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 highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

In 2023, India's average temperature recorded between June and September was 28.77 degrees Celsius. The average temperature registered during the given period increased slightly compared to the previous year, up from 28.38 degrees Celsius. The annual average temperature in India stood at 26.15 degrees Celsius in 2022.

This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2018), with the data processed through September 30, 2018. The primary data for water year 2018 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2018) and processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) is computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation, and disaggregated to hourly PET by using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as "backup". Temporal variations in the statistical properties of the data resulting from changes in measurement and data ...

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including Time series mean annual BAWAP …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including Time series mean annual BAWAP rainfall from 1900 - 2012. Long term average BAWAP rainfall and Penman Potentail Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P (precipitation); (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009). There are 4 csv files here: BAWAP_P_annual_BA_SYB_GLO.csv Desc: Time series mean annual BAWAP rainfall from 1900 - 2012. Source data: annual BILO rainfall P_PET_monthly_BA_SYB_GLO.csv long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month Climatology_Trend_BA_SYB_GLO.csv Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009). Dataset History Dataset was created from various BAWAP source data, including Monthly BAWAP rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET, Correlation coefficient data. Data were extracted from national datasets for the GLO subregion. BAWAP_P_annual_BA_SYB_GLO.csv Desc: Time series mean annual BAWAP rainfall from 1900 - 2012. Source data: annual BILO rainfall P_PET_monthly_BA_SYB_GLO.csv long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month Climatology_Trend_BA_SYB_GLO.csv Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009). Dataset Citation Bioregional Assessment Programme (2014) GLO climate data stats summary. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/afed85e0-7819-493d-a847-ec00a318e657. Dataset Ancestors Derived From Natural Resource Management (NRM) Regions 2010 Derived From Bioregional Assessment areas v03 Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Contained within the 4th Edition (1974) of the Atlas of Canada is a set of six maps showing the average daily minimum temperature and average daily maximum temperature for September, October and November.

Over recent decades, the southeastern United States (Southeast) has become increasingly well represented by the terrestrial climate proxy record. However, while the paleo proxy records capture the region’s hydroclimatic history over the last several centuries, the understanding of near-surface air temperature variability is confined to the comparatively shorter observational period (1895-present). Here, we detail the application of blue intensity (BI) methods on a network of tree-ring collections and examine their utility for producing robust paleotemperature estimates. Results indicate that maximum latewood BI (LWBI) chronologies exhibit positive and temporally stable correlations (r = 0.28- 0.54, p < 0.01) with summer maximum temperatures. As such, we use a network of LWBI chronologies to reconstruct the August-September average maximum temperatures for the Southeast spanning the period 1760-2010 CE. Our work demonstrates the utility of applying novel dendrochronological techniques..., , , # August-September average maximum temperature reconstruction for the southeastern United States (1760-2022 CE)

https://doi.org/10.5061/dryad.mpg4f4r76

We provide a tree-ring-based reconstruction of the August-September average maximum air temperatures (degrees Celsius) spanning the period 1760-2022 CE for the southeastern United States. Reconstruction estimates are based on tree-ring maximum latewood blue intensity data from populations of Picea rubens (red spruce) and Tsuga canadensis (eastern hemlock) from numerous sites across the southern Appalachian mountains. In the attached dataset, we provide the reconstruction estimates as anomalies (degrees Celsius), calculated relative to the 1901-1980 CE average. We also include the raw tree-ring blue intensity data used for the reconstruction.

Datasets included:

1) Reconstructed August-September average maximum air temperatures for the Southeastern US. Data are uploaded as text (.txt) ...

average monthly maximum temperature (°C * 10) These layers (grid data) cover the global land areas except Antarctica. spatial resolution is 30 seconds.

WorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. They can be used for mapping and spatial modeling in a GIS or other computer program. The data are described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2017), with the processed data through September 30, 2017. The primary data for each year is downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2017) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) in thousandths of an inch is computed from average daily air temperature in degrees Fahrenheit (°F), average daily dewpoint temperature in degrees Fahrenheit (°F), daily total wind movement in miles (mi), and daily total solar radiation in Langleys per day (Lg/d) and disaggregated to hourly PET in thousandths of an inch using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as “backup”. Temporal variations in the statistical properties of the data resulting from changes in measurement and data storage methodologies were adjusted to match the statistical properties resulting from the data collection procedures that have been in place since January 1, 1989 (Over and others, 2010). The adjustments were computed based on the regressions between the primary data series from ANL and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. Each hourly value is assigned a corresponding data source flag that indicates the source of the value and its transformations. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2015) station at St. Charles, Illinois is used as "backup" for the air temperature, solar radiation and wind speed data. Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2017) station at Chicago O'Hare International Airport is used as "backup" for the dewpoint temperature and wind speed data. Each data source flag is of the form "xyz" that allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2017, Meteorological data, accessed on October 25, 2017, at URL http://gonzalo.er.anl.gov/ANLMET/. Bera, M., and Over, T. M., 2017, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/F7SJ1HS5. Midwestern Regional Climate Center, 2017, Meteorological data, accessed on December 5, 2017, at URL http://mrcc.isws.illinois.edu/CLIMATE/welcome.jsp. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, 2015, Illinois Climate Network: Champaign, Ill., Illinois State Water Survey, accessed on December 5, 2017, at http://dx.doi.org/10.13012/J8MW2F2Q.

Contained within the 4th Edition (1974) of the Atlas of Canada is a set of two maps. One map shows the average precipitation for April to September. The second shows the average precipitation for October to March.

To better understand the physiological effects of climate change and ocean acidification on marine organisms, information on the environmental conditions experienced in their natural habitats is required. Data from long-term monitoring studies capture in situ variability of environmental parameters that are used to relate experimental findings with field conditions. Elands Bay on the west coast of South Africa is a key location for such research and monitoring. It is a popular location for West Coast rock lobster fishing and therefore an important sentinel site for a commercial fishery species and the benthic communities upon which it depends. Low pH conditions exist along the west coast due to effects of upwelling, while cold-bottom waters in Elands Bay often result in low oxygen events responsible for mass walkouts of rock lobster. Additional exposure to extreme stressors associated with climate change can exacerbate impacts on their physiological processes. For example, acute thermal stress experienced during a marine heatwave may cause a rapid deterioration of cellular processes and performances beyond tolerance limits, affecting survival, growth and development. In South Africa, occurrences of marine heatwaves are increasing all along the coastline, and occur on average at least once a year. Data on temperature extremes are therefore important to design experiments and calculate thermal windows. We initiated long-term monitoring of inshore environmental parameters in Elands Bay by deploying temperature loggers in representative habitat types: intertidal rock pools varying in surface area, volume and position along the shore, sun-exposed habitats, and subtidal habitats. The sun-exposed logger is situated at the nearby Fisheries Research office where it is attached underneath the gutter close to the top of the roof (facing the sun but shaded). Here we present the cleaned up version of temperature data from an exposed habitat from 1 to 30 September 2023.

Digital grid dataset of minimum monthly temperature (September) for the period of 1950-2000 of Hindu Kush Himalayan (HKH) Region. The dataset is derived from WorldClim (http://www.worldclim.org/), and major climate databases compiled by the Global Historical Climatology Network (GHCN),the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet. Monthly Precipitation data set consists of 12 raster files, one for each month, showing minimum mean values derived from monthly temperature readings. The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid.

England's highest monthly mean air temperatures are typically recorded in July and August of each year. Since 2015, the warmest mean temperature was measured in July 2018 at 18.8 degrees Celsius. On the other hand, February of that same year registered the coolest temperature, at 2.6 degrees Celsius. In February 2025, the mean air temperature was five degrees Celsius, 50 percent lower than the same month the previous year. The English weather England is the warmest region in the United Kingdom and the driest. In 2024, the average annual temperature in England amounted to 10.73 degrees Celsius – around 1.1 degrees above the national mean. That same year, precipitation in England stood at about 1,020 millimeters. By contrast, Scotland – the wettest region in the UK – recorded over 1,500 millimeters of rainfall in 2024. Temperatures on the rise Throughout the last decades, the average temperature in the United Kingdom has seen an upward trend, reaching a record high in 2022. Global temperatures have experienced a similar pattern over the same period. This gradual increase in the Earth's average temperature is primarily due to various human activities, such as burning fossil fuels and deforestation, which lead to the emission of greenhouse gases. This phenomenon has severe consequences, including more frequent and intense weather events, rising sea levels, and adverse effects on human health and the environment.

