The United Kingdom's average minimum temperature in July 2021 measured 12.1 degrees Celsius. This month, recorded the highest minimum temperature during the reported period. Since 2015, the lowest monthly minimum temperature in the UK was recorded in February 2018, at -0.7 degrees Celsius. This was the first time during this period that the average monthly minimum temperature dropped below zero degrees Celsius, while in January 2021 the second time took place, at -0.5 degrees Celsius. Further information about the weather in the United Kingdom can be found here.

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

England's highest monthly mean air temperatures are typically recorded in July and August of each year. Since 2015, the warmest mean temperature was measured in July 2018 at 18.8 degrees Celsius. On the other hand, February of that same year registered the coolest temperature, at 2.6 degrees Celsius. In September 2025, the mean air temperature was 13.8 degrees Celsius, matching the figure recorded the same month the previous year. The English weather England is the warmest region in the United Kingdom and the driest. In 2024, the average annual temperature in England amounted to 10.73 degrees Celsius – around 1.1 degrees above the national mean. That same year, precipitation in England stood at about 1,020 millimeters. By contrast, Scotland – the wettest region in the UK – recorded over 1,500 millimeters of rainfall in 2024. Temperatures on the rise Throughout the last decades, the average temperature in the United Kingdom has seen an upward trend, reaching a record high in 2022. Global temperatures have experienced a similar pattern over the same period. This gradual increase in the Earth's average temperature is primarily due to various human activities, such as burning fossil fuels and deforestation, which lead to the emission of greenhouse gases. This phenomenon has severe consequences, including more frequent and intense weather events, rising sea levels, and adverse effects on human health and the environment.

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

What does the data show?

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

Limitations of the data

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

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

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

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

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

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

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

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the monthly averages of temperature for 2050-2079 were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

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

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

Data source

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

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

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

UKCP18 v20190731 (downloaded 04/11/2021)

Useful links

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

These layers are the outputs of research which developed a national river temperature model for Scotland capable of predicting both daily maximum river temperature and sensitivity to climate change. The layers show the following: summer_max_tw_2015_16 – Predictions of maximum daily river temperatures for the hottest day between July 2015 and June 2016. summer_max_tw_2003 – Predictions of maximum daily river temperatures for the hottest year in the last 20 years (2003). summer_climate_change_sensitivity – Predictions of the change in river temperature that would result from a 1°C increase in air temperature. A fourth layer has been developed to combine the outputs from “summer_max_tw_2003” and “summer_climate_change_sensitivity” into a single layer that can be used to prioritise management where the relative importance of maximum temperature and temperature change are considered to be equal. This was achieved by (1) dividing the predictions of ‘summer_max_tw_2003’ and ‘summer_climate_change_sensitivity’ into 5 equal categories between the minimum and maximum observed values (2) assigning these categories a value ranging from 1 (the hottest / most sensitive rivers) to 5 (the coolest / least sensitive rivers) (3) sum the rankings (-1) to produce an overall priority ranking (1:9) where rivers ranked as 1 are the highest priority for management (i.e. high river temperature and high climate sensitivity) and 9 the lowest. Management_Priority_Layer – Management priority on a scale of 1:9 where 1 is the highest priority (i.e. high river temperature and high climate sensitivity) and 9 the lowest. Please Note * This layer was derived by the Scottish Government from a licensed dataset. It is not downloadable or routinely available. The data can be shared on request if a user provides evidence that they hold a licence from the UK Centre for Ecology and Hydrology (UKCEH) for the 1:50,000 Digital River Network (https://www.ceh.ac.uk/data/15000-watercourse-network) Data and Resources SRTMN - Predictions of maximum daily river temperatures for the hottest day between July 2015 and June 2016 View summer_max_tw_2015_16 on Marine Scotland Maps portal SRTMN - Predictions of maximum daily river temperatures for the hottest year in the last 20 years (2003) View summer_max_tw_2003 on Marine Scotland Maps portal SRTMN - Predictions of the change in river temperature that would result from a 1°C increase in air temperature View summer_climate_change_sensitivity on Marine Scotland Maps portal

Monthly maximum temperatures in the United Kingdom (UK) tend to follow a similar pattern, with maximum temperatures typically highest in July and August. The warmest maximum temperature was in July 2018, at 22.6 degrees Celsius. During this period the lowest maximum temperature, 4.9 degrees Celsius, was measured in January 2021.

