The annual mean temperature in the United Kingdom has fluctuated greatly since 1990. Temperatures during this period were at their highest in 2022, surpassing 10 degrees Celsius. In 2010, the mean annual temperature stood at 7.94 degrees, the lowest recorded during this time. Daily temperatures Average daily temperatures have remained stable since the turn of the century, rarely dropping below 10 degrees Celsius. In 2010, they dropped to a low of nine degrees Celsius. The peak average daily temperature was recorded in 2022 when it reached 11.2 degrees. This was an increase of one 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 22.6 degrees Celsius. In comparison, the lowest monthly minimum temperature was in February of the same year, at just minus 0.6 degrees. This was an especially cold February, as the previous year the minimum temperature for this month was 2.6 degrees.

Temperature in the United Kingdom increased to 10.14 celsius in 2023 from 10.13 celsius in 2022. This dataset includes a chart with historical data for the United Kingdom Average Temperature.

The average temperature across the United Kingdom presented a trend of continuous growth since 1961. During the first period, from 1961 to 1990, the country recorded an average temperature of 8.3 degrees Celsius. In the next period, from 1991 to 2020, the UK's average temperature increased by 0.8 degrees Celsius and increased further by 0.5 degrees Celsius between 2014 and 2023. In the latter year, figures remained at 10 degrees Celsius, 1.7 degrees warmer than the average recorded between 1961 and 1990, illustrating the effects of climate change. Nevertheless, 2022 was the warmest year in the United Kingdom.

The United Kingdom's hottest summer ever recorded was in 2018, with an average temperature of 15.76 degrees Celsius. Meanwhile, 2023 saw the eighth hottest summer in the UK, with an average temperature of 15.35 degrees. In the last couple of decades, five of the top 10 warmest summers in the UK were recorded. New temperature records in 2022 In summer 2022, record-breaking temperatures of more than 40 degrees Celsius were recorded at several locations across the UK. Accordingly, 2022 was also the UK's warmest year on record, with the average annual temperature rising above 10 degrees Celsius for the first time. Since temperature recording began in 1884, the hottest years documented in the country have all occurred after 2003. England: the warmest country in the UK Amongst the countries that comprise the United Kingdom, England has generally seen the highest annual mean temperatures. In 2022, England’s average temperature also reached a new record high, at nearly 11 degrees Celsius. And while it’s not a typical sight in the United Kingdom, England also registered the most hours of sunshine on average, with Scotland being the gloomiest country out of the four.

The United Kingdom recorded its hottest-ever year in 2022, with an average temperature of 10.03 degrees Celsius. Since the start of temperature recording in 1884, the 10 warmest years recorded in the UK have been from 2003 onwards. Weather conditions are predicted to become more extreme due to climate change.

Temperatures have risen in the last 100 years around the world. In the 1910s, global average temperatures were some 0.38 degrees Celsius lower than the average temperatures between 1910 and 2000. In the most recent decade, the world experienced temperatures that were 1.21 degrees Celsius over the average.

1999-2017 - London SWT Weather data

1. Site: SWT
2. Country\City\Location: UK\London\ Southwark WoodsfordTower SWT
3. Latitude: 51.487760; Longitude: -0.091069
4. Anemometer height: 60 m
5. Weather station owner: Bill Legassick, Southwark Council. Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705
6. Email: Bill.Legassick@southwark.gov.uk

1. Sensor:
2. Sensor type:---- Model -------------: Date Installed
3. Anemometer : CDL Windset (EC8) ......... : 1999
4. Rain gauge : Campbell Scientific ARG-100 : 1999
5. Temperature: T107_C .....................: 1999
6. Humidity : HMP45A .....................: 1999

1. Files: Are Zipped
2. Filenames: Weather_Data_2008.CSV
3. Filetype: comma delimited

4. Header Row:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly AverageDirection,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,MaximumHourly Rainfall Rate,Time of Rainfall

