ISF is an independent research institute within the University of Technology Sydney. They conduct trans-disciplinary, project-based research in line with their vision of creating positive change towards sustainable futures.

Annual Estimates of Global Anthropogenic Methane Emissions: 1860-1994

The tool summarises the relevant climate-related risks and opportunities for banks, insurers and asset managers based on the business activities, products, or risks of the firm and the materiality of different lending exposure types, underwriting classes, asset classes, and economic sectors for the firm. The tool can generate two types of report: an Institutional Report which gives a report tailored to your institution a Sector Report which allows users to see the content for an individual sector

The Climate Financial Risk Forum (CFRF) list of data and tools providers is a collection of currently-available climate risk data, tools and products for financial institutions. It has been created to serve as an illustrative list of current climate risk offerings, highlighting the variety and scope of what is currently available in the marketplace. This is to ultimately support research and decision making around climate risk product procurement. The database has been designed to provide practitioners with relevant information in a digestible and searchable format.

The map visualises the 352 million assets included in the Climate TRACE emissions inventory. The default view when the page first loads shows emissions sources from all sectors across all geographies for the most-recent calendar year. It is possible to look up emissions estimates for nearly every major source of emissions in the world, from power plants, refineries, and road networks to areas of deforestation.

Emissions Pathways are transformational process that delivers long-term emissions reductions and sustainable development in collaboration with local communities, businesses and other key actors. This dataset is published via Climate Watch, a free online platform designed to empower policymakers, researchers, media and other stakeholders with the open climate data, visualizations and resources they need to gather insights on national and global progress on climate change. The Paris Agreement requires participating countries to submit emission inventories that are based on activities within their territory. All the inventories on Climate Watch are based on this production-based accounting.

Human-caused greenhouse gas (GHG) emissions drive climate change. This dataset includes time series of emissions data by country using four measures that differ in scope and methodology: Climate Watch, PIK PRIMAP-hist, UNFCCC and GCP. The Paris Agreement requires participating countries to submit emission inventories that are based on activities within their territory. All the inventories on Climate Watch are based on this production-based accounting. This dataset is published via Climate Watch, a free online platform designed to empower policymakers, researchers, media and other stakeholders with the open climate data, visualizations and resources they need to gather insights on national and global progress on climate change.

Under the Paris Agreement, countries are invited to communicate “mid-century long-term low greenhouse gas emissions development strategies” (long-term strategies, or LTS). These strategies are central to the goal of limiting global warming to well below 2°C and to pursue efforts to limit the increase to 1.5°C, representing a significant opportunity for countries to lay out their vision for achieving a low-carbon economy by 2050 while also pursuing sustainable development. It is advantageous for countries to align their NDCs and long-term strategies for consistency and to avoid the lock-in of carbon-intensive behavior, technologies and policies. This dataset is published via Climate Watch, a free online platform designed to empower policymakers, researchers, media and other stakeholders with the open climate data, visualizations and resources they need to gather insights on national and global progress on climate change. All the inventories on Climate Watch are based on this production-based accounting.

Under the Paris Agreement, nearly every nation made a commitment to tackle climate change and strengthen their efforts over time. To explore the content of these Nationally Determined Contributions (NDCs), search for key terms. Also, this dataset allows you to analyze and compare NDCs using over 150 structured indicators. Parties (representing 171 countries) have submitted their new or updated NDCs. This dataset is published via Climate Watch, a free online platform designed to empower policymakers, researchers, media and other stakeholders with the open climate data, visualizations and resources they need to gather insights on national and global progress on climate change. All the inventories on Climate Watch are based on this production-based accounting.

Identify potential alignment between the targets, actions, policy measures, and needs in countries' Nationally Determined Contributions (NDCs) and the Sustainable Development Goals (SDGs) targets. The analysis covers NDCs submitted prior to May 2021. This dataset is published via Climate Watch, a free online platform designed to empower policymakers, researchers, media, and other stakeholders with the open climate data, visualizations, and resources they need to gather insights on national and global progress on climate change. All the inventories on Climate Watch are based on this production-based accounting.

This API supports searching for emitting assets by sector, owner, location; query to get emissions and other asset details; and lookup of aggregated emission by countries.

Groundwater recharge indicates the existence of renewable groundwater resources and is therefore an important component in sustainability studies. However, recharge is also one of the least understood, largely because it varies in space and time and is difficult to measure directly. For most studies, only a relatively small number of measurements is available, which hampers a comprehensive understanding of processes driving recharge and the validation of hydrogeological model formulations for small- and large-scale applications. We present a new global recharge dataset encompassing >5000 locations. In order to gain insights into recharge processes, we provide a systematic analysis between the dataset and other global-scale datasets, such as climatic or soil-related parameters. Precipitation rates and seasonality in temperature and precipitation were identified as the most important variables in predicting recharge. The high dependency of recharge on climate indicates its sensitivity to climate change. We also show that vegetation and soil structure have an explanatory power for recharge. Since these conditions can be highly variable, recharge estimates based only on climatic parameters may be misleading. The freely available dataset offers diverse possibilities to study recharge processes from a variety of perspectives. By noting the existing gaps in understanding, we hope to encourage the community to initiate new research into recharge processes and subsequently make recharge data available to improve recharge predictions.

