The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

This Global Summaries dataset, known as GSOY for Yearly, contains a yearly resolution of meteorological elements from 1763 to present with updates applied weekly. The major parameters are: – average annual temperature, average annual minimum and maximum temperatures; total annual precipitation and snowfall; departure from normal of the mean temperature and total precipitation; heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; extreme annual minimum and maximum temperatures; number of days with fog; and number of days with thunderstorms. The primary input data source is the Global Historical Climatology Network - Daily (GHCN-Daily) dataset. The Global Summaries datasets also include a monthly resolution of meteorological elements in the GSOM (for Monthly) dataset. See associated resources for more information. These datasets are not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which are not available in GSOM and GSOY. The GSOM and GSOY datasets replace the legacy U.S. COOP Summaries (DSI-3220), and have been expanded to include non-U.S. (global) stations. U.S. COOP Summaries (DSI-3220) only includes National Weather Service (NWS) COOP Published, or "Published in CD", sites.

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the CW100. The CW100 Model is based on physics, not statistics or airport observations. As a result, it can achieve significantly better accuracy than statistical models, especially for non-airport locations. While other forecast models are designed to forecast the entire atmosphere, the CW100 greatly reduces computational requirements by focusing entirely on conditions near the ground. This reduction of computations allows it to resolve additional physical processes near the ground that are not resolved by other models. It also allows the CW100 to operate at a much higher resolution, typically 100x finer than standard models and other gridded forecasts.

Detailed Forecasts:
Features a detailed 48-hour outlook broken into four segments per day: morning, afternoon, evening, and overnight. Each segment provides condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, 6-hr forecasted precip amounts, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Extended Forecasts Days 1-15:
Features condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Hour-by-Hour Forecasts: Features Hour-by-Hour forecasts. The product is available as 12 hour, 48 hour and 168 hour blocks. Each hourly forecast includes weather descriptions, wind conditions, temperature, dew point, humidity, visibility, rainfall totals, snowfall totals, and precipitation probability. Available for over 85,000 forecast points globally. Updated four times per day.

Historical Longer Term Forecasts: Includes historical hourly and/or daily forecast data from 2009 until present date. Data will include condition descriptions, high/low temperatures, wind speed and direction, dew point, humidity, comfort level, UV index, probability of precipitation, rainfall and snowfall amounts. Available for over 85,000 forecast points globally. The information is updated four times per day.

Below are available time periods per each type of forecast from the GFS model and from CustomWeather's proprietary CW100 model:

GFS: 7-day hourly forecasts from August 2nd 2009; 48-hour to 5-day detailed forecasts from August 4th 2009; 15-day forecasts from October 9th 2008.

CW100: 7-day hourly forecasts from November 27, 2012; 48-hour detailed forecasts from November 12, 2011; 7-day forecasts from December 6, 2010, 15-day forecasts from August 6, 2012. CW100 is CustomWeather's proprietary model.

MOS: (Model Output Statistics) for any global location using archive of model and observation data. 0.25 degree resolution. 15-day hourly forecasts from January 1, 2017; 15-day forecasts from April 19, 2017.

