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Dataset

 

CRU TS4.01: Climatic Research Unit (CRU) Time-Series (TS) version 4.01 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2016)

Update Frequency: Not Planned
Latest Data Update: 2017-09-22
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2017-09-22
DOI Publication Date: 2017-12-04
Download Stats: last 12 months
Dataset Size: 393 Files | 32GB
  Citations: 43
This dataset has been superseded. See Latest Version here
Abstract

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.01 data are month-by-month variations in climate over the period 1901-2016, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia.

The CRU TS4.01 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 2016.

The CRU TS4.01 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 TS4.01 is a full release, differing only in methodology from the parallel release, v3.25. Both are released concurrently to support comparative evaluations between these two versions, however, this will be the last release of version 3.

The CRU TS4.01 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. (2017): CRU TS4.01: Climatic Research Unit (CRU) Time-Series (TS) version 4.01 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2016). Centre for Environmental Data Analysis, 04 December 2017. doi:10.5285/58a8802721c94c66ae45c3baa4d814d0. https://dx.doi.org/10.5285/58a8802721c94c66ae45c3baa4d814d0
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 4.01 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

 CRU User Guide
 CRU TS 4.01 file formats guide
 Climate Research Unit (CRU) data Homepage
 CRU TS Version 4.01 Google Earth Interface
 CRU TS 4.01 Release Notes
 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: 43

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.

