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 1895. 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.

This dataset contains temperature exposure statistics for Europe (e.g. percentiles) derived from the daily 2 metre mean, minimum and maximum air temperature for the entire year, winter (DJF: December-January-February) and summer (JJA: June-July-August). These statistics were derived within the C3S European Health service and are available for different future time periods and using different climate change scenarios. Temperature percentiles are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts, and they allow to identify a common threshold and comparison between different cities/areas. The temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. The statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided.

This figure shows how annual average air temperatures have changed in different parts of the United States since the early 20th century (since 1901 for the contiguous 48 states and 1925 for Alaska). The data are shown for climate divisions, as defined by the National Oceanic and Atmospheric Administration.

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.

When comparing somatic growth thermal performance curves (TPCs), higher somatic growth across experimental temperatures is often observed for populations originating from colder environments. Such countergradient variation has been suggested to represent adaptation to seasonality, or shorter favorable seasons in colder climates. Alternatively, populations from cold climates may outgrow those from warmer climates at low temperature, and vice versa at high temperature, representing adaptation to temperature. Using modelling, we show that distinguishing between these two types of adaptation based on TPCs requires knowledge about (i) the relationship between somatic growth rate and population growth rate, which in turn depends on the scale of somatic growth (absolute or proportional), and (ii) the relationship between somatic growth rate and mortality rate in the wild. We illustrate this by quantifying somatic growth rate TPCs for three populations of Daphnia magna where population growth scales linearly with proportional somatic growth. For absolute somatic growth, the northern population outperformed the two more southern populations across temperatures, and more so at higher temperatures, consistent with adaptation to seasonality. In contrast, for the proportional somatic growth TPCs, and hence population growth rate, TPCs tended to converge towards the highest temperatures. Thus, if the northern population pays an ecological mortality cost of rapid growth in the wild, this may create crossing population growth TPCs consistent with adaptation to temperature. Future studies within this field should be more explicit in how they extrapolate from somatic growth in the lab to fitness in the wild. Methods D. magna ephippia were obtained from three populations: a pond in Værøy, Norway (67.687°N 12.672°E), a pond in Park Midden-Limburg, Zonhoven, Belgium (50.982°N 5.318°E), and a rice field which is flooded and dries out annually in the Delta del Ebro, Riet Vell, Spain (40.659°N 0.775°E). In the following, these three populations are referred to as the Norway, Belgium and Spain populations, respectively. We used 10 clones (originating from 10 different ephippia) from each population in the experiments, and these were reared at 17°C with a 16L:8D photoperiod for three to four parthenogenetic generations prior to the experiment. During this period, individuals were fed three times a week with Shellfish Diet 1800 (Reed Mariculture Inc, USA) at final concentration of algae 4 × 105 cells/ml, and the ADaM medium was changed once a week. For the experiment, second or later clutch neonates were collected and photographed less than 24 hours after birth. After photographing, neonates were placed individually in 50 ml tubes containing 17°C ADaM medium. Each tube was placed in a Memmert Peltier-cooled incubator IPP 260plus (Memmert, Germany). We used a 16L:8D photoperiod and the temperature in different cabinets was set to 12.0, 15.0, 17.0, 19.0, 22.0, 24.0 and 26.0 °C. Each temperature treatment received eight individuals from each of the 10 clones. Animals were fed every second day with concentrations that had previously been established to represent ad lib rations. Due to logistic constraints, the different temperature treatments were run simultaneously for one population at a time (Norway May-June 2015, Spain December-February 2018, Belgium July-September 2018). All individuals were checked daily for mortality and sexual maturity (presence of eggs in the brood chamber). Tubes were rotated daily within the climate cabinets during the maturity checks to avoid positional effects. Upon maturation individuals were photographed and terminated.

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).

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 dataset presents monthly statistics for relative humidity and temperature in the State of Qatar. It includes minimum and maximum values for temperature (°C) and relative humidity (%), categorized by month and year. The data supports environmental and climate research by providing essential atmospheric measurements.

How the ability to acclimate will impact individual performance and ecological interactions under climate change remains poorly understood. Theory predicts that the benefit an organism can gain from acclimating depends on the rate at which temperatures change relative to the time it takes to induce beneficial acclimation. Here, we present a conceptual model showing how slower seasonal changes under climate change can alter species’ relative performance when they differ in acclimation rate and magnitude. To test predictions from theory, we performed a microcosm experiment where we reared a mid- and a high-latitude damselfly species alone or together under the rapid seasonality currently experienced at 62°N and the slower seasonality predicted for this latitude under climate change and measured larval growth and survival. To separate acclimation effects from fixed thermal responses, we simulated growth trajectories based on species’ growth rates at constant temperatures and quantified how much and how fast species needed to acclimate to match the observed growth trajectories. Consistent with our predictions, the results showed that the midlatitude species had a greater capacity for acclimation than the high-latitude species. Furthermore, since acclimation occurred at a slower rate than seasonal temperature changes, the midlatitude species had a small growth advantage over the high-latitude species under the current seasonality but a greater growth advantage under the slower seasonality predicted for this latitude under climate change. In addition, the two species did not differ in survival under the current seasonality, but the midlatitude species had higher survival under the predicted climate change scenario, possibly because rates of cannibalism were lower when smaller heterospecifics were present. These findings highlight the need to incorporate acclimation rates in ecological models.

