Abstract
Abstract. We investigate the impact of model formulation and horizontal resolution on the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa. Two RCMs (SMHI-RCA4 and HCLIM38-ALADIN) are utilized for downscaling the ERA-Interim reanalysis over Africa at four different resolutions: 25, 50, 100, and 200 km. In addition to the two RCMs, two different parameter settings (configurations) of the same RCA4 are used. By contrasting different downscaling experiments, it is found that model formulation has the primary control over many aspects of the precipitation climatology in Africa. Patterns of spatial biases in seasonal mean precipitation are mostly defined by model formulation, while the magnitude of the biases is controlled by resolution. In a similar way, the phase of the diurnal cycle in precipitation is completely controlled by model formulation (convection scheme), while its amplitude is a function of resolution. However, the impact of higher resolution on the time-mean climate is mixed. An improvement in one region/season (e.g. reduction in dry biases) often corresponds to a deterioration in another region/season (e.g. amplification of wet biases). At the same time, higher resolution leads to a more realistic distribution of daily precipitation. Consequently, even if the time-mean climate is not always greatly sensitive to resolution, the realism of the simulated precipitation increases as resolution increases. Our results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation related improvements are strongly model dependent and can, in general, not be considered as an added value of downscaling.
Highlights
Regional climate modelling is a dynamical downscaling method widely used for downscaling coarse-scale global climate models (GCMs) to provide richer regional spatial information for climate assessments and for impact and adaptation studies (Giorgi and Gao, 2018; Giorgi and Mearns, 1991; Laprise, 2008; Rummukainen, 2010)
Our results show that improvements in the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa compared to their driving reanalysis in many cases are related to model formulation and not necessarily to higher resolution
We conclude that for both RCA4 and HCLIM-ALADIN, spatial bias patterns are similar and more related to model formulation, while magnitude of biases are more sensitive to resolution
Summary
Regional climate modelling is a dynamical downscaling method widely used for downscaling coarse-scale global climate models (GCMs) to provide richer regional spatial information for climate assessments and for impact and adaptation studies (Giorgi and Gao, 2018; Giorgi and Mearns, 1991; Laprise, 2008; Rummukainen, 2010). Perceived added value from RCMs may have different causes, and it may not always be for the right reason where “right reason” would result from an improved representation of regional processes at smaller scales. Such improvement leads to more accurate simulations on local scales, and can, to some extent, reduce large-scale GCM biases. Added value may be attributed to different reasons, not directly related to higher resolution in RCMs but to different model formulation in the RCMs and their driving GCMs. It is possible that the physics of a RCM has been targeted for processes specific to the region it is being run for, giving it a local advantage over GCMs that may have had their physics developed for global application. RCMs can either reduce or amplify GCM biases, sometimes even changing their signs (Chan et al, 2013)
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