The Intertropical Convergence Zone (ITCZ) is responsible for most of the weather and climate in equatorial regions along with many tropical-midlatitude interactions. It is therefore important to understand how models represent its structure and variability. Most ITCZ-associated precipitation is convective, making it unclear how horizontal resolution impacts its representation. To assess this, we introduce a novel technique that involves calculation of the precipitation field's decorrelation length scale (DCLS) using model data sets that share a common lineage with horizontal resolutions from 16 to 160 km. All resolutions captured the ITCZ's mean structure; however, imprints of topography, such as Hawaii and sea surface temperature, including the variability associated with upwelling cold water off the coast of South America, are more clearly represented at higher resolutions. The DCLS analysis indicates that there are changes in the spatial variability of the ITCZ's precipitation that are not reflected in its mean structure, thus confirming its utility as a diagnostic.
The United Kingdom (UK) is experiencing rapid growth in wind farm development, primarily in the south as well as in offshore regions. As such, reliable long‐term wind statistics are integral in planning future development. Given the scarcity of in situ data, atmospheric reanalyses are beginning to be used for this purpose. However, most reanalyses have resolutions of ∼50 km or lower – scales too coarse to capture many topographic and coastal effects. To address this, dynamical downscaling has been used to obtain higher‐resolution data. However, it is unclear how the downscaling process impacts the temporal and spatial representation of the wind field. Here a set of reanalysis and analysis datasets with a common lineage and with horizontal resolutions ranging from ∼75 to ∼9 km are used to investigate the impact of model resolution on the representation of the spatial and temporal variability in the wind field in the UK. To assess this impact, the decorrelation length and temporal scale – DCLS and DCTS, respectively – are used to characterize spatial and temporal variability. The results can be classified into two categories – resolution‐dependent and resolution‐independent features. Resolution‐dependent results suggest that even resolutions of ∼30 km do not capture the wind field's variability, especially in coastal and mountainous regions. However, resolution‐independent results suggest that there are regions, such as central Ireland, over the oceans, and southeastern England, where the 30 km resolution captures the wind fields' variability. Results also support the historic choice of wind farm development in the UK, namely, the southern/southwestern tip of England as well as off the southern shore.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.