The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
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).
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
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A. M., Takacs, L. L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R. H., et al. (2017) · Journal of Climate · doi:10.1175/jcli-d-16-0758.1 · Cited by 9,261
The JRA-55 Reanalysis: General Specifications and Basic Characteristics
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., et al. (2015) · Journal of the Meteorological Society of Japan Ser II · doi:10.2151/jmsj.2015-001 · Cited by 4,768
Huang, B., Thorne, P., Banzon, V. F., Boyer, T., Chepurin, G. A., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., & Zhang, H. (2017) · Journal of Climate · doi:10.1175/jcli-d-16-0836.1 · Cited by 3,245
Self-citationGlobal surface temperature change
Hansen, J., Ruedy, R., Sato, M., & Lo, K. (2010) · Reviews of Geophysics · doi:10.1029/2010rg000345 · Cited by 3,081 · cited 8×
“As shown in Hansen et al. (2010), the effect of the urban adjustment on global temperature change is on the order of 0.01 ◦C.”
BackgroundMethodInfluentialSelf-citationProcesses and impacts of Arctic amplification: A research synthesis
Serreze, M. C., & Barry, R. G. (2011) · Global and Planetary Change · doi:10.1016/j.gloplacha.2011.03.004 · Cited by 2,752
Recent Arctic amplification and extreme mid-latitude weather
Cohen, J. L., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D. P., Coumou, D., Francis, J. A., Dethloff, K., Entekhabi, D., Overland, J. E., et al. (2014) · Nature Geoscience · doi:10.1038/ngeo2234 · Cited by 2,613
Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850
Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B., & Jones, P. D. (2006) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2005jd006548 · Cited by 2,040
Morice, C. P., Kennedy, J. J., Rayner, N. A., & Jones, P. D. (2012) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2011jd017187 · Cited by 1,657
Climate Impact of Increasing Atmospheric Carbon Dioxide
Hansen, J. E., Johnson, D., Lacis, A. A., Lebedeff, S. A., Lee, P., Rind, D. H., & Russell, G. L. (1981) · Science · doi:10.1126/science.213.4511.957 · Cited by 1,074
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Ishihara K., 2006, Calculation of global surface temperature anomalies with COBE‐SST, Weather Service Bulletin, 73, S19—S25
- 10.4172/2327-4581.1000103 · DOI · Paper page
Cited by
Sorted by citations
Items 501–600 of 961
Frequency-Domain Evidence for Climate Change
Mangat, M. K., & Reschenhofer, E. (2020) · Econometrics · doi:10.3390/econometrics8030028 · Cited by 6
“Secondary datasets were the NASA Goddard Institute for Space Studies (GISS) hemispheric annual means from 1880 to 2019, which are available at the website https://data.giss.nasa.gov/gistemp/ (Lenssen et al. 2019).”
MethodDeath valley: world‐beating temperatures, no sweat
Matthews, T. K. R. (2020) · Weather · doi:10.1002/wea.3858 · Cited by 6
Smith, C., Zięba, G., & Przybylski, M. (2021) · Functional Ecology · doi:10.1111/1365-2435.13846 · Cited by 6
Khodaee, S. M. M., Hashemi, M., Mirlohi, A., Majidi, M. M., Sukumaran, S., Moghaddam, M. E., & Abdollahi, M. R. (2021) · Rhizosphere · doi:10.1016/j.rhisph.2021.100413 · Cited by 6
Bahadur, J., Mehta, S., Singh, S., Das, A., Maity, A., Youngs, T. G. A., Sen, D., & Polshettiwar, V. (2022) · Materials Advances · doi:10.1039/d2ma00785a · Cited by 6
Li, K., Zheng, F., Luo, D.-Y., Sun, C. J., & Zhu, J. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac8dab · Cited by 6
CLIMATE AGENDA IN CURRENT SITUATION: ADVICES FOR RUSSIAN ECONOMY
ROGINKO, S. A. (2022) · Scientific Works of the Free Economic Society of Russia · doi:10.38197/2072-2060-2022-236-4-447-465 · Cited by 6
A Long-Term Cloud Albedo Data Record Since 1980 from UV Satellite Sensors
Weaver, C. J., Wu, D. L., Bhartia, P. K., Labow, G. J., & Haffner, D. P. (2020) · Remote Sensing · doi:10.3390/rs12121982 · Cited by 6
Quantifying heat-related mortality attributable to human-induced climate changePreprint
Stuart-Smith, R. F., Vicedo-Cabrera, A. M., Li, S., Otto, F. E. L., Belesova, K., Haines, A., Harrington, L. J., Hess, J., Venkatraman, R., Wetzer, T., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-2702337/v2 · Cited by 6
Ishii, M., NISHIMURA, A., YASUI, S., & Hirahara, S. (2024) · Journal of the Meteorological Society of Japan Ser II · doi:10.2151/jmsj.2025-002 · Cited by 5
“LSAT analyses compared with COBE-LSAT3 are the Climatic Research Unit temperature version 4 of the University of East Anglia, UK (CRUTEMP5; Osborn and Jones 2014) and the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP4; Lenssen et al. 2019).”
