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The accumulation of dust on the surface of solar panels reduces the amount of sunlight reaching the solar cells and results in a decrease in panel performance. To avoid this loss of production and thus, to improve the performance capacity, solar panels must be cleaned frequently. The West African region is well known for its high solar energy potential. However, this potential can be reduced by the high occurrence of dust storms during the year. This article aims to provide a contribution to the construction of a meteorological information service for solar panel cleaning operations at Diass solar plant in Senegal (Western Sahel). It is based on a full year in situ experiment comparing the power loss due to dust between solar panels cleaned at different frequencies and those not cleaned. The model to determine the cleaning frequencies is based on the deposition rate of airborne particles, the concentration of airborne particles, and the density of the dust that has a major impact on the power loss. Cleaning frequencies are presented at seasonal scale because in the study area, dust episodes differ according to the seasons. A cost–benefit analysis is also performed to demonstrate the advantage of using weather information service to support the dust cleaning operations at Diass plant. As results, it is found that cleaning every 3 weeks is required during the dry seasons, December–January–February and March–April–May. During the rainy season, cleaning every 5 weeks is recommended in June–July–August, while in September–October–November cleaning every 4 weeks is sufficient to maintain an optimal performance of the solar panel. The total costs of cleaning operations based on these results are reduced compared to the current costs of cleaning and the benefits are much higher than without cleaning action.

This study examines changes in extreme precipitation over the greater Caribbean and their correlation with large-scale sea surface temperature (SST) for the period 1985 to 2015. The data used for this study were derived from two satellite products: Climate Hazards Group InfraRed Precipitation (CHIRPS) and NOAA DOISST (Daily Optimum Interpolation Sea Surface Temperature version 2.1) with resolutions of 5 km and 25 km, respectively. Then, change in the characteristics of six(6) extreme precipitation indices defined by the WMO ETCCDI (World Meteorological Organization Expert Team on Climate Change Detection and Indices) is analyzed, and Spearman's correlation coefficient has been used and evaluated by t-test to investigate the influence of a few large-scale SST indices: (i) Caribbean Sea Surface Temperature (SST-CAR); (ii) Tropical South Atlantic (TSA); (iii) Southern Oscillation Index (SOI); (iv) Northern Oscillation Index (NAO). The results show that at the regional scale, +NAO contributes significantly to a decrease in heavy precipitation (R95p), daily precipitation intensity (SDII), and total precipitation (PRCPTOT), whereas +TSA is associated with a significant increase in daily precipitation intensity (SDII). At an island scale, in Puerto Rico and southern Cuba, the positive phase of +TSA, +SOI, and +SST-CAR is associated with an increase in daily precipitation intensity (SDII) and heavy precipitation (R95p). However, in Jamaica and northern Haiti, the positive phases of +SST-CAR and +TSA are also associated with increased indices (SDII, R95p). In addition, the SST warming of the Caribbean Sea surface temperature and the positive phase of the Southern Oscillation (+SOI) significantly increases with the number of rainy days (RR1) and the maximum duration of consecutive rainy days (CWD) over the Dominican Republic and in southern Haiti. [ABSTRACT FROM AUTHOR]

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the "signal-tonoise paradox" and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of two planetary boundary parameterizations in simulating the mean climate of West Africa using the Regional Climate Model version 5 (RegCM5). These planetary boundary parameterizations are the Holtslag scheme and the University of Washington scheme. Two sets of three one-year simulations were carried out at 25 km horizontal resolution with three different initial conditions. The first set of simulations used the Holtslag scheme (hereafter referred to as Hol), while the second used the University of Washington (UW) scheme (hereafter referred to as UW). The results displayed in this study are an average of the three simulations. During the JJAS rainy season, with respect to GPCP, both models overestimated total rainfall in the orographic regions. The UW experiment represented total rainfall fairly well compared to its counterpart, Hol. Both models reproduced convective rainfall well, with a relatively weak dry bias over the Guinean coast subregion. Globally, UW performed better than Hol in simulating precipitation. The pattern of near-surface temperature in both models was well reproduced with a higher bias with Hol than with UW. Indeed, the UW scheme led to a cooling effect owing to the reduction in eddy heat diffusivity in the lower troposphere contributing to reduce the bias. As a consequence, the height of the planetary boundary layer (PBL) was best simulated using the UW scheme but was underestimated compared to ERA5, while using the Hol scheme failed to capture the height of the PBL. This is coherent with the distribution of total cloud cover, which was better simulated with the UW scheme compared to the Hol scheme. This study shows that use of both planetary boundary parameterizations leads to a good simulation of most of the climatological characteristics of the West African region. Nevertheless, use of the UW scheme contributes to a better performance than use of the Hol scheme, and the differentiation between the two schemes is significant along the Guinea Coast and in orographic regions. In these topographically complex regions, UW appears to be more appropriate than Hol. This study emphasizes the importance of planetary boundary parameterizations for accurately simulating climate variables and for improving climate forecasts and projections in West Africa. [ABSTRACT FROM AUTHOR]

