Downscaling large-scale climate variability using a regional climate model: the case of ENSO over Southern Africa

Abstract

International audienceThis study documents methodological issues arising when downscaling modes of large-scale atmospheric variability with a regional climate model, over a remote region that is yet under their influence. The retained case study is El Niño Southern Oscillation and its impacts on Southern Africa and the South West Indian Ocean. Regional simulations are performed with WRF model, driven laterally by ERA40 reanalyses over the 1971-1998 period. We document the sensitivity of simulated climate variability to the model physics, the constraint of relaxing the model solutions towards reanalyses, the size of the relaxation buffer zone towards the lateral forcings and the forcing fields through ERA-Interim driven simulations. The model's internal variability is quantified using 15-member ensemble simulations for seasons of interest, single 30-year integrations appearing as inappropriate to investigate the simulated interannual variability properly. The incidence of SST prescription is also assessed through additional integrations using a simple ocean mixed-layer model. Results show a limited skill of the model to reproduce the seasonal droughts associated with El Niño conditions. The model deficiencies are found to result frombiased atmospheric forcings and/or biased response to these forcings, whatever the physical package retained. In contrast, regional SST forcing over adjacent oceans favor realistic rainfall anomalies over the continent, although their amplitude remains too weak. These results confirm the significant contribution of nearby ocean SST to the regional effects of ENSO, but also illustrate that regionalizing large-scale climate variability can be a demanding exercise

Performance assessment of three convective parameterization schemes in WRF for downscaling summer rainfall over South Africa

“…This is an important outcome of the present study since some of the previous studies (Joubert et al 1999;Engelbrecht et al 2002;Hudson and Jones 2002;Boulard et al 2012) had difficulties to reproduce the observed ENSO-southern Africa rainfall relationship in their regional climate modeling experiments. The present study has successfully reproduced the strength of observed ENSO-South Africa rainfall relationship over the period Fig.…”

“…The weather research and forecasting (WRF) model (Skamarock et al 2008) is increasingly being used as RCM for downscaling studies over southern Africa (Cre´tat et al 2011(Cre´tat et al , 2012Ratnam et al 2012Ratnam et al , 2013Cre´tat and Pohl 2012;Boulard et al 2012;Vigaud et al 2012). However, previous WRF sim-ulations aimed at studying summer rainfall over southern Africa consist of either relatively coarse grid resolutions, or addressed only a few seasons.…”

The Role of Mesoscale Convective Complexes in Southern Africa Summer Rainfall

“…Although they are of opposing ENSO polarity, one cannot assume that they are representative of ENSO in general. Furthermore, the ENSO-southern Africa summer rainfall relationships are nonlinear (Fauchereau et al 2009;Boulard et al 2013). Nevertheless, it does provide some insight to factors that may influence weather patterns in the region and, hence, MCC development.…”

Evaluating Climate Models with an African Lens

“…SB1), most GCMs overestimate precipitation (Christensen et al 2007;Cook and Vizy 2012), with a large range in magnitude (Lazenby et al 2016). Relative to Central and East Africa, there has been more research into understanding the modeled circulation (e.g., Shongwe et al 2009;Tozuka et al 2014;Lazenby et al 2016), modes of variability (e.g., Kataoka et al 2012;Boulard et al 2013;Dieppois et al 2015), and mechanisms of future change (Engelbrecht et al 2009), as well as efforts to characterize atmospheric states based on winds, moisture, and temperature, which suggests more similarity between models based on atmospheric circulation than precipitation (Hewitson and Crane 2006). There has also been an emphasis on regional climate models (RCMs; e.g., Crétat et al 2012;Kalognomou et al 2013;Meque and Abiodun 2015;Shongwe et al 2015;Favre et al 2016;Pinto et al 2016).…”