Name: Global Warning Weather Dataset
Creator: Kaggle
License: http://opendatacommons.org/licenses/dbcl/1.0/

About this Dataset

Global Warning Weather Dataset

Dataset Source:

https://climatedata.imf.org/pages/climatechange-data

Citation

Umeokwobia, R., Owusu-Afriye, J. and Abu, J., 2025. Climate Economics and Finance.

Fornino, M., Kutlukaya, M., Lepore, C. and López, J.U., 2024. A multi-country study of forward-looking economic losses from floods and tropical cyclones. International Monetary Fund.

Annual Surface Temperature Change

This indicator presents the mean surface temperature change during the period 1961-2021, using temperatures between 1951 and 1980 as a baseline. Use the drop-down menus to search for temperature changes by country.

This data is provided by the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and is based on publicly available GISTEMP data from the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA GISS).

World Monthly Atmospheric Carbon Dioxide Concentrations

This indicator presents the concentration of carbon dioxide in the atmosphere, on a monthly and yearly basis, dating back to 1958. Use the drop-down menus to search for a specific time period, and to see the rate of year-on-year growth.

The source data for these visualizations comes from the National Oceanic and Atmospheric Association Global Monitoring Laboratory.

Change in Mean Sea Levels

This indicator gives estimates of the rise of global sea levels, based on measurements from satellite radar altimeters. These are produced by measuring the time it takes a radar pulse to make a round-trip from the satellite to the sea surface and back again.

The graphs below show the data obtained by four satellite altimeters – TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 – which have monitored the same surfaces since 1992. On the right, you’ll see mean sea level changes for each of the world’s main seas and oceans.

Forest and Carbon

Forests contain the largest stock of land-based carbon. Changes in forests, both in their extent and condition, can lead to low carbon storage and result in faster global warming. Indices on the extent of forests and carbon stored in forests provide a high-level summary of the state of forests across countries.

Land Cover and Land Cover Altering Indicator

Land cover has important linkages to climate regulation. Land’s ability to sequester carbon or store (retain) carbon impacts that amount of CO2 in the atmosphere. These indicators look at changes in land cover over time, grouping land cover into those types that have climate influencing, climate regulating and climate neutral impacts.

Climate-related Disasters Frequency

The links between climate change and natural disasters are well documented in a wide variety of climate change literature. This graph depicts the trend in these climate-related disasters over time.

Tables

Change In Mean Sea Levels

@kaggle.aritra100_global_warning_weather_dataset.change_in_mean_sea_levels

487.21 kB
41,693 rows
13 columns

CREATE TABLE change_in_mean_sea_levels (
  "objectid" BIGINT,
  "country" VARCHAR,
  "iso2" VARCHAR,
  "iso3" VARCHAR,
  "indicator" VARCHAR,
  "unit" VARCHAR,
  "source" VARCHAR,
  "cts_code" VARCHAR,
  "cts_name" VARCHAR,
  "cts_full_descriptor" VARCHAR,
  "measure" VARCHAR,
  "date" VARCHAR,
  "value" DOUBLE
);

Climate Indicators Annual Mean Global Surface Temperature

@kaggle.aritra100_global_warning_weather_dataset.climate_indicators_annual_mean_global_surface_temperature

