The lowest temperature ever recorded on Earth was at Dome Fuji in the Antarctica at -93.2 degrees Celsius. However, scientists have discovered that under the right conditions, the temperature in this place can probably drop to -100 degrees Celsius, which is estimated to be the coldest it can be on Earth.

Lowest Recorded Temps by City

Climate Data Over Time

By Gary Hoover [source]

About this dataset

This dataset includes information about the lowest recorded temperatures from 2015 for various US cities. It's a chilling reminder of just how cold winter can be - and that each city has its own unique climate! From scorching summer days to frigid winter ones, this dataset gives insight into the temperatures extremes experienced across the country. Whether you live in a balmy beach town or an icy mountain village, you can explore your city's yearly temperature range and prepare accordingly for whatever Mother Nature throws your way!

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Research Ideas

• Analyzing the melting of snow and ice based on temperatures in US cities to observe shifts in global climate change.
• Comparing and plotting city temperature trends over 2015, to develop targeted energy efficiency programs for colder climates or regions
• Estimating extreme weather events for 2016 by extrapolating from 2015 data - understanding low temperatures helps predict when local authorities need to be prepared and take safety measures during extreme cold spells

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: Lowest Temperature on Record through 2015 by US City.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | CITY | The name of the city. (String) | | JAN | The lowest recorded temperature in January. (Float) | | FEB | The lowest recorded temperature in February. (Float) | | MAR | The lowest recorded temperature in March. (Float) | | APR | The lowest recorded temperature in April. (Float) | | MAY | The lowest recorded temperature in May. (Float) | | JUN | The lowest recorded temperature in June. (Float) | | JUL | The lowest recorded temperature in July. (Float) | | AUG | The lowest recorded temperature in August. (Float) | | SEP | The lowest recorded temperature in September. (Float) | | OCT | The lowest recorded temperature in October. (Float) | | NOV | The lowest recorded temperature in November. (Float) | | DEC | The lowest recorded temperature in December. (Float) | | ANN | The average annual lowest recorded temperature. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Gary Hoover.

The state of Tlaxcala recorded the coldest minimum average temperature across Mexico in 2023, at just *** degrees Celsius. Meanwhile, Quintana Roo registered the highest minimum temperature that year – with values reaching ** degrees Celsius – closely followed by the state of Tabasco. Accordingly, both states were amongst Mexico's warmest states on average in 2023.

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

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.

In 2023, Mexico's coldest months were registered during the winter, with January recording the lowest minimum temperature that year, at just *** degrees Celsius. On the other hand – with nearly ** degrees Celsius – July had the warmest minimum temperature of 2023. That same year, the state of Tlaxcala reached the lowest annual minimum temperature across Mexico.

Context This dataset provides weather data for Tokyo, covering the period from June 26, 2018, to June 26, 2024. The data is sourced from the Japan Meteorological Agency (JMA). According to their usage terms, numerical figures are not subject to copyright and can be freely used.

Content The dataset includes daily weather data for Tokyo with the following columns:

• Date: The date of the recorded data.
• Average Temperature (°C): The average temperature for the day.
• Highest Temperature (°C): The highest temperature recorded for the day.
• Highest Temperature (°C) Datetime: The date and time when the highest temperature was recorded.
• Lowest Temperature (°C): The lowest temperature recorded for the day.
• Lowest Temperature (°C) Datetime: The date and time when the lowest temperature was recorded.
• Total Precipitation (mm): The total precipitation for the day.
• Sunshine Duration (hours): The total duration of sunshine for the day.
• Maximum Snow Depth (cm): The maximum snow depth recorded for the day.
• Maximum Snow Depth (cm) Datetime: The date and time when the maximum snow depth was recorded.
• Total Snowfall (cm): The total snowfall for the day.
• Average Wind Speed (m/s): The average wind speed for the day.
• Maximum Wind Speed (m/s): The maximum wind speed recorded for the day. Maximum wind speed refers to the highest 10-minute average wind speed observed within a specific time period.
• Maximum Wind Speed (m/s) Datetime: The date and time when the maximum wind speed was recorded.
• Maximum Wind Speed (m/s) Direction: The direction of the maximum wind speed recorded.
• Maximum Gust Speed (m/s): The maximum gust speed recorded for the day. Maximum gust speed refers to the highest instantaneous wind speed observed, typically measured over a 3-second period.
• Maximum Gust Speed (m/s) Datetime: The date and time when the maximum gust speed was recorded.
• Maximum Gust Speed (m/s) Direction: The direction of the maximum gust speed recorded.
• Most Frequent Wind Direction (16-point compass): The most frequent wind direction recorded using a 16-point compass.
• Average Vapor Pressure (hPa): The average vapor pressure for the day.
• Average Humidity (%): The average humidity for the day.
• Minimum Relative Humidity (%): The minimum relative humidity for the day.
• Minimum Relative Humidity (%) Datetime: The date and time when the minimum relative humidity was recorded.