5. Data: hourly averages

The United Kingdom experienced an average of 1,242.1 millimeters of rainfall in 2024, a decrease of 5.8 percent in comparison to the previous year. While 2024 saw substantial rainfall, it did not surpass the thus-far peak of the century, with 1,373 millimeters of rain recorded in 2000. Regional variations and seasonal patterns Rainfall distribution across the UK is far from uniform, with Scotland and Wales consistently receiving the highest annual precipitation. In 2024, they recorded an average of 1,571.7 millimeters and 1,600.8 millimeters, respectively, significantly above the UK’s average. This disparity is largely due to both countries’ mountainous terrain, which is more susceptible to Atlantic weather systems. Seasonally, the wettest months in the UK typically occur in the winter, with the highest precipitation levels seen between November and February. Climate change impact on UK weather Climate change is influencing UK weather patterns, leading to warmer and wetter conditions overall. While annual rainfall fluctuates, there is a trend towards more extreme weather events. For example, 2020 and 2022 saw rain deviations from the long-term mean in the UK of more than 100 millimeters in February. As weather patterns continue to evolve, monitoring rainfall trends remains crucial for understanding and adapting to a changing climate.

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 UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2022.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.

At many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.

Liquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.

2011\ London\ SWT Weather data
Data Type: Weather station
Site information:
Latitude: 51.487760
Longitude: -0.091069
Anemometer height: 60 m
Owner: Bill Legassick, Southwark Council. Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705
Email: Bill.Legassick@southwark.gov.uk
Sensor information
Sensor type Model Date installed
Anemometer CDL Windset (EC8) 1999
Rain gauge Campbell Scientific ARG-100 1999
Temperature probe T107_C 1999
Humidity probe HMP45A 1999
Files: Are Zipped
Filenames: Weather_Data_2008.CSV
Filetype: comma delimited
Header Row:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly Average Direction,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,Maximum Hourly Rainfall Rate,Time of Rainfall
Data: hourly averages2011\ London\ SWT Weather data

2012 - London SWT Weather data

1. Site: SWT
2. Country\City\Location: UK\London\ Southwark W Tower SWT
3. Latitude: 51.487760
4. Longitude: -0.091069
5. Anemometer height: 60 m
6. Owner: Bill Legassick, Southwark Council. Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705
7. Email: Bill.Legassick@southwark.gov.uk

1. Sensor information
2. Sensor type:---- Model -------------: Date Installed
3. Anemometer : CDL Windset (EC8) ......... : 1999
4. Rain gauge : Campbell Scientific ARG-100 : 1999
5. Temperature: T107_C .....................: 1999
6. Humidity : HMP45A .....................: 1999

1. Files: Are Zipped
2. Filenames: Weather_Data_2008.CSV
3. Filetype: comma delimited
4. Header Row:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly AverageDirection,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,MaximumHourly Rainfall Rate,Time of Rainfall
5. Data: hourly averages

2010\ London\ SWT Weather data

Data Type: Weather station
Site information:
Latitude: 51.487760
Longitude: -0.091069
Anemometer height: 60 m
Owner: Bill Legassick, Southwark Council. Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705
Email: Bill.Legassick@southwark.gov.uk
Sensor information
Sensor type Model Date installed
Anemometer CDL Windset (EC8) 1999
Rain gauge Campbell Scientific ARG-100 1999
Temperature probe T107_C 1999
Humidity probe HMP45A 1999
Files: Are Zipped
Filenames: Weather_Data_2008.CSV
Filetype: comma delimited
Header Row:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly Average Direction,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,Maximum Hourly Rainfall Rate,Time of Rainfall
Data: hourly averages2010\ London\ SWT Weather data

