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

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• LandAverageTemperature: global average land temperature in celsius

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• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

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• LandMaxTemperature: global average maximum land temperature in celsius

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

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• LandMinTemperature: global average minimum land temperature in celsius

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

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• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

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

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**Other files include: **

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

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

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

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• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

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The raw data comes from the Berkeley Earth data page.

As of 2019, respondents among organizations that are already involved in the scaling of artificial intelligence (AI) projects for climate action anticipate that greenhouse gas emissions will be cut by almost ** percent through AI-enabled projects in the next 3 to 5 years. The use of AI-enabled climate action projects are also expected to improve power and industrial efficiency, reduce waster and dead weight assets, and assist in cost savings, according to respondents.

The Long Beach Climate Action Plan (LB CAP) was adopted by the City Council on August 16, 2022. The implementation of the LB CAP is an ongoing, collaborative process between the City, its partners, and the community to make Long Beach a safer, healthier, and more sustainable place to live, work, and play. The City aims to accomplish this by implementing LB CAP action items that work to reduce greenhouse gas emissions, mitigate the effects of climate change, enhance economic vitality, and improve the quality of life in Long Beach.Powering the Climate Portal2021 GHG Inventory Report2023 GHG Inventory Report

169 US Cities that have signed the Global Covanent for Climate and Energy. Things coded include population size, per capita income, partisanship (2020 election), city energy manager, presence of a university, ACEEE score, energy plan ambition.

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.

Climate change is viewed as a major concern globally, with around 90 percent of respondents to a 2023 survey viewing it as a serious threat to humanity. developing nations often show the highest levels of concern, like in the Philippines where 96.7 percent of respondents acknowledge it as a serious threat. Rising emissions despite growing awareness Despite widespread acknowledgment of climate change, global greenhouse gas emissions continue to climb. In 2023, emissions reached a record high of 53 billion metric tons of carbon dioxide equivalent, marking a 60 percent increase since 1990. The power industry remains the largest contributor, responsible for 28 percent of global emissions. This ongoing rise in emissions has significant implications for global climate patterns and environmental stability. Temperature anomalies reflect warming trend In 2024, the global land and ocean surface temperature anomaly reached 1.29 degrees Celsius above the 20th-century average, the highest recorded deviation to date. This consistent pattern of positive temperature anomalies, observed since the 1980s, highlights the long-term warming effect of increased greenhouse gas accumulation in the atmosphere. The warmest years on record have all occurred within the past decade.

In an effort to address the threat of climate change, the Commonwealth of Pennsylvania released an updated Climate Action Plan in April 2019, the first version of the Plan to include greenhouse gas (GHG) emissions reduction goals. The Plan includes over 100 actions that leaders can take to reduce emissions and combat climate change, fifteen of which are quantitatively modeled for environmental and economic impact. Empowering local leaders to take action was one of the driving factors for releasing this Plan. As part of this plan, the Pennsylvania DEP partnered with ICLEI to initiate a municipal outreach program to help municipalities conduct a greenhouse gas inventory and develop a climate action plan for themselves. The program envisions the municipality working with a student intern at a local college to conduct the greenhouse gas inventory in the fall semester and begin developing a climate action plan in the spring semester. The City of Allentown was one of twenty municipalities that participated in DEP’s program for the 2020- 2021 academic year. In the fall of 2020, a Muhlenberg College student intern worked with the City of Allentown Environmental Advisory Council to obtain the needed data and establish a GHG emissions inventory baseline with guidance from PA DEP and ICLEI. The full GHG Inventory report is available on the City’s website and is attached below as Attachment A. In addition to this fundamental first step, in the spring of 2021 the City of Allentown began developing a comprehensive inventory of climate actions that the City has already implemented, those that the City is in the process of implementing, those that the City has considered but not yet begun to implement, and those that the City has not yet considered but are being recommended by the City’s Environmental Advisory Council. This work was begun by the same Muhlenberg intern under DEP’s municipal outreach program. The climate action inventory will be made available on the City’s website when it is finalized. The comprehensive climate action inventory will be an important component of the City’s input to the regional climate action plan currently under development by the Lehigh Valley Planning Commission. The City intends to develop its own climate action plan in conjunction with the LVPC’s regional effort. In light of the greenhouse gas inventory and comprehensive climate action inventory, the City of Allentown decided in the spring of 2021 to respond CDP’s Climate Change Questionnaire for the first time. A copy of the response is available on the City’s website and attached below as Attachment B. The results of the GHG inventory and many of the actions identified to date in the climate action inventory are reflected in the City’s CDP Questionnaire response. The process of responding to the CDP questionnaire has enabled the City to think through many of the elements of a climate action plan, including climate hazards, adaptation measures, adaptation goals and mitigation actions.

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

Maximum temperature and rainfall observed data files were downloaded from the IRI Data Library as well as the model predicted 850-to-500 geopotential thickness fields (used to predict maximum temperature over southern Africa) and 850 circulation data fields (predictor for rainfall). Model Output statistics in CPT - climate predictability tool, was set up using CCA - canonical correlation analysis to produce retroactive forecasts. MATLAB was further utilized to post-process / fine-tune the output from CPT and to produce other results. The researcher used the output from the global climate model to develop a statistical model for maximum temperature seasonal forecasts for Southern Africa.

