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We demonstrate improvements in CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations) dust extinction retrievals over northern Africa and Europe when corrections are applied regarding the Saharan dust lidar ratio assumption, the separation of the dust portion in detected dust mixtures, and the averaging scheme introduced in the Level 3 CALIPSO product. First, a universal, spatially constant lidar ratio of 58 sr instead of 40 sr is applied to individual Level 2 dust-related backscatter products. The resulting aerosol optical depths show an improvement compared with synchronous and collocated AERONET (Aerosol Robotic Network) measurements. An absolute bias of the order of −0.03 has been found, improving on the statistically significant biases of the order of −0.10 reported in the literature for the original CALIPSO product. When compared with the MODIS (Moderate-Resolution Imaging Spectroradiometer) collocated aerosol optical depth (AOD) product, the CALIPSO negative bias is even less for the lidar ratio of 58 sr. After introducing the new lidar ratio for the domain studied, we examine potential improvements to the climatological CALIPSO Level 3 extinction product: (1) by introducing a new methodology for the calculation of pure dust extinction from dust mixtures and (2) by applying an averaging scheme that includes zero extinction values for the nondust aerosol types detected. The scheme is applied at a horizontal spatial resolution of 1° × 1° for ease of comparison with the instantaneous and collocated dust extinction profiles simulated by the BSC-DREAM8b dust model. Comparisons show that the extinction profiles retrieved with the proposed methodology reproduce the well-known model biases per subregion examined. The very good agreement of the proposed CALIPSO extinction product with respect to AERONET, MODIS and the BSC-DREAM8b dust model makes this dataset an ideal candidate for the provision of an accurate and robust multiyear dust climatology over northern Africa and Europe.

Extreme precipitation events have substantial implications for water resources, ecosystems, and human populations in Central Africa (CA). Consequently, understanding the spatial variability of these events is crucial for effective climate change adaptation and water management strategies. In this study, we assess the performance of the state-of-the-art global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating extreme precipitation events over CA. By considering three observational datasets, we evaluated the ability of sixteen CMIP6 models as well their multi-model ensemble (MME), to capture the patterns of extreme precipitation events. We then focus on key metrics such as duration and intensity, based on a total of ten indices of extreme precipitation events, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results showed that the individual models as well as the MME exhibited acceptable performance in reproducing the patterns of extreme precipitation events, especially when dealing with wet-day amount, frequency, dry spell duration and persistence of extreme precipitation over the two northern CA subregions (i.e., Sudano-Sahelian and Northern Equatorial). Moreover, the analyses revealed that CMIP6 models generally lack the ability of in simulating not only the wet spell duration, but also heavy precipitation indices, particularly over the central and southeastern CA subregions (i.e., Equatorial East and Southern Equatorial, respectively). These results highlight the remaining challenges in reproducing local-scale processes governing precipitation variability and extreme precipitation events over the region. The results of this study provide an understanding of both the strengths and limitations of the CMIP6 models over CA, which would help to improve regional climate projections and strengthen the ability of decision makers to assess the future risks associated with extreme precipitation events in the region.

This study examines the role of terrestrial forcings on the regional climate of sub-Saharan Africa through the application of a multivariate statistical method, stepwise generalized equilibrium feedback assessment, to an array of observational, reanalysis, and remote sensing data products. By applying multiple datasets, data uncertainty and the robustness of assessed land surface feedbacks are considered. The approach from our 2017 study is expanded to decompose the relative contribution of vegetation, soil moisture, and oceanic forcings; investigate the role of evapotranspiration (ET) partitioning in terrestrial feedbacks; and compare land surface feedbacks among four key regions, namely the Sahel, Greater Horn of Africa, West African monsoon region, and Congo. ET partitioning differs notably among sub-Saharan regions and between available observational datasets. Across sub-Saharan Africa as a whole, oceanic and terrestrial forcings impose a relatively comparable impact on year-round atmospheric conditions. The land surface feedbacks are most pronounced across the semi-arid Sahel and Greater Horn of Africa, although with unique seasonality of such feedbacks between regions. Moisture recycling is the dominant mechanism in these regions, with positive soil moisture–vegetation–rainfall feedbacks. The direct feedback of soil moisture anomalies on atmospheric conditions outweighed that of leaf area index anomalies. There is a clear need for more extensive observations of ET, its partitioning, and soil moisture across sub-Saharan Africa, as these data uncertainties propagate into the reliability of assessed soil moisture–ET feedbacks, particularly across the Sahel.

This study was funded by National Aeronautics and Space Administration Land-Cover/Land-Use Change Program (NASA LCLUC) Project # NNX09AI25G, titled ‘‘The Role of Socioeconomic Institutions in Mitigating Impacts of Climate Variability and Climate Change in Southern Africa’’, and National Science Foundation Integrative Graduate Education and Research Traineeship (NSF-IGERT) 0504422 Adaptive Management of Water, Wetlands and Watershed.

Urban quality of life (QoL) is becoming a subject of urban research mainly for western and Asian countries. Such attention is due to an increasing awareness of the contribution of QoL studies in identifying intervention areas and in monitoring urban planning policies. However, most studies are carried out at city or country level that can average out details at small scales. In this paper we present a case study where the urban QoL at small scale is measured and its variability is evaluated for Kirkos sub-city of Addis Ababa, Ethiopia. The study is based on data from a household survey and some secondary data. Geographic information system (GIS) is applied to extract proximity information (e.g., distance to school facilities) and visualize the spatial distribution of QoL. Statistical methods such as factor analysis are applied to establish an index of objective QoL while coefficient of variation is applied to evaluate spatial variability of subjective QoL. The results of this study reveal that the subjective quality of life (QoL) scores show large variation in the sub-city. The mean QoL score also indicates that the respondents in the sub-city, on average, are dissatisfied with the quality of their life. Respondents with higher education level and income are on average, however, more satisfied with their QoL in the sub-city. The results reveal that the lower the QoL in the Kebele, the larger the variability of QoL within the Kebele. Such indicates how aggregation at large scale can average out the variation of QoL at small scales. The results reveal the presence of QoL variability at small scales. The comparison between the subjective and the objective QoL at Kebele level indicated a state of dissonance, adaptation, deprivation or well-being. Such results suggest that the two measures do not always indicate the same level of QoL.