Inspiration 1. Climate Change Analysis Analyze Tokyo's weather data to identify climate change impacts and trends over the past four years, aiding in the development of accurate climate models and policies. Note that certain usage restrictions apply under specific laws such as the Meteorological Service Act; see ML-17 and ML-23 for details.

1. Renewable Energy Optimization Optimize solar and wind energy deployment in Tokyo by assessing sunshine and wind data, improving renewable energy efficiency and supporting sustainability goals.

2. Urban Planning and Public Health Use Tokyo's weather data to enhance urban planning and public health initiatives, mitigating heat islands, reducing flood risks, and improving residents' quality of life.

Acknowledgements Special thanks to the Japan Meteorological Agency for providing this valuable data. When using this data, please attribute it as follows:

出典：気象庁ホームページ　https://www.jma.go.jp/jma/kishou/info/coment.html

This NOAA Fundamental Climate Data Record (FCDR) from Colorado State University (CSU) contains brightness temperatures that have been improved and quality-controlled over the observational time period. The temperature data are from the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) series of passive microwave radiometers carried onboard the Defense Meteorological Satellite Program (DMSP) satellites, and from the Advanced Microwave Scanning Radiometer 2 (AMSR2) carried onboard the Global Change Observation Mission 1st - Water (GCOM-W1) satellite. The dataset encompasses data from a total of 11 satellites including the SSM/I sensors on board DMSP satellites F08, F10, F11, F13, F14, and F15, the SSMIS sensors on board DMSP satellites F16, F17, F18, and F19, as well as AMSR2 on board GCOM-W1. The DMSP satellites F09 and F12 were not used. The data record covers the time period from July 1987 beginning with SSM/I through the present with a 7 to 10 day latency. There are roughly 15 orbits per satellite per day with a swath width of approximately 1400 km resulting in nearly global daily coverage. The spatial and temporal resolutions of the FCDR files correspond to the original resolution of the source Temperature Data Record (TDR) observations. The spatial resolution of the data is a function of the sensor/channel and varies from approximately ~50 km for the lowest frequency channels to ~15km for the high-frequency channels. The output parameters include the observed brightness temperatures for each channel at the original sensor channel resolution along with latitude and longitude for each pixel, time, quality flags, and view angle information. Interim updates to the FCDR, notated as ICDR, are produced on an operational basis as new source data become available. The initially-produced interim files or ICDR files are produced prior to the final FCDR production. Once the data has been fully checked, updates for the final FCDR files are provided approximately every year. There are several changes to Version 2 from Version 1 of the FCDR. 1) The addition of inter-calibrated data from the GCOM-W1 AMSR2 instrument for the period from July 2, 2012 to the present. 2) A change from relative inter calibration to SSM/I on board DMSP F13 to an absolute calibration based on the well-documented and published calibration of the GPM GMI instrument. 3) Multiple updates to geolocation and calibration corrections to the SSM/I and SSMIS brightness temperature data including improved geolocation and pointing information, updated cross-track bias corrections, updated corrections for solar intrusions, and updated inter calibration adjustments incorporating corrections over both cold and warm scenes. The file format is netCDF-4 with added metadata that follow the Climate and Forecast (CF) Conventions and Attribute Convention for Dataset Discovery (ACDD).

Temperature Time-Series for some Brazilian cities

Do you ever wonder how are temperatures in Brazilian cities? Too hot? Cold weather sometimes? And what about climate changes? Is Brazil getting hotter?

This is your chance to check it out!

Context

This datasets are collected in order to provide some answers for the above question through Data Analysis. Maybe you want to try some Machine Learning model in order to practice and predict the evolution of temperature in some Brazilian cities.

Content

The content is provided by NOAA GHCN v4 and post-processed by NASA's GISTEMP v4.

In summary, each data file contains a temperature time series for a station named according to the city. The time series provides temperature records by month for each year. Some mean measurement is calculated, like metANN and D-J-F. I can't give details about these quantities, nor how they are calculated. Please refer for NASA GISTEMP website in this regard. The most important seems to be metANN, which is an annual temperature mean.