Wind speed averages in the United Kingdom are generally highest in the first and fourth quarters of each calendar year – the winter months. Since 2010, the UK’s highest wind speed average was recorded in the first quarter of 2020, at 11.5 knots. During this period, 2010 was the only year that had the greatest wind speeds outside the winter months, with an average of 8.4 knots in the third quarter. In 2024, wind speeds ranged between a low of 7.9 knots in the third quarter and 9.4 knots in the first quarter. With few exceptions, UK wind speeds generally average at least eight knots annually. 2015 marked the year with the highest average wind speed in the UK (since the beginning of the reporting period in 2001), reaching an average of 9.4 knots. Wind power The UK has some of the best wind conditions in Europe for wind power. By 2023, there were 39 offshore wind farms operating across the UK, by far the most in Europe. Meanwhile, offshore wind power additions in the UK reached 1.14 gigawatts that same year. Quarterly rainfall Another weather phenomenon, UK rainfall also tends to be heaviest in the winter months. The average rainfall in the second quarter of 2024 was 254.5 millimeters, with figures in 2011 spiking to 738.6 millimeters. That year, precipitation levels in some parts of Scotland were the highest in one hundred years, while southern parts of England kept remarkably dry.

This dataset contains derived annual mean globally-averaged variables from an existing global coupled carbon-climate Earth System Model and a novel atmosphere-ocean box model to understand surface warming response in terms of changes in global carbon inventories, empirical heat budget, and variation in time with carbon emissions. The source model outputs were generated by Thomas Froelicher in 2015 using a 1000-year simulation of the global coupled carbon-climate Earth System Model developed at the Geophysical Fluid Dynamics Laboratory (GFDL ESM2M). A scenario was forced of a 1% annual rate increase in carbon dioxide from preindustrial levels until global mean surface air temperature increased by 2 degrees Celsius since the preindustrial, after this point emissions of carbon were set to zero and all other non-carbon dioxide greenhouse gases were kept at preindustrial levels. Output parameters included: ocean temperature; salinity; dissolved inorganic carbon; ocean alkalinity; dissolved inorganic phosphate; surface air temperature; atmospheric carbon dioxide; cumulative carbon emission. Annual mean variables were then derived from these data. This was determined by calculated changes in: ocean carbon inventory; ocean carbon under saturation; saturated dissolved inorganic carbon; ocean dissolved inorganic carbon; radiative forcing from carbon dioxide; ocean heat uptake. Additionally the dependence of radiative forcing on carbon emissions, dependence of surface warming on radiative forcing and surface warming dependence on radiative forcing were determined. The box model consists of three homogeneous layers: a well‐mixed atmosphere, an ocean mixed layer with 100‐m thickness, and an ocean interior with 3,900‐m thickness - all assumed to have the same horizontal area. The model solves for the heat and carbon exchange between these layers, including physical and chemical transfers, however ignoring biological transfers, and sediment and weathering interactions. The model is forced from an equilibrium by carbon emitted into the atmosphere with a constant rate of 20 PgC/year for 100 years and integrated for 1,000 years. Ocean ventilation is represented by the ocean interior taking up the heat and carbon properties of the mixed layer on an e-folding time scale of 200 years. These datasets were generated as part of the Natural Environment Research Council (NERC) Discovery Science project "Mechanistic controls of surface warming by ocean heat and carbon uptake" standard grant reference NE/N009789/1 lead by Principal Investigator - Professor Ric Williams, University of Liverpool and Co-Investigator - Dr Philip Goodwin, University of Southampton. Data are acrvhived at the British Oceanographic Data Centre.

We present a high resolution (~20 to 100 years temporal resolution) reconstruction of hydrological changes in the Makassar Strait over the last 14 kyr from Core SO217-18517 retrieved off the Mahakam Delta (1°32.198'S, 117°33.756'E; 698 m water depth) during the SO217 Makassar-Java (MAJA) Cruise. Sea surface temperatures, based on Mg/Ca of Globigerinoides ruber and alkenone UK'37, and sea water d18O reconstructions, based on G. ruber d18O and Mg/Ca, in combination with sortable silt grain-size measurements and X-ray fluorescence (XRF) core scanner derived elemental data provide evidence for increased precipitation during the Bølling-Allerød (BA) and early Holocene and for warmer and more saline surface waters and a decrease in the intensity of the Indonesian Throughflow (ITF) during the Younger Dryas (YD). XRF derived Log (Zr/Rb) records, sortable silt data and increased sedimentation rates indicate decreased winnowing, interpreted as a slowdown of the ITF thermocline flow during the YD. We attribute this decline in ITF intensity to slowdown of the Atlantic Meridional Overturning Circulation (AMOC) during the YD. We suggest that changes in Makassar Strait surface hydrology during this interval of northern hemisphere cooling and southern hemisphere warming were related to a southward displacement of the Intertropical Convergence Zone.

