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Fisseha Berhane, Benjamin Zaitchik, and Hamada S. Badr

Abstract

This paper characterizes the influence of the Madden–Julian oscillation (MJO) on spring rainy season (March–June) convection variability over equatorial West Africa (EWA) and investigates mechanisms of association. It is found that the MJO has a significant impact on convection and precipitation anomalies over the region. Over large portions of EWA, MJO impacts on rainfall constitute a difference on the order of 20%–50% from average daily rain rates for the season. This impact is primarily due to the direct influence of the eastward movement of the MJO convective core into EWA, which is associated with westerly low-level wind anomalies that advect moisture from the Atlantic Ocean to the region. In addition, equatorial Rossby and Kelvin waves triggered by MJO convection anomalies over the Indian Ocean have a significant and systematic influence on EWA spring rainy season precipitation. The Kelvin wave contribution and the relative strength of the direct MJO convective influence compared to that of equatorial wave activity differs from findings of studies that have examined MJO influence on EWA during boreal summer. In addition, MJO is found to influence precipitation extremes during spring rains in a manner that is not observed in summer. Importantly, in this analysis the influences of MJO convection and each of the MJO-associated convectively coupled equatorial waves frequently coincide, reaching EWA approximately 20 days after MJO convection initiates in the Indian Ocean. This coincident timing enhances the total MJO impact on the region, and it also implies that MJO events have potential for prediction of regional-scale convection and rainfall anomalies over EWA in this season.

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Hamada S. Badr, Benjamin F. Zaitchik, and Seth D. Guikema

Abstract

Rainfall in the Sahel region of Africa is prone to large interannual variability, and it has exhibited a recent multidecadal drying trend. The well-documented social impacts of this variability have motivated numerous efforts at seasonal precipitation prediction, many of which employ statistical techniques that forecast Sahelian precipitation as a function of large-scale indices of surface air temperature (SAT) anomalies, sea surface temperature (SST), surface pressure, and other variables. These statistical models have demonstrated some skill, but nearly all have adopted conventional statistical modeling techniques—most commonly generalized linear models—to associate predictor fields with precipitation anomalies. Here, the results of an artificial neural network (ANN) machine-learning algorithm applied to predict summertime (July–September) Sahel rainfall anomalies using indices of springtime (April–June) SST and SAT anomalies for the period 1900–2011 are presented. Principal component analysis was used to remove multicollinearity between predictor variables. Predictive accuracy was assessed using repeated k-fold random holdout and leave-one-out cross-validation methods. It was found that the ANN achieved predictive accuracy superior to that of eight alternative statistical methods tested in this study, and it was also superior to that of previously published predictive models of summertime Sahel precipitation. Analysis of partial dependence plots indicates that ANN skill is derived primarily from the ability to capture nonlinear influences that multiple major modes of large-scale variability have on Sahelian precipitation. These results point to the value of ANN techniques for seasonal precipitation prediction in the Sahel.

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Saleh Satti, Benjamin F. Zaitchik, Hamada S. Badr, and Tsegaye Tadesse

Abstract

Improving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July–September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology’s atmosphere–ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented.

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Hamada S. Badr, Amin K. Dezfuli, Benjamin F. Zaitchik, and Christa D. Peters-Lidard

Abstract

Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 1981–2014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios.

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Amin K. Dezfuli, Benjamin F. Zaitchik, Hamada S. Badr, Jason Evans, and Christa D. Peters-Lidard

Abstract

Rainfall variability in the Tigris–Euphrates headwaters is a result of interaction between topography and meteorological features at a range of spatial scales. Here, the Weather Research and Forecasting (WRF) Model, driven by the NCEP–DOE AMIP-II reanalysis (R-2), has been implemented to better understand these interactions. Simulations were performed over a domain covering most of the Middle East. The extended simulation period (1983–2013) enables us to study seasonality, interannual variability, spatial variability, and extreme events of rainfall. Results showed that the annual cycle of precipitation produced by WRF agrees much more closely with observations than does R-2. This was particularly evident during the transition months of April and October, which were further examined to study the underlying physical mechanisms. In both months, WRF improves representation of interannual variability relative to R-2, with a substantially larger benefit in April. This improvement results primarily from WRF’s ability to resolve two low-level, terrain-induced flows in the region that are either absent or weak in R-2: one parallel to the western edge of the Zagros Mountains, and one along the east Turkish highlands. The first shows a complete reversal in its direction during wet and dry days: when flowing southeasterly it transports moisture from the Persian Gulf to the region, and when flowing northwesterly it blocks moisture and transports it away from the region. The second is more directly related to synoptic-scale systems and carries moist, warm air from the Mediterranean and Red Seas toward the region. The combined contribution of these flows explains about 50% of interannual variability in both WRF and observations for April and October precipitation.

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