The Temperature Record

Outline

• Dataset Documentation
• Global Datasets
• CONUS Datasets
• Issues Related to the Instrumental Record
• Absolute Temperature vs Anomalies
• Bias Correction and Homogenization
• The Urban Heat Island Effect

1. Dataset Documentation and Uncertainty Analysis

1.1 GMST and GMAT Datasets

1.1.1 GloSAT

[1] Morice, C. P., Berry, D. I., Cornes, R. C., Cowtan, K., Cropper, T., Hawkins, E., Kennedy, J. J., Osborn, T. J., Rayner, N. A., Recinos Rivas, B., Schurer, A. P., Taylor, M., Teleti, P. R., Wallis, E. J., Winn, J., and Kent, E. C.: An observational record of global gridded near-surface air temperature change over land and ocean from 1781, Earth Syst. Sci. Data, 17, 7079–7100, https://doi.org/10.5194/essd-17-7079-2025, 2025.

[2] Morice, C.P.; Berry, D.I.; Cornes, R.C.; Cowtan, K.; Cropper, T.; Hawkins, E.; Kennedy, J.J.; Osborn, T.; Rayner, N.A.; Rivas, B.R.; Schurer, A.; Taylor, M.; Teleti, P.R.; Wallis, E.J.; Winn, J.P.; Kent, E.C. (2025): GloSATref.1.0.0.0: An observational record of global gridded near surface air temperature change over land and ocean from 1781. NERC EDS Centre for Environmental Data Analysis, 19 June 2025. doi:10.5285/a2519624a593402a83246bd359d098be. https://dx.doi.org/10.5285/a2519624a593402a83246bd359d098be

[3] Kennedy, John; Becker, Amani; Cowtan, Kathryn D; Hawkins, Ed; Hegerl, Gabi; Middleton, Stuart E.; et al. (2025). Illustration introducing the GloSAT Global Surface Air Temperature project. figshare. Figure. doi: 10.6084/m9.figshare.28001222

1.1.2 HadCRUT

[1]  Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick, R. E., et al. (2021). An updated assessment of near-surface temperature change from 1850: the HadCRUT5 data set. Journal of Geophysical Research: Atmospheres, 126, e2019JD032361. https://doi.org/10.1029/2019JD032361

[2] Morice, C. P., et al. “Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set.” Journal of Geophysical Research: Atmospheres 117.D8 (2012): D08101.
https://doi.org/10.1029/2011JD017187

[3] Brohan P et al. “Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850.” Journal of Geophysical Research: Atmospheres 111.D12 ( 2006) https://doi.org/10.1029/2005JD006548

1.1.3 NASA GISTEMP

[1] Lenssen, N., G.A. Schmidt, M. Hendrickson, P. Jacobs, M. Menne, and R. Ruedy, 2024: A GISTEMPv4 observational uncertainty ensemble. J. Geophys. Res. Atmos., 129, no. 17, e2023JD040179, doi:10.1029/2023JD040179.

[2] Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307-6326, doi:10.1029/2018JD029522.

1.1.4 NOAA Global Temp

[1] Huang, B., and Coauthors, 2020: Uncertainty Estimates for Sea Surface Temperature and Land Surface Air Temperature in NOAAGlobalTemp Version 5. J. Climate, 33, 1351–1379, https://doi.org/10.1175/JCLI-D-19-0395.1.

[2] Zhang, H.-M, J.H. Lawrimore, B. Huang, M.J. Menne, X. Yin, A. Sȧnchez-Lugo, B.E. Gleason, R.S. Vose, D. Arndt, J.J. Rennie, and C.N. Williams, 2019: Updated Temperature Data Give a Sharper View of Climate Trends. Eos, 100, https://doi.org/10.1029/2019EO128229

[3] Vose, R.S., B. Huang, X. Yin., D. Arndt, D.R. Easterling, J.H. Lawrimore, M.J. Menne, A. Sanchez-Lugo, H.-M. Zhang, 2021: Implementing full spatial coverage in NOAA’s global temperature analysis. Geophysical Research Letters, 48, e2020GL090873. https://doi.org/10.1029/2020GL090873

