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 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.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in August 2025, the average temperature across the North American country stood at 22.98 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.

'''DEFINITION'''

The OMI_CLIMATE_SST_BAL_trend product includes the cumulative/net trend in sea surface temperature anomalies for the Baltic Sea from 1982-2024. The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The cumulative trend is the rate of change (°C/year) scaled by the number of years (43 years). The SST Level 4 analysis products that provide the input to the trend calculations are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2024. The product has a spatial resolution of 0.02 in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). See the Copernicus Marine Service Ocean State Reports for more information on the OMI product (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). The times series of monthly anomalies have been used to calculate the trend in SST using Sen’s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).

'''CONTEXT'''

SST is an essential climate variable that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2oC from 1982 to 2012 considering all months of the year and 3-5oC when only July- September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018).

'''CMEMS KEY FINDINGS'''

SST trends were calculated for the Baltic Sea area and the whole region including the North Sea, over the period January 1982 to December 2024. The average trend for the Baltic Sea domain (east of 9°E longitude) is 0.039°C/year, which represents an average warming of 1.68°C for the 1982-2023 period considered here. When the North Sea domain is included, the trend decreases to 0.026°C/year corresponding to an average warming of 1.19°C for the 1982-2024 period. Trends are highest for the Baltic Sea and the North Sea, compared to other regions.

'''DOI (product):''' https://doi.org/10.48670/moi-00206

'''DEFINITION'''

OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2024, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 product . The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product.

'''CONTEXT'''

Sea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 °C when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018).

'''CMEMS KEY FINDINGS'''

The basin-average trend of SST anomalies for Baltic Sea region amounts to 0.039±0.003°C/year over the period 1982-2024 which corresponds to an average warming of 1.68°C. Adding the North Sea area, the average trend amounts to 0.026±0.002°C/year over the same period, which corresponds to an average warming of 1.19°C for the entire region since 1982.

'''DOI (product):''' https://doi.org/10.48670/moi-00205

Version 1.2 (March 2025): Now with longer time series, expanded stream gauge coverage, meteorological data from additional sources, soil moisture time series, and observed rainfall time series from 11,853 rain gauges.

This is the CAMELS-BR dataset (Catchment Attributes and MEteorology for Large-sample Studies – Brazil) accompanying the paper: Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.

CAMELS-BR provides daily observed streamflow time series for 4,025 stream gauges, daily observed rainfall for 11,853 rain gauges, daily meteorological time series and 65 attributes for 897 catchments in Brazil.

The daily hydrometeorological time series include (i) observed streamflow accompanied by quality control information, (ii) precipitation extracted from five products, (iii) actual evapotranspiration extracted from three products, (iv) potential evapotranspiration extracted from two products, (v) reference evapotranspiration extracted from one product, (vi) minimum, mean, and maximum temperature extracted from three products, and (vii) soil moisture extracted from two products.

The 65 catchment attributes cover properties such as (i) topography, (ii) climate, (iii) hydrology, (iv) land cover, (v) geology, (vi) soil, and (vii) human intervention.

The data follow the same standards as other CAMELS datasets such as for the United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), and Great Britain (https://doi.org/10.5194/essd-2020-49).

How to cite: Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.

Changes in CAMELS-BR version 1.2:

Major changes

Updated streamflow time series up to February 2025 (where available), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Some historical records have changed slightly due to ANA's quality control procedures. For eight gauges (see the readme.txt file), data are merged from 2025 and 2019 records (i.e. from CAMELS-BR version 1.1).

Increased the stream gauge coverage to 4025 stream gauges (including both quality-controlled and non-quality-controlled series), up from 3679 in version 1.1.

Added daily observed rainfall time series for 11853 rain gauges (not catchment averages), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Data include quality flags but are mostly not quality-controlled.

Added a GeoPackage file with coordinates for 11853 rain gauges.

Updated precipitation time series (catchment averages) up to October 2024 (where available). Now derived from: CHIRPS v2.0; CPC; ERA5-Land; MSWEP v2.8; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil – 864 catchments).

Updated actual evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from: GLEAM v4.2a; ERA5-Land; and MGB-SA.

Updated potential evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from GLEAM v4.2a and ERA5-Land.

Added reference evapotranspiration time series (catchment averages). Derived from BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).

Updated daily maximum, mean, and minimum temperature time series (catchment averages) up to October 2024 (where available). Now derived from: CPC; ERA5-Land; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).

Added daily soil moisture time series (catchment averages) up to December 2024 (where available). Computed from GLEAM v4.2a and ERA5-Land.

Improved meteorological data processing. Catchment averages now account for pixel fraction coverage.

Reformatted meteorological time series files. Files now includes data from different products, with columns renamed for clarity.

Hydrological and climatic indices were not updated, despite the new streamflow and meteorological data.

Minor changes

Updated stream gauge coordinates based on ANA's website on 27 February 2025. Coordinates were updated for 73 gauges in the 897 selected catchments and for 298 gauges across all catchments.

Streamflow time series now include quality flag values from 0 to 7 (see the readme.txt file), previously from 0 to 4 in CAMELS-BR version 1.1. Flags from 5 to 7 may be present only in the last few years of data.

Streamflow time series files for the 897 selected gauges now include values in both millimeters per day and cubic meters per second.

Removed streamflow time series with fewer than 180 days of measurement.

Converted gauge and catchment spatial data from Shapefile (.shp) to GeoPackage (.gpkg).

Catchment areas computed by GSIM (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") flagged as "caution" for quality were set to "nan" due to low reliability.

Updated catchment areas computed by ANA (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") to reflect the newest ANA's data from 27 February 2025. Streamflow values in millimeters per day remain unchanged because unit conversions rely on GSIM areas.

Set catchment areas with zero squared kilometers, as computed by ANA, to "nan".

Removed CPC daily mean temperature time series (catchment averages) because they were a simple average of minimum and maximum temperatures. For daily mean temperatures, refer to ERA5-Land data (now included) as they are computed from hourly data.

Annual Frost Free Period is the expected number of days between the last freezing temperature in spring (January-July) and the first freezing temperature in fall (August-December). The number of days is based on the probability that the values for the standard normal period will be exceeded in 5 years out of 10. For more information see the Natural Resources Conservation Service"sSoil Survey Manual. Dataset SummaryPhenomenon Mapped: Length of frost-free seasonGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: DaysCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS).The value for frost free period is derived from the gSSURGO component table field Frost Free Days - Representative Value (ffd_r). The value in this layer is the average value for all components of each map unit weighted by component percent (comppct_r). What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selectingAddthenBrowse Living Atlas Layers. A window will open. Type "frost free" in the search box and browse to the layer. Select the layer then clickAdd to Map. In ArcGIS Pro, open a map and selectAdd Datafrom the Map Tab. SelectDataat the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expandPortalif necessary, then selectLiving Atlas. Type "frost free" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer"s built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many otherbeautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.