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.

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.

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.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

READER (REference Antarctic Data for Environmental Research) is a project of the Scientific Committee on Antarctic Research (SCAR http://www.scar.org/) and has the goal of creating a high quality, long term dataset of mean surface and upper air meteorological measurements from in-situ Antarctic observing systems. These data will be of value in climate research and climate change investigations.

The primary sources of data are the Antarctic research stations and automatic weather stations. Data from mobile platforms, such as ships and drifting buoys are not being collected since our goal is to derive time series of data at fixed locations.

Surface and upper air data are being collected and the principal statistics derived are monthly and annual means. Daily data will not be provided in order to keep the data set to a manageable size. With the resources available to the project, it is clearly not possible to collect all the information that could be required by the whole range of investigations into change in the Antarctic. Instead a key set of meteorological variables (surface temperature, mean sea level pressure and surface wind speed, and upper air temperature, geopotential height and wind speed at standard levels) are being assembled and a definitive set of measurements presented for use by researchers.

A lot of stations have been operated in the Antarctic over the years; many for quite short periods. However, our goal here is to provide information on the long time series that can provide insight into change in the Antarctic. So to be included, the record from a station must extend for 25 years, although not necessarily in a continuous period, or be currently in operation and have operated for the last 10 years. In READER we have chosen to use only data from year-round stations.

1. There is growing interest in the effects of wind on wild animals, given evidence that wind speeds are increasing and becoming more variable in some regions, particularly at temperate latitudes. Wind may alter movement patterns or foraging ability, with consequences for energy budgets and, ultimately, demographic rates. 2. These effects are expected to vary among individuals due to intrinsic factors such as sex, age or feeding proficiency. Furthermore, this variation is predicted to become more marked as wind conditions deteriorate, which may have profound consequences for population dynamics as the climate changes. However, the interaction between wind and intrinsic effects has not been comprehensively tested. 3. In many species, in particular those showing sexual size dimorphism, males and females vary in foraging performance. Here, we undertook year-round deployments of data loggers to test for interactions between sex and wind speed and direction on foraging effort in adult European shags Phalacrocorax aristotelis, a pursuit-diving seabird in which males are c. 18% heavier. 4. We found that foraging time was lower at high wind speeds but higher during easterly (onshore) winds. Furthermore, there was an interaction between sex and wind conditions on foraging effort, such that females foraged for longer than males when winds were of greater strength (9% difference at high wind speeds vs. 1% at low wind speeds) and when winds were easterly compared with westerly (7% and 4% difference, respectively). 5. The results supported our prediction that sex-specific differences in foraging effort would become more marked as wind conditions worsen. Since foraging time is linked to demographic rates in this species, our findings are likely to have important consequences for population dynamics by amplifying sex-specific differences in survival rates.

This dataset contains gridded meteorological data associated with challenging periods of weather for highly-renewable UK and European electricity systems of the future collected during the Adverse Weather Scenarios for Future Electricity Systems project. This project is a collaboration between the Met Office, the National Infrastructure Commission and the Climate Change Committee. More details about the project can be found in the associated documentation.

Two categories of challenging weather conditions; long duration adverse events and short duration wind ramping events, are provided.

Long duration events

The long duration event component of the dataset provides daily time series at 60 x 60 km spatial resolution, covering a European domain, for surface temperature, 100 m wind speed and net surface solar radiation data, representative of a selection of adverse weather scenarios. Each adverse weather scenario is contained within a time slice of data. For summer-time events, one calendar year (January - December) of data is provided, with the summer-time event occurring in the summer of that year. For winter-time events, two calendar years of data are provided, with the winter-time event occurring in the winter (October-March) intersecting the two calendar years. In all cases, the start date, duration and severity of the adverse weather event, contained within the time slice of data, are given in the NetCDF global ttributes.