Dunn, R., Blannin, J., Willett, K. M., Gobron, N., Morris, G. A., Ades, M., Adler, R. F., Alexe, M., Allan, R. P., Anderson, J., et al. (2025) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-25-0102.1 · Cited by 10
Geophysical Trends Inferred From 20 Years of AIRS Infrared Global Observations
DeSouza‐Machado, S., Strow, L. L., & Kramer, R. J. (2025) · Journal of Geophysical Research Atmospheres · doi:10.1029/2025jd043501 · Cited by 5
“The NASA Goddard Institute of Space Studies (GISS) v4 surface temperature data (GISTEMP Team, 2025; Lenssen et al., 2019, 2024) is a 1,200 km smoothed monthly data set based primarily on near surface temperatures land stations, and data from ships and buoys.”
Tropical impacts of the Southern Ocean underestimated by mean-state biases
Dong, Y., Lu, K., Hwang, Y., Hu, R. J., Ceppi, P., Breul, P., Roach, L. A., & Deser, C. (2026) · Science Advances · doi:10.1126/sciadv.aed1936 · Cited by 5
“5, we use TS observations from GISTEMP v4 dataset ( 62 ) to compute the equatorial eastern Pacific TS trend over 1990–2021.”
Odden Ice Melt Linked to Labrador Sea Ice Expansions and the Great Salinity Anomalies of 1970–1995
Allan, D., & Allan, R. P. (2024) · Journal of Geophysical Research Oceans · doi:10.1029/2023jc019988 · Cited by 5
Stoian, I. M., Pârvu, S., & Mincă, D. G. (2024) · MAEDICA – a Journal of Clinical Medicine · doi:10.26574/maedica.2024.19.1.94 · Cited by 5
Large-Scale Climate Modes Drive Low-Frequency Regional Arctic Sea Ice Variability
Wyburn-Powell, C., & Jahn, A. (2024) · Journal of Climate · doi:10.1175/jcli-d-23-0326.1 · Cited by 5
Lenard, T., & Ejankowski, W. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1384435 · Cited by 5
McDougal, T. L., & Patterson, J. H. (2021) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2021.102073 · Cited by 6
“Climate anomaly data were obtained from the GISS Surface Temperature Analysis (GISTEMP) dataset, version 4, produced by the NASA Goddard Institute for Space Studies [53,54].”
BackgroundMethodMulti-decadal oscillations of surface temperatures and the impact on temperature increases
Kalicinsky, C., & Koppmann, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-24448-3 · Cited by 5
Panchen, Z. A. (2022) · Plant Ecology & Diversity · doi:10.1080/17550874.2022.2147804 · Cited by 5
“The Arctic is experiencing unprecedented climate change with temperatures rising at three times the global average (Lenssen et al. 2019; GISTEMP Team 2020; AMAP 2021; MassonDelmotte et al. 2021).”
