This file is based on the new high-resolution Berkeley Earth global temperature data set. It expands upon the previous Berkeley Earth temperature data set by including predictive structures based on historical weather patterns and increasing the underlying resolution to 0.25° x 0.25° latitude-longitude.

Files based on this new data set are being provided as part of an early preview to aid in the identification of any remaining bugs or errors. While, we believe the current data set to be accurate and useful, it is still in development and substantial revisions remain possible if significant issues are identified.

This file contains a detailed summary of the changes in Earth's global average surface temperature estimated by combining the new high-resolution Berkeley Earth land-surface temperature field with a reinterpolated version of the HadSST4 ocean temperature field.

As a preliminary data product, no citation for this work currently exists.

This global data product merges land-surface air temperatures with ocean sea surface water temperatures. For most of the ocean, sea surface temperatures are similar to near-surface air temperatures; however, air temperatures above sea ice can differ substantially from the water below the sea ice. In sea ice regions, temperature anomalies are extrapolated from the land-surface air temperatures when ice is present, and from the ocean temperatures when ice is absent.

The percent coverage of sea ice was taken from the HadISST v2 dataset and varies by month and location. In the typical month, between 3.5% and 5.5% of the Earth's surface is covered with sea ice. For more information on the processing and use of HadISST and HadSST refer to the description file for the combined gridded data product.

Temperature data contributing to this analysis include (but are not limited to):

GHCN-Monhtly v4, Menne et al. 2018, https://doi.org/10.1175/JCLI-D-18-0094.1

Global Summary of the Day, https://www.ncei.noaa.gov/products/global-summary-day MET-Reader, Scientific Committee for Antaractic Research, British Antarctic Survey, https://legacy.bas.ac.uk/met/READER/ HADSST4, Kennedy et al. 2019, https://doi.org/10.1029/2018JD029867

Ice mask data comes from:

HadISST2, Titcher and Rayner 2014, https://doi.org/10.1002/2013JD020316 Sea Ice Index, NSIDC, https://nsidc.org/data/g02135/versions/3

High-resolution downscaling algorithms were trained using high-resolution data, though none of this data is used directly in the reconstruction. High-resolution datasets used in training include:

ERA5 from the Copernicus Climate Change Service, Hersbach et al. (2018), http://doi.org/10.24381/cds.adbb2d47 https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels

The above list of data sources is only a partial list. For a more complete set of references please refer to Berkeley Earth's previous description papers.

Temperatures are in Celsius and reported as anomalies relative to the Jan 1951-Dec 1980 average. Uncertainties represent the 95% confidence interval for statistical and spatial undersampling effects as well as ocean biases.

The land analysis was run on 06-Mar-2023 02:09:12 The ocean analysis was published on 13-Mar-2023 02:52:51

The land component is based on 50498 time series with 21081445 monthly data points

The ocean component is based on 456950592 instantaneous water temperature observations

Estimated Jan 1951-Dec 1980 global mean temperature (°C): 14.148 +/- 0.019

As Earth's land is not distributed symmetrically about the equator, there exists a mean seasonality to the global average temperature.

Estimated Jan 1951-Dec 1980 monthly absolute temperature: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 12.31 12.52 13.15 14.07 15.01 15.68 15.91 15.72 15.17 14.30 13.33 12.59 +/- 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.03 0.02 0.03 0.03

For each month, we report the estimated global surface temperature anomaly for that month and its uncertainty. We also report the corresponding values for 12-month, five-year, ten-year, and twenty-year moving averages CENTERED about that month (rounding down if the center is in between months). For example, the annual average from January to December 1950 is reported at June 1950.

The table Global Temperatures by City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://redivis.com/datasets/1e0a-f4931vvyg. It contains 8599212 rows across 7 variables.

Since 1880, the annual global land temperature anomaly has fluctuated, showing an overall upward tendency. In 2023, the global land surface temperature stood at 1.81 degrees Celsius above the global average between 1901 to 2000. This was the highest annual temperature anomaly recorded during the period in consideration. Anomalies in global ocean surface temperature followed a similar trend over the same period of time.

Man-made change

The Earth's temperature increases naturally over time as the planet goes through cyclic changes. However, the scientific community has concluded that human interference, particularly deforestation and the consumption of fossil fuels, has acted as a catalyst in recent centuries. Increases in the unprecedented number of natural disasters in the past few decades, such as tropical cyclones, wildfires and heatwaves, have been attributed to this slight man-made increase in the Earth's surface temperature.

End of an ice age?

