Benchmarking the performance of pairwise homogenization of surface temperatures in the United States
Abstract
Changes in the circumstances behind in situ temperature measurements often lead to biases in individual station records that, collectively, can also bias regional temperature trends. Since these biases are comparable in magnitude to climate change signals, homogeneity “corrections” are necessary to make the records suitable for climate analysis. To quantify the effectiveness of U.S. surface temperature homogenization, a randomized perturbed ensemble of the USHCN pairwise homogenization algorithm was run against a suite of benchmark analogs to real monthly temperature data. Results indicate that all randomized versions of the algorithm consistently produce homogenized data closer to the true climate signal in the presence of widespread systematic errors. When applied to the real‐world observations, the randomized ensemble reinforces previous understanding that the two dominant sources of bias in the U.S. temperature records are caused by changes to time of observation (spurious cooling in minimum and maximum) and conversion to electronic resistance thermometers (spurious cooling in maximum and warming in minimum). Error bounds defined by the ensemble output indicate that maximum temperature trends are positive for the past 30, 50 and 100 years, and that these maximums contain pervasive negative biases that cause the unhomogenized (raw) trends to fall below the lower limits of uncertainty. Moreover, because residual bias in the homogenized analogs is one‐tailed under biased errors, it is likely that maximum temperature trends have been underestimated in the USHCN. Trends for minimum temperature are also positive over the three periods, but the ensemble error bounds encompass trends from the unhomogenized data.
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The PHA has undergone extensive evaluation and benchmarking and has been shown to be effective at finding a wide variety of shifts in realistic simulated monthly temperature datasets.
“The PHA has undergone extensive evaluation and benchmarking (e.g., Menne and Williams 2009; Venema et al. 2012; Williams et al. 2012) and has been shown to be effective at finding a wide variety of shifts in realistic simulated monthly temperature datasets.”
From The Global Historical Climatology Network Monthly Temperature Dataset, Version 4 (2018) · cites this paper as Williams et al. 2012
The total homogenization uncertainty can be broken down into parametric uncertainty and uncertainty associated with incomplete homogenization caused by shifts that remain undetected by the algorithm.
“The total homogenization uncertainty can be broken down into two parts: 1) uncertainty associated with the use of the PHA itself-termed the parametric uncertainty (e.g., Williams et al. 2012) and 2) uncertainty associated with incomplete homogenization caused by shifts that remain undetected by the algorithm.”
From The Global Historical Climatology Network Monthly Temperature Dataset, Version 4 (2018) · cites this paper as Williams et al. 2012
The setting combinations used for the PHA ensemble were determined to have higher skill compared to the purely random set of combinations described in Williams et al. (2012).
“In our case, however, the setting combinations used (e.g., minimum number of neighbors required) were determined to have higher skill compared to the purely random set of combinations described in Williams et al. (2012).”
From The Global Historical Climatology Network Monthly Temperature Dataset, Version 4 (2018) · cites this paper as Williams et al. (2012)
Cites
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Self-citationAssessing bias and uncertainty in the HadAT-adjusted radiosonde climate record.
McCarthy, M. P., Titchner, H. A., Thorne, P. W., Tett, S. F. B., Haimberger, L., & Parker, D. E. (2008) · Journal of Climate · doi:10.1175/2007jcli1733.1 · Cited by 56 · see citing works
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Improvements in the GISTEMP uncertainty model
Lenssen, N. J. L., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A., Ruedy, R., & Zyss, D. (2019) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2018jd029522 · Cited by 962 · see citing works
Self-citationU.S. Climate Reference Network after one decade of operations: Status and assessment
Diamond, H. J., Karl, T. R., Palecki, M. A., Baker, C. B., Bell, J. E., Leeper, R. D., Easterling, D. R., Lawrimore, J. H., Meyers, T. P., Helfert, M. R., et al. (2013) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-12-00170.1 · Cited by 415 · see citing works
Self-citationBenchmarking homogenization algorithms for monthly data
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., et al. (2012) · Climate of the Past · doi:10.5194/cp-8-89-2012 · Cited by 368 · see citing works
Self-citationThe Global Historical Climatology Network Monthly Temperature Dataset, Version 4
Menne, M. J., Williams, C. N., Gleason, B. E., Rennie, J. J., & Lawrimore, J. H. (2018) · Journal of Climate · doi:10.1175/jcli-d-18-0094.1 · Cited by 281 · see citing works
“The PHA has undergone extensive evaluation and benchmarking (e.g., Menne and Williams 2009; Venema et al. 2012; Williams et al. 2012) and has been shown to be effective at finding a wide variety of shifts in realistic simulated monthly temperature datasets.”
BackgroundMethodResultInfluentialRennie, J. J., Lawrimore, J. H., Gleason, B. E., Thorne, P. W., Morice, C. P., Menne, M. J., Williams, C. N., de Almeida, W. G., Christy, J. R., Flannery, M., et al. (2014) · Geoscience Data Journal · doi:10.1002/gdj3.8 · Cited by 123 · see citing works
Self-citationHausfather, Z., Cowtan, K., Menne, M. J., & Williams, C. N. (2016) · Geophysical Research Letters · doi:10.1002/2015gl067640 · Cited by 32 · see citing works
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- Authors
- Williams, C. N., Menne, M. J., & Thorne, P. W.
- Affiliations
- National Oceanic and Atmospheric Administration (US)
- Venue
- Journal of Geophysical Research: Atmospheres
- Publisher
- American Geophysical Union (AGU)
- Published
- 2012-03-08
- Volume
- 117
- Issue
- D5
- ISSN
- 0148-0227 (ISSN-L)
- OpenAlex
- W2109411334
- Cited by
- 121
- References
- 4
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 5 · 2022: 9 · 2021: 8 · 2020: 6 · 2019: 8 · 2018: 9 · 2017: 8 · 2016: 7 · 2015: 11 · 2014: 14 · 2013: 12 · 2012: 12
- FWCI
- 13.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Geophysical Research Atmospheres (publishedVersion)
- Maynooth University ePrints and eTheses Archive (Maynooth University) (acceptedVersion)
- Funders
- Department for Environment, Food and Rural Affairs, UK Government
- Met Office
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Physical Sciences › Engineering › Aerospace Engineering › Calibration and Measurement Techniques
- Keywords
- resistance temperature detectors, maximum temperature trends, minimum temperature trends, climate change signal, United States
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