Robust detection of forced warming in the presence of potentially large climate variability
Sippel, S.; Meinshausen, N.; Székely, E.; Fischer, E.; Pendergrass, A.G.; Lehner, F.; Knutti, R. (2021). Robust detection of forced warming in the presence of potentially large climate variability. Science Advances 7(43): eabh4429. https://dx.doi.org/10.1126/sciadv.abh4429
In: Science Advances. AAAS: New York. ISSN 2375-2548; e-ISSN 2375-2548, meer
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| Auteurs | | Top |
- Sippel, S.
- Meinshausen, N.
- Székely, E.
- Fischer, E.
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- Pendergrass, A.G.
- Lehner, F.
- Knutti, R.
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| 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. |
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