Acknowledgements

These datasets are provided through NASA's GISTEMP v4 and recorded by NOAA GHCN v4. Thanks for researchers and staffs for the really nice work!

These rasters provide the local mean annual extreme low temperature from 1976 to 2005 in an 800m x 800m grid covering the USA (including Puerto Rico) based on interpolation of data from more than a thousand weather stations. Each location's Plant Hardiness Zone is calculated based on classifying that temperature into 5 degree bands. The classified rasters are then used to create print and interactive maps. A complex algorithm was used for this edition of the USDA Plant Hardiness Zone Map (PHZM) to enable more accurate interpolation between weather reporting stations. This new method takes into account factors such as elevation changes and proximity to bodies of water, which enabled mapping of more accurate zones.Temperature station data for this edition of the USDA PHZM came from several different sources. In the eastern and central United States, Puerto Rico, and Hawaii, nearly all the data came from weather stations of the National Weather Service. In the western United States and Alaska, data from stations maintained by USDA Natural Resources Conservation Service, USDA Forest Service, U.S. Department of the Interior (DOI) Bureau of Reclamation, and DOI Bureau of Land Management also helped to better define hardiness zones in mountainous areas. Environment Canada provided data from Canadian stations, and data from Mexican stations came from the Global Historical Climate Network.All of these data were carefully examined to ensure that only the most reliable were used in the mapping. In the end, data from a total of 7,983 stations were incorporated into the maps. The USDA PHZM was produced with the latest version of PRISM, a highly sophisticated climate mapping technology developed at Oregon State University. The map was produced from a digital computer grid, with each cell measuring about a half a mile on a side. PRISM estimated the mean annual extreme minimum temperature for each grid cell (or pixel on the map) by examining data from nearby stations; determining how the temperature changed with elevation; and accounting for possible coastal effects, temperature inversions, and the type of topography (ridge top, hill slope, or valley bottom).Information on PRISM can be obtained from the PRISM Climate Group website (http://prism.oregonstate.edu).Once a draft of the map was completed, it was reviewed by a team of climatologists, agricultural meteorologists, and horticultural experts. If the zone for an area appeared anomalous to these expert reviewers, experts doublechecked for errors or biases.For example, zones along the Canadian border in the Northern Plains initially appeared slightly too warm to several members of the review team who are experts in this region. It was found that there were very few weather reporting stations along the border in the United States in that area. Data from Canadian reporting stations were added, and the zones in that region are now more accurately represented. In another example, a reviewer noted that areas along the relatively mild New Jersey coastline that were distant from observing stations appeared to be too cold. This was remedied by increasing the PRISM algorithm’s sensitivity to coastal proximity, resulting in a mild coastal strip that is more consistently delineated up and down along the shoreline.On the other hand, a reviewer familiar with Maryland’s Eastern Shore thought the zones there seemed too warm. The data were doublechecked and no biases were found; the zone designations remained unchanged.The zones in this edition were calculated based on 1976-2005 temperature data. Each zone represents the average annual extreme minimum temperature for an area, reflecting the temperatures recorded for each of the years 1976-2005. This does not represent the coldest it has ever been or ever will be in an area, but it reflects the average lowest winter temperature for a given geographic area for this time period. This average value became the standard for assigning zones in the 1960s. The previous edition of the USDA Plant Hardiness Zone Map, which was revised and published in 1990, was drawn from weather data from 1974 to 1986.A detailed explanation of the mapmaking process and a discussion of the horticultural applications of the new PHZM are available from the articles listed below.Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson. 2012. Development of a new USDA Plant Hardiness Zone Map for the United States. Journal of Applied Meteorology and Climatology, 51: 242-264. Link to articleWidrlechner, M.P., C. Daly, M. Keller, and K. Kaplan. 2012. Horticultural Applications of a Newly Revised USDA Plant Hardiness Zone Map. HortTechnology, 22: 6-19. Link to article