[4] Huang, B., X. Yin, M. J. Menne, R. Vose, and H. Zhang, 2022: Improvements to the Land Surface Air Temperature Reconstruction in NOAAGlobalTemp: An Artificial Neural Network Approach. Artif. Intell. Earth Syst., 1, e220032, https://doi.org/10.1175/AIES-D-22-0032.1

[5] Yin, X., B. Huang, M. Menne, R.S. Vose, H.-M. Zhang, A. Adeyeye, S. Applequist, K. Gleason, C. Liu, and A. Sanchez-Lugo, 2024: NOAAGlobalTemp Version 6: An AI-Based Global Surface Temperature Dataset. Bull. Amer. Meteor. Soc., 105, E2184-E2193. https://doi.org/10.1175/BAMS-D-24-0012.1

[6] Huang, B., X. Yin, M. J. Menne, R. Vose, and H. Zhang, NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 6.0.0 [indicate subset used]. NOAA National Centers for Environmental Information. https://doi.org/10.25921/rzxg-p717

 

1.1.5 Berkeley Earth

[1] Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature Record, Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, 2020.

[2] Rohde, R. “Berkeley earth temperature averaging process.” Geoinformatics & Geostatistics: An Overview 1.2 (2013):1000103.
https://www.scitechnol.com/berkeley-earth-temperature-averaging-process-IpUG.php?article_id=582

1.1.6 ERA5

[1] Hersbach, et al. 2020. The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society 146 (730), 1999–2049. https://doi.org/10.1002/qj.3803

[2] Bell, et al. 2021. The ERA5 global reanalysis: Preliminary extension to 1950, Quarterly Journal of the Royal Meteorological Society 147(741), 4186–4227. https://doi.org/10.1002/qj.4174

[3] Copernicus Climate Change Service (C3S). 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), https://cds.climate.copernicus.eu/cdsapp#!/home

1.1.7 JRA-3Q

[1] Naoe, H., C. Kobayashi, S. Kobayashi, Y. Kosaka, and K. Shibata, 2025: Representation of quasi-biennial oscillation in JRA-3Q. J. Meteor. Soc. Japan, 103, https://doi.org/10.2151/jmsj.2025-012.

1.2 CONUS Datasets

1.2.1 USCRN

[1] Diamond, H. J., T. R. Karl, M. A. Palecki, C. B. Baker, J. E. Bell, R. D. Leeper, D. R. Easterling, J. H. Lawrimore, T. P. Meyers, M. R. Helfert, G. Goodge, and P. W. Thorne, 2013 : U.S. Climate Reference Network after one decade of operations: status and assessment. Bull. Amer. Meteor. Soc. , 94 , 489-498.
doi: 10.1175/BAMS-D-12-00170.1

1.2.2 nClimDiv

[1] Vose, R. S., and Coauthors, 2014: Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions. J. Appl. Meteor. Climatol., 53, 1232–1251, https://doi.org/10.1175/JAMC-D-13-0248.1.

[2] Vose, Russell S.; Applequist, Scott; Squires, Mike; Durre, Imke; Menne, Matthew J.; Williams, Claude N., Jr.; Fenimore, Chris; Gleason, Karin; Arndt, Derek (2014): NOAA Monthly U.S. Climate Divisional Database (NClimDiv). NOAA National Climatic Data Center. doi:10.7289/V5M32STR

1.2.3 USHCN (Deprecated in 2014)

[1] Menne, M. J., C. N. Williams Jr., and M. A. Palecki (2010), On the reliability of the U.S. surface temperature record,J. Geophys. Res., 115, D11108, doi:10.1029/2009JD013094.

[2] Hausfather, Z., M. J. Menne, C. N. Williams, T. Masters, R. Broberg, and D. Jones (2013), Quantifying the effect of urbanization on U.S. Historical Climatology Network temperature records, J. Geophys. Res. Atmos., 118, 481–494, doi:10.1029/2012JD018509.

