Dataset
CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2012)
Abstract
The gridded Climatic Research Unit (CRU) TS (time-series) 3.21 datasets are month-by-month variations in climate over the period 1901-2012, on high-resolution (0.5 x 0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.
CRU TS 3.21 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. 2012.
CRU TS 3.21 data were produced using the same methodology as for the 3.20 datasets.
In addition to updating the dataset with 2012 data, the v3.21 release corrects two errors in the v3.20 dataset. Please see the release notes in the docs section, which contain details of the errors.
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 3.21 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.
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__ACTIVITY_0c08abfc-f2d5-11e2-a948-00163e251233
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| Access rules: |
Access to these data is available to any registered CEDA user. Please Login or Register for a CEDA account to gain access.
Use of these data is covered by the following licence(s): 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. CRU TS 3.21 was produced in July 2013. |
| Data Quality: |
Please see Mitchell and Jones, 2005 (http://dx.doi.org/10.1002/joc.1181).
The CRU TS3.00 files were produced by CRU and include data up to the end of 2006. However, only data up to June 2006 should be used. For dates later than this, any subsequent updates/corrections to the original data have not been included, so the values may be misleading.
Please note, CRU TS 3.00 NetCDF data are experimental, not all attributes are correctly set yet. The data themselves should be identical to those in the ASCII versions.
For CRU TS 3.10, the data have been examined in detail for consistency with CRU TS 3.00 by the CRU. Small differences may occur where additional station data have become available since CRU TS 3.00 was generated and are now included in the analysis. The data from the entire period 1901-2009 can be used, and all of the attributes in the NetCDF files are correctly set.
For each new release of the CRU TS data, the underlying datasets have been improved. New stations have been added. Some stations have been removed for quality control purposes. This explains why data may not be exactly the same for a common time period.
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| File Format: |
The CRU TS 3.21 data (i.e. climate variables) are available in both compressed (.gz extension) ASCII ".dat" and netcdf ".nc" file formats. CRU TS 3.21 metadata files (i.e. stations and observations) are available in ASCII. To understand how to read the CRU TS 3.21 data and metadata files, please refer to the CRU_TS_3.21_File_Formats_explained PDF documentation (under Linked Documentation below).
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Related Documents
| Climate Research Unit (CRU) data Homepage |
| Harris et al. (2014) Reference |
| CRU TS 3.21 file formats guide |
| CRU User Guide |
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.
| Arneth, A., Sitch, S., Pongratz, J., et al. (2017) Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nature Geoscience 10, 79–84. https://doi.org/10.1038/ngeo2882 https://doi.org/10.1038/ngeo2882 |
| Atlaskina, K., Berninger, F. & de Leeuw, G. (2015) Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere. https://doi.org/10.5194/tcd-9-2745-2015 https://doi.org/10.5194/tcd-9-2745-2015 |