This e-book is a quick primer on earth observation of water resources and has been developed jointly by the World Bank and NASA. It provides a basic introduction to hydrologic processes and the types of in-situ and earth observation monitoring approaches to gain a global perspective to help address problems in the real world such as floods, droughts, cyclones, and forecasting for agriculture and water-related disease management applications. It provides a primer for accessing useful NASA data, modeling tools, related interactive viewers and useful links in this regard, that showcase interactive maps to visualize precipitation and even groundwater data and trends and near-real time flood potential from space. This e-book provides an illustrative overview of the use of increasingly powerful free data from satellites that can be critical for monitoring and managing watersheds and aquifers around the world.

The Global Carbon Budget tracks the trends in global carbon emissions and sinks and is a key measure of progress towards the goals of the Paris Agreement. The data and model output provided are freely available and were furnished by individual scientists who encourage their use. Citation: Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022b, ESSD) for all data. Acknowledgement: Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank [relevant modelling groups] for producing and making available their model output.”

The Global Climate Action portal is an online platform where actors from around the globe - countries, regions, cities, companies, investors and other organisations - can register their commitments to act on climate change.

Prompted by the urgency to scale up climate action, state, non-state and subnational actors have committed to facilitate the achievement of the Paris Agreement. With this Actor Tracker one can explore the individual actors and their engagements in climate action by using the different filters, or explore the progress tracking framework by navigating to a specific actor page.

The Global Climate Change Litigation database was created in 2011 and is updated regularly. It currently includes 644 cases, with links to 1278 case documents. At present, the Global database features cases from over 40 countries. The database also includes climate litigation cases brought before international or regional courts or tribunals.

Global Earth Observation Integrated Data Environment

This report analyzes water availability across all potentially commercial shale resources worldwide. It also reveals that water availability could limit shale resource development on every continent except Antarctica.

The Global Wind Atlas is a free, web-based application developed to help policymakers, planners, and investors identify high-wind areas for wind power generation virtually anywhere in the world, and then perform preliminary calculations. The Global Wind Atlas facilitates online queries and provides freely downloadable datasets based on the latest input data and modeling methodologies. Users can additionally download high-resolution maps of the wind resource potential, for use in GIS tools, at the global, country, and first-administrative unit (State/Province/Etc.) level in the Download section. Information on the datasets and methodology used to create the Global Wind Atlas can be found in the Methodology and Datasets sections.

HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) provides hydrographic information in a consistent and comprehensive format for regional and global-scale applications. HydroSHEDS offers a suite of geo-referenced data sets in raster and vector format, including stream networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances, and river topology information. Recently available data derived from HydroSHEDS include comprehensive layers of major basins and smaller sub-basins (~100-2,500 km2) across the globe. These data layers are available to support watershed analyses, hydrological modeling, and freshwater conservation planning at a quality, resolution, and extent that had previously been unachievable in many parts of the world. Data includes Void-Filled elevation, Hydrologically conditioned elevation, drainage directions, flow accumulation, river network, basin outlines, HydroBASINS License information: https://www.hydrosheds.org/page/license

IRENA Avoided Emissions Calculator1 provides an estimate of the emissions that have been avoided due to a country’s uptake of renewables in a given year. This value will vary greatly depending on the nonrenewable mix that has been replaced by renewables. This calculator therefore can only be used to provide a general indication of avoided emissions for various scenarios rather than an absolute number which will require further information on baseline electricity use and technology displacement options.

ISF is an independent research institute within the University of Technology Sydney. They conduct trans-disciplinary, project-based research in line with their vision of creating positive change towards sustainable futures.

Interactive maps and downloadable data for regional and global Geology, Geochemistry, Geophysics, and Mineral Resources, provided by USGS. Multiple useful links for materials to help understand the geology of locations.

This dataset contains monthly generation, emissions and demand data for 85 geographies representing more than 90% of global power demand. Data is collected from multi-country datasets (EIA, Eurostat, BP, UN) as well as national sources (e.g China data from the National Bureau of Statistics).

