Data challenges in climate sensitivity studies

The problem with data starts even before it’s been generated. I was reminded of this while standing in my garden shed, staring at a thermometer. It's "simply" measuring the temperature of the air. Sounds objective, right? A number. A unit. A fact. But let’s look closer. - Is the thermometer placed in direct sunlight or shade? - What’s the elevation? The ground cover? The wind exposure? - What type of sensor is being used, and when was it last calibrated? - What’s the timestamp? How often is it sampled? - And what happens when the equipment is replaced or relocated? None of these are trivial. They’re confounding factors, and they introduce noise, drift, and bias, long before we even analyze the data. Now imagine trying to: - Compare global temperature changes across time - Integrate datasets from different weather stations - Model long-term climate patterns Without standardized methodology, clear metadata, and deep contextual awareness, even basic measurements become ambiguous at best ... and misleading at worst. This is not just a climate data issue. It’s a universal data issue. In healthcare. In drug development. In AI. The illusion of clean, objective data often collapses the moment you examine how that data was generated. Before you interpret, compare, or model any data, ask first: What are you actually measuring? And what might you be missing?

“High-resolution gridded precipitation products”   High-quality, multi-decadal precipitation data are essential for research and decision-making. Such records enable rigorous assessment of variability and long-term trends and provide a robust foundation for model calibration and evaluation. Reliable long-term observations also underpin integrated and adaptive management of water resources, supporting sustainable planning for agriculture, ecosystems, and regional development. Spatial limitations, nevertheless, remain a persistent challenge in precipitation measurement.  Although gauge networks provide the foundation for precipitation observation, gauge distribution is often sparse and uneven. These deficiencies are most pronounced in complex terrain and other data-sparse regions. This recent study assessed temporal inhomogeneities in six datasets – Daymet, gridMET, nClimGrid, PRISM AN (All Networks), PRISM LT (Long Term), and TerraClimate – across the southeastern U.S. during the1980–2024 period.   Annual precipitation totals derived from monthly and daily products were compared with a regional reference time series constructed from 120 U.S. Cooperative Observer Program (COOP) gauges. Residual-mass curves were used to diagnose departures from temporal homogeneity, and split-sample Mann–Whitney U tests identified statistically significant discontinuities.   Results revealed that the gridded precipitation datasets commonly used for hydroclimatic analyses in the southeastern U.S. exhibited SUBSTANTIAL temporal inhomogeneities that can DISTORT long-term trend assessments.   The six high-resolution products and their combinations evaluated here during 1980–2024 revealed statistically SIGNIFICANT discontinuities in most series, with shifts clustering in 1991–1993, 2002, 2005, 2011–2012, and 2016 and largely coinciding with changes in gauge networks or data processing.   WETTING biases in Daymet and PRISM AN reflected the expansion of CoCoRaHS and decline of COOP gauges, whereas a DRYING bias in nClimGrid and, to a lesser degree, PRISM LT were associated with increasing influence of ASOS tipping-bucket gauges.   Abrupt step changes in TerraClimate and gridMET corresponded to documented shifts in input data and processing. These inhomogeneities produced precipitation trends ranging from 19 to 48 mm per decade, compared with a non-significant reference trend of 30 mm per decade. These findings pertain to temporal consistency rather than overall accuracy. Thus, datasets identified as temporally stable MAY NOT be optimal for applications requiring accurate day-to-day or location-specific estimates. Overall, the findings underscore the necessity of explicitly evaluating TEMPORAL CONSISTENCY before applying gridded precipitation products in long-term hydroclimatic analyses. See J.E. Diem (2026) in HESS, EGUsphere, “Temporal inhomogeneities in high-resolution gridded precipitation products for the southeastern United States”