Multimodel seasonal forecasting of global drought onset

Abstract

[1] The capability of seasonal forecasting of global drought onset at local scales (1°) has been investigated using multiple climate models with 110 realizations. Climate models increase the global mean probability of drought onset detection from the climatology forecast by 31%-81%, but only increase equitable threat score by 21%-50% due to a high false alarm ratio. The multimodel ensemble increases the drought detectability over some tropical areas where individual models have better performance, but cannot help more over most extratropical regions. On average, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with low potential predictability and weak antecedent El Niño-Southern Oscillation signal. Given the high false alarms, the reliability is very important for a skillful probabilistic drought onset forecast. This raises the question of whether seasonal forecasting of global drought onset is essentially a stochastic forecasting problem.

Forecasting droughts in East Africa

“…This followed the methodology initially described by Dutra et al (2013b) and was applied on a basin scale. Similar approaches have been also described by Mo et al (2012) and Yuan and Wood (2013). The results display the ensemble mean of the SPI forecasts; the ensemble mean variance was rescaled for each lead time to conserve a standard deviation of 1 (see Dutra et al, 2014, for further details).…”

Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA

“…Therefore, as different models have different abilities 430 depending on seasonality and lead times, strategic multi-model averaging procedures may help increase the 431 forecasting skill of these extreme flood and drought events (e.g., Luo and Wood 2008, Bradley et al 2015, 432 especially in locations with strong antecedent ENSO signal (e.g., Yuan and Wood, 2013). 433…”

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

“…However, NCEP's National Multi-Model Ensemble system (NMME; http://www.cpc.ncep.noaa.gov/products/NMME/) includes five other models aside from CFSv2. Recent studies have demonstrated the value of using multimodel ensembles of seasonal forecasts relative to using just one of the models (Hagedorn et al, 2005;Kirtman et al, 2014;Lavers et al, 2009;Yuan and Wood, 2013). Therefore, we plan to use NMME model ensembles to generate climate scenarios.…”