Scaling fluctuation analysis and statistical hypothesis testing of anthropogenic warming

• Published in: Climate dynamics 2014-05, Vol.42 (9-10), p.2339-2351
• Main Author: Lovejoy, S
• Format: Article
• Language: English
• Subjects: anthropogenic activities; Anthropogenic factors; Carbon dioxide; climate; Climate change; Climate models; Climate sensitivity; Climatology; Climatology. Bioclimatology. Climate change; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Exact sciences and technology; External geophysics; General Circulation Models; Geophysics. Techniques, methods, instrumentation and models; Geophysics/Geodesy; Global warming; Hypothesis testing; Influence; Meteorology; Oceanography; probability; probability distribution; Radiative transfer; Statistical analysis
• ISSN: 0930-7575; 1432-0894
• DOI: 10.1007/s00382-014-2128-2

Although current global warming may have a large anthropogenic component, its quantification relies primarily on complex General Circulation Models (GCM’s) assumptions and codes; it is desirable to complement this with empirically based methodologies. Previous attempts to use the recent climate record have concentrated on “fingerprinting” or otherwise comparing the record with GCM outputs. By using CO₂ radiative forcings as a linear surrogate for all anthropogenic effects we estimate the total anthropogenic warming and (effective) climate sensitivity finding: ΔT ₐₙₜₕ = 0.87 ± 0.11 K, [Formula: see text]. These are close the IPPC AR5 values ΔT ₐₙₜₕ = 0.85 ± 0.20 K and [Formula: see text] (equilibrium) climate sensitivity and are independent of GCM models, radiative transfer calculations and emission histories. We statistically formulate the hypothesis of warming through natural variability by using centennial scale probabilities of natural fluctuations estimated using scaling, fluctuation analysis on multiproxy data. We take into account two nonclassical statistical features—long range statistical dependencies and “fat tailed” probability distributions (both of which greatly amplify the probability of extremes). Even in the most unfavourable cases, we may reject the natural variability hypothesis at confidence levels >99 %.