{"query_echo":{"q":"","filters":{"climate_tags":["temperature"]},"warnings":[]},"total":{"value":17,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?climate_tags=temperature","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"clipolin-001024","url":"https://climateinsightsdirectory.org/document/clipolin-001024","title":"Assessing Climate Risks to India’s Power Sector: A District-Level and Asset-Level Methodology for Climate-Resilient Power Systems","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"report","language":"en","date_published":"2026-05","source_url":"https://www.climatepolicyinitiative.org/wp-content/uploads/2026/05/Annexure-Assessing-Climate-Risks-to-Indias-Power-Sector.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The report details a framework for assessing climate risks to India's power sector, distinguishing between chronic stresses (e.g., temperature, drought) and acute extremes (e.g., floods, cyclones). It introduces the Asset-Specific Climate Risk Factor (ACRF) for spatial screening and Expected Annual Impact (EAI)—measured as Expected Annual Loss (EAL) for generation and Energy Not Served (ENS) for networks—to quantify operational disruptions in MWh. The methodology includes a cost-benefit analysis (CBA) framework to evaluate the economic viability of resilience measures by comparing investment costs against avoided energy losses and repair expenditures.","ai_generated":true,"unit":"clipolin-001024#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology employs a 'hazard–exposure–vulnerability' construct to generate two primary outputs: the Asset-Specific Climate Risk Factor (ACRF), a dimensionless index for spatial risk mapping, and the Expected Annual Impact (EAI), which quantifies energy losses in MWh per year.","ai_generated":true,"unit":"clipolin-001024#insight-1","date_modified":"2026-10-23"},{"text":"Climate hazards are categorized into chronic and acute pathways based on their physical impact mechanisms. Chronic hazards, including temperature, rainfall, wind speed, and drought-induced water stress, cause gradual performance degradation or operational constraints. Acute hazards, such as floods, cyclones, and landslides, cause sudden physical failures and forced outages.","ai_generated":true,"unit":"clipolin-001024#insight-2","date_modified":"2026-10-23"},{"text":"Asset exposure is defined differently across the power sector: generation assets are mapped to a single district based on rated installed capacity; transmission assets are distributed across districts based on line length; and distribution assets are mapped to a single district based on average load served.","ai_generated":true,"unit":"clipolin-001024#insight-3","date_modified":"2026-10-23"},{"text":"The framework quantifies operational impacts through Expected Annual Loss (EAL) for generation assets and Energy Not Served (ENS) for transmission and distribution assets. ENS incorporates a redundancy/load-transfer factor to account for the grid's ability to reroute power, preventing the overestimation of impacts in meshed networks.","ai_generated":true,"unit":"clipolin-001024#insight-4","date_modified":"2026-10-23"},{"text":"A cost-benefit analysis (CBA) framework is provided to evaluate resilience measures, such as flood protection walls or equipment hardening. It compares the present value of lifecycle costs (CAPEX and O&M) against the discounted stream of avoided energy losses and avoided direct repair and replacement expenditures.","ai_generated":true,"unit":"clipolin-001024#insight-5","date_modified":"2026-10-23"}],"climate_tags":["Climate risk assessment","electric power industry","climate resilience","temperature","flood","tropical cyclone","landslide"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","risk framework","economic appraisal"]},{"id":"climcent-000013","url":"https://climateinsightsdirectory.org/document/climcent-000013","title":"More Frequent Fire Weather","publisher":{"name":"Climate Central","organization":"climate-central"},"document_type":"research paper","language":"en","date_published":"2025-05-07","source_url":"https://www.climatecentral.org/climate-matters/more-frequent-fire-weather","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The analysis reveals a significant increase in fire weather days across much of the contiguous U.S., with the western U.S. experiencing far more frequent fire weather than the east. While the western U.S. has seen strong increases, particularly in the Southwest during spring and summer, parts of the eastern U.S. have also seen impactful increases, especially in the Southeast and Northeast. The study emphasizes that while human activity causes 87% of wildfires, fire weather primes the landscape for these fires to ignite and