According to a survey in Summer 2024, social media is the media source people trust the least regarding climate change in the United Kingdom. More traditional information sources, like the TV and the radio, are the most trusted providers of accurate information on climate change, with trust rates over 60 percent.

Social media is a transformative digital technology, collapsing the "six degrees of separation" which have previously characterised many social networks, and breaking down many of the barriers to individuals communicating with each other. Some commentators suggest that this is having profound effects across society, that social media has revolutionised the communication of controversial public issues such as climate change, and that this has significantly increased the volume and variety of scientists, politicians, journalists, non-governmental organisations, think tanks and members of the public in contact with each other. Tweets were collected in response to the airing of the BBC programme "Climate Change: The Facts", broadcast on April 18th, 2019. https://www.imdb.com/title/tt10095266/ https://www.bbc.co.uk/iplayer/episode/m00049b1/climate-change-the-facts The data deposited is a list of 87,177 tweet IDs, which can be used to retrieve tweets.

Social media is a transformative digital technology, collapsing the "six degrees of separation" which have previously characterised many social networks, and breaking down many of the barriers to individuals communicating with each other. Some commentators suggest that this is having profound effects across society, that social media has revolutionised the communication of controversial public issues such as climate change, and that this has significantly increased the volume and variety of scientists, politicians, journalists, non-governmental organisations, think tanks and members of the public in contact with each other. For example, in 2012 over 4000 tweets about climate change were sent every day.

Social media communication can act as a trusted source of public information about climate change, foster public participation in climate science, be a campaigning tool and trigger polarising events with far-reaching effects (e.g. Climategate). However, despite these broad changes in the communication environment, we lack a detailed understanding of the characteristics of social media climate change communications, the wider contexts for these communications, and what the social media revolution means for the relationship between science, politics and publics. Using an innovative interdisciplinary methodological approach that combines social media big data analysis with fine grained ethnographic description, this project aims to: 1) discover the key contributors to social media climate change communication, the content they discuss, and how these change over time and space; 2) locate the connections between contributors, explore how social media usage is influenced by personal, professional and intellectual backgrounds, and how these influences vary over time and space; 3) identify the opportunities and challenges presented by social media for future public discussions of climate change. In this way, Making Climate Social will establish the contributors, content, connections and contexts which make up social media climate change communications, how these change over time and space, and what they mean for future public discussions of the science and politics of climate change.

The Met Office is a project partner, hosting a knowledge exchange visit by the PI, where he will interact with key climate scientists, the Communications Team and Customer Centre and give a seminar to research staff in both climate and weather research. The PI will also meet regularly with the Met Office, Department for Energy and Climate Change and the cross-sector project Advisory Board to ensure that research findings reach and affect relevant audiences: i) academic audiences in science and technology studies, climate change communication and social media researchers; ii) publics interested in climate change and/or social media usage; iii) government, scientific organisations and universities with responsibility for supporting social media usage by climate change researchers. The project will achieve this through: i) high-quality research articles published in leading journals across a range of specialist academic journals; ii) a dedicated project blog, Twitter account @MakCliSoc, and series of Guardian blogposts to build awareness with, and disseminate findings to, a broad range of stakeholders and publics; iii) the Climate Change Social Radar: an innovative and interactive collaboration with digital developers to provide an engaging web interface through which to explore project data and reflect on broader ethical issues of social media; iv) succinct policy briefings tailored for key stakeholders and written in plain English.

The long term goal of this project is to make Making Climate Social a trusted source of information that tracks the dynamics of social media climate change communications, providing a counterpart to the Media and Climate Change Observatory (Colorado) which focuses on traditional media coverage of climate change.

The latest National Statistics on forestry produced by the Forestry Commission were released on 24 September 2015 according to the arrangements approved by the UK Statistics Authority.

Detailed statistics are published in the web publication Forestry Statistics 2015, with an extract in Forestry Facts & Figures 2015. They include UK statistics on woodland area, planting, timber, trade, climate change, environment, recreation, employment and finance & prices as well as some statistics on international forestry. Where possible, figures are also provided for England, Wales, Scotland and Northern Ireland.