NEXRAD is a network of 160 high-resolution Doppler weather radars operated by the NOAA National Weather Service (NWS), the Federal Aviation Administration (FAA), and the U.S. Air Force (USAF). Doppler radars detect atmospheric precipitation and winds, which allow scientists to track and anticipate weather events, such as rain, ice pellets, snow, hail, and tornadoes, as well as some non-weather objects like birds and insects. NEXRAD stations use the Weather Surveillance Radar - 1988, Doppler (WSR-88D) system. The NEXRAD products are divided in two data processing levels. The lower Level 2 data are base products at original resolution. Level 2 data are recorded at all NWS and most USAF and FAA WSR-88D sites. From the Level 2 quantities, computer processing generates numerous meteorological analysis Level 3 products. The Level 3 data consists of reduced resolution, low-bandwidth, base products as well as many derived, post-processed products. Level 3 products are recorded at most U.S. sites, though non-US sites do not have Level 3 products. There are over 40 Level 3 products available from the NCDC. General products for Level 3 include the base and composite reflectivity, storm relative velocity, vertical integrated liquid, echo tops and VAD wind profile. Precipitation products for Level 3 include estimated ground accumulated rainfall amounts for one and three hour periods, storm totals, and digital arrays. Estimates are based on reflectivity to rainfall rate (Z-R) relationships. Overlay products for Level 3 are alphanumeric data that give detailed information on certain parameters for an identified storm cell. These include storm structure, hail index, mesocyclone identification, tornadic vortex signature, and storm tracking information. Radar messages for Level 3 are sent by the radar site to users in order to know more about the radar status and special product data. NEXRAD data are provided to the NOAA National Climatic Data Center for archiving and dissemination to users. Data coverage varies by station and ranges from May 1992 to 1 day from present. Most stations began observing in the mid-1990s, and most period of records are continuous.Daily GHCN is part of the Global Historical Climatology Network - Daily (GHCN-Daily) dataset. GHCN-Daily integrates daily climate observations from approximately 30 different data sources. Version 3 was released in September 2012 with the addition of data from two additional station networks. Changes to the processing system associated with the version 3 release also allowed for updates to occur 7 days a week rather than only on most weekdays. Version 3 contains station-based measurements from well over 90,000 land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. Over 25,000 stations are regularly updated with observations from within roughly the last month. The dataset is also routinely reconstructed (usually every week) from its roughly 30 data sources to ensure that GHCN-Daily is generally in sync with its growing list of constituent sources. During this process, quality assurance checks are applied to the full dataset. Where possible, GHCN-Daily station data are also updated daily from a variety of data streams. Station values for each daily update also undergo a suite of quality checks.Local Climatological Data (LCD) are summaries of climatological conditions from airport and other prominent weather stations managed by NWS, FAA, and DOD. The product includes hourly observations and associated remarks, and a record of hourly precipitation for the entire month. Also included are daily summaries summarizing temperature extremes, degree days, precipitation amounts and winds. The tabulated monthly summaries in the product include maximum, minimum, and average temperature, temperature departure from normal, dew point temperature, average station pressure, ceiling, visibility, weather type, wet bulb temperature, relative humidity, degree days (heating and cooling), daily precipitation, average wind speed, fastest wind speed/direction, sky cover, and occurrences of sunshine, snowfall and snow depth. The source data is global hourly (DSI 3505) which includes a number of quality control checks.Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.The global summaries data set contains a monthly (GSOM) resolution of meteorological elements (max temp, snow, etc) from 1763 to present with updates weekly. The major parameters are: monthly mean maximum, mean minimum and mean temperatures; monthly total precipitation and snowfall; departure from normal of the mean temperature and total precipitation; monthly heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; and extreme daily temperature and precipitation amounts. The primary source data set source is the Global Historical Climatology Network (GHCN)-Daily Data set. The global summaries data set also contains a yearly (GSOY) resolution of meteorological elements. See associated resources for more information. This data is not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which will not be available in GSOM and GSOY. The GSOM and GSOY data set is going to replace the legacy DSI-3220 and expand to include non-U.S. (a.k.a. global) stations. DSI-3220 only included National Weather Service (NWS) COOP Published, or "Published in CD", sites.The global summaries data set contains a yearly (GSOY) resolution of meteorological elements (max temp, snow, etc) from 1763 to present with updates weekly. The major parameters are: monthly mean maximum, mean minimum and mean temperatures; monthly total precipitation and snowfall; departure from normal of the mean temperature and total precipitation; monthly heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; and extreme daily temperature and precipitation amounts. The primary source data set source is the Global Historical Climatology Network (GHCN)-Daily Data set. The global summaries data set also contains a monthly (GSOM) resolution of meteorological elements. See associated resources for more information. This data is not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which will not be available in GSOM and GSOY. The GSOM and GSOY data set is going to replace the legacy DSI-3220 and expand to include non-U.S. (a.k.a. global) stations. DSI-3220 only included National Weather Service (NWS) COOP Published, or "Published in CD", sites.The U.S. Annual Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters that provide users with many tools to understand typical climate conditions for thousands of locations across the United States, as well as U.S.

