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Slides presented at the 102 Annual American Meteorological Society Meeting, as part of the session "Major Weather Events and Impacts of 2021" (paper 6.3 - It's Getting Hot in Here: Real-Time Climate Fingerprints Applied to the 2021 Extreme Heat Season) For more information, please reach out to Daniel Gilford at dgilford@climatecentral.org. Presentation Abstract: Extreme heat was observed and experienced across large portions of the United States in 2021, including during notable record-breaking events in the Pacific Northwest, the Southwest, and along the East coast. The contiguous US experienced its hottest June on record, and excess heat related deaths stretched into the thousands. While more frequent and intense periods of extreme heat are expected consequences of anthropogenic climate change, rapidly and continuously assessing the degree to which human emissions of greenhouse gases increase the likelihood of a specific event remains a challenging technical process. In this study we introduce the Realtime Climate attribution framework and illustrate its application through an analysis of observed 2021 extreme heat events. The framework implements one model-based and two observation-based approaches to produce three distinct attribution assessments, including best estimates and uncertainties. The framework is designed to be flexible across a range of variables and scales, computationally lightweight, and adaptable for impact studies. Using a suite of global climate models, observed global mean temperatures, and local observed daily temperatures, we quantify the extent to which human-driven climate change made 2021 maximum and minimum daily temperature extremes more likely across the United States. Results confirm the continued and growing influence of human-driven climate change in local weather extremes. For instance, we find that the record-breaking high temperatures in June near Phoenix, AZ, were at least 3.25 times more likely because of human activity. Through this framework, we are building the capacity to produce attribution estimates while an event is unfolding. Furthermore, the ability to estimate attribution levels continuously will enhance studies of extreme heat impacts on human health, along with other socioeconomic or influences.

------------------------------------------------------------------------------------------------------------------------------------------- This version has been superseded. The latest version is at https://doi.org/10.5281/zenodo.5517550 ------------------------------------------------------------------------------------------------------------------------------------------- Eddy covariance flux tower datasets of all Urban-PLUMBER sites, associated with the manuscript: "Harmonized, gap-filled dataset from 20 urban flux tower sites" Use of any data must give credit through citation of the above manuscript and other sources as appropriate. We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant contacts are included in timeseries metadata. For site information and timeseries plots see https://urban-plumber.github.io/sites. For processing code see https://github.com/matlipson/urban-plumber_pipeline. Within each site folder: - `index.html`: A summary page with site characteristics and timeseries plots. - `SITENAME_sitedata_vX.csv`: comma seperated file for numerical site characteristics e.g. location, surface cover fraction etc. - `timeseries/` (following files available as netCDF and txt) - `SITENAME_raw_observations_vX`: site observed timeseries before project-wide quality control. - `SITENAME_clean_observations_vX`: site observed timeseries after project-wide quality control. - `SITENAME_metforcing_vX`: site observed timeseries after project-wide quality control and gap filling. - `SITENAME_era5_corrected_vX`: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset. - `log_processing_SITENAME_vX.txt`: a log of the print statements through running the create_dataset_SITENAME scripts. Authors Mathew Lipson, Sue Grimmond, Martin Best, Andreas Christen, Andrew Coutts, Ben Crawford, Bert Heusinkveld, Erik Velasco, Helen Claire Ward, Hirofumi Sugawara, Je-Woo Hong, Jinkyu Hong, Jonathan Evans, Joseph McFadden, Keunmin Lee, Krzysztof Fortuniak, Leena Järvi, Matthias Roth, Nektarios Chrysoulakis, Nigel Tapper, Oliver Michels, Simone Kotthaus, Stevan Earl, Sungsoo Jo, Valéry Masson, Winston Chow, Wlodzimierz Pawlak, Yeon-Hee Kim. Corresponding author: Mathew Lipson <m.lipson@unsw.edu.au>

