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.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, 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
• 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)

The raw data comes from the Berkeley Earth data page.

(1) This is the dataset simulated by high resolution atmospheric model of which horizontal resolution is 60km-mesh over the globe (GCM), and 20km over Japan and surroundings (RCM), respetively. The climate of the latter half of the 20th century is simulated for 6000 years (3000 years for the Japan area), and the climates 1.5 K(*2), 2 K (*1) and 4 K warmer than the pre-industrial climate are simulated for 1566, 3240 and 5400 years, respectivley, to see the effect of global warming. (2) Huge number of ensembles enable not only with statistics but also with high accuracy to estimate the future change of extreme events such as typoons and localized torrential downpours. In addtion, this dataset provides the highly reliable information on the impact of natural disasters due to climate change on future societies. (3) This dataset provides the climate projections which adaptations against global warming are based on in various fields, for example, disaster prevention, urban planning, environmetal protection, and so on. It would realize the global warming adaptations consistent not only among issues but also among regions. (4) Total size of this dataset is 3 PB (3 × the 15th power of 10 bytes).

(*1) Datasets of the climates 2K warmer than the pre-industorial climate (d4PDF 2K) is available on 10th August, 2018. (*2) Datasets of the climates 1.5K warmer than the pre-industorial climate (d4PDF 1.5K) is available on 8th February, 2022.

This dataset includes processed climate change datasets related to climatology, hydrology, and water operations. The climatological data provided are change factors for precipitation and reference evapotranspiration gridded over the entire State. The hydrological data provided are projected stream inflows for major streams in the Central Valley, and streamflow change factors for areas outside of the Central Valley and smaller ungaged watersheds within the Central Valley. The water operations data provided are Central Valley reservoir outflows, diversions, and State Water Project (SWP) and Central Valley Project (CVP) water deliveries and select streamflow data. Most of the Central Valley inflows and all of the water operations data were simulated using the CalSim II model and produced for all projections.

These data were originally developed for the California Water Commission’s Water Storage Investment Program (WSIP). The WSIP data used as the basis for these climate change resources along with the technical reference document are located here: https://data.cnra.ca.gov/dataset/climate-change-projections-wsip-2030-2070. Additional processing steps were performed to improve user experience, ease of use for GSP development, and for Sustainable Groundwater Management Act (SGMA) implementation. Furthermore, the data, tools, and guidance may be useful for purposes other than sustainable groundwater management under SGMA.

Data are provided for projected climate conditions centered around 2030 and 2070. The climate projections are provided for these two future climate periods, and include one scenario for 2030 and three scenarios for 2070: a 2030 central tendency, a 2070 central tendency, and two 2070 extreme scenarios (i.e., one drier with extreme warming and one wetter with moderate warming). The climate scenario development process represents a climate period analysis where historical interannual variability from January 1915 through December 2011 is preserved while the magnitude of events may be increased or decreased based on projected changes in precipitation and air temperature from general circulation models.

2070 Extreme Scenarios Update, September 2020

DWR has collaborated with Lawrence Berkeley National Laboratory to improve the quality of the 2070 extreme scenarios. The 2070 extreme scenario update utilizes an improved climate period analysis method known as "quantile delta mapping" to better capture the GCM-projected change in temperature and precipitation. A technical note on the background and results of this process is provided here: https://data.cnra.ca.gov/dataset/extreme-climate-change-scenarios-for-water-supply-planning/resource/f2e1c61a-4946-4863-825f-e6d516b433ed.

Note: the original version of the 2070 extreme scenarios can be accessed in the archive posted here: https://data.cnra.ca.gov/dataset/sgma-climate-change-resources/resource/51b6ee27-4f78-4226-8429-86c3a85046f4

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a "change consistent with warming" or a "change not consistent with warming", based on information from the underlying studies.

GCCMPD covers a large range of policies amount to 73,625 policies of 216 entities. With expert knowledge-based dictionary mapping, probability statistics methods, and advanced natural language processing technology, GCCMD delivers comprehensive and consistent information of sectoral policy instruments regarding their objectives, target sectors, instruments, legal compulsion, administrative entities and so on.

Description: This dataset offers annual surface temperature data for nearly all countries spanning the years 1960 to 2022. The dataset is provided in two formats, allowing flexibility in data analysis and visualization: tidy format and wide format.

Tidy Format: The tidy format is structured with columns including Country, ISO code, Year, and Temperature.

Wide Format: The wide format is designed for quick reference and easy comparisons across countries and years, with columns for each year from 1960 to 2022.