Although a one- or two-degree anomaly may not seem like a large difference, changes in the ocean and land temperatures have significant consequences for the entire planet. A five-degree drop triggered the last major ice age – the Quaternary Glaciation – over 20,000 years ago, which technically is still continuing today. This ice age is in its final interglacial period, and it will not officially end until the remnants of the final ice sheets melt, of which there are only two left today, in Antarctica and Greenland.

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a blended product from two independent analysis products: the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the land surface temperature (LST) analysis using the Global Historical Climatology Network (GHCN) temperature database. The data is merged into a monthly global surface temperature dataset dating back from 1880 to the present. The monthly product output is in gridded (5 degree x 5 degree) and time series formats. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. The changes from version 4 to version 5 include an update to the primary input datasets: ERSST version 5 (updated from v4), and GHCN-M version 4 (updated from v3.3.3). Version 5 updates also include a new netCDF file format with CF conventions. This dataset is formerly known as Merged Land-Ocean Surface Temperature (MLOST).

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

In this dataset, we have include several files:

＊ Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

 Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
 LandAverageTemperature: global average land temperature in celsius
 LandAverageTemperatureUncertainty: the 95% confidence interval around the average
 LandMaxTemperature: global average maximum land temperature in celsius
 LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
 LandMinTemperature: global average minimum land temperature in celsius
 LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
 LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
 LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

＊ Other files include:

 Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
 Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
 Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
 Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

Source: Kaggle

https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Raw data: Berkeley Earth data page http://berkeleyearth.org/data/

The table Global Temperatures by Major City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://redivis.com/datasets/1e0a-f4931vvyg. It contains 239177 rows across 7 variables.

Based on policies and actions in place as of December 2023, the global temperature increase is estimated to reach a median of 2.7 degrees Celsius in 2100. In the best-case scenario, where all announced net-zero targets, long-term targets, and Nationally Determined Contributions (NDCs) are fully implemented, the global temperature is still expected to rise by 1.8 degrees Celsius, when compared to the pre-industrial average.

In 2015, Paris Agreement parties pledged to limit global warming to well below two degrees Celsius above pre-industrial levels, with the aim of reaching a maximum of 1.5 degrees. As of 2023, a warming of 1.3 degrees above the pre-industrial average was recorded.

The problem being investigated involves analyzing global land temperature changes for major cities as well as globally. This topic was chosen as it is representative of the international issue of global warming. The severity of the issue is reflected in The Paris Agreement, which is a legally binding treaty with the goal of limiting global warming to the target rate of 1.5°C (degrees Celsius), or at least 2°C within this century (United Nations, 2021). Global warming has been linked to intensification of extreme weather phenomena, which lead to fatalities, environmental damage, community devastation, and financial costs. For example, extreme heat can lead to drought, wildfires, and create the urban heat island effect (Center for Climate and Energy Solutions, 2018). The temperature of the ocean will be explored as well as this factor is significant since theoretically, changes in the ocean would take longer – which in turn provides clarity on the severity of climate change.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

Variables in the dataset/application are: Precipitation, Temperature, Temperature anomaly

What does the data show?

This data shows the monthly averages of surface temperature (°C) for 1981-2010 from CRU TS (v. 4.06) dataset. It is provided on the WGS84 grid which measures approximately 60km x 60km (latitude x longitude) at the equator. This is the same as the 60km grid used by UKCP18 global datasets.

What are the naming conventions and how do I explore the data?

This data contains a field for each month’s average over the period. They are named 'tas' (temperature at surface) and the month. E.g. ‘tas March’ is the average of the daily average surface air temperatures in March throughout 1981-2010.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas January’ values.

Data source

CRU TS v. 4.06 - (downloaded 12/07/22)

Useful links

Further information on CRU TS Further information on understanding climate data within the Met Office Climate Data Portal

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a merged land&ocean surface temperature analysis (formerly known as MLOST) It is a spatially gridded (5° - 5°) global surface temperature dataset, with monthly resolution from January 1880 to present. We combine a global sea surface (water) temperature (SST) dataset with a global land surface air temperature dataset into this merged dataset of both the Earth's and land's and ocean surface temperatures. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) version 5. The land surface air temperature dataset is similar to ERSST but uses data from the Global Historical Climatology Network Monthly (GHCN-M) database, version 4.

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

%3C!-- --%3E

• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

%3C!-- --%3E

• LandMaxTemperature: global average maximum land temperature in celsius

%3C!-- --%3E

• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

%3C!-- --%3E

• LandMinTemperature: global average minimum land temperature in celsius

%3C!-- --%3E

• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

%3C!-- --%3E

• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

%3C!-- --%3E

• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

%3C!-- --%3E

**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

%3C!-- --%3E

• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

%3C!-- --%3E

• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

%3C!-- --%3E

• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

%3C!-- --%3E

The raw data comes from the Berkeley Earth data page.

