Guiding the creation of a comprehensive surface temperature resource for twenty-first-century climate science
Open accessAbstract
Creating SurfaCe temperature DataSetS to meet twenty-firSt-Century ChallengeSWhat: An international group of climatologists, statisticians, metrologists (measurement scientists), software experts, and others met to plan the best way forward to create a comprehensive, traceable data bank of global surface temperature and to facilitate the creation of high-quality, robust data products to meet the needs of the twenty-first century When: 7-9 September 2010 Where: Exeter, United Kingdom t HE BASIC SCIENCE CHALLENGE .Historically, temperature measurements were made by a variety of individuals and organizations to primarily serve weather-related needs where modest precision (a degree or two) was satisfactory to observe a passing cold front, day-to-night cooling, or winter-to-summer warming.However, one man's signal is another man's noise: noise arising from changing measurement errors, due to changes to instrumentation, siting, and observing practices, can be acceptable, or even desirable, if the changes result in more absolutely accurate or timely data for weather purposes.Such change has therefore been ubiquitous.This noise becomes problematic for characterizing climate change, however, where signals of tenths of a kelvin per decade are noteworthy.While great progress has been made in developing methods to identify and adjust for these complex, sometimes subtle, nonclimatic influences while retaining the true data characteristics, even greater attention to this is required.Add to the mix a substantially varying geographical coverage and density-and the fact that some areas of the globe have never been, and probably never will be, directly observed by in situ measurements-and the challenge becomes substantial.Hence, we need to fully quantify and understand the uncertainties.
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- Authors
- Thorne, P. W., Willett, K. M., Allan, R. J., Bojinski, S., Christy, J. R., Fox, N., Gilbert, S., Jolliffe, I., Kennedy, J. J., Kent, E., Tank, A. K., Lawrimore, J., Parker, D. E., Rayner, N., Simmons, A., Song, L., Stott, P. A., & Trewin, B.
- Affiliations
- North Carolina State University (US)
- Met Office (GB)
- World Meteorological Organization (CH)
- University of Alabama in Huntsville (US)
- National Physical Laboratory (GB)
- University of Exeter (GB)
- National Oceanography Centre (GB)
- Royal Netherlands Meteorological Institute (NL)
- National Oceanic and Atmospheric Administration (US)
- European Centre for Medium-Range Weather Forecasts (GB)
- China Meteorological Administration (CN)
- Bureau of Meteorology (AU)
- ORCID
- Venue
- Bulletin of the American Meteorological Society
- Publisher
- American Meteorological Society
- Published
- 2011-11
- Volume
- 92
- Issue
- 11
- Pages
- ES40-ES47
- ISSN
- 0003-0007 (ISSN-L)
- 1520-0477
- OpenAlex
- W2023843250
- Semantic Scholar
- 317b1872d343a228c7367405b8e092dd53ecbd3f
- Cited by
- 76
- References
- 11
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 4 · 2023: 1 · 2022: 7 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 6 · 2017: 2 · 2016: 6 · 2015: 5 · 2014: 9 · 2013: 10 · 2012: 10
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bulletin of the American Meteorological Society (publishedVersion)
- Maynooth University ePrints and eTheses Archive (Maynooth University) (acceptedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- NERC Open Research Archive (Natural Environment Research Council) (submittedVersion)
- Funders
- Department for Environment, Food and Rural Affairs, UK Government
- Met Office
- Natural Environment Research Council (noc010012)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Scientific Measurement and Uncertainty Evaluation
- Physical Sciences › Engineering › Aerospace Engineering › Calibration and Measurement Techniques
- Keywords
- global surface temperature, climate data record, measurement uncertainty, homogenization, data quality, climate change detection, data coverage gaps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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