Three types of long-duration adverse weather scenarios are represented: winter-time wind-drought-peak-demand events, summer-time wind-drought-peak-demand events, and summer-time surplus generation events. These are provided at various extreme levels (1 in 2, 5, 10, 20 ,50 and 100-year events); and for a range of current and nominal future climate change warming levels (1.2 [current day, early 2020s], 1.5, 2, 3, and 4 degrees Celsius above pre-industrial level), representative of events impacting either just the UK, or Europe as a whole.

The data provided are derived from the Met Office decadal prediction system hindcast (https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/climate-models/depresys), according to the climate change impacts identified from UKCP18 (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index).

Short duration events The short duration event component of the dataset provides hourly time series at 4 x 4 km spatial resolution, covering a UK and surrounding offshore area domain, for 100 m wind speed, representative of a selection of wind generation ramping events. Each adverse weather scenario is contained within a time slice of data with up to one week before and one week after the day on which the event occurs (up to 15 days in total) provided. For the majority of events provided, the full 15 days are available, however for a small number of events which occur less than one week from the beginning or end of the underlying data used to derive this dataset, this is not possibly to supply, and these events are listed below. The start date and time along with the direction and magnitude of the ramp (change in wind capacity factor) contained within the time slice of data, are given in the NetCDF global attributes.

The short duration wind generation ramping events are representative of events impacting five separate regions of Great Britain and surrounding offshore areas, as defined in the accompanying documentation. These regions are Scotland, the East England, West England and Wales offshore North and offshore South. The events are defined by changes in wind capacity factors occurring over different length time windows (1-hour, 3-hour, 6-hour, 12-hour and 24-hour windows). These are provided at various extreme levels (1 in 2, 5, 10, 20 ,50 and 100-year events) for the 1.2 degrees Celsius above pre-industrial level (I.e. representative of early 2020s climate) and through the analysis outlined in the accompanying documentation are though to also be representative of the 2, 3, and 4 degrees Celsius above pre-industrial level nominal future climate change warming levels.

The data provided are derived from the UKCP18 local projections (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index).

The methods developed for characterising and representing these adverse weather scenarios, and the approach used to compile the final dataset are presented in the accompanying documentation.

Use of this data is subject to the terms of the Open Government Licence (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/). The following acknowledgment must be given when using the data: © Crown Copyright 2021, Met Office, funded by the National Infrastructure Commission.

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

Columns:

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

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

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

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021, but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).

The changes for v1.1.0.0 HadUK-Grid datasets are as follows:

• The addition of data for calendar year 2021

• The addition of 30 year averages for the new reference period 1991-2020

• An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.

• A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.

Net changes to the input station data used to generate this dataset:

-Total of 122664065 observations

-118464870 (96.5%) unchanged

-4821 (0.004%) modified for this version

-4194374 (3.4%) added in this version

-5887 (0.005%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021, but the start time is dependent on climate variable and temporal resolution. The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost. This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). The changes for v1.1.0.0 HadUK-Grid datasets are as follows: * The addition of data for calendar year 2021 * The addition of 30 year averages for the new reference period 1991-2020 * An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively. * A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836. Net changes to the input station data used to generate this dataset: -Total of 122664065 observations -118464870 (96.5%) unchanged -4821 (0.004%) modified for this version -4194374 (3.4%) added in this version -5887 (0.005%) deleted from this version The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

The mean temperature in the United Kingdom in 2023 was 0.8 degrees Celsius higher than the long-term mean from 1991 to 2020. Since 2001, the deviation of the mean annual temperature in the United Kingdom has remained below one degree Celsius with the exception of 2010.

UK ECN Meteorology Measurements

Long-term Automatic Weather Station Data

By data.world's Admin [source]

About this dataset

The UK Environmental Change Network (ECN) is a pioneering long-term environmental monitoring programme backed by 14 government departments and agencies. It has been collecting meteorology data from its terrestrial sites since 1991, using Automatic Weather Stations (AWSs) that measure essential variables such as albedo, solar radiation, air temperature and humidity, rainfall, surface wetness and soil moisture, as well as wind direction and velocity.

