Congo Basin Hydrology, Climate, and Biogeochemistry. Группа авторов

4.8 Spatial and seasonal distributions at 925 hPa of zonal wind in colour (m/s) and wind intensity (in m/s vector) for the period 2001–2006 of ERA5 (a–d) and the different experiments (e–l).

Figure 4.9 Spatial and seasonal distributions at 200 hPa of zonal wind in colour (m/s) and wind intensity (in m/s vector) for the period 2001–2006 of ERA5 (a–d) and the different experiments (e–l).

Figure 4.9 shows wind at 200 hPa pressure level. We observe that ERA 5 has the TEJ located between 5°S–5°N latitude, which extends from the Indian Ocean to the Atlantic Ocean during the DJF, MAM, and SON seasons. The experiments tend to reasonably reproduce this TEJ but with an overestimation of the intensity and spatial extent. As far as wind direction and intensity are concerned, the experiments manage to represent them during the DJF, MAM, and SON seasons. A comparative analysis of the different experiments reveals that performance is similar, but there are important differences.

4.4. CONCLUSION

Central Africa’s climate presents a considerable challenge for climate modeling. This is due to its complexity and the diversity of dynamic and physical processes involved in the establishment of the Central African monsoon system. Understanding these processes is crucial for researchers in this region (Jenkins et al., 2005). Ocean–air interaction is one of the region’s complex processes. The uniqueness of this work resides in the use of version 4.6 of the Regional Climate Model (RegCM) to examine the influence of slab‐ocean parameterization, which tells us about ocean–atmosphere interaction using SST. The objective of this analysis was to evaluate the RegCM’s response to the SOM in terms of the representation of the seasonal spatial distribution of precipitation, temperature, and wind. The model was integrated into Central Africa according to Grell’s convective scheme with the Fritsch‐Chappell closure assumption (GFC). The ability of the model was assessed by running two series of simulations. The first simulation is designed to force the surface boundaries of the RegCM with the weekly OISSTs (optimum interpolation sea‐surface temperatures) (interpolated daily), the second simulation is designed to couple the RegCM with the SOM, which provides mutual interaction between the ocean and the atmosphere. The two experiments were initiated on 1 January 2000 for seven years, with one year of “spin‐up” excluded in the study period. These experiments are all forced by the ECMWF ERA‐Interim (ERA‐15) reanalysis. The model’s ability to reproduce the seasonality of rain, temperature, and wind for the period from 2001 to 2006 was assessed and then cross‐compared. The comparison of experiments was made with ARC2, GPCP observation data, and ERA 5 and ERA‐Interim reanalyses.

      The results showed that the experiments satisfactorily reproduced the main characteristics of the rainfall regime, surface temperature, and wind in Central Africa and the Congo Basin in all seasons, despite a lower performance in terms of temperature. The position of the rainfall maxima and minima is fairly well represented. The surface temperature is well represented, but with an underestimation of 2 to 3 °C. Also, the experiments satisfactorily reproduce the different phases of the seasonal cycle of rain and temperature. Finally, the experiments manage to faithfully reproduce the main characteristics of the atmospheric wind dynamics at the surface (925 hPa) and at altitude (200 hPa): the positioning of the monsoon flow is satisfactory and agrees well with the ERA 5 reanalysis. A comparative analysis reveals subtle differences between the two experiments: RegCM_CTR and RegCM_SLAB. This difference can be attributed to a large variability associated with slab‐ocean convection, which takes into account ocean–atmosphere interaction. These results are similar to those of Umakanth and Kesarkar (2017) conducted in India. Generally, it is understood that the parameterization of the slab‐ocean, which provides information on ocean–atmosphere interaction, considerably improves the performance of version 4.6 of the RegCM regional climate model for simulating the Central African monsoon. This work opens new perspectives in the regional climate modeling of Central Africa: It would be appropriate to repeat sensitivity experiments of RegCM to different convective schemes and process‐based assessment.

ACKNOWLEDGMENTS

      The authors thank ICTP for providing the RegCM4.6 regional climate model. We wish to thank the data producers GPCP, ARC2, ERA‐Interim, ERA 5, and OISST. Our thanks also go to the three anonymous reviewers whose criticisms and suggestions made it possible to significantly improve the manuscript.

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