This statistic shows cities in the United States with the highest average annual temperatures. Data is based on recordings from 1981 to 2010. In San Antonio, Texas the average temperature is 80.7 degrees Fahrenheit. Some cities that have the hottest maximum summer temperatures will not be included in this list due to their extreme temperature variance.

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

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.

Njal.Rollinson.EggSize.Amphibians.DryadData on life-history traits, environmental temperature with each species geographic range, and range size for 718 species of amphibians.Dryad.US.Climate.DataMean maximum monthly temperatures (degrees C) for 7493 cities in the USA. Simulations in table A1 of the main text randomly selected 1000 of these cities as the basis for simulation. Data are provided in case readers wish to randomly select different cities for simulation.Dryad.Monthly.Temperature.SimulationR code to exactly replicate the simulation in Table A1 of the main text.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Fort Keogh (FTK) contains air temperature (mean maximum ) measurements in celsius units and were aggregated to a monthly timescale.

AimEucalypts are important and popular urban tree species across cities worldwide. However, little is known about how their climatic niche breadth (CNB) and functional traits predict their success, and vulnerability, to current climate change in cities. We assessed the relationship between the CNB of eucalypts and key traits to understand their tolerance to climate change.LocationGlobal urban areas, 66 cities in 21 countries.Time period1981 to 2022.Major species studiedFifty ‘eucalypt’ species belonging to the genera Eucalyptus, Angophora and Corymbia.MethodsWe used the species' safety margin concept to determine cities where eucalypts were planted outside the limits of their CNB, as defined from the native range, considering two extreme variables, maximum temperature of the warmest month (MTWM) and precipitation of the driest month (PDM). We assessed correlations between functional traits (leaf δ13C, leaf dry mass, leaf length, leaf N per dry mass, wood density) and negative safety margins, indicative of tolerance to non-native conditions.ResultsIn total, 42 species planted in 40 cities exceeded their safety margins for MTWM, while 43 species in 38 cities exceeded their safety margins for PDM. Within 24 cities, all species exceeded their native CNB for both MTWM and PDM. The cities of Atakpame (Togo), Chennai (India), Chongqing (China) and the US cities of Phoenix and Riverside had the highest richness of eucalypt species growing outside their native CNB. Broadly, species with wide CNB, small leaves, high δ13C, high leaf N per dry mass and high wood density were more likely to persist in cities where climatic conditions exceeded their native CNB.Main conclusionsEucalypts occupy many cities experiencing climatic conditions outside their native CNB. Species with traits characteristic of heat and drought tolerance are more often planted in cities where climatic conditions may exceed their CNB native limits.

Surface Weather Observation 1001 Forms is a set of historical manuscript records for the period 1893-1948. The collection includes two very similar form types: Form 1001, in use by the US Weather Bureau, and Form 1, in use by the US Army and Navy. NCDC Climate Data Modernization Program (CDMP) scanned the vast majority of these forms in order to fill in the observation gap prior to the time when commercial aviation began in the U.S. Many forms contiaining observations taken on foreign soil have not yet been scanned. Observations were recorded two to four times daily beginning as early as 1893 at city Weather Bureau Offices. During the 1930s many of these city stations gradually moved to airport locations.

Through 1936 observations were taken twice daily; then in 1937 the general practice was to record four observations per day. The data elements are as follows: station pressure, sea level pressure, dry and wet bulb temperature, dew point, maximum and minimum temperature, wind direction and speed, precipitation, cloud amount and type, ceiling, state of weather and visibility. It should be noted that not all elements are present for all stations in this dataset, and that ceiling and visibility observations did not begin at the city offices until the 1930's. Official surface weather observation standards can be found in the Circular N manuals. The vast majority of records are available online, but some records are still only available in the physical format only.

This case follows the evolution of a low pressure system through its life cycle as it moved from Arkansas to Illinois, and provides a good example of the effects of model biases in the AVN model. The errors introduced to the AVN model due to inherent biases resulted in an incorrect prediction of strong cyclogenesis over the Gulf of Mexico.

For more information, see: http://data.eol.ucar.edu/codiac/projs?COMET_CASE_015