[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 contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. Daytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class. The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width. The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml). LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. Dataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

Projected regional average change in seasonal and annual temperature and precipitation extremes for the IPCC SREX regions for CMIP5. The data were produced in 2013 by the Intergovernmental Panel on Climate Change (IPCC) Working Group II (WGII) Chapter 14 supplementary material (SM) author team for the IPCC Fifth Assessment Report (AR5). Regional average seasonal and annual temperature and precipitation extremes for the periods 2016-2035, 2046-2065 and 2081-2100 for CMIP5 General Circulation Model (GCM) projections are compared to a baseline of 1986-2005 from each model's historical simulation. The temperature and precipitation data are based on the difference between the projected periods and the historical baseline for which the 25th, 50th and 75th percentiles, and the lowest and highest responses among the 32 models which are expressed for temperature as degrees Celsius change and for precipitation as a per cent change. The temperature responses are averaged over the boreal winter and summer seasons; December, January, February (DJF) and June, July and August (JJA) respectively. The precipitation responses are averaged over half year periods, boreal winter (BW); October, November, December, January, February and March (ONDJFM) and boreal summer (BS); April, May, June, July, August and September (AMJJAS). Regional averages are based on the SREX regions defined by the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (IPCC, 2012: also known as "SREX"). Added to the SREX regions are additional regions containing the two polar regions, the Caribbean, Indian Ocean and Pacific Island States. The data are further categorised by the land and sea mask for each SREX region.

Subpolar regions are key areas to study natural climate variability, due to their high sensitivity to rapid environmental changes, particularly through sea surface temperature (SST) variations. Here, we have tested three independent organic temperature proxies (UK'37, TEX86 and LDI) on their potential applicability for SST reconstruction in the subpolar region around Iceland. UK'37, TEX86 and TEXL86 temperature estimates from suspended particulate matter showed a substantial discrepancy with instrumental data, while long chain alkyl diols were below detection limit in most of the stations. In the northern Iceland Basin, sedimenting particles revealed a seasonality in lipid fluxes i.e. high fluxes of alkenones and GDGTs were measured during late spring-summer, and high fluxes of long chain alkyl diols during late summer. The flux-weighted average temperature estimates had a significant negative (ca. 2.3°C for UK'37) and positive (up to 5°C for TEX86) offset with satellite-derived SSTs and temperature estimates derived from the underlying surface sediment. UK'37 temperature estimates from surface sediments around Iceland correlate well with summer mean sea surface temperatures, while TEX86 derived temperatures correspond with both annual and winter mean 0-200 m temperatures, suggesting a subsurface temperature signal. Anomalous LDI-SST values in surface sediments, and low mass flux of 1,13- and 1,15-diols compared to 1,14-diols, suggest that Proboscia diatoms are the major sources of long chain alkyl diols in this area rather than eustigmatophyte algae, and therefore the LDI cannot be applied in this region.

In 2024, the average summer temperature in the United Kingdom was ***** degrees Celsius. Over the time period from 1990 through 2024, the average summer temperature in the UK fluctuated from a low of ***** degrees in 1993 to a high of ***** degrees in 2018.

