The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.03 data are month-by-month variations in climate over the period 1901-2018, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia.

The CRU TS4.03 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 2018.

The CRU TS4.03 data were produced using angular-distance weighting (ADW) interpolation. All version 4 releases used triangulation routines in IDL. Please see the release notes for full details of this version update.

The CRU TS4.03 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.

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).

The gridded CRU TS (time-series) 3.10 data are month-by-month variations in climate over the period 1901-2009, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia. CRU TS 3.10 includes variables such as cloud cover, diurnal temperature range, PET, daily mean temperature, monthly average daily minimun/maximum temperature, and vapour pressure for the period 1901-2009. Note that a corrected run of precipitation data, based on the v3.10 precipitation station data are available (e.g cru_ts_3_10_01.1901.2009.pre.dat). 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. CRU TS 3.10 data were produced using the same methodology as for the 3.00 dataset. The main differences is that the 3.10 dataset extends from 1901-2009, and all of the data in this period can now be used. Slight differences may be noticed between the results for a given time/location between the 3.00 and 3.10 versions, due to additional data now being available. CRU have examined the 3.10 dataset in detail and are confident that such differences are not significant. The CRU TS 3.10 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 gridded CRU TS (time-series) 3.26 data are month-by-month variations in climate over the period 1901-2017, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.

This version of CRU TS supersedes version 3.25, additionally however these data are superseded by the CRU TS 4.02 data which has a new processing methodology. This concurrent release of CRU TS 3.26 and CRU TS 4.02 is made to support users during the transition to the CRU TS version 4 data. No further releases of version 3 are planned.

CRU TS 3.26 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. 2017.

CRU TS 3.26 data were produced using the same methodology as for the 3.21, 3.22, 3.23, 3.24.01, 3.26 datasets. This version contains updates the dataset with 2016 data, some new stations have been added for TMP and PRE only. This release is the latest release of the CRU TS data. Known issues predating this release remain.

The CRU TS 3.26 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.

This dataset contains key characteristics about the data described in the Data Descriptor Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

The gridded CRU TS (time-series) 3.20 data are month-by-month variations in climate over the period 1901-2011, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia. CRU TS 3.20 data were produced using the same methodology as for the 3.00 and 3.10 datasets.

CRU TS 3.20 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum temperature, vapour pressure and wet day frequency for the period 1901-2011.

The CRU TS 3.20 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.

This set of files includes downscaled projections of monthly totals, and derived annual, seasonal, and decadal means of monthly average temperature (in degrees Celsius, no unit conversion necessary) from 1901 - 2006 (CRU TS 3.0) or 2009 (CRU TS 3.1) at 771 x 771 meter spatial resolution.

These datasets contain precipitation and temperature data over the period 1901 to 1995 for each of the stands studied (83). These data are from the CRU.TS.v.4.03 dataset (Harris et al 2014).

The CRU JRA V1.1 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is same as the CRU TS grid, though the set of variables is different) . The data are available at a 6 hourly time-step from January 1901 to December 2017. The dataset is constructed by combining data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA) and adjusted where possible to align with the CRU TS 3.26 data (see the Process section and the ReadMe file for full details). The CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details). The CRU JRA dataset is intended to be a replacement of the CRUNCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRUNCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. If this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following: Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology 34, 623-642. Kobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and Basic Characteristics. J. Met. Soc. Jap., 93(1), 5-48 https://dx.doi.org/10.2151/jmsj.2015-001

[Source: NASA/GISS]

These pages provide access to graphical extracts of land surface precipitation data for the period 1901-2000. The original data come from the CRU TS 2.0 dataset and are used here courtesy of Dr. T.D. Mitchell of the Tyndall Centre for Climate Change Research, University of East Anglia, U.K.

The source CRU TS 2.0 dataset comprises 1200 monthly grids of observed climate, for the period 1901-2000, and covering the global land surface at 0.5 degree x 0.5 degree; resolution. There are five climatic variables available: cloud cover, DTR, precipitation, temperature, vapour pressure. The original data are publicly available from the Climatic Research Unit at the University of East Anglia, along with guidance for their use and other documentation.

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.

CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.

CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

The "Standardized Precipitation Index" (SPI) is used to describe extremely dry or wet climate situations. The World Meteorological Organization (WMO) recommends, that all national meteorological and hydrological services should use the SPI for monitoring of dry spells (Press report December 2009, WMO No. 872). The advantages of SPI usage are: Only precipitation data are needed for the calculation of the index. The index is a standardized measure for precipitation in different climatic regions and for seasonal differences. Calculated for different time scales: meteorological, agricultural-economic and hydrological. SPI Classes: SPI ≤ -2: Extremely dry, -2 < SPI ≤ -1.5: Severely dry, -1.5 < SPI ≤ -1: Moderately dry, -1 < SPI ≤ 1: Near normal, 1 < SPI ≤ 1.5: Moderately wet, 1.5 < SPI ≤ 2: Severely wet, SPI ≥ 2: Extremely wet. Calculation: The SPI, presented here, is different from the original SPI definition of McKee et al. 1993. An enhanced SPI is used, that significantly reduces errors resulting from the determination of the precipitation's distribution (Sienz et al. 2011). MC Kee et al. 1993 shifted the time series of the SPI one time step into the future, but this is not done for the calculation of the SPI presented here. The SPI was calculated from two precipitation data sets: European Climate and Data Assessment (ECA&D), E-OBS gridded dataset Version 4.0 (1951 - 2010) for Europe Climate Research Unit (CRU), Version: CRU TS 2.1 (1901 - 2002) for Europe and USA

The Climatic Research Unit (CRU) Country (CY) data version 4.03 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency; including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and potential evapotranspiration. This version uses the updated set of country definitions, please see the appropriate Release Notes. This dataset was produced in 2019 by CRU at the University of East Anglia and extends the CRU CY4.02 data to include 2018. The data are available as text files with the extension '.per' and can be opened by most text editors. Spatial averages are calculated using area-weighted means. CRU CY4.03 is derived directly from the CRU time series (TS) 4.03 dataset. CRU CY version 4.03 spans the period 1901-2018 for 292 countries. To understand the CRU CY4.03 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.03. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.03 release notes listed in the online documentation on this record. CRU CY data are available for download to all CEDA users.

The CRU Time Series 4.05 dataset was developed and has been subsequently updated, improved and maintained with support from a number of funders, principally the UK's Natural Environment Research Council (NERC) and the US Department of Energy. Long-term support is currently provided by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre. Current gridded products (CRU TS) are presented either as ASCII grids, or in NetCDF format. The gridding process used in Brohan et al.. (2006) and earlier publications assigns each station to the 5 degree latitude/longitude box within which it is located. The gridding then simply averages all available station temperatures (as anomalies from 1961-90) within each grid box for each month from 1851. No account is taken of the station's elevation or location within the grid box (anomalies show little consistent dependence on altitude). A more up-to-date location for a station is not important for the gridding, unless a site change were to move the station to an adjacent grid box. In this instance, the data was derived as a subset of the original dataset. CRU publishes the data in NetCDF file format, however for data visualisation purposes the datasets was tranformed into tidy tables, represented in the South African Risk and Vulnerability Atlas (SARVA) by the South African Environmental Observation Network's uLwazi Node. Citation: University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, 2021. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681

Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes.

Land surface properties:

• TreeCover - Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from DiMiceli et al. 2015, regridded as per Kelley et al. 2019
• NonTreeCover - VCF fractional herb cover
• Urban cover from the History Database of the Global Environment, Version 3.1 (HYDE) Klein Goldewijk et al. 2011
• Crop cover (from HYDE)
• Pasture Cover (from HYDE)
• PopDen (population density from HYDE)

Climate:

• MAP_CRU - Mean annual precipitation from version 4.01 of the Climatic Research Unit Time Series high resolution gridded dataset (CRU TS v4.01) (Harris & Jones 2017)
• MAT - Mean annual temperature from CRU)
• MADD_CRU - Mean annual dry days from CRU - i.e seasonality of rainfall
• MTWM - Mean Maximum Temperature of the warmest month from CRU
• MTCM - Mean Minumum Temperature of the coldest month from CRU
• SW1 - direct downwards SW simulated using the SLASH model using CRU cload cover
• SW2 - diffuse downwards SW simulated using the SLASH model using CRU cload cover
• BurntArea_GFED_four_s - Burnt area from Global Fire Emissions Database, Version 4.1 (GFEDv4.1) (Van Der Werf et al. 2017)
• MaxWind (Mean Max Windspeed from CRU-(National Centers for Environmental Prediction (Harris 2019)

Dimiceli, C., Carroll, M., Sohlberg, R., Kim, D. H.,Kelly, M., and Townshend, J. R. G. (2015). Mod44bmodis/terra vegetation continuous fields yearly l3global 250m sin grid v006 (v006).

Harris, I. (2019). CRU JRA v1. 1: A forcings dataset ofgridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data, January1901–December 2017, University of East Anglia Climatic Research Unit, Centre for Environmental DataAnalysis.

Harris, I. and Jones, P. (2017). CRU TS4. 01: Climatic Re-search Unit (CRU) Time-Series (TS) version 4.01 ofhigh-resolution gridded data of month-by-month vari-ation in climate (Jan. 1901–Dec. 2016).Centre forEnvironmental Data Analysis, 25.

Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Marthews,T. R., and Dong, N. (2019). How contemporary biocli-matic and human controls change global fire regimes

Klein Goldewijk, K., Beusen, A., Van Drecht, G., and DeVos, M. (2011). The HYDE 3.1 spatially explicitdatabase of human-induced global land-use changeover the past 12,000 years.Global Ecology and Bio-geography, 20(1):73–86.

Van Der Werf, G. R., Randerson, J. T., Giglio, L.,Van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M.,Van Marle, M. J., Morton, D. C., Collatz, G. J., et al.(2017). Global fire emissions estimates during 1997–2016.Earth System Science Data, 9(2):697–720.

What does the data show?

This data shows the monthly averages of minimum surface temperature (°C) for 2070-2099 using a combination of the CRU TS (v. 4.06) and UKCP18 global RCP2.6 datasets. The RCP2.6 scenario is an aggressive mitigation scenario where greenhouse gas emissions are strongly reduced.

The data combines a baseline (1981-2010) value from CRU TS (v. 4.06) with an anomaly from UKCP18 global. Where the anomaly is the change in temperature at 2070-2099 relative to 1981-2010.

The data is provided on the WGS84 grid which measures approximately 60km x 60km (latitude x longitude) at the equator.

Limitations of the data

We recommend the use of multiple grid cells or an average of grid cells around a point of interest to help users get a sense of the variability in the area. This will provide a more robust set of values for informing decisions based on the data.

What are the naming conventions and how do I explore the data?

This data contains a field for each month’s average over the period. They are named 'tmin' (temperature minimum), the month and ‘upper’ ‘median’ or ‘lower’. E.g. ‘tmin Mar Lower’ is the average of the daily minimum temperatures in March throughout 2070-2099, in the second lowest ensemble member.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tmin Jan Median’ values.

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

To select which ensemble members to use, the monthly averages of minimum surface temperature for the period 2070-2099 were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

Data source

CRU TS v. 4.06 - (downloaded 12/07/22)

UKCP18 v.20200110 (downloaded 17/08/22)

Useful links

Further information on CRU TS Further information on the UK Climate Projections (UKCP) Further information on understanding climate data within the Met Office Climate Data Portal

The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.

Another data download site is Loess plateau Scientific Data Center (http://loess.geodata.cn/). This is a Chinese website. This website publishes the updated histrorical dataset and future downscaled monthly precipitation under multiple SSP Scenarios and GCMs, with 1 km spatial resolution.

/*************/ The dataset is updated yearly. Now, the period of the dataset is from 1901.1 to 2020.12.

/*************/ The future 1km dataset from 2021-2100 is published.

The data provider recommended the below publication as the reference.
Peng Shouzhang, Ding Yongxia, Liu Wenzhao, Li Zhi. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 2019, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019.

The Climatic Research Unit (CRU) Country (CY) data version 4.08 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.

This dataset was produced in 2024 by CRU at the University of East Anglia and extends the CRU CY4.07 data to include 2023. The data are available as text files with the extension '.per' and can be opened by most text editors.

Spatial averages are calculated using area-weighted means. CRU CY4.08 is derived directly from the CRU time series (TS) 4.07 dataset. CRU CY version 4.08 spans the period 1901-2023 for 292 countries.

To understand the CRU CY4.08 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.07. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.08 release notes listed in the online documentation on this record.

CRU CY data are available for download to all CEDA users.

What does the data show?

This data shows the monthly averages of maximum surface temperature (°C) for 2040-2069 using a combination of the CRU TS (v. 4.06) and UKCP18 global RCP2.6 datasets. The RCP2.6 scenario is an aggressive mitigation scenario where greenhouse gas emissions are strongly reduced.

The data combines a baseline (1981-2010) value from CRU TS (v. 4.06) with an anomaly from UKCP18 global. Where the anomaly is the change in temperature at 2040-2069 relative to 1981-2010.

The data is provided on the WGS84 grid which measures approximately 60km x 60km (latitude x longitude) at the equator.

Limitations of the data

We recommend the use of multiple grid cells or an average of grid cells around a point of interest to help users get a sense of the variability in the area. This will provide a more robust set of values for informing decisions based on the data.

What are the naming conventions and how do I explore the data?

This data contains a field for each month’s average over the period. They are named 'tmax' (temperature maximum), the month and ‘upper’ ‘median’ or ‘lower’. E.g. ‘tmax Mar Lower’ is the average of the daily minimum temperatures in March throughout 2040-2069, in the second lowest ensemble member.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tmax Jan Median’ values.

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

To select which ensemble members to use, the monthly averages of maximum surface temperature for the period 2040-2069 were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

Data source

CRU TS v. 4.06 - (downloaded 12/07/22)

UKCP18 v.20200110 (downloaded 17/08/22)

Useful links

Further information on CRU TS Further information on the UK Climate Projections (UKCP) Further information on understanding climate data within the Met Office Climate Data Portal