The mean annual temperature in North America stood at -4.5 degrees Celsius in 1995. It is expected that, 30 years later in 2025, the average temperature will increase by 1.6 degrees Celsius due to the effects of global warming, under a scenario where global temperatures increase by 1.5 degree Celsius.

Historical Land and Ocean Temperatures

Historical Land and Ocean Temperatures from 1750 to Present

By Data Society [source]

About this dataset

This dataset contains global average land and ocean temperature data from 1750 to present, providing the ability to investigate global climate change. It contains several data sets that provide insight on the past and current temperatures of our planet. By accessing this dataset, anyone can research and form their own views on this contentious yet important subject.

We have included several files in this data set: GlobalTemperatures.csv which holds records of global average land temperature in Celsius, LandAverageTemperatureUncertainty representing the 95% confidence interval around that average, and LandMaxTemperatureUncertainty having the same level of uncertainty around its maximum land temperatures as well as more detailed information when substituting country or city in place off landindicating a more localized view of climate change patterns globally speaking. Other sources within this dataset include GlobalLandTemperaturesByCountry which is a collection of data relative to country boundaries held over time best showcase actual heat temperature datapoints manipulated by human activity, GlobalLandTemperaturesByState which provides similar insights however done so through national borders rather than by territories - giving researchers another avenue for honing into dedicated population centres for their analyses; finally GlobalLandTemperaturesByMajorCity focuses solely on major metropolitan areas world wide over various years giving researchers further clarity about local changes in climate due to year-round urban activities influencing outcomes too localized for country / state sourced datasets to account for without directly connecting eyewitness accounts with geo located coordinates manually associated with respective cities therein within same database making it even easier store & parse new information .

Source: Kaggle Raw Data: Berkeley Earth Data Page

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How to use the dataset

Before you start exploring different analyses, it helps to understand what type of data is being used by reviewing the file descriptions. The primary dataset that is included is GlobalTemperatures.csv which contains average land and ocean temperatures at a given date, as well as confidence intervals around each value for indicating uncertainty when measuring over time (e.g., 70 years ago). Additionally provided are separate datasets for global climate change by country (GlobalLandTemperaturesByCountry.csv), state (GlobalLandTemperaturesByState.csv), major city (GlobalLandTemperaturesByMajorCity) , or city (GlobalLandTemperaturesByCity) levels detailing average temperatures specific to geo-locations throughout each area/time period respectively;each dataset includes confidence intervals around each value for indicating uncertainty when measuring over time . For example: LandAverageTemperature describes the average land temperature providing further breakouts between Minimum/Maximum temperatures both with the respective uncertainty values associated with them per Time Period observed.

Research Ideas

• Developing climate change models that track global average temperature trends by country and major cities.
• Analyzing the correspondence between average temperatures and the locations of different natural resources or phenological events, such as insect swarming behavior or acclimation of trees to temperatures.
• Conducting studies to understand how shifts in temperatures are impacting different ecological systems and how land-use changes can mitigate these effects

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: GlobalLandTemperaturesByCountry.csv | Column name | Description | |:----------------------------------|:--------------------------------------------------------------| | dt | Date of the temperature measurement. (Date) | | AverageTemperature | Average temperature of the region. (Float) | | AverageTemperatureUncertainty | Uncertainty of the average temperature measurement. (Float) | | Country | Country where the temperature measurement was taken. (String) |

File: GlobalLandTemperaturesByMajorCity.csv | Column name | Description | |:----------------------------------|:-----------------------------------------------------------------------| | dt | Date of the temperature measurement. (Date) | | AverageTemperature | Average temperature of the region. (Float) | | AverageTemperatureUncertainty | Uncertainty of the average temperature measurement. (Float) | | Country | Country where the temperature measurement was taken. (String) | | City | Name of the city where the temperature measurement was taken. (String) | | Latitude | Latitude coordinate of the city. (Float) | | Longitude | Longitude coordinate of the city. (Float) |

File: GlobalLandTemperaturesByState.csv | Column name | Description | |:----------------------------------|:--------------------------------------------------------------| | dt | Date of the temperature measurement. (Date) | | AverageTemperature | Average temperature of the region. (Float) | | AverageTemperatureUncertainty | Uncertainty of the average temperature measurement. (Float) | | Country | Country where the temperature measurement was taken. (String) |

