The West Africa region (5 circle\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>\circ$$\end{document} to 20 circle\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>\circ$$\end{document}N and 10 circle\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>\circ$$\end{document}E to 20 circle\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>\circ$$\end{document}W) is particularly vulnerable to climate change due to a combination of unique geographic features, meteorological conditions, and socio-economic factors. Drastic changes in precipitation (e.g., droughts or floods) in the region can have dramatic impacts on rain-fed agriculture, water availability, and disease risks for the region's population. Quantifying these risks requires localized climate projections at a higher resolution than is generally available from general circulation models. Using self-organizing maps, we produce station-based downscaled precipitation projections for medium and high-emission climate scenarios for this region. Compared to historical observations, the downscaled values are able to match the historical range of the distribution, and recreate the seasonal variability for the inland portions of the region, but struggle with the seasonal cycle along the coast. We find a decrease in the interior Sahel region by an average of 10% by 2100 under the high greenhouse gas-emission scenario of Shared Socioeonomic Pathway 5-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}8.5. Precipitation decreases in the Sahel are primarily driven by reductions in the number of rainy days during the wet season, rather than by consistent decreases in the magnitude of the precipitation amounts or decreases in the average length of the wet season.

Future climate projections are made with global numerical models whose spatial resolution often exceed 100s of km2. These scales are too large to resolve many weather events, leaving a gap between the climate information needed to understand the impact of climate change on many human activities, and the information that can be provided by global models. Regional climate projections generated using statistical downscaling methods can provide an essential bridge between global climate models and the high spatial resolution data needed. As the demand for localized climate information continues to grow, new software tools are necessary to provide downscaled climate information. In this article, we describe CCdownscaling, a software package that provides multiple statistical climate downscaling methods to the station scale, including the Self Organizing Maps method. CCdownscaling includes several evaluation metrics for assessing the skill of downscaled climate information in various applications, and we demonstrate these features on an example dataset.

For future climate projections to be useful they must be actionable at the local level. In this study, we develop daily temperature and precipitation climate scenarios suitable for use in projections of drought, energy use, water use, and crop production. We investigate the magnitude of future changes to air temperature and precipitation in the Midwest United States in response to three future climate change scenarios. Results are used to assess changes to incidence of precipitation extremes and human comfort (using heat index) associated with the anticipated climate changes in the region. We use self‐organizing maps and random forest based techniques to generate daily realizations of temperature and precipitation for 279 weather stations in a region centred on Illinois. We determine that the random forest model performs best for maximum and minimum temperatures, while the self‐organizing map performs best for precipitation. Using nine models from the Coupled Model Inter‐Comparison Project Phase 5, downscaled daily temperature and precipitation values are generated for low, moderate, and high greenhouse gas emissions scenarios for historical and future periods. Based on recent trends, we focus our results on the high emissions scenario, and show an average increase of 4.3°C in maximum daily air temperature across the region for the 2071–2100 period. Precipitation decreases by up to 15% in the southern half of the study region, with a similar percentage increase in the northern half of the region. The regional environmental changes result in an increase of 5.8° in average summer heat index, and increase of 48% in the number of days likely to produce extreme heat, and a decrease in the average value of the standardized precipitation and evapotranspiration index of 1.9 (indicating increased drought) across the region by 2100.

Abstract. Statistical downscaling methods provide an essential bridge between low resolution global climate models and localized information needed by decision makers. As the demand for localized climate information continues to grow to make projections for a wide variety of applications, the need for software that can provide this sort of downscaled data grows with it. The CCdownscaling package described in the article provides a number of downscaling methods, including Self Organizing Maps, as well as a number of evaluation metrics for assessing downscale model skill. In this article, we describe the features of the CCdownscaling package, and show an example use case for downscaling temperature and precipitation. It is open-source and freely available for use in generating downscaled projections.

Eastern Africa is a common region of African easterly wave (AEW) onset and AEW early-life. How the large-scale environment over east Africa relates to the likelihood of an AEW subsequently undergoing tropical cyclogenesis in a climatology has not been documented. This study addresses the following hypothesis: AEWs that undergo tropical cyclogenesis (i.e., developing AEWs) initiate and propagate under a more favorable monsoon large-scale environment over eastern Africa when compared to non-developing AEWs. Using a 21-year August-to-September (1990-2010) climatology of AEWs, differences in the large-scale environment between developers and non-developers are identified and are propose to be used as key predictors of subsequent tropical cyclone formation and could informtropical cyclogenesis prediction. TC precursors when compared to non-developing AEWs experience: an anomalously active West African Monsoon, stronger northerly flow, more intense zonal Somali jet, anomalous convergence over the Marrah Mountains (region of AEW forcing), and a more intense and elongated African easterly jet (AEJ). These large-scale conditions are linked to near-trough attributes of developing AEWs which favor more moisture ingestion, vertically aligned circulation, a stronger initial 850-hPa vortex, deeper wave pouch, and arguably more AEW and Mesoscale convective systems interactions. AEWs that initiate over eastern Africa and cross the west coast of Africa are more likely to undergo tropical cyclogenesis than those initiating over central or west Africa. Developing AEWs are more likely to be southern-track AEWs than non-developing AEWs.