Monthly averages of global maximum surface temperatures (C) for 1981-2010 from CRU TS data, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'tmax' (temperature maximum) and the month. E.g. 'tmax April' is the mean of daily-maximum temperatures in April throughout 1981-2010.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km. This is the same as the 60km grid used by UKCP18.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global maximum surface temperatures (°C) for 2040-2069 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'tmax' (temperature maximum), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'tmax March Median' is the mean of daily-maximum temperatures in March throughout 2040-2069, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with an anomaly (the temperature change in °C relative to 1981-2010) from UKCP18.The anomaly data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2040-2069 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global maximum surface temperatures (°C) for 2070-2099 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'tmax' (temperature maximum), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'tmax March Median' is the mean of daily-maximum temperatures in March throughout 2070-2099, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with an anomaly (the temperature change in °C relative to 1981-2010) from UKCP18.The anomaly data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2070-2099 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global minimum surface temperatures (C) for 1981-2010 from CRU TS data, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'tmin' (temperature minimum) and the month. E.g. 'tmin April' is the mean of daily-minimum temperatures in April throughout 1981-2010.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km. This is the same as the 60km grid used by UKCP18.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global minimum surface temperatures (°C) for 2040-2069 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'tmin' (temperature minimum), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'tmin March Median' is the mean of daily-minimum temperatures in March throughout 2040-2069, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with an anomaly (the temperature change in °C relative to 1981-2010) from UKCP18.The anomaly data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2040-2069 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global rainfall amount (mm) for 1981-2010 from CRU TS data, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'pr' (precipitation) and the month. E.g. 'pr March' is the mean of monthly-total rainfall in March throughout 1981-2010.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km. This is the same as the 60km grid used by UKCP18.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global rainfall amount (mm) for 2040-2069 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'pr' (precipitation), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'pr March Median' is the mean of monthly-total rainfall in March throughout 2040-2069, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.Data has been removed and replaced with 'Null' where the baseline 1981-2010 value was <1mm/month. This is because the percentage change may be unreliable with a very small baseline. 'Null' means that data is not provided, it doesn't mean 0mm precipitation. The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with a percentage change relative to 1981-2010 from UKCP18. Where the baseline value was <1mm/month, the projection value has been replaced with 'Null' because the percentage change may be unreliable with a very small baseline.The percentage change data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2040-2069 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global rainfall amount (mm) for 2070-2099 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'pr' (precipitation), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'pr March Median' is the mean of monthly-total rainfall in March throughout 2070-2099, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.Data has been removed and replaced with 'Null' where the baseline 1981-2010 value was <1mm/month. This is because the percentage change may be unreliable with a very small baseline. 'Null' means that data is not provided, it doesn't mean 0mm precipitation. The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with a percentage change relative to 1981-2010 from UKCP18. Where the baseline value was <1mm/month, the projection value has been replaced with 'Null' because the percentage change may be unreliable with a very small baseline.The percentage change data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2070-2099 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global surface temperature (C) for 1981-2010 from CRU TS data, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'tas' (temperature at surface) and the month. E.g. 'tas March' is the mean of daily-mean temperatures in March throughout 1981-2010.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km. This is the same as the 60km grid used by UKCP18.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global surface temperatures (°C) for 2040-2069 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'tas' (temperature at surface), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'tas March Median' is the mean of daily-mean temperatures in March throughout 2040-2069, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with an anomaly (the temperature change in °C relative to 1981-2010) from UKCP18.The anomaly data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2040-2069 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global surface temperatures (°C) for 2070-2099 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'tas' (temperature at surface), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'tas March Median' is the mean of daily-mean temperatures in March throughout 2070-2099, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with an anomaly (the temperature change in °C relative to 1981-2010) from UKCP18.The anomaly data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2070-2099 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

The net zero tracker aims to increase transparency and accountability of net zero targets pledged by nations, states and regions, cities and companies. They collect data on targets set and on many factors that indicate the integrity of those targets — essentially, how serious the entity setting the target is about meaningfully cutting its net emissions to zero.

A collection of mapped water, disaster, climate, environment, and water-related social and economic indicators, from various sources, including: Lake Levels, Standardized Precipitation Index, GEOGloWS Streamflow Explorer, City Water Intakes, Glacker Inventory, Surface Water Explorer, Cropping Extent, Water Conflicts, Population Density, Land Cover Comparison, Water Stress (Aqueduct), Livestock Density, GRACE Groundwater, Forest Cover, Satellite Temperature, MODIS Layers, Night LIghts, Satellite Precipitation (NASA GPM), Hydroviewer, Global urban Footprint, Watershed Mapper, Annual Precipitation, Soil Moisture, Water Transition, Global Wetlands, Land-Water Changes, Snow Cover, Martime Boundaries, Illustrative Watershed Tool, Water Security Dashboard, Major Ports, World Water Quality, World Wind Map, ESRI WATER Balance, GEOGRLAM RAPP, GEOSS Portal, GRACE Groundwater Portal, Open Street Map Data, Servir Tehys Apps, NASA Disasters Portal, GEOGLOWS Streamflow Estimater, GFMS, MODIS Flood Explorer, IWMI Water Data Portal, GDACS Tool, GDACS Floods, Flood Exposure, Country Data Visualizations, Drought Exposure, Flood Risk, NOAA Buoy Data Center, Flood Frequency, Flood Observatory, Climate Engine, GADS (Agriculture) Climate (KNMI) Stations, Fishing Watch, MIT Trad Atlas, GRDC, Climate Stations, Dams, Hydropower Plants, All Power Plants, Tsunamis, Groundwater Climate Vulnerability, Climate Change Knowledge Portal, Projected Dry Spells, Protected Areas, Erosion Risks, Toxic Sites, Water Quality (Chlorophyll-A), Sea Surface Temperature, Crop Nutrient Balance, Landsat Archive, WorldPop, UN Cities Population, Google Earth Engine Datasets, Open Data Cube, FAO WaPOR, CIESIN Pop Grid, DFO App, Google Earth

