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  Conventional and Bayesian approach to climate-change detection and attribution

Hasselmann, K. F. (1998). Conventional and Bayesian approach to climate-change detection and attribution. Quarterly Journal of the Royal Meteorological Society, 124, 2541-2565. doi:10.1002/qj.49712455202.

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 Creators:
Hasselmann, Klaus F.1, Author              
Affiliations:
1MPI for Meteorology, Max Planck Society, Bundesstraße 53, 20146 Hamburg, DE, ou_913545              

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Free keywords: GREENHOUSE-GAS; TEMPERATURE TRENDS; FINGERPRINT METHOD; SIGNALS; ATMOSPHERE; SATELLITE; MODELMeteorology & Atmospheric Sciences; attribution; detection; climate change;
 Abstract: The conventional multi-variate, multi-fingerprint theory of climate-change detection and attribution, expressed in terms of existing frequency distributions, is reviewed and generalized to a Bayesian approach based on subjective probabilities. Bayesian statistics enable a quantitative determination of the impact of climate-change detection tests on prior subjective assessments of the probability of an externally forced climate change. The Bayesian method also provides a potentially powerful tool for enhancing statistical detection and attribution tests by combining a number of different climate-change indicators that are not amenable to standard signal-to-noise analyses because of inadequate information on the associated natural-variability statistics. The relation between the conventional and Bayesian approach is illustrated by examples taken from recent conventional analyses of climate-change detection and attribution for three cases of climate-change forcing by increasing greenhouse-gas concentrations, increasing greenhouse-gas and aerosol concentrations, and variations, in solar insolation. The enhancement of detection and attribution levels through a joint Bayesian anal:lsis of a number of different climate-change indices is demonstrated in a further example. However, this advantage of the Bayesian approach can be achieved only within the framework of a subjective rather than objective analysis. The conventional and Bayesian approach both exhibit specific advantages and shortcomings, so that a parallel application of both methods is probably the most promising detection and attribution strategy.

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Language(s): eng - English
 Dates: 1998
 Publication Status: Published in print
 Pages: 25
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: ISI: 000078052700001
DOI: 10.1002/qj.49712455202
 Degree: -

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Title: Quarterly Journal of the Royal Meteorological Society
Source Genre: Journal
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Publ. Info: Reading, Berkshire, England [etc.] : Royal Meteorological Society.
Pages: - Volume / Issue: 124 Sequence Number: - Start / End Page: 2541 - 2565 Identifier: ISSN: 0035-9009
CoNE: https://pure.mpg.de/cone/journals/resource/954925442598

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Title: Report / Max-Planck-Institut für Meteorologie
  Other : MPI Report
Source Genre: Series
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Publ. Info: Hamburg : Max-Planck-Institut für Meteorologie
Pages: - Volume / Issue: 260 Sequence Number: - Start / End Page: - Identifier: ISSN: 0937-1060
CoNE: https://pure.mpg.de/cone/journals/resource/0937-1060