The recent experiments performed at the Hadley Centre have used the new Unified Model (Cullen, 1993). These experiments represent a large step forward in the way climate change is modelled by GCMs and raises new possibilities for scenario construction. This experiment has overcome some of the major difficulties that were associated with the previous generations of equilibrium (circa IPCC 1990) and cold-start transient (circa IPCC 1992) climate change experiments. HadCM2 has a spatial resolution of 2.5 degrees x 3.75 degrees (latitude by longitude) and the representation produces a grid box resolution of 96 x 73 grid cells. This produces a surface spatial resolution of about 417km x 278 km reducing to 295 x 278km at 45 degrees North and South (comparable to a spectral resolution of T42). The equilibrium climate sensitivity (DT2x) of HadCM2, that is the global-mean temperature response to a doubling of effective CO2 concentration, is approximately 2.5 degrees C, although, this quantity varies with the time-scale considered. This is somewhat lower than most other GCMs (IPCC, 1992). In order to undertake a 'warm-start' experiment it is necessary to perturb the model with a forcing from an early historical era, when the radiative forcing was relatively small compared to the present. The Hadley Centre started their experiments performed with HadCM2 with forcing from the middle industrial era, about 1860 Mitchell et al., 1995 and Johns et al., 1995. The greenhouse gas only integrations, HadCM2GG, used the combined forcing of all the greenhouse gases as an equivalent CO2 concentration. A further series of integrations, HadCM2GS, used the combined equivalent CO2 concentration plus the negative forcing from sulphate aerosols. The HadCM2GG integrations simulated the change in forcing of the climate system by greenhouse gases since the early industrial period (taken by HadCM2 to be 1860). The addition of the negative forcing effects of sulphate aerosols represents the direct radiative forcing due to anthropogenic sulphate aerosols by means of an increase in clear-sky surface albedo proportional to the local sulphate loading (refer to Mitchell et al., 1995 for details of this method). The indirect effects of aerosols were not simulated. The modelled control climate shows a negligible long term trend in surface air temperature over the first 400 years. The trend is about +0.04 degrees C per century, which is comparable to other such experiments. HadCM2CON represents an improvement over previous generations of GCMs that have been used at the Hadley Centre (Johns et al., 1995 and Airey et al., 1995). The experiments performed have simulated the observed climate system using estimated forcing perturbations since 1860. Johns et al., (1995) and Mitchell et al., (1995) have established that HadCM2's sensitivity is consistent with the real climate system. The agreement between the observed global-mean temperature record and that produced in these experiments is better for HadCM2GS than for HadCM2GG. This implies that HadCM2Gs has captured the observed signal of global-mean temperature changes better than HadCM2GG for the recent 100-year record. The climate sensitivity of HadCM2 is about 2.5 degrees C For the A2 emissions scenario the main emphasis is on a strengthening of regional and local culture, with a return to family values in many regions. The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attention on contributing to the local community. Elsewhere, the trend is towards increased investment in education and science and growth in economic productivity. Social and political structures diversify, with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. The A2 world sees more international tensions and less cooperation than in A1 or B1. People, ideas and capital are less mobile so that technology diffuses slowly. International disparities in productivity, and hence income per capita, are maintained or increased. With the emphasis on family and community life, fertility rates decline only slowly, although they vary among regions. Hence, this scenario family has high population growth (to 15 billion by 2100) with comparatively low incomes per capita relative to the A1 and B1 worlds, at US$7,200 in 2050 and US$16,000 in 2100.Technological change is rapid in some regions and slow in others as industry adjusts to local resource endowments, culture, and education levels. Regions with abundant energy and m... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.227.1 for complete metadata about this dataset.

The BASE Temperature Data Record (TDR) dataset from Colorado State University (CSU) is a collection of the raw unprocessed antenna temperature data that has been written into single orbit granules and reformatted into netCDF-4. The temperature data are from the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) series of passive microwave radiometers carried onboard the Defense Meteorological Satellite Program (DMSP) satellites. This dataset encompasses data from a total of nine satellites including the SSM/I sensors on board DMSP satellites F08, F10, F11, F13, F14, and F15 as well as the SSMIS sensors on board DMSP satellites F16, F17, F18, and F19. The data record covers the time period from July 1987 through the present with a 7 to 10 day latency. The spatial and temporal resolutions of the BASE files correspond to the original resolution of the raw source TDR observations. There are roughly 15 orbits per day with a swath width of approximately 1400 km resulting in nearly global daily coverage. The spatial resolution of the data is a function of the sensor/channel and varies from approximately ~50 km for the lowest frequency channels to ~15km for the high-frequency channels. These files contain all of the information from the original source TDR files with the following changes/additions. The BASE files have been reorganized into single orbit granules with duplicate scans removed, and spacecraft position and velocity based on the TLE (two line element) data have been added for calculating geolocation. With the exception of duplicate scans, none of data from the original TDR files was changed or removed. This BASE TDR dataset is used by CSU as input for the subsequent processing of the final intercalibrated Fundamental Climate Data Record (FCDR). The file format is netCDF-4 with added metadata that follow the Climate and Forecast (CF) Conventions and Attribute Convention for Dataset Discovery (ACDD).