spread.","ai_generated":true,"unit":"climcent-000013#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The western U.S. experiences significantly more fire weather than the eastern U.S., averaging 31 fire weather days annually compared to 8 days in the east.","ai_generated":true,"unit":"climcent-000013#insight-1","date_modified":"2026-10-23"},{"text":"Since 1973, the western U.S. has seen a strong increase in annual fire weather days, with some areas in Southern California, New Mexico, Texas, and Arizona experiencing an increase of approximately two months per year. Some locations in California, Nevada, New Mexico, and Texas now see fire weather twice as often as in the early 1970s.","ai_generated":true,"unit":"climcent-000013#insight-2","date_modified":"2026-10-23"},{"text":"Seasonal increases in fire weather are most prominent during spring across much of the country. In the western U.S., the Southwest has seen the largest jumps in spring and summer, with parts of New Mexico, Arizona, and Colorado gaining about a month of fire weather in spring, and Southern California, Utah, and Arizona gaining about three weeks in summer.","ai_generated":true,"unit":"climcent-000013#insight-3","date_modified":"2026-10-23"},{"text":"In the eastern U.S., while many areas saw only a few additional days of fire weather since 1973, the Northeast and Southeast have experienced the greatest increases. Specifically, northern New Jersey and coastal New York have seen at least 10 additional days annually, and the northern Piedmont area in North Carolina has seen nearly two additional weeks.","ai_generated":true,"unit":"climcent-000013#insight-4","date_modified":"2026-10-23"},{"text":"Human activity was responsible for 87% of U.S. wildfires from 2001 to 2024, accounting for nearly half of all burned acres, though fire weather conditions (temperature, relative humidity, and wind) are what prime the landscape for these fires to spread.","ai_generated":true,"unit":"climcent-000013#insight-5","date_modified":"2026-10-23"}],"climate_tags":["wildfire","climate change","temperature"],"geography_discussed":["Nevada","New Mexico","Washington","Oregon","California","Texas","Arizona","North Dakota","South Dakota","Colorado","Utah","Rhode Island","Florida","Connecticut","Michigan","Wisconsin","Kentucky","Indiana","Maine","North Carolina","New Jersey","New York"],"frame_perspective":[],"analysis_type":["trend analysis","meteorological analysis"]},{"id":"iai-000055","url":"https://climateinsightsdirectory.org/document/iai-000055","title":"El Niño-driven changes in global fire 2015/16","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"research paper","language":"en","date_published":"2025-01-09","source_url":"https://iai.int/es/pub_acad/el-nino-driven-changes-in-global-fire-2015-16/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The study finds that the 2015/16 El Niño led to a 4% increase in global burned area and a 5% increase in fire emissions compared to a 'No El Niño' scenario, resulting in a 4% reduction in the terrestrial carbon sink. South America experienced the most significant impact, with a 13% increase in burned area and emitted carbon, primarily driven by temperature increases between August and October in central-southern Brazil.","ai_generated":true,"unit":"iai-000055#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The 2015/16 El Niño caused a global increase in burned area by 4% and fire emissions by 5% when compared to a scenario without El Niño, which contributed to a 4% decrease in the terrestrial carbon sink.","ai_generated":true,"unit":"iai-000055#insight-1","date_modified":"2026-10-23"},{"text":"South America exhibited the most substantial fire response to the El Niño, recording a 13% increase in both burned area and emitted carbon, which aligned with the most significant decrease in carbon uptake.","ai_generated":true,"unit":"iai-000055#insight-2","date_modified":"2026-10-23"},{"text":"In central-southern Brazil, peak fire activity occurred from August to October, with temperature identified as the primary driver for the increase in burned area during this timeframe.","ai_generated":true,"unit":"iai-000055#insight-3","date_modified":"2026-10-23"},{"text":"The model indicates variable precipitation responses, with drier conditions in East Asia, southern Africa, and northern South America, while most global areas experienced temperature increases.","ai_generated":true,"unit":"iai-000055#insight-4","date_modified":"2026-10-23"}],"climate_tags":["El Niño southern oscillation","wildfire","carbon sink","precipitation","temperature"],"geography_discussed":["South America","Africa","Asia","Brazil"],"frame_perspective":[],"analysis_type":["simulation","comparative analysis"]},{"id":"wwatt-000091","url":"https://climateinsightsdirectory.org/document/wwatt-000091","title":"Climate