Assessing the Capabilities of Three Regional Climate Models over CORDEX Africa in Simulating West African Summer Monsoon Precipitation

Akinsanola, A. A.; Ogunjobi, K. O.; Gbode, Imoleayo E.; Ajayi, Vincent O.

doi:10.1155/2015/935431

Cited by 63 publications

(34 citation statements)

References 65 publications

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“…In the literature, model performance evaluations have been achieved by computing statistical indicators, such as the mean bias, the Nash–Sutcliffe Efficiency (NSE), the root‐mean‐square error (RMSE) and the RMSE‐observations standard deviation ratio (RSR) (e.g. Gbobaniyi et al ., ; Zhou et al ., ; Akinsanola et al ., ; McMahon et al ., ). The optimal values of the statistical indicators are: 0.0 for mean bias; 1.0 for NSE; the lower, the better for RMSE; and 0.0 for RSR.…”

Section: Methodsmentioning

confidence: 97%

Climate change scenarios for Angola: an analysis of precipitation and temperature projections using four RCMs

Carvalho

Santos

Pulquério

2016

Intl Journal of Climatology

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Climate change is expected to have a major impact on the world's environment, economy and society, especially in African countries such as Angola due to the dependence of many vital sectors on climate variability (e.g. agriculture) and their low adaptive capacity. With the advances in the availability of climate model data sets provided by programs such as the CORDEX, Angola can benefit from studies on future climate change using high‐spatial‐resolution data, which have previously been absent for this region. The purpose of this study is to analyse the projected changes in temperature and precipitation over Angola during the 21st century based on four regional climate models (RCMs). The spatial and temporal changes were analysed using a high‐ and medium‐low radiative forcing scenario to consider the uncertainty in greenhouse gas emissions. Changes in the average annual precipitation, average annual temperature and a drought index are studied for short‐, mid‐ and long‐term projections. Compared to the reference period, relevant changes are projected in temperature and precipitation indices for the different models under both emission scenarios. These changes include an increase in both the maximum and minimum temperature of up to 4.9 °C by the end of the century and an intensification of droughts. The precipitation projections are highly variable – increasing and decreasing – across the region and dependent on the RCMs. Despite these differences, the precipitation generally decreases over time (approximately −2% by 2100), with the southern region experiencing a stronger decrease in precipitation. Further investigation is needed to minimize projections uncertain, as the annual mean precipitation was overestimated by three of the RCMs (especially in the northern coastal units). Overall, these projected changes in climate can have important implications for the future of Angola, because they are expected to magnify existing problems, thus creating new risks for human and natural systems.

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Section: Methodsmentioning

confidence: 97%

Climate change scenarios for Angola: an analysis of precipitation and temperature projections using four RCMs

Carvalho

Santos

Pulquério

2016

Intl Journal of Climatology

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“…These models are chosen because many studies (Akinsola, yacouba, Alioune, etc.) [13][14][15] The second are the simulated data. They are the historical and future projections (RCP4.5 and RCP8.5 scenarios) of rainfall and PET.…”

Section: Datamentioning

confidence: 99%

“…These models are chosen because many studies (Akinsola, yacouba, Alioune, etc.) [13][14][15] in the West African region have recognized their ability to reproduce the rainfall regime. Their characteristics are shown in Table 1.…”

Section: Datamentioning

confidence: 99%

Assessment of Future Water Resources Availability under Climate Change Scenarios in the Mékrou Basin, Benin

