Congo Basin Hydrology, Climate, and Biogeochemistry. Группа авторов

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СКАЧАТЬ of Yaoundé I, Yaoundé, Cameroon

       2 Laboratory for Mechanics and Modeling of Physical Systems, Department of Physics, University of Dschang, Dschang, Cameroon

       3 Section of Earth System Physics, The Abdus Salam International Centre for Theoretical Physics, Trieste, Italy

       4 Climate Change Research Laboratory, National Institute of Cartography, Yaoundé, Cameroon

       5 Climate Application and Prediction Center for Central Africa, ECCAS Regional Climate Centre, Douala, Cameroon

       6 Agency for Aerial Navigation Safety in Africa and Madagascar, Dakar, Senegal

      ABSTRACT

      The study aims to assess the local response of the regional climate model version 4.6 (RegCM4.6) to the coupling of ocean–atmosphere interaction in Central Africa. The ability of the model was evaluated over six years (1 January 2001 to 31 December 2006) by conducting two different experiments with the Grell convective scheme. The experiments were carried out monthly with a spatial resolution of 40 km. The model was forced by ERA‐Interim reanalyses and validated by GPCP (Global Precipitation Climatology Project) observational data, ERA 5 and ERA‐Interim reanalyses. To evaluate the influence of the “slab‐ocean,” we carried out two different experiments: The first experiment was designed to produce the climatology and force the surface limits of RegCM with the sea surface temperature. The second experiment was designed to couple RegCM with the “slab‐ocean,” which provides mutual interaction between the ocean and the atmosphere. Using statistical tools, we evaluated the model’s ability to simulate precipitation, surface temperature, and wind. Both experiments reasonably reproduced the main characteristics of the rainfall regime, temperature, and wind. A comparative analysis of the different experiments revealed that the performances of the experiments were similar in Central Africa and in the different homogeneous sub‐regions as far as rainfall is concerned, but there were subtle differences. Slab‐ocean improvement varied from season to season and from sub‐region to sub‐region. However, we noted a significant improvement in temperature and rainfall over the Indian Ocean.

4.1. INTRODUCTION

Central Africa is the second‐largest tropical forest basin in the world after the Amazon. Available studies highlight that a large part of the population of this region makes a living from agriculture, goods, and services derived from the Congo Basin forest (Bele et al., 2013; Haensler et al., 2013a; Sonwa et al., 2012), which makes it vulnerable to climate change (Haensler et al., 2013a). Despite the great improvement in climate studies in recent decades, the climate of Central Africa, particularly that of the Congo Basin, is not yet well studied because of a poor spatial and temporal distribution of data from weather stations. The scientific community is interested in the study of this climate, but the complexity of the climate system in Central Africa considerably limits the ability of researchers to understand and predict the fluctuations of this climate. One of the means available to understand the mechanisms of climate is based on the use of climate numerical models (Cubasch et al., 1994; Murphy & Mitchell, 1995). Furthermore, the current computational resources do not allow us to have atmospheric general circulation models (AGCMs) with sufficiently fine horizontal resolutions and sufficiently detailed physical parameterization to adequately represent the mesoscale continental phenomena and ocean–atmosphere interaction when coupling the AGCMs to the ocean (Umakanth & Kesarkar, 2017). The ocean is a very important part of the climate system because it dominates by its calorific capacity, which modulates the variability of tropical precipitation through sea‐surface temperature (SST). Dezfuli et al. (2015) showed that SST in the Indian and Atlantic Oceans influences atmospheric convection and circulation in the Congo Basin. Despite the crucial role of SST in the climate of the sub‐region, very little work has been done on the evaluation of coupled regional climate models (RCMs) compared to autonomous RCM simulations. In a study, Ratnam et al. (2011) used the mixed layer of the slab‐ocean model (SOM) to couple the regional weather research and forecasting (WRF) model to simulate rainfall over the southern African region. The study confirms that the WRF model coupled with the SOM allows it to better simulate the climate of the South African region compared to the stand‐alone WRF. Umakanth and Kesarkar (2017) coupled the SOM to the regional climate model (RegCM4.4) to simulate the sub‐seasonal variability of the Indian summer monsoon. The result is that the coupling improves RegCM’s performance by simulating the spatiotemporal characteristics of the Indian monsoon regime. Furthermore, studies by Singh et al. (2007), Chow and Chan (2009), and Hartmann and Kristin (2002) have shown that in a regional climate model, the same convective pattern cannot give accurate results over all parts of the globe because the convective process in the tropics is very different from that in mid‐latitudes and polar regions. Several studies using the regional climate model have already been carried out in Central Africa (Fotso‐Kamga et al, 2020; Fotso‐Nguemo et al, 2016, 2017; Igri et al., 2015, 2018; Mbienda et al., 2016; Rockel and Geyer, 2008; Taguela et al, 2020; Tchotchou and Kamga, 2009; Tanessong et al., 2013; Vondou & Haensler, 2017; Vondou et al, 2017), but none of these studies ever used the RCM while taking into consideration the SOM. Nevertheless, these studies underline the importance of the choice of convective scheme and initial conditions for rainfall simulation in Central Africa. Therefore, it is important to identify appropriate convective schemes in a model before conducting a study. Thus, the physical parameters of the model set in the RegCM4 sensitivity experiments in Central Africa by Mbienda et al. (2016) are used in this study. Given the crucial role of SOM in modulating rainfall in Central Africa, this study motivated us to use an approach similar to that of the studies that coupled the SOM with the regional climate model. The overall objective of this study is to assess the response of RegCM version 4.6 to ocean–atmosphere coupling over Central Africa. The specific objectives of this work are to evaluate, under the effect of ocean–atmosphere coupling, (i) the spatial distribution of average seasonal rainfall; (ii) the spatial distribution of mean seasonal surface temperatures; and (iii) the spatial distribution of seasonal wind averages. The rest of the chapter is organized as follows: Section 4.2 describes the model, experimental protocol, data, and methodology; the results are presented and discussed in Section 4.3; and the conclusion is in Section 4.4.

4.2. DESCRIPTION OF THE MODEL, EXPERIMENTAL PROTOCOL, DATA, AND METHODOLOGY

      4.2.1. Model Description

The RegCM regional climate model is a model that was developed by the group of atmospheric physicists and climatologists of the Abdus Salam International Centre for Theoretical Physics (ICTP; Giorgi et al., 1993). Since the release of RegCM3, the model has undergone substantial development in terms of both software code and physical representations, leading to the development of a fourth version of the model (RegCM4), which was published by ICTP in June 2010 as a prototype (RegCM4.0) and in May 2011 as the first full version (RegCM4.1). Since the RegCM4.5 version, the model has used a non‐hydrostatic dynamic core, which makes it possible to obtain small horizontal resolutions of the order of a few kilometers. Radiation parameterization was adapted from the NCAR CCM3 radiation transfer scheme (Kiehl et al., 1996). The non‐local boundary layer developed by Holtslag and Boville (1993) is used for the representation of the planetary boundary layer. Large‐scale precipitation is calculated by the SUBEX scheme (Pal et al., 2000) and the biosphere–atmosphere transfer system (BATS) of Dickinson et al. (1993). The BATS scheme takes into account the transfer of energy, mass, and momentum between the atmosphere and the biosphere. The model includes options for an ocean flow parameterization scheme, interactive aerosols, microphysics, lake models, etc. The RegCM4.6 version provides five different convective schemes (Giorgi et al., 2012): the modified Kuo scheme (Anthes СКАЧАТЬ