Teal is a free visual tool that enables you to explore climate variables for the past 70+ years, from 1950 to near real time at annual, monthly, seasonal, and daily frequency.

The AGS Utilities Tool offers schema validation, data validation and conversion of geotechnical AGS files. The API is publicly available for use in stakeholders’ own analysis, processing or software.

The Global Systemic Risk Assessment Tool (G-SRAT) is a data and analytics portal covering hazards, exposure, vulnerability and risk to infrastructure and people around the world. This tool aims to support climate adaptation decision-making by identifying spatial vulnerabilities and risks under current and future climate scenarios.

In accordance with Articles 4 and 12 of the Climate Change Convention and the relevant decisions of the Conference of the Parties, countries that are Parties to the Convention submit national greenhouse gas (GHG) inventories to the Climate Change Secretariat. These submissions are made in accordance with the reporting requirements adopted under the Convention, such as the revised “Guidelines for the preparation of national communications by Parties included in Annex I to the Convention, Part I: UNFCCC reporting guidelines on annual greenhouse gas inventories” (decision 24/CP.19) for Annex I Parties and “Guidelines for the preparation of national communications for non-Annex I Parties” (decision 17/CP.8). The inventory data are provided in the annual GHG inventory submissions by Annex I Parties and in the national communications and biennial update reports by non-Annex I Parties.

"The UNFCCC secretariat has prepared this greenhouse gas (GHG) emissions calculator to provide the general public with a free and up-to-date methodology for estimating GHG emissions. This Spreadsheet aims only to support organisations to estimate their GHG emissions in order to raise awareness and to promote climate action. However, the UNFCCC secretariat makes no representations as to the accuracy, completeness, suitability or validity of any information on this Spreadsheet and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an “as-is” basis. All data and information provided on this Spreadsheet are for reference purposes only. The emission factors used on this Spreadsheet are publicly available on third parties’ websites and the links are provided in the tab ‘Info and sources’. The contents in, and linked to, this spreadsheet do not reflect the policy or position of the UNFCCC nor the UNFCCC secretariat’s and do not imply UNFCCC secretariat’s endorsement. Under no circumstances shall the UNFCCC be liable for any loss, damage, liability or expense incurred or suffered that is claimed to have resulted from the use of this Spreadsheet, its data or its methodology, or from the conduct of any user. Use of this Spreadsheet and reliance upon the content in or linked to it is solely at the user’s own risk.

From the site: "Information on carbonatite deposits containing niobium and rare earth elements from around the world with grade and tonnage models, geologic setting, mineralogy, with data allowing locations of these deposits to be plotted using GIS."

From the site: "MRDS is a collection of reports describing metallic and nonmetallic mineral resources throughout the world. Included are deposit name, location, commodity, deposit description, geologic characteristics, production, reserves, resources, and references. It subsumes the original MRDS and MAS/MILS. MRDS is large and complex. This service provides a subset of the database comprised of those data fields deemed most useful and which most frequently contain some information, but full reports of most records are available as well."

From the site: "Geochemical analysis of rock samples collected and analyzed by the USGS. This dataset includes and supersedes rock data formerly released as "Geochemistry of igneous rocks in the US extracted from the PLUTO database". The database contains 414,304 records."

From the site: "Location, geologic and mineral economic data for world rare earth mines, deposits, and occurrences compiled from published and non-published sources. Geographic coordinates are provided for 577 of the 799 deposits described here."

From the site: "Information on sediment-hosted zinc-lead deposits from around the world with grade and tonnage models, a general classification based on geologic setting, mineralogy, with data allowing locations of these deposits to be plotted using GIS."

CKAN-compatible API to the WRI’s data catalog.

This dataset contains yearly electricity generation, capacity, emissions, import and demand data for over 200 geographies. Data is collected from multi-country datasets (EIA, Eurostat, BP, UN) as well as national sources (e.g China data from the National Bureau of Statistics).