143.64 kB
231 rows
74 columns

CREATE TABLE climate_indicators_annual_mean_global_surface_temperature (
  "objectid" BIGINT,
  "country" VARCHAR,
  "iso2" VARCHAR,
  "iso3" VARCHAR,
  "indicator" VARCHAR,
  "unit" VARCHAR,
  "source" VARCHAR,
  "cts_code" VARCHAR,
  "cts_name" VARCHAR,
  "cts_full_descriptor" VARCHAR,
  "n_1961" DOUBLE  -- 1961,
  "n_1962" DOUBLE  -- 1962,
  "n_1963" DOUBLE  -- 1963,
  "n_1964" DOUBLE  -- 1964,
  "n_1965" DOUBLE  -- 1965,
  "n_1966" DOUBLE  -- 1966,
  "n_1967" DOUBLE  -- 1967,
  "n_1968" DOUBLE  -- 1968,
  "n_1969" DOUBLE  -- 1969,
  "n_1970" DOUBLE  -- 1970,
  "n_1971" DOUBLE  -- 1971,
  "n_1972" DOUBLE  -- 1972,
  "n_1973" DOUBLE  -- 1973,
  "n_1974" DOUBLE  -- 1974,
  "n_1975" DOUBLE  -- 1975,
  "n_1976" DOUBLE  -- 1976,
  "n_1977" DOUBLE  -- 1977,
  "n_1978" DOUBLE  -- 1978,
  "n_1979" DOUBLE  -- 1979,
  "n_1980" DOUBLE  -- 1980,
  "n_1981" DOUBLE  -- 1981,
  "n_1982" DOUBLE  -- 1982,
  "n_1983" DOUBLE  -- 1983,
  "n_1984" DOUBLE  -- 1984,
  "n_1985" DOUBLE  -- 1985,
  "n_1986" DOUBLE  -- 1986,
  "n_1987" DOUBLE  -- 1987,
  "n_1988" DOUBLE  -- 1988,
  "n_1989" DOUBLE  -- 1989,
  "n_1990" DOUBLE  -- 1990,
  "n_1991" DOUBLE  -- 1991,
  "n_1992" DOUBLE  -- 1992,
  "n_1993" DOUBLE  -- 1993,
  "n_1994" DOUBLE  -- 1994,
  "n_1995" DOUBLE  -- 1995,
  "n_1996" DOUBLE  -- 1996,
  "n_1997" DOUBLE  -- 1997,
  "n_1998" DOUBLE  -- 1998,
  "n_1999" DOUBLE  -- 1999,
  "n_2000" DOUBLE  -- 2000,
  "n_2001" DOUBLE  -- 2001,
  "n_2002" DOUBLE  -- 2002,
  "n_2003" DOUBLE  -- 2003,
  "n_2004" DOUBLE  -- 2004,
  "n_2005" DOUBLE  -- 2005,
  "n_2006" DOUBLE  -- 2006,
  "n_2007" DOUBLE  -- 2007,
  "n_2008" DOUBLE  -- 2008,
  "n_2009" DOUBLE  -- 2009,
  "n_2010" DOUBLE  -- 2010,
  "n_2011" DOUBLE  -- 2011,
  "n_2012" DOUBLE  -- 2012,
  "n_2013" DOUBLE  -- 2013,
  "n_2014" DOUBLE  -- 2014,
  "n_2015" DOUBLE  -- 2015,
  "n_2016" DOUBLE  -- 2016,
  "n_2017" DOUBLE  -- 2017,
  "n_2018" DOUBLE  -- 2018,
  "n_2019" DOUBLE  -- 2019,
  "n_2020" DOUBLE  -- 2020,
  "n_2021" DOUBLE  -- 2021,
  "n_2022" DOUBLE  -- 2022,
  "n_2023" DOUBLE  -- 2023,
  "n_2024" DOUBLE  -- 2024
);

Climate Indicators Monthly Atmospheric Carbon Dioxide, 87a86402

@kaggle.aritra100_global_warning_weather_dataset.climate_indicators_monthly_atmospheric_carbon_dioxide__87a86402

31.03 kB
1,602 rows
12 columns

CREATE TABLE climate_indicators_monthly_atmospheric_carbon_dioxide__87a86402 (
  "objectid" BIGINT,
  "country" VARCHAR,
  "iso2" VARCHAR,
  "iso3" VARCHAR,
  "indicator" VARCHAR,
  "unit" VARCHAR,
  "source" VARCHAR,
  "cts_code" VARCHAR,
  "cts_name" VARCHAR,
  "cts_full_descriptor" VARCHAR,
  "date" VARCHAR,
  "value" DOUBLE
);

Forest And Carbon

@kaggle.aritra100_global_warning_weather_dataset.forest_and_carbon

367.61 kB
1,333 rows
41 columns

CREATE TABLE forest_and_carbon (
  "objectid" BIGINT,
  "country" VARCHAR,
  "iso2" VARCHAR,
  "iso3" VARCHAR,
  "indicator" VARCHAR,
  "unit" VARCHAR,
  "source" VARCHAR,
  "cts_code" VARCHAR,
  "cts_name" VARCHAR,
  "cts_full_descriptor" VARCHAR,
  "f1992" DOUBLE,
  "f1993" DOUBLE,
  "f1994" DOUBLE,
  "f1995" DOUBLE,
  "f1996" DOUBLE,
  "f1997" DOUBLE,
  "f1998" DOUBLE,
  "f1999" DOUBLE,
  "f2000" DOUBLE,
  "f2001" DOUBLE,
  "f2002" DOUBLE,
  "f2003" DOUBLE,
  "f2004" DOUBLE,
  "f2005" DOUBLE,
  "f2006" DOUBLE,
  "f2007" DOUBLE,
  "f2008" DOUBLE,
  "f2009" DOUBLE,
  "f2010" DOUBLE,
  "f2011" DOUBLE,
  "f2012" DOUBLE,
  "f2013" DOUBLE,
  "f2014" DOUBLE,
  "f2015" DOUBLE,
  "f2016" DOUBLE,
  "f2017" DOUBLE,
  "f2018" DOUBLE,
  "f2019" DOUBLE,
  "f2020" DOUBLE,
  "f2021" DOUBLE,
  "f2022" DOUBLE
);

