| 02 Apr 2026
The humidity paradox: how drier conditions and fewer clouds amplify terrestrial warming due to global climate change
Abstract. Clouds are a controlling factor determining the planetary albedo and thus strongly contribute to establishing the radiation balance at the top of the atmosphere as well as the energy balance at the surface. Terrestrial warming rates, divergent to oceans, have accelerated substantially since around 1980, along with significant changes in humidity and cloud cover. We analyse spatiotemporal changes in warming rates compared to changes in Earth’s radiation and humidity, considering land globally for the period 1979–2023, using reanalysis and satellite data. We find statistically significant increases in top of the atmosphere net solar radiation and surface net terrestrial radiation, causing a net warming, together with decreasing cloud cover. These changes coincide with drier land surface conditions and are associated with the humidity paradox: insufficient supply of water vapour causing a decrease in relative humidity. Reduced evaporative cooling of the land surface is an additional positive feedback and has likely contributed to the ocean-land warming contrast. The oceans have, on the other hand, effectively unlimited water to evaporate and can therefore cool in a warming climate by evaporating more and more water. Inhibited cloud formation over land and an increase in solar radiation provide an amplifying feedback loop for the observed rapid terrestrial warming in recent decades due to a CO2-driven humidity deficit. A decrease in precipitation over land regionally is a strong indication of perturbed surface water balance that is driven by increases in absorbed infrared and solar radiation.
The study by Paul Glantz et al. addresses an interesting and scientifically sounded idea, namely, it looks at the effect of differential skin surface temperature trends over land and the ocean. In theory, slower warming of the ocean surface should limit water vapor supply to the atmosphere over land, and thus, cause reduction of atmospheric relative humidity and cloudiness. In practice, the atmospheric hydrological cycle is very complex, mediated by numerous poorly understood feedbacks and even less understood atmospheric dynamics, especially in the turbulent planetary boundary layer. The study does not attempt to penetrate atmospheric physics or dynamics. It focuses on a broad search for statistical evidence of terrestrial warming amplification through reduced moisture and cloudiness. If properly implemented, the study could be an important contribution to physical climatology and climate change science. Unfortunately, in my opinion, the study failed in addressing its scientific goals. The reported results are messy and do not warrant publication.
The major of the study is that it does not apply proper statistical instruments for the analysis. It rather loosely browses across different statistics derived from different diverse datasets. We find here model-based atmospheric reanalysis (ERA5), satellite products of different quality, resolution, and coverage (CLARA, CERES), in situ datasets (HadISDN, GPCP, GPCC). What does this zoo of datasets make to the question in scope? It makes the text long, logically fractured, and complicated to read. I don’t understand the needs for such complications. Just opposite, in my opinion, if ERA5 – as an internally consistent and complete dataset – demonstrates the constraining effects of differential warming on the hydrological cycle, it should be sufficient to analyze ERA5, and then, in Discussion, to show how good/bad the reanalysis is with respect to observational datasets (and whether it does/doesn’t matter).
So, the most important part of any study – the method – is actually missing in the Data and Method section. This is for a good reason though because the authors do not have any logically justified analysis method for their study, but just a more or less trivial set of popular statistical indicators, mostly trends, of more or less relevant variables. The presented set of statistics does not support or question the main hypothesis of this study.
My other comments are less significant but still pinpoint serios weaknesses and problems with the manuscript.