An evaluation of COSMO‐CLM regional climate model in simulating precipitation over Central Africa

Abstract

In this study, an analysis of present day climate simulation (1998–2008) is presented for the Central African (CA) region with the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, forced by the ERA‐Interim (ERAINT) reanalysis data. The ability of the CCLM to simulate the observed precipitation with particular focus on the mean spatial pattern, low‐level circulation, seasonal cycles, and daily characteristics is evaluated. Likewise, the added value of the regional model CCLM compared to the driving ERAINT reanalysis is also investigated. It is shown that ERAINT and CCLM exhibit quite different sign of bias, which is an indication of the importance of internal variability and fine scale processes representation for the simulation of surface climate. Despite the CCLM is constantly dry over southern CA, the model succeeds to reproduce reasonably the mean spatial patterns of precipitation and low‐level circulation features, along with the associated seasonal cycles over the whole CA and majority of the five selected analysis sub‐regions. Results also show that daily precipitation indices are well represented, although the better performance greatly depends on the considered seasons. Nevertheless, CCLM substantially outperforms the ERAINT daily precipitation characteristics, thus highlighting the added value of the downscaling exercise over the region. The analysis of daily precipitation indices also reveals that the dry character of the model could probably be connected to the underestimation of the simulated less intense events, which in turn result to an overestimation of the simulated dry spell duration.

Changes in extreme precipitation events in the Zambezi River basins based on CORDEX‐CORE models: Part I—Evaluation of historical simulation

“…However, there are notable differences in the frequency of rainy days among observations. Previous studies have confirmed discrepancies in extreme precipitation for gridded observational datasets over Africa (Sylla et al ., 2013; Pinto et al ., 2016; Fotso‐Kamga et al ., 2020). These studies attributed discrepancies among observations to various factors (e.g., differences in spatial resolution across observations; merging and interpolation techniques).…”

Simulated impact of global warming on extreme rainfall events over Cameroon during the 21st century

“…These downscaling experiments, which have been performed over the African continent at a horizontal resolution of 50km (∼0.44°), cover the period from 1950 to 2100 and have been forced by the higher GHG representative concentration pathways scenario (RCP8.5; Moss et al, 2010). Most of these RCMs have recently been extensively validated in previous studies conducted over Central Africa (Fotso‐Nguemo et al, 2016; Pokam et al, 2018; Tamoffo et al, 2019; Fotso‐Nguemo et al, 2019; Taguela et al, 2020; Fotso‐Kamga et al, 2020). They conclude that not only does the model bias vary with respect to season and the considered subregion but also that the multi‐model ensemble mean outperforms any individual models.…”

How do coupled models represent the African Easterly Jets and their associated dynamics over Central Africa during the September–November rainy season?

“…In particular, investigations of processes regulating the energy balance that raises the near-surface temperature, rather than focusing only on processes explaining rainfall bias in models in particular over Central Africa (e.g. Dommo et al 2018;Fotso-Kamga et al 2020, Fotso-Nguemo et al 2016, 2017Washington 2018, 2019;Tamoffo et al 2019Tamoffo et al , 2021aTaguela et al 2020Taguela et al , 2022a. These surface thermal processes depend strongly on insolation, vegetation cover and albedo.…”