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.

The zip file contains high-resolution projections of the annual averaged temperature and precipitation over 50 regions and 150 municipalities in the Province of Ontario from year 1981 to 2099, under the representative concentration pathways (RCPs) 2.6, 4.5, 6.0 and 8.5 (emission scenarios defined by the Inter-governmental Panel for Climate Change (IPCC)’s 5th assessment report (AR5). This data is provided in partnership with York University. More data and visualizations are available at the user-friendly "https://yorku.ca/ocdp">Ontario Climate Data Portal (OCDP) developed and maintained by York University.

According to a 2021 survey, sourcing electricity from renewable or zero-emissions power sources was one of the most supported governmental initiatives. Around 59 percent of respondents believed their government should be taking such climate action. Joining other countries to commit to climate change goals was the second most selected option in the same survey.

According to a 2024 survey conducted among UK residents, almost 80 percent had some concern about climate change. In comparison, 19 percent were not concerned, with four percent of those having no concerns at all. The survey was conducted by the Department for Business, Energy & Industrial Strategy (BEIS) as part of its Net Zero and Climate Change Public Attitudes Tracker. Climate change causesIn a recent BEIS survey, it was found that 38 percent of respondents believed climate change is mainly caused by human activity. 13 percent believed it is caused entirely by human activity, whilst one percent felt that there is no such thing as climate change. Climate change is the term used for global weather phenomena which results in new weather patterns, increasing global temperatures. This term also includes the climate effects these increasing temperatures cause. A move towards green energyOver the last decade, electricity generation from renewable sources in the UK has increased significantly, surpassing 122 terawatt-hours in 2021. In the same period of time, the UK has seen its greenhouse gas emissions decrease by nearly 30 percent – from approximately 609 MtCO2e in 2010 to 427 MtCO2e in 2021.

In order to meet science-based targets established by the UN, About You has committed to exclusively using renewable electricity by 2026. As of financial year 2022/2023, the percentage is already at 99.2 percent. The fashion retailer also pledged to reduce Scope 3 greenhouse gases from private label products by 35.3 percent per unit of value added. The goal appears quite ambitious, since the measured reduction stood at 7.7 percent as of the latest financial year.

This database contains historical temperature and precipitation data aggregated from 2-degree gridded data to the country and basin levels.

Executive order showing Providence's commitment to eliminating city-wide carbon emissions and preparing the city for the long-term impacts of climate change.

The Sustainable Development Goals set by the United Nations aim to solve social, economic and environmental development problems in an integrated manner during the period from 2015 to 2030. The 13th SDG is to “Take urgent action to combat climate change and its impacts”, concerning sub-goal 13.1 “Strengthen resilience and adaptive capacity to climate related hazards and disasters in all countries”. SDG13.1.1 has been defined to be a specific, effective indicator that can be used to quantitatively monitor and evaluate governments' response to climate change. It is defined as the “number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population”. The EM-DAT contains important core data on the occurrence and impact of more than 220,000 large-scale disasters worldwide from 1900 to the present. As a global disaster database, the EM-DAT provides a large amount of natural and technological disaster data for international projects and scientific research. By making use of the EM-DAT big data and SGD13.1.1 indicators, it is possible to quantify disaster information at a large geographical scale and to conduct valuable disaster assessments of different countries and regions, as well as to improve the monitoring and assessment of disaster risk reduction capabilities, and strengthen the ability of countries to adapt to, resist, and reduce extreme disasters caused by climate change. In our related paper "Disaster Assessment for the “Belt and Road” Region based on SDG landmarks", disaster assessment for the ‘Belt and Road’ region was carried out in relation to the SGD13.1.1 indicator, based on the EM-DAT (The Emergency Events Database) database. A new method for diagnosing trends in SGD13.1.1 was proposed, and an overview of disaster records is used to quantify disasters for a total of 73 countries using the data available in the EM-DAT. The following data are supplementary materials for this article,including:the calculation variables of the SDG13.1.1 indicator;disaster types of each country in the whole Belt and Road region; calulated SDG13.1.1 value and trend values of each country in the Belt and Road countries.

Financial overview and grant giving statistics of Maine Climate Action Now

Financial overview and grant giving statistics of The Us Climate Action Network

Developing countries in Asia and the Pacific are historically the least responsible for greenhouse gas emissions that result in climate change, but are most vulnerable to its environmental, economic and social impacts. Priority responses to the challenge of global warming include strategies to reduce vulnerability; climate-proofing infrastructure to protect lives and assets; investing in adaptation strategies; strengthening resilience; and reducing emissions. This will require significant investments by both public and private sectors. Global estimates of the cumulative investment needed to stay within a 2ºC temperature increase by 2030-35 range between $55 and $93 trillion. Developing Asia alone needs an estimated US$3.6 billion per annum up to 2030 to transition toward net zero emissions and increased resilience as required by the Paris Agreement. This economic transition also presents a unique opportunity for private finance. Estimates suggest that the Paris Agreement has opened up nearly $23 trillion in opportunities for climate-smart investments in emerging markets up to 2030.