This dataset contains continuous hourly records of the monitoring findings measured at ECN archaeological sites – presented in terms of site codes (SITECODE), AWS numbers (AWSNO), fieldnames (FIELDNAME), date ranges for collection period start/end dates(FROM_DATE/TO_DATE), datatype (DATETYPE)and verbal descriptions of the recorded data items(DESCRIPTION). Spanning 24 years and over 50 locations across the UK’s most fragile ecosystems - this invaluable data set is profoundly useful for future environmental studies & research projects. Whether looking to evaluate past climate fluctuations or ascertain current day improvements to the environment – no other resource more concisely captures both these essential elements than this one!

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How to use the dataset

This dataset provides useful information on meteorology data from the UK Environmental Change Network's (ECN) terrestrial sites. The data collected here spans 1991 to 2015 and contains information on variables such as albedo (ground and sky), temperature, relative humidity, radiation, rainfall, surface wetness, soil moisture, wind direction and speed. This dataset can be used for various applications related to research and analysis of the data being collected in order to have a better insight into climate change at these sites over time.

• SITECODE – Unique identifier for the site
• AWSNO – Unique identifier for the Automatic Weather Station
• FIELDNAME – Name of the meteorological variable being measured
• FROM_DATE – Date when the measurement began
• TO_DATE - Date when the measurement ended
• DATETYPE - Type of data being measured (hourly, daily etc.)

• DESCRIPTION - Description of the meteorological variable being measured

  • CONTINUING - Whether or not a measurement is ongoing

By exploring each column we can get a better understanding of what type datasets it holds within it. From this we can determine what parameters are important when analyzing this dataset and which methods are best suited for this task depending on its structure. With further exploration one could learn how to read CSV files in python or use libraries such as pandas and numpy manipulate this dataset or other tools like Tableau to visualize our results in interactive charts/graphs with ease so that we can gain valuable insights from our findings

Research Ideas

• Comparing long-term trends in temperature and other meteorological variables across different sites to detect signs of climate change.
• Analyzing seasonal changes in wind direction and speed to identify areas of the UK most vulnerable to extreme weather events over time.
• Using the data on surface wetness, rainfall, and soil moisture levels to map areas with increased soil fertility for sustainable agriculture practices

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ECN_MA_qtext.csv | Column name | Description | |:----------------|:--------------------------------------------------------------------| | SITECODE | Unique identifier for the site. (String) | | AWSNO | Automatic weather station number. (Integer) | | FIELDNAME | Name of meteorological variable being measured. (String) | | FROM_DATE | Date when measurement began. (Date) | | TO_DATE | Date when measurement ended. (Date) | | DATETYPE | Type of data being measured. (String) | | CONTINUING | Whether measurement is ongoing or not. (Boolean) | | DESCRIPTION | Description of the meteorological variable being meas...

This data has been sourced from the Met Office's historical station data available here: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data

I have included the python script used to generate the dataset.

The data is held at a monthly level and contains: - max_temp: Mean daily maximum temperature - min_temp: Mean daily minimum temperature - air_frost_days: Days of air frost - rain_mm: Total rainfall - sun: Total sunshine duration (hours) - station: the station of the observation - lat: latitude of the station - long: longitude of the station - month_year: month date of the observation

A network for snow depth measurements was started with 4 Automatic Weather Stations in 1987 around Terra Nova Bay (North Victoria Land). It provides continuous meteo-climatological data and information, according to standard and reliable procedures, making use of robust methodologies and testing new procedures. At present, we operate a network of 13 Automatic Weather Stations working year-round plus 3 working during summer only. Data are achieved, validated, archived and diffused, and instruments are maintained.

A network for incoming solar radiation measurements was started with 4 Automatic Weather Stations in 1987 around Terra Nova Bay (North Victoria Land). It provides continuous meteo-climatological data and information, according to standard and reliable procedures, making use of robust methodologies and testing new procedures. At present, we operate a network of 13 Automatic Weather Stations working year-round plus 3 working during summer only. Data are achieved, validated, archived and diffused, and instruments are maintained.

