An improved ensemble of land surface air temperatures since 1880 using revised pair-wise homogenization algorithms accounting for autocorrelation
An improved ensemble of land surface air temperatures since 1880 using revised pair-wise homogenization algorithms accounting for autocorrelation
Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA's monthly Global Historical Climatology Network (GHCNm) averages 0.028C cooler between 1880 and 1940 than Berkeley Earth's and 0.148C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA's GHCNm(V4), PHA0, we propose two revisions to account for autocorrelation in climate variables. One version, PHA1, makes minimal modification to PHA0 by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA2, uses penalized likelihood to detect breakpoints through optimizing a modelselection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA2. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA1 and PHA2 both similarly outperform PHA0 in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA1- and 50 PHA2-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880-2022 trend by 0.16 [0.12, 0.19]8C century21 (95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]8C over 2010-22 relative to 1880-1900.
Bias, Changepoint analysis, Climate change, Climate records, Temperature
2325-2345
Chan, Duo
4c1278dc-7f39-4b67-b1cd-3f81f55f4906
Gebbie, Geoffrey
b175e22b-563d-4925-9649-1eb980c2a315
Huybers, Peter
48e9a517-aa2a-40f1-96ef-06d76b19291c
1 April 2024
Chan, Duo
4c1278dc-7f39-4b67-b1cd-3f81f55f4906
Gebbie, Geoffrey
b175e22b-563d-4925-9649-1eb980c2a315
Huybers, Peter
48e9a517-aa2a-40f1-96ef-06d76b19291c
Chan, Duo, Gebbie, Geoffrey and Huybers, Peter
(2024)
An improved ensemble of land surface air temperatures since 1880 using revised pair-wise homogenization algorithms accounting for autocorrelation.
Journal of Climate, 37 (7), .
(doi:10.1175/JCLI-D-23-0338.1).
Abstract
Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA's monthly Global Historical Climatology Network (GHCNm) averages 0.028C cooler between 1880 and 1940 than Berkeley Earth's and 0.148C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA's GHCNm(V4), PHA0, we propose two revisions to account for autocorrelation in climate variables. One version, PHA1, makes minimal modification to PHA0 by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA2, uses penalized likelihood to detect breakpoints through optimizing a modelselection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA2. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA1 and PHA2 both similarly outperform PHA0 in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA1- and 50 PHA2-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880-2022 trend by 0.16 [0.12, 0.19]8C century21 (95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]8C over 2010-22 relative to 1880-1900.
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Accepted/In Press date: 12 March 2024
Published date: 1 April 2024
Keywords:
Bias, Changepoint analysis, Climate change, Climate records, Temperature
Identifiers
Local EPrints ID: 488787
URI: http://eprints.soton.ac.uk/id/eprint/488787
ISSN: 0894-8755
PURE UUID: fc6a1b2f-ecb4-410b-a1eb-0a928db918f6
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Date deposited: 05 Apr 2024 16:40
Last modified: 08 May 2024 02:07
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Contributors
Author:
Duo Chan
Author:
Geoffrey Gebbie
Author:
Peter Huybers
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