This dataset is a model output, from the Grid-to-Grid hydrological model driven by weather@home2 climate model data. It provides a 100-member ensemble of monthly mean flow (m3/s) and soil moisture (mm water/m soil) on a 1 km grid for the following time periods: historical baseline (HISTBS: 1900-2006), near-future (NF: 2020-2049) and far-future (FF: 2070-2099). It also includes a baseline period (BS: 1975-2004). To aid interpretation, two additional spatial datasets are provided: - Digitally-derived catchment areas on a 1km x 1km grid - Estimated locations of flow gauging stations on a 1km x 1km grid and as a csv file. The data were produced as part of MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity), which was a UK NERC-funded research project (2014-2017) that developed a risk-based approach to drought and water scarcity.

Risk of Flooding from Rivers and the Sea (RoFRS) shows the chance of flooding from rivers and the sea taking into account the presence and condition of flood defences. It is our main way of communicating flood risk from rivers and sea to the public through our ‘Check Your Long Term Flood Risk’ service on gov.uk. Climate change scenarios have been produced for this dataset to indicate the predicted impacts of climate change on future risk.

While flood defences reduce the level of risk they do not completely remove it. For example, water can flow over the top of the defence, or they can fail in extreme weather conditions or if they are in poor condition. As a result, the RoFRS maps may show that there is risk to areas behind some flood defences.

RoFRS 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 four 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) but greater than or equal to 0.1% (1 in 1,000) chance in any given year 
Very Low - less than 0.1% chance in any given year (1 in 1,000) 

Our climate change allowances include anticipated changes to:

- Peak river flow 
- Sea level rise 
- Offshore wind speed and extreme wave height 

The climate change allowances are based on the latest UK Climate Projections (UKCP18) from the Met Office, using the Representative Concentration Pathway (RCP) 8.5.

Our Flood risk assessment: climate change allowances include several different allowances reflecting the range of possible future climates. They also provide allowances for different periods of time, acknowledging that some users will want to look further into the future than others. The periods of time vary for each source of risk because equivalent datasets for each source are not always available.

Check Your Long-Term Flood Risk is aimed at supporting individuals, communities and organisations making short- and medium-term decisions to manage future flood risk. We have therefore chosen:

- the ‘Central’ allowance for the 2050s epoch (2040-2069) for risk of flooding from rivers 
- the ‘Higher Central’ allowance for risk of flooding from the sea, accounting for cumulative sea level rise to 2065 

This data also presents the likelihood of flooding for the following depths:

0.2m 
0.3m 
0.6m 
0.9m 
1.2m 

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

This dataset is a model output, from the Grid-to-Grid hydrological model driven by weather@home2 climate model data. It provides a 100-member ensemble of daily mean river flow (m3/s) for 260 catchments, for the following time periods: historical baseline (HISTBS: 1900-2006), near-future (NF: 2020-2049) and far-future (FF: 2070-2099). It also includes a baseline period (BS: 1975-2005). The catchments correspond to locations of NRFA gauging stations (http://nrfa.ceh.ac.uk/). The data were produced as part of MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity), which was a UK NERC-funded research project (2014-2017) that developed a risk-based approach to drought and water scarcity.