File: GlobalTemperatures.csv | Column name | Description | |:----------------------------------------------|:-----------------------------------------------------------------------------------| | dt | Date of the temperature measurement. (Date) | | LandAverageTemperature | Average land temperature in Celsius. (Float) | | LandAverageTemperatureUncertainty | Uncertainty interval of the average land temperature in Celsius. (Float) | | LandMaxTemperature | Maximum land temperature in Celsius. (Float) | | LandMaxTemperatureUncertainty | Uncertainty interval of the maximum land temperature in Celsius. (Float) | | LandMinTemperature | Minimum land temperature in Celsius. (Float) | | LandMinTemperatureUncertainty | Uncertainty interval of the minimum land temperature in Celsius. (Float) | | LandAndOceanAverageTemperature | Average land and ocean temperature in Celsius. (Float) | | LandAndOceanAverageTemperatureUncertainty | Uncertainty interval of the average land and ocean temperature in Celsius. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Data Society.

Estimates of changes in the mean surface temperature are presented, in Degree Celsius, for the years 1961-2021 by country and for World.Source: Food and Agriculture Organization of the United Nations (FAO). 2022. FAOSTAT Climate Change, Climate Indicators, Temperature change. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/ET. Accessed on 2023-03-28.Category: Climate Change DataData series: Temperature change with respect to a baseline climatology, corresponding to the period 1951-1980 Metadata:Statistics are acquired from the FAO. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).Methodology:The time series temperature change at a point is calculated as a weighted average of the GISTEMP data over all stations within a given radius, with the closest stations weighted most heavily. The details of the method adopted by FAO for estimating Annual country level and global temperature change are available at – https://fenixservices.fao.org/faostat/static/documents/ET/ET_e.pdfDisclaimer:Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The mean of the 12 months each year is displayed here. Each annual update is available around the 15th of the following January (e.g., 2020 is available Jan 15th, 2021). The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report summary report by NOAA NCEI is available here. GHCN monthly mean station averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here.What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each year since 1880. Be sure to configure the time settings in your web map to view the timeseries correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. For a more detailed temporal analysis, a monthly mean is available here.

My submission to the data science competition "Data Science vs Fake News" put on by KDNuggets.com, Data4Democracy, and Data.World. The goal of my submission is to highlight CO2 as the primary driver of climate change and why. This is in response to Scott Pruitt, EPA Administrator, claiming that CO2 is not a primary cause.

Title:

It's Getting Hot In Here

The Burning Effect of Fake News on Climate Change

Austin Schwinn

March 10, 2017

REFERENCES

• DiChristopher, Tom. "EPA Chief Scott Pruitt Says Carbon Dioxide Is Not a Primary Contributor to Global Warming." CNBC. CNBC, 10 Mar. 2017. Web. 10 Mar. 2017.

• Kohske. "Dual Axis in ggplot2." RPubs.com. 2013. 9 Mar. 2017.

• “Climate Change Indicators in the United States." EPA. Environmental Protection Agency, 19 Dec. 2016. Web. 9 Mar. 2017.

• Schipani, Vanessa. "Precision in Climate Science." FactCheck.org. SciCheck, 3 Mar. 2017. Web. 9 Mar. 2017.

• "Climate Change 2013: The Physical Science Basis." Intergovernmental Panel on Climate Change. WMO UNEP, 2013. Web. 9 Mar. 2017.

• "Berkeley Earth Global Climate Change Data." Data.World. Data Society, 2015. Web. 9 Mar. 2017.

• Butler, James H. "The NOAA Annual Greenhouse Gas Index (AGGI)." NOAA Earth System Research Laboratory. Global Monitoring Division, Spring 2016. Web. 9 Mar. 2017.

• "National Climate Assessment." GlobalChange.gov. US Global Change Research Program, 2014. Web. 9 Mar. 2017.

• Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe, Eds., 2014: Highlights of Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program, 148 pp.

All the following are the 10 EPA combined underlying datasets:

• EPICA Dome C and Vostok Station, Antarctica: approximately 796,562 BCE to 1813 CE: Lüthi, D., M. Le Floch, B. Bereiter, T. Blunier, J.-M. Barnola, U. Siegenthaler, D. Raynaud, J. Jouzel, H. Fischer, K. Kawamura, and T.F. Stocker. 2008. High-resolution carbon dioxide concentration record 650,000–800,000 years before present. Nature 453:379–382. www.ncdc.noaa.gov/paleo/pubs/luethi2008/luethi2008.html.

