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.
REFERENCES
1 Anthes, R. A., Hsie, E. Y., Kuo, Y. H. (1987). Description of the Penn State/NCAR Mesoscale Model Version 4 (MM4). Technical Report No. 282. Boulder, CO: National Center for Atmospheric Research.
2 Balas, N., Nicholson, S. E., & Klotter, D. (2007). The relationship of rainfall variability in West Central Africa to sea‐surface temperature fluctuations. International Journal of Climatology, 27(10), 1335–1349. doi: 10.1002/joc.1456
3 Bele, M. Y., Tiani, A. M., Somorin, O. A., & Sonwa, D. J. (2013). Exploring vulnerability and adaptation to climate change of communities in the forest zone of Cameroon. Climatic Change, 119(3–4), 875–889. doi: 10.1007/s10584‐013‐0738‐z
4 Chow, K., & Chan, J. C. (2009). A Dual‐scheme approach of cumulus parameterization for simulating the Asian summer monsoon. Meteorological Applications, 17(3). doi: 10.1002/met.169
5 Cubasch, U., Santer, B. D., Hellbach, A., Hegerl, G., Höck, H., Maier‐Reimer, E., et al. (1994). Monte Carlo climate change forecasts with a global coupled ocean‐atmosphere model. Climate Dynamics, 10(1–2), 1–19. doi: 10.1007/bf00210333
6 Dezfuli, A. K., Zaitchik, B. F., & Gnanadesikan, A. (2015). Regional atmospheric circulation and rainfall variability in South Equatorial Africa. Journal of Climate, 28(2), 809–818. doi: 10.1175/jcli‐d‐14‐00333.1
7 Dickinson, R. E., Henderson‐Sellers, A., Kennedy, P. J. (1993). Biosphere‐atmosphere transfer scheme (BATS) version 1e as coupled to the NCAR community climate model. Technical Report No. TN‐387+STR. Boulder, CO: National Center for Atmospheric Research.
8 Dosio, A., Panitz, H., Schubert‐Frisius, M., & Lüthi, D. (2014). Dynamical downscaling of CMIP5 global circulation models over CORDEX‐Africa with COSMO‐CLM: Evaluation over the present climate and analysis of the added value. Climate Dynamics, 44(9–10), 2637–2661. doi: 10.1007/s00382‐014‐2262‐x
9 Dyer, E. L., Jones, D. B., Nusbaumer, J., Li, H., Collins, O., Vettoretti, G., & Noone, D. (2017). Congo Basin precipitation: Assessing seasonality, regional interactions, and sources of moisture. Journal of Geophysical Research: Atmospheres, 122(13), 6882–6898. doi: 10.1002/2016jd026240
10 Emanuel, K. A. (1991). A scheme for representing cumulus convection in large‐scale models. Journal of the Atmospheric Sciences, 48(21), 2313–2329. doi: 10.1175/1520‐0469(1991)0482.0.co;2
11 Fotso‐Kamga, G., Fotso‐Nguemo, T. C., Diallo, I., Yepdo, Z. D., Pokam, W. M., Vondou, D. A., & Lenouo, A. (2020). An evaluation of COSMO‐CLM regional climate model in simulating precipitation over Central Africa. International Journal of Climatology, 40(5), 2891–2912. doi: 10.1002/joc.6372
12 Fotso‐Nguemo, T. C., Vondou, D. A., Tchawoua, C., & Haensler, A. (2016). Assessment of simulated rainfall and temperature from the regional climate model REMO and future changes over Central Africa. Climate Dynamics, 48, 3685–3705. doi:10.1007/s00382‐016‐3294‐1
13 Fotso‐Nguemo, T. C., Vondou, D. A., Pokam, W. M., Djomou, Z. Y., Diallo, I., Haensler, A., et al. (2017). On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa. Climate Dynamics, 49, 3813–3838. doi: 10.1007/s00382‐017‐3547‐7
14 Giorgi, F., Marinucci, M. R., & Bates, G. T. (1993). Development of a second‐generation regional climate model (RegCM2). Part I: Boundary‐layer and radiative transfer processes. Monthly Weather Review, 121(10), 2794–2813. doi: 10.1175/1520‐0493(1993)1212.0.co;2
15 Giorgi, F., & Mearns, L. O. (1999). Introduction to special section: Regional climate modeling revisited. Journal of Geophysical Research: Atmospheres, 104(D6), 6335–6352. doi: 10.1029/98jd02072
16 Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M., Bi, X., et al. (2012). RegCM4: Model description and preliminary tests over multiple CORDEX domains. Climate Research, 52, 7–29. doi: 10.3354/cr01018
17 Grell, G. A. (1993). Prognostic evaluation of assumptions used by cumulus parameterizations.
