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Improvements in the GISTEMP Uncertainty Model
Open accessAbstract
We outline a new and improved uncertainty analysis for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys, and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses, and incorporate independently derived estimates for ocean data processing, station homogenization, and other structural biases. The resulting 95% uncertainties are near 0.05 °C in the global annual mean for the last 50 years and increase going back further in time reaching 0.15 °C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date) and conclude with 87% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).
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“Based on an assessment of January–November data from Berkeley Earth (Rohde and Hausfa-ther, 2020), NASA GISTEMP (Lenssen et al., 2019), NOAA-GlobalTemp v6 (Yin et al., 2024), HadCRUT5 (Morice et al., 2021 ), and ERA5 (Hersbach et al., 2023), GMST in 2025 is expected to approximately tie with 2023…”
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“Additional data-sets for the same period are also used for uncertainty quantification, including the NASA GISS Surface Temperature Analysis (GISTEMP) product (at 2° × 2° grids) (Hansen et al., 2010; Lenssen et al., 2019), the NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp) product (at 5° ×…”
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Spatially resolved evaluation of Earth system models with satellite column-averaged CO 2
Gier, B. K., Buchwitz, M., Reuter, M., Cox, P. M., Friedlingstein, P., & Eyring, V. (2020) · Biogeosciences · doi:10.5194/bg-17-6115-2020 · Cited by 21
Greening of Eurasia's center driven by low-latitude climate warming
Wang, S., Chen, F., Chen, Y., Torbenson, M. C. A., Esper, J., Zhao, X., Hu, M., Zhang, H., Yue, W., & Cao, H. (2025) · Forest Ecosystems · doi:10.1016/j.fecs.2025.100330 · Cited by 21
Could the Recent Taal Volcano Eruption Trigger an El Niño and Lead to Eurasian Warming?
Liu, F., Xing, C., Li, J., Wang, B., Chai, J., Gao, C., Huang, G., Liu, J., & Chen, D. (2020) · Advances in Atmospheric Sciences · doi:10.1007/s00376-020-2041-z · Cited by 20
“In this study, we used the reconstructed ENSO index from Li et al. (2011) over the period 900−1869, and the observed boreal-winter Niño3.”
BackgroundMethodGreening Hiatus in Eurasian Boreal Forests Since 1997 Caused by a Wetting and Cooling Summer Climate
Gao, X., Liang, S., & Sauer, J. (2020) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2020jg005662 · Cited by 20
“Since the early 1970s, the high northern latitude land regions have warmed slightly more than three times faster than the rest of the world (Lenssen et al., 2019), and such a significant warming trend in climate is deemed as the major cause of increases in vegetation productivity from 1982 to 1997…”
BackgroundConstruction of homogenized daily surface air temperature for the city of Tianjin during 1887–2019
Si, P., Li, Q., & Jones, P. D. (2021) · Earth system science data · doi:10.5194/essd-13-2211-2021 · Cited by 20
“Based on a few recently reported studies (Li et al., 2020a; Lenssen et al., 2019; Xu et al., 2018; Menne et al., 2018), we employ the station series or the interpolated tempera- ture series using neighboring grid boxes from three global land surface temperature observation series (Table 2) as…”
BackgroundMethodAizawa, T., Oshima, N., & Yukimoto, S. (2022) · Geophysical Research Letters · doi:10.1029/2021gl097093 · Cited by 23
“, 2021), GISTEMP v4 (Lenssen et al., 2019), and NOAAGlobalTemp version 5 (Huang et al.”
BackgroundRangel‐Peraza, J. G., Sanhouse-García, A. J., Flores-González, L. M., Monjardín-Armenta, S. A., Mora-Félix, Z. D., Rentería-Guevara, S. A., & Terrones, Y. A. B. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123249 · Cited by 20
How Reliable Are Decadal Climate Predictions of Near-Surface Air Temperature?
Verfaillie, D., Doblas-Reyes, F. J., Donat, M. G., Pérez‐Zanón, N., Solaraju-Murali, B., Torralba, V., & Wild, S. (2020) · Journal of Climate · doi:10.1175/jcli-d-20-0138.1 · Cited by 20
“2012), while GISTEMP does (Lenssen et al. 2019), which might explain some differences—at least in terms”
BackgroundMethodInfluentialPauling, A. G., Bushuk, M., & Bitz, C. M. (2021) · Geophysical Research Letters · doi:10.1029/2021gl092558 · Cited by 20
“We also compare the global mean surface temperature evolution following volcanic eruptions with the GISTEMP (GISTEMP Team, 2020; Lenssen et al., 2019) and Berkeley Earth (Rohde et al., 2013) observational datasets.”
Future heat stress to reduce people’s purchasing power
Kuhla, K., Willner, S. N., Otto, C., Wenz, L., & Levermann, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251210 · Cited by 21
A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics
Kong, Q., & Huber, M. S. (2025) · Scientific Data · doi:10.1038/s41597-025-04527-6 · Cited by 30
“However, as articulated below, future periods corresponding to these warming levels are located with respect to the baseline by accounting for the observed temperature change of 0.21°C between the baseline and preindustrial 110 .”