The NI Land classification was developed using the Merlewood method of multivariate land classification. This involved selecting a sample of 700 1km grid squares, representing 5% of NI land area. For each of these squares, attributes on climate, elevation & topography, vegetation, hydrology, settlement, geology, and soils were recorded from maps. Using TWINSPAN, this data was used to produce a land classification hierarchy, yielding 23 land classes for N.Ireland. This was then used to classify all remaining 1km grid squares in N. Ireland. In total, 14,377 1km grid squares were assigned to one of the 23 land classes.For detailed information on the methodology and descriptions of each of the land classes see, Land Classification report edited.pdfA brief description of the Land Classification Groups identified in the dataset are listed below, for more detail see the report above. GroupLand Class GroupGeographic LocationClimateElevation, topography and hydrologySettlementGeologySoils11, 2, 3, 4Mainly South-East, Co. Armagh and Co. Down.Also present in Co. Fermanagh between Upper Lough Erne and Lough Macnean.Land class 1 is coastal, Strangford Lough and the Co. Down coast.Sleet/snow, low.January temperature, moderate – high.July temperature, moderate – high.Lowland.Elevation 0-500 ft (0-152 m).Flat, undulating and drumlin landscape.River and stream network.Source of river/stream.Developed road network (all types).Urban land.Intermediate number of buildings.Ordovician/Silurian shales and greywackes.Basic igneous.Mixed limestone, shale and sandstone.Sandstones and conglomerates. Acid brown earths.Gleys25Mainly South-West, Co. Fermanagh.Lakeland, in particular Lough Erne but also Lough Neagh.North Co. Derry coast, in particular Magilligan.River Foyle.Sleet/snow, low – moderate.January temperature, moderate – high.July temperature, low – moderate.Lowland.Elevation, 0-500 ft (0 – 152 m).Flat undulating landscape.Stream and river network.Inland water bodies.Developed road network (all types).Moderately low number of buildings.Mixed limestone, shale and sandstone.Limestones (other than Precambrian).Sandstones and conglomerates.Gleys.Brown/grey podzolics.36,7,8 Adjacent to west side of Antrim plateau and Belfast Hills.West of Mourne Mountains and Slieve Croob.Adjacent of Sperrins and North Derry Mountains.Lowland, Co. Tyrone and east Co. Fermanagh.Land class 6 is more characteristic of the South-West, land class 7 the South and land class 8 the North. Sleet/snow, moderate – high.January temperature, low – moderate.July temperature, low – moderate.Lowland.Elevation, 200-500 ft (62-152 m).Flat and undulating landscape and drumlin bottoms.River and stream network.Source of river/stream. Developed road network (all types).Intermediate number of buildings. Sandstones and conglomerates.Basic igneous (Antrim lavas).Mixed limestone, shale and sandstone.GleysAcid brown earths.Brown/grey brown podzolics.49, 10, 11, 12Lowlands between Lough Neagh and the north coast and surrounding Lough Neagh. Land classes 9 and 10 largely surround Lough Neagh.Land classes 11 and 12 are characteristic of land between Lough Neagh and the north coast.Sleet/snow, moderate.January temperature, low - moderate. July temperature, low - high.Lowland.Elevation 0-500 ft (0-152 m).Flat and undulating landscape.River and stream network.Developed road network (all types). Urban land.Intermediate number of buildings.Basic igneous (Antrim lavas).Acid brown earths.Gleys.Blanket peat (basin and low level).513, 14, 15, 16Widely dispersed but particularly common at lowland/upland margin.Land class 13 is characteristic west of the Mourne Mountains and Slieve Croob in Co.Down. It is also present adjacent to the Sperrin Mountains.Land class 14 is widely dispersed.Land class 15 has a predominantly south-west distribution.Land class 16 is associated with the coast, river valleys and glens in the North and North-East.Sleet/snow, low - moderate.January temperature, moderate - high. July temperature, low - moderate.Lowland.Elevation, 0-500 ft (0-152m). Sloping and drumlin landscape. River and stream network.Developed road network (all types). Urban land.Intermediate number of buildings.Basic igneous (Antrim lavas). Sandstones and conglomerates. Mixed limestone, shale and sandstone. Precambrian limestones.Basic igneous.Shales and mudstone (including coal measures). Ordovician/Silurian, shales and greywackes.Gleys.Acid brown earths. Brown/grey brown podzolics.617, 18, 19Mourne Mountains and Slieve Croob. South Armagh.Centi-al Co. Tyrone and east Co. Fermanagh. Sperrin Mountains.Antrim plateau.Land class 17 is particularly common in the South-East (Mourne Mountains, Slieve Croob and south Armagh).Land class 18 is dispersed throughout but is centred on the south-west of the Antrim Hills.Land class 19 is mainly located in Co. Tyrone and east Co. Fermanagh.Sleet/snow, low - high.January temperature, low - moderate. July temperature, moderate - high.Upland.Elevation, 500-800 ft (153-244 m). Sloping and hilly landscape.Stream network.Source of river/stream.Secondary, tertiary and minor road network. Moderate number of buildings.Basic igneous.Granite, gneiss.Basic igneous (Antrim lavas). Ordovician/Silurian, shales and greywackes. Sandstones and conglomerates.Acid brown earths.Gleys.Brown/grey brown podzolics. Peaty podzols.720, 21Antrim plateau, Belfast Hills.North De1'ry Hills, Sperrin Mountains.Mountain areas in Co. Fermanagh and Co. Tyrone.Land class 20 is more characteristic than land class 21 in the South-West (Co. Fermanagh, Co. Tyrone).Sleet/snow, moderate - high.January temperature, moderate - high. July temperature, low - moderate.Upland/mountains.Elevation 500-800 ft (153-244 m). Sloping and upland plateau landscape. Stream network.Source of river/stream.Secondary, tertiary and minor road network. Low number of buildings.Schists.Basic igneous (Antrim Lavas).Mixed limestone, shale and sandstone. Precambrian, undifferentiated.Sandstones and conglomerates. Chalk and related rock.Precambrian limestones.Blanket peat (basalt and high level). Peaty gleys.Gleys.Brown/grey brown podzolics.822, 23Mountain areas throughout.Land class 22 is more characteristic in the South-West (Co. Fermanagh, Co. Tyrone) than land class 23.Land class 23 is more common than land class 22 in the Mourne Mountains.Sleet/snow, low - high.January temperature, moderate - high. July temperature, low - moderate.Mountains.Elevation > 800 ft (> 244 m).Sloping and mountain plateau landscape. Stream network.Source of river/stream.Minor road network.Few buildings.Schists.Basic igneous (Antrim Lavas).Mixed limestone, shale and sandstone. Basic igneous.Granite gneiss.Blanket peat (basalt and high level). Peaty gleys.Peaty podzols.Gleys.