This data set contains the temperature anomaly series for each quarter and month of the years from January, 1951 to December, 2006 on the Tibetan Plateau. Based on the “China Homogenized Historical Temperature Data Set (1951–2004) Version 1.0” and the daily average temperature data from 2005 to 2006, the monthly average temperature of 123 sites on the Tibetan Plateau and its neighboring areas were gridded using the Climate Anomaly Method (CAM). Further, the average monthly temperature anomaly sequences from 1951 to 2006 were established using the area weighting factor method. To maximize the use of the observation data, the method using the data at a nearby reference station to correct the short series of the climatic standard values of the air temperature data is emphatically discussed. Reference: Yu Ren, Xueqin Zhang, Lili Peng. Construction and Analysis of Mean Air Temperature Anomaly Series for the Qinghai-Xizang Plateau during 1951-2006. Plateau Meteorology, 2010. The “China Homogenized Historical Temperature Data Set (1951–2004) Version 1.0” and the daily average temperature data from 2005 to 2006 meet the relevant national standards. There are five fields in the monthly temperature anomaly data table. Field 1: Year Field 2: Month Field 3: Number of grids Number of grids included in the calculation Field 4: Number of sites Number of sites included in the calculation Field 5: Monthly Temperature Anomaly Unit °C There are five fields in the year and quarter temperature anomaly data table. Field 1: Year Field 2: Quarter Field 3: Number of grids Number of grids included in the calculation Field 4: Number of sites Explanation: Number of sites included in the calculation Field 5: Temperature anomaly °C In the quarter field: 1. If it is null, it is the annual temperature anomaly 2. DJF: Winter (Last December to this February) temperature anomaly °C 3. MAM: Spring (March-May) temperature anomaly °C 4. JJA: Summer (June-August) temperature anomaly °C 5. SON: Fall (September-November) temperature anomaly °C Data accuracy: the monthly average temperature anomaly to the third decimal places, the annual and quarterly average temperature anomaly to the second decimal places.

Watershed Data Management (WDM) database file ARGN19.WDM is an update of ARGN18.WDM (Bera, 2019) with the processed data from October 1, 2018 through September 30, 2019, appended to it. The primary data were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2019) and processed following the guidelines documented in Over and others (2010). ARGN19.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2019. Daily potential evapotranspiration (PET) were computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup”. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2019) station at St. Charles, Illinois, was used as "backup" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2019) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service at the station at O'Hare International Airport and used as "backup". Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). To open this file user needs to install any of the utilities described in the section "Related External Resources" on this page. References Cited: Argonne National Laboratory, 2019, Meteorological data, accessed on November 6, 2019, at http://www.atmos.anl.gov/ANLMET/. Bera, M., 2019, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2018: U.S. Geological Survey data release, ​https://doi.org/10.5066/P9H8P0F7. Midwestern Regional Climate Center, 2019, Meteorological data, accessed on November 6, 2019, at https://mrcc.illinois.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2019. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on November 6, 2019, at http://dx.doi.org/10.13012/J8MW2F2Q.

description: Daily maximum air temperature data were converted from tenths of degrees Celsius ( C) to degrees Fahrenheit. Maximum air temperature data were evaluated for; data were excluded if values were less -0.04 F or if there were no data. Recorded maximum temperature ranged from -0.04 F on several days in Georgia and Alabama to 120.20 F on September 17, 1909 at the Millen 4 N, GA weather station. Median of maximum temperature was 80.06 F.; abstract: Daily maximum air temperature data were converted from tenths of degrees Celsius ( C) to degrees Fahrenheit. Maximum air temperature data were evaluated for; data were excluded if values were less -0.04 F or if there were no data. Recorded maximum temperature ranged from -0.04 F on several days in Georgia and Alabama to 120.20 F on September 17, 1909 at the Millen 4 N, GA weather station. Median of maximum temperature was 80.06 F.