change more than doubled the likelihood of extreme fire weather conditions in eastern Canada","publisher":{"name":"World Weather Attribution","organization":"world-weather-attribution"},"document_type":"research paper","language":"en","date_published":"2023-08-22","source_url":"https://www.worldweatherattribution.org/climate-change-more-than-doubled-the-likelihood-of-extreme-fire-weather-conditions-in-eastern-canada/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The research finds that human-induced climate change significantly increased the intensity and probability of extreme fire weather in eastern Canada in 2023, particularly in Québec, where the cumulative severity of the fire season was approximately 50% more intense and at least seven times more likely to occur.","ai_generated":true,"unit":"wwatt-000091#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"In the current climate, the intense fire weather observed between May and July 2023 is considered a moderately extreme event with a 4-5% annual probability, meaning it is expected to occur once every 20-25 years.","ai_generated":true,"unit":"wwatt-000091#insight-1","date_modified":"2026-10-23"},{"text":"Human-induced climate change increased the cumulative severity of the 2023 fire season in Québec up to the end of July by approximately 50% and made seasons of this severity at least seven times more likely.","ai_generated":true,"unit":"wwatt-000091#insight-2","date_modified":"2026-10-23"},{"text":"Peak fire weather intensity increased by about 20% due to human-induced climate change, and such peak events are now at least twice as likely to occur.","ai_generated":true,"unit":"wwatt-000091#insight-3","date_modified":"2026-10-23"},{"text":"The extreme fire weather was driven by human-induced increases in temperature and decreases in humidity, which were further compounded in 2023 by unusually low precipitation.","ai_generated":true,"unit":"wwatt-000091#insight-4","date_modified":"2026-10-23"},{"text":"The 2023 wildfires caused significant societal impacts, including at least 17 direct fatalities, the evacuation of over 150,000 people, and damage to at least 200 structures. Air quality was severely degraded across North America, with New York City reaching a hazardous AQI of 341 on June 7th.","ai_generated":true,"unit":"wwatt-000091#insight-5","date_modified":"2026-10-23"}],"climate_tags":["wildfire","air quality","temperature","precipitation"],"geography_discussed":["Canada","United States","Alberta","British Columbia","Saskatchewan","Northwest Territories","New York City","Ontario","Ottawa","Toronto","Nova Scotia","New York","New Jersey","Connecticut","North America"],"frame_perspective":[],"analysis_type":["attribution study"]},{"id":"sei-000745","url":"https://climateinsightsdirectory.org/document/sei-000745","title":"Climate Change Sittaung River","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2022-11-01","source_url":"https://www.sei.org/publications/climate-change-sittaung-river/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The study uses a Water Evaluation and Planning (WEAP) model and five CMIP5 GCMs to project climate impacts on the Sittaung river basin. Findings indicate that while increased riverine flows will likely benefit domestic water supply and hydropower generation, unmet agricultural demands for food production are expected to increase.","ai_generated":true,"unit":"sei-000745#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Future climate projections for the Sittaung river basin indicate an increase in average temperature and rainfall, with December and January becoming drier and warmer, and rainy months becoming wetter and warmer.","ai_generated":true,"unit":"sei-000745#insight-1","date_modified":"2026-10-23"},{"text":"Riverine flows at the basin outlet are projected to increase from a baseline of 1091 m3/s by 7–10% in the near future (2021–2050) and by 16–19% in the far future (2051–2080).","ai_generated":true,"unit":"sei-000745#insight-2","date_modified":"2026-10-23"},{"text":"Climate change is expected to have divergent impacts across sectors: unmet domestic demand (baseline 1.3 MCM) should decrease by approximately 50%, and total energy generation from nine hydropower projects (baseline 4.12 million MWh) is expected to increase by 9–11% in the near future and 16–17% in the far future; conversely, unmet agricultural demand (baseline 667 MCM) is estimated to increase by 11–15% in the near future and 14–19% in the far future.","ai_generated":true,"unit":"sei-000745#insight-3","date_modified":"2026-10-23"}],"climate_tags":["climate change","water management","precipitation","temperature"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["impact assessment","climate projection"]},{"id":"climcent-000290","url":"https://climateinsightsdirectory.org/document/climcent-000290","title":"FIRE WEATHER","publisher":{"name":"Climate Central","organization":"climate-central"},"document_type":"briefing","language":"en","date_published":"2021-08-25","source_url":"https://assets.ctfassets.net/cxgxgstp8r5d/4FPon4YHSWtIeFmIv1tJtw/d3f61fa19c0886cc17f2d0ba59d496e4/Climate_Central_Fire_Weather_Analysis_2021.