BackgroundA Machine Learning hourly analysis on the relation the Ionosphere and Schumann Resonance Frequency
Cano-Domingo, C., Stoean, R., Joya, G., Novas, N., Fernández-Ros, M., & Parra, J. A. G. (2023) · Measurement · doi:10.1016/j.measurement.2022.112426 · Cited by 5
Bennedsen, M., Hillebrand, E., & Lykke, J. Z. (2023) · Journal of Climate · doi:10.1175/jcli-d-22-0460.1 · Cited by 5
“…that in the IPCC Global Warming of 1.5◦C report (IPCC, 2018), which includes: the GISS Surface Temperature Analysis (GISTEMP) (GISTEMP Team, 2021; Lenssen et al., 2019), the NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp) (NOAA National Centers for Environmental…”
MethodInfluentialChen, X., Hu, C., & Lian, T. (2023) · Atmospheric Research · doi:10.1016/j.atmosres.2023.106969 · Cited by 5
Schmidt, G. A., Jones, G. S., & Kennedy, J. (2023) · Geophysical Research Letters · doi:10.1029/2022gl102530 · Cited by 5
Self-citationCraigmile, P. F., & Guttorp, P. M. (2023) · Earth and Space Science · doi:10.1029/2022ea002468 · Cited by 5
Temporal characteristics of extreme high temperatures in Wuhan since 1881
Zheng, X., Ren, G., He, J., Zhao, Y., Ren, Y., & Yang, G. (2023) · Climate Research · doi:10.3354/cr01730 · Cited by 5
Anthropogenic Aerosols Offsetting Ocean Warming Less Efficiently Since the 1980s
Sohail, T., Irving, D., Zika, J. D., & Gregory, J. M. (2023) · Geophysical Research Letters · doi:10.1029/2023gl105374 · Cited by 5
First comprehensive assessment of industrial-era land heat uptake from multiple sources
García‐Pereira, F., González-Rouco, J. F., Melo‐Aguilar, C., Steinert, N. J., García‐Bustamante, E., de Vrese, P., Jungclaus, J. H., Lorenz, S. J., Hagemann, S., Cuesta‐Valero, F. J., et al. (2024) · Earth System Dynamics · doi:10.5194/esd-15-547-2024 · Cited by 6
Extreme Tibetan Plateau cooling caused by tropical volcanism
Zhu, X., Ji, Z., Liu, F., Dong, W., & Gao, C. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00672-8 · Cited by 5
“Additionally, we incorporated long-term monthly global temperature data from the NASA Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP v4), which employed a spatial smoothing technique withascaleof1200 kmandcoversthetimeperiodfrom1880to2020 49 .”
Skeie, R. B., Aldrin, M., Berntsen, T. K., Holden, M., Huseby, R. B., Myhre, G., & Storelvmo, T. (2024) · Earth System Dynamics · doi:10.5194/esd-15-1435-2024 · Cited by 11
Frolov, D. M. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1614/1/012078 · Cited by 5
Characterizing unforced decadal climate variability in global climate model large ensembles
Yao, B., Xu, Y., Dessler, A. E., & Liu, C. (2021) · Climate Dynamics · doi:10.1007/s00382-021-05900-y · Cited by 5
The Representation of the Southern Annular Mode Signal in the Brazilian Earth System Model
Prado, L. F., Wainer, I. E. K. C., & de Souza, R. B. (2021) · Atmosphere · doi:10.3390/atmos12081045 · Cited by 5
“The GISS Surface Temperature Analysis v4 (GISTEMP v4) [39] uses the timeframe 1951–1980 as the reference period and combines Global Historical Climatology Network v4 (GHCN) air temperature over land with Extended Reconstructed SST v5 (ERSST), on a 2◦ × 2◦ grid.”
MethodMapping environmental suitability for anthrax reemergence in the Arctic
Stella, E., Mari, L., Gabrieli, J., Barbante, C., & Bertuzzo, E. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac2527 · Cited by 5
INDUSTRIAL TOURISM AS A TOOL FOR SUSTAINABLE DEVELOPMENT OF THE URAL MACROREGION
TIMAKOVA, R. T., & Pyankova, S. G. (2022) · Scientific Works of the Free Economic Society of Russia · doi:10.38197/2072-2060-2022-236-4-329-344 · Cited by 5
Choi, N., & Lee, S. (2023) · International Journal of Climatology · doi:10.1002/joc.8247 · Cited by 5
“…and Space Administration Goddard Institute for Space Studies (NASA GISS) surface temperature, the global mean temperature anomaly in August 2016 is recorded as the highest at 2.31 C compared to the 1980 – 2015 average, followed by 2.25 C in 2022 (GISTEMP Team, 2023; Lenssen et al., 2019).”
