Quantifying Uncertainty in Climate Sensitivity Metrics Using Large Ensemble Observational Datasets
Year: 2026
Type: preprint
Abstract: Gridded surface temperature observational datasets are an important line of evidence for estimating climate sensitivity metrics. Climate sensitivity metrics are often derived from comparing historical warming in these observations against climate model simulations, an approach that is known to be sensitive to the choice of observational dataset. With observational products now providing large ensembles that sample their own methodological uncertainty, we use these large ensembles, together with a spatial-temporal model-observation framework, to assess the sensitivity of climate sensitivity metrics to observational uncertainty. We find that the uncertainty within a single dataset is now the dominant source of observational uncertainty, five to six times larger than the typical uncertainty captured when only the median estimate from each data product is considered. The additional datasets from these observational products further allow us to isolate where this uncertainty originates in the construction of a dataset. Uncertainty in how the land record is constructed contributes at least seven times more than uncertainty in the construction of the ocean record for climate sensitivity metrics, and we find that the choice of whether to spatially infill the observational record shifts estimates by about 0.2◦C. Applying the large ensembles to a global, time-mean energy balance framework, we find that uncertainty from a single dataset can produce a difference of about 1.1◦C in the median estimate of equilibrium climate sensitivity.
DOI: 10.31223/x5sz2z
Authors: Vikrant Sapkota, Chris Forest
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7204757537