DataSheet1_Historical global and regional spatiotemporal patterns in daily temperature.docx

• Main Authors: Md Adilur Rahim, Robert V. Rohli, Rubayet Bin Mostafiz, Nazla Bushra, Carol J. Friedland
• Format: Dataset
• Language: unknown
• Published: 2024
• Subjects: Environmental Science; Climate Science; Environmental Impact Assessment; Environmental Management; Soil Biology; Water Treatment Processes; Environmental Engineering Design; Environmental Engineering Modelling; Environmental Technologies; global climate change; global warming; ERA5 land skin temperature; regional climate anomalies; seasonal temperature change; Arctic; Climate change; Global warming
• Online Access: https://doi.org/10.3389/fenvs.2023.1294456.s001; https://figshare.com/articles/dataset/DataSheet1_Historical_global_and_regional_spatiotemporal_patterns_in_daily_temperature_docx/25038017

The abrupt increase in surface air temperature over the last few decades has received abundant scholarly and popular attention. However, less attention has focused on the specific nature of the warming spatially and seasonally, using high-resolution reanalysis output based on historical temperature observations. This research uses the European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5) output to identify spatiotemporal features of daily mean surface air temperature, defined both as the mean of the maximum and minimum temperatures over the calendar day (“meanmaxmin”) and as the mean of the 24 hourly observations per day (“meanhourly”), across the terrestrial Earth. Results suggest temporal warming throughout the year, with several “hot spots” of significantly increasing temperature, including in the Arctic transition seasons, Northern Hemisphere mid-latitudes in July, Eurasia in spring, Europe and the lower latitudes in summer, and tropical autumn. Cooling is also observed, but generally at rates more likely to be statistically insignificant than warming rates. These trends are nearly identical regardless of whether calculated as “meanmaxmin” or “meanhourly.” These results may assist scientists and citizens to understand more fully observed agricultural, commercial, ecological, economic, and recreational trends in light of climate change considerations.