The highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

What does the data show?

This data shows the monthly averages of 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 'tas' (temperature at surface), the month and ‘upper’ ‘median’ or ‘lower’. E.g. ‘tas Mar Lower’ is the average of the daily average 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 ‘tas 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 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

UKCP09: 5 km gridded data - Annual averages for the extreme temperature range. The data set contains 12 files (one for each month for the 1961-1990 average period). The individual grids are named according to the following convention: variablename_mmm_Average_Actual.txt where mmm is the month name (e.g. Jan).

The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/.

To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/research/climate/climate-monitoring/UKCP09/register

What does the data show?

This data shows monthly averages of surface temperature (°C) for 2050-2079 from the UKCP18 regional climate projections. The data is for the high emissions scenario (RCP8.5).

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 the average over the period. They are named 'tas' (temperature at surface), the month, and 'upper' 'median' or 'lower'. E.g. 'tas July Median' is the median value for July.

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 ‘tas January 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.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the monthly averages of temperature for 2050-2079 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

tas_rcp85_land-rcm_uk_12km_12_mon-30y_200912-207911.nc (median)

tas_rcp85_land-rcm_uk_12km_05_mon-30y_200912-207911.nc (lower)

tas_rcp85_land-rcm_uk_12km_04_mon-30y_200912-207911.nc (upper)

UKCP18 v20190731 (downloaded 04/11/2021)

Useful links

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

Temperature in the United Kingdom decreased to 9.88 celsius in 2024 from 10.14 celsius in 2023. This dataset includes a chart with historical data for the United Kingdom Average Temperature.

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

ukcp09-Gridded datasets based on surface observations have been generated for a range of climatic variables. The primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. This data set includes monthly ukcp09-Gridded datasets at 5 x 5 km resolution. A grid for each month covering the whole of the UK, downloadable in 10-year blocks. The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/. To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/research/climate/climate-monitoring/UKCP09/register

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.08 data are month-by-month variations in climate over the period 1901-2023, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.

The CRU TS4.08 variables are cloud cover, diurnal temperature range, frost day frequency, wet 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 2023.

The CRU TS4.08 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update.

The CRU TS4.08 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.

What does the data show?

This data shows the monthly averages of 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 'tas' (temperature at surface), the month and ‘upper’ ‘median’ or ‘lower’. E.g. ‘tas Mar Lower’ is the average of the daily average 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 ‘tas 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 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 daily average temperature in the United Kingdom (UK) has remained relatively stable since 2001, with temperatures rarely straying below 10 degrees Celsius. In 2024, the UK had an average daily temperature of 11.9 degrees Celsius. This was the highest average daily temperature recorded since the turn of the century. British summertime Britain is not known for its blisteringly hot summer months, with the average temperatures in this season varying greatly since 1990. In 1993, the average summer temperature was as low as 13.39 degrees Celsius, whilst 2018 saw a peak of 15.8 degrees Celsius. In that same year, the highest mean temperature occurred in July at 17.2 degrees Celsius. Variable weather Due to its location and the fact that it is an island, the United Kingdom experiences a diverse range of weather, sometimes in the same day. It is in an area where five air masses meet, creating a weather front. Each brings different weather conditions, such as hot, dry air from North Africa and wet and cold air from the Arctic. Temperatures across the UK tend to be warmest in England.

Datasets provides long-term climate data for large Asian cities with populations over 500,000. The dataset includes data on cloud cover, temperature range, number of frost days, potential evapotranspiration, precipitation, minimum temperature, mean temperature, maximum temperature, relative humidity, and number of wet days. The dataset includes data for 831 cities.

Columns:

• ID
• Date
• Latitude
• Longitude
• cld: Cloud cover (%)
• dtr: Temperature range (°C)
• frs: Number of frost days
• pet: Potential evapotranspiration (mm)
• pre: Precipitation (mm)
• tmn: Minimum temperature (°C)
• tmp: Mean temperature (°C)
• tmx: Maximum temperature (°C)
• vap: Relative humidity (%)
• wet: Number of wet days

Inspiration:
Are you interested in predicting the future weather conditions in your city or one of the 831 cities in our climate dataset? Our climate dataset contains data on various climate metrics, including temperature, precipitation, cloud cover, wind speed, and humidity. This data can be used to train a machine learning model that can predict future weather conditions with high accuracy. Imagine using a machine learning model to predict the weather in your city for the next week, month, or year. This information could be used to make decisions about planning, adaptation, and risk mitigation.

Please note:
This dataset contains satellite-derived climate data from the website https://crudata.uea.ac.uk. Satellite data are measured using sensors that may be subject to error. Therefore, it is possible that these data may differ from ground-based observations, which are typically used to generate real-world data. This difference is generally greater in remote areas and regions with high cloud.

This is a data set of mean monthly surface climate data over global land areas, excluding Antarctica, for nearly all of the twentieth century. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. All variables have mean monthly values for the period 1901-1995, several have data as recent as 1998, and more data will be added by the data originators. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al. (2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre. Access to the complete year-by-year monthly data set or to data more recent than posted here can be achieved by making a request with the Climate Impacts LINK Project at the Climatic Research Unit (email: d.viner@uea.ac.uk, web site: www.cru.uea.ac.uk/link ).