# ggACene (global gridded Air Conditioning energy) projections ### Output AC and AC electricity gridded data This repository hosts output data for SSPs126, 245, 370 and 585 on the estimated and future projected ownership of residential air conditioning (% of households), the related energy consumption (TWh/yr.), and the underlying population counts (useful to quantify the per-capita average consumption or the headcount of people affected by the cooling gap). These data are contained in the multi-layer .nc (NCDF) files, which can be opened and processed in any GIS software/library, or visualised in softwares such as Panoply. ### Input data and analysis replication The repository also hosts input data to replicate the entire data generating process. A twin Github repository hosts code (https://github.com/giacfalk/ggACene) to run the model generating the ggACene (global gridded Air Conditioning energy) projections dataset. ## InstructionsTo reproduce the model and generate the dataset from scratch, please refer to the following steps:- Download input data "replication_package_input_data.7z" by cloning the repository- Decompress the folder using 7-Zip (https://www.7-zip.org/download.html)- Open RStudio and adjust the path folder in the sourcer.R script- Run the sourcer.R script to train the ML model, make projections, and represent result files ### Figures replication package Finally, the source_code_data_replication_figures.zip archive contains an R script and processed input data to replicate all the figures contained in the manuscript. ### ReferenceFalchetta, G., De Cian, E., Pavanello, F., & Wing, I. S. Inequalities in global residential cooling energy use to 2050. Nature Communications. https://www.nature.com/articles/s41467-024-52028-8

The potential for global forest cover The restoration of forested land at a global scale could help capture atmospheric carbon and mitigate climate change. Bastin et al. used direct measurements of forest cover to generate a model of forest restoration potential across the globe (see the Perspective by Chazdon and Brancalion). Their spatially explicit maps show how much additional tree cover could exist outside of existing forests and agricultural and urban land. Ecosystems could support an additional 0.9 billion hectares of continuous forest. This would represent a greater than 25% increase in forested area, including more than 200 gigatonnes of additional carbon at maturity.Such a change has the potential to store an equivalent of 25% of the current atmospheric carbon pool. Science , this issue p. 76 ; see also p. 24

The data is available for download at the AR6 Scenario Explorer hosted by IIASA. As part of the IPCC's 6th Assessment Report (AR6), authors from Working Group III on Mitigation of Climate Change undertook a comprehensive exercise to collect and assess quantitative, model-based scenarios related to the mitigation of climate change. Building on previous assessments, such as those undertaken for the 5th Assessment Report (AR5) and the Special Report on Global Warming of 1.5°C (SR15), the calls for AR6 for scenarios have been expanded and includes economy-wide GHG emissions, energy, and sectoral scenarios from global to national scales, thus more broadly supporting the assessment across multiple chapters (see Annex III, Part 2 of the WGIII report for more details). The compilation and assessment of the scenario ensemble was conducted by authors of the IPCC AR6 report, and the resource is hosted by the International Institute for Applied Systems Analysis (IIASA) as part of a cooperation agreement with Working Group III of the IPCC. The scenario ensemble contains 3,131 quantitative scenarios with data on socio-economic development, greenhouse gas emissions, and sectoral transformations across energy, land use, transportation, buildings and industry. These scenarios derive from 191 unique modelling frameworks, 95+ model families that are either globally comprehensive, national, multi-regional or sectoral. The criteria for submission included that the scenario is presented in a peer-reviewed journal accepted for publication no later than October 11th, 2021, or published in a report determined by the IPCC WG III Bureau to be eligible grey literature by the same date. The AR6 scenario database is documented in Annex III.2 of the Sixth Assessment Report of Working Group III. For the purpose of the assessment, scenarios have been grouped in various categories relating to, among other things, climate outcomes, overshoot, technology availability and policy assumptions. For ease of use, the dataset is split into multiple files: Scenarios data for the Global region Scenarios data for R5 regions Scenarios data for R6 regions Scenarios data for R10 regions Scenarios data for ISO-3 (country) regions Global metadata indicators file National metadata indicators file The data is available for download at the AR6 Scenario Explorer hosted by IIASA. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information. In addition to the data you may find more relevant information and cite one of the relevant chapters of the WG III report. If working with global or regional (R6, R10) data: Keywan Riahi, Roberto Schaeffer, et al. Mitigation Pathways Compatible with Long-Term Goals, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ If working with national data (ISO region data): Franck Lecocq, Harald Winkler, et al. Mitigation and development pathways in the near- to mid-term, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ If you find the metadata files particularly useful: Celine Guivarch, Elmar Kriegler, Joana Portugal Pereira, et al. Annex III: Scenarios and Modelling Methods, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ Scenarios data also supports analysis in Chapters 2, 5, 6, 7, 9, 10, 12 and 15