This dataset includes cleavage rates of embryos of three species of Antarctic embryos that were reared at a range of temperatures up to the 32-cell stage.

Massive corals are used as environmental recorders throughout the tropics and subtropics to study environmental variability during time periods preceding ocean-observing instrumentation. However, careful testing of paleoproxies is necessary to validate the environmental-proxy record throughout a range of conditions experienced by the recording organisms. As part of the USGS Coral Reef Ecosystems Studies project (http://coastal.er.usgs.gov/crest/), we tested the hypothesis that the coral Siderastrea siderea faithfully records sea-surface temperature (SST) in the Sr/Ca record throughout the subtropical (Florida, USA) seasonal cycle along 350 km of reef tract. The datasets included in this data release are comprised of data collected between 2009 and 2013. Coral samples were analyzed from thirty-nine corals growing in 3- to 4-meter water depths at Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS), Sombrero Reef (FKNMS), and Pulaski Shoal (Dry Tortugas National Park). Temperatures were recorded with Onset® HOBO® Water Temp Pro V2 (U22-001) data loggers in duplicate at each site. Sr/Ca, Mg/Ca, calcification rate, and select underwater temperature data are provided here. The results of this experiment are interpreted in Kuffner and others (2017). A larger temperature dataset, including the data provided here, is found in another data release Kuffner (2016), and a larger calcification-rate dataset is interpreted in Kuffner and others (2013).

Time rate of change of temperature (storage component only) over 30 minutes at each measurement level along the vertical tower profile. Gap-filling is not applicable. This data product is bundled into DP4.00200, Bundled data products - eddy covariance, and is not available as a stand-alone download.

This dataset contains records from implanted and not implanted temperature logging tags, body sizes of fish implanted with temperature logging tags, times the fish/tags were transferred from one temperature to another, and the time the temperature recorded by the tag equilibrated with ambient water conditions. Data were collected to compare thermal equilibration rate of temperature recording tags implanted in the coelomic cavity of lab reared brook trout to temperature recording tags not implanted in fish. Tagged fish and not implanted tags were moved rapidly between 8 C and 16 C and also subjected to slow (2 C per hour) thermal ramp.

Present summary statistics for biometeorological variables for NEON weather stations at core TIS sites. Statistics will include means, standard deviations, maxima, and minima for periods of days, months, and years. Engineering-grade product only.

Per-capita growth rates for T. pseudonana selected at low and high temperatures for ~350 generations and assayed at 10 temperatures.

These data were published in O'Donnell et al (2018) Global Change Biol. and can also be found in Supporting Information archive gcb14360-sup-0001-SupInfoS1.zip. The filename is ODonnell_etal_2018_temp_gr.rate_data_T.pseudonana_0318.csv. These data can also be seen in Figure 1 of the main text.

AbstractClimate change may soon threaten much of global biodiversity, especially if species cannot adapt to changing climatic conditions quickly enough. A critical question is how quickly climatic niches change, and if this speed is sufficient to prevent extinction as climates warm. Here, we address this question in the grass family (Poaceae). Grasses are fundamental to one of Earth's most widespread biomes (grasslands), and provide roughly half of all calories consumed by humans (including wheat, rice, corn and sorghum). We estimate rates of climatic niche change in 236 species and compare these with rates of projected climate change by 2070. Our results show that projected climate change is consistently faster than rates of niche change in grasses, typically by more than 5000-fold for temperature-related variables. Although these results do not show directly what will happen under global warming, they have troubling implications for a major biome and for human food resources. Usage notesESM_guideSupplementary Figure S1Visual summary and flowchart of the methods used in this study.Appendix_S1Supplementary MethodsAppendix_S2Summary of climatic data for 170 species from Edwards & Smith [13] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Climatic data for each species for each climatic variable are summarized by the minimum, median and maximum values among localities within its distribution, as well as the mean value across all localities that was used to calculate rates. Temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S3Summary of climatic data for 62 species from Tree 1 of Spriggs et al. [2] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Climatic data for each species for each climatic variable are summarized by the minimum, median and maximum values identified within its distribution, as well as the mean value across all localities that was used to calculate rates. Temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S4Summary of climatic data for 60 species from Tree 2 of Spriggs et al. [2] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Climatic data for each species for each climatic variable are summarized by the minimum, median and maximum values identified within its distribution, as well as the mean value across all localities that was used to calculate rates. Temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S5Climatic data for all localities (including latitude and longitude) extracted from WorldClim database at 30 second resolution for 170 species from the tree of Edwards & Smith [13] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Note that temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S6Climatic data for all localities (including latitude and longitude) extracted from WorldClim database at 30 second resolution for 62 species from Tree 1 in Spriggs et al. [2] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Note that temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S7Climatic data for all localities (including latitude and longitude) extracted from WorldClim database at 30 second resolution for 60 species from Tree 2 in Spriggs et al. [2] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Note that temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S8Estimated node ages (in millions of years) and rates of climatic niche change for three trees under three different models of evolution (BM = Brownian Motion; OU = Ornstein-Uhlenbeck; Lambda = estimated lambda) for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12), along with estimated rates of future climate change under three scenarios...