Columns (Tidy Format): 1. Country: The name of the country. 2. ISO: The ISO 3166-1 alpha-3 country code. 3. Year: The year of the temperature record. 4. Temperature: The annual surface temperature (in degrees Celsius) for the corresponding year and country.

Columns (Wide Format): - Country: The name of the country. - ISO: The ISO 3166-1 alpha-3 country code. - Temperature (1960) to Temperature (2022): Columns representing annual surface temperatures (in degrees Celsius) for each year from 1960 to 2022.

Note: - Missing temperature values are represented as NaN in both formats. - The wide format allows for straightforward data manipulation and visualization, while the tidy format is suitable for structured data analysis.

Source: The temperature data is sourced from reputable climate monitoring agencies and research institutions, ensuring its reliability and accuracy.

Usage: Researchers, climate scientists, and data analysts can utilize this dataset for a wide range of climate-related studies, including trend analysis, temperature comparisons, and climate change assessments. It provides a comprehensive overview of temperature variations across countries over the years.

Acknowledgments: We extend our gratitude to the dedicated scientists and organizations worldwide for their contributions to climate data collection and research.

License: The dataset is made available under an open data license, permitting free use and distribution for research and educational purposes.

The Intergovernmental Panel on Climate Change Fifth Assessment Report (AR5) Observed Climate Change Impacts Database, Version 2.01 contains observed responses to climate change across a wide range of systems as well as regions. These responses include systems for which climate change has played a major role in observed changes, regional-scale impacts where climate change has played a minor role, and sub-regional impacts. Impacts on physical, biological, and human systems were differentiated, and the area impacted can vary from specific locations to broad areas such as a major river basin.

The data this week comes from the NASA GISS Surface Temperature Analysis (GISTEMP v4). This datasets are tables of global and hemispheric monthly means and zonal annual means. They combine land-surface, air and sea-surface water temperature anomalies (Land-Ocean Temperature Index, L-OTI). The values in the tables are deviations from the corresponding 1951-1980 means.

The GISS Surface Temperature Analysis version 4 (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), combined as described in their publications Hansen et al. (2010) and Lenssen et al. (2019). These updated files incorporate reports for the previous month and also late reports and corrections for earlier months.

When comparing seasonal temperatures, it is convenient to use “meteorological seasons” based on temperature and defined as groupings of whole months. Thus, Dec-Jan-Feb (DJF) is the Northern Hemisphere meteorological winter, Mar-Apr-May (MAM) is N.H. meteorological spring, Jun-Jul-Aug (JJA) is N.H. meteorological summer and Sep-Oct-Nov (SON) is N.H. meteorological autumn. String these four seasons together and you have the meteorological year that begins on Dec. 1 and ends on Nov. 30 (D-N). The full year is Jan to Dec (J-D). Brian Bartling

This folder, titled "Data," contains the MATLAB code, final products, tables, and figures used in Parker, L.E., Zhang, N., Abatzoglou, J.T. et al. A variety-specific analysis of climate change effects on California winegrapes. Int J Biometeorol 68, 1559–1571 (2024). https://doi.org/10.1007/s00484-024-02684-8 Data Collection: Climatological data (daily maximum and minimum temperatures, precipitation, and reference evapotranspiration) were obtained from the gridMET dataset for the contemporary period (1991-2020) and from 20 global climate models (GCMs) for the mid-21st century (2040-2069) under RCP 4.5.Phenology Modeling: Variety-specific phenology models were developed using published climatic thresholds to assess chill accumulation, budburst, flowering, veraison, and maturity stages for the six winegrape varieties.Agroclimatic Metrics: Fourteen viticulturally important agroclimatic metrics were calculated, including Growing Degree Days (GDD), Cold Hardiness, Chilling Degree Days (CDD), Frost Damage Days (FDD), and others.Analysis Tools: MATLAB was used for data processing, analysis, and visualization. The MATLAB code provided in this dataset includes scripts for analyzing climate data, running phenology models, and generating visualizations.MATLAB Code: Scripts and functions used for data analysis and modeling.Processed Data: Results from phenology and agroclimatic analyses, including the projected changes in phenological stages and climate metrics for the selected varieties and AVAs.Tables: Detailed results of phenological changes and climate metrics, presented in a clear and structured format.Figures: Visual representations of the data and results, including charts and maps illustrating the impacts of climate change on winegrape development stages and agroclimatic conditions. Research Description: This study investigates the impacts of climate change on the phenology and agroclimatic metrics of six winegrape varieties (Cabernet Sauvignon, Chardonnay, Pinot Noir, Zinfandel, Pinot Gris, Sauvignon Blanc) across multiple California American Viticultural Areas (AVAs). Using climatological data and phenology models, the research quantifies changes in key development stages and viticulturally important climate metrics for the mid-21st century.