The average temperature in December 2024 was 38.25 degrees Fahrenheit in the United States, the fourth-largest country in the world. The country has extremely diverse climates across its expansive landmass. Temperatures in the United States On the continental U.S., the southern regions face warm to extremely hot temperatures all year round, the Pacific Northwest tends to deal with rainy weather, the Mid-Atlantic sees all four seasons, and New England experiences the coldest winters in the country. The North American country has experienced an increase in the daily minimum temperatures since 1970. Consequently, the average annual temperature in the United States has seen a spike in recent years. Climate Change The entire world has seen changes in its average temperature as a result of climate change. Climate change occurs due to increased levels of greenhouse gases which act to trap heat in the atmosphere, preventing it from leaving the Earth. Greenhouse gases are emitted from various sectors but most prominently from burning fossil fuels. Climate change has significantly affected the average temperature across countries worldwide. In the United States, an increasing number of people have stated that they have personally experienced the effects of climate change. Not only are there environmental consequences due to climate change, but also economic ones. In 2022, for instance, extreme temperatures in the United States caused over 5.5 million U.S. dollars in economic damage. These economic ramifications occur for several reasons, which include higher temperatures, changes in regional precipitation, and rising sea levels.

The Worldwide Airfield Summary contains a selection of climatological data produced by the U.S. Air Force, Air Weather Service. The reports were compiled from dozens of domestic and international sources. It consists of summaries for approximately 4,000 airfield stations worldwide and the climatic areas in which they are located, for various periods ending at or before May 1974. A keyed subset (June 1969) is available digitally upon request.

The data presented are monthly and annual summaries of: 1. Absolute maximum and minimum temperatures (Deg. F), 2. Mean daily maximum and minimum temperatures (Deg. F), 3. Mean number of days with maximum temperature equal to or greater than 90 Deg. F, 4. Mean number of days with minimum temperature equal to or less than 32 or 0 Deg. F, 5. Mean dew-point temperature (Deg. F), 6. Mean relative humidity (%), 7. Mean pressure altitude (feet), 8. Mean precipitation (in.), 9. Mean snowfall (in.), 10. Mean number of days with precipitation equal to or greater than 0.1 inch, 11. Mean number of days with snowfall equal to or greater than 1.5 inch, 12. Mean number of days with an occurrence of visibility less than 0.5 mile, 13. Mean number of days with thunderstorms, 14. Percent frequency surface wind speed equal to or greater than 17 knots, 15. Percent frequency surface wind speed equal to or greater than 28 knots, 16. Percent frequency ceiling less than 5,000 feet and/or visibility less than 5 miles, 17. Percent frequency ceiling less than 1,500 feet and/or visibility less than 3 miles by 3-hourly increments, 18. Percent frequency ceiling less than 300 feet and/or visibility less than 1 mile by 3-hourly increments, 19. Mean number of days with ceiling equal to or greater than 1,000 feet and visibility equal to or greater than 3 miles, 20. Mean number of days with ceiling equal to or greater than 2,500 feet and visibility equal to or greater than 3 miles, 21. Mean number of days with ceiling equal to or greater than 6,000 feet and visibility equal to or greater than 3 miles, 22. Mean number of days with ceiling equal to or greater than 10,000 feet and visibility equal to or greater than 3 miles, 23. Mean number of days ceiling equal to or greater than 2,000 feet and visibility equal to or greater than 3 miles with surface wind speed less than 10 knots, 24. Mean number of days with surface wind speed equal to or greater than 17 knots and no precipitation, 25. Mean number of days with surface wind speed 4 to 10 knots and temperature 33 to 89 Deg. F and no precipitation, 26. Mean number of days with sky cover less than 3/10ths and visibility equal to or greater than 3 miles.

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the CW100. The CW100 Model is based on physics, not statistics or airport observations. As a result, it can achieve significantly better accuracy than statistical models, especially for non-airport locations. While other forecast models are designed to forecast the entire atmosphere, the CW100 greatly reduces computational requirements by focusing entirely on conditions near the ground. This reduction of computations allows it to resolve additional physical processes near the ground that are not resolved by other models. It also allows the CW100 to operate at a much higher resolution, typically 100x finer than standard models and other gridded forecasts.

Detailed Forecasts:
Features a detailed 48-hour outlook broken into four segments per day: morning, afternoon, evening, and overnight. Each segment provides condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, 6-hr forecasted precip amounts, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Extended Forecasts Days 1-15:
Features condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Hour-by-Hour Forecasts: Features Hour-by-Hour forecasts. The product is available as 12 hour, 48 hour and 168 hour blocks. Each hourly forecast includes weather descriptions, wind conditions, temperature, dew point, humidity, visibility, rainfall totals, snowfall totals, and precipitation probability. Available for over 85,000 forecast points globally. Updated four times per day.

