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Dataset

 

CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018)

Update Frequency: Not Planned
Latest Data Update: 2019-08-01
Status: Ongoing
Online Status: ONLINE
Publication State: Citable
Publication Date: 2019-06-05
DOI Publication Date: 2020-01-22
Download Stats: last 12 months
Dataset Size: 398 Files | 32GB
  Citations: 31
This dataset has been superseded. See Latest Version here
Abstract

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.03 data are month-by-month variations in climate over the period 1901-2018, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia.

The CRU TS4.03 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 2018.

The CRU TS4.03 data were produced using angular-distance weighting (ADW) interpolation. All version 4 releases used triangulation routines in IDL. Please see the release notes for full details of this version update.

The CRU TS4.03 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 integer 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. The missing value code for 'stn' is -999.

All CRU TS output files are actual values - NOT anomalies.

Citable as:  University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D. (2020): CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018). Centre for Environmental Data Analysis, 22 January 2020. doi:10.5285/10d3e3640f004c578403419aac167d82. https://dx.doi.org/10.5285/10d3e3640f004c578403419aac167d82
Abbreviation: Not defined
Keywords: CRU, CRU TS, atmosphere, earth science, climate

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
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.
Data lineage:

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 4.03 was provided to CEDA for archival in May 2019.

CRU TS 4.02 was provided to CEDA for archival in December 2018.

CRU TS 4.01 was provided to CEDA for archival in September 2017.

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.
In March 2013, CRU TS observation databases for TMP and PRE variables were provided by CRU. Others are in preparation. In july 2013, two errors were found in the PRE and WET variables of CRU TS v3.20. These have been repaired in CRU TS v3.21. Details of the errors found are available in the Release Notes in the archive.

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.
Data Quality:
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.
File Format:
Data are provided in ASCII and NetCDF formats.

Related Documents

 Climate Research Unit (CRU) data Homepage
 CRU User Guide
 CRU TS 4.03 Release Notes
 CRU TS 4.03 file formats guide
 Code Readme
 Code archive for the database
 Code archive for the dataset
 CRU TS Version 4.03 Google Earth Interface
 Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3
 Publication of version 4 of the CRU TS monthly high-resolution gridded climate dataset

Citations: 31

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.