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

This metadata references the scientific article: "Boreal temperature variability inferred from maximum latewood density and tree-ring width data, Wrangell Mountain region, Alaska", which can be download from here. Authors: Nicole K. Davi,(a,)* Gordon C. Jacoby,(a) and Gregory C. Wiles(b) (a)Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA (b)Department of Geology, The College of Wooster, Wooster, OH 44691, USA Variations in both width and density of annual rings from a network of tree chronologies were used to develop high-resolution proxies to extend the climate record in the Wrangell Mountain region of Alaska. We developed a warm-season (July-September) temperature reconstruction that spans A.D. 1593-1992 based on the first eigenvector from principal component analysis of six maximum latewood density (MXD) chronologies. The climate/tree-growth model accounts for 510f the temperature variance from 1958 to 1992 and shows cold in the late 1600s early 1700s followed by a warmer period, cooling in the late 1700s early 1800s, and warming in the 20th century. The 20th century is the warmest of the past four centuries. Several severely cold warm-seasons coincide with major volcanic eruptions. The first eigenvector from a ring-width (RW) network, based on nine chronologies from the Wrangell Mountain region (A.D. 1550-1970), is correlated positively with both reconstructed and recorded Northern Hemisphere temperatures. RW shows a temporal history similar to that of MXD by increased growth (warmer) and decreased growth (cooler) intervals and trends. After around 1970 the RW series show a decrease in growth, while station data show continued warming, which may be related to increasing moisture stress or other factors. Both the temperature history based on MXD and the growth trends from the RW series are consistent with well-dated glacier fluctuations in the Wrangell Mountains and some of the temperature variations also correspond to variations in solar activity. 2003 University of Washington. Published by Elsevier Inc. All rights reserved. Keywords: Paleoclimate; Tree rings; Maximum latewood density; Ring width; Temperature; Boreal; Alaska CRN003, PI Dr. Brian Luckman.

Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC): Ensemble member output from Unified Model runs as described in Flack et al. (2018): Convective-Scale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405

The dataset contains ensemble run output from 36 hour long runs under different model set ups (see details below) for 6 case studies (see Flack et al. 2018 for greater detail). The case studies (and model output available in the dataset) chosen related to a spectrum of 'convective adjustment time scales', defined as the ratio between the convective available potential energy (CAPE) and its rate of release at the convective scale. 'control' run files contain large scale rainfall rates and amounts whilst the 'control_multilevel' files contain various parameters on various levels, including mean sea level pressure, zonal, meridional and vertical wind components, specific humidity and temperature.

• Case A: 20th April 2012, part of the Dynamical and Microphysical Evolution of Convective Storms (DYMECS) field experiment (Stein et al. 2015), showing typical conditions for scattered showers in the United Kingdom.
• Case B: 12 August 2013, for a case where a surface low was situated over Scandinavia and the Azores high was beginning to build, leading to persistent northwesterly flow.
• Case C: 23rd July 2013, relating to the fifth intensive observation period (IOP 5) of the Convective Precipitation Experiment (COPE; Leon et al. 2016). A low pressure system was centered to the west of the United Kingdom with several fronts ahead of the main center, which later decayed.
• Case D: 2nd August 2013, covering IOP 10 of the COPE field campaign, with convection initiating at 1100 UTC. The synoptic situation shows a low pressure system centered to the west of Scotland, which led to southwesterly winds and a convergence line being set up along the North Cornish coastline (in southwest England).
• Case E: 27th July 2013, covers the period of IOP 7 of the COPE field campaign where two mesoscale convective systems (MCS) influenced the U.K.’s weather throughout the forecast period.
• Case F: 5th August 2013, was chosen for the complex situation for considering convective-scale perturbation grown and a second case driven by the boundary conditions as seen during IOP 12 of the COPE campaign

A brief description of the model run IDs and model setup is given below.

The model used to create these ensembles is the Met Office Unified Model (MetUM). The United Kingdom Variable resolution (UKV) configuration is used, and so the data has a grid spacing of approximately 1.5 km. This was run at version 8.2 and run with the MetUM Graphical User Interface (GUI).

run ID: xkyib

This is the control experiment and everything is kept identical to the operational running of this configuration of the MetUM.

run ID: xldef

Here the Gaussian potential temperature perturbations are added into the model. Full details of the perturbation method are described in Flack et al. (2018) Convective-Scale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405, however a brief overview is given below:

A Gaussian distribution (defined using random numbers between +/- 1 at each grid point, with the seed determined by the time the model is ran) is created at every grid point in the domain. A superposition is created and rescaled to 0.1 K so as to be an appropriate amplitude for boundary layer noise. Each of the Gaussian distributions have a standard deviation of 9km so as to be added onto an appropriate scale for the model. The perturbations are added in at a model hybrid height of 261.6 m (approximately the 8th model level).

The wettest months in the United Kingdom tend to be at the start and end of the year. In the period of consideration, the greatest measurement of rainfall was nearly 217 millimeters, recorded in December 2015. The lowest level of rainfall was recorded in April 2021, at 20.6 millimeters. Rainy days The British Isles are known for their wet weather, and in 2024 there were approximately 164 rain days in the United Kingdom. A rainday is when more than one millimeter of rain falls within a day. Over the past 30 years, the greatest number of rain days was recorded in the year 2000. In that year, the average annual rainfall in the UK amounted to 1,242.1 millimeters. Climate change According to the Met Office, climate change in the United Kingdom has resulted in the weather getting warmer and wetter. In 2022, the annual average temperature in the country reached a new record high, surpassing 10 degrees Celsius for the first time. This represented an increase of nearly two degrees Celsius when compared to the annual average temperature recorded in 1910. In a recent survey conducted amongst UK residents, almost 80 percent of respondents had concerns about climate change.

Meteorological data acquired on board the R/V Hespérides with an AANDERAA Scanning Unit 3010 Station in continuous mode during the ELEFANTE-12 cruise