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  • Author or Editor: Lance Leslie x
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M. Issa Lélé
,
Lance M. Leslie
, and
Peter J. Lamb

Abstract

The major objective of this study is to re-evaluate the ocean–land transport of moisture for rainfall in West Africa using 1979–2008 NCEP–NCAR reanalysis data. The vertically integrated atmospheric water vapor flux for the surface–850 hPa is calculated to account for total low-level moisture flux contribution to rainfall over West Africa. Analysis of mean monthly total vapor fluxes shows a progressive penetration of the flux into West Africa from the south and west. During spring (April–June), the northward flux forms a “moisture river” transporting moisture current into the Gulf of Guinea coast. In the peak monsoon season (July–September), the southerly transport weakens, but westerly transport is enhanced and extends to 20°N owing to the strengthening West African jet off the west coast. Mean seasonal values of total water vapor flux components across boundaries indicate that the zonal component is the largest contributor to mean moisture transport into the Sahel, while the meridional transport contributes the most over the Guinea coast. For the wet years of the Sahel rainy season (July–September), active anomalies are displaced farther north compared to the long-term average. This includes the latitude of the intertropical front (ITF), the extent of moisture flux, and the zone of strong moisture flux convergence, with an enhanced westerly flow. For the dry Sahel years, the opposite patterns are observed. Statistically significant positive correlations between the zonal moisture fluxes and Sudan–Sahel rainfall totals are most pronounced when the zonal fluxes lead by 1–4 pentads. However, although weak, they still are statistically significant at lags 3 and 4 for meridional moisture fluxes.

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Irenea L. Corporal-Lodangco
,
Lance M. Leslie
, and
Peter J. Lamb

Abstract

This study investigates the El Niño–Southern Oscillation (ENSO) contribution to Philippine tropical cyclone (TC) variability, for a range of quarterly TC metrics. Philippine TC activity is found to depend on both ENSO quarter and phase. TC counts during El Niño phases differ significantly from neutral phases in all quarters, whereas neutral and La Niña phases differ only in January–March and July–September. Differences in landfalls between neutral and El Niño phases are significant in January–March and October–December and in January–March for neutral and La Niña phases. El Niño and La Niña landfalls are significantly different in April–June and October–December. Philippine neutral and El Niño TC genesis cover broader longitude–latitude ranges with similar long tracks, originating farther east in the western North Pacific. In El Niño phases, the mean eastward displacement of genesis locations and more recurving TCs reduce Philippine TC frequencies. Proximity of La Niña TC genesis to the Philippines and straight-moving tracks in April–June and October–December increase TC frequencies and landfalls. Neutral and El Niño accumulated cyclone energy (ACE) values are above average, except in April–June of El Niño phases. Above-average quarterly ACE in neutral years is due to increased TC frequencies, days, and intensities, whereas above-average El Niño ACE in July–September is due to increased TC days and intensities. Below-average La Niña ACE results from fewer TCs and shorter life cycles. Longer TC durations produce slightly above-average TC days in July–September El Niño phases. Fewer TCs than neutral years, as well as shorter TC durations, imply less TC days in La Niña phases. However, above-average TC days occur in October–December as a result of higher TC frequencies.

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Zewdu T. Segele
,
Michael B. Richman
,
Lance M. Leslie
, and
Peter J. Lamb

Abstract

An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June–September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble.

For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed–predicted correlation r (SSClim) being +0.81 (65%) for 1970–2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society.

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