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 doubled. This work addresses a long-standing limitation in attributing warming to external forcing and opens up opportunities even in the case of large model differences in decadal-scale variability, model structural uncertainty, and limited observational records. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000513307Publication status
publishedExternal links
Journal / series
Science AdvancesVolume
Pages / Article No.
Publisher
AAASOrganisational unit
03777 - Knutti, Reto / Knutti, Reto
03990 - Meinshausen, Nicolai / Meinshausen, Nicolai
Funding
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