We analysed the global geographical characteristics of how extreme surface air temperature and rainfall have evolved, based on the recurrence rate of record-breaking events, and found hot spots with anomalously high as well as regions with anomalously low numbers of record-breaking events. The recurrence rate was defined as the proportion of the actual count of record-breaking events over time to the number expected in a hypothetically stable climate. In a stable climate, the data is independent and identically distributed (iid) if the data is sampled at intervals that makes the autocorrelation between data points negligible. Anomalous recurrence rates indicate shifts in the tails of statistical distributions, and our analysis of record-high annual mean surface air temperatures revealed highest recurrence rates in the tropics, as opposed to the polar regions with the fastest warming. We present new evidence for extremely hot years becoming more common and widespread over the 1950-2023 period, based on recurrence rates as well as the global surface area fraction with daily mean surface air temperature exceeding 30°C and 40°C. A similar analysis for annual total precipitation highlights regions with increasingly more extreme annual precipitation as well as record-low annual precipitation typically associated with drought conditions. A multi-model ensemble of 306 runs with global climate models (CMIP6 SSP2-45) reproduced the statistics of record-breaking high annual mean surface air temperatures, but there were some differences with the reanalysis on annual total precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates, especially over parts of the Arctic. Methods Analysis using R and R-markdown script. Data from the ERA5 and NCEP2 reanalyses as well as global glimate models (CMIP6 SSP2-45).

In 2024, the global ocean surface temperature was 0.97 degrees Celsius warmer than the 20th-century average. Oceans are responsible for absorbing over 90 percent of the Earth's excess heat from global warming. Departures from average conditions are called anomalies, and temperature anomalies result from recurring weather patterns or longer-term climate change. While the extent of these temperature anomalies fluctuates annually, an upward trend has been observed over the past several decades. Effects of climate change Since the 1980s, every region of the world has consistently recorded increases in average temperatures. These trends coincide with significant growth in the global carbon dioxide emissions, greenhouse gas, and a driver of climate change. As temperatures rise, notable decreases in the extent of arctic sea ice have been recorded. Outlook An increase in emissions from the use of fossil fuels is projected for the coming decades. Nevertheless, global investments in clean energy have increased dramatically since the early 2000s.

Method to access data for "Montague H.C. Neate-Clegg, Matthew A. Etterson, Morgan W. Tingley, William D. Newmark, The combined effects of temperature and fragment area on the demographic rates of an Afrotropical bird community over 34 years, Biological Conservation, Volume 282, 2023, 110051, ISSN 0006-3207, https://doi.org/10.1016/j.biocon.2023.110051.". This dataset is not publicly accessible because: N/A. It can be accessed through the following means: To request data, email monteneateclegg@gmail.com at UCLA. Format: N/A. This dataset is associated with the following publication: Neate-Clegg, M., M. Etterson, M. Tingley, and W. Newmark. The combined effects of temperature and fragment area on the demographic rates of an Afrotropical bird community over 34 years. BIOLOGICAL CONSERVATION. Elsevier Science Ltd, New York, NY, USA, 282: 110051, (2023).

The temperature database is used for calculating the magnitudes and rates of climate change in 47 time intervals from early Ordovician (478 Ma) to early Miocene (16 Ma). This database is composed of the most significant warming/cooling events over the past 480 million years and consists of original proxy data, calculated temperature data, locations, geologic age, time span, and relevant references. Paleotemperatures of surface seawater were calculated from multiple proxies such as oxygen isotope from carbonate and apatite fossils (δ¹⁸O), carbonate clumped isotope (Δ47), and organic geochemical proxy (TEX₈₆).