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"Climate Central's analysis of weather station data from 225 locations across 17 U.S. states reveals a significant increase in fire weather days since the 1970s, driven primarily by warming and decreasing relative humidity. The report highlights that interior regions have seen higher increases than coastal areas, with some parts of New Mexico experiencing two additional months of fire weather compared to nearly 50 years ago. Beyond increasing wildfire risk, these conditions have led utilities like PG&E to implement public safety power shutoffs (PSPSs) to prevent equipment-related ignitions, resulting in significant economic impacts and health risks. The document argues that while land management and localized grids can mitigate risk, long-term stabilization requires reducing carbon pollution to net zero by 2050.","ai_generated":true,"unit":"climcent-000290#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Analysis of data from 1973 to 2020 shows strong increases in both the total number and the percentage of annual fire weather days across the Western U.S., with some climate divisions in California, Oregon, Texas, and Washington experiencing these conditions more than twice as often as in the early 1970s.","ai_generated":true,"unit":"climcent-000290#insight-1","date_modified":"2026-10-23"},{"text":"New Mexico has seen some of the most significant increases in fire weather, with certain areas now experiencing two more months of such weather annually than they did nearly half a century ago.","ai_generated":true,"unit":"climcent-000290#insight-2","date_modified":"2026-10-23"},{"text":"The increase in fire weather days is more pronounced in interior regions than in coastal regions, as the higher humidity levels typical of coasts make it more difficult to reach the low relative humidity thresholds required for fire weather.","ai_generated":true,"unit":"climcent-000290#insight-3","date_modified":"2026-10-23"},{"text":"A small number of states in the northern plains, including North Dakota, South Dakota, Kansas, and Nebraska, saw a decline in fire weather frequency, which the report attributes to temporary cooling in springs and summers potentially influenced by natural variation and agricultural irrigation.","ai_generated":true,"unit":"climcent-000290#insight-4","date_modified":"2026-10-23"},{"text":"Warming nights have reduced overnight relative humidity, which has negatively impacted the ability of firefighters to gain control over wildfires.","ai_generated":true,"unit":"climcent-000290#insight-5","date_modified":"2026-10-23"},{"text":"To avoid equipment-related ignitions during fire weather, California utilities have implemented public safety power shutoffs (PSPSs); in 2019, these shutoffs left 12 million people in Northern California without power and had an estimated economic impact of $10 billion.","ai_generated":true,"unit":"climcent-000290#insight-6","date_modified":"2026-10-23"},{"text":"Beyond weather, other factors fueling modern wildfires include the buildup of fuels due to the loss of indigenous land management, the spread of invasive grasses, urbanization of forested areas, and beetle-caused tree mortality linked to rising temperatures.","ai_generated":true,"unit":"climcent-000290#insight-7","date_modified":"2026-10-23"}],"climate_tags":["wildfire","temperature","climate change","urbanization","renewable energy","carbon dioxide emissions","Net-zero emissions","fossil fuel"],"geography_discussed":["New Mexico","Texas","California","Oregon","Washington","North Dakota","South Dakota","Kansas","Nebraska","Northern California"],"frame_perspective":[],"analysis_type":["historical trend analysis","meteorological analysis"]},{"id":"iisd-003728","url":"https://climateinsightsdirectory.org/document/iisd-003728","title":"Climate and Climate Change","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"fact sheet","language":"en","date_published":"2021-05","source_url":"https://www.iisd.org/system/files/2021-06/winnipeg-river-basin-sheet-1-climate-change.