Meeting future food demand will require transformations toward sustainable and resilient food systems that simultaneously increase production, minimize environmental impacts, and adapt to climate change. With fluctuations in temperature and precipitation exercising a growing influence on production stability across the planet, a detailed understanding of where cropping patterns are vulnerable to climatic stresses is a missing yet critical step for developing solutions that enhance the climate resilience of crop production. Here we address this urgent need by combining gridded climate data, spatially-explicit agricultural statistics, and process-based crop modeling to quantify global patterns of rainfed and irrigated crop climate sensitivity (measured as the percent reduction in median yield under extreme climate conditions) and climate-associated production losses for 17 major crops, accounting for 75% of global primary production. This climate sensitivity metric is ideally suited for identifying locations where each crop tends to be subject to high climate variability and where crop production may be susceptible to high climate-related production losses. We estimate -10.1% and -6.8% losses in global rainfed and irrigated production (respectively) under historically observed extreme climate conditions - enough calories to feed 2.1 billion people - and find hotspots of climate sensitivity in the central US, eastern Brazil, the Mediterranean basin, and South Asia, among other regions. We then focus on monsoon cereals (rice, maize, millet, sorghum) to illustrate how sustainable irrigation expansion and targeted crop switching could reduce climate sensitivity, finding that 62% of production losses could be avoided while increasing overall production by 14%. Our new scalable and universal approach to measuring the climate sensitivity of crops enables the assessment of where climate-related production losses tend to be largest and where mitigating actions and investments can be proactively targeted to better ensure the stability and increased supply of global crop production.