This dataset covers statistics on carbon in forests, the Woodland Carbon Code and public attitudes to climate change. Attribution statement:

[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 0.09°C.]What does the data show? This dataset shows the change in summer average temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, summer is defined as June-July-August. 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 summer 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 summer 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 Summer 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 Summer 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 summer 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 summer change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tas summer change 2.0 median' is named 'tas_summer_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 summer 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 Summer 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 latest National Statistics on forestry produced by the Forestry Commission were released on 28 September 2017 according to the arrangements approved by the UK Statistics Authority.

Detailed statistics are published in the web publication Forestry Statistics 2017, with an extract in Forestry Facts & Figures 2017. They include UK statistics on woodland area, planting, timber, trade, climate change, environment, recreation, employment and finance & prices as well as some statistics on international forestry. Where possible, figures are also provided for England, Wales, Scotland and Northern Ireland.

This dataset covers statistics on carbon in forests, the Woodland Carbon Code and public attitudes to climate change. Attribution statement:

Derived climate model projections data produced as part of the UK Climate Projections 2018 (UKCP18) project. The data produced by the UK Met Office Hadley Centre provides information on changes in 21st century climate for the UK helping to inform adaptation to a changing climate.

The derived climate model projections are estimated using a methodology based on time shift and other statistical approaches applied to a set of 28 projections comprising of 15 coupled simulations produced by the Met Office Hadley Centre, and 13 coupled simulations from CMIP5. The derived climate model projections exist for the RCP2.6 emissions scenario and for 2°C and 4°C global warming above pre-industrial levels.

The derived climate model projections are provided on a 60km spatial grid for the UK region and the projections consist of time series for the RCP2.6 emissions scenario that cover 1900-2100 and a 50 year time series for each of the global warming levels.

This dataset contains realisations scenario with global warming stabilised at 2°C.

[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 0.0.]What does the data show? The Annual Count of Extreme Summer Days is the number of days per year where the maximum daily temperature is above 35°C. It measures how many times the threshold is exceeded (not by how much) in a year. Note, the term ‘extreme summer days’ is used to refer to the threshold and temperatures above 35°C outside the summer months also contribute to the annual count. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Extreme Summer Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of extreme summer days to previous values.What are the possible societal impacts?The Annual Count of Extreme Summer Days indicates increased health risks, transport disruption and damage to infrastructure from high temperatures. It is based on exceeding a maximum daily temperature of 35°C. Impacts include:Increased heat related illnesses, hospital admissions or death affecting not just the vulnerable. Transport disruption due to overheating of road and railway infrastructure.Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Hot Summer Days (days above 30°C) and the Annual Count of Tropical Nights (where the minimum temperature does not fall below 20°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Extreme Summer Days 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 Annual Count of Extreme Summer Days, an average is taken across the 21 year period. Therefore, the Annual Count of Extreme Summer Days show the number of extreme summer days that could occur each year, for each given level of warming. 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?This data contains a field for each global warming level and two baselines. They are named ‘ESD’ (where ESD means Extreme Summer Days, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Extreme Summer Days 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Extreme Summer Days 2.5 median’ is ‘ExtremeSummerDays_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘ESD 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 Annual Count of Extreme Summer Days 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 ‘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.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods 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 linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

This dataset provides daily-mean sea surface temperatures (SST), presented on global 0.05° latitude-longitude grid, spanning 1980 to present. This is a Level 4 product, with gaps between available daily observations filled by statistical means.

The SST CCI Analysis product contains estimates of daily mean SST and sea ice concentration. Each SST value has an associated uncertainty estimate.

The dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.

Data from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS).

This CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:

• Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)

• Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)

• Improved retrieval with respect to desert-dust aerosols

• Addition of dual-view SLSTR data from 2016 onwards

• Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s

• Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)

• Inclusion of L2P passive microwave AMSR data

Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/

When citing this dataset please also cite the associated data paper:

Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w

Global climate model projections for the CMIP5 RCP8.5 emissions scenario produced as part of the UK Climate Projection 2018 (UKCP18) project. Data has been produced by the UK Met Office Hadley Centre, and provides information on changes in 21st century climate for the UK, helping to inform adaptation to a changing climate. The set of 28 projections is a combination of 15 coupled model simulations produced by the Met Office Hadley Centre, and 13 coupled simulations from CMIP5 contributed by different climate modelling centres. This data set provides information on changes in climate across the entire globe from 1900 to 2100 for RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across many climate variables at different times and spatial locations. This dataset contains regional averages for 23 river basin regions across the UK.

