Abstract
The Congo Basin, being one of the major basins in the tropics, is important to the global climate, yet its hydrology is perhaps the least understood. Although various reanalysis/analysis datasets have been used to improve our understanding of the basin’s hydroclimate, they have been historically difficult to validate due to sparse in situ measurements. This study analyzes the impact of model resolution on the spatial variability of the Basin’s hydroclimate using the Decorrelation Length Scale (DLCS) technique, as it is not subject to uniform model bias. The spatial variability within the precipitation (P), evaporation/evapotranspiration (E), and precipitation-minus-evaporation (P-E) fields were investigated across four spatial resolutions using reanalysis/analysis datasets from the ECMWF ranging from 9–75 km. Results show that the representation of P and P-E fields over the Basin and the equatorial Atlantic Ocean are sensitive to model resolution, as the spatial patterns of their DCLS results are resolution-dependent. However, the resolution-independent features are predominantly found in the E field. Furthermore, the P field is the dominant source of spatial variability of P-E, occurring over the land and the equatorial Atlantic Ocean, while over the Southern Atlantic, P-E is mainly governed by the E field, with both showing weak spatial variability.
Highlights
Since the decorrelation length scale (DCLS) value represents the average radius of the prescribed contour, its specific magnitude depends upon the prescribed contour level
If a region has a local minimum in DCLS values, this implies that the region has a higher degree of variability compared to surrounding regions
Fields with smaller slope values are less sensitive to the choice of DCLS value, whereas fields with larger slope values may indicate higher sensitivity—suggesting the possibility that a larger threshold is needed to capture the variability as it may occur on smaller spatial scales
Summary
Scale (DLCS) technique, as it is not subject to uniform model bias. The spatial variability within the precipitation (P), evaporation/evapotranspiration (E), and precipitation-minus-evaporation (P-E). Fields were investigated across four spatial resolutions using reanalysis/analysis datasets from the ECMWF ranging from 9–75 km. Results show that the representation of P and P-E fields over the. Basin and the equatorial Atlantic Ocean are sensitive to model resolution, as the spatial patterns of their DCLS results are resolution-dependent. The resolution-independent features are predominantly found in the E field. The P field is the dominant source of spatial variability of P-E, occurring over the land and the equatorial Atlantic Ocean, while over the Southern. Central Africa has been identified as one of the regions where the effects of land surface conditions on regional climate and dynamics are most pronounced [1].
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