Skill of seasonal rainfall and temperature forecasts for East Africa

Skilful seasonal forecasts can provide useful information for decision makers, particularly in regions heavily dependent on agriculture, such as East Africa. We analyse prediction skill for seasonal East African rainfall and temperature one to four months ahead from two seasonal forecasting systems: the US National Centers for Environmental Prediction (NCEP) Coupled Forecast System Model Version 2 (CFSv2) and the UK Met Office (UKMO) Global Seasonal Forecast System Version 5 (GloSea5). We focus on skill for low or high temperature and rainfall, below the 25th or above the 75th percentile respectively, as these events can have damaging effects in this region. We find skill one month ahead for both low and high rainfall from CFSv2 for December-January-February in Tanzania, and from GloSea5 for September-October-November in Kenya. Both models have higher skill for temperature than for rainfall across Ethiopia, Kenya and Tanzania, two months ahead in some cases. Performance for rainfall and temperature change in the two models during certain El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) phases, the impacts of which vary by country, season and sometimes by model. While most changes in performance are within the range of uncertainty due to the relatively small sample size in each phase, they are significant in some cases. For example, La Niña lowers performance for Kenya September-October-November rainfall in CFSv2 but does not affect skill in GloSea5

The effect of increased convective entrainment on Asian monsoon biases in the MetUM General Circulation Model

We demonstrate that summer precipitation biases in the South Asian monsoon domain are sensitive to increasing the convective parametrisation’s entrainment and detrainment rates in the Met Office Unified Model. We explore this sensitivity to improve our understanding of the biases and inform efforts to improve convective parametrisation. We perform novel targeted experiments in which we increase the entrainment and detrainment rates in regions of especially large precipitation bias. We use these experiments to determine whether the sensitivity at a given location is a consequence of the local change to convection or is a remote response to the change elsewhere. We find that a local change leads to different mean-state responses in comparable regions. When the entrainment and detrainment rates are increased globally, feedbacks between regions usually strengthen the local responses. We choose two regions of tropical ascent that show different mean-state responses, the western equatorial Indian Ocean and western north Pacific, and analyse them as case studies to determine the mechanisms leading to the different responses. Our results indicate that several aspects of a region’s mean-state, including moisture content, sea surface temperature and circulation, play a role in local feedbacks that determine the response to increased entrainment and detrainment

Contribution of tropical cyclones to atmospheric moisture transport and rainfall over East Asia

The coastal region of East Asia (EA) is one of the regions with the most frequent impacts from tropical cyclones (TCs). In this study, rainfall and moisture transports related to TCs are measured over the EA, and the contribution of TCs to the regional water budget is compared with other contributors, especially the mean circulation of the EA summer monsoon (EASM). Based on ERA-Interim re-analysis (1979–2012), the trajectories of TCs are identified using an objective feature tracking method. Over 60% of TCs occur from July to October (JASO). During JASO, TC rainfall contributes 10-30% the of monthly total rainfall over the coastal region of EA; this contribution is highest over the south/southeast coast of China in September. TCs make a larger contribution to daily extreme rainfall (above the 95th percentile): 50-60% over the EA coast and as high as 70% over Taiwan island. Compared with the mean EASM, TCs transport less moisture over the EA. However, as the peak of the mean seasonal cycle of TCs lags two months behind that of the EASM, the moisture transported by TCs is an important source for the water budget over the EA region when the EASM withdraws. This moisture transport is largely performed by westward-moving TCs. These results improve our understanding of the water cycle of EA and provide a useful test bed for evaluating and improving seasonal forecasts and coupled climate models

Forecasting annual maximum water level for the Negro River at Manaus using dynamical seasonal predictions

Early and skilful prediction of the Negro River maximum water levels at Manaus is critical for effective mitigation measures to safeguard lives and livelihoods. Using dynamical seasonal prediction hindcasts, from six prediction centres, we investigate extending the lead time of previously developed statistical models, which issue forecasts in March for Manaus. The original statistical forecast models used observed rainfall as the major predictor. We advance the capability to issue skilful forecasts earlier, in February. We develop ensemble forecasts by combining predictor data from observations and seasonal hindcasts. We compare those forecasts against the original statistical forecast models and forecasts using the observed climatology or persistence of predictors. The ensemble-mean forecasts, issued in February, using European Centre for Medium-Range Weather Forecasts (ECMWF) hindcast input, perform similarly as the original forecasts issued in March and gain one month of lead time. The ECMWF-based ensemble forecasts skilfully predict the likelihood of water levels exceeding the severe flood level of 29 m. Forecast performance reduces and ensemble spread increases with increasing lead time from February to January. We conclude that forecasts for Manaus maximum water levels can be produced using combined input from observations and real-time ECMWF forecasts