Global sea level was projected to increase until the end of the century, under all greenhouse gas (GHG) emissions scenarios. At a very low GHG emission scenario, global sea level was expected to increase by 38 centimeters, when compared to a 1995-2014 baseline. In contrast, in a high emissions scenario, sea level rise worldwide was expected to be twice as high, at 77 centimeters.

Introduction

Global Warming Statistics: Global warming, most people think it is just about the Earth getting hotter. But the truth is, it is much more than that. It is about rising temperatures, melting ice, stronger storms, changing seasons, and changing lives. Now, when we look at the global warming statistics, we are not only looking at numbers on a chart. These stats tell the real story of how our planet is changing and what it means for us.

Think of it this way. If the Earth had a health report, global warming statistics would be the test results. They show how much the temperature has gone up, how fast the seas are rising, how greenhouse gases are building up in the atmosphere, and how many species are struggling to survive.

The reason we dive into these statistics is that numbers don’t lie. When scientists say carbon dioxide has crossed 420 parts per million or that sea levels have risen by 20 centimeters since 1900, those are hard facts. And these facts help us understand the scale of the problem. Without these stats, global warming would remain a vague idea, but with them, we can see the evidence in clear and measurable ways.

In this article, I’m going to walk you through the most important global warming statistics. We’ll look at how temperatures have changed, how much ice we are losing, how seas are rising, and even how these changes affect our health, food, and economy. By the end, you’ll see the real impact of global warming. Let’s get into it.

More than 170 million people worldwide may be internally displaced by 2050, due to slow-onset impacts of climate change. This is based on a pessimistic scenario of high greenhouse gases emissions and unequal development. In a more climate-friendly scenario, this figure would still add up to some 125 million climate migrants, while a more inclusive development would lead to roughly 78 million migrants. In all three scenarios, Sub-Saharan Africa would see the largest number of displacements resulting from shifts in water availability, crop productivity, and sea level rise within the six regions evaluated.

Adapting agriculture to the impacts of climate change in Sub-Sahara Africa will cost around 15.5 billion U.S. dollars per year by 2050. The estimate included expenses for research, water management, infrastructure, and climate information services. Agricultural activities in Africa are primarily rainfed and highly depend on the weather. According to the source, the non-adoption of adaptation measures against climate change would cost over 200 billion U.S. dollars.

Climate Change Analysis
This dataset falls under the category Environmental Data Air Quality Data.
It contains the following data: Average temperature
This dataset was scouted on 2022-02-10 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.kaggle.com/bimal1990/climate-change-analysis/dataSee URL for data access and license information.

The majority of U.S. adults believe that non-government scientists and educators are the most trustworthy sources for information about climate change, with **** percent of respondents in 2022. By comparison, nearly ** percent of respondents said they considered environmental groups trustworthy, and some ** percent said they considered college professors/educators trustworthy.

The preferred citation when using this dataset is: Stevens, A., & Lamie, C., Eds. (2024). New York State Climate Impacts Assessment: Understanding and preparing for our changing climate. The New York State Climate Impacts Assessment is an investigation into how climate change will affect New York State’s communities, ecosystems, and economy. The data and information presented will help New Yorkers plan and prepare for the impacts of climate change. The assessment also strives to show how addressing climate change provides opportunities to enhance equity and reduce the vulnerability of those most at risk. As part of the assessment, Columbia University developed climate change projections for temperature and precipitation, extreme events, degree days, and sea level rise, downscaled to 12 regions of New York State. This dataset includes those projections of future climate conditions in New York State, for the 2030s through 2100. For more information on these projections or to read the full NYS Climate Impacts Assessment, visit the assessment website at https://nysclimateimpacts.org/. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

This dataset consists of spatially explicit (1 km gridded) metrics of climate change 'exposure' (i.e. an index of the amount of expected change in a location) derived from quantifying the difference in observed historical and predicted future climatic conditions. Four comparisons are included between five discrete time periods: 1901–1930 v. 1961–1990; 1961–1990 v. 2010–2019; 2010–2019 v. 2021–2040; and 2021–2040 v. 2061–2080.

(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 x the 15th power of 10 bytes).

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

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.

This article explores the phenomenon of the use of ICT for climate change activism in the Pacific.

Relevant datasets for "Projecting the Suicide Burden of Climate Change in the United States" (Belova et al.). Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Climate change, that is a threat to ecosystems and the livelihoods of those that depend on them, is increasingly manifesting as an increased frequency and intensity of severe weather events such as droughts and floods (Déqué et al., 2017). Climate change has created an urgent need for early warning aids or models to enhance the sub-Saharan African health systems ability to prepare for, and cope with escalations in treatment needs of climate sensitive diseases (Nhamo & Muchuru, 2019). This dataset was created from the health and weather data of nine purposively selected study districts in Uganda, whose health and weather data were available for the development of an early warning health model (https://github.com/CHAIUGA/chasa-model) and an accompanying prediction web app (https://github.com/CHAIUGA/chasa-webapp). The districts were selected based on the following criteria: (a) were experiencing climate change and variability, (b) represented different climatologic, and agro-ecological zones, (c) availability of climate information and health information from a health facility within a 40 kilometres radius of a functional weather station. Historical weather data was retrieved from the Uganda National Meteorological Association databases, as monthly averages. The weather variables in this data included: atmospheric pressure, rainfall, solar radiation, humidity, temperature (maximum, minimum and mean), and wind (gusts and average wind speed). The monthly health aggregated data for the period starting September 2018 to December 2019, was retrieved from the National Health Repository (DHIS2) for referral hospitals within the selected districts. Only data for a selection of climate-sensitive disease aggregates was obtained. The dataset contains 436 complete matched disease and weather records. Ethical issues: Both the de-identified aggregate monthly disease diagnosis count data and weather data in this dataset are from national data available to the public on request.

