Representation of Extreme Precipitation Events Leading to Opposite Climate Change Signals over the Congo Basin

Abstract

Abstract:We investigate the reasons for the opposite climate change signals in precipitation between the regional climate model REMO and its driving earth system model MPI-ESM over the greater Congo region. Three REMO simulations following three RCP scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) are conducted, and it is found that the opposite signals, with REMO showing a decrease and MPI-ESM an increase in the future precipitation, diverge strongly as we move from a less extreme to a more extreme scenario. It has been shown that REMO simulates a much higher number of extreme rainfall events than MPI-ESM. This results in higher surface runoff and thus less soil infiltration, which leads to lower amounts of soil moisture in REMO. This further leads to less moisture recycling via evapotranspiration, which in turn results in less precipitation over the region. In the presence of a strong radiative forcing, the hydrological cycle becomes less intense in REMO and a downward trend in hydrological variables is observed. Contrary to this, the higher amounts of soil-moisture due to the lack of extreme rainfall events in MPI-ESM enhance the hydrological cycle. In the presence of strong radiative forcing, higher amounts of soil moisture result in increased evapotranspiration which in turn results in the higher amount of precipitation. It is concluded that the land-atmosphere coupling over the Congo region is very sensitive to the change in soil moisture amounts, which is likely to play a major role in global warming conditions. Therefore, adequate and improved representation of soil processes in climate models is essential to study the effects of climate change. However, the better representation of extreme rainfall events in REMO compared to OPEN ACCESSAtmosphere 2013, 4 255 MPI-ESM can be regarded as an added value of the model.

Assessing the robustness of projected precipitation changes over central Africa on the basis of a multitude of global and regional climate projections

“…In the central tropics (zone 3) the majority of the GCMs agree on an increase in annual total precipitation, whereas the RCM sub-ensemble indicates a decrease (even though some of the forcing model projections show a substantial increase in total rainfall). In a recent study by Saeed et al (2013) it is shown that the projected drying in the REMO RCM projections (which comprises the majority of the RCM sub-ensemble -see Table S1) is partly related to the depletion of soil moisture caused by high-intensity rainfall events, which leads on the long run to a less intense hydrological cycle over the region. Fig.…”

How Does a Regional Climate Model Modify the Projected Climate Change Signal of the Driving GCM: A Study over Different CORDEX Regions Using REMO

“…The global and regional model processes leading to the opposite precipitation signals of the regional and the global simulations in the projected climate over central Africa are presently investigated by Saeed et al [68]. They show that more frequent heavy precipitation events occur in REMO and lead to higher surface runoff than in the GCM.…”

“…At the same time, temperature increase is stronger in the regional simulations. The study of Saeed et al [68] refers the drier and warmer climate conditions projected by the regional model in central Africa to much more frequent heavy precipitation events and thus stronger surface runoff and less infiltration of water into the soil, leading to reduced local water recycling and less evaporative cooling in this region. If similar processes occur within the tropical climate types in South America and Indonesia, it will be investigated in following studies.…”

Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

“…One aspect of the variability of particular interest is the oc- currence of heavy-precipitation events, which can result in flooding, affect disease incidence and reduce crop yields (IPCC, 2015). Changes in the frequency of these events can also affect trends in total precipitation due to non-linearity in land-surface processes (Saeed et al, 2013). Figure 7a shows the frequency distribution of daily-mean precipitation rates averaged over 2.5…”