[3] Shen, S. S. P., C. K. Lee, and J. Lawrimore, 2012: Uncertainties, Trends, and Hottest and Coldest Years of U.S. Surface Air Temperature since 1895: An Update Based on the USHCN V2 TOB Data. J. Climate, 25, 4185–4203, https://doi.org/10.1175/JCLI-D-11-00102.1.

[4] Climate Reference Network (2002), Site information handbook, NOAA/NESDIS CRN Ser. X030, CRN Rep. NOAA-CRN/OSD-2002-0002R0UD0, Natl. Climatic Data Cent., Asheville, N. C. (Available at ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/documentation/program/X030FullDocumentD0.pdf).

2. Issues Related to the Instrumental Record

2.1 Absolute Temperature vs Anomalies

[1] Svante Arrhenius. On the Influence of Carbonic Acid in the Air upon the Temperature of the Ground. Philosophical Magazine and Journal of Science Series 5, Volume 41, April 1896, pages 237-276.

[2] Hansen, J.E., and S. Lebedeff, 1987: Global trends of measured surface air temperature. J. Geophys. Res., 92, 13345-13372, doi:10.1029/

[3] Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the uncertainty model in the Goddard Institute for Space Studies Surface Temperature (GISTEMP) analysis, J. Geophys. Res. Atmos., in press, doi:10.1029/2018JD029522.

[4] Kevin Cowtan and others, Statistical analysis of coverage error in simple global temperature estimators, Dynamics and Statistics of the Climate System, Volume 3, Issue 1, 2018, dzy003, https://doi.org/10.1093/climsys/dzy003

[5] Wang, K. Sampling Biases in Datasets of Historical Mean Air Temperature over Land. Sci Rep 4, 4637 (2014). https://doi.org/10.1038/srep0463
https://www.nature.com/articles/srep04637

[6] Chan, D., Gebbie, G., Huybers, P. et al. A Dynamically Consistent Ensemble of Temperature at the Earth surface since 1850 from the DCENT dataset. Sci Data 11, 953 (2024). https://doi.org/10.1038/s41597-024-03742-x

2.2 Bias Correction and UHI

2.2.1 Homogenization and Bias Correction

[1] Jones P. D., T. J. Osborn, and K. R. Briffa. “Estimating sampling errors in large-scale temperature averages.” Journal of Climate 10.10 (1997): 2548–2568. https://doi.org/10.1175/1520-0442(1997)010%3C2548:ESEILS%3E2.0.CO;2

[2] Folland C. K., et al. “Global temperature change and its uncertainties since 1861.” Geophysical Research Letters 28.13 (2001): 2621–2624.
https://doi.org/10.1029/2001GL012877

[3] Menne, M.J. and C. N. Williams Jr. “Homogenization of Temperature Series via Pairwise Comparisons.” Journal of Climate 22 (2009): 1700-1717. http://dx.doi.org/10.1175/2008JCLI2263.1

[4] Smith, Thomas M. and Richard W. Reynolds, “Bias Corrections for Historical Sea Surface Temperatures Based on Marine Air Temperatures,” Journal of Climate 15 (2002): 73-87.
https://www.ncdc.noaa.gov/monitoring-references/docs/Smith_Reynolds_2002.pdf

[5] Karl et al, “A Model to Estimate the Time of Observation Bias Associated with Monthly Mean Maximum, Minimum and Mean Temperatures for the United States,” Journal of Applied Meteorology 25.2(1986):145-160
https://doi.org/10.1175/1520-0450(1986)025<0145:AMTETT>2.0.CO;2
https://www1.ncdc.noaa.gov/pub/data/ushcn/papers/karl-etal1986.pdf

[6] Vose et al, “An evaluation of the time of observation bias adjustment in the U.S. Historical Climatology Network,” Gheophysical Research Letters 30.20 (2003).
https://www1.ncdc.noaa.gov/pub/data/ushcn/papers/vose-etal2003.pdf

[7] Willett, K. et al 2014. A framework for benchmarking of homogenisation algorithm performance on the global scale. Geosci. Instrum, Method. Data Syst., 3, 187-200.

