On the invalidity of the ordinary least squares estimate of the equilibrium climate sensitivity

Dukpa Kim

doi:10.1007/s00704-021-03719-5

On the invalidity of the ordinary least squares estimate of the equilibrium climate sensitivity

Dukpa Kim^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

The equilibrium climate sensitivity is often estimated by the ordinary least squares applied to annual data of observed/calculated temperature and forcing series. One of the conditions under which the ordinary least squares estimator is consistent is the uncorrelatedness of the regressor and regression error. However, this condition can fail in a regression using historical data of temperature and forcing. Alternative estimators established in econometrics are shown to mitigate the impact of the correlated regressor and regression error and deliver a more reliable estimate of the equilibrium climate sensitivity.

Original language	English
Pages (from-to)	21-27
Number of pages	7
Journal	Theoretical and Applied Climatology
Volume	146
Issue number	1-2
DOIs	https://doi.org/10.1007/s00704-021-03719-5
Publication status	Published - 2021 Oct

Bibliographical note

Funding Information:
This research is supported by the Korea University Grant (K1911951).

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

ASJC Scopus subject areas

Atmospheric Science

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10.1007/s00704-021-03719-5

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abstract = "The equilibrium climate sensitivity is often estimated by the ordinary least squares applied to annual data of observed/calculated temperature and forcing series. One of the conditions under which the ordinary least squares estimator is consistent is the uncorrelatedness of the regressor and regression error. However, this condition can fail in a regression using historical data of temperature and forcing. Alternative estimators established in econometrics are shown to mitigate the impact of the correlated regressor and regression error and deliver a more reliable estimate of the equilibrium climate sensitivity.",

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N2 - The equilibrium climate sensitivity is often estimated by the ordinary least squares applied to annual data of observed/calculated temperature and forcing series. One of the conditions under which the ordinary least squares estimator is consistent is the uncorrelatedness of the regressor and regression error. However, this condition can fail in a regression using historical data of temperature and forcing. Alternative estimators established in econometrics are shown to mitigate the impact of the correlated regressor and regression error and deliver a more reliable estimate of the equilibrium climate sensitivity.

AB - The equilibrium climate sensitivity is often estimated by the ordinary least squares applied to annual data of observed/calculated temperature and forcing series. One of the conditions under which the ordinary least squares estimator is consistent is the uncorrelatedness of the regressor and regression error. However, this condition can fail in a regression using historical data of temperature and forcing. Alternative estimators established in econometrics are shown to mitigate the impact of the correlated regressor and regression error and deliver a more reliable estimate of the equilibrium climate sensitivity.

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On the invalidity of the ordinary least squares estimate of the equilibrium climate sensitivity

Abstract

Bibliographical note

ASJC Scopus subject areas

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