Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia

In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 - 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted seasonal rainfall for the better performing models—GFDL-CM2p5-FLOR-A06, CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012—is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1- CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.

Climate drivers of vector-borne diseases in Africa and their relevance to control programmes

Background:Climate-based disease forecasting has been proposed as a potential tool in climate change adaptation for the health sector.Here we explore the relevance of climate data,drivers and predictions for vector-borne disease control efforts in Africa.Methods:Using data from a number of sources we explore rainfall and temperature across the African continent,from seasonality to variability at annual,multi-decadal and timescales consistent with climate change.We give particular attention to three regions defined as WHO-TDR study zones in Western,Eastern and Southern Africa.Our analyses include 1)time scale decomposition to establish the relative importance of year-to-year,decadal and long term trends in rainfall and temperature;2)the impact of the El Niño Southern Oscillation(ENSO)on rainfall and temperature at the Pan African scale;3)the impact of ENSO on the climate of Tanzania using high resolution climate products and 4)the potential predictability of the climate in different regions and seasons using Generalized Relative Operating Characteristics.We use these analyses to review the relevance of climate forecasts for applications in vector borne disease control across the continent.Results:Timescale decomposition revealed long term warming in all three regions of Africa-at the level of 0.1-0.3°C per decade.Decadal variations in rainfall were apparent in all regions and particularly pronounced in the Sahel and during the East African long rains(March-May).Year-to-year variability in both rainfall and temperature,in part associated with ENSO,were the dominant signal for climate variations on any timescale.Observed climate data and seasonal climate forecasts were identified as the most relevant sources of climate information for use in early warning systems for vector-borne diseases but the latter varied in skill by region and season.Conclusions:Adaptation to the vector-borne disease risks of climate variability and change is a priority for government and civil society in African countries.Understanding rainfall and temperature variations and trends at multiple timescales and their potential predictability is a necessary first step in the incorporation of relevant climate information into vector-borne disease control decision-making.