Sahel rainfall shows pronounced decadal variability and a negative trend between wet conditions in the 1950s-1960s and dry ones in the 1970s-1980s. Using continuous wavelet transform, the quasi-decadal variability (QDV) of rainfall reveals zonal contrasts. The highest QDV is identified in the 1950s-1960s over western Sahel and in the 1970s-1980s over eastern Sahel. The quasi-decadal atmospheric anomalies have been reconstructed using Fourier transform for the 1950s-1960s and the 1970s-1980s, respectively, and assessed by the composite analysis of the QDV phases for the periods before and after 1968. Over western Sahel, the rainfall QDV in the 1950s-1960s is related to the North Atlantic sea surface temperature (SST) variability, as highlighted by the wavelet coherence. A southward shift trend of the Intertropical Convergence Zone (ITCZ) is identified through an enhancement of northeasterly fluxes and moisture convergence over the western part of West Africa. A decrease (increase) of southern (northern) subtropical sinking motions seems to be involved. In the 1970s-1980s, a strengthening of cross-equatorial Atlantic SST and pressure gradients is related to an increase of monsoon flow from lower troposphere up to the midtroposphere and to the northward shift of the ITCZ, mainly over eastern Sahel. The Pacific SST influence is also identified, which involves changes in the global zonal circulation. Key Points Zonal contrasts are detected in Sahel rainfall at quasi-decadal scale Quasi-decadal signals of the WAM are identified in both wet and dry periods Modifications of SST forcing are observed between wet and dry periods ©2013. American Geophysical Union. All Rights Reserved.

The spatiotemporal variability of meteorological droughts, its changes and hot spots location across Africa are analysed for the period spanning 1928–2017 using the Standardized Precipitation Index (SPI) applied to the precipitation products from the Climatic Research Unit (CRU), University of Delaware (UDEL) and Global Precipitation Climatology Centre (GPCC). Spatially, an analysis based on rotational empirical orthogonal function identifies five regions of similar drought variability, namely the Sahel, East Africa, East Southern Africa, West Southern Africa and the Gulf of Guinea. Temporally, the most common periods of drought occurrence are the 1970s, the 1980s and, to a lesser extent, the 1990s. Changes in drought characteristics for the intermediate past (1958–1987) and recent past (1988–2017) compared to the far past (1928–1957) indicate robust increases of drought duration, frequency and severity in the Sahel, and to a lower extent in the Gulf of Guinea, some areas of Central Africa, part of Southern Africa and over Madagascar. These changes are stronger (weaker) along the Sahel during the intermediate past (recent past) and stronger (weaker) over Central and Southern Africa and Madagascar during the recent past (intermediate past). As a consequence, drought hot spots, mostly driven by severity during the regions' wet season, are identified in areas confined in the Sahel during the intermediate past and in regions mainly over Central and Southern Africa and Madagascar during the recent past. Our results are useful for drought disaster risk management across Africa and provide a valuable reference for future drought analysis under global warming conditions. [ABSTRACT FROM AUTHOR]

This study aims to provide improved knowledge and evidence on current (1986–2015) climate variation based on six rainfall indices over five West African countries (Senegal, Niger, Burkina Faso, Ivory Coast, and Benin) using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset. On average, precipitation has increased over the central Sahel and the western Sahel. This increase is associated with increase in the number of rainy days, longer wet spells and shorter dry spells. Over the Guinea Coast, the slight increase in precipitation is associated with an increase in the intensity of rainfall with a shorter duration of wet spells. However, these mean changes in precipitation are not all statistically significant and uniform within a country. While previous studies are focused on regional and sub-regional scales, this study contributes to deliver a climate information at a country level that is more relevant for decision making and for policy makers, and to document climate-related risks within a country to feed impact studies in key sectors of the development, such as agriculture and water resources.