• Law Dome, Antarctica, 75-year smoothed: approximately 1010 CE to 1975 CE: Etheridge, D.M., L.P. Steele, R.L. Langenfelds, R.J. Francey, J.-M. Barnola, and V.I. Morgan. 1998. Historical CO2 records from the Law Dome DE08, DE08-2, and DSS ice cores. In: Trends: A compendium of data on global change. Oak Ridge, TN: U.S. Department of Energy. Accessed September 14, 2005. http://cdiac.ornl.gov/trends/co2/lawdome.html.

• Siple Station, Antarctica: approximately 1744 CE to 1953 CE: Neftel, A., H. Friedli, E. Moor, H. Lötscher, H. Oeschger, U. Siegenthaler, and B. Stauffer. 1994. Historical carbon dioxide record from the Siple Station ice core. In: Trends: A compendium of data on global change. Oak Ridge, TN: U.S. Department of Energy. Accessed September 14, 2005. http://cdiac.ornl.gov/trends/co2/siple.html.

• Mauna Loa, Hawaii: 1959 CE to 2015 CE: NOAA (National Oceanic and Atmospheric Administration). 2016. Annual mean carbon dioxide concentrations for Mauna Loa, Hawaii. Accessed April 14, 2016. ftp://ftp.cmdl.noaa.gov/products/trends/co2/co2_annmean_mlo.txt.

• Barrow, Alaska: 1974 CE to 2014 CE: Cape Matatula, American Samoa: 1976 CE to 2014 CE

• South Pole, Antarctica: 1976 CE to 2014 CE: NOAA (National Oceanic and Atmospheric Administration). 2016. Monthly mean carbon dioxide concentrations for Barrow, Alaska; Cape Matatula, American Samoa; and the South Pole. Accessed April 14, 2016. ftp://ftp.cmdl.noaa.gov/data/trace_gases/co2/in-situ/surface. Cape Grim, Australia: 1992 CE to 2006 CE

• Shetland Islands, Scotland: 1993 CE to 2002 CE: Steele, L.P., P.B. Krummel, and R.L. Langenfelds. 2007. Atmospheric CO2 concentrations (ppmv) derived from flask air samples collected at Cape Grim, Australia, and Shetland Islands, Scotland. Commonwealth Scientific and Industrial Research Organisation. Accessed January 20, 2009. http://cdiac.esd.ornl.gov/ftp/trends/co2/csiro.

• Lampedusa Island, Italy: 1993 CE to 2000 CE: Chamard, P., L. Ciattaglia, A. di Sarra, and F. Monteleone. 2001. Atmospheric carbon dioxide record from flask measurements at Lampedusa Island. In: Trends: A compendium of data on global change. Oak Ridge, TN: U.S. Department of Energy. Accessed September 14, 2005. http://cdiac.ornl.gov/trends/co2/lampis.html.

A station observation-based global land monthly mean surface air temperature dataset at 0.5 0.5 latitude-longitude resolution for the period from 1948 to the present . It uses a combination of two large individual data sets of station observations collected from the Global Historical Climatology Network version 2 and the Climate Anomaly Monitoring System (GHCN + CAMS), so it can be regularly updated in near real time with plenty of stations and (2) some unique interpolation methods, such as the anomaly interpolation approach with spatially-temporally varying temperature lapse rates derived from the observation-based Reanalysis for topographic adjustment.

Data Source: https://www.metoffice.gov.uk/hadobs/hadcrut4/index.html

Inspiration: http://www.usatoday.com/story/weather/2016/05/10/climate-spiral-rising-global-temperatures/84185746/

h/t: https://makeovermonday.co.uk/data/data-sets-2016/ Week 20

Weather catastrophes caused economic losses of 329 billion U.S. dollars worldwide in 2021. Sudden cataclysmic disasters cause devastation on impact. Some weather and climate-related extreme events are storms, floods, heat waves, cold waves, droughts, and forest fires. Climate-related hazards pose risks to human health and can lead to substantial economic losses.

Global natural disaster economic loss The economic damage caused by disasters varies based on geography and affects natural resources. Capital assets and infrastructure, along with the loss of life, disrupt the economic structure. In 2021, the economic loss due to natural disasters globally was about 343 billion U.S. dollars, and flooding generated the highest loss that year.

Billion-dollar natural disaster events in the United States

The United States experienced nearly two dozen billion-dollar disasters in 2021. At an economic loss of around 75 billion U.S. dollars, Hurricane Ida, a Category 4 storm that landed on the Louisiana coast in August, was the costliest.