The latest National Statistics on forestry produced by the Forestry Commission were released on 27 September 2018 according to the arrangements approved by the UK Statistics Authority.

Detailed statistics are published in the web publication Forestry Statistics 2018, with an extract in Forestry Facts & Figures 2018. They include UK statistics on woodland area, planting, timber, trade, climate change, environment, recreation, employment and finance & prices as well as some statistics on international forestry. Where possible, figures are also provided for England, Wales, Scotland and Northern Ireland.

This dataset covers statistics on carbon in forests, the Woodland Carbon Code and public attitudes to climate change. Attribution statement:

What does the data show?

The data shows projections of population age structure (thousands of people per age class) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The age structure is split into 19 age classes e.g. 10-14 and is available for the end of each decade. For more information see the table below.

This dataset contains only SSP2, the 'Middle of the Road' scenario.

Indicator

Demography

Metric

Age Structure

Unit

Thousands per age class

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

19 age classes

Baseline Data Source

ONS 2019

Projection Trend Source

IIASA

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

This data contains a field for the year at the end of each decade. A separate field for 'Age Class' allow the data to be filtered e.g. by age class '10-14'.

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 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:

Further information on the UK SSPs can be found on the UK SSP project site and in this storymap. Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

This dataset is derived from modelled changes to the distributions of >12,700 terrestrial mammal and bird species under four different climate scenarios, projected to 2070. It contains national-level projections of species richness change under each climate scenario, based on species' modelled climatic niches, as well as projected range shifts in relation to political borders globally.

This dataset includes six sets of model output from JULES/IMOGEN simulations. Each set includes output from JULES (the Joint UK Land Environment Simulator) run with 34 climate change patterns from 2000-2099. The outputs provide carbon stocks and variables related to the surface energy budget to understand the implications of land-based climate mitigation.

[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.

This data represents the probabilistic climate projections component of the past (observed) and future climate scenario projections data, produced as part of the UK Climate Projections 2018 (UKCP18) project. Data has been produced by the UK Met Office Hadley Centre, and provides information on changes in 21st century climate for the UK, helping to inform adaptation to a changing climate.

The data represents anomalies with respect to the baseline period 1981-2000, and cover the period 1 Dec 1960 to 30 Nov 2099. Data for 16 administrative regions in the UK is provided.

The Probabilistic Projections were updated on 4th August 2022, to make improvements to the methodology to improve: consistency between maximum, minimum and mean temperature; consistency in the downscaling; statistical treatment of precipitation particularly at the wet and dry extremes; representation of annual and decadal variability; and adjustment of the data in the 1981-2000 baseline period to ensure the anomalies average to zero. The combination of the improvements means that all variables are modified to some degree. For more information, please refer to the UKCP news article and the documents it links to.

On 11th February 2025, the Probabilistic Projections were updated to include information at global warming levels. This information is available for each of the RCP scenarios as cumulative distribution frequency data. Further information about this global warming levels data and approach can be found in the relevant UKCP news article and the guidance document it links to.

[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 percentage change between the 'lower' values before and after this update is -1%.]What does the data show? A Heating Degree Day (HDD) is a day in which the average temperature is below 15.5°C. It is the number of degrees above this threshold that counts as a Heating Degree Day. For example if the average temperature for a specific day is 15°C, this would contribute 0.5 Heating Degree Days to the annual sum, alternatively an average temperature of 10.5°C would contribute 5 Heating Degree Days. Given the data shows the annual sum of Heating Degree Days, this value can be above 365 in some parts of the UK.Annual Heating Degree Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of HDD to previous values.What are the possible societal impacts?Heating Degree Days indicate the energy demand for heating due to cold days. A higher number of HDD means an increase in power consumption for heating, therefore this index is useful for predicting future changes in energy demand for heating.What is a global warming level?Annual Heating Degree Days are 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 Annual Heating Degree Days, an average is taken across the 21 year period. Therefore, the Annual Heating Degree Days show the number of heating degree days that could occur each year, for each given level of warming. 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?This data contains a field for each warming level and two baselines. They are named ‘HDD’ (Heating Degree Days), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'HDD 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'HDD 2.5 median' is 'HDD_25_median'. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘HDD 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, Annual Heating Degree Days 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.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods 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 linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.