The recent experiments performed at the Hadley Centre have used the new Unified Model (Cullen, 1993). These experiments represent a large step forward in the way climate change is modelled by GCMs and raises new possibilities for scenario construction. This experiment has overcome some of the major difficulties that were associated with the previous generations of equilibrium (circa IPCC 1990) and cold-start transient (circa IPCC 1992) climate change experiments. HadCM2 has a spatial resolution of 2.5 degrees x 3.75 degrees (latitude by longitude) and the representation produces a grid box resolution of 96 x 73 grid cells. This produces a surface spatial resolution of about 417km x 278 km reducing to 295 x 278km at 45 degrees North and South (comparable to a spectral resolution of T42). The equilibrium climate sensitivity (DT2x) of HadCM2, that is the global-mean temperature response to a doubling of effective CO2 concentration, is approximately 2.5 degrees C, although, this quantity varies with the time-scale considered. This is somewhat lower than most other GCMs (IPCC, 1992). In order to undertake a 'warm-start' experiment it is necessary to perturb the model with a forcing from an early historical era, when the radiative forcing was relatively small compared to the present. The Hadley Centre started their experiments performed with HadCM2 with forcing from the middle industrial era, about 1860 Mitchell et al., 1995 and Johns et al., 1995. The greenhouse gas only integrations, HadCM2GG, used the combined forcing of all the greenhouse gases as an equivalent CO2 concentration. A further series of integrations, HadCM2GS, used the combined equivalent CO2 concentration plus the negative forcing from sulphate aerosols. The HadCM2GG integrations simulated the change in forcing of the climate system by greenhouse gases since the early industrial period (taken by HadCM2 to be 1860). The addition of the negative forcing effects of sulphate aerosols represents the direct radiative forcing due to anthropogenic sulphate aerosols by means of an increase in clear-sky surface albedo proportional to the local sulphate loading (refer to Mitchell et al., 1995 for details of this method). The indirect effects of aerosols were not simulated. The modelled control climate shows a negligible long term trend in surface air temperature over the first 400 years. The trend is about +0.04 degrees C per century, which is comparable to other such experiments. HadCM2CON represents an improvement over previous generations of GCMs that have been used at the Hadley Centre (Johns et al., 1995 and Airey et al., 1995). The experiments performed have simulated the observed climate system using estimated forcing perturbations since 1860. Johns et al., (1995) and Mitchell et al., (1995) have established that HadCM2's sensitivity is consistent with the real climate system. The agreement between the observed global-mean temperature record and that produced in these experiments is better for HadCM2GS than for HadCM2GG. This implies that HadCM2Gs has captured the observed signal of global-mean temperature changes better than HadCM2GG for the recent 100-year record. The climate sensitivity of HadCM2 is about 2.5 degrees C For the A2 emissions scenario the main emphasis is on a strengthening of regional and local culture, with a return to family values in many regions. The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attention on contributing to the local community. Elsewhere, the trend is towards increased investment in education and science and growth in economic productivity. Social and political structures diversify, with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. The A2 world sees more international tensions and less cooperation than in A1 or B1. People, ideas and capital are less mobile so that technology diffuses slowly. International disparities in productivity, and hence income per capita, are maintained or increased. With the emphasis on family and community life, fertility rates decline only slowly, although they vary among regions. Hence, this scenario family has high population growth (to 15 billion by 2100) with comparatively low incomes per capita relative to the A1 and B1 worlds, at US$7,200 in 2050 and US$16,000 in 2100.Technological change is rapid in some regions and slow in others as industry adjusts to local resource endowments, culture, and education levels. Regions with abundant energy and m... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.208.1 for complete metadata about this dataset.