BackgroundInfluentialGlobal and regional climate in 2021
Kennedy, J., Christidis, N., Dunn, R., McCarthy, M., Morice, C., & Rayner, N. A. (2022) · Weather · doi:10.1002/wea.4256 · Cited by 5
The Econometrics of Global WarmingPreprint
Razzak, W. A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3952220 · Cited by 5
Chapter 3 : Earth Systems Processes. Fifth National Climate Assessment
Leung, L.-Y. R., Terando, A. J., Joseph, R., Tselioudis, G., Bruhwiler, L., Cook, B. I., Deser, C., Hall, A. D., Hamlington, B. D., Hoell, A., et al. (2023) · doi:10.7930/nca5.2023.ch3 · Cited by 5
On the ability to study regional hydrometeorological changes using GPS and GRACE measurements
Leńczuk, A., Olivera-Guerra, L., Kłos, A., & Bogusz, J. (2024) · Progress in Earth and Planetary Science · doi:10.1186/s40645-024-00665-4 · Cited by 4
“In the case of global surface temperature change, we use the GISS Surface Temperature Analysis (Hansen and Lebedeff 1987) version 4 (GISTEMP v4) (Lenssen et al. 2019) that contains combined changes of sea surface temperature records and meteorological station measurements over land and ocean areas.”
Tropical Pacific Warming Patterns Influence Future Hydroclimate Shifts and Extremes in the Americas
Heede, U. K., Lenssen, N., Karnauskas, K. B., & Deser, C. (2025) · Earth s Future · doi:10.1029/2025ef006014 · Cited by 4
“In general, land warms faster than the sea surface, polar regions warm faster than the subtropics, and the Northern Hemisphere has warmed more than the Southern Hemisphere over the industrial era (Gulev et al., 2021; Lenssen et al., 2019; Morice et al., 2021).”
Self-citationShi, G., Zhang, L., Liu, Y., Zhou, C., Zhang, D., Hu, Y., & Hong, M. (2025) · Journal of Geophysical Research Atmospheres · doi:10.1029/2025jd044008 · Cited by 4
“…data sets in polar science, including Goddard Institute for Space Studies Surface Temperature product version 4, GISTEMP v4 (GISTEMP, 2024, Lenssen et al., 2019), Met Office Hadley Centre and Climatic Research Unit global surface temperature data set version 5, HadCRUT5 (Morice et al.,…”
Atypical warming pattern of strong 2023-24 El Niño boosts global temperatures to new 1.5 °C record
Jiang, N., Zhu, C., McPhaden, M. J., Hu, Z., Lian, T., Zhou, C., & Chen, D. (2025) · Communications Earth & Environment · doi:10.1038/s43247-025-02971-1 · Cited by 4
“Dataset accessed 2025-01-20 at https://data.giss.nasa.gov/gistemp/] 71 .”