Tropical cyclone ensemble track forecasts from 153 initialization times during 2017-18 are clustered using regression mixturemodels. Clustering is performed on a four-ensemble dataset [ECMWF1GEFS1UKMET1 CMC (EGUC)], and a three-ensemble dataset that excludes the CMC (EGU). For both datasets, five-cluster partitions are selected to analyze, and the relationship between cluster properties (size, ensemble composition) and 96-144-h cluster-mean error is evaluated. For both datasets, small clusters produce very large errors, with the least populous cluster producing the largest error in more than 50% of forecasts. The mean of the most populous EGUC cluster outperforms the most accurate (EGU) ensemble mean in only 43% of forecasts; however, when the most populous EGUC cluster from each forecast contains $30% of the ensemble population, its average cluster-mean error is significantly reduced compared to when the most populous cluster is smaller. Forecasts with a highly populous EGUC cluster also appear to have smaller EGUC-, EGU-, and ECMWF-mean errors. Cluster-mean errors also vary substantially by the ensembles composing the cluster. The most accurate clusters are EGUC clusters that contain threshold memberships of ECMWF, GEFS, and UKMET, but not CMC. The elevated accuracy of EGUC CMC-excluding clusters indicates the potential utility of including the CMC in clustering, despite its large ensemble-mean errors. Pruning ensembles by removing members that belong to small clusters reduces 96-144-h forecast errors for both EGUC and EGU clustering. For five-cluster partitions, a pruning threshold of 10% affects 49% and 35% of EGUC and EGU ensembles, respectively, improving 69%-74% of the forecasts affected by pruning.

The topography of eastern Africa, namely, the Ethiopian Highlands and Marrah Mountains have been shown to play a key role in the genesis of African Easterly Waves (AEWs) through convective initiation in that region. Topographic influences on the African Easterly Jet, evolution and energetics of AEWs, and rainfall production across northern tropical Africa are examined here. The Weather Research and Forecasting model is employed to simulate the climate over a 60‐day period for three years (2004, 2005, and 2006) for three cases with varying topography: realistic, half‐height, and no topography. An energetics analysis for the resulting AEWs reveals that wave development by barotropic and baroclinic processes weakens when topography is flattened. These results show that topography in Africa plays a significant role in the wave development as they propagate westward, not only in their initiation over East Africa.

This study introduces the development of the Tracking Algorithm for Mesoscale Convective Systems (TAMS), an algorithm that allows for the identifying, tracking, classifying, and assigning of rainfall to mesoscale convective systems (MCSs). TAMS combines area-overlapping and projected-cloud-edge tracking techniques to maximize the probability of detecting the progression of a convective system through time, accounting for splits and mergers. The combination of projection on area overlapping is equivalent to setting the background flow in which MCSs are moving on. Sensitivity tests show that area-overlapping technique with no projection (thus, no background flow) underestimates the real propagation speed of MCSs over Africa. The MCS life cycles and propagation derived using TAMS are consistent with climatology. The rainfall assignment is also more reliable than with previous methods as it utilizes a combination of regridding through linear interpolation with high temporal and spatial resolution data. This makes possible the identification of extreme rainfall events associated with intense MCSs more effectively. TAMS will be utilized in future work to build an AEW-MCS dataset to study tropical cyclogenesis.

An African easterly wave (AEW) and associated mesoscale convective systems (MCSs) dataset has been created and used to evaluate the propagation of MCSs, AEWs, and, especially, the propagation of MCSs relative to the AEW with which they are associated (i.e., wave-relative framework). The thermodynamic characteristics of AEW-MCS systems are also analyzed. The analysis is done for both AEW-MCS systems that develop into tropical cyclones and those that do not to quantify significant differences. It is shown that developing AEWs over West Africa are associated with a larger number of convective cloud clusters (CCCs; squall-line-type systems) than nondeveloping AEWs. The MCSs of developing AEWs propagate at the same speed of the AEW trough in addition to being in phase with the trough, whereas convection associated with nondeveloping AEWs over West Africa moves faster than the trough and is positioned south of it. These differences become important for the intensification of the AEW vortex as this slower-moving convection (i.e., moving at the same speed of the AEW trough) spends more time supplying moisture and latent heat to the AEW vortex, supporting its further intensification. An analysis of the rainfall rate (MCS intensity), MCS area, and latent heating rate contribution reveals that there are statistically significant differences between developing AEWs and nondeveloping AEWs, especially over West Africa where the fraction of extremely large MCS areas associated with developing AEWs is larger than for nondeveloping AEWs.