The data is available for download at the AR6 Scenario Explorer hosted by IIASA.<<< click here. As part of the IPCC's 6th Assessment Report (AR6), authors from Working Group III on Mitigation of Climate Change undertook a comprehensive exercise to collect and assess quantitative, model-based scenarios related to the mitigation of climate change. Building on previous assessments, such as those undertaken for the 5th Assessment Report (AR5) and the Special Report on Global Warming of 1.5°C (SR15), the calls for AR6 for scenarios have been expanded and includes economy-wide GHG emissions, energy, and sectoral scenarios from global to national scales, thus more broadly supporting the assessment across multiple chapters (see Annex III, Part 2 of the WGIII report for more details). The compilation and assessment of the scenario ensemble was conducted by authors of the IPCC AR6 report, and the resource is hosted by the International Institute for Applied Systems Analysis (IIASA) as part of a cooperation agreement with Working Group III of the IPCC. The scenario ensemble contains 3,131 quantitative scenarios with data on socio-economic development, greenhouse gas emissions, and sectoral transformations across energy, land use, transportation, buildings and industry. These scenarios derive from 191 unique modelling frameworks, 95+ model families that are either globally comprehensive, national, multi-regional or sectoral. The criteria for submission included that the scenario is presented in a peer-reviewed journal accepted for publication no later than October 11th, 2021, or published in a report determined by the IPCC WG III Bureau to be eligible grey literature by the same date. The AR6 scenario database is documented in Annex III.2 of the Sixth Assessment Report of Working Group III. For the purpose of the assessment, scenarios have been grouped in various categories relating to, among other things, climate outcomes, overshoot, technology availability and policy assumptions. The AR6 Scenarios Database is jointly published by the Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis. The data is available for download at the AR6 Scenario Explorer hosted by IIASA.<<< click here. For ease of use, the database is provided as multiple files: Filename Description Region coverage Uncompressed Size (MB) Standard files for assessment AR6_Scenarios_Database_World_v1.1.csv All data reported for the World region, primarily from integrated assessment models (IAMs), as well as variables from the climate assessment World only 353 AR6_Scenarios_Database_R5_regions_v1.1.csv All data reported and aggregated to R5 regions, primarily from IAMs. 5 global regions 847 AR6_Scenarios_Database_R6_regions_v1.1.csv All data reported and aggregated to R6 regions (as preferred by IPCC), primarily from IAMs. 6 global regions 408 AR6_Scenarios_Database_R10_regions_v1.1.csv All data reported and aggregated to R10 regions, primarily from IAMs. 10 global regions 1,266 AR6_Scenarios_Database_ISO3_v1.1.csv Ass data reported at the country level, primarily from national integrated assessment and energy systems models, but also IAMs for major countries. Country level 1,155 AR6_Scenarios_Database_metadata_indicators_v1.1.xlsx Wide range of categorical and numerical indicators calculated for each model-scenario. Primarily world data 3 Additional "climate assessment" files New in v1.1 AR6_Scenarios_Database_World_ALL_CLIMATE_v1.1.csv Same as World snapshot above, but with all the climate assessment data for MAGICC and FaIR models included World only 3,006 AR6_Climate_Diagnostics_CICERO-SCM_v1.1.csv Climate assessment data for the CICERO-SCM model World only 743 AR6_Climate_Diagnostics_metadata_indicators_v1.1.xlsx Full set of categorical and numerical indicators relating to the climate assessment, calculated for each model-scenario World only 2 AR6_historical_emissions.csv Historical CO2 and GHGs for world region used in climate assessment World only 0.01 The data is available for download at the AR6 Scenario Explorer hosted by IIASA. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information. In addition to the data you may find more relevant information and cite one of the relevant chapters of the WG III report. If working with global or regional (R6, R10) data: Keywan Riahi, Roberto Schaeffer, et al. Mitigation Pathways Compatible with Long-Term Goals, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ If working with national data (ISO region data): Franck Lecocq, Harald Winkler, et al. Mitigation and development pathways in the near- to mid-term, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ If you find the metadata files particularly useful: Celine Guivarch, Elmar Kriegler, Joana Portugal Pereira, et al. Annex III: Scenarios and Modelling Methods, in "Mitigation of Climate Change". Intergovernmental Panel on Climate Change, Geneva, 2022. url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ Scenarios data also supports analysis in the Summary for Policy Makers, Technical Summary and Chapters 2, 5, 6, 7, 9, 10, 12 and 15. Climate assessment of global emissions pathways The climate assessment of the long-term global emissions scenarios was undertaken as part of the Chapter 3 assessment. The workflow is available at https://github.com/iiasa/climate-assessment and published in Kikstra et al. 2022. The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures. Geoscientific Model Development https://doi.org/10.5194/egusphere-2022-471. Scripts for this assessment are at https://doi.org/10.5281/zenodo.7304736 For these purposes, the full climate assessment data is provided, as documented in the table above. Release notes for v1.1 Following feedback and identification of some issues between the versions available to authors in preparation of the published report and the v1.0 public release, updates are made to v1.1.Changes made here are made with the intention of facilitating and improving the reproducibility of the IPCC report. There are no resulting corrections to the report and its findings, as these issues were identified by authors and manually addressed. Full list of release notes is published on the Downloads page https://data.ene.iiasa.ac.at/ar6/#/downloads The data is available for download at the AR6 Scenario Explorer hosted by IIASA.<<< click here.

This represents the necessary input data for running the California Biomass Residue Emissions Characterization (C-BREC) Model. This is supplemental data for C-BREC release v1.2.1. The C-BREC Model code can be found on GitHub at https://github.com/schatzcenter/CBREC.