| Basile, S.J., Lin, X., Wieder, W.R., Hartman, M.D. & Keppel-Aleks, G. (2020) Leveraging the signature of heterotrophic respiration on atmospheric CO<sub>2</sub> for model benchmarking. Biogeosciences 17, 1293–1308. https://doi.org/10.5194/bg-17-1293-2020 https://doi.org/10.5194/bg-17-1293-2020 |
| Bayer, A. (2016) Comment to Reviewer #1. https://doi.org/10.5194/esd-2016-24-ac3 https://doi.org/10.5194/esd-2016-24-ac3 |
| Bonsor, H.C., MacDonald, A.M., Ahmed, K.M., et al. (2017) Hydrogeological typologies of the Indo-Gangetic basin alluvial aquifer, South Asia. Hydrogeology Journal 25, 1377–1406. https://doi.org/10.1007/s10040-017-1550-z https://doi.org/10.1007/s10040-017-1550-z |
| Creutzig, F., Bren d’Amour, C., Weddige, U., Fuss, S., Beringer, T., Gläser, A., Kalkuhl, M., Steckel, J.C., Radebach, A. & Edenhofer, O. (2019) Assessing human and environmental pressures of global land-use change 2000–2010. Global Sustainability 2. https://doi.org/10.1017/sus.2018.15 https://doi.org/10.1017/sus.2018.15 |
| Dass, P., Rawlins, M.A., Kimball, J.S. & Kim, Y. (2016) Environmental controls on the increasing GPP of terrestrial vegetation across northern Eurasia. Biogeosciences 13, 45–62. https://doi.org/10.5194/bg-13-45-2016 https://doi.org/10.5194/bg-13-45-2016 |
| Dirmeyer, P.A., Yu, L., Amini, S., Crowell, A.D., Elders, A. & Wu, J. (2016) Projections of the shifting envelope of Water cycle variability. Climatic Change 136, 587–600. https://doi.org/10.1007/s10584-016-1634-0 https://doi.org/10.1007/s10584-016-1634-0 |
| Duku, C., Rathjens, H., Zwart, S.J. & Hein, L. (2015) Towards ecosystem accounting: a comprehensive approach to modelling multiple hydrological ecosystem services. https://doi.org/10.5194/hessd-12-3477-2015 https://doi.org/10.5194/hessd-12-3477-2015 |
| Duku, C., Zwart, S.J. & Hein, L. (2018) Impacts of climate change on cropping patterns in a tropical, sub-humid watershed. ed. by P.K. Subudhi. PLOS ONE 13, e0192642. https://doi.org/10.1371/journal.pone.0192642 https://doi.org/10.1371/journal.pone.0192642 |
| Gommes, R., Wu, B., Zhang, N., Feng, X., Zeng, H., Li, Z. & Chen, B. (2016) CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture. International Journal of Biometeorology 61, 199–215. https://doi.org/10.1007/s00484-016-1199-7 https://doi.org/10.1007/s00484-016-1199-7 |
| Hashimoto, S., Carvalhais, N., Ito, A., Migliavacca, M., Nishina, K. & Reichstein, M. (2015) Global spatiotemporal distribution of soil respiration modeled using a global database. Biogeosciences 12, 4121–4132. https://doi.org/10.5194/bg-12-4121-2015 https://doi.org/10.5194/bg-12-4121-2015 |
| Hashimoto, S., Carvalhais, N., Ito, A., Migliavacca, M., Nishina, K. & Reichstein, M. (2015) Global spatiotemporal distribution of soil respiration modeled using a global database. https://doi.org/10.5194/bgd-12-4331-2015 https://doi.org/10.5194/bgd-12-4331-2015 |
| Krause, A., Pugh, T.A.M., Bayer, A.D., Lindeskog, M. & Arneth, A. (2016) Impacts of land-use history on the recovery of ecosystems after agricultural abandonment. https://doi.org/10.5194/esd-2016-11 https://doi.org/10.5194/esd-2016-11 |
| Ladau, J., Shi, Y., Jing, X., He, J.-S., Chen, L., Lin, X., Fierer, N., Gilbert, J.A., Pollard, K.S. & Chu, H. (2017) Climate change will lead to pronounced shifts in the diversity of soil microbial communities. https://doi.org/10.1101/180174 https://doi.org/10.1101/180174 |
| Ladau, J., Shi, Y., Jing, X., He, J.-S., Chen, L., Lin, X., Fierer, N., Gilbert, J.A., Pollard, K.S. & Chu, H. (2018) Existing Climate Change Will Lead to Pronounced Shifts in the Diversity of Soil Prokaryotes. ed. by O. Mason. mSystems 3. https://doi.org/10.1128/msystems.00167-18 https://doi.org/10.1128/msystems.00167-18 |
| Le Quéré, C., Peters, G.P., Andres, R.J., et al. (2013) Global carbon budget 2013. https://doi.org/10.5194/essdd-6-689-2013 https://doi.org/10.5194/essdd-6-689-2013 |
| Libanda, B. & Paeth, H. (2023) Future photovoltaic solar power resources in Zambia: a CORDEX-CORE multi-model synthesis. Meteorology and Atmospheric Physics 135. https://doi.org/10.1007/s00703-023-00990-1 https://doi.org/10.1007/s00703-023-00990-1 |
| Myers-Smith, I.H. & Myers, J.H. (2018) Comment on “Precipitation drives global variation in natural selection”. Science 359. https://doi.org/10.1126/science.aan5028 https://doi.org/10.1126/science.aan5028 |
| Naesseth, C.A., Lindsten, F. & Schön, T.B. (2015) Nested Sequential Monte Carlo Methods. https://doi.org/10.48550/ARXIV.1502.02536 https://doi.org/10.48550/arxiv.1502.02536 |
| not a doi https://doi.org/1983/0ba390c0-7c3d-42c2-98ef-9535325acef3 |
| not a doi https://doi.org/1871.1/c26796d2-6eeb-4486-aa51-2e4c15c5bbb4 |
| Palazzi, E., von Hardenberg, J., Terzago, S. & Provenzale, A. (2014) Precipitation in the Karakoram-Himalaya: a CMIP5 view. Climate Dynamics 45, 21–45. https://doi.org/10.1007/s00382-014-2341-z https://doi.org/10.1007/s00382-014-2341-z |
| Purwaningsih, A. & Hidayat, R. (2016) Performance of Decadal Prediction in Coupled Model Intercomparisson Project Phase 5 (CMIP5) on Projecting Climate in Tropical Area. Procedia Environmental Sciences 33, 128–139. https://doi.org/10.1016/j.proenv.2016.03.064 https://doi.org/10.1016/j.proenv.2016.03.064 |
| Quesada-Montano, B., Wetterhall, F., Westerberg, I.K., Hidalgo, H.G. & Halldin, S. (2018) Characterising droughts in Central America with uncertain hydro-meteorological data. Theoretical and Applied Climatology 137, 2125–2138. https://doi.org/10.1007/s00704-018-2730-z https://doi.org/10.1007/s00704-018-2730-z |
| Quetin, G.R. & Swann, A.L.S. (2017) Empirically Derived Sensitivity of Vegetation to Climate across Global Gradients of Temperature and Precipitation. Journal of Climate 30, 5835–5849. https://doi.org/10.1175/jcli-d-16-0829.1 https://doi.org/10.1175/jcli-d-16-0829.1 |
| Robinson, E.L., Blyth, E.M., Clark, D.B., Finch, J. & Rudd, A.C. (2017) Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. Hydrology and Earth System Sciences 21, 1189–1224. https://doi.org/10.5194/hess-21-1189-2017 https://doi.org/10.5194/hess-21-1189-2017 |
| Sharma, S., Gray, D.K., Read, J.S., et al. (2015) A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009. Scientific Data 2. https://doi.org/10.1038/sdata.2015.8 https://doi.org/10.1038/sdata.2015.8 |
| Siepielski, A.M., Morrissey, M.B., Buoro, M., et al. (2018) Response to Comment on “Precipitation drives global variation in natural selection”. Science 359. https://doi.org/10.1126/science.aan5760 https://doi.org/10.1126/science.aan5760 |
| Werner, A.T., Schnorbus, M.A., Shrestha, R.R., Cannon, A.J., Zwiers, F.W., Dayon, G. & Anslow, F. (2019) A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America. Scientific Data 6. https://doi.org/10.1038/sdata.2018.299 https://doi.org/10.1038/sdata.2018.299 |
| Wu, Z., Boke-Olén, N., Fensholt, R., Ardö, J., Eklundh, L. & Lehsten, V. (2018) Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions. ed. by J.M. Dias. PLOS ONE 13, e0199383. https://doi.org/10.1371/journal.pone.0199383 https://doi.org/10.1371/journal.pone.0199383 |
Process overview
| 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: 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
1901-01-01T00:00:00
2012-12-31T00:00:00
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-60.0000° |