CORDEX Multi‐RCM Hindcast Over Central Africa: Evaluation Within Observational Uncertainty

Abstract

This paper investigates the performance of 10 Regional Climate Models (RCMs) hindcasts from the Coordinated Regional Climate Downscaling Experiments (CORDEX) over Central Africa, covering the period 1998–2008 and performed over a common model grid spacing 0.44° ( ∼50 km). Multiple observational data sets are used to evaluate model performances over four targeted subregions. Throughout the work, a measure of observational uncertainty is made and we discuss whether or not the models are found within or outside the range of observational uncertainty. Results indicate that RCMs generally capture rainfall and temperature basic features, though important biases exist and vary for models and seasons. Dry (wet) biases are common features over the Congo basin (northern and southern part of the domain). In terms of precipitation and temperature in both seasonal and annual scale, most RCMs along with their ensemble mean generally fall in the range of observational uncertainty. Furthermore, most RCMs show a good spread of grid points where the added value of RCMs is found although the added value in temperature is not as great as with precipitation. UC‐WRF is among models adding less value on ERAINT and this could explain why whatever the time scale of variability, UC‐WRF outputs are generally out from the observational uncertainty. The multimodel ensemble mean is generally found within observational range when most models are there as well. This highlights the fact that the ensemble mean, built from the equal treatment of RCMs, does not generally outperform individual RCMs realization as it is reported in several previous studies.

What Can We Know About Recent Past Precipitation Over Africa? Daily Characteristics of African Precipitation From a Large Ensemble of Observational Products for Model Evaluation

“…The precipitation time series over the reference period (1981–2010) is constructed using the last 25 years of the historical runs (1981–2005) and the first 5 years (2006–2010) of the scenario runs under the Representative Concentration Pathway RCP8.5, as done for instance in other studies over Africa (e.g.. Dosio et al., 2019) and Europe (e.g., Dosio & Fischer, 2018). It is important to note that here we do not evaluate the results of RCMs driven by reanalysis (the so‐called evaluation runs, as in e.g., Taguela et al., 2020) but, rather, those of the downscaled GCMs; in these runs, biases are a combination of those inherited through the boundary conditions and those resulting from the RCMs parameterizations (e.g., Dosio et al., 2015). Although this may limit the evaluation, as, for instance, modeled trends can be the result of unforced internal variability and, therefore not directly comparable to the observed ones, our scope is not to find the “best” performing RCM, rather, to verify if the GCM‐RCM ensemble captures the daily characteristics of the African precipitation within the limits of the observational uncertainty therefore ensuring its fitness for purpose when used for future projections.…”

“…The precipitation time series over the reference period (1981-2010) is constructed using the last 25 years of the historical runs and the first 5 years (2006)(2007)(2008)(2009)(2010) of the scenario runs under the Representative Concentration Pathway RCP8.5, as done for instance in other studies over Africa (e.g.. Dosio et al, 2019) and Europe (e.g., Dosio & Fischer, 2018). It is important to note that here we do not evaluate the results of RCMs driven by reanalysis (the so-called evaluation runs, as in e.g., Taguela et al, 2020) but, rather, those of the downscaled GCMs; in these runs, biases are a combination of those inherited through Note. Simulations are those available through the Earth System Grid Federation (ESGF) server.…”

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.…”

Process‐Based Analysis of the Added Value of Dynamical Downscaling Over Central Africa

“…It is still not well understood whether the AV of an RCM's precipitation over CA is indeed associated with that in the related drivers. This analysis can therefore be beneficial to increase our confidence in the model's fitness for purpose to simulate present and future precipitation climatology, especially in a region featuring large model uncertainties such as Africa (e.g., Dosio et al, 2019Dosio et al, , 2020Taguela et al, 2020).…”