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document describes the humid continental climate of the lower Winnipeg River basin and details historical increases in river discharge. It presents climate projections for the 2050s, indicating rising temperatures, precipitation, and runoff, which may impact hydroelectric development and aquatic ecosystems.","ai_generated":true,"unit":"iisd-003728#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The lower Winnipeg River basin (LWRB) is characterized by a humid, warm-summer continental climate (Köppen Dfb) with four distinct seasons. Based on data from 1981 to 2010 at the Pinawa WNRE station in Manitoba, the region has a mean daily temperature of 2.8°C and a mean annual precipitation of 578.3 mm.","ai_generated":true,"unit":"iisd-003728#insight-1","date_modified":"2026-10-23"},{"text":"Contrary to the trend of decreasing water flows seen in most Canadian rivers during the 20th century, discharge in the Winnipeg River has increased since 1924. This increase is attributed to higher precipitation in summer and fall, increased winter discharge, and human activities such as water diversion from Lake St. Joseph.","ai_generated":true,"unit":"iisd-003728#insight-2","date_modified":"2026-10-23"},{"text":"Climate model simulations for the 2050s period (2040 to 2069) relative to a 1981–2010 baseline project an ensemble median increase in mean annual temperature of 2.8°C, mean annual precipitation of 6.9%, and mean runoff of 3.3%.","ai_generated":true,"unit":"iisd-003728#insight-3","date_modified":"2026-10-23"},{"text":"While there is strong agreement among simulations regarding temperature increases, there is higher uncertainty concerning changes in runoff. These future variations could affect water quality, river morphology, nutrient/contaminant loading, aquatic life, and hydroelectric development.","ai_generated":true,"unit":"iisd-003728#insight-4","date_modified":"2026-10-23"}],"climate_tags":["climate change","precipitation","temperature","water quality"],"geography_discussed":["Winnipeg River basin","Manitoba","Ontario","United States","Minnesota","Canada"],"frame_perspective":[],"analysis_type":["climate projection","historical trend analysis"]},{"id":"sei-001447","url":"https://climateinsightsdirectory.org/document/sei-001447","title":"Escenarios de Cambio Climático: Método k-NN","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"briefing","language":"es","date_published":"2020-06","source_url":"https://www.sei.org/wp-content/uploads/2020/06/knndiscussionbrief-2.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines a methodology employed by the Stockholm Environment Institute (SEI) to bridge the gap between global climate models and local water resource planning. Because General Circulation Models (GCMs) from the CMIP5 project lack the spatial resolution (40–120km) necessary for local hydrological assessment, SEI uses the k-NN statistical downscaling method. This process involves selecting high-performing GCMs based on eight climate attributes and using them to create synthetic daily series for temperature and precipitation from 2020 to 2100. These series are then integrated into the Water Evaluation and Planning (WEAP) system to explore robust adaptation alternatives for water service providers and environmental agencies.","ai_generated":true,"unit":"sei-001447#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"General Circulation Models (GCMs) from the CMIP5 project are the primary source for long-term climate change scenarios, but their spatial resolution of 40–120km is insufficient for local water resource planning.","ai_generated":true,"unit":"sei-001447#insight-1","date_modified":"2026-10-23"},{"text":"The k-NN method is a non-parametric statistical downscaling technique that generates synthetic daily climate series for temperature and precipitation for the period 2020–2100, which are then used to inform the Water Evaluation and Planning (WEAP) system.","ai_generated":true,"unit":"sei-001447#insight-2","date_modified":"2026-10-23"},{"text":"The k-NN methodology preserves attributes from historical series and captures daily resolution climate attributes while maintaining spatial correlation, allowing for the construction of climate series tailored to specific planning needs.","ai_generated":true,"unit":"sei-001447#insight-3","date_modified":"2026-10-23"},{"text":"The process for generating synthetic series involves eight steps, including the compilation of at least two decades of historical daily meteorology, the selection of GCMs based on eight quantitative and qualitative climate attributes, and the application of k-NN to predict local climate attributes (Y) using GCM attributes (X).","ai_generated":true,"unit":"sei-001447#insight-4","date_modified":"2026-10-23"}],"climate_tags":["climate change scenario","hydrological model","temperature","precipitation","climate change adaptation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological briefing"]},{"id":"bc3res-000019","url":"https://climateinsightsdirectory.org/document/bc3res-000019","title":"Modos de trasmisión y factores que afectan a la exposición/trasmisión","publisher":{"name":"Basque