Abstract

The first detailed reconstruction of the terrestrial paleoclimate development of the UK Neogene (?Langhian to Piacenzian) is presented. The paleoclimate data are derived from the paleobotanical record using two probability-based reconstruction techniques CREST (Climate REconstruction SofTware) (Chevalier et al. 2014) and CRACLE (Climate Reconstruction Analysis using Coexistence Likelihood Estimation) (Harbert & Nixon 2015) that use Bayesian and likelihood estimation probability respectively. The results of these reconstructions are presented alongside reconstructions using the widely-applied Co-existence Approach (CA) (Utescher et al. 2014) for comparison. While all three techniques use the climate requirements of their Nearest Living Relatives as the basis of their reconstruction, they use different database observations. CREST and CRACLE use the GBIF (Global Biodiverstiy Information Facility) (GBIF, 2021) as well as WorldClim inputs for the 19 bioclimate variables used by BIOCLIM (http://www.worldclim.org/bioclim). Meanwhile, the CA uses the Palaeoflora database, meaning the input for the three models is different. The reconstructions for the UK Neogene palaeoclimate come from 4 localities (12 samples total) spanning the Middle Miocene (Langhian) to Pliocene (Piacenzian): Trwyn y Parc, Anglesey (Middle Miocene), Brassington Formation, Derbyshire (Serravallian-Tortonian), Coralline Crag Formation (latest Zanclean-earliest Piacenzian) and Red Crag Formation (Piacenzian-Gelasian) of southeast England. We present CREST and CRACLE reconstructions of Mean Annual Temperature (MAT), Mean Temperature of Warmest Quarter (MTWQ), Mean Temperature of Coldest Quarter (MTCQ), Mean Annual Precipitation (MAP) and precipitation seasonality (CoV ×100). The CA does not reconstruct MTWQ, MTCQ or precipitation seasonality. Instead, the CA reconstructs Warmest Month Mean Temperature (WMMT) and Coldest Month Mean Temperature (CMMT). The proportion of rainfall falling in the wettest months of the year (RMPwet(%)) was used as a proxy for precipitation seasonality following the methodology of Jacques et al. (2011) and Utescher et al. (2015). The CREST R-code output provides 0.5 and 0.95 (2-σ) uncertainties as well as an optimum and mean for each variable. The CRACLE R-code output provides both parametric and non-parametric joint likelihoods (P-CRACLE and N-CRACLE) with 0.95 (2-σ) uncertainties and a mean that is based on P-CRACLE. The CA generates a minimum and maximum likelihood which together comprise the coexistence interval. The Neogene climate reconstruction of the UK shows a cooling trend from the Langhian to the Pliocene-Pleistocene boundary. CREST and CRACLE produce trends and values consistent with Co-existence Approach data with 0.95 uncertainties overlapping with the CA coexistence interval.

File Descriptions

Table S1 displays the complete reconstruction for the UK Neogene using CREST, CRACLE and the Co-existence Approach.

Table S2 displays detailed site information including: modern and paleo latitude and longitude, dating technique, modern climatology and fossil assemblage diversity (number of fossil taxa versus number of NLRs used for climate reconstruction). Modern climatology has been included to serve as a comparison to the reconstructed Neogene climate. This data has been extracted from WorldClim 2.1 (Fick & Hijmans, 2017).

Data Set S1 contains the list of fossil spore and pollen taxa per site and associated Nearest Living Relatives (NLRs), where identifiable, used as the input for CREST, CRACLE and the Co-existence Approach. Relic taxa are included and highlighted in red.

Data Set S2 is included to show the effect relic taxa have on paleoclimate reconstructions. The relic taxa are removed following the protocol of Utescher et al. (2014) whereby known relic taxa are removed from analyses to avoid biased reconstructions. Relic taxa removed from analyses include Cathaya, Cryptomeria, Pinus sylvestris and Sciadopitys when present.

Data Set S3 is included to show the effects of removing family-level identifications in CRACLE reconstructions. Removing families is shown to generate a less informative reconstruction. Including both genera- and family-level classifications of NLR (Nearest Living Relative) is recommended, however we suggest identifying NLRs (Nearest Living Relatives) to genera-level wherever possible.