As part of this national strategy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) had launched a 5-year (FY2007 - 2011) initiative called the Innovative Program of Climate Change Projection for the 21st Century (KAKUSHIN Program), using the Earth Simulator (ES) to address emerging research challenges, such as those derived from the outcomes of the MEXT's Kyosei Project (FY2002 - 2006), that had made substantial contributions to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The KAKUSHIN Program was expected to further contribute to the Fifth Assessment Report (AR5).

The research items include the advancement and forecasting of global warming models, the quantification and reduction of model uncertainty, and the evaluation of the impacts of natural disasters based on forecast information. Much of the data submitted to CMIP5 from Japan were generated under this KAKUSHIN program using the global climate models and the Earth system models developed in Japan. This dataset is the result of using the Global Climate Model MIROC4h.

All CMIP5 data are collected, managed, and published by the Earth System Grid Federation (ESGF), and DIAS serves as an ESGF node. All public datasets, including this dataset, are available from ESGF. For information on how to use these datasets, including this dataset, see "CMIP5 Data - Getting Started" (URL is available in the online information below). Please note that an ESGF account is required to download the CMIP5 data.

Because the terms of use for CMIP5 data are different from CMIP6 in many respects, please check the following Terms of Use carefully: https://pcmdi.llnl.gov/mips/cmip5/terms-of-use.html Currently, all CMIP5 data, including this dataset, is classified as "unrestricted" within it.

Inland fishes provide important ecosystem services to communities worldwide and are especially vulnerable to the impacts of climate change. Fish respond to climate change in diverse and nuanced ways which creates challenges for practitioners of fish conservation, climate change adaptation, and management. Although climate change is known to affect fish globally, a comprehensive online, public database of how climate change has impacted inland fishes worldwide and adaptation or management practices that may address these impacts does not exist. We conducted an extensive, systematic primary literature review to identify peer-reviewed journal publications describing projected and documented examples of climate change impacts on inland fishes. From this standardized Fish and Climate Change database, FiCli, researchers and managers can query fish families, species, response types, or geographic locations to obtain summary information on inland fish responses to climate change and recommended management actions. The FiCli provides access to comprehensive published information to inform inland fish Inland fishes provide important ecosystem services to communities worldwide and are especially vulnerable to the impacts of climate change.

Global warming vs climate change

Many people use these two terms interchangeably, but we think it’s important to acknowledge their differences. Global warming is an increase in the Earth’s average surface temperature from human-made greenhouse gas emissions. On the other hand, climate change refers to the long-term changes in the Earth’s climate, or a region on Earth, and includes more than just the average surface temperature. For example, variations in the amount of snow, sea levels, and sea ice can all be consequences of climate change.

Content

Worldwide Climate Change & Global Warming keyword / topic search in Google Search Engine from 2004 - present

Acknowledgements

Google Trends Lab

This dataset provides a detailed exploration of global warming and climate change trends across 195 countries from 1900 to 2023. It includes 1,00,000 rows and 26 columns, capturing environmental, economic, and societal factors impacting global warming. Key indicators such as temperature anomalies, CO2 emissions, deforestation rates, sea-level rise, and renewable energy usage are included, making this dataset suitable for climate change prediction and analysis.

Whether you're a beginner exploring trends or an advanced data scientist building models, this dataset is an excellent resource for learning, experimentation, and insights into one of the most pressing challenges of our time.

Insights to Explore:

For Beginners:

Trend Analysis:

Track how global temperature anomalies have changed over the decades. Identify countries with the highest and lowest CO2 emissions. Explore population growth trends and their correlation with CO2 emissions.

Visualization Practice:

Create line charts showing changes in renewable energy usage over time. Develop bar charts comparing extreme weather events between countries.

For Intermediate Users:

Correlation Analysis:

Analyze relationships between deforestation rates and temperature anomalies. Explore how GDP and fossil fuel usage correlate with CO2 emissions. Feature Engineering:

Create new features like Per Capita CO2 Emissions or Energy Efficiency Score to enhance predictive modeling. Clustering:

Group countries based on their environmental policies and renewable energy usage.

