Bringing physical reasoning into statistical practice in climate-change science
Abstract The treatment of uncertainty in climate-change science is dominated by the far-reaching influence of the ‘frequentist’ tradition in statistics, which interprets uncertainty in terms of sampling statistics and emphasizes p -values and statistical significance. This is the normative standard...
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Springer Science and Business Media LLC
2021
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Online Access: | http://dx.doi.org/10.1007/s10584-021-03226-6 https://link.springer.com/content/pdf/10.1007/s10584-021-03226-6.pdf https://link.springer.com/article/10.1007/s10584-021-03226-6/fulltext.html |
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crspringernat:10.1007/s10584-021-03226-6 2023-05-15T15:11:33+02:00 Bringing physical reasoning into statistical practice in climate-change science Shepherd, Theodore G. 2021 http://dx.doi.org/10.1007/s10584-021-03226-6 https://link.springer.com/content/pdf/10.1007/s10584-021-03226-6.pdf https://link.springer.com/article/10.1007/s10584-021-03226-6/fulltext.html en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Climatic Change volume 169, issue 1-2 ISSN 0165-0009 1573-1480 Atmospheric Science Global and Planetary Change journal-article 2021 crspringernat https://doi.org/10.1007/s10584-021-03226-6 2022-01-04T11:17:22Z Abstract The treatment of uncertainty in climate-change science is dominated by the far-reaching influence of the ‘frequentist’ tradition in statistics, which interprets uncertainty in terms of sampling statistics and emphasizes p -values and statistical significance. This is the normative standard in the journals where most climate-change science is published. Yet a sampling distribution is not always meaningful (there is only one planet Earth). Moreover, scientific statements about climate change are hypotheses, and the frequentist tradition has no way of expressing the uncertainty of a hypothesis. As a result, in climate-change science, there is generally a disconnect between physical reasoning and statistical practice. This paper explores how the frequentist statistical methods used in climate-change science can be embedded within the more general framework of probability theory, which is based on very simple logical principles. In this way, the physical reasoning represented in scientific hypotheses, which underpins climate-change science, can be brought into statistical practice in a transparent and logically rigorous way. The principles are illustrated through three examples of controversial scientific topics: the alleged global warming hiatus, Arctic-midlatitude linkages, and extreme event attribution. These examples show how the principles can be applied, in order to develop better scientific practice. “La théorie des probabilités n’est que le bon sens reduit au calcul.” (Pierre-Simon Laplace, Essai Philosophiques sur les Probabilités , 1819). “It is sometimes considered a paradox that the answer depends not only on the observations but on the question; it should be a platitude.” (Harold Jeffreys, Theory of Probability , 1st edition, 1939). Article in Journal/Newspaper Arctic Climate change Global warming Springer Nature (via Crossref) Arctic Laplace ENVELOPE(141.467,141.467,-66.782,-66.782) Climatic Change 169 1-2 |
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Springer Nature (via Crossref) |
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English |
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Atmospheric Science Global and Planetary Change |
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Atmospheric Science Global and Planetary Change Shepherd, Theodore G. Bringing physical reasoning into statistical practice in climate-change science |
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Atmospheric Science Global and Planetary Change |
description |
Abstract The treatment of uncertainty in climate-change science is dominated by the far-reaching influence of the ‘frequentist’ tradition in statistics, which interprets uncertainty in terms of sampling statistics and emphasizes p -values and statistical significance. This is the normative standard in the journals where most climate-change science is published. Yet a sampling distribution is not always meaningful (there is only one planet Earth). Moreover, scientific statements about climate change are hypotheses, and the frequentist tradition has no way of expressing the uncertainty of a hypothesis. As a result, in climate-change science, there is generally a disconnect between physical reasoning and statistical practice. This paper explores how the frequentist statistical methods used in climate-change science can be embedded within the more general framework of probability theory, which is based on very simple logical principles. In this way, the physical reasoning represented in scientific hypotheses, which underpins climate-change science, can be brought into statistical practice in a transparent and logically rigorous way. The principles are illustrated through three examples of controversial scientific topics: the alleged global warming hiatus, Arctic-midlatitude linkages, and extreme event attribution. These examples show how the principles can be applied, in order to develop better scientific practice. “La théorie des probabilités n’est que le bon sens reduit au calcul.” (Pierre-Simon Laplace, Essai Philosophiques sur les Probabilités , 1819). “It is sometimes considered a paradox that the answer depends not only on the observations but on the question; it should be a platitude.” (Harold Jeffreys, Theory of Probability , 1st edition, 1939). |
format |
Article in Journal/Newspaper |
author |
Shepherd, Theodore G. |
author_facet |
Shepherd, Theodore G. |
author_sort |
Shepherd, Theodore G. |
title |
Bringing physical reasoning into statistical practice in climate-change science |
title_short |
Bringing physical reasoning into statistical practice in climate-change science |
title_full |
Bringing physical reasoning into statistical practice in climate-change science |
title_fullStr |
Bringing physical reasoning into statistical practice in climate-change science |
title_full_unstemmed |
Bringing physical reasoning into statistical practice in climate-change science |
title_sort |
bringing physical reasoning into statistical practice in climate-change science |
publisher |
Springer Science and Business Media LLC |
publishDate |
2021 |
url |
http://dx.doi.org/10.1007/s10584-021-03226-6 https://link.springer.com/content/pdf/10.1007/s10584-021-03226-6.pdf https://link.springer.com/article/10.1007/s10584-021-03226-6/fulltext.html |
long_lat |
ENVELOPE(141.467,141.467,-66.782,-66.782) |
geographic |
Arctic Laplace |
geographic_facet |
Arctic Laplace |
genre |
Arctic Climate change Global warming |
genre_facet |
Arctic Climate change Global warming |
op_source |
Climatic Change volume 169, issue 1-2 ISSN 0165-0009 1573-1480 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1007/s10584-021-03226-6 |
container_title |
Climatic Change |
container_volume |
169 |
container_issue |
1-2 |
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1766342394986364928 |