Search

This study focuses on evaluating the High-Resolution Model Inter-comparison Project (HighResMIP), Atmospheric Model Intercomparison Project (AMIP), and Coupled Model simulations within the framework of the Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). We used fifteen Models to explore how CMIP6 reproduced the June–September (JJAS) precipitation features over the Greater Horn of Africa (GHA) during the 1979–2014 historical simulation periods. Rainfall from the Global Precipitation Climatology Center (GPCC) and Climatic Research Unit (CRU) are used to validate the model simulations. Overall, the AMIP multi-model ensemble mean (MME) is able to reproduce the observed seasonal mean, the annual cycle, the frequency distribution of cumulative rainfall, spatial and trend patterns of precipitation over GHA. Particularly, long-term mean of JJAS season precipitation is well reproduced over the western part of Sudan Republic, much of South Sudan, over some isolated parts of north-western Uganda, Ethiopian Highlands, and western Ethiopia. However, consistent with previous studies, coupled models MME shows substantial discrepancies compared to AMIP in simulating JJAS rainfall climatology by exhibiting dry bias relative to both GPCC and CRU rainfall. In contrast, the HighresMIP experiments reveal wet bias over most parts of the GHA. The annual cycles of observed rainfall are well captured in AMIP, CMIP, and HighresMIP experiments and with further improvement in MMEs mean. In addition, the spatial rainfall pattern correlation between GPCC (CRU) and model simulations is as high as 0.89 (0.94), whereas the maximum trend pattern correlation is 0.47(0.72) with GPCC (CRU) respectively. Employing a multicriteria decision-making algorithm (MCDM) based on eight performance metrics as the selection criterion, we identified four, three, and two models and their MMEs out of AMIP, CMIP, and HighresMIP experiments, respectively, having superior skills over Ethiopian Highlands. In contrast, the study shows substantial biases in a number of models from AMIP, CMIP and HighresMIP experiments over GHA relative to GPCC and CRU observations that need to be improved with either bias correction or through further tuning of the models to improve their skills. [ABSTRACT FROM AUTHOR]

Regional climate models (RCM) are commonly used to downscale the coarse resolution general circulation models (GCMs) to produce climate variables at spatially high-resolution grids. The quality of the downscaled data depends on the skills of both GCMs and RCMs. In this study, 10 GCMs are used to constrain the boundary and provide initial conditions of three RCMs. A total of 18 GCM-RCMs combinations are employed to produce simulations over East Africa (EA). The accuracy of simulated rainfalls is evaluated with respect to Climate Research Unit (CRU) rainfall to identify the best GCM-RCM combinations. Bias, root mean squared error (RMSE), correlation coefficient, and MAE-based model skill score have shown that MPI-REMO, MIROC-REMO, MPI-RCA4, IPSL-RCA4, CCCMA-RCA4, MOHC-CCLM, MOHC-REMO, and CNRM-RCA4 during spring season; ICHEC-REMO, MIROC-REMO, MOHC-REMO, MIROC-RCA4, CSIRO-RCA4, and MPI-REMO during autumn season; CSIRO-RCA4, MIROC-RCA4, CCCMA-RCA4, MIROC-REMO, CNRM-RCA4, and MOHC-RECA during boreal summer; and ICHEC-REMO, NOAA-RCA4, MOHC-REMO, MOHC-CCLM, MIROC-REMO, MPI-REMO, and IPSL-RCA4 during boreal winter season are the best performing GCM-RCM combination. It is also evident that the skills of the models are better in autumn than their skills in boreal spring and summer. Moreover, summer rain in EA is the most difficult for models to simulate. Comparison of annual mean with the CRU rainfall shows that MPI-REMO, MIROC-REMO, CSIRO-RCA4, MOHC-REMO, CCCma-RCA4, IPSL-RCA4, and CNRM-RCA4 are also the best GCM-RCM combinations as observed from strong significant spatial correlation, as well as low bias, RMSE, and positive skill score as high as 0.7. Therefore, the GCM-RCM combinations that exhibit superior performance over EA in most seasons as well as in capturing observed annual mean are CCCMA-RCA4, MIROC-REMO, MPI-REMO, IPSL-RCA4, CSIRO-RCA4, MOHC-REMO, and MIROC-RCA4. The difference in skills between models as well as variation of the same model skill both spatially and seasonally implies the role of several factors such as local topography, vegetation, and surface type as well as robustness of model physics in capturing small scale processes such as mesoscale convection in boreal summer (e.g., over Ethiopian highlands). [ABSTRACT FROM AUTHOR]

The scarcity of ground-based observations, poor global coverage and resolution of satellite observations necessitate the use of data generated from models to assess spatio-temporal variations of atmospheric CO2 concentrations in a near continuous manner in a global and regional scale. Africa is one of the most data scarce region as satellite observation at the equator is limited by cloud cover and there are very limited number of ground based measurements. As a result, use of simulations from models are mandatory to fill this data gap. However, the first step in the use of data from models requires assessment of model skill in capturing limited existing observations. Even though, the NOAA Carbon Tracker model is evaluated using TCCON and satellite observations at a global level, its performance should be assessed at a regional scale, specifically in a regions like Africa with a highly varying climatic responses and a growing local source. In this study, NOAA CT2016 CO2 is compared with the ACOS GOSAT observation over Africa using five years datasets covering the period from April 2009 to June 2014. In addition, NOAA CT2016 CO2 is compared with OCO-2 observation over Africa using two years data covering the period from January 2015 to December 2016. The results show that the XCO2 retrieved from GOSAT and OCO-2 are lower than CT2016 model simulation by 0.42 and 0.93 ppm on average respectively, which lie within the range of the errors associated with the GOSAT and OCO-2 XCO2 retrievals. The mean correlations of 0.73 and 0.6, a regional precisions of 3.49 and 3.77 ppm, and the relative accuracies of 1.22 and 1.95 ppm were found between the model and the two data sets implying the performance of the model in Africa's land regions is reasonably good despite shortage of in-situ observations over the region assimilated in the model. These differences, however, exhibit spatial and seasonal scale variations. Moreover, the model shows some weakness in capturing the whole distribution. For example, the probability of detection ranges from 0.6 to 1 and critical success index ranges from 0.4 to 1 over the continent when the analysis includes data above the 95th percentile and the whole data respectively. This shows the model misses the higher extreme ends of the CO2 distribution. Spatially, GOSAT and OCO-2 XCO2 are lower than that of CT2016 by upto 4 ppm over North Africa (10°-35° N) whereas it exceeds CT2016 XCO2 by 3 ppm over Equatorial Africa (10° S-10° N). Larger spatial mean biases of 2.11 and 1.8 ppm, 1.25 and 0.73 ppm in CT2016 XCO2 with respect to that of GOSAT and OCO-2 are observed during winter (DJF) and spring (MAM) while small biases of -0.15 and 0.21 ppm, and 0.2 and -1.14 ppm are observed during summer (JJA) and autumn (SON) respectively. The model simulation has the ability to capture seasonal cycles with a small discrepancy over the North Africa and during winter seasons over all regions. In these cases, the model overestimates the local emissions and underestimate CO2 loss. [ABSTRACT FROM AUTHOR]