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021, but the start time is dependent on climate variable and temporal resolution. The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost. This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). The changes for v1.1.0.0 HadUK-Grid datasets are as follows: * The addition of data for calendar year 2021 * The addition of 30 year averages for the new reference period 1991-2020 * An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively. * A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836. Net changes to the input station data used to generate this dataset: -Total of 122664065 observations -118464870 (96.5%) unchanged -4821 (0.004%) modified for this version -4194374 (3.4%) added in this version -5887 (0.005%) deleted from this version The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

'''DEFINITION''' The subsurface temperature anomaly has been derived from the regional reanalysis of the CMEMS NWS MFC group for the North-West European Shelf Seas (product reference NWSHELF_MULTIYEAR_PHY_004_009). Horizontal averaging has been conducted over just the shelf, defined as the contiguous region surrounding the UK where sea depth is no more than 200m. The profiles of the annual mean temperature anomaly have been calculated relative to the reference period of 1993-2019. The time series shows monthly anomalies calculated from monthly means. CMEMS Ocean State Report (Mulet et al., 2018) gives the broader context for these anomalies.

'''CONTEXT''' The North-West European Shelf Seas are the waters on the continental shelf adjoining the North-East Atlantic. Geographically, they can be divided into 5 regions: the North Sea, the English Channel, the Celtic Sea, the Irish Sea, and the North-Western Approaches. The temperatures in these seas is moderated by inflow from the North Atlantic Drift (a continuation of the Gulf Stream), into the Irish Sea and English Channel from the south-west and into the North Sea from the north and north-west. In Winter, this Atlantic water maintains surface temperatures no colder than 10°C in western regions. Cooler temperatures, below 3°C, can occur in eastern parts near continental coasts. In Summer, those eastern coastal regions can warm to as much as 18°C, especially in the shallow waters of the southern North Sea. Stratification occurs in the Summer months, starting around May in the northern part of the domain, and then extending southwards (Paramor et al., 2009). Currents are dominated by the strong semi-diurnal tides which act to mix the water and reduce stratification. This is especially noticeable in the shallow southern North Sea.

'''CMEMS KEY FINDINGS''' Interannual variations of the subsurface temperature anomaly averaged across the domain range from -1°C to +1°C within the upper 100-m deep layer over the period 1993-2019. There is some long-term variability, with warm anomalies for most of the first decade of the 21st century. This is a decade in which 6 of the 10 warmest years on record occurred for air temperature over the UK (Kendon et al., 2019). The 4 other warmest years for UK climate were 2011, 2014, 2017 and 2018. 2011 and 2017 correspond to warm anomalies through the depth of the Shelf. 2018 saw an unusually hot Summer heatwave for the atmosphere, which is reflected in a short-lived and shallow warm anomaly (to 25m depth) in the reanalysis. 2014 was for UK climate the hottest year on record. This corresponds to a stronger and longer-lived anomaly in the reanalysis, but only down to around 75m depth. Temperatures in deeper layers may be moderated by inflow to the region from the North Atlantic drift. 2019 was overall slightly warmer than average in near-surface layers, and slightly cooler below 100m depth.

Chemistry of the Antarctic Boundary Layer and the Interface with Snow (CHABLIS) is a Natural Environment Research Council (NERC) and Antarctic Funding Initiative (AFI) funded project, aimed at studying the chemistry of the Antarctic Boundary Layer in greater detail, and for a longer duration, than has previously been attempted. Field measurements were carried out at the British Antarctic Survey station, Halley, at the Clean Air Sector Laboratory (CASLab). Year-round measurements began in February 2004, and a summer campaign focussing on oxidants ran during January/February 2005, after which CHABLIS fieldwork ended. This dataset contains weather observations taken at Halley for the period February 2004 to February 2005. Access to this dataset is now public.

The United Kingdom experienced an average of ******* millimeters of rainfall in 2024, a decrease of *** percent in comparison to the previous year. While 2024 saw substantial rainfall, it did not surpass the thus-far peak of the century, with ***** 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 ******* millimeters and ******* 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.