Abstract
<jats:p>Observational science, such as climate science, is experiencing unprecedented development owing to the enormous volume of data generated every day and the increasing use of artificial intelligence, which significantly facilitates and accelerates data processing, analysis, and interpretation. Any significant change in the main measured or calculated variables used for environmental monitoring represents a challenge for the scientific community and requires a sound physical explanation. One of the most important indicators used to monitor global warming is the global mean surface temperature, which is conventionally calculated as a linear combination of temperatures measured throughout the year at selected locations. As demonstrated by the author in previous studies, this widely accepted method is not fully adequate because it lacks a direct physical interpretation and an explicit relationship with the Earth's energy budget. A simple new method for the relative assessment of global warming is proposed in this paper. The analysis is based on the ERA5 reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), using 2-m air temperature data. Hourly data covering the period 1950–2025 were used on a global grid consisting of approximately 1.04 million cells representing the Earth's entire surface. The proposed method confirms the long-term warming trend and provides a comparative analysis with the results obtained using the conventional method.</jats:p>