Multimodel ensemble simulations of present and future climates over West Africa: Impacts of vegetation dynamics

Abstract

In this study, we take an ensemble modeling approach using the regional climate model RegCM4.3.4‐CLM‐CN‐DV (RCM) to study the impact of including vegetation dynamics on model performance in simulating present‐day climate and on future climate projections over West Africa. The ensemble consists of four global climate models (GCMs) as lateral boundary conditions for the RCM, and simulations with both static and dynamic vegetation are conducted. The results demonstrate substantial sensitivity of the simulated precipitation, evapotranspiration, and soil moisture to vegetation representation. Although including dynamic vegetation in the model eliminates potential inconsistencies between surface climate and the bioclimatic requirements of the prescribed vegetation, it enhances model biases causing climate drift. For present‐day climate, the ensemble average generally outperforms individual members due to cancelation of model biases. For future changes, although the original GCMs project contradicting future rainfall trends over West Africa, the RCMs‐produced trends are generally consistent. The multimodel ensemble projects significant decreases of rainfall over a major portion of West Africa and significant increases over eastern Sahel and East Africa. Projected future changes of evapotranspiration and soil moisture are consistent with those of precipitation, with significant decreases (increases) for western (eastern) Sahel. Accounting for vegetation‐climate interactions has localized but significant impacts on projected future changes of precipitation, with a wet signal over a belt of projected increase of woody vegetation cover; the impact on the projected future changes of evapotranspiration and soil moisture over west and central Africa is much more profound.

Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin, Texas

“…The spatial distribution of satellite bias and errors is also important for various hydrometeorological applications such as global/regional climate modeling, data assimilation, and flood modeling and forecasting (Afshari et al, 2016;Erfanian et al, 2016;Tavakoly et al, 2017). The spatial distributions of all performance measures of all satellite products are shown in The spatial distribution of all performance measures is examined for each product.…”

Changes in hydro-meteorological conditions over tropical West Africa (1980–2015) and links to global climate

“…Studies of changes in global climate and how they impact on meteorological and hydrological processes, are without doubt, emerging as active research. So far, our understanding of global climate has improved due to significant progress and advances made in global and regional climate models (i.e., GCMs and RCMs) (see, e.g., Tall et al, 2016;Erfanian et al, 2016;Prudhomme et al, 2014;Dimri et al, 2013;Schewe et al, 2013;Mishra et al, 2012;Li et al, 2004;Lebel et al, 2000). However, in regions where strong hydrological variability have been linked to multiple environmental phenomena such as large scale ocean-atmosphere phenomenon (e.g., Joly and Voldoire, 2010;Redelsperger and Lebel, 2009), land use changes (e.g., Favreau et al, 2009;Descroix et al, 2009), and other human interventions (e.g., surface water schemes) (e.g., Ngom et al, 2016;Ndehedehe et al, 2017a;Ahmed et al, 2014), the skills of climate and hydrological models may be restricted.…”

Ensemble‐based Reconstructed Forcing (ERF) for regional climate modeling: Attaining the performance at a fraction of cost

“…(CLM4.5) [ Oleson et al , ] to the International Centre for Theoretical Physics Regional Climate Model version 3.4 (RegCM4.3.4) [ Giorgi et al , ]. Tropical Africa is used as a case study where climate impact adaptation (hence availability of reliable climate projections) is a high priority, yet considerable intermodel variation exists in both the present‐day simulation of precipitation and future projections [ Giorgi and Mearns , ; Cook , ; Wang et al , ; Erfanian et al , ]. The model performance was validated through several previous studies with the same resolution and domain [ Ji et al , , ; Yu et al , ; Wang et al , ; Erfanian et al , ].…”