Historical and Future Changes in Sub-Saharan African Climate

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (12 May 2021) | Viewed by 16368

Special Issue Editors


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Chief Guest Editor
Department of Meteorology, University of Reading, Reading RG6 6BB, UK
Interests: decadal climate variability; climate change; decadal prediction; Sahel precipitation

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Guest Editor
Environmental Modeling Group, Max Planck Institute for Meteorology, Environmental Modeling Group, 20146 Hamburg, Germany

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Guest Editor
Centre for Agroecology, Water and Resilience (CAWR), Coventry University, Coventry CV1 5FB, UK
Interests: decadal variability and predictability of regional hydroclimates and extreme events; internal climate modes of variability (ENSO, NAO, AMO); non-stationary behaviour of land-atmosphere interactions; statistical Regional climate modelling; ocean-atmosphere coupled climate model simulations; Hydrological Modelling

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Guest Editor
Department of Geography, University of Georgia, Athens, Georgia, GA 30602, USA
Interests: climate models; climate dynamics; tropical and mid-latitude precipitation

Special Issue Information

Dear Colleagues,

Changes in external forcing (e.g., greenhouse gas concentration and anthropogenic aerosol emissions) are associated with climate changes over Africa. However, projections are uncertain. Projections of sub-Saharan precipitation change suffer from large uncertainties, and understanding the effects of climate change, up to the end of the 21st century, remains a challenging issue.

In addition, precipitation variability is associated with internal modes of sea surface temperature variability and local feedback (e.g., land cover, soil moisture, aerosols). Sub-Saharan precipitation varies on a range of different time scales, including the low-frequency variability that is associated with the Atlantic and Pacific oceans. For instance, climate variability led, over the Sahel, to a drought in the 1970s and 1980s and to a limited recovery after the 1990s, partly due to changes in Atlantic sea surface temperatures.

Therefore, future climate changes will not occur smoothly, but will experience strong decadal to multi-decadal trends. In this context, local policies for climate adaptation and mitigation will be difficult to put in place. Understanding these variations is a challenging issue, but is expected to provide useful outcomes, and should be the topic of further investigations.

Authors are encouraged to contribute to this Special Issue with their original results, aiming to understand projections in climate variability, to reduce uncertainty in simulations and to anticipate impacts over Africa.


Dr. Paul-Arthur Monerie
Dr. Adrien Deroubaix
Dr. Bastien Dieppois
Dr. Akintomide Afolayan Akinsanola
Guest Editors

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Keywords

  • tropical precipitation;
  • climate variability;
  • climate change;
  • decadal trends;
  • uncertainty

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Published Papers (3 papers)

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Research

25 pages, 11065 KiB  
Article
Multi-Decadal Variability and Future Changes in Precipitation over Southern Africa
by Kenny Thiam Choy Lim Kam Sian, Jianhong Wang, Brian Odhiambo Ayugi, Isaac Kwesi Nooni and Victor Ongoma
Atmosphere 2021, 12(6), 742; https://doi.org/10.3390/atmos12060742 - 9 Jun 2021
Cited by 44 | Viewed by 6362
Abstract
The future planning and management of water resources ought to be based on climate change projections at relevant temporal and spatial scales. This work uses the new regional demarcation for Southern Africa (SA) to investigate the spatio-temporal precipitation variability and trends of centennial-scale [...] Read more.
The future planning and management of water resources ought to be based on climate change projections at relevant temporal and spatial scales. This work uses the new regional demarcation for Southern Africa (SA) to investigate the spatio-temporal precipitation variability and trends of centennial-scale observation and modeled data, based on datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The study employs several statistical methods to rank the models according to their precipitation simulation ability. The Theil–Sen slope estimator is used to assess precipitation trends, with a Student’s t-test for the significance test. The comparison of observation and model historical data enables identification of the best-performing global climate models (GCMs), which are then employed in the projection analysis under two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The GCMs adequately capture the annual precipitation variation but with a general overestimation, especially over high-elevation areas. Most of the models fail to capture precipitation over the Lesotho-Eswatini area. The three best-performing GCMs over SA are FGOALS-g3, MPI-ESM1-2-HR and NorESM2-LM. The sub-regions demonstrate that precipitation trends cannot be generalized and that localized studies can provide more accurate findings. Overall, precipitation in the wet and dry seasons shows an initial increase during the near future over western and eastern SA, followed by a reduction in precipitation during the mid- and far future under both projection scenarios. Madagascar is expected to experience a decrease in precipitation amount throughout the twenty-first century. Full article
(This article belongs to the Special Issue Historical and Future Changes in Sub-Saharan African Climate)
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16 pages, 3785 KiB  
Article
Identification of a Representative Stationary Period for Rainfall Variability Description in the Sudano-Sahelian Zone of West Africa during the 1901–2018 Period
by Boubacar Ibrahim, Yahaya Nazoumou, Tazen Fowe, Moussa Sidibe, Boubacar Barry, Gil Mahé and Jean-Emmanuel Paturel
Atmosphere 2021, 12(6), 716; https://doi.org/10.3390/atmos12060716 - 2 Jun 2021
Cited by 1 | Viewed by 2384
Abstract
Many studies have been undertaken on climate variability in West Africa since the drastic drought of 1970s. These studies rely in many cases on different baseline periods chosen with regard to the reference periods defined by the World Meteorological Organization. A method is [...] Read more.
Many studies have been undertaken on climate variability in West Africa since the drastic drought of 1970s. These studies rely in many cases on different baseline periods chosen with regard to the reference periods defined by the World Meteorological Organization. A method is developed in this study to determine a stationary baseline period for rainfall variability analysis. The method is based on an application of three statistic tests (on deviation and trend) and a test of shifts detection in rainfall time series. The application of this method on six different gridded rainfall data and observations from 1901 to 2018 shows that the 1917–1946 period is the longest stationary period. An assessment of the significance of the difference between the mean annual rainfall amount during this baseline period and the annual rainfall amount during the other years shows that the “Normal” annual rainfall amount is defined by an interval delineated by ±the standard deviation (STD). With regard to this interval, a very wet/dry year is defined with a surplus/gap over/below the STD. Overall the 1901–2018 period, the 1950–1970 period presents the most important number of significant wet years and the 1971–1990 period presents the most important number of significant dry years. Full article
(This article belongs to the Special Issue Historical and Future Changes in Sub-Saharan African Climate)
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25 pages, 16185 KiB  
Article
Evaluation of the Performance of CMIP6 Models in Reproducing Rainfall Patterns over North Africa
by Hassen Babaousmail, Rongtao Hou, Brian Ayugi, Moses Ojara, Hamida Ngoma, Rizwan Karim, Adharsh Rajasekar and Victor Ongoma
Atmosphere 2021, 12(4), 475; https://doi.org/10.3390/atmos12040475 - 9 Apr 2021
Cited by 65 | Viewed by 6695
Abstract
This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used [...] Read more.
This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models. Full article
(This article belongs to the Special Issue Historical and Future Changes in Sub-Saharan African Climate)
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