Nicklas, J. M., Fox‐Kemper, B., & Lawrence, C. E. (2024) · Journal of Climate · doi:10.1175/jcli-d-23-0580.1 · Cited by 13
Ziółkowski, P., Witanowski, Ł., Klonowicz, P., & Mikielewicz, D. P. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.121655 · Cited by 4
Okada, C., Hou, Z., Imoto, H., Naka, K., Kikutani, T., & Takasaki, M. (2024) · ACS Omega · doi:10.1021/acsomega.4c07631 · Cited by 4
Yoo, M., Yoo, H., Lee, Y., Lee, S., Moon, S., Jung, T., Cho, C., Jeong, H., & Cho, D. (2025) · Advanced Energy and Sustainability Research · doi:10.1002/aesr.202400306 · Cited by 4
Panchen, Z. A., Doubt, J., Saarela, J. M., & Kharouba, H. M. (2025) · New Phytologist · doi:10.1111/nph.70386 · Cited by 4
Gu, P., Gan, B., Cai, W., & Wu, L. (2024) · Environmental Research Letters · doi:10.1088/1748-9326/ad2eea · Cited by 4
Early 20th century Southern Hemisphere cooling
Brönnimann, S., Brugnara, Y., & Wilkinson, C. (2024) · Climate of the past · doi:10.5194/cp-20-757-2024 · Cited by 4
Clearing roadblocks that stymie city climate action
Rosenzweig, C. E., SOLECKI, W. D., Pathak, M., Barau, A. S., Barata, M. M. D. L., & Dombrov, M. (2025) · Nature Cities · doi:10.1038/s44284-025-00205-1 · Cited by 4
Caporale, G. M., Gil‐Alana, L. A., & Carmona-González, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-92990-x · Cited by 4
“The series are monthly and span the periods from 1880 to 2022; they are the combined global land and ocean temperature anomalies, i.e., deviations from the 1901–2000 mean for each of the two hemispheres and from the 1910–2000 one for the Arctic and Antarctic (Please note that there exist other sources for Arctic tempe…”
A Generic Framework and Library for Exploration of Small Multiples through Interactive Piling
Lekschas, F., Zhou, X., Chen, W., Gehlenborg, N., Bach, B., & Pfister, H. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3028948 · Cited by 7
Tobin, S. (2020) · Journal of Southern Hemisphere Earth System Science · doi:10.1071/es19037 · Cited by 4
Polyphony of Short-Term Climatic Variations
Sonechkin, D. M., & Vakulenko, N. V. (2021) · Atmosphere · doi:10.3390/atmos12091145 · Cited by 4
“Data Availability Statement: GISS: https://data.giss.nasa.gov/gistemp/, accessed on 29 August 2021; NCDC: https://www.ncdc.noaa.gov/cag/global/time-series/globe/land_ocean/p12/1/1958 -2016, accessed on 29 August 2021; HADCRUT4: https://crudata.uea.ac.uk/cru/data/temperature/, accessed on 29 August 2021.”
ResultFernández, A., & Somos‐Valenzuela, M. A. (2022) · Journal of Glaciology · doi:10.1017/jog.2022.16 · Cited by 4
Strand, S. M., Christiansen, H. H., & Gilbert, G. (2022) · Journal of Geophysical Research Earth Surface · doi:10.1029/2021jf006502 · Cited by 4
“1°C during the 1991–2020 climate normal period, based on the NASA GISS Surface Temperature Analysis (GISTEMP v4) data set (GISTEMP Team, 2021; Lenssen et al., 2019).”
BackgroundMethodInfluentialWei, T., Sun, C. J., & Li, J. (2023) · Journal of Climate · doi:10.1175/jcli-d-22-0783.1 · Cited by 5
Braithwaite, R. J., & Hughes, P. D. (2023) · Annals of Glaciology · doi:10.1017/aog.2023.54 · Cited by 4
Feng, Y., Zhu, Y., & He, L. (2024) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-235022 · Cited by 4
“In 2019, Lenssen et al. [23] outlined a new and improved uncertainty analysis for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4).”
ENSO Index Variations and Links with Solar and Volcanic Activity
Zharkova, V. V., & Vasilieva, I. (2024) · Natural Science · doi:10.4236/ns.2024.164004 · Cited by 4
“The Oceanic Ni˜no Index (ONI) is taken from https : //www.climate.gov/news − features/understanding − climate/climate − variability − oceanic − nino − index ).”
BackgroundInfluentialThe Added Value of Statistical Seasonal Forecasts
Krikken, F., Geertsema, G., Nielsen, K., & Troccoli, A. (2024) · Climate · doi:10.3390/cli12060083 · Cited by 4
Global and regional climate in 2019
Kennedy, J., Kendon, M., Killick, R., Dunn, R., Allan, R. J., Rayner, N. A., & McCarthy, M. (2020) · Weather · doi:10.1002/wea.3822 · Cited by 4
Aerosol and Meteorological Influences on Mixed‐Phase Stratiform Clouds at North Slope of Alaska
Stauffer, C. L., Tan, I., & Matrosov, S. Y. (2025) · Geophysical Research Letters · doi:10.1029/2025gl114815 · Cited by 3
“Arctic Amplification (AA) refers to the observed rate of warming in the Arctic being more than twice the average rate of warming of the rest of the globe (Lenssen et al., 2019).”
