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.

This dataset consists of spatially explicit (1 km gridded) metrics of climate change “exposure” (i.e. an index of the amount of expected change in a location) derived from quantifying the difference in observed historical and predicted future climatic conditions. Four comparisons are included between five discrete time periods: 1901–1930 v. 1961–1990; 1961–1990 v. 2010–2019; 2010–2019 v. 2021–2040; and 2021–2040 v. 2061–2080. Full details about this dataset can be found at https://doi.org/10.5285/d370cda8-7d3d-4b62-8d09-23711aa18ac2

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:

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 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.

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:

[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 0.26°C.]What does the data show? This dataset shows the change in summer maximum air 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. The dataset uses projections of daily maximum air temperature from UKCP18. For each year, the highest daily maximum temperature from the summer period is found. These are then averaged to give values for the 1981-2000 baseline, 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 maximum 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 Maximum 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 Maximum 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 'tasmax 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. 'tasmax 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. 'tasmax summer change 2.0 median' is named 'tasmax_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 ‘tasmax 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 Maximum 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 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.

Natural England has developed a national scale model that undertakes an analysis of current datasets to provide an assessment of the relative vulnerability of priority habitats to climate change based on principles of adaptation for biodiversity. Through this model we are able to spatially represent the relative vulnerability of priority habitats to enable better spatial prioritisation of adaptation action. The methodology uses a 200m2 Geographic Information System (GIS) grid model to assess priority habitats for their overall vulnerability based on 5 metrics habitat sensitivity, habitat fragmentation, topographic variety, condition and management and conservation value.

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.

These shapefiles show the locations of climate change hotspots and climate change refugia in the UK EEZ, as identified from spatial meta-analysis conducted as part of the Marine Spatial Planning Addressing Climate Effects (MSPACE) program. Analysis was run on selected climate modelling and/or species distribution model outputs relevant to three sectors of interest (conservation, fisheries and aquaculture). We compared a present day reference period (2006-2025) to each possible 20 year time period between 2026 and 2069 (e.g. 2026-2045, 2027-2046, 2028-2047 etc.) under two different GHG emissions scenarios – RCP4.5 (strong curbs in global emissions toward climate change mitigation, from 2050 onwards, leading to a mean global warming by the end of the century of approx 2.4 degrees Celsius) and RCP8.5 (emissions continue to rise steadily throughout the 21st century, leading to mean global warming approx 4.3 degrees Celsius). In this way, we were able to determine whether or not the marine environment, as described by the modelling layers included in the analysis, changes significantly between the reference period and the future period of interest. The analysis method identifies ecosystem-wide climate change signals, allowing the investigation of the effects of climate change as a holistic process that species respond to through changes in species distributions, affecting the activity of sectors that rely on them. This allows for a mapping of the emergence of climate change hotspots (areas where climate driven trends lead these ecosystem components into a new state beyond their natural variability) over space and time, and so indicating areas where the current level of activity of sectors reliant on those species and habitats may no longer be sustainable. Importantly, because planners need to know also about what can be done, not just what will be lost, this methodology also allows the identification of climate change refugia, where the ecosystem underpinning a sector remains in its current state, and thus where current uses may be sustainable. Specific analyses were conducted for each focal sector, and each shapefile corresponds to the results of one of these analyses. In the conservation sector, analyses focused on: pelagic habitats, benthic habitats, megafauna exploiting pelagic habitats, megafauna exploiting benthic habitats and climate services (e.g. carbon sequestration ability of benthic habitats). In the fisheries sector, analyses focused on: pelagic fisheries and benthic/demersal fisheries. In the aquaculture sector, analyses focused on: pelagic aquaculture (activities taking place in the water column such as salmon cages and suspended mussel culture) and benthic aquaculture (activities that take place on the seabed such as oyster trestles).

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

This project contains the Stata code as well as additional information used for the following paper:Randell, H & C Gray (Forthcoming). Climate Change and Educational Attainment in the Global Tropics. Proceedings of the National Academy of Sciences.The data are publicly available and can be accessed freely. The census data were obtained from IPUMS-International (https://international.ipums.org/international/) and the climate data were obtained from the CRU-Time Series Version 4.00 (http://data.ceda.ac.uk//badc/cru/data/cru_ts/cru_ts_4.00/).We include three do-files in this project:"Climate_-1_to_5.do" -- this file was used to convert the climate data into z-scores of climatic conditions experienced during ages -1 to 5 years among children in the sample. "ClimEducation_PNAS_FINAL.do" -- this file was used to process the census data downloaded from IPUMS-International, link it to the climate data, and perform all of the analyses in the study."Climate_6-10_and_11-current.do" -- this file was used to convert the climate data into z-scores of climatic conditions experienced during ages 6-10 and 11-current age among children in the sample.In addition, we include a shapefile (as well as related GIS files) for the final sample of analysis countries. The attribute "birthplace" is used to link the climate data to the census data. We include Python scripts for extracting monthly climate data for each 10-year temperature and precipitation file downloaded from CRU. "py0_60" extracts data for years one through five, and "py61_120" extracts data for years six through ten.Lastly, we include an excel file with inclusion/exclusion criteria for the countries and censuses available from IPUMS.

Risk of Flooding from Surface Water (RoFSW) map is an assessment of where surface water flooding may occur when rainwater does not drain away through the normal drainage systems or soak into the ground, but lies on or flows over the ground instead. It includes information about flooding extents and depths. It is produced using national scale modelling and enhanced with compatible, locally produced modelling from lead local flood authorities (LLFAs).

RoFSW is a probabilistic product, meaning that it shows the overall risk, rather than the risk associated with a specific event or scenario. In externally published versions of this dataset, risk is displayed as one of three likelihood bandings: High - greater than or equal to 3.3% chance in any given year (1 in 30) Medium - less than 3.3% (1 in 30) but greater than or equal to 1% (1 in 100) chance in any given year Low - less than 1% (1 in 100) chance in any given year

This dataset presents the risk which takes account of the following climate change allowances based on the latest UK Climate Projections (UKCP18) from the Met Office, using the Representative Concentration Pathway (RCP) 8.5:

- the ‘Central’ allowance for the 2050s epoch (2040-2060) for risk of flooding from surface water.

These allowances include anticipated changes to peak rainfall intensity.

NB. This is a complex dataset, with preview available only on certain zoom levels. The Web Mapping service has been set to 1:50 000 in the

Abstract copyright UK Data Service and data collection copyright owner.

Public Perceptions of Climate Change and Energy Futures in Britain, 2010 is a nationally representative survey of Great Britain which examines perceptions and cognitions relating to climate change and the energy future of Britain. The survey aimed to examine how these perceptions have changed since previous evaluations but also has a theoretical goal of examining how context influences perceptions. Context was examined in a number of ways including how location impacts peoples’ perceptions. For example, does having personal experience of the recent flooding change beliefs? Also the significance of whether people consider climate change and energy issues in terms of themselves personally or in terms of society as a whole was explored, as well as how awareness of other issues such as energy security influences perceptions.

The survey also involved a local sample which was collected in two power generation locations (East Aberthaw in Wales, and Hinkley Point in Somerset). In addition, over sampling in Scotland and Wales allows statistical comparisons to be made between these and the England results.

Further information is available from the Understanding Risk research group web site and the ESRC Award web page.


Main Topics:

The following four main areas are investigated:

• perceptions of climate change and energy futures
• how the framework of climate change and security of supply have impacted on perceptions of energy futures
• how personal individual differences influence how information is received and processed
• how local environments and proximity to energy sources impact perceptions