Spatio‐temporal variability of rainfall in a high tropical island: Patterns and large‐scale drivers in Réunion Island

Abstract

Weather forecasting is challenging because of the complex interplay between local conditions and regional atmospheric forcings. In this article, we analyse the relationships between local daily rainfall and large‐scale synoptic patterns in the geographical context of Réunion Island, a high volcanic island in the southwestern Indian Ocean basin. Given the critical role of trade winds on weather conditions at island scale, we analyse those relationships across seasons defined with respect to yearly trade‐wind regimes. The analysis of the distribution of inversion events' elevation and frequency allows us to characterize the trade‐wind inversion layer (TWIL) and identify four seasons with homogeneous distributions. We characterize the spatio‐temporal variability of rainfall measured at island scale by a dense network of rain‐gauges over 37 years and relate it to large‐scale weather regimes identified using geopotential height meteorological data. After seasonal signal removal using Fourier transforms and dimension reduction via Principal Component Analysis, we perform Canonical Correlation Analysis to identify canonical variables relating rainfall and geopotential height patterns at the two different studied spatial scales. We then combine Ascending Hierarchical Classification and partitioning k‐means methods to identify homogeneous large‐scale synoptic conditions within each season. From this information, we build composite maps of geopotential height that characterize weather regimes and further analyse rainfall patterns at island scale in each regime. Elevation and orientation are used as descriptive variables at island scale as they strongly structure the patterns. Overall, four homogeneous seasons were identified based on trade‐wind regimes, within which six large‐scale weather regimes were identified for summer, the most variable season, and four for the others. Finally, rainfall patterns at island scale are described in relation to the highlighted synoptic regimes and local descriptive variables.

Cloud Radar Observations of Diurnal and Seasonal Cloudiness over Reunion Island

“…Furthermore, one can also observe large differences occurring at mid-levels. While the vertical cloud extension is more or less limited to 4 km in 2018, in good agreement with previous climatological studies of [30,31], clouds occur in much more significant proportions up to an altitude of 6 km and even beyond (frequency of occurrence of 1% at 8 km) in 2017. In the following, large-scale environmental conditions inferred from the analysis of ERA5 data are investigated in order to understand the possible origins of these differences.…”

“…In winter, as the ITCZ moves back northwards, the local weather becomes mostly influenced by the subsided southern branch of the Hadley cell. The resulting temperature inversion tends to inhibit atmospheric upward vertical motion above~3 km [28,29] and is further amplified by the trade wind inversion [30][31][32]. Due to its complex orography, Reunion Island is also affected all year long by thermally induced sea breeze circulations that can force onshore moist flow at low levels and further favors the development of low-level clouds during daytime [30,33].…”

“…These gaps reflect the aforementioned attenuation of the radar signal by strong precipitation, which principally occurs for instantaneous rain rates higher than 15 mm h −1 (Figures 2a and 3). According to the climatological study of [31], which provides a detailed analysis of the spatio-temporal variability of local rainfall patterns over Reunion Island, such situations are nevertheless uncommon in the Saint-Denis area, and not frequent enough to significantly impact the results that will be presented in the following. gauge measurements, is maximised around mid-day (9-11 UTC, 13-15 LST) and dissipates in the early evening.…”

Stochastic daily rainfall generation on tropical islands with complex topography

“…The first component consists of rain types, which summarize island-scale rain statistics. Unlike weather type based approaches [Ailliot et al, 2015] [Réchou et al, 2019, we define rain types based on rain features only, i.e., no information about meteorological covariates or large-scale circulation are included during the classification step. This leads to a classification centered on rainfall intensity and spatial distribution, which allows us to explore how island-scale rainfall variability is impacted by orographic effects (section 3.1).…”

“…In the present case, we selected the meteorological covariates according to our initial knowledge about rain generation mechanisms in high tropical islands, and their links with the state of the atmosphere [Elison Timm et al, 2014] [Réchou et al, 2019] [Sanfilippo, 2020; this led to the following five covariates.…”