CRU TS4.00: Climatic Research Unit (CRU) Time-Series (TS) version 4.00 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2015)
The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.00 data are month-by-month variations in climate over the period 1901-2015, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia.
The CRU TS4.00 variables are cloud cover, diurnal temperature range, frost day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2015.
The CRU TS4.00 data were produced using angular-distance weighting (ADW) interpolation. All version 3 releases used triangulation routines in IDL. Please see the release notes for full details of this version update. CRU TS4.00 is a full release, differing only in methodology from the existing current release, v3.24.01. Both are released concurrently to support comparative evaluations between these two versions.
The CRU TS4.00 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation.
All CRU TS output files are actual values - NOT anomalies.
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Access to these data is available to any registered CEDA user. Please Login or Register for an account to gain access.
Use of these data is covered by the following licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
The CRU TS data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. Previous releases of the CRU TS data include:
CRU TS 4.00 was provided to CEDA for archival in March 2017.
CRU TS 3.24.01 was provided to CEDA for archival in January 2017. This is the latest version available and is a replacement of the withdrawn dataset 3.24, it supersedes all previous data versions (which are available to allow user comparisons)
CRU TS 3.24 was provided to CEDA for archival in July 2016. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons), v3.24 has been withdrawn.
CRU TS 3.23 was provided to CEDA in October 2015 by CRU. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons).
CRU TS 3.22 was provided to CEDA for archival in July 2014 by CRU.
CRU TS 3.21 was provided to CEDA for archival in July 2013 by CRU.
CRU TS 3.20 was produced in December 2012.
CRU TS 3.10.01 In July 2012, systematic errors were discovered in the CRUTS v3.10 process. The effect was, in some cases, to reduce the gridded values for PRE and therefore WET. Values of FRS were found to be unrealistic in some areas due to the algorithms used for synthetic generation. The files (pre, frs and wet) were immediately removed from BADC. The corrected run for precipitation, based on the v3.10 precipitation station data, was generated as a direct replacement and given the version number 3.10.01. There were no corrected runs produced for wet and frs.
CRU TS 3.00 data files acquired directly from CRU in 2007. CRU provided the BADC with software to generate the CRU datasets in 2010, and this was used to produce CRU TS 3.10 at the BADC in early 2011.
The data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harries et al. 2014 and the release notes, links to both can be found in the documentation.
Data are provided in ASCII and NetCDF formats.
The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.
|Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958â2015. Scientific Data, 5(1). https://doi.org/10.1038/sdata.2017.191|
|Baudouin, J.-P., Herzog, M., & Petrie, C. A. (2019). Cross-validating precipitation datasets in the Indus River basin. https://doi.org/10.5194/hess-2019-303|
|Becker, M., Papa, F., Frappart, F., Alsdorf, D., Calmant, S., da Silva, J. S., Prigent, C., & Seyler, F. (2018). Satellite-based estimates of surface water dynamics in the Congo River Basin. International Journal of Applied Earth Observation and Geoinformation, 66, 196â209. https://doi.org/10.1016/j.jag.2017.11.015|
|Dallmeyer, A., Claussen, M., & Brovkin, V. (2019). Harmonising plant functional type distributions for evaluating Earth system models. Climate of the Past, 15(1), 335â366. https://doi.org/10.5194/cp-15-335-2019|
|Herrera-Soto, G., GonzÃ¡lez-CÃ¡sares, M., Pompa-GarcÃa, M., Camarero, J., & SolÃs-Moreno, R. (2018). Growth of Pinus cembroides Zucc. in Response to Hydroclimatic Variability in Four Sites Forming the Species Latitudinal and Longitudinal Distribution Limits. Forests, 9(7), 440. https://doi.org/10.3390/f9070440|
|KonÃ©, B., Diedhiou, A., Diawara, A., Anquetin, S., TourÃ©, N. E., Bamba, A., & Kobea, A. T. (2020). Influence of initial soil moisture in a Regional Climate Model study over West Africa: Part 1: Impact on the climate mean. https://doi.org/10.5194/hess-2020-112|
|KonÃ©, B., Diedhiou, A., Diawara, A., Anquetin, S., TourÃ©, N. E., Bamba, A., & Kobea, A. T. (2022). Influence of initial soil moisture in a regional climate model study over West Africa â PartÂ 1: Impact on the climate mean. Hydrology and Earth System Sciences, 26(3), 711â730. https://doi.org/10.5194/hess-26-711-2022|
