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Updated high‐resolution grids of monthly climatic observations – the CRU TS3 .10 Dataset
Open accessAbstract
This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3 .10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet‐day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well‐known formulae. Time series for hemispheric averages and 20 large sub‐continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3 .10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series ( http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/ ). © 2013 Royal Meteorological Society
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Ziese, M., Schneider, U., Meyer-Christoffer, A., Schamm, K., Vido, J., Finger, P., Bissolli, P., Pietzsch, S., & Becker, A. (2014) · Earth System Science Data · doi:10.5194/essd-6-285-2014 · Cited by 66
“Overall, droughts are defined as a shortage of available water (Heim, 2002; Wilhite and Glantz, 1985). In detail, droughts are, for example, defined as “a sustained extended deficiency in precipitation” (WMO, 1986) or a “naturally occurring phenomenon that exists when precipitation has been significantly below normal…”
BackgroundMethodInfluentialCritical impacts of global warming on land ecosystems
(2013) · doi:10.5194/esd-4-347-2013 · Cited by 58
(2022) · doi:10.5194/essd-14-163-2022 · Cited by 56
Ecological and conceptual consequences of Arctic pollution
Kirdyanov, A. V., Krusic, P. J., Shishov, V. V., Vaganov, E. A., Fertikov, A. I., Myglan, V. S., Barinov, V. V., Browse, J., Esper, J., Ilyin, V. A., et al. (2020) · Ecology Letters · doi:10.1111/ele.13611 · Cited by 60
“, 2011, 2013), which exist from 1901 to 2018 (CRU TS4.01; Harris et al., 2014).”
MethodShi, H., Zhang, J., Zhao, B., Xia, X., Hu, B., Chen, H., Wei, J., Liu, M., Bian, Y., Fu, D., et al. (2021) · Geophysical Research Letters · doi:10.1029/2020gl091105 · Cited by 44
Maddux, S. D., Yokley, T. R., Svoma, B. M., & Franciscus, R. G. (2016) · American Journal of Physical Anthropology · doi:10.1002/ajpa.23032 · Cited by 41
Representation of multidecadal sahel rainfall variability in 20th century reanalyses.
Berntell, E., Zhang, Q., Chafik, L., & Körnich, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-29217-9 · Cited by 39
Wang, G., Zhang, W., Sun, W., Li, T., & Han, P. (2017) · Atmospheric Chemistry and Physics · doi:10.5194/acp-17-11849-2017 · Cited by 37
Ledo, A., Heathcote, R., Hastings, A., Smith, P., & Hillier, J. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2017.12.005 · Cited by 37
“…it necessary to take into account the effect of seasonal and inter-annual variability of climate for the following reasons: for the IBM, crop rotations are longer than 5e10 years, so positive and negative effects of the climate variability will largely cancel out over time (Harris et al., 2014).”
BackgroundInfluentialKere, E. N., Choumert, J., Combes Motel, P., Combes, J. L., Santoni, O., & Schwartz, S. (2017) · Ecological Economics · doi:10.1016/j.ecolecon.2017.02.018 · Cited by 35
Temperature, maize yield, and civil conflicts in sub-Saharan Africa
Jun, T. (2017) · Climatic Change · doi:10.1007/s10584-017-1941-0 · Cited by 26
Casagranda, E., Navarro, C., Grau, H. R., & Izquierdo, A. E. (2019) · Regional Environmental Change · doi:10.1007/s10113-019-01514-7 · Cited by 22
Pattanayak, S., Nanjundiah, R. S., & Kumar, D. N. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa9664 · Cited by 19
Does the expansion of biofuels encroach on the forest?
Keles, D., Choumert-Nkolo, J., Combes Motel, P., & Nazindigouba Kéré, E. (2018) · Journal of Forest Economics · doi:10.1016/j.jfe.2018.11.001 · Cited by 16
Müller, R. C., Kim, J.-S., Lee, H., Muri, H., Tjiputra, J., Yoon, J.-H., & Schaepman-Strub, G. (2024) · Communications Earth & Environment · doi:10.1038/s43247-024-01329-3 · Cited by 9
Metadata
- Authors
- Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H.
- Venue
- International Journal of Climatology
- Publisher
- Wiley
- Published
- 2013-05-21
- Volume
- 34
- Issue
- 3
- Pages
- 623-642
- ISSN
- 0899-8418 (ISSN-L)
- 1097-0088
- OpenAlex
- W1867005924
- Cited by
- 6,094
- References
- 1
- FWCI
- 275.26
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- Science and Technology Facilities Council
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.
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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.