The recent experiments performed at the Hadley Centre have used the new Unified Model (Cullen, 1993). These experiments represent a large step forward in the way climate change is modelled by GCMs and raises new possibilities for scenario construction. This experiment has overcome some of the major difficulties that were associated with the previous generations of equilibrium (circa IPCC 1990) and cold-start transient (circa IPCC 1992) climate change experiments. HadCM2 has a spatial resolution of 2.5 degrees x 3.75 degrees (latitude by longitude) and the representation produces a grid box resolution of 96 x 73 grid cells. This produces a surface spatial resolution of about 417km x 278 km reducing to 295 x 278km at 45 degrees North and South (comparable to a spectral resolution of T42). The equilibrium climate sensitivity (DT2x) of HadCM2, that is the global-mean temperature response to a doubling of effective CO2 concentration, is approximately 2.5 degrees C, although, this quantity varies with the time-scale considered. This is somewhat lower than most other GCMs (IPCC, 1992). In order to undertake a 'warm-start' experiment it is necessary to perturb the model with a forcing from an early historical era, when the radiative forcing was relatively small compared to the present. The Hadley Centre started their experiments performed with HadCM2 with forcing from the middle industrial era, about 1860 Mitchell et al., 1995 and Johns et al., 1995. The greenhouse gas only integrations, HadCM2GG, used the combined forcing of all the greenhouse gases as an equivalent CO2 concentration. A further series of integrations, HadCM2GS, used the combined equivalent CO2 concentration plus the negative forcing from sulphate aerosols. The HadCM2GG integrations simulated the change in forcing of the climate system by greenhouse gases since the early industrial period (taken by HadCM2 to be 1860). The addition of the negative forcing effects of sulphate aerosols represents the direct radiative forcing due to anthropogenic sulphate aerosols by means of an increase in clear-sky surface albedo proportional to the local sulphate loading (refer to Mitchell et al., 1995 for details of this method). The indirect effects of aerosols were not simulated. The modelled control climate shows a negligible long term trend in surface air temperature over the first 400 years. The trend is about +0.04 degrees C per century, which is comparable to other such experiments. HadCM2CON represents an improvement over previous generations of GCMs that have been used at the Hadley Centre (Johns et al., 1995 and Airey et al., 1995). The experiments performed have simulated the observed climate system using estimated forcing perturbations since 1860. Johns et al., (1995) and Mitchell et al., (1995) have established that HadCM2's sensitivity is consistent with the real climate system. The agreement between the observed global-mean temperature record and that produced in these experiments is better for HadCM2GS than for HadCM2GG. This implies that HadCM2Gs has captured the observed signal of global-mean temperature changes better than HadCM2GG for the recent 100-year record. The climate sensitivity of HadCM2 is about 2.5 degrees C For the A2 emissions scenario the main emphasis is on a strengthening of regional and local culture, with a return to family values in many regions. The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attention on contributing to the local community. Elsewhere, the trend is towards increased investment in education and science and growth in economic productivity. Social and political structures diversify, with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. The A2 world sees more international tensions and less cooperation than in A1 or B1. People, ideas and capital are less mobile so that technology diffuses slowly. International disparities in productivity, and hence income per capita, are maintained or increased. With the emphasis on family and community life, fertility rates decline only slowly, although they vary among regions. Hence, this scenario family has high population growth (to 15 billion by 2100) with comparatively low incomes per capita relative to the A1 and B1 worlds, at US$7,200 in 2050 and US$16,000 in 2100.Technological change is rapid in some regions and slow in others as industry adjusts to local resource endowments, culture, and education levels. Regions with abundant energy and m... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.222.1 for complete metadata about this dataset.

Contains global weather station locations with data for monthly means from 1981 through 2010 for: Daily Mean Temperature °C Daily Maximum Temperature °C Daily Minimum Temperature °C Precipitation in mm Highest Daily Temperature °C Lowest Daily Temperature °C Additional monthly fields containing the equivalent values in °F and inches are available at the far right of the attribute table. GHCND stations were included if there were at least fifteen average daily values available in each month for all twelve months of the year, and for at least ten years between 1981 and 2010. 3,197 of the 7,480 stations did not collect or lacked sufficient precipitation data. These data are compiled from archived station values which have not undergone rigorous curation, and thus, there may be unexpected values, particularly in the daily extreme high and low fields. Esri is working to further curate this layer and will make updates as improvements are found. If your area of study is within the United States, we recommend using the U.S. Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer because the data in that service were compiled from web services produced by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc., thus the data in the U.S. service is of higher quality. Revision History: Initially Published: 6 Feb 2019 Updated: 12 Feb 2019 - Improved initial extraction algorithm to remove stations with extreme values. This included values higher than the highest temperature ever recorded on Earth, or those with mean values that were considerably different than adjacent neighboring stations.Updated: 18 Feb 2019 - Updated after finding an error in initial processing that excluded a 2,870 stations. Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 635 of 7,452 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as:

Esri, 2019: World Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed April 2019. https://www.arcgis.com/home/item.html?id=ed59d3b4a8c44100914458dd722f054f Source Data: Station locations compiled from: Initially compiled using station locations from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24 Amended to use the most recent station locations from Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Access Date: April 10, 2019 doi:10.7289/V5NV9G8D. Station Monthly Means compiled from Daily Data: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24

This Version 7 NOAA Fundamental Climate Data Record (CDR) from Remote Sensing Systems (RSS) contains brightness temperatures that have been inter-calibrated and homogenized over the observation time period. The temperature data are from the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) series of passive microwave radiometers carried onboard the Defense Meteorological Satellite Program (DMSP) satellites. These satellite sensors measure the natural microwave emission coming from the Earth’s surface in the spectral band from 19 to 85 GHz. This dataset encompasses data from a total of seven satellites including the SSM/I sensors on board DMSP satellites F08, F10, F11, F13, F14, and F15 as well as the SSMIS sensors on board DMSP satellite F17. The data record covers the time period from July 1987 through the present with a one month latency. The spatial and temporal resolutions of the CDR files correspond to the original resolution of the source SSMI(S) observations. There are roughly 15 orbits per day with a swath width of approximately 1400 km resulting in nearly global daily coverage. The spatial resolution of the data is a function of the sensor/channel and varies from approximately 50 km for the lowest frequency channels to approximately 15km for the high-frequency channels. The output parameters include the observed brightness temperatures for each of the seven SSM/I channels and 24 SSMIS channels at the original sensor channel resolution along with latitude and longitude information, time, quality flags, and view angle information. The file format is netCDF-4 with added metadata that follow the Climate and Forecast (CF) Conventions and Attribute Convention for Dataset Discovery (ACDD). There are three major changes in the Version 7 processing: (1) the water vapor continuum absorption model was re-derived, (2) the clear-sky bias in cloud water was removed and the data format for cloud water was changed, and (3) the beamfilling correction in the rain algorithm was modified. Relative to Version 6, Version 7 has: (1) increased vapor values in the range of 50-60 mm by 1%, (2) increased vapor values above 60 mm by 2-3%, (3) cloud data changed to the range of cloud water values: -0.05 to 2.45 mm (cloud data format has changed), and (4) increased the global mean rain rates by about 16% (mostly due to changes in the extratropical values).

Like politics, you might say that all climate is local. As researchers seek to help the public better understand climate and climate change, a sensible approach would include helping people know more about changes in their own backyards. High and low temperatures are something that all of us pay attention to each day; when they are extreme (flirting with or setting records) they generate tremendous interest, largely because of the potential for significant impacts on human health, the environment, and built infrastructure. Changes through time in record high and low temperatures (extremes) are also an important manifestation of climate change (Sect. 3.8 in Trenberth et al. 2007; Peterson et al. 2008; Peterson et al. 2012). Meehl et al. (2009) found that currently, about twice as many high temperature records are being set as low temperature records over the conterminous U.S. (lower 48 states) as a whole. As the climate warms further, this ratio is expected to multiply, mainly because when the whole temperature distribution for a location or region shifts, it changes the "tails" of the distribution (in the case of warming this means fewer extreme cold temperatures and more extreme hot temperatures; see Page 2, Figure ES.1 of Karl et al. 2008). The Meehl et al. (2009) findings were covered pretty well by the online media, but, as is the case for all types of scientifc studies, it's safe to say that most of the public are not aware of these basic findings, and they would benefit from additional ways to get climate extremes information for their own areas and assess it. One such way is the National Climatic Data Center's (NCDC) U.S. Records Look-Up page. But how do most people typically hear about their area's high and low temperature records? Likely via the evening news, when their local on-air meteorologist notes the high/low for the day at a nearby airport then gives the years when the all-time high and low for the date were set (perhaps not at that same airport). The year of the record is an interesting bit of information on its own but it doesn't do much to place things in context. What about the local history of record temperatures and how things may be changing? Here we present a daily temperature records data product that we hope will serve the scientist and non-scientist alike in exploring and analyzing high and low temperature records and trends at hundreds of locations across the U.S. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/climate/temp/us_recordtemps/dayrec.html

INDICATOR DEFINITION Monthly means of three-hourly temperatures for Australian Antarctic stations Casey, Davis, Mawson, Macquarie Island and Heard Island.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: CONDITION

RATIONALE FOR INDICATOR SELECTION Global climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. Because of this, there is interest in observations of temperature across the globe, including Antarctica. Extensive high-quality observations from fixed locations are essential to serve as direct indicators of temperature changes and also confirm climate model output.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5" S, long 110 degrees 31' 39.4" E), Davis (lat 68 degrees 34' 35.8" S, long 77 degrees 58' 02.6" E), Mawson (lat 67 degrees 36' 09.7" S, long 62 degrees 52' 25.7" E), Macquarie Island (lat 54 degrees 37' 59.9" S, long 158 degrees 52' 59.9" E), Atlas Cove, Heard Island (lat 53 degrees 1' 8" S, long 73 degrees 23' 30" E) and Spit Bay, Heard Island (lat 53 degrees 6' 30" S, 73 degrees 43' 21" E).