Watershed Data Management (WDM) database file ARGN21.WDM is an update of ARGN20.WDM (Bera, 2021) with the processed data from October 1, 2020 through September 30, 2021, appended to it. The primary data were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2022) and processed following the guidelines documented in Over and others (2010). ARGN21.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2021. Daily potential evapotranspiration (PET) were computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as "backup." The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2022) station at St. Charles, Illinois, was used as "backup" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2022) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service from the station at O'Hare International Airport and used as "backup." Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). To open this file user needs to install the utility described in the section "Related External Resources" on this page. References Cited: Argonne National Laboratory, 2022, Meteorological data, accessed on January 17, 2022, at https://www.atmos.anl.gov/ANLMET/numeric/. Bera, M., 2021, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9GP8COF. Midwestern Regional Climate Center, 2022, Meteorological data, accessed on March 2, 2022, at https://mrcc.purdue.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2022. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on January 4, 2022, at http://dx.doi.org/10.13012/J8MW2F2Q.

Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This set of files includes downscaled projections of decadal means of monthly mean temperatures (in degrees Celsius, no unit conversion necessary) for each month of decades 2020-2029, 2050-2059, and 2060-2069 at 2x2 kilometer spatial resolution. Each file represents a mean monthly mean in a given decade.

The spatial extent is clipped to a Seward REA boundary bounding box.

Overview:

Most of SNAP#8217;s climate projections come in multiple versions. There are 5 climate models, one 5 model average, 3 climate scenarios, 12 months, and 100 years. This amounts to 21,600 files per variable. Some datasets are derived products such as monthly decadal averages or specific seasonal averages, among others. This specific dataset is one subset of those.

Each set of files originates from one of five top ranked global circulation models or is calculated as a 5 Model Average. These models are referred to by the acronyms: cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, miroc3_2_medres, or 5modelavg.

For a description of the model selection process, please see Walsh et al. 2008. Global Climate Model Performance over Alaska and Greenland. Journal of Climate. v. 21 pp. 6156-6174

Each set of files also represents one projected emission scenario referred to as: sresb1, sresa2, or sresa1b.

Emmission scenarios in brief:

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) created a range of scenarios to explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting greenhouse gas emissions. The B1 scenario describes a convergent world, a global population that peaks in mid-century, with rapid changes in economic structures toward a service and information economy. The Scenario A1B assumes a world of very rapid economic growth, a global population that peaks in mid-century, rapid introduction of new and more efficient technologies, and a balance between fossil fuels and other energy sources. The A2 scenario describes a very heterogeneous world with high population growth, slow economic development and slow technological change.

These files are bias corrected and downscaled via the delta method using PRISM (http:prism.oregonstate.edu) 1961-1990 2km data as baseline climate. Absolute anomalies are utilized for temperature variables. Proportional anomalies are utilized for precipitation variables. Please see http:www.snap.uaf.eduabout for a description of the downscaling process.

File naming scheme:

[variable]_[metric]_[units]_[format]_[assessmentReport] [groupModel][scenario]_[timeFrame].[fileFormat]

[variable] pr, tas, logs, dot, dof, veg, age, dem etc

[metric] mean, total, decadal mean monthly mean, etc

[units] mm, C, in, km

[format] optional, if layer is formatted for special use

[assessmentReport] ar4, ar5

[groupModel] cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, miroc3_2_medres, 5modelavg, cru_ts30

[scenario] sresb1, sresa2, sresa1b

[timeFrame] yyyy or mm_yyyy or yyyy_yyyy or mm_yyyy_mm_yyyy

[fileFormat] txt, png, pdf, bmp, tif

examples:

tas_mean_C_ar4_cccma_cgcm3_1_sresb1_05_2034.tif

this file represents mean May, 2034 temperature from the 4th Assessment Report on Climate Change from the CCCMA modeling group, using their CGCM3.1 model, under the B1 climate scenario.

pr_total_mm_ar4_5modelAvg_sresa1b_09_2077.tif

this file represents total September, 2077 precipitation from the 4th Assessment Report on Climate Change from the 5 Model Average, under the A1B climate scenario.

tas = near-surface air temperature

pr = precipitation including both liquid and solid phases