Abstract:
The Galápagos Archipelago exhibits a unique and high endemic biodiversity that is strongly affected by climate variability, mainly caused by the El Niño-Southern Oscillation phenomenon. However, there exist few climate datasets for the islands and a long-term climate dataset at the meso-scale is not available. We present the Galápagos Archipelago Refined analysis dataset (GAR), a dynamically downscaled dataset of 2 h temporal resolution and 2 km horizontal grid spacing for the Galápagos Archipelago, that is based on ERA5 reanalysis data. The GAR is produced by the Weather Research and Forecasting Model (WRF V.4.3.3). Sensitivity experiments focused on precipitation and air temperature led to the selection of a suitable model setup for the region, which was developed using observational data from the Darwin Measurement Network (DMN) and the Charles Darwin Research Station (CDRS). We evaluated the performance of the model by reproducing the measured daily mean values at the Cerro Crocker (CC) and Puerto Ayora (PA) stations for the period from 01 April 2022 to 31 March 2023. The results show very strong correlations (ρT,CC=0.94 and ρT,PA=0.94) for air temperatures. For daily precipitation rates, measured by rain gauges, the GAR yields medium to strong correlation (ρPg,CC=0.66 and ρPg,PA=0.44). Specific humidity very strongly correlates with the measurements (ρSH,CC=0.88 and ρSH,PA=0.97). Analysis of the spatial patterns of precipitation, specific humidity, and temperature on the meso-scale indicated a strong dependency on altitude. Precipitation for the dry season is triggered mainly by orographic lifting, while wet season precipitation is driven by thermally induced convection. The GAR fulfils the need for high spatio-temporal resolution data on the Galápagos climate and serves as a valuable source for scientific research in this area. The GAR data are publicly available, and together with the downscaling approach evaluated here, this dataset can easily be extended into the future.