Historical Longer Term Forecasts: Includes historical hourly and/or daily forecast data from 2009 until present date. Data will include condition descriptions, high/low temperatures, wind speed and direction, dew point, humidity, comfort level, UV index, probability of precipitation, rainfall and snowfall amounts. Available for over 85,000 forecast points globally. The information is updated four times per day.

Below are available time periods per each type of forecast from the GFS model and from CustomWeather's proprietary CW100 model:

GFS: 7-day hourly forecasts from August 2nd 2009; 48-hour to 5-day detailed forecasts from August 4th 2009; 15-day forecasts from October 9th 2008.

CW100: 7-day hourly forecasts from November 27, 2012; 48-hour detailed forecasts from November 12, 2011; 7-day forecasts from December 6, 2010, 15-day forecasts from August 6, 2012. CW100 is CustomWeather's proprietary model.

MOS: (Model Output Statistics) for any global location using archive of model and observation data. 0.25 degree resolution. 15-day hourly forecasts from January 1, 2017; 15-day forecasts from April 19, 2017.

This folder contains all of the data used in The Pudding essay Greetings from Mars published in February 2018.

Below you'll find metadata for each file.

mars-weather.csv - What is this?: Data representing the weather conditions on Mars from Sol 1 (August 7, 2012 on Earth) to Sol 1895 (February 27, 2018 on Earth). - Source(s) & Methodology: This data was measured and transmitted via the Rover Environmental Monitoring Station (REMS) on-board the Curiosity Rover. The data was made publicly available by NASA’s Mars Science Laboratory and the Centro de Astrobiología (CSIC-INTA). The Centro de Astrobiología offers a widget and a disclaimer regarding the data collected by Curiosity here. You can also find more in-depth weather reports from the Centro de Astrobiología team here. - Data Disclaimer: The Centro de Astrobiología includes the following disclaimer for these data:

The information contained in this file is provided by Centro de Astrobiologia (CAB) and is intended for outreach purposes only. Any other use is discouraged. CAB will take no responsibility for any result or publication made base on the content of this file. To access REMS scientific data, visit PDS. The environmental magnitudes given in this file are obtained from the values read by the Rover Environmental Monitoring Station (REMS) on board the Mars Science Laboratory (MSL) rover on Mars. This file provides the environmental magnitudes at REMS location, so MSL rover influences those magnitudes (rover position, rover temperature, rover orientation, rover shade, dust depositions on the rover, etc.) REMS does not take measurements continuously and it takes measurements at different times from one day to another. This fact has influence on the variation of the values given in this file from one day to another . For different reasons (instrument maintenance, instrument calibration, instrument degradation, etc.), some or all of the magnitudes in this file may not be available.

• Last Modified: March 2, 2018 (a daily-updated version in JSON-format can be found here).
• Contact Information: Amber Thomas
• Spatial Applicability: Gale Crater, Mars (just south of Mars' equator). More information on Curiosity's location here.
• Temporal Applicability: Sol 1 (August 7, 2012 on Earth) to Sol 1895 (February 27, 2018 on Earth)
• Observations (Rows): Each row represents the weather information collected at some point on a single sol (Martian day).

• Variables (Columns):

• id The identification number of a single transmission number

• terrestrial_date The date on Earth (formatted as month/day/year or m/dd/yy). date

• sol The number of elapsed sols (Martian days) since Curiosity landed on Mars. number

• ls The solar longitude or the Mars-Sun angle, measured from the Northern Hemisphere. In the Northern Hemisphere, the spring equinox is when ls = 0. Since Curiosity is in the Southern Hemisphere, the following ls values are of importance:
  • ls = 0: autumnal equinox
  • ls = 90 : winter solstice
  • ls = 180 : spring equinox
  • ls = 270 : summer solstice number

• month The Martian Month. Similarly to Earth, Martian time can be divided into 12 months. Helpful information can be found here. text

• min_temp The minimum temperature (in °C) observed during a single Martian sol. number

• max_temp The maximum temperature (in °C) observed during a single Martian sol. number

• pressure The atmospheric pressure (Pa) in Curiosity's location on Mars. number

• wind_speed The average wind speed (m/s) measured in a single sol. Note: Wind Speed data has not be transmitted to Earth since Sol 1485. Missing values are coded as NaN. number

• atmo_opacity Description of the overall weather conditions on Mars for a given sol based on atmospheric opacity (e.g., Sunny). text

earthWeather.json Access the data here.

