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Dataset

 

CRU TS3.23: Climatic Research Unit (CRU) Time-Series (TS) Version 3.23 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2014)

Update Frequency: Not Planned
Latest Data Update: 2017-05-10
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2015-10-15
DOI Publication Date: 2015-11-09
Download Stats: last 12 months
Dataset Size: 642 Files | 34GB

This dataset has been superseded. See Latest Version here
Abstract

The gridded CRU TS (time-series) 3.23 data are month-by-month variations in climate over the period 1901-2014, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.

CRU TS 3.23 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period Jan. 1901 - Dec. 2014.

CRU TS 3.23 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2014 data, some new stations have been added for TMP and PRE only. Known issues predating this release remain; the 4.00 release, due soon, will address these.

The 4.00 release will utilise Angular-Distance Weighting (ADW) gridding, promising more accurate results with far greater adjustability and logging. It will cover the same spatial, temporal and variate spaces as version 3.23 (land areas excluding Antarctica at 0.5°x0.5°, monthly from 1901 to 2014 with no missing values, 10 variables).

Versions 3.23 and 4.00 will run concurrently until 2016, after which the new (ADW) approach will be used. This is to allow comparisons between the methods and results to be made by users of the dataset.

The CRU TS 3.23 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. (2015): CRU TS3.23: Climatic Research Unit (CRU) Time-Series (TS) Version 3.23 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2014). Centre for Environmental Data Analysis, 09 November 2015. doi:10.5285/4c7fdfa6-f176-4c58-acee-683d5e9d2ed5. https://dx.doi.org/10.5285/4c7fdfa6-f176-4c58-acee-683d5e9d2ed5
Abbreviation: cru_ts_3.23
Keywords: CRU TS, ATMOSPHERE, EARTHSCIENCE

Details

Previous Info:

2015-11-09 DOI issued for CRU TS3.23. Dataset now citeable. 2015-11-09 DOI issued for CRU TS3.23. Dataset now citeable.

2015-11-06 The WET variable now available (v.3.23.01)! 2015-11-06 The WET variable now available (v.3.23.01)!

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:

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.

CRU TS 3.22 was produced in July 2014.

In October 2015, CRU TS 3.23 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 3.23 data (i.e. climate variables) are available in both compressed (.gz extension) ASCII ".dat" and netcdf ".nc" file formats. CRU TS 3.23 metadata files (i.e. stations and observations) are available in ASCII. To understand how to read the CRU TS 3.23 data and metadata files, please refer to the CRU_TS_3.23_File_Formats_explained PDF documentation (under Linked Documentation below).

Citations: 24

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.

