Robust detection of forced warming in the presence of potentially large climate variability.

ABSTRACT: Climate warming is unequivocal and exceeds internal climate variability. However, estimates of the magnitude of decadal-scale variability from models and observations are uncertain, limiting determination of the fraction of warming attributable to external forcing. Here, we use statistical learning to extract a fingerprint of climate change that is robust to different model representations and magnitudes of internal variability. We find a best estimate forced warming trend of 0.8°C over the past 40 years, slightly larger than observed. It is extremely likely that at least 85% is attributable to external forcing based on the median variability across climate models. Detection remains robust even when evaluated against models with high variability and if decadal-scale variability were double

SUBMITTER: Sippel S 

PROVIDER: S-EPMC8535853 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature