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)
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.
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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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: |
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. 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.
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| File Format: |
Data are provided in ASCII and NetCDF formats.
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Related Documents
Citations: 48
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, 1161–1172. https://doi.org/10.1175/waf-d-19-0023.1 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, 307–334. https://doi.org/10.5194/cp-15-307-2019 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. https://doi.org/10.1038/s41467-020-17710-7 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. https://doi.org/10.1029/2018jd028378 https://doi.org/10.1029/2018jd028378 |
| Centella-Artola, A., Bezanilla-Morlot, A., Taylor, M.A., et al. (2020) Evaluation of Sixteen Gridded Precipitation Datasets over the Caribbean Region Using Gauge Observations. Atmosphere 11, 1334. https://doi.org/10.3390/atmos11121334 https://doi.org/10.3390/atmos11121334 |
| Chen, C., Park, T., Wang, X., et al. (2019) China and India lead in greening of the world through land-use management. Nature Sustainability 2, 122–129. https://doi.org/10.1038/s41893-019-0220-7 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, 1791–1809. https://doi.org/10.1007/s00382-021-05992-6 https://doi.org/10.1007/s00382-021-05992-6 |
| Craven, D., Eisenhauer, N., Pearse, W.D., et al. (2018) Multiple facets of biodiversity drive the diversity–stability relationship. Nature Ecology & Evolution 2, 1579–1587. https://doi.org/10.1038/s41559-018-0647-7 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, 655–677. https://doi.org/10.1007/s11069-021-04962-9 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 https://doi.org/10.20944/preprints202007.0317.v1 |
| Ge, J., Pitman, A.J., Guo, W., Wang, S. & Fu, C. (2019) Do Uncertainties in the Reconstruction of Land Cover Affect the Simulation of Air Temperature and Rainfall in the CORDEX Region of East Asia? Journal of Geophysical Research: Atmospheres 124, 3647–3670. https://doi.org/10.1029/2018jd029945 https://doi.org/10.1029/2018jd029945 |
| 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, 3215–3249. https://doi.org/10.5194/gmd-14-3215-2021 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. https://doi.org/10.1038/s41598-018-29286-w 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, 3151. https://doi.org/10.3390/rs14133151 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, 4495–4513. https://doi.org/10.5194/bg-15-4495-2018 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 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, 135–144. https://doi.org/10.1007/s00704-018-2476-7 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 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, 3331–3359. https://doi.org/10.5194/hess-24-3331-2020 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, 125–147. https://doi.org/10.1016/j.jssas.2021.07.006 https://doi.org/10.1016/j.jssas.2021.07.006 |
| Ise, T. & Oba, Y. (2019) VARENN: Graphical representation of spatiotemporal data and application to climate studies. https://doi.org/10.48550/ARXIV.1907.09725 https://doi.org/10.48550/arxiv.1907.09725 |
| Ise, T. & Oba, Y. (2020) VARENN: graphical representation of periodic data and application to climate studies. npj Climate and Atmospheric Science 3. https://doi.org/10.1038/s41612-020-0129-x 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. https://doi.org/10.1038/s41597-020-00587-y 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. Mountain Landscapes in Transition, 385–401. https://doi.org/10.1007/978-3-030-70238-0_15 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. Mountain Landscapes in Transition, 403–416. https://doi.org/10.1007/978-3-030-70238-0_16 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 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. https://doi.org/10.1038/s41598-018-34993-5 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, 625–642. https://doi.org/10.1007/s00376-021-1075-1 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. https://doi.org/10.1057/s41599-019-0302-1 https://doi.org/10.1057/s41599-019-0302-1 |
| Lyu, H., Dong, Z., Roobavannan, M., Kandasamy, J. & Pande, S. (2020) Prospects of interventions to alleviate rural–urban migration in Jiangsu Province, China based on sensitivity and scenario analysis. Hydrological Sciences Journal 65, 2175–2184. https://doi.org/10.1080/02626667.2020.1802030 https://doi.org/10.1080/02626667.2020.1802030 |
| Makula, E.K. & Zhou, B. (2021) Changes in March to May rainfall over Tanzania during 1978–2017. International Journal of Climatology 41, 5663–5675. https://doi.org/10.1002/joc.7146 https://doi.org/10.1002/joc.7146 |
| not a doi https://doi.org/1887/81918 |
| Parker, S.E., Harrison, S.P., Comas-Bru, L., Kaushal, N., LeGrande, A.N. & Werner, M. (2021) A data–model approach to interpreting speleothem oxygen isotope records from monsoon regions. Climate of the Past 17, 1119–1138. https://doi.org/10.5194/cp-17-1119-2021 https://doi.org/10.5194/cp-17-1119-2021 |
| Peng, Q., Wang, R., Jiang, Y., Li, C. & Guo, W. (2021) The change of hydrological variables and its effects on vegetation in Central Asia. Theoretical and Applied Climatology 146, 741–753. https://doi.org/10.1007/s00704-021-03730-w https://doi.org/10.1007/s00704-021-03730-w |
| Rabaey, K., Vandekerckhove, T., de Walle, A.V. & Sedlak, D.L. (2020) The third route: Using extreme decentralization to create resilient urban water systems. Water Research 185, 116276. https://doi.org/10.1016/j.watres.2020.116276 https://doi.org/10.1016/j.watres.2020.116276 |
| Rezsöhazy, J., Goosse, H., Guiot, J., Gennaretti, F., Boucher, E., André, F. & Jonard, M. (2020) Application and evaluation of the dendroclimatic process-based model MAIDEN during the last century in Canada and Europe. Climate of the Past 16, 1043–1059. https://doi.org/10.5194/cp-16-1043-2020 https://doi.org/10.5194/cp-16-1043-2020 |
| Russo, E., Kirchner, I., Pfahl, S., Schaap, M. & Cubasch, U. (2019) Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain. Freie Universität Berlin. https://doi.org/10.17169/REFUBIUM-26076 https://doi.org/10.17169/refubium-26076 |
| Serra-Maluquer, X., Gazol, A., Sangüesa-Barreda, G., Sánchez-Salguero, R., Rozas, V., Colangelo, M., Gutiérrez, E. & Camarero, J.J. (2019) Geographically Structured Growth decline of Rear-Edge Iberian Fagus sylvatica Forests After the 1980s Shift Toward a Warmer Climate. Ecosystems 22, 1325–1337. https://doi.org/10.1007/s10021-019-00339-z https://doi.org/10.1007/s10021-019-00339-z |
| Shepherd, A., Littleton, E., Clifton‐Brown, J., Martin, M. & Hastings, A. (2020) Projections of global and UK bioenergy potential from Miscanthus × giganteus—Feedstock yield, carbon cycling and electricity generation in the 21st century. GCB Bioenergy 12, 287–305. https://doi.org/10.1111/gcbb.12671 https://doi.org/10.1111/gcbb.12671 |
| Sinha, E., Michalak, A.M., Balaji, V. & Resplandy, L. (2022) India’s Riverine Nitrogen Runoff Strongly Impacted by Monsoon Variability. Environmental Science & Technology 56, 11335–11342. https://doi.org/10.1021/acs.est.2c01274 https://doi.org/10.1021/acs.est.2c01274 |
| Sun, G. & Mu, M. (2021) Impacts of two types of errors on the predictability of terrestrial carbon cycle. Ecosphere 12. https://doi.org/10.1002/ecs2.3315 https://doi.org/10.1002/ecs2.3315 |
| Tian, Y., Gao, Y. & Guo, D. (2021) The Relationship between Melt Season Sea Ice over the Bering Sea and Summer Precipitation over Mid-Latitude East Asia. Advances in Atmospheric Sciences 38, 918–930. https://doi.org/10.1007/s00376-021-0348-z https://doi.org/10.1007/s00376-021-0348-z |
| Weitzel, N., Hense, A. & Ohlwein, C. (2019) Combining a pollen and macrofossil synthesis with climate simulations for spatial reconstructions of European climate using Bayesian filtering. Climate of the Past 15, 1275–1301. https://doi.org/10.5194/cp-15-1275-2019 https://doi.org/10.5194/cp-15-1275-2019 |
| Wu, T., Yu, R., Lu, Y., et al. (2021) BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model. Geoscientific Model Development 14, 2977–3006. https://doi.org/10.5194/gmd-14-2977-2021 https://doi.org/10.5194/gmd-14-2977-2021 |
| Xu, K., Wang, X., Jiang, C. & Sun, O.J. (2020) Additional file 1 of Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity. figshare. https://doi.org/10.6084/M9.FIGSHARE.12153837.V1 https://doi.org/10.6084/m9.figshare.12153837.v1 |
| Xu, K., Wang, X., Jiang, C. & Sun, O.J. (2020) Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity. Forest Ecosystems 7. https://doi.org/10.1186/s40663-020-00239-y https://doi.org/10.1186/s40663-020-00239-y |
| Yang, R., Gui, S. & Cao, J. (2019) Bay of Bengal‐East Asia‐Pacific Teleconnection in Boreal Summer. Journal of Geophysical Research: Atmospheres 124, 4395–4412. https://doi.org/10.1029/2019jd030332 https://doi.org/10.1029/2019jd030332 |
| Zermeño‐Díaz, D.M. (2022) Diagnostics of observed dry trends in Caribbean precipitation. International Journal of Climatology 42, 6927–6943. https://doi.org/10.1002/joc.7621 https://doi.org/10.1002/joc.7621 |
Process overview
| 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
Temporal Range
1901-01-01T00:00:00
2016-12-31T23:59:59
Geographic Extent
90.0000° |
||
-180.0000° |
180.0000° |
|
-60.0000° |