• What is this?: Data representing the weather conditions from 1,000 cities on Earth.
• Source(s) & Methodology: This data is Powered by Dark Sky, meaning that it uses the Dark Sky weather API to query the weather conditions in 1,000 cities daily. Last Modified: Daily.
• Contact Information: Amber Thomas
• Spatial Applicability: 1,000 cities around the world (at a minimum, the capital city of every country -worldwide and the capital city of every US state).
• Temporal Applicability: Due to timezones on Earth, 3 different calendar days are existing simultaneously at some place on the planet. To account for this, the data is updated at UTC Midnight and records 3 days worth of data. Days in the future are forecasted weather conditions.

• Variables (Columns):

• latitude The latitude of the city in decimal notation (e.g., 24.453884) number

• longitude The longitude of the city in decimal notation (e.g.,...

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in April 2025, the average temperature across the North American country stood at 12.02 degrees Celsius. Rising temperatures Globally, 2016, 2019, 2021 and 2024 were some of the warmest years ever recorded since 1880. Overall, there has been a dramatic increase in the annual temperature since 1895. Within the U.S. annual temperatures show a great deal of variation depending on region. For instance, Florida tends to record the highest maximum temperatures across the North American country, while Wyoming recorded the lowest minimum average temperature in recent years. Carbon dioxide emissions Carbon dioxide is a known driver of climate change, which impacts average temperatures. Global historical carbon dioxide emissions from fossil fuels have been on the rise since the industrial revolution. In recent years, carbon dioxide emissions from fossil fuel combustion and industrial processes reached over 37 billion metric tons. Among all countries globally, China was the largest emitter of carbon dioxide in 2023.

This collective of data from reports from worldwide CLIMAT stations is based on original data that are routinely disseminated by the responsible national weather services. All of the time series stored in the DWD database are made available. This data is quality-checked by the DWD for the purpose of climatological or climate-related applications. Further information: https://opendata.dwd.de/climate_environment/CDC/observations_global/CLIMAT/monthly/qc/precipGE1mm_days/historical/BESCHREIBUNG_obsglobal_monthly_qc_precipGE1mm_days_historical_de.pdf

Most of the existing carbon emission studies based on the IPAT framework considered the size effect rather than structure effect of population. However, it is proved with the micro-data household evidence that the demographic structure explains the unexpected trends better. To complete the framework, this study integrated the structure effects with the STIRPAT model base on the household life-cycle consumption theory as different age groups differ in carbon consumption behaviors. For further analysis with the frequent extreme weather events caused by global warming and their catastrophic effect on human activities, this study also harmonized Köppen criteria with the theories model by Syukuro Manabe and Klaus Hasselmann and considers climate factors precipitation (PRE), annual degree-day (DD), and temperature anomaly (TA) with the extended model to investigate whether population aging trend provides room for or creates barriers to carbon reduction. NASA night-time light (NTL) data DMSP/OLS and VIIRS/DNB is adopted as the proxy for population density to weight the relevant climate data from over 30,000 weather stations worldwide. The combined dataset is from 150 countries, and the period is during 1970–2013. The Panel Seemingly Unrelated Regression (SUR) method is used to solve the problems of cross-sectional correlation, non-stationarity, and endogeneity since sample countries are closely linked in the global meteorological system which make each cross-sectional disturbance term likely to be contemporaneously correlated, and endogeneity of carbon emission under the same global agreement constraint. The empirical results show that the age structure had significant and different impacts on carbon emissions. The general influence of age growth is an inverted U shape as the younger group consumes less than the older group, and offspring leave the family when the householder turns 50. The EKC theory is also checked with the threshold model of per capita income on carbon emissions to determine how many countries reached carbon peak. This study proved that the aggregated carbon consumption pattern is aligned with the microlevel evidence on household energy consumption. Another distinguished finding is that population aging may generally lead to an increase in heat and electricity carbon emissions, contrary to what some household energy consumption models would predict. We explain the uplifted tail as the “effect caused by the narrowed adaptation temperature range” when people are getting older and vulnerable. It should be noted that as the aging trend becomes severe worldwide and extreme weather events happen with higher frequency, the potential energy spending and thus carbon emission on air conditioning will undoubtfully overgrow. One important method is to improve the building energy efficiency by retrofitting old buildings’ insulations. Implementing new green building standards in carbon reduction must not be ignored. Evidence shows that if the insulation of pre-1990s houses is reconstructed with modern materials, carbon emissions caused by residential cooling and heating can be reduced by about 20% every year. Overall, promoting an efficient building style provides reduction capacity for the industrial sector, and it is a way to achieve sustainable growth.

NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.

The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.

The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.

The latest data version update includes hourly-based source ARD, in addition to enhanced daily, monthly, annual, and climatology data. The daily time series for meteorology is available from 1981, while solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning from 1984 for meteorology and from 2001 for solar-based parameters. The hourly data equips users with the ARD needed to model building system energy performance, providing information directly amenable to decision support tools introducing the industry standard EnergyPlus Weather file format.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

The Global Historical Climatology Network - Daily (GHCN-Daily/GHCNd) dataset integrates daily climate observations from approximately 30 different data sources. Version 3 was released in September 2012 with the addition of data from two additional station networks. Changes to the processing system associated with the version 3 release also allowed for updates to occur 7 days a week rather than only on most weekdays. Version 3 contains station-based measurements from well over 90,000 land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. Over 25,000 stations are regularly updated with observations from within roughly the last month. The dataset is also routinely reconstructed (usually every week) from its roughly 30 data sources to ensure that GHCNd is generally in sync with its growing list of constituent sources. During this process, quality assurance checks are applied to the full dataset. Where possible, GHCNd station data are also updated daily from a variety of data streams. Station values for each daily update also undergo a suite of quality checks.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Surface synoptic weather reports from ships and land stations worldwide were processed to produce a global cloud climatology which includes: total cloud cover, the amount and frequency of occurrence of nine cloud types within three levels of the troposphere, the frequency of occurrence of clear sky and of precipitation, the base heights of low clouds, and the non-overlapped amounts of middle and high clouds. Synoptic weather reports are made every three hours; the cloud information in a report is obtained visually by human observers. The reports used here cover the period 1971-96 for land and 1954-2008 for ocean. This digital archive provides multi-year monthly, seasonal, and annual averages in 5x5-degree grid boxes (or 10x10-degree boxes for some quantities over the ocean). Daytime and nighttime averages, as well as the diurnal average (average of day and night), are given. Nighttime averages were computed using only those reports that met an "illuminance criterion" (i.e., made under adequate moonlight or twilight), thus minimizing the "night-detection bias" and making possible the determination of diurnal cycles and nighttime trends for cloud types. The phase and amplitude of the first harmonic of both the diurnal cycle and the annual cycle are given for the various cloud types. Cloud averages for individual years are also given for the ocean for each of 4 seasons, and for each of the 12 months (daytime-only averages for the months). [Individual years for land are not gridded, but are given for individual stations in a companion data set, CDIAC's NDP-026D).] This analysis used 185 million reports from 5388 weather stations on continents and islands, and 50 million reports from ships; these reports passed a series of quality-control checks. This analysis updates (and in most ways supercedes) the previous cloud climatology constructed by the authors in the 1980s. Many of the long-term averages described here are mapped on the University of Washington, Department of Atmospheric Sciences Web site. The Online Cloud Atlas containing NDP-026E data is available via the University of Washington. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp026e/ndp026e.html

Abstract: The Annual Global High-Resolution Extreme Heat Estimates (GEHE), 1983-2016 data set provides global 0.05 degrees (~5 km) gridded annual counts of the number of days where the maximum Wet Bulb Globe Temperature (WBGTmax) exceeded dangerous hot-humid heat thresholds for the period 1983 to 2016. The thresholds are based on the International Standards Organization (ISO) criteria for occupational heat-related risk, defined as days where WBGTmax > 28, 30, and 32 degrees Celsius. This data set also includes the annual rate of change in the number of extreme humid-heat days that exceeded these thresholds. GEHE has a wide array of applications for mapping and quantifying extreme humid-heat dynamics over a 34-year time period, and is the highest resolution data set of its kind to date. GEHE provides scientific researchers and decision makers from a wide range of arenas, including climate change, public and occupational health, urban planning and design, hazards risk reduction, and food security, insights into how humid-heat has impacted human and environmental systems worldwide. The data set can be used to pinpoint how changes in extreme humid-heat impact human health and well-being, as well as ecological systems, across scales of analysis, from local, to national, to global.Purpose: To provide a high-resolution, longitudinal global data set of annual extreme humid-heat.Variables: Wet Bulb Globe TemperatureWBGTmax28 WBGTmax28-trendWBGTmax30WBGTmax30-trendWBGTmax32WBGTmax32-trendLegendSymbol Pixel Value Description High: 366A high-resolution, longitudinal global data set of annual extreme humid-heatLow: 0 Citation: Tuholske, C., P. Peterson, C. Funk, and K. Caylor. 2023. Annual Global High-Resolution Extreme Heat Estimates (GEHE), 1983-2016. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/hff0-k565. Publication References:Tuholske, C., K. Caylor, C. Funk, A. Verdin, S. Sweeney, K. Grace, P. Peterson, and T. Evans. 2021. Global Urban Population Exposure to Extreme Heat. Proceedings of the National Academy of Sciences 118(41), e2024792118. DOI: https://doi.org/10.1073/pnas.2024792118Contact:For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum.