Super El Niño events drive climate regime shifts with enhanced risks under global warming
Xue, A., Geng, X., Jin, F., Shin, Y., Sung, M., & Kug, J. (2025) · Nature Communications · doi:10.1038/s41467-025-66143-7 · Cited by 3
Robust intensification of projected regional precipitation extremes over Africa
Akinsanola, A. A., Taguela, T. N., & Bobde, V. (2026) · Nature Communications · doi:10.1038/s41467-026-73246-2 · Cited by 3
“For gridded temperature observations, we use the HadCRUT4 (Hadley Center/Climatic Research Unit Temperature version 4) 87 and GIS-TEMP4 (Goddard Institute for Space Studies Surface Temperature Analysis version 4) 88 datasets.”
Ilyas, M., Nychka, D. W., Brierley, C., & Guillas, S. (2021) · Atmospheric measurement techniques · doi:10.5194/amt-14-7103-2021 · Cited by 3
Persistence of temperature and precipitation: from local to global anomalies
Takalo, J. (2021) · Annals of Geophysics · doi:10.4401/ag-8618 · Cited by 3
“01 database for 1910-2017 (blue stars) and Land-Ocean temperatures from Gistemp Land-Ocean Temperature Index (LOTI) for 1910-2019 (magenta triangles) [Lenssen et al., 2019].”
BackgroundMethodInfluentialPerformance of Doppler shift compensation varies with environmental temperature and humidity in bats
Ding, J., Han, F., Zhang, K., Lin, A., Jiang, T., Feng, J., & Liu, Y. (2023) · Animal Behaviour · doi:10.1016/j.anbehav.2023.08.009 · Cited by 3
Gunasinghe, D. S., Weerasingha, D. B., & Ratnayake, A. S. (2024) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2024.101197 · Cited by 3
Pfeiffer, M., Takayanagi, H., Reuning, L., Watanabe, T. K., Ito, S., Garbe‐Schönberg, D., Watanabe, T., Chung‐Che, W., Shen, C., Zinke, J., et al. (2025) · Climate of the past · doi:10.5194/cp-21-211-2025 · Cited by 3
Global Warming Is Likely Affecting Regional Drought Across Eurasia
Marvel, K., Cook, B. I., & Cook, E. R. (2025) · AGU Advances · doi:10.1029/2024av001289 · Cited by 4
“We estimate T ( t ) and its uncertainty from 3 published data sets (HadCRUT, Morice et al., 2021), Berkeley Earth (Rohde & Hausfather, 2020), and GISTEMP (N. J. Lenssen et al., 2019), see (Appendix B).”
Chalif, J. I., Osterberg, E. C., & Partridge, T. F. (2025) · AGU Advances · doi:10.1029/2024av001399 · Cited by 3
Streptococcus agalactiae in childbearing age immigrant women in Comunitat Valenciana (Spain)
Sahuquillo-Arce, J. M., Hernández-Cabezas, A., Castaño-Aroca, M. J., Chouman-Arcas, R., Díaz-Aguirre, E., Boga, B. A., & López-Hontangas, J. L. (2020) · Scientific Reports · doi:10.1038/s41598-020-66811-2 · Cited by 5
Global Temperature Forecast Based on ARIMA Model
Naiqian, G., Guan, Y., & Xue-lian, S. (2019) · International Conference on Communication and Information Systems (ICCIS) · doi:10.1109/iccis49662.2019.00026 · Cited by 3
“Instead of using the whole data set for setting models, we leave the last 200 points for out-of-sample forecasting, checking the precise level of this model.[14-25]”
MethodTrewin, B. C. (2020) · Journal of Southern Hemisphere Earth System Science · doi:10.1071/es20007 · Cited by 3
“Itwasthesecond-warmestspring on record for the southern hemisphere, after 2015, in the NOAA (Huang et al. 2020) and GISS (Lenssen et al. 2019) data sets, and third-warmest in the HadCRUT4 data set (Morice et al. 2012).”
