The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets
Open accessAbstract
The ERA‐Interim and JRA‐55 reanalyses of synoptic data and several conventional analyses of monthly climatological data provide similar estimates of global‐mean surface warming since 1979. They broadly agree on the character of interannual variability and the extremity of the 2015/2016 warm spell to which a strong El Niño and low Arctic sea‐ice cover contribute. Nevertheless global and regional averages differ on various time‐scales due to differences in data coverage and sea‐surface temperature analyses; averages from those conventional datasets that infill where they lack direct observations agree better with the averages from the reanalyses. The latest warm event is less extreme when viewed in terms of atmospheric energy, which gives more weight to variability in the Tropics, where the thermal signal has greater vertical penetration and latent energy is a larger factor. Surface warming from 1998 to 2012 is larger than indicated by earlier versions of the conventional datasets used to characterize what the Fifth Assessment Report of the Intergovernmental Panel on Climate Change termed a hiatus in global warming. None of the datasets exhibit net warming over the Antarctic since 1979. Centennial trends from the conventional datasets, HadCRUT4 on the one hand and GISTEMP and NOAAGlobalTemp on the other, differ mainly because sea‐surface temperatures differ. Infilling of values where direct observations are lacking is more questionable for the data‐sparse earlier decades. Change since the eighteenth century is inevitably more uncertain than change over and after a modern baseline period. The latter is arguably best estimated separately for taking stock of actions to limit climate change, exploiting reanalyses and using satellite data to refine the conventional approach. Nevertheless, early in 2016 the global temperature appears to have first touched or briefly breached a level 1.5 °C above that early in the Industrial Revolution, having touched the 1.0 °C level in 1998 during a previous El Niño.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Global surface temperature change
Hansen, J., Ruedy, R., Sato, M., & Lo, K. (2010) · Reviews of Geophysics · doi:10.1029/2010rg000345 · Cited by 3,081 · cited 1×
“Comparisons are also made with the GISTEMP (Hansen et al. , 2010) dataset produced by the US National Aeronautics and Space Administration (NASA).”
The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., & Wimmer, W. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.10.017 · Cited by 1,394
Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends
Cowtan, K., & Way, R. G. (2014) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.2297 · Cited by 645
Making sense of the early-2000s warming slowdown
Fyfe, J. C., Meehl, G. A., England, M. H., Mann, M. E., Santer, B. D., Flato, G. M., Hawkins, E., Gillett, N. P., Xie, S.-P., Kosaka, Y., et al. (2016) · Nature Climate Change · doi:10.1038/nclimate2938 · Cited by 394
Cited by
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al. (2020) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3803 · Cited by 31,882
Self-citationImprovements 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
The ERA5 global reanalysis: Preliminary extension to 1950
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Radu, R., Schepers, D., et al. (2021) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4174 · Cited by 689
Self-citation
Metadata
- Authors
- Simmons, A. J., Berrisford, P., Dee, D. P., Hersbach, H., Hirahara, S., & Thépaut, J. ‐.
- Venue
- Quarterly Journal of the Royal Meteorological Society
- Publisher
- Wiley
- Published
- 2016-12-05
- Volume
- 143
- Issue
- 702
- Pages
- 101-119
- ISSN
- 0035-9009 (ISSN-L)
- 1477-870X
- OpenAlex
- W2533922453
- Cited by
- 179
- References
- 4
- FWCI
- 11.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets. https://app.cochat.ai/papers/10.1002/qj.2949
- The paper
- Simmons, A. J., Berrisford, P., Dee, D. P., Hersbach, H., Hirahara, S., & Thépaut, J. ‐. (2017). A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.2949
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.