The annual mean temperature in the United Kingdom has fluctuated greatly since 1990. Temperatures during this period were at their highest in 2022, surpassing ** degrees Celsius. In 2010, the mean annual temperature stood at **** degrees, the lowest recorded during this time. Daily temperatures Average daily temperatures have remained stable since the turn of the century, rarely dropping below ** degrees Celsius. In 2010, they dropped to a low of **** degrees Celsius. The peak average daily temperature was recorded in 2022 when it reached **** degrees. This was an increase of *** degree Celsius compared to the long-term mean, and the most positive deviation during the period of consideration. Highs and lows The maximum average temperature recorded across the UK since 2015 was in July 2018. This month saw a maximum temperature of **** degrees Celsius. In comparison, the lowest monthly minimum temperature was in February of the same year, at just minus *** degrees. This was an especially cold February, as the previous year the minimum temperature for this month was *** degrees.

The Coastal Temperature Network consists of Cefas (and predecessor) originated data and data from external suppliers, who have agreed their data can be published as part of the network (Jones, 1981). The earliest data are from 1875 (Owers Light vessel) and have been supplied by the Met Office. The longest continuous record provided here is from Eastbourne (1892–2014). Sampling is from piers and breakwaters 50-200m from the shore where possible (Jones, 1981). The present network covers the temperature condition of coastal waters around the coast of England and Wales and was operationally combined with the salinity and temperature conditions across the Southern Bight of the North Sea. Individuals on behalf of Cefas, councils, companies and other organisations have obtained records of coastal sea surface temperature, for some stations, of more than 100-year duration. Approximately half of the stations started recording coastal temperatures in the mid–1960s. There are 41 stations in England and Wales where 20 out of 41 are still in operation. Cefas observers record coastal sea surface temperature using calibrated thermometers approximately 6 – 14 times per month, usually close to the time of high water. Other organisations record sea surface temperature ranging from daily values to monthly means. Since 2012, the data from Dover Council is recorded every minute. Data are published as monthly means (Joyce, 2006); the extracted data are the measurements used to calculate the means. The Cefas instruments are calibrated at Lowestoft to an accuracy of ±0.1°C. The accuracy of other instruments is not known, but is thought to be at least to an accuracy of ±0.2°C. The ferry route observers record offshore sea surface temperature from the ships main seawater pipe using a calibrated thermometer 4 times a month. The temperatures are recorded to at least an accuracy of ±0.2°C. The seawater samples are taken from the sea water main pipe to the harbour pump about 1.5 metres inboard. Quality assurance checks are applied to the data for each station by comparing the current dataset with either a 5 or 10 year running mean for each month. The data is first tested to see whether it is normally distributed i.e. whether all the data are close to average. The standard deviation is calculated to see how tightly the data are clustered around the mean; three standard deviations are then calculated to account for 99% of the data. If the data are outside this range (3 std dev) then the value is flagged and removed from subsequent analysis. See Joyce (2006) for details of the duration and history of individual datasets. Inevitably, there are changes in the number and location of monitoring stations over such a long period. At its peak the network reported on about 100 locations. This has reduced to around 30 in the late 20th century. Jones & Jeffs (1991) show the locations of early coastal stations. In addition, operating sites are moved and data recording upgraded, e.g. Eastbourne from a manual coastal site (see Joyce, 2006) to, in 2013, an electronic logging system mounted on an offshore buoy. These changes are reflected in the positions associated with the extracted data. See https://www.cefas.co.uk/cefas-data-hub/sea-temperature-and-salinity-trends/_ for a full description of the originating system which has sea-surface temperature (and sometimes salinity) data collected at a number of coastal sites around England and Wales, some operated by volunteers, some operated by local councils and some associated with power stations. The longest time-series include those from Eastbourne (1892 - present), Dover (1926 - present) and Port Erin, Isle of Man (1903 - present) although most time series began in the 1960s or 1970s.

.. _https://www.cefas.co.uk/cefas-data-hub/sea-temperature-and-salinity-trends/: https://www.cefas.co.uk/cefas-data-hub/sea-temperature-and-salinity-trends/

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 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.

This is a data set of mean monthly surface climate data over Southern Africa for nearly all of the twentieth century. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. All variables have mean monthly values for the period 1901-1995, several have data as recent as 1998, and more data will be added by the data originators. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al. (2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre. Access to the complete year-by-year monthly data set or to data more recent than posted here can be achieved by making a request with the Climate Impacts LINK Project at the Climatic Research Unit (email: d.viner@uea.ac.uk, web site: www.cru.uea.ac.uk/link ).

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.21°C.]What does the data show? This dataset shows the change in winter average temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, winter is defined as December-January-February. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions.The dataset uses projections of daily average air temperature from UKCP18 which are averaged over the winter period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare winter average temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.PeriodDescription1981-2000 baselineAverage temperature (°C) for the period2001-2020 (recent past)Average temperature (°C) for the period2001-2020 (recent past) changeTemperature change (°C) relative to 1981-20001.5°C global warming level changeTemperature change (°C) relative to 1981-20002°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-20003°C global warming level changeTemperature change (°C) relative to 1981-20004°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Winter Average Temperature Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Winter Average Temperature Change, an average is taken across the 21 year period.We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?These data contain a field for each warming level and the 1981-2000 baseline. They are named 'tas winter change' (change in air 'temperature at surface'), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'tas winter change 2.0 median' is the median value for winter for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tas change winter 2.0 median' is named 'tas_winter_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas winter change 2.0°C 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.For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Winter Average Temperature Change was 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 ‘higher’ 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 higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

The Climatic Research Unit (CRU) Country (CY) data version 4.05 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 2021 by CRU at the University of East Anglia and extends the CRU CY4.04 data to include 2020. 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 CY4.05 is derived directly from the CRU time series (TS) 4.05 dataset. CRU CY version 4.05 spans the period 1901-2020 for 292 countries. To understand the CRU CY4.05 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.05. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.05 release notes listed in the online documentation on this record. CRU CY data are available for download to all CEDA users.

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.