BackgroundCOMPARISON OF CRYOSAT-2 AND ICESAT-2 ON WATER LEVEL MONITORING OF NAM CO LAKE
Zhao, H., Xu, R., & Qiao, G. (2021) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b3-2021-527-2021 · Cited by 3
“In the past 50 years, the TP has warmed rapidly, and its heating rate is about twice the global average (Lenssen et al., 2019), and most”
BackgroundTaylor, L. M., & Hakim, G. J. (2023) · Earth and Space Science · doi:10.1029/2022ea002679 · Cited by 3
“We also verify forecasts on the GISS Surface Temperature Analysis (GISTEMP; Lenssen et al., 2019), available from 1880 to 2019 C.”
MethodAssessing homogeneity of land surface air temperature observations using sparse‐input reanalyses
Gillespie, I. M., Haimberger, L., Compo, G. P., & Thorne, P. (2022) · International Journal of Climatology · doi:10.1002/joc.7822 · Cited by 3
“We compare CRUTEMv5 (Osborn et al., 2020), GHCNMv4 (Menne et al., 2018), GISTemp (Lenssen et al., 2019), Berkeley (Rohde et al., 2013) and CLSAT (Xu et al., 2017) to the four new adjusted datasets developed herein (using the averaging as in section 5).”
MethodImpact of ocean data assimilation on climate predictions with ICON-ESM
Pohlmann, H., Brune, S., Fröhlich, K., Jungclaus, J. H., Sgoff, C., & Baehr, J. (2022) · Climate Dynamics · doi:10.1007/s00382-022-06558-w · Cited by 3
Record High Warm 2021 February Temperature over East Asia
Xie, J. (2022) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-22-0139.1 · Cited by 3
Climate Changing Impact on Microbes and Their Interactions with Plants: An Overview
Singh, N., & Das, P. P. (2023) · Climate change management · doi:10.1007/978-3-031-21079-2_12 · Cited by 3
Su, C.-H., & Kiang, J. (2022) · Atmosphere · doi:10.3390/atmos13122130 · Cited by 3
Record High 2022 September-Mean Temperature in Western North America
Xie, J., Tang, Q., Golaz, J., & Lin, W. (2024) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-23-0148.1 · Cited by 3
Schumann Resonance Modes and Ionosphere Parameters: An Annual Variability Comparison
Cano-Domingo, C., Novas, N., Stoean, R., Fernández-Ros, M., & Parra, J. A. G. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3194912 · Cited by 3
Ghate, V. P., Carlton, A. G., Surleta, T., & Burns, A. M. (2022) · Journal of Climate · doi:10.1175/jcli-d-22-0073.1 · Cited by 3
“The Goddard Institute of Space Studies (GISS) surface temperature analysis version 4 (GISTEMP) data are used in this study (Lenssen et al. 2019).”
Identifying robust strategies for assisted migration in a competitive stochastic metacommunityPreprint
Backus, G. A., & Baskett, M. L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/811497 · Cited by 3
“…an annual temporal standard deviation of ψ = 0.1639 °C, equal to the standard deviation of mean combined global land-surface air and sea-surface water temperature anomalies from 1880 to 1979 (GISTEMP Team 2019; Lenssen et al. 2019), and high-stochasticity environments had four times that amount.”
BackgroundInfluentialCloud‐Radiation Interactions and Cloud‐Climate Feedbacks From an Active‐Sensor Satellite Perspective
Cesana, G., Ackerman, A. S., de Guélis, T. V., & Henderson, D. S. (2023) · Geophysical monograph · doi:10.1002/9781119700357.ch4 · Cited by 3
A century-long China homogenized daily surface air temperature dataset (CUG-CMA CHDT)
Zheng, X., Ren, Y., Ren, G., Zhang, P., He, J., Yang, G., Qin, Y., Wen, K., Xue, X., & Ren, C. (2024) · Scientific Data · doi:10.1038/s41597-024-03758-3 · Cited by 3
“…observational surface air temperature datasets in global land: Global Historical Climatology Network (GHCN)-monthly (GHCNm) dataset 6,7 , Climatic Research Unit (CRU) dataset 8,9 , Goddard Institute for Space Studies (GISTEMP) dataset 10,11 and Berkeley Earth Surface Temperature (BEST) 12 .”
