ENS). First and principally studied in wintertime, when they are more stronger, four main states have been defined, namely the positive North Atlantic Oscillation phases (NAO+), the negative NAO (NAO−), the blocking regime and the Atlantic Ridge regime They are well known to play an important role in creating large-scale conditions that either favour or inhibit precipitation in Europe (Plaut and Simonnet, 2001; Yiou and Nogaj, 2004), especially in extreme events
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