For Advanced Users:

Predictive Modeling:

Build time-series models to forecast future temperature anomalies or sea-level rise. Develop machine learning models to predict CO2 emissions based on socioeconomic factors. Anomaly Detection:

Detect outliers in extreme weather events or CO2 emissions.

Deep Learning Applications:

Train deep learning models to predict Arctic ice extent using multi-year trends.

This is an adjusted dataset with a base from Berkeley Earth’s on Kaggle – Climate Change: Earth Surface Temperature Data. The second source is Our World in Data’s Average monthly surface temperature.

This dataset combines historical land temperatures (1743-2013) with modern surface temperatures (1940-2024) to create a seamless, standardized global temperature record. With limited absolute temperature datasets available for recent years, this dataset helps bridge that gap, supporting climate analysis and long-term trend research.

The historical land temperature data includes monthly air temperature records measured 2 meters above the ground. The modern surface temperature data covers both land and ocean temperatures, recorded using satellites, buoys, and weather stations.

Since modern surface temperatures were slightly lower than historical land measurements, an adjustment factor was applied to correct for differences in measurement methods and ocean influence. A mean temperature ratio was calculated from overlapping years to align the datasets while preserving trends.

The final dataset provides monthly temperature records for each country from 1750 to 2024. The dataset was validated by comparing long-term trends with temperature anomalies, confirming its accuracy.

This dataset is useful for climate research, machine learning models, and studying global warming impacts, offering a more complete absolute temperature record than many existing datasets on Kaggle.

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a "change consistent with warming" or a "change not consistent with warming", based on information from the underlying studies.

The database contains a time series of daily air temperature projections of two meters in the period 1981-2100 for the scenario of greenhouse gas emissions RCP2.6 above Slovenia in the correct grid, resolution 0.125°.

For RCP2.6, simulations of two regional climate models are available. The simulations are the result of regional models of the EURO-CORDEX project. Their resolution is 0.11°. The data are corrected according to measurements in Slovenia in the period 1981-2010 (the so-called bias correction). Find out more about the EURO-CORDEX project through additional links.

The data is in NetCDF format files, which are divided into 30-year periods due to their size. There are four files available for each model. Each 30-year period is marked with the year of the last year of the period.

Model projection files have names that contain information about the name of the variable, models, projection time, version, etc. separated by underscores. The components of the names are: name of variable (tas: average air temperature), model resolution (12 km: 0.125°), the name of the global climate model that gave marginal conditions to the regional, abbreviated greenhouse gas emissions scenario (rcp26: RCP2.6), ensemble parameters (e.g. r1i1p1), regional climate model name, projection version, projection time step (day: 1 day) and start and end dates of the projection (as YYYYMMDD where YYYY is year, MM month and DD day).

The model results represent the physically possible states of the climate system in the future relative to the day, which in this case is the path of greenhouse gas concentration. As greenhouse gas concentrations cannot be predicted, the International Panel on Climate Change (IPCC) in its fifth report from 2014 produced four plausible scenarios for it, given the socio-economic evolution of humanity in the future. Since model results differ from one another and each of them represents a possible state of the climate, their results must be statistically processed. The differences between them form the basis for assessing the uncertainty of the projections. A summary of analyses of climate change by the end of the century in Slovenia can be found among additional links. There is also the first synthesis report on climate change in Slovenia, which contains a more detailed description of the methodology and results of climate projections. About climate projections are discussed in Chapter 1, on regional climate models, Chapter 3.2, and on the correction of errors or bias of models Chapter 4.3.

One of the options for working with NetCDF files (extraction, aggregation, etc.) is the CDO program. Reading and statistical processing on NetCDF files are also possible with the statistical package R, especially its ncdf4 and raster packages. Links to CDO and R programs can be found in additional links.

The costs of climate change are often estimated in monetary terms but this raises ethical issues. Here we express them in terms of numbers of people left outside the ‘human climate niche’ – defined as the historically highly-conserved distribution of relative human population density with respect to mean annual temperature (MAT). We show that climate change has already put ~9% of people (>600 million) outside this niche. By end-of-century (2080-2100), current policies leading to around 2.7 °C global warming could leave one third (22-39%) of people outside the niche. Reducing global warming from 2.7 to 1.5 °C results in a ~5-fold decrease in the population exposed to unprecedented heat (MAT ≥29 °C). The lifetime emissions of ~3.5 global average citizens today (or ~1.2 average US citizens) expose 1 future person to unprecedented heat by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.