2017

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Abstract:This work aims to evaluate future water availability in the Mékrou catchment under climate change scenarios. To reach this goal, data from Regional Climate Models (RCMs) were used as the input for four rainfall-runoff models which are ModHyPMA (Hydrological Model based on Least Action Principe), HBV (Hydrologiska Byråns Vattenbalansavdelning), AWBM (Australian Water Balance Model), and SimHyd (Simplified Hydrolog). Then the mean values of the hydro-meteorological data of three different projected periods (2011-2040, 2041-2070 and 2071-2100) were compared to their values in the baseline period. The results of calibration and validation of these models show that the meteorological data from RCMs give performances that are as good as performances obtained with the observed meteorological data in the baseline period. The comparison of the mean values of the hydro-meteorological data of the baseline period to their values for the different projected periods indicates that for PET there is a significantly increase until 2100 for both Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios. Therefore, the rate's increase of potential evapotranspiration (PET) under the RCP8.5 scenario is higher than that obtained under the RCP8.5 scenario. Changes in rainfall amounts depend on the scenario of climate change and the projected periods. For the RCP4.5 scenario, there is a little increase in the annual rainfall amounts over the period from 2011 to 2040, while there is a decrease in the rainfall amounts over the other two projected periods. According to the RCP8.5 scenario, the contrary of changes observed with the RCP4.5 scenario are observed. At a monthly scale, the rainfall amounts will increase for August and September and decrease for July and October. These changes in rainfall amounts greatly affect yearly and monthly discharge at the catchment outlet. Over the three projected periods and for both RCP4.5 and RCP8.5, the mean annual discharge will significantly increase related to the baseline periods. However, the magnitude of increases will depend on the projected period and the RCP scenario. At a monthly scale, it was found that runoff increases significantly from August to November for all projected periods and the climate change scenario.

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“…GCMs are therefore a fundamental tool for understanding climate variations and dynamics. Unlike other climate data sources, such as satellite, radar, and on-site measurement data where only the past and present and climatology represent the key contributions [24][25][26][27] in terms of performance evaluation over the region.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics

Gnitou

Tan

et al. 2019

Atmosphere

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Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of assessment, but mostly to serve as a basis for data quality enhancement activities. In this study, we assessed the Rossby Centre Regional Climate Model (RCA4) over West Africa without targeting any application-specific feature, while jointly evaluating its boundary conditions and accounting for observational uncertainties. Results from this study revealed that the RCA4 signal highly modifies the boundary conditions (global climate models (GCMs) and reanalysis data), resulting in a significant reduction of their biases in the dynamically downscaled outputs. The results, with respect to the observational ensemble members, are in line with the differences between the observation datasets. Among the RCA4 simulations, the ensemble mean outperformed all individual simulations regardless of the statistical metric and the reference data used. This indicates that the RCA4 adds value to GCMs over West Africa, with no influence of observational uncertainty, and its ensemble mean reduces model-related uncertainties. climate is measured or estimated, GCM outputs are provided with past, present, and future climate variables. This feature thus allows a variety of applications that were infeasible with past and present climate data repositories. Although useful for general climate information purposes, GCMs suffer from uncertainty in process representation, error propagation, uncertainty in observational data, and sensitivity in resolution [1]. This final drawback is particularly common when GCMs are used to resolve regional-scale features due to being originally designed to serve global needs in terms of providing likely accurate climate information. A common and widely adopted solution within the climate research community is the use of high-resolution regional climate models (RCMs) driven by GCMs or reanalysis data as boundary conditions of the domain area being modeled [2].Like GCMs, RCMs result from the representation of climate processes, but with a particular emphasis on resolving regional-scale climate features. As improved and regionally resolved versions of GCMs, the availability of RCMs for past, present, and future climate data has provided new possibilities not only for understanding regional-scale historical climate phenomena, but also for inferring their likelihood in the future. RCMs are thus important tools for local governments, institutions, researchers, and local communities [1,3].The improvements in resolution provided by RCMs are not error-free. Being derived from GCM data means RCMs might be subjected to GCM-inherited errors [4]. The various uses of regional climate data suggest that different assumptions and considerations are required for different features and metrics with respect to the domain of application and highlight the...

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Assessing the Capabilities of Three Regional Climate Models over CORDEX Africa in Simulating West African Summer Monsoon Precipitation

Cited by 63 publications

References 65 publications

Climate change scenarios for Angola: an analysis of precipitation and temperature projections using four RCMs

Climate change scenarios for Angola: an analysis of precipitation and temperature projections using four RCMs

Assessment of Future Water Resources Availability under Climate Change Scenarios in the Mékrou Basin, Benin

Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics

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