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The document outlines a 20-hour self-guided virtual course on satellite data for environmental monitoring, specifically targeting researchers in Latin America and the Caribbean. The curriculum focuses on using Google Earth Engine to analyze tropical forests, land cover, and climate extremes through eight technical modules and six sessions. Participants engage in case studies involving deforestation, drought modeling, and air pollution, culminating in a final assessment requiring a 70% score for certification.

The report identifies critical gaps in the management of climate and biodiversity information in the Andean region, noting that data is often too technical, fragmented, or inaccessible for policymakers. It emphasizes the need to integrate traditional local knowledge with scientific data and proposes the implementation of National Environmental Information Systems to bridge the gap between long-term scientific research and immediate political needs. Using Colombia's SIAC as an example, the authors suggest that framing biodiversity conservation through the lens of ecosystem services and social stability is more effective for securing political support and sustainable development.

The 'Satellite Data to Monitor Regional Environmental Change' course is a 20-hour self-paced online program for Latin American researchers. It focuses on using Google Earth Engine to analyze multi-decadal satellite records, with specific applications in tropical forest health, deforestation, forest fire monitoring, and climate projection. The curriculum is divided into five modules covering image processing, time series analysis, vector data calculations, land cover change, and the creation of public-facing interactive applications.

The project addresses the lack of long-term climate data in the tropical Andes by combining dendrochronology, improved hydrometeorological networks, in-situ digital sensors, and global circulation models. These diverse data streams—including tree-ring chronologies spanning 100 to 200 years and simulations for 2011-2030 and 2045-2065—are integrated to reduce uncertainty in climate projections and inform the NatureServe vulnerability index for conservation management.

The IISD Experimental Lakes Area (ELA) is a research facility in northwestern Ontario consisting of 58 lakes and their watersheds used for whole-ecosystem experiments. For over 50 years, the facility has provided long-term data sets used for global climate modelling and has influenced environmental policies, such as those restricting phosphorus to prevent algal blooms. Current and past research focuses include the impacts of climate change, oil spills (specifically diluted bitumen), microplastics, pharmaceuticals (such as synthetic estrogen), and other contaminants like mercury and acid rain.

The document compiles qualitative feedback from scientists at various U.S. federal agencies and the National Center for Atmospheric Research (NCAR) on the state of climate research integrity. The responses highlight systemic concerns regarding political interference, funding cuts, and restrictions on public communication, while some scientists express dissenting views on the extent of these issues or the validity of certain climate models.

The report projects substantial warming and shifting precipitation patterns for the US Midwest, with extreme heat events and heavy rainfall becoming more frequent. Under a higher emissions scenario, end-of-century temperatures could rise by up to 10°F, significantly increasing the frequency of severe heat waves and reducing snow cover. While annual precipitation may only increase slightly, winter and spring are expected to become wetter, while summer precipitation may decrease under higher emissions. These changes are projected to increase heat-related mortality and flooding risks across the region.

The document details the technical framework of Aqueduct's future projections, which utilize the HYPFLOWSCI6 dataset and five General Circulation Models (GCMs) to estimate water supply, use, stress, depletion, and variability. It describes three socioeconomic and climate scenarios (SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5) with temperature projections ranging from 1.3°C to 5.7°C by 2100. The guide emphasizes that these scenarios represent different development pathways rather than simple best- or worst-case outcomes, noting that higher economic activity in optimistic scenarios can actually increase water stress. Technical processing includes spatial aggregation at the HydroBASINS Level 6 sub-basin level and temporal aggregation using 10-year trailing moving averages for the years 2030, 2050, and 2080. Limitations are noted regarding irrigation data (projected only to 2050) and livestock water demand (held constant after 2014).

The document argues that open data—datasets freely available for use and redistribution—is essential for a coordinated, whole-of-society response to climate change. It identifies five primary benefits of open data: improved data coordination and quality, more informed decision-making, the creation of novel partnerships, the democratization of climate modeling, and enhanced monitoring of policies and programs. The paper draws on a literature review, expert consultations, and pilot projects in Chile and Uruguay (conducted between August 2019 and August 2020) to provide a methodology for stakeholders to assess and improve the openness of climate-relevant datasets.

The document outlines a framework for nonexperts to evaluate climate mitigation models, emphasizing that no single model is ideal for all policy questions. It suggests assessing studies across seven dimensions: primary goals, scope, modeling framework, scenarios and data inputs, uncertainty, results, and process aspects to ensure transparency regarding assumptions and inputs.

The document examines the purpose, elements, and implementation of long-term climate strategies, emphasizing the need to align near-term actions (NDCs) with mid-century goals. It covers the role of modeling, the integration of adaptation and sustainable development, the necessity of climate-aligned finance, and the importance of a 'just transition' for workers in carbon-intensive industries.

The document serves as a manual for journalists to avoid common errors when linking specific weather events to climate change. It details the methodology of 'event attribution studies'—which use climate models to compare the current world with a pre-industrial one—and provides specific guidance on reporting heatwaves, floods, tropical cyclones, heavy snowfall, droughts, and wildfires. The guide emphasizes that while climate change increases the probability and intensity of many extremes, it is rarely the sole cause, and local factors like land use and water management often play critical roles in turning a weather event into a disaster.

The document explains the methodology of 'extreme event attribution' to help journalists avoid common errors, such as ignoring climate change or attributing an event to it as a sole cause. It details how scientists use climate models to compare the modern world with a pre-industrial one to determine if an event was more likely or intense due to global warming. The guide provides specific scientific consensus and limitations for heatwaves, floods, tropical cyclones, heavy snowfall, droughts, and wildfires, emphasizing that while some connections are robust (e.g., heatwaves), others are more complex and depend on regional data and human vulnerability.

The document explains the science of extreme event attribution, detailing how researchers use climate models and historical data to determine if climate change made a specific event more likely or intense. It provides specific guidance on reporting various phenomena—heatwaves, floods, tropical cyclones, snowfall, droughts, and wildfires—highlighting where scientific confidence is high and where uncertainties remain.

The study finds that the July 2019 heatwave in Western Europe was made significantly more likely and intense by human-induced climate change. In France and the Netherlands, the event would have been extremely improbable without human influence, with return periods exceeding 1,000 years. Across all analyzed locations, the heatwave was 1.5 to 3 °C hotter than it would have been in an unchanged climate. The analysis utilized a combination of observational data and eight climate model ensembles, noting that while models generally underestimate temperature trends and overestimate variability, they still simulate large probability changes.

The 2022 UK heatwave, which saw temperatures reach 40.3°C in Coningsby, was made at least 10 times more likely by human-caused climate change. Observational analysis indicates such an event would have been about 4°C cooler in pre-industrial times, though climate models systematically underestimate this trend. The event had significant impacts on human health, with projected excess mortality of over 840 people in England and Wales, and exposed critical vulnerabilities in UK infrastructure and urban planning.

The document explains 'extreme event attribution'—the scientific process of determining how climate change affects the likelihood and intensity of specific weather events. It provides a framework for journalists to avoid common reporting errors, such as ignoring climate change or attributing an event to it without evidence. The guide details the specific influence of global warming on heatwaves, floods, tropical cyclones, heavy snowfall, droughts, and wildfires, noting that while some links (like heatwaves) are globally certain, others (like droughts and floods) vary by region and are influenced by socio-economic vulnerabilities.

The study concludes that the June 2019 heat wave in France was made at least five times more likely due to human-induced climate change. Observational data suggests a much higher increase in probability and a temperature rise of approximately 4ºC compared to a century ago, though climate models generally show lower trends and systematic biases in representing heat wave variability.