Abiotic constraints, such as drought and heat driven by climate change, negatively impact the production of the common bean (Phaseolus vulgaris L.), an essential grain legume worldwide. The ability to tolerate drought and heat stress in common bean can be improved by introducing genetic variation from related species, such as tepary bean (Phaseolus acutifolius A. Gray), which has recently gained attention because of its adaptation to drought and heat stresses and potential use as a genetic resource and alternative crop. To better understand the phenotypic response of tepary bean to drought and heat stress in multiple environments and trials and to select highly adapted tepary beans, we conducted two field experiments. In Experiment 1, we compared the adaptation to drought stress of tepary bean (n = 10), common bean (n = 10), and Lima bean (Phaseolus lunatus L.; n = 9) by assessing the reduction in grain yield under terminal drought compared to well-irrigated conditions in two California locations with arid summer conditions. Of the three species, tepary bean showed the statistically strongest adaptation to terminal drought, followed by Lima bean and common bean. In Experiment 2, we evaluated a set of 22 tepary beans from contrasting origins for drought and heat stresses across multi-environment trials (METs), in California, Nebraska, and Colombia, with common bean as a control. We found a considerable variation in the tepary bean phenotypic response to these MET conditions, as a result of a strong genotype x environment (G x E) interaction. Also, we identified tepary bean accessions adapted to drought, heat, and well-irrigated conditions across multiple climate zones. Understanding the performance of tepary bean across multiple environments and identifying tepary beans with broad and target-specific adaptation will maximize the potential use of the species. Methods Experiment 1: Drought evaluation of three domesticated Phaseolus species To compare the drought stress response among key domesticated Phaseolus species, ten genotypes of each of the common bean and tepary bean and nine genotypes of the Lima bean (Supplemental Table S1) were evaluated under well-irrigated and terminal drought conditions during the summer of 2016 in two locations in California (Table 1). These 29 genotypes are representative of the genetic diversity in these three species as they include representatives of the two domestications in common and Lima beans (e.g., Kwak and Gepts, 2009; Garcia et al., 2021) and their breeding status (landraces vs. improved varieties). Field trials were conducted at the University of California, Agricultural and Natural Resources West Side Research and Extension Center (WSREC) (Bsk climate, i.e. arid, cold steppe type climate, according to the Köppen-Geiger classification: Peel et al. 2007; Rubel & Kottek, 2010; Beck et al., 2018), and the University of California, Davis, Plant Science Field Facility (UC Davis) (Csa climate: temperate with hot, dry summer), which differ in altitude, soil type, and average weather data such as precipitation, temperature, and relative humidity (Table 1). The plant material was obtained from the seed inventory of the Bean Breeding Program, Department of Plant Sciences, University of California, Davis. Drought/irrigated field trials in both locations were planted side by side in a row-column randomized design with three replications. Individual plots consisted of two 3 m-rows (50 seeds/row), spaced 0.76 m apart. The drought stress was terminal, with irrigation reduced by 50% at WSREC and 75% at UC Davis between flowering and harvesting compared with the well-irrigated trial. Trials were agronomically maintained using standard common bean commercial procedures (Long et al., 2010). To control for spatial variability in each trial, best linear unbiased estimators (BLUEs) and best linear unbiased predictors (BLUPs) of yield (kg/ha) were fitted using the SPATs package (Rodríguez-Álvarez et al., 2018) in the Mr. Bean App (Aparicio et al., 2019) (Figures 3 and 4). Genetic correlations of the locations were obtained using the statistical program EchidnaMMS (Gilmour, 2020). The analysis of variance (ANOVA) was conducted using the lme4 package (Bates et al, 2015) in R (R Core Team, 2023). The model, which was fitted with the lmer function (Bates et al., 2015), included species, genotype, locations, and irrigation/drought treatment as fixed effects. ANOVA calculations utilized the Kenward and Roger (1997) method for determining the denominator degrees of freedom to enhance the accuracy of F-tests