Land Cover Accounts

@kaggle.aritra100_global_warning_weather_dataset.land_cover_accounts

604.66 kB
3,033 rows
42 columns

CREATE TABLE land_cover_accounts (
  "objectid" BIGINT,
  "country" VARCHAR,
  "iso2" VARCHAR,
  "iso3" VARCHAR,
  "indicator" VARCHAR,
  "unit" VARCHAR,
  "source" VARCHAR,
  "cts_code" VARCHAR,
  "cts_name" VARCHAR,
  "cts_full_descriptor" VARCHAR,
  "climate_influence" VARCHAR,
  "f1992" DOUBLE,
  "f1993" DOUBLE,
  "f1994" DOUBLE,
  "f1995" DOUBLE,
  "f1996" DOUBLE,
  "f1997" DOUBLE,
  "f1998" DOUBLE,
  "f1999" DOUBLE,
  "f2000" DOUBLE,
  "f2001" DOUBLE,
  "f2002" DOUBLE,
  "f2003" DOUBLE,
  "f2004" DOUBLE,
  "f2005" DOUBLE,
  "f2006" DOUBLE,
  "f2007" DOUBLE,
  "f2008" DOUBLE,
  "f2009" DOUBLE,
  "f2010" DOUBLE,
  "f2011" DOUBLE,
  "f2012" DOUBLE,
  "f2013" DOUBLE,
  "f2014" DOUBLE,
  "f2015" DOUBLE,
  "f2016" DOUBLE,
  "f2017" DOUBLE,
  "f2018" DOUBLE,
  "f2019" DOUBLE,
  "f2020" DOUBLE,
  "f2021" DOUBLE,
  "f2022" DOUBLE
);

Physical Risks Climate Related Disasters Frequency

@kaggle.aritra100_global_warning_weather_dataset.physical_risks_climate_related_disasters_frequency

125.84 kB
1,972 rows
55 columns

CREATE TABLE physical_risks_climate_related_disasters_frequency (
  "objectid" BIGINT,
  "country" VARCHAR,
  "iso2" VARCHAR,
  "iso3" VARCHAR,
  "indicator" VARCHAR,
  "unit" VARCHAR,
  "source" VARCHAR,
  "cts_code" VARCHAR,
  "cts_name" VARCHAR,
  "cts_full_descriptor" VARCHAR,
  "n_1980" DOUBLE  -- 1980,
  "n_1981" DOUBLE  -- 1981,
  "n_1982" DOUBLE  -- 1982,
  "n_1983" DOUBLE  -- 1983,
  "n_1984" DOUBLE  -- 1984,
  "n_1985" DOUBLE  -- 1985,
  "n_1986" DOUBLE  -- 1986,
  "n_1987" DOUBLE  -- 1987,
  "n_1988" DOUBLE  -- 1988,
  "n_1989" DOUBLE  -- 1989,
  "n_1990" DOUBLE  -- 1990,
  "n_1991" DOUBLE  -- 1991,
  "n_1992" DOUBLE  -- 1992,
  "n_1993" DOUBLE  -- 1993,
  "n_1994" DOUBLE  -- 1994,
  "n_1995" DOUBLE  -- 1995,
  "n_1996" DOUBLE  -- 1996,
  "n_1997" DOUBLE  -- 1997,
  "n_1998" DOUBLE  -- 1998,
  "n_1999" DOUBLE  -- 1999,
  "n_2000" DOUBLE  -- 2000,
  "n_2001" DOUBLE  -- 2001,
  "n_2002" DOUBLE  -- 2002,
  "n_2003" DOUBLE  -- 2003,
  "n_2004" DOUBLE  -- 2004,
  "n_2005" DOUBLE  -- 2005,
  "n_2006" DOUBLE  -- 2006,
  "n_2007" DOUBLE  -- 2007,
  "n_2008" DOUBLE  -- 2008,
  "n_2009" DOUBLE  -- 2009,
  "n_2010" DOUBLE  -- 2010,
  "n_2011" DOUBLE  -- 2011,
  "n_2012" DOUBLE  -- 2012,
  "n_2013" DOUBLE  -- 2013,
  "n_2014" DOUBLE  -- 2014,
  "n_2015" DOUBLE  -- 2015,
  "n_2016" DOUBLE  -- 2016,
  "n_2017" DOUBLE  -- 2017,
  "n_2018" DOUBLE  -- 2018,
  "n_2019" DOUBLE  -- 2019,
  "n_2020" DOUBLE  -- 2020,
  "n_2021" DOUBLE  -- 2021,
  "n_2022" DOUBLE  -- 2022,
  "n_2023" DOUBLE  -- 2023,
  "n_2024" DOUBLE  -- 2024
);