Ardilouze, C., Batté, L., Decharme, B., & Déqué, M. (2019). On the Link between Summer Dry Bias over the U.S. Great Plains and Seasonal Temperature Prediction Skill in a Dynamical Forecast System. Weather and Forecasting, 34(4), 1161–1172. https://doi.org/10.1175/waf-d-19-0023.1
Bothe, O., Wagner, S., & Zorita, E. (2019). Inconsistencies between observed, reconstructed, and simulated precipitation indices for England since the year 1650 CE. Climate of the Past, 15(1), 307–334. https://doi.org/10.5194/cp-15-307-2019
Burrell, A. L., Evans, J. P., & De Kauwe, M. G. (2020). Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17710-7
Careto, J. A. M., Cardoso, R. M., Soares, P. M. M., & Trigo, R. M. (2018). Land-Atmosphere Coupling in CORDEX-Africa: Hindcast Regional Climate Simulations. Journal of Geophysical Research: Atmospheres, 123(19), 11,048-11,067. Portico. https://doi.org/10.1029/2018jd028378
Centella-Artola, A., Bezanilla-Morlot, A., Taylor, M. A., Herrera, D. A., Martinez-Castro, D., Gouirand, I., Sierra-Lorenzo, M., Vichot-Llano, A., Stephenson, T., Fonseca, C., Campbell, J., & Alpizar, M. (2020). Evaluation of Sixteen Gridded Precipitation Datasets over the Caribbean Region Using Gauge Observations. Atmosphere, 11(12), 1334. https://doi.org/10.3390/atmos11121334
Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturvedi, R. K., Fuchs, R., Brovkin, V., Ciais, P., Fensholt, R., Tømmervik, H., Bala, G., Zhu, Z., Nemani, R. R., & Myneni, R. B. (2019). China and India lead in greening of the world through land-use management. Nature Sustainability, 2(2), 122–129. https://doi.org/10.1038/s41893-019-0220-7
Chinta, V., Chen, Z., Du, Y., & Chowdary, J. S. (2021). Influence of the Interdecadal Pacific Oscillation on South Asian and East Asian summer monsoon rainfall in CMIP6 models. Climate Dynamics, 58(5–6), 1791–1809. https://doi.org/10.1007/s00382-021-05992-6
Craven, D., Eisenhauer, N., Pearse, W. D., Hautier, Y., Isbell, F., Roscher, C., Bahn, M., Beierkuhnlein, C., Bönisch, G., Buchmann, N., Byun, C., Catford, J. A., Cerabolini, B. E. L., Cornelissen, J. H. C., Craine, J. M., De Luca, E., Ebeling, A., Griffin, J. N., Hector, A., … Manning, P. (2018). Multiple facets of biodiversity drive the diversity–stability relationship. Nature Ecology Evolution, 2(10), 1579–1587. https://doi.org/10.1038/s41559-018-0647-7
Dar, M. A., Ahmed, R., Latif, M., & Azam, M. (2021). Climatology of dust storm frequency and its association with temperature and precipitation patterns over Pakistan. Natural Hazards, 110(1), 655–677. https://doi.org/10.1007/s11069-021-04962-9
EL CHAMI, D., & Galli, F. (2020). A Preliminary Assessment of Growth Regulators in Agricultural: Innovation for Sustainable Vegetable Nutrition. https://doi.org/10.20944/preprints202007.0317.v1
Glotfelty, T., Ramírez-Mejía, D., Bowden, J., Ghilardi, A., & West, J. J. (2021). Limitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0). Geoscientific Model Development, 14(6), 3215–3249. https://doi.org/10.5194/gmd-14-3215-2021
Grotjahn, R., & Huynh, J. (2018). Contiguous US summer maximum temperature and heat stress trends in CRU and NOAA Climate Division data plus comparisons to reanalyses. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-29286-w
Guo, Z., Lou, W., Sun, C., & He, B. (2022). Trend Changes of the Vegetation Activity in Northeastern East Asia and the Connections with Extreme Climate Indices. Remote Sensing, 14(13), 3151. https://doi.org/10.3390/rs14133151
Haughton, N., Abramowitz, G., De Kauwe, M. G., & Pitman, A. J. (2018). Does predictability of fluxes vary between FLUXNET sites? Biogeosciences, 15(14), 4495–4513. https://doi.org/10.5194/bg-15-4495-2018
Hellwig, N., Walz, A., Markovic, D. (2019). Climatic and socioeconomic effects on land cover changes across Europe. Postprints Der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, 764. https://doi.org/10.25932/publishup-43788
Hong, T., Dong, W., Ji, D., Dai, T., Yang, S., & Wei, T. (2018). The response of vegetation to rising CO2 concentrations plays an important role in future changes in the hydrological cycle. Theoretical and Applied Climatology, 136(1–2), 135–144. https://doi.org/10.1007/s00704-018-2476-7
Hulsman, P., Savenije, H. H. G., & Hrachowitz, M. (2020). Learning from satellite observations: increased understanding of catchment processes through stepwise model improvement. https://doi.org/10.5194/hess-2020-191
Hulsman, P., Winsemius, H. C., Michailovsky, C. I., Savenije, H. H. G., & Hrachowitz, M. (2020). Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region. Hydrology and Earth System Sciences, 24(6), 3331–3359. https://doi.org/10.5194/hess-24-3331-2020
Ibanga, O. A., Idehen, O. F., & Omonigho, M. G. (2022). Spatiotemporal variability of soil moisture under different soil groups in Etsako West Local Government Area, Edo State, Nigeria. Journal of the Saudi Society of Agricultural Sciences, 21(2), 125–147. https://doi.org/10.1016/j.jssas.2021.07.006
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Ise, T., & Oba, Y. (2020). VARENN: graphical representation of periodic data and application to climate studies. Npj Climate and Atmospheric Science, 3(1). https://doi.org/10.1038/s41612-020-0129-x
Karger, D. N., Schmatz, D. R., Dettling, G., & Zimmermann, N. E. (2020). High-resolution monthly precipitation and temperature time series from 2006 to 2100. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-00587-y
Kharuk, V. I., Im, S. T., & Petrov, I. A. (2021). Conifer Growth During Warming Hiatus in the Altay-Sayan Mountain Region, Siberia. Sustainable Development Goals Series, 385–401. https://doi.org/10.1007/978-3-030-70238-0_15
Kharuk, V. I., Im, S. T., Petrov, I. A., Shushpanov, A. S., & Dvinskaya, M. L. (2021). Climate-Induced Fir (Abies sibirica Ledeb.) Mortality in the Siberian Mountains. Sustainable Development Goals Series, 403–416. https://doi.org/10.1007/978-3-030-70238-0_16
Kumar, A., Sanyal, P., & Agrawal, S. (2019). Spatial distribution of δ18O values of water in the Ganga river basin: Insight into the hydrological processes. Journal of Hydrology, 571, 225–234. https://doi.org/10.1016/j.jhydrol.2019.01.044
Lausier, A. M., & Jain, S. (2018). Overlooked Trends in Observed Global Annual Precipitation Reveal Underestimated Risks. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-34993-5
Li, J., Xie, T., Tang, X., Wang, H., Sun, C., Feng, J., Zheng, F., & Ding, R. (2021). Influence of the NAO on Wintertime Surface Air Temperature over East Asia: Multidecadal Variability and Decadal Prediction. Advances in Atmospheric Sciences, 39(4), 625–642. https://doi.org/10.1007/s00376-021-1075-1
Lyu, H., Dong, Z., Roobavannan, M., Kandasamy, J., & Pande, S. (2019). Rural unemployment pushes migrants to urban areas in Jiangsu Province, China. Palgrave Communications, 5(1). https://doi.org/10.1057/s41599-019-0302-1
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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
  • long_name: Atmospheric Phenomena
  • gcmd_url: http://vocab.ndg.nerc.ac.uk/term/P131/4/GTER0022
  • gcmd_keyword: Atmospheric Phenomena
  • names: http://vocab.ndg.nerc.ac.uk/term/P131/4/GTER0022, Atmospheric Phenomena
  • 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: tmn
  • units: degrees Celsius
  • long_name: near-surface temperature minimum
  • 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:
2016-12-31T23:59:59
Geographic Extent

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