and t-tests for fixed effects. Mean comparisons were performed with the emmeans function using the Tukey test to compare the estimated marginal means (Bates et al. 2015). Experiment 2: Evaluation of tepary beans to drought and heat stress throughout multi-environment trials (MET) studyDrought stress trials To determine the drought tolerance of tepary beans across different environments, we evaluated 22 tepary bean genotypes (Table 2) in three drought-stressed and well-irrigated trials in three locations in 2017 and 2018. These 22 lines were chosen to represent germplasm accessions or landraces (G entries) and improved cultivars (TARS-Tep entries) (Table 2) and were selected from a wide range of annual mean temperature and annual rainfall (Figure 2). For the purpose of comparison, four P. vulgaris breeding lines were also added (DOR, SEN, and SEF lines (Table 2). Field trials were conducted in a humid tropical environment at the International Center for Tropical Agriculture (now called Alliance Bioversity & CIAT), Palmira, Colombia (DrPAL; Af climate); a semi-arid temperate environment at the University of California, Davis, CA (DrUCD); and a semi-arid temperate climate at the University of Nebraska, Panhandle Research and Extension Center, Scottsbluff, NE (DrUNL; Bsk climate) (Table 1). Trials were planted during the summer seasons in the two temperate locations and during the low precipitation season in the tropical location. Environmental conditions such as altitude, soil type, and weather data such as precipitation, temperature, and relative humidity differed across the three locations (Table 1). Drought stress in Palmira was intermittent and moderate, with water supply reduced by 28% compared to the well-irrigated trial. Drought stress in Davis was terminal and severe, with water supply to the crop reduced by ~75% compared to the well-irrigated trial. Drought stress in Scottsbluff was terminal and severe, with water supply reduced by 74% compared to the well-irrigated trial (Table 1). Drought and well-irrigated trials were planted side-by-side in a randomized complete block design with three replicates in Palmira, Colombia (PAL), five in Davis, CA (UC Davis), and two in Scottsbluff, NE. Individual plots in Davis consisted of two rows (50 seeds/row), 3 m-long, and 0.76 m apart; in Palmira, the trials were planted in plots 4-row, 3-m-long plots, spaced 0.6 m apart; Scottsbluff trials were planted in 2-row, 3.65 m-long plots, 0.6 m apart. Trials were agronomically maintained using local, standard common bean commercial procedures. High heat trials To evaluate the heat tolerance of tepary beans across different environments, we evaluated the same 22 drought-tolerance-tested tepary bean genotypes (Table 2) under heat stress in three distinct locations in 2017 and 2018 (Table 1). It has been reported that minimum temperatures (i.e., night temperatures) above 20˚C and maximum temperatures (i.e., day temperatures) above 30˚C negatively impact the yield production in the common bean (Porch & Jahn, 2001; Porch, 2006). Thus, locations above the 20/30˚C night/day threshold were selected to conduct the heat stress trials. Field trials were planted in two hot and humid tropical locations in Alvarado (HtALV; Department of Tolima, Colombia; Af climate) and Caribia (HtCAR; Department of Magdalena, Colombia; Af climate), and one desert climate at the University of California, Desert Research and Extension Center, Holtville, CA (HtDREC; Bwh climate) (Table 1). Reduction of water supplied (by rainfall and irrigation, Table 1) provided an additional environment in Holtville that combined heat and drought stress. High heat trials were planted at low altitudes in tropical locations and during the summer season in the desert. Environmental conditions such as altitude, soil type, and weather data such as precipitation, temperature, and relative humidity differed across the three locations (Table 1). Field trials were planted in a randomized complete block experimental design, with three replicates in all three locations. Plots in Alvarado consisted of two rows (50 seeds/row), 3 m long, and 0.6 m apart; in Caribia, the plots consisted of 4 rows (50 seeds/row), 3 m long, and 0.6 m apart; in Holtville, California, plots included 2 rows (50 seeds/row), 3 m long, and 0.76 m apart. Trials were agronomically maintained using standard common bean commercial procedures.

Reference evapotranspiration per day with a spatial resolution of 0.1 degree. Unit: mm day-1. The dataset contains daily values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.