Teleconnections between Ethiopian summer rainfall and sea surface temperature: part II. Seasonal forecasting

A seasonal forecasting system that is capable of skilfully predicting rainfall totals on a regional scale would be of great value to Ethiopia. Here, we describe how a statistical model can exploit the teleconnections described in part 1 of this pair of papers to develop such a system. We show that, in most cases, the predictors selected objectively by the statistical model can be interpreted in the light of physical teleconnections with Ethiopian rainfall, and discuss why, in some cases, unexpected regions are chosen as predictors. We show that the forecast has skill in all parts of Ethiopia, and argue that this method could provide the basis of an operational seasonal forecasting system for Ethiopia.

Evidence for long-term regional changes in precipitation on the East Coast Mountains in Mauritius

Global climate change and its impacts are being increasingly studied and precipitation trends are one of the measures of quantifying climate change especially in the tropics. This study uses daily rainfall data to determine if there are changes in the long-term trends in rainfall variability in the East Coast Mountains of Mauritius during the last few decades, and to investigate the factors influencing the trends in the inter-annual to inter-decadal rainfall variability. Statistical modelling has been used to investigate the trends in total seasonal rainfall, the number of rain days and the mean amount of rain per rainy days and the local, regional and large-scale factors that affect them on inter-annual to inter-decadal time scales. The strongest inter-decadal trend was found in the number of rain days for both rainfall seasons, and the other variables were found to have weak or insignificant trends. Both local factors, such as the surrounding sea surface temperatures and large-scale phenomena such as Indian Monsoon and the El Niño Southern Oscillation were found to influence rainfall patterns.

Simulated global-mean sea level changes over the last half-millennium

Simulations of the last 500 yr carried out using the Third Hadley Centre Coupled Ocean-Atmosphere GCM (HadCM3) with anthropogenic and natural (solar and volcanic) forcings have been analyzed. Global-mean surface temperature change during the twentieth century is well reproduced. Simulated contributions to global-mean sea level rise during recent decades due to thermal expansion (the largest term) and to mass loss from glaciers and ice caps agree within uncertainties with observational estimates of these terms, but their sum falls short of the observed rate of sea level rise. This discrepancy has been discussed by previous authors; a completely satisfactory explanation of twentieth-century sea level rise is lacking. The model suggests that the apparent onset of sea level rise and glacier retreat during the first part of the nineteenth century was due to natural forcing. The rate of sea level rise was larger during the twentieth century than during the previous centuries because of anthropogenic forcing, but decreasing natural forcing during the second half of the twentieth century tended to offset the anthropogenic acceleration in the rate. Volcanic eruptions cause rapid falls in sea level, followed by recovery over several decades. The model shows substantially less decadal variability in sea level and its thermal expansion component than twentieth-century observations indicate, either because it does not generate sufficient ocean internal variability, or because the observational analyses overestimate the variability.

Validation of improved TAMANN neural network for operational satellite-derived rainfall estimation in Africa

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms-a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.