BackgroundDeep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023
ziqi, M., Huang, J., Zhang, X., Luo, Y. M., Dou, T., & Ding, M. (2025) · Scientific Data · doi:10.1038/s41597-025-05175-6 · Cited by 3
“1 57 , GISTEMPv4 58 and HadCRUT5 59 .”
Guo, Z., Li, C., & Li, Q. (2024) · Theoretical and Applied Climatology · doi:10.1007/s00704-024-04966-y · Cited by 2
“0 (Li et al. 2021a, b), CRUTEM5 (Osborn et al. 2021), Berkeley Earth (Rohde and Hausfather 2020), Cowton & Way (Cowtan and Way 2014), and GISTEMP (Lenssen et al. 2019).”
A new global land–ocean merged surface temperature dataset since the 1850s: the CMA-GMST dataset
Chen, L., Xu, W., Zhou, Z., Cao, L., Yang, S., & Xu, C. (2025) · Climate Dynamics · doi:10.1007/s00382-025-07614-x · Cited by 2
“…Centre/ Climatic Research Unit global ST (HadCRUT) dataset (Morice et al. 2021), the Goddard Institute for Space Studies ST (GISTEMP) dataset (Lenssen et al. 2019), the NOAA Merged Global Land–Ocean ST (NOAAGlobalTemp) data-set (Vose et al. 2021), the Berkeley Earth Land/Ocean ST (BEST)…”
Becker, E. J., Kirtman, B. P., & Min, D. (2025) · Weather and Forecasting · doi:10.1175/waf-d-24-0123.1 · Cited by 2
Morlini, I., & Orlandini, S. (2025) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-025-02990-6 · Cited by 2
“The NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP v4) provides Earth and space scientists with estimates of global mean surface air temperature changes (GISTEMP Team 2021; Lenssen et al. 2019).”
Beaugrand, G., Reid, P. C., & Conversi, A. (2025) · Climate Research · doi:10.3354/cr01756 · Cited by 2
Ma, C., & Yuan, N. (2025) · Climate Dynamics · doi:10.1007/s00382-025-07791-9 · Cited by 2
“In spite of great efforts by multiple sectors of the human society, recent studies show that we are still approaching the 1.5 °C threshold (Lenssen et al. 2019; Rohde and Hausfather 2020; Morice et al. 2021; Forster et al. 2023).”
InfluentialReversed tropical-Arctic teleconnection under climate change
Tang, W., Sun, B., Keenlyside, N. S., Li, F., He, S., Li, H., Zhou, B., & Wang, H. (2025) · npj Climate and Atmospheric Science · doi:10.1038/s41612-025-01243-1 · Cited by 2
Defossilization of Industrial Glass Production via Carbon Capture and Utilization of Flue Gas
Drünert, F., Rahmat, Y., Fleischmann, B., & Dietrich, R. (2026) · Glass Europe · doi:10.52825/glass-europe.v4i.2861 · Cited by 2
“This goal is approximately equivalent to a 20-year global average temperature of 16 °C [4].”
GloMarGridding : A Python Toolkit for Flexible Spatial Interpolation in Climate Applications
Cornes, R., Chan, S. C., Cable, A., Duo, C. H. A. N., Faulkner, A., Kent, E. C., & Siddons, J. T. (2026) · Geoscience Data Journal · doi:10.1002/gdj3.70064 · Cited by 2
“The techniques used in GloMarGridding build on established methods that are used for generating surface temperature fields (e.g., Lenssen et al. 2019; Rohde and Hausfather 2020; Morice et al. 2021).”
A Reconciled Satellite Record Reveals a Negative Low Cloud Feedback Over the Past 47 Years
Cesana, G., & Arouf, A. (2026) · Geophysical Research Letters · doi:10.1029/2026gl124158 · Cited by 2
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
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