Cosentino, B.J., Moore, J., Karraker, N.E., Ouellet, M. & Gibbs, J.P. (2017) Evolutionary response to global change: Climate and land use interact to shape color polymorphism in a woodland salamander. Ecology and Evolution 7, 5426–5434. doi.org/10.1002/ece3.3118
da Silva, R.S., Kumar, L., Shabani, F., da Silva, E.M., da Silva Galdino, T.V. & Picanço, M.C. (2016) Spatio-temporal dynamic climate model for Neoleucinodes elegantalis using CLIMEX. International Journal of Biometeorology 61, 785–795. doi.org/10.1007/s00484-016-1256-2
Guillod, B.P., Jones, R.G., Bowery, A., et al. (2017) weather@home 2: validation of an improved global–regional climate modelling system. Geoscientific Model Development 10, 1849–1872. doi.org/10.5194/gmd-10-1849-2017
Krishnamurthy, L., Muñoz, Á.G., Vecchi, G.A., Msadek, R., Wittenberg, A.T., Stern, B., Gudgel, R. & Zeng, F. (2018) Assessment of summer rainfall forecast skill in the Intra-Americas in GFDL high and low-resolution models. Climate Dynamics 52, 1965–1982. doi.org/10.1007/s00382-018-4234-z
Li, Z., Chen, Y., Wang, Y. & Fang, G. (2016) Dynamic changes in terrestrial net primary production and their effects on evapotranspiration. Hydrology and Earth System Sciences 20, 2169–2178. doi.org/10.5194/hess-20-2169-2016
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S., von Bloh, W., Stoorvogel, J.J. & Müller, C. (2018) Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage). doi.org/10.5194/gmd-2018-255
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S., von Bloh, W., Stoorvogel, J.J. & Müller, C. (2019) Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage). Geoscientific Model Development 12, 2419–2440. doi.org/10.5194/gmd-12-2419-2019
Marty, C., Tilg, A.-M. & Jonas, T. (2017) Recent Evidence of Large-Scale Receding Snow Water Equivalents in the European Alps. Journal of Hydrometeorology 18, 1021–1031. doi.org/10.1175/jhm-d-16-0188.1
not a doi doi.org/1871.1/73dc4b66-a0ac-426d-b535-05492c430ceb
not a doi doi.org/11336/98776
Ranhotra, P.S., Shekhar, M., Roy, I. & Bhattacharyya, A. (2022) Holocene Climate and Glacial Extents in the Gangotri Valley, Garhwal Himalaya, India: A Review. Springer Climate, 125–142. doi.org/10.1007/978-3-030-92782-0_6
Roy, I., Ranhotra, P.S., Tomar, N., Shekhar, M., Agrawal, S., Bhattacharyya, A., Kumar, P., Patil, S.K. & Sharma, R. (2022) Reconstruction of the late Holocene climate variability from the summer monsoon dominated Bhagirathi valley, western Himalaya. Journal of Asian Earth Sciences 227, 105080. doi.org/10.1016/j.jseaes.2022.105080
Schaphoff, S., Forkel, M., Müller, C., et al. (2018) LPJmL4 - A dynamic global vegetation model with managed land - Part 2: Model evaluation. doi.org/10.34657/3739
Schaphoff, S., Forkel, M., Müller, C., et al. (2018) LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation. Geoscientific Model Development 11, 1377–1403. doi.org/10.5194/gmd-11-1377-2018
Schaphoff, S., von Bloh, W., Rammig, A., et al. (2018) LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description. Geoscientific Model Development 11, 1343–1375. doi.org/10.5194/gmd-11-1343-2018
Shi, H., Li, T. & Wei, J. (2017) Evaluation of the gridded CRU TS precipitation dataset with the point raingauge records over the Three-River Headwaters Region. Journal of Hydrology 548, 322–332. doi.org/10.1016/j.jhydrol.2017.03.017
Sörensson, A.A. & Ruscica, R.C. (2018) Intercomparison and Uncertainty Assessment of Nine Evapotranspiration Estimates Over South America. Water Resources Research 54, 2891–2908. https://doi.org/10.1002/2017wr021682 doi.org/10.1002/2017WR021682
Stuecker, M.F., Tigchelaar, M. & Kantar, M.B. (2018) Climate variability impacts on rice production in the Philippines. ed. by V. Magar. PLOS ONE 13, e0201426. doi.org/10.1371/journal.pone.0201426
Tigchelaar, M., Battisti, D.S., Naylor, R.L. & Ray, D.K. (2018) Future warming increases probability of globally synchronized maize production shocks. Proceedings of the National Academy of Sciences 115, 6644–6649. doi.org/10.1073/pnas.1718031115
Valcheva, R. (2021) Climate Change Projections for Bulgaria According to RCP45 Scenario Until 2099. Springer Proceedings in Complexity, 125–129. doi.org/10.1007/978-3-662-63760-9_19
Vautard, R., Christidis, N., Ciavarella, A., et al. (2018) Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe. Climate Dynamics 52, 1187–1210. doi.org/10.1007/s00382-018-4183-6
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K. & Zaehle, S. (2018) Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0). Geoscientific Model Development 11, 2789–2812. doi.org/10.5194/gmd-11-2789-2018
Wang, Z., Wu, R. & Huang, G. (2017) Low‐frequency snow changes over the Tibetan Plateau. International Journal of Climatology 38, 949–963. doi.org/10.1002/joc.5221
Wu, M., Schurgers, G., Rummukainen, M., Smith, B., Samuelsson, P., Jansson, C., Siltberg, J. & May, W. (2016) Vegetation–climate feedbacks modulate rainfall patterns in Africa under future climate change. Earth System Dynamics 7, 627–647. doi.org/10.5194/esd-7-627-2016

Process overview

This dataset was generated by the computation detailed below.
Title

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

Abstract

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

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: 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
  • units: mm/day
  • long_name: potential evapotranspiration
  • var_id: pet
  • var_id: pre
  • long_name: precipitation
  • units: mm/month
  • 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:
-
End time:
-
Geographic Extent

 
90.0000°
 
-180.0000°
 
180.0000°
 
-60.0000°