Sectoral impact modeling data upon which the climate damage functions are based. See main paper and supplemental material for additional detail and metadata. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

The primary climate-change data and information analysis center of the U.S. Department of Energy (DOE) and includes the World Data Center for Atmospheric Trace Gases, serving the climate change-related data and information needs of users worldwide. CDIAC''s data holdings include estimates of carbon dioxide emissions from fossil-fuel consumption and land-use changes; records of atmospheric concentrations of carbon dioxide and other radiatively active trace gases; carbon cycle and terrestrial carbon management datasets and analyses; and global/regional climate data and time series. CDIAC provides scientific and data management support for projects sponsored by a number of agencies, including the AmeriFlux Network, continuous observations of ecosystem level exchanges of CO2, water, energy and momentum at different time scales for sites in the Americas; the Ocean CO2 Data Program of CO2 measurements taken aboard ocean research vessels; DOE-supported FACE experiments, which evaluate plant and ecosystem response to elevated CO2 concentrations; and the HIPPO project, which is analyzing the atmospheric carbon cycle and greenhouse gas concentrations from pole to pole over the Pacific Ocean. For those wishing to contribute data to the CDIAC data collection, review the guide and then contact one of the CDIAC staff members to discuss further data submission plans. Data providers may submit data in a variety of ways including via email, direct deposit to a secure CDIAC File Transfer Protocol (FTP) server, on transfer media (e.g. CD-ROM), or by having CDIAC mirror a location at the investigator''s institution.

Abstract:

Analyzing the spread of information related to a specific event in the news has many potential applications. Consequently, various systems have been developed to facilitate the analysis of information spreadings such as detection of disease propagation and identification of the spreading of fake news through social media. There are several open challenges in the process of discerning information propagation, among them the lack of resources for training and evaluation. This paper describes the process of compiling a corpus from the EventRegistry global media monitoring system. We focus on information spreading in three domains: sports (i.e. the FIFA WorldCup), natural disasters (i.e. earthquakes), and climate change (i.e.global warming). This corpus is a valuable addition to the currently available datasets to examine the spreading of information about various kinds of events.Introduction:Domain-specific gaps in information spreading are ubiquitous and may exist due to economic conditions, political factors, or linguistic, geographical, time-zone, cultural, and other barriers. These factors potentially contribute to obstructing the flow of local as well as international news. We believe that there is a lack of research studies that examine, identify, and uncover the reasons for barriers in information spreading. Additionally, there is limited availability of datasets containing news text and metadata including time, place, source, and other relevant information. When a piece of information starts spreading, it implicitly raises questions such as asHow far does the information in the form of news reach out to the public?Does the content of news remain the same or changes to a certain extent?Do the cultural values impact the information especially when the same news will get translated in other languages?Statistics about datasets:

Statistics about datasets:

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# Domain Event Type Articles Per Language Total Articles

1 Sports FIFA World Cup 983-en, 762-sp, 711-de, 10-sl, 216-pt 2679

2 Natural Disaster Earthquake 941-en, 999-sp, 937-de, 19-sl, 251-pt 3194

3 Climate Changes Global Warming 996-en, 298-sp, 545-de, 8-sl, 97-pt 1945

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Files contain: 1) expected annual damages summed by HUC10, for baseline climate and future climates of 1C-5C warmer than baseline, and 2) expected annual damages due to adaptation based on benefit cost ratios of 1, 2, and 4, and net benefits of adaptation under each BCR scenario. Tabs are results for discount rates of 1, 3, and 6 (see text). Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

The Greenhouse Gas Reporting Program (GHGRP) collects information on greenhouse gas (GHG) emissions annually from facilities across Canada. It is a mandatory program for those who meet the requirements. Facilities that emit 10 kilotonnes or more of GHGs, in carbon dioxide (CO2) equivalent (eq.) units, per year must report their emissions to Environment and Climate Change Canada. The emissions data is available in two files, each presenting emissions by different breakdowns and offered in two convenient formats for downloads: .xlsx and .csv. The Emissions by Gas file, covering data from 2004 to present, contains emissions (in tonnes and tonnes of CO2 eq.) for each facility categorized by gas type, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFC), perfluorocarbons (PFC), and sulphur hexafluoride (SF6). The Emissions by Source file, starting from 2022, includes emissions data (in tonnes and tonnes of CO2 eq.) broken down by source category, encompassing Stationary Fuel Combustion, Industrial Process, On-site Transportation, Waste, Wastewater, Venting, Flaring, and Leakage. For additional information and usage guidelines, please refer to the accompanying "Lisez Moi - Read Me" file. Additionally, our data search tool can assist you in efficiently navigating and extracting specific information from the GHGRP's data. Supplemental Information Learn more about the GHGRP: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions/facility-reporting.html Overview of Reported Emissions - An annual summary report of the facility-reported emissions and trends: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions/facility-reporting/data.html Canada's Greenhouse Gas Emissions: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions.html Contact us: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions/contact-team.html