|Lalande, M., MÃ©nÃ©goz, M., Krinner, G., Naegeli, K., & Wunderle, S. (2021). Climate change in the High Mountain Asia in CMIP6. https://doi.org/10.5194/esd-2021-43|
|Lalande, M., MÃ©nÃ©goz, M., Krinner, G., Naegeli, K., & Wunderle, S. (2021). Climate change in the High Mountain Asia in CMIP6. Earth System Dynamics, 12(4), 1061â1098. https://doi.org/10.5194/esd-12-1061-2021|
|LewiÅska, K. E., Buchner, J., Bleyhl, B., Hostert, P., Yin, H., Kuemmerle, T., & Radeloff, V. C. (2021). Changes in the grasslands of the Caucasus based on Cumulative Endmember Fractions from the full 1987â2019 Landsat record. Science of Remote Sensing, 4, 100035. https://doi.org/10.1016/j.srs.2021.100035|
|Lu, S., & Zuo, H. (2021). Sensitivity of South Asian summer monsoon simulation to land surface schemes in Weather Research and Forecasting model. International Journal of Climatology, 41(15), 6805â6824. Portico. https://doi.org/10.1002/joc.7278|
|Milly, P. C. D., Kam, J., & Dunne, K. A. (2018). On the Sensitivity of Annual Streamflow to Air Temperature. Water Resources Research, 54(4), 2624â2641. Portico. https://doi.org/10.1002/2017wr021970|
|Park, S., Park, H., Im, J., Yoo, C., Rhee, J., Lee, B., & Kwon, C. (2019). Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches. PLOS ONE, 14(10), e0223362. https://doi.org/10.1371/journal.pone.0223362|
|Peng, D., Zhou, T., Zhang, L., & Zou, L. (2019). Detecting human influence on the temperature changes in Central Asia. Climate Dynamics, 53(7â8), 4553â4568. https://doi.org/10.1007/s00382-019-04804-2|
|Saikia, P., Kumar, A., Diksha, Lal, P., Nikita, & Khan, M. L. (2020). Ecosystem-Based Adaptation to Climate Change and Disaster Risk Reduction in Eastern Himalayan Forests of Arunachal Pradesh, Northeast India. Disaster Resilience and Green Growth, 391â408. https://doi.org/10.1007/978-981-15-4712-6_22|
|Salunke, P., Jain, S., & Mishra, S. K. (2018). Performance of the CMIP5 models in the simulation of the Himalaya-Tibetan Plateau monsoon. Theoretical and Applied Climatology, 137(1â2), 909â928. https://doi.org/10.1007/s00704-018-2644-9|
|Sato, H., & Ise, T. (2022). Predicting global terrestrial biomes with the LeNet convolutional neural network. Geoscientific Model Development, 15(7), 3121â3132. https://doi.org/10.5194/gmd-15-3121-2022|
|Schickhoff, U., Bobrowski, M., BÃ¶hner, J., BÃ¼rzle, B., Chaudhary, R. P., MÃ¼ller, M., Scholten, T., Schwab, N., & Weidinger, J. (2023). The Treeline Ecotone in Rolwaling Himal, Nepal: Pattern-Process Relationships and Treeline Shift Potential. Ecology of Himalayan Treeline Ecotone, 95â145. https://doi.org/10.1007/978-981-19-4476-5_5|
|Schwab, N., Kaczka, R., Janecka, K., BÃ¶hner, J., Chaudhary, R., Scholten, T., & Schickhoff, U. (2018). Climate Change-Induced Shift of Tree Growth Sensitivity at a Central Himalayan Treeline Ecotone. Forests, 9(5), 267. https://doi.org/10.3390/f9050267|
|Sidibe, M., Dieppois, B., MahÃ©, G., Paturel, J.-E., Amoussou, E., Anifowose, B., & Lawler, D. (2018). Trend and variability in a new, reconstructed streamflow dataset for West and Central Africa, and climatic interactions, 1950â2005. Journal of Hydrology, 561, 478â493. https://doi.org/10.1016/j.jhydrol.2018.04.024|
|Wozniak, M. C., & Steiner, A. L. (2017). A prognostic pollen emissions model for climate models (PECM1.0). Geoscientific Model Development, 10(11), 4105â4127. https://doi.org/10.5194/gmd-10-4105-2017|
UEA Climatic Research Unit (CRU) high resolution gridding software deployed on UEA CRU computer system for v4.00
This computation involved: UEA Climate Research Unit (CRU) High Resolution gridding software deployed on UEA Climate Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. (2020) - see Details/Docs tab, moderated by the Release Notes for v4.00 (which outline the new gridding process)
- long_name: Atmospheric Phenomena
- gcmd_keyword: Atmospheric Phenomena
- gcmd_url: http://vocab.ndg.nerc.ac.uk/term/P131/4/GTER0022
- names: http://vocab.ndg.nerc.ac.uk/term/P131/4/GTER0022, Atmospheric Phenomena
- units: percentage
- long_name: cloud cover
- var_id: cld
- units: degrees Celsius
- long_name: diurnal temperature range
- var_id: dtr
- long_name: ground frost frequency
- var_id: frs
- units: days
- units: degrees_north
- long_name: latitude
- var_id: lat
- long_name: longitude
- units: degrees_east
- var_id: lon
- units: degrees Celsius
- long_name: near-surface temperature
- var_id: tmp
- long_name: near-surface temperature maximum
- units: degrees Celsius
- var_id: tmx
- long_name: near-surface temperature minimum
- units: degrees Celsius
- var_id: tmn
- units: mm/day
- var_id: pet
- long_name: potential evapotranspiration
- units: mm/month
- var_id: pre
- long_name: precipitation
- var_id: stn
- var_id: time
- long_name: time
- units: hPa
- var_id: vap
- long_name: vapour pressure
- long_name: wet day frequency
- var_id: wet
- units: days