Frequency: Monthly

Measurement Technique: Thermometry

RESEARCH ISSUES There is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in site location or exposure, and for changes in instrumentation or observing practices.

Some of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.

Before the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.

LINKS TO OTHER INDICATORS SOE Indicators 2 - Highest monthly record of daily maximum air temperatures SOE Indicators 3 - Lowest monthly record of daily minimum air temperatures SOE Indicators 4 - Monthly mean of daily radiosonde temperatures at the 100hPa level (deg C) SOE Indicators 5 - Monthly mean of daily radiosonde temperatures at the 500hPa level (deg C) SOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C) SOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa) SOE Indicators 11 - Atmospheric concentrations of greenhouse gas species SOE Indicators 12 - Noctilucent cloud observations at Davis SOE Indicators 14 - Midwinter atmospheric temperature at altitude 87km SOE Indicators 16 - Extent of summer surface glacial melt (sq km) SOE Indicators 42 - Antarctic sea ice extent and concentration SOE Indicators 43 - Fast ice thickness at Davis and Mawson SOE Indicators 56 - Monthly fuel usage of the generator sets and boilers SOE Indicators 59 - Monthly electricity usage SOE Indicators 62 - Water levels of Deep Lake, Vestfold Hills

Note - Station codes in the data are as follows: 300000 - Davis 300001 - Mawson 300004 - Macquarie Island 300005 - Atlas Cove, Heard Island 300017 - Casey 300028 - Spit Bay, Heard Island

The fields for this dataset are: Temperature Mean Air Temperature Year Month Station Station Code Field Value Enough Oobservations Number Observations

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.

INDICATOR DEFINITION Monthly lowest temperatures obtained from observed daily minimum temperatures for Australian Antarctic stations Casey, Davis, Mawson and Macquarie Island.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: CONDITION

RATIONALE FOR INDICATOR SELECTION Global climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. Because of this, there is interest in observations of temperature across the globe, including Antarctica. Extensive high-quality observations from fixed locations are essential to serve as direct indicators of temperature changes and also confirm climate model output.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5" S, long 110 degrees 31' 39.4" E), Davis (lat 68 degrees 34' 35.8" S, long 77 degrees 58' 02.6" E), Mawson (lat 67 degrees 36' 09.7" S, long 62 degrees 52' 25.7" E) and Macquarie Island (lat 54 degrees 37' 59.9" S, long 158 degrees 52' 59.9" E).

Temporal scale: Monthly.

Measurement Technique: Thermometry.

RESEARCH ISSUES There is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in site location or exposure, and for changes in instrumentation or observing practices.

Some of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.

Before the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.

LINKS TO OTHER INDICATORS SOE Indicators 1 - Monthly mean air temperatures for Australian Antarctic stations SOE Indicators 2 - Monthly highest air temperatures for Australian Antarctic stations SOE Indicators 4 - Monthly mean of daily radiosonde temperatures at the 100hPa level (deg C) SOE Indicators 5 - Monthly mean of daily radiosonde temperatures at the 500hPa level (deg C) SOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C) SOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa) SOE Indicators 11 - Atmospheric concentrations of greenhouse gas species SOE Indicators 14 - Midwinter atmospheric temperature at altitude 87km SOE Indicators 16 - Extent of summer surface glacial melt (sq km) SOE Indicators 42 - Antarctic sea ice extent and concentration SOE Indicators 43 - Fast ice thickness at Davis and Mawson SOE Indicators 56 - Monthly fuel usage of the generator sets and boilers SOE Indicators 59 - Monthly electricity usage

Note - Station codes in the data are as follows: 300000 - Davis 300001 - Mawson 300004 - Macquarie Island 300017 - Casey

The fields in this dataset are: Temperature Lowest Air Temperature Year Month Station Station Code Field Value Enough Observations Number Observations