A novel statistical decomposition of the historical change in global mean surface temperature

Qian, Gangzhen, Li, Qingxiang, Li, Chao, Li, Haiyan, Wang, Xiaolan L, Dong, Wenjie and Jones, Phil ORCID: https://orcid.org/0000-0001-5032-5493 (2021) A novel statistical decomposition of the historical change in global mean surface temperature. Environmental Research Letters, 16 (5). ISSN 1748-9326

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According to the characteristics of forced and unforced components to climate change, sophisticated statistical models were used to fit and separate multiple scale variations in the global mean surface temperature (GMST) series. These include a combined model of the multiple linear regression and autoregressive integrated moving average models to separate the contribution of both the anthropogenic forcing (including anthropogenic factors (GHGs, aerosol, land use, Ozone, etc) and the natural forcing (volcanic eruption and solar activities)) from internal variability in the GMST change series since the last part of the 19th century (which explains about 91.6% of the total variances). The multiple scale changes (inter-annual variation, inter-decadal variation, and multi-decadal variation) are then assessed for their periodic features in the remaining residuals of the combined model (internal variability explains the rest 8.4% of the total variances) using the ensemble empirical mode decomposition method. Finally, the individual contributions of the anthropogenic factors are attributed using a partial least squares regression model.

Item Type: Article
Uncontrolled Keywords: sdg 13 - climate action,sdg 15 - life on land ,/dk/atira/pure/sustainabledevelopmentgoals/climate_action
Faculty \ School: Faculty of Science > School of Environmental Sciences
UEA Research Groups: Faculty of Science > Research Groups > Climatic Research Unit
Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
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Depositing User: LivePure Connector
Date Deposited: 21 May 2021 00:08
Last Modified: 15 Jun 2023 00:07
URI: https://ueaeprints.uea.ac.uk/id/eprint/80063
DOI: 10.1088/1748-9326/abea34

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