[8] Quayle, R. G., Easterline, D. R., Karl, T. R., & Hughes, P. Y. (1991). Effects of Recent Thermometer Changes in the Cooperative Station Network, Bulletin of the American Meteorological Society, 72(11), 1718-1724. https://journals.ametsoc.org/view/journals/bams/72/11/1520-0477_1991_072_1718_eortci_2_0_co_2.xml

[9] Williams, C. N., M. J. Menne, and P. W. Thorne (2012), Benchmarking the performance of pairwise homogenization of surface temperatures in the United States, J. Geophys. Res., 117, D05116, doi:10.1029/2011JD016761.
https://www.ncei.noaa.gov/pub/data/ushcn/papers/williams-etal2012.pdf

[10] Menne, M. J., C. N. Williams Jr., and M. A. Palecki (2010), On the reliability of the U.S. surface temperature record, J. Geophys. Res., 115, D11108, doi:10.1029/2009JD013094.

[11] Hubbard, K. G., and X. Lin (2006), Reexamination of instrument change effects in the U.S. Historical Climatology Network, Geophys. Res. Lett., 33, L15710, doi:10.1029/2006GL027069.

[12] Venema, V. et al 2012. Benchmarking homogenization algorithms for monthly data. Clim. Of the Past, 8, 89-115.

[13] Mestre, O. et al 2013. HOMER: HOMogenisation software in R – methods and applications. Idöjárás, 117, 47-67.

[14] Szentimrey, T. 2008. Development of MASH homogenization procedure for daily data. Proceedings, 5th seminar for homogenization and quality control in climatological databases, Budapest, Hungary, 2006, WCDMP- No.71, 123-130.

[15] Wang, X.L., Chen, H., Wu, Y., Feng, Y. and Pu, Q. 2010. New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 2416-2436.

[16] Thompson, D. W. J., Kennedy, J. J., Wallace, J. M., & Jones, P. D. (2008). A large discontinuity in the mid-twentieth century in observed global-mean surface temperature. Nature, 453(7195), 646–649. doi:10.1038/nature06982

[17] Sippel, S., Kent, E.C., Meinshausen, N. et al. Early-twentieth-century cold bias in ocean surface temperature observations. Nature 635, 618–624 (2024). https://doi.org/10.1038/s41586-024-08230-1

[18] Tim Osborn, John Kennedy. Revised historical record sharpens perspective on global warming. Nature News and Views. November 20, 2024. https://www.nature.com/articles/d41586-024-03551-7

2.3 Papers Specific to Urbanization and UHI

[1] Peterson and Owen, “Urban Heat Island Assessment: Metadata Are Important” Journal of Climate 18 (2005): 2637-2646.
https://journals.ametsoc.org/doi/10.1175/JCLI3431.1

[2] Jones, P. D., Lister, D. H., and Li, Q. (2008), Urbanization effects in large‐scale temperature records, with an emphasis on China, J. Geophys. Res., 113, D16122, doi:10.1029/2008JD009916.https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008JD009916

[3] Parker, D. E., 2006: A demonstration that large-scale warming is not urban. J. Climate, 19, 2882–2895.
https://journals.ametsoc.org/doi/10.1175/JCLI3730.1

[4] Hausfather, Z., M. J. Menne, C. N. Williams, T. Masters, R. Broberg, and D. Jones (2013), Quantifying the effect of urbanization on U.S. Historical Climatology Network temperature records, J. Geophys. Res. Atmos., 118, 481–494, doi:10.1029/2012JD018509.

[6] Wickham C, Rohde R, Muller RA, Wurtele J, Curry J, et al. (2013) Influence of Urban Heating on the Global Temperature Land Average using Rural Sites Identified from MODIS Classifications. Geoinfor Geostat: An Overview 1:2. doi:10.4172/2327-4581.1000104
https://www.scitechnol.com/2327-4581/2327-4581-1-104.pdf