Berg, P., Almén, F., & Bozhinova, D. (2021). HydroGFD3.0 (Hydrological Global Forcing Data): a 25 km global precipitation and temperature data set updated in near-real time. Earth System Science Data, 13(4), 1531–1545. https://doi.org/10.5194/essd-13-1531-2021
Bothe, O., & Zorita, E. (2019). Proxy surrogate reconstructions for Europe and the estimation of their uncertainties. https://doi.org/10.5194/cp-2019-81
Chenaoui, C., Miled, S. B., Ndongo, M. S., Ndiaye, P. I., Rekik, M., & Darghouth, M. A. (2021). A climate-based model for tick life cycle: an infinite system of differential equation approach. https://doi.org/10.1101/2021.09.02.458669
Chen, T., Guo, R., Yan, Q., Chen, X., Zhou, S., Liang, C., Wei, X., & Dolman, H. (2022). Land Management Contributes significantly to observed Vegetation Browning in Syria during 2001–2018. Biogeosciences, 19(5), 1515–1525. https://doi.org/10.5194/bg-19-1515-2022
Fahim, T. C., & Sikder, B. B. (2022). Exploring farmers’ perception of climate-induced events and adaptation practices to protect crop production and livestock farming in the Haor area of north-eastern Bangladesh. Theoretical and Applied Climatology, 148(1–2), 441–454. https://doi.org/10.1007/s00704-021-03907-3
Friedlingstein, P., O’Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S., Aragão, L. E. O. C., Arneth, A., Arora, V., Bates, N. R., … Zaehle, S. (2020). Global Carbon Budget 2020. Earth System Science Data, 12(4), 3269–3340. https://doi.org/10.5194/essd-12-3269-2020
García-García, A., Cuesta-Valero, F. J., Beltrami, H., González-Rouco, J. F., & García-Bustamante, E. (2022). WRF v.3.9 sensitivity to land surface model and horizontal resolution changes over North America. Geoscientific Model Development, 15(2), 413–428. https://doi.org/10.5194/gmd-15-413-2022
Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0453-3
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., … Vera, C. S. (2020). An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth System Science Data, 12(4), 2959–2970. https://doi.org/10.5194/essd-12-2959-2020
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cimadevilla, E., Cofiño, A. S., Cerezo-Mota, R., Di Luca, A., Faria, S. H., Gorodetskaya, I., Hauser, M., Herrera, S., Hewitt, H. T., Hennessy, K. J., Jones, R. G., Krakovska, S., Manzanas, R., Marínez-Castro, D., Narisma, G. T., … Vera, C. S. (2020). An update of IPCC climate reference regions for subcontinental analysis of climate model data: Definition and aggregated datasets. https://doi.org/10.5194/essd-2019-258
Krauer, F., Viljugrein, H., & Dean, K. R. (2021). The influence of temperature on the seasonality of historical plague outbreaks. Proceedings of the Royal Society B: Biological Sciences, 288(1954), 20202725. https://doi.org/10.1098/rspb.2020.2725
Liu, D., & Fu, D. (2022). The water yield pattern for annual and monthly scales from a unifying catchment water balance model. Stochastic Environmental Research and Risk Assessment, 36(12), 4057–4072. https://doi.org/10.1007/s00477-022-02244-9
Lovino, M. A., Müller, G. V., Pierrestegui, M. J., Espinosa, E., & Rodríguez, L. (2022). Extreme precipitation events in the Austral Chaco region of Argentina. International Journal of Climatology, 42(11), 5985–6006. Portico. https://doi.org/10.1002/joc.7572
Malles, J.-H., & Marzeion, B. (2020). 20th century global glacier mass change: an ensemble-based modelreconstruction. https://doi.org/10.5194/tc-2020-320
Meng, M., Huang, N., Wu, M., Pei, J., Wang, J., & Niu, Z. (2019). Vegetation change in response to climate factors and human activities on the Mongolian Plateau. PeerJ, 7, e7735. Portico. https://doi.org/10.7717/peerj.7735
Midhun, M., Lekshmy, P. R., Thirumalai, K., & Ramesh, R. (2022). Coherent Indian Summer Monsoon and Sahel Rainfall Variability Revealed by Ethiopian Rainfall δ 18 O. Journal of Geophysical Research: Atmospheres, 127(22). Portico. https://doi.org/10.1029/2022jd037160
Mu, Y., Wei, Y., Wu, J., Ding, Y., Shangguan, D., & Zeng, D. (2020). Variations of Mass Balance of the Greenland Ice Sheet from 2002 to 2019. Remote Sensing, 12(16), 2609. https://doi.org/10.3390/rs12162609
not a doi https://doi.org/1983/1d70b2ed-77fa-47ea-9425-bb437b94bb0d
not a doi https://doi.org/11370/1a9ae91c-41ca-45e5-88fc-3f221eac2955
not a doi https://doi.org/10216/129524
not a doi https://doi.org/1871.1/df5d5631-7834-47f0-9237-32d8b0401334
Pu, B., Jin, Q., Ginoux, P., & Yu, Y. (2022). Compound Heat Wave, Drought, and Dust Events in California. Journal of Climate, 35(24), 8133–8152. https://doi.org/10.1175/jcli-d-21-0889.1
Scafetta, N. (2020). A Proposal for Isotherm World Maps to Forecast the Seasonal Evolution of the SARS-CoV-2 Pandemic. https://doi.org/10.20944/preprints202004.0063.v1
Scafetta, N. (2020). Distribution of the SARS-CoV-2 Pandemic and Its Monthly Forecast Based on Seasonal Climate Patterns. International Journal of Environmental Research and Public Health, 17(10), 3493. https://doi.org/10.3390/ijerph17103493
Sheng, C., He, B., Wu, G., Liu, Y., Zhang, S., & Zhang, P. (2022). Interannual Impact of the North Atlantic Tripole SST Mode on the Surface Potential Vorticity Over the Tibetan Plateau During Boreal Summer. Journal of Geophysical Research: Atmospheres, 127(9). Portico. https://doi.org/10.1029/2021jd036369
Simpson, C., Hosking, J. S., Mitchell, D., Betts, R. A., & Shuckburgh, E. (2021). Regional disparities and seasonal differences in climate risk to rice labour. Environmental Research Letters, 16(12), 124004. https://doi.org/10.1088/1748-9326/ac3288
Thomas, L., Balakrishna, R., Chaturvedi, R., Mukhopadhyay, P., & Ghate, R. (2021). What Influences Rural Poor in India to Refill Their LPG? Climate Change and Community Resilience, 191–204. https://doi.org/10.1007/978-981-16-0680-9_13
Williams, J. J., Freeman, R., Spooner, F., & Newbold, T. (2021). Vertebrate population trends are influenced by interactions between land use, climatic position, habitat loss and climate change. Global Change Biology, 28(3), 797–815. Portico. https://doi.org/10.1111/gcb.15978
Yun, K.-S., Timmermann, A., & Stuecker, M. F. (2020). Synchronized spatial shifts of Hadley and Walker circulations. https://doi.org/10.5194/esd-2020-70
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Zhang, T., Feng, Y., & Chen, H. (2023). Revealing the Formation of the Dipole Mode of Eurasian Snow Cover Variability During Late Autumn. Journal of Geophysical Research: Atmospheres, 128(6). Portico. https://doi.org/10.1029/2022jd038233

Process overview

This dataset was generated by the computation detailed below.
Title

UEA Climatic Research Unit (CRU) high resolution gridding software deployed on UEA CRU computer system for v4.00
Abstract

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)
Input Description

None
Output Description

None
Software Reference

None
  • var_id: cld
  • units: percentage
  • long_name: cloud cover
  • var_id: dtr
  • units: degrees Celsius
  • long_name: diurnal temperature range
  • units: days
  • var_id: frs
  • long_name: ground frost frequency
  • units: degrees_north
  • long_name: latitude
  • var_id: lat
  • units: degrees_east
  • long_name: longitude
  • var_id: lon
  • var_id: tmp
  • units: degrees Celsius
  • long_name: near-surface temperature
  • var_id: tmx
  • units: degrees Celsius
  • long_name: near-surface temperature maximum
  • units: mm/day
  • long_name: potential evapotranspiration
  • var_id: pet
  • var_id: pre
  • long_name: precipitation
  • units: mm/month
  • var_id: stn
  • long_name: time
  • var_id: time
  • units: hPa
  • var_id: vap
  • long_name: vapour pressure
  • units: days
  • var_id: wet
  • long_name: wet day frequency

Co-ordinate Variables

Coverage
Temporal Range
Start time:
1901-01-01T00:00:00
End time:
2018-12-31T23:59:59
Geographic Extent

 
90.0000°
  
-180.0000°
  
180.0000°
 
-60.0000°
  
Related parties
Authors (3)
Publishers (1)
Principal Investigators (2)