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CRU TS3.22: Climatic Research Unit (CRU) Time-Series (TS) Version 3.22 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2013)

Update Frequency: Not Planned
Latest Data Update: 2017-05-10
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2014-09-17
DOI Publication Date: 2014-09-23
Download Stats: last 12 months
Dataset Size: 637 Files | 32GB

This dataset has been superseded. See Latest Version here

The gridded CRU TS (time-series) 3.22 data are month-by-month variations in climate over the period 1901-2013, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.

CRU TS 3.22 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and wet day frequency for the period Jan. 1901 - Dec. 2013.

CRU TS 3.22 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2013 data, the v3.22 release corrects an error in the v3.21 dataset. This is summarised in the document, CRU_Advisory_v3.2x_NE_Africa.txt, and affects PRE and WET variables only. There are several known issues with the current dataset which cannot be resolved in the timeframe of this release; they will be addressed in the future. This directory also contains an advisory note regarding an issue with 35 Mozambique stations that were new. After an investigation by the CRU, the comparison plots show that the only countries affected in a possibly significant way are Egypt and Eritrea. The details of these can be found in this directory.

The CRU TS 3.22 data are monthly gridded fields based on monthly observational data, which are 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.

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

CRU TS data are available for download to all CEDA users. The CEDA Web Processing Service (WPS) may be used to extract a subset of the data (please see link to WPS below).

Citable as:  University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D. (2014): CRU TS3.22: Climatic Research Unit (CRU) Time-Series (TS) Version 3.22 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2013). NCAS British Atmospheric Data Centre, 23 September 2014. doi:10.5285/18BE23F8-D252-482D-8AF9-5D6A2D40990C.
Abbreviation: cru_ts_3.22


Previous Info:

2014-09-24 DOI issued for CRU TS3.22. Dataset is now citeable. 2014-09-24 DOI issued for CRU TS3.22. Dataset is now citeable.

2014-09-23 CRU TS 3.22 now available for extraction via the CEDA WPS. 2014-09-23 CRU TS 3.22 now available for extraction via the CEDA WPS.

Previously used record 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: When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

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.

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 3.20 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 3.21 was produced in July 2013.

In July 2014, CRU TS 3.22 provided by CRU, is the latest version available, superseding all previous data versions (which are available to allow user comparisons).

File Format:
The CRU TS 3.22 data (i.e. climate variables) are available in both compressed (.gz extension) ASCII ".dat" and netcdf ".nc" file formats. CRU TS 3.22 metadata files (i.e. stations and observations) are available in ASCII. To understand how to read the CRU TS 3.22 data and metadata files, please refer to the CRU_TS_3.22_File_Formats_explained PDF documentation (under Linked Documentation below).

Citations: 25

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.

Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., & Pineda, L. (2020). Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation. Hydrology and Earth System Sciences, 24(2), 535–559.
Basha, G., Kishore, P., Ratnam, M. V., Jayaraman, A., Agha Kouchak, A., Ouarda, T. B. M. J., & Velicogna, I. (2017). Historical and Projected Surface Temperature over India during the 20th and 21st century. Scientific Reports, 7(1).
Berg, P., Donnelly, C., & Gustafsson, D. (2018). Near-real-time adjusted reanalysis forcing data for hydrology. Hydrology and Earth System Sciences, 22(2), 989–1000.
Camara, M., Diba, I., & Diedhiou, A. (2022). Effects of Land Cover Changes on Compound Extremes over West Africa Using the Regional Climate Model RegCM4. Atmosphere, 13(3), 421.
Choudhary, A., & Dimri, A. P. (2018). Performance of an ensemble of CORDEX-SA simulations in representing maximum and minimum temperature over the Himalayan region. Theoretical and Applied Climatology, 136(3–4), 1047–1072.
Dunn, G., Johnson, G. D., Balk, D. L., & Sembajwe, G. (2019). Spatially varying relationships between risk factors and child diarrhea in West Africa, 2008-2013. Mathematical Population Studies, 27(1), 8–33.
Gričar, J., Prislan, P., de Luis, M., Gryc, V., Hacurová, J., Vavrčík, H., & Čufar, K. (2015). Plasticity in variation of xylem and phloem cell characteristics of Norway spruce under different local conditions. Frontiers in Plant Science, 6.
Hentgen, L., Ban, N., Kröner, N., Leutwyler, D., & Schär, C. (2019). Clouds in Convection‐Resolving Climate Simulations Over Europe. Journal of Geophysical Research: Atmospheres, 124(7), 3849–3870. Portico.
Katzfey, J., Nguyen, K., McGregor, J., Hoffmann, P., Ramasamy, S., Nguyen, H. V., Khiem, M. V., Nguyen, T. V., Truong, K. B., Vu, T. V., Nguyen, H. T., Thuc, T., Phong, D. H., Nguyen, B. T., Phan-Van, T., Nguyen-Quang, T., Ngo-Duc, T., & Trinh-Tuan, L. (2016). High-resolution simulations for Vietnam - methodology and evaluation of current climate. Asia-Pacific Journal of Atmospheric Sciences, 52(2), 91–106.
Kowalczyk, E. A., Stevens, L. E., Law, R. M., Harman, I. N., Dix, M., Franklin, C. N., & Wang, Y.-P. (2016). The impact of changing the land surface scheme in ACCESS(v1.0/1.1) on the surface climatology. Geoscientific Model Development, 9(8), 2771–2791.
Law, R. M., Ziehn, T., Matear, R. J., Lenton, A., Chamberlain, M. A., Stevens, L. E., Wang, Y.-P., Srbinovsky, J., Bi, D., Yan, H., & Vohralik, P. F. (2017). The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 1: Model description and pre-industrial simulation. Geoscientific Model Development, 10(7), 2567–2590.
Liu-Helmersson, J., Quam, M., Wilder-Smith, A., Stenlund, H., Ebi, K., Massad, E., & Rocklöv, J. (2016). Climate Change and Aedes Vectors: 21st Century Projections for Dengue Transmission in Europe. EBioMedicine, 7, 267–277.
Liu, Y., Li, Y., Li, S., & Motesharrei, S. (2015). Spatial and Temporal Patterns of Global NDVI Trends: Correlations with Climate and Human Factors. Remote Sensing, 7(10), 13233–13250.
Malavelle, F. F., Haywood, J. M., Mercado, L. M., Folberth, G. A., Bellouin, N., Sitch, S., & Artaxo, P. (2019). Studying the impact of biomass burning aerosol radiative and climate effects on the Amazon rainforest productivity with an Earth system model. Atmospheric Chemistry and Physics, 19(2), 1301–1326.
Manatsa, D., Mushore, T., & Lenouo, A. (2015). Improved predictability of droughts over southern Africa using the standardized precipitation evapotranspiration index and ENSO. Theoretical and Applied Climatology, 127(1–2), 259–274.
Mianabadi, A., Coenders-Gerrits, M., Shirazi, P., Ghahraman, B., & Alizadeh, A. (2017). A simple global Budyko model to partition evaporation into interception and transpiration.
Nengker, T., Choudhary, A., & Dimri, A. P. (2017). Assessment of the performance of CORDEX-SA experiments in simulating seasonal mean temperature over the Himalayan region for the present climate: Part I. Climate Dynamics, 50(7–8), 2411–2441.
Ongoma, V., Chen, H., & Gao, C. (2018). Evaluation of CMIP5 twentieth century rainfall simulation over the equatorial East Africa. Theoretical and Applied Climatology, 135(3–4), 893–910.
Ongoma, V., Chen, H., Gao, C., & Sagero, P. O. (2017). Variability of temperature properties over Kenya based on observed and reanalyzed datasets. Theoretical and Applied Climatology, 133(3–4), 1175–1190.
Papadopoulos, A., & Pantera, A. (2016). Dendrochronological Investigations of Valonia Oak Trees in Western Greece. South-East European Forestry, 7(1).
Wang, Y., Xie, Y., Dong, W., Ming, Y., Wang, J., & Shen, L. (2017). Adverse effects of increasing drought on air quality via natural processes. Atmospheric Chemistry and Physics, 17(20), 12827–12843.
Warren, R., Price, J., VanDerWal, J., Cornelius, S., & Sohl, H. (2018). The implications of the United Nations Paris Agreement on climate change for globally significant biodiversity areas. Climatic Change, 147(3–4), 395–409.
Zhou, L. (2016). Desert Amplification in a Warming Climate. Scientific Reports, 6(1).
Zhu, L., Zhang, Y., Li, Z., Guo, B., & Wang, X. (2016). A 368-year maximum temperature reconstruction based on tree-ring datain the northwestern Sichuan Plateau (NWSP), China. Climate of the Past, 12(7), 1485–1498.
Ziehn, T., Lenton, A., Law, R. M., Matear, R. J., & Chamberlain, M. A. (2017). The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations. Geoscientific Model Development, 10(7), 2591–2614.

Process overview

This dataset was generated by the computation detailed below.

UEA Climatic Research Unit (CRU) High Resolution gridding software deployed on UEA Climatic Research Unit (CRU) computer system


This computation involved: UEA Climatic Research Unit (CRU) High Resolution gridding software deployed on UEA Climatic Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. (2014) - see link below.

Input Description


Output Description


Software Reference


  • long_name: Atmospheric Phenomena
  • gcmd_url:
  • gcmd_keyword: Atmospheric Phenomena
  • names:, 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: tmx
  • units: degrees Celsius
  • long_name: near-surface temperature maximum
  • var_id: tmn
  • units: degrees Celsius
  • long_name: near-surface temperature minimum
  • units: mm/day
  • long_name: potential evapotranspiration
  • var_id: pet
  • units: mm
  • var_id: pre
  • long_name: precipitation
  • 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

Temporal Range
Start time:
End time:
Geographic Extent