InfluentialClark, J. B., Mannino, A., Spencer, R. G. M., Tank, S. E., & McClelland, J. W. (2023) · Global Biogeochemical Cycles · doi:10.1029/2023gb007854 · Cited by 19
“Annually averaged a 300 was greatest in the Lena and the Ob' and lowest in the Mackenzie, while the DOC to a 300 ratio was highest in the Kolyma and Lena Rivers, followed by the Yukon (Table S1 in Supporting Information S1).”
BackgroundInfluentialCan urban shrinkage contribute to mitigating surface air temperature warming?
Ma, F., & Yoon, H. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105730 · Cited by 19
Drivers and mechanisms contributing to excess warming in Europe during recent decades
Dong, B., & Sutton, R. T. (2025) · npj Climate and Atmospheric Science · doi:10.1038/s41612-025-00930-3 · Cited by 19
“The other observed monthly mean SAT data sets used are the HadCRUT5 dataset 52 , the NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp) 53 , the GISS Surface Temperature Analysis version 4 (GISTEMPv4) 54 ,andBerkeleyEarthSurfaceTemperatures(BEST) 55 .”
Observed trend in Earth energy imbalance may provide a constraint for low climate sensitivity models
Myhre, G., Hodnebrog, Ø., Loeb, N. G., & Forster, P. M. D. F. (2025) · Science · doi:10.1126/science.adt0647 · Cited by 19
Carbon cycle in tropical peatlands and coastal seas
Rixen, T., Wit, F., Hutahaean, A. A., Schlüter, A., Baum, A., Klemme, A., Müller, M., Pranowo, W. S., Samiaji, J., & Warneke, T. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-815050-4.00011-0 · Cited by 21
Chhokar, R. S., Sharma, R. K., Kumar, N., Singh, R., & Singh, G. P. (2022) · International Journal of Plant Production · doi:10.1007/s42106-022-00216-1 · Cited by 19
Soon, W., Connolly, R., Connolly, M., Akasofu, S. ‐., Baliunas, S. L., Berglund, J., Bianchini, A., Briggs, W. M., Butler, C., Cionco, R. G., et al. (2023) · Climate · doi:10.3390/cli11090179 · Cited by 21
Rollings, M., & Merlis, T. M. (2021) · Journal of Climate · doi:10.1175/jcli-d-20-0410.1 · Cited by 19
“Lu, J., G. Chen, and D. M. Frierson, 2008: Response of the zonal mean atmospheric circulation to El Niño versus global warming.”
BackgroundInvestigation of Aeolian Dust Deposition Rates in Different Climate Zones of Southwestern Iran
Foroushani, M. A., Opp, C., & Groll, M. (2021) · Atmosphere · doi:10.3390/atmos12020229 · Cited by 19
Zhang, Y., Zhao, R., Nie, X., Deng, S., Xu, W., Zhu, Y. M., & Zhao, L. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.134626 · Cited by 19
Recent grain production boom in Russia in historical context
Kirilenko, A., & Dronin, N. M. (2022) · Climatic Change · doi:10.1007/s10584-022-03332-z · Cited by 19
“Tsurf250 from the National Aeronautics Space Administration (NASA) Goddard Institute for Space Studies (GISS), gridded at 1 × 1 degrees of geographical latitude and longitude with application of a 250-km smoothing filter (Hansen and Lebedeff 1987; Lenssen et al. 2019).”
MethodThe fractional energy balance equation for climate projections through 2100
Procyk, R., Lovejoy, S. M., & Hébert, R. (2022) · Earth System Dynamics · doi:10.5194/esd-13-81-2022 · Cited by 20
Clarke, B., Barnes, C. R., Rodrigues, R. R., Zachariah, M., Alves, L. M., Haarsma, R., Pinto, I., Yang, W., Vahlberg, M., Vecchi, G. A., et al. (2026) · npj natural hazards. · doi:10.1038/s44304-025-00162-8 · Cited by 23
“As a measure of anthropogenic climate change, we use the (low-pass fi ltered) global mean surface temperature (GMST), where GMST is taken fromtheNationalAeronauticsandSpaceAdministration(NASA)Goddard InstituteforSpaceScience(GISS)surfacetemperatureanalysis (GISTEMP) 66,67 .”
Metadata
- Authors
- Lenssen, N. J. L., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A., Ruedy, R., & Zyss, D.
- Affiliations
- Goddard Institute for Space Studies (US)
- Columbia University (US)
- National Oceanic and Atmospheric Administration (US)
- Corresponding author
- Lenssen, Nathan J. L.
- Venue
- Journal of Geophysical Research: Atmospheres
- Publisher
- American Geophysical Union (AGU)
- Published
- 2019-06-24
- Volume
- 124
- Issue
- 12
- Pages
- 6307-6326
- ISSN
- 0148-0227 (ISSN-L)
- 2169-897X
- 2169-8996
- OpenAlex
- W2946160190
- Cited by
- 962
- References
- 48
- Citations by year
- 2026: 65 · 2025: 101 · 2024: 153 · 2023: 159 · 2022: 162 · 2021: 189 · 2020: 114 · 2019: 18
- FWCI
- 62.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Geophysical Research Atmospheres (publishedVersion)
- NOAA Institutional Repository (publishedVersion)
- Funders
- National Science Foundation (NSF DGE 16-44869)
- 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 › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
- Keywords
- surface temperature anomalies, uncertainty analysis, instrumental temperature records, station homogenization, station coverage, ocean data processing, systematic bias, land cover change
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