The Climate Change Knowledge Portal (CCKP) is the World Bank's designated climate data service. CCKP offers a comprehensive suite of climate data and products that are derived from the latest generation of climate data archives. CCKP implements a systematic way of pre-processing the raw observed and model-based projection data to enable inter-comparable use across a broad range of applications. Data is available across an expansive range of climate variables and can be extracted per individual spatial units, variables, select timeframes, climate projection scenarios, across ensembles or individual models. Data is available as global gridded or spatially aggregated to national, subnational, watershed, and Exclusive Economic Zone scaled.

The Observed Climate Data, CRU ts4.07 0.5-degree dataset, CRU TS (Climatic Research Unit gridded Time Series) is the most widely used observational climate dataset. Data is presented on a 0.5° latitude by 0.5° longitude grid over all land domains except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. The CRU TS version 4.07 gridded dataset is derived from observational data and provides quality-controlled temperature and rainfall values from thousands of weather stations worldwide, as well as derivative products including monthly climatologies and long term historical climatologies. Data products are derived from the raw data produced by the Climatic Research Unit (CRU) of the University of East Anglia (UEA).

Global gridded NetCDF files can be accessed via https://registry.opendata.aws/wbg-cckp/

Pre-computed statistics for spatially aggregated data is available as API or xls via

https://climateknowledgeportal.worldbank.org/download-data

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:

Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019

Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).

Climate change impact data

File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.

File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv

Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019).

Climate change mitigation cost data

The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].

File 4: REMIND_scenario_results_economic_data.csv

File 5: REMIND_scenarios_climate_data.csv

Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.

In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.

The first dimension specifies the climate policy regime (delayed action, baseline scenarios):

1xx: climate action from 2010
5xx: climate action from 2015
2xx climate action from 2020 (used in this study)
3xx climate action from 2030
4x1 weak policy baseline (before Paris agreement)

The second dimension specifies the technology portfolio and assumptions:

x1x Full technology portfolio (used in this study)
x2x noCCS: unavailability of CCS
x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed
x4x NucPO: phase out of investments into nuclear energy
x5x Limited SW: penetration of solar and wind power limited
x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)
x6x noBECCS: unavailability of CCS in combination with bioenergy

The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).

xx1 0$/tCO2 (baseline)
xx2 10$/tCO2
xx3 30$/tCO2
xx4 50$/tCO2
xx5 100$/tCO2
xx6 200$/tCO2
xx7 500$/tCO2
xx8 40$/tCO2
xx9 20$/tCO2
xx0 5$/tCO2

For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).

[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.

[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

Global Land and Surface Temperature Trends Analysis

Assessing climate change year by year

By IBM Watson AI XPRIZE - Environment [source]

About this dataset

This dataset from Kaggle contains global land and surface temperature data from major cities around the world. By relying on the raw temperature reports that form the foundation of their averaging system, researchers are able to accurately track climate change over time. With this dataset, we can observe monthly averages and create detailed gridded temperature fields to analyze localized data on a country-by-country basis. The information in this dataset has allowed us to gain a better understanding of our changing planet and how certain regions are being impacted more than others by climate change. With such insights, we can look towards developing better responses and strategies as our temperatures continue to increase over time

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Introduction

This guide will show you how to use this dataset to explore global climate change trends over time.

Exploring the Dataset

• Select one or more countries by using df[df['Country']=='countryname'] command in order to filter out any unnecessary information that is not related to those countries;

• Use df.groupby('City')['AverageTemperature'] command in order to group all cities together with their respective average temperatures;

• Compute basic summary statistics such as mean or median for each group with df['AverageTemperature'].{mean(),median()}, where {} can be replaced with mean or median according various statistic requirements;

4 .Plot a graph comparing these results from line plots or bar charts with pandas plot function such as df[column].plot(kind='line'/'bar'), etc., which can help visualize certain trends associated form these groups

You can also use latitude/longitude coordinates provided alongwith every record further decompose records by location using folium library within python such as folium maps that provide visualization features & zoomable maps alongwith many other rendering options within them like mapping locations according different color shades & size based on different parameters given.. These are just some ways you could visualize your data! There are plenty more possibilities!

Research Ideas

• Analyzing temperature changes across different countries to identify regional climate trends and abnormalities.
• Investigating how global warming is affecting urban areas by looking at the average temperatures of major cities over time.
• Comparing historic average temperatures for a given region to current day average temperatures to quantify the magnitude of global warming in that region.

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 for the given date. (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...