Centre for Climate Change","organization":"basque-centre-for-climate-change"},"document_type":"research paper","language":"es","date_published":"2020-05-11","source_url":"https://www.bc3research.org/es/2020/05/modos-de-trasmision-del-sars-cov-2-y-factores-que-pueden-influenciar-la-enfermedad/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document identifies three primary transmission modes for SARS-CoV-2: direct physical contact, intermediate inanimate objects, and respiratory droplets or aerosols. It highlights that fine particles (<10 µm) can reach pulmonary alveoli, while larger droplets (>100 µm) contribute primarily to contact transmission. Environmental factors such as low temperatures and specific relative humidity levels are noted to increase virus survival, while solar ultraviolet radiation acts as a natural virucide. The report discusses correlations between high levels of atmospheric pollutants, such as NO2 and PM2.5, and increased COVID-19 mortality and incidence in regions including Northern Italy, Spain, China, and the United States, suggesting that pollution may predispose individuals to more severe infections.","ai_generated":true,"unit":"bc3res-000019#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"SARS-CoV-2 transmission is suspected to occur via three main routes: direct physical contact with saliva or mucus, intermediate inanimate objects (such as metal, plastic, glass, or paper), and respiratory droplets and aerosols.","ai_generated":true,"unit":"bc3res-000019#insight-1","date_modified":"2026-10-23"},{"text":"The size of respiratory particles determines their transmission path: particles larger than 100 µm deposit quickly on surfaces and contribute to contact transmission, while particles smaller than 10 µm can reach the pulmonary alveoli.","ai_generated":true,"unit":"bc3res-000019#insight-2","date_modified":"2026-10-23"},{"text":"Environmental conditions significantly impact virus viability; low temperatures increase survival across all relative humidity levels, and solar ultraviolet radiation is an efficient natural agent for inactivating the virus.","ai_generated":true,"unit":"bc3res-000019#insight-3","date_modified":"2026-10-23"},{"text":"Indoor environments increase infection risk compared to outdoors; one study in Japan indicated that contagion is 19 times more likely in indoor spaces.","ai_generated":true,"unit":"bc3res-000019#insight-4","date_modified":"2026-10-23"},{"text":"High concentrations of NO2 and specific climatic conditions that prevent air dispersion were linked to severe outbreaks in Northern Italy and Central Spain, where 78% of 4,443 deaths were concentrated in five regions.","ai_generated":true,"unit":"bc3res-000019#insight-5","date_modified":"2026-10-23"},{"text":"Chronic exposure to air pollutants (NO2, O3, PM2.5, and PM10) in Northern Italy over the last decade (2010-2019) significantly correlated with COVID-19 cases across up to 71 provinces.","ai_generated":true,"unit":"bc3res-000019#insight-6","date_modified":"2026-10-23"},{"text":"Global warming may influence transmission by altering bird migration patterns or changing human behavior, such as increasing the use of air conditioning and indoor crowding in very hot temperatures.","ai_generated":true,"unit":"bc3res-000019#insight-7","date_modified":"2026-10-23"}],"climate_tags":["air pollution","temperature","particulates","climate change","indoor air quality"],"geography_discussed":["Japan","South Korea","United States","Iowa","People's Republic of China","Jinan","Italy","Spain","France","Germany","Beijing","Wuhan"],"frame_perspective":[],"analysis_type":["literature review","correlation analysis"]},{"id":"sei-001388","url":"https://climateinsightsdirectory.org/document/sei-001388","title":"GMET in Bolivia: building gridded climate datasets for water resource studies","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"briefing","language":"en","date_published":"2019-02","source_url":"https://www.sei.org/wp-content/uploads/2019/03/gmet-in-bolivia.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Stockholm Environment Institute (SEI), in collaboration with the National Center for Atmospheric Research (NCAR) and Bolivian partners, applied the Gridded Meteorological Ensemble Tool (GMET) to generate daily climate grids for Bolivia and parts of neighbouring countries from 1980 to 2016. Using a database of over 400 stations, the project demonstrated GMET's ability to incorporate prediction errors and uncertainty into spatial precipitation and temperature data, though it found that satellite-based data remained necessary to fill gaps in data-sparse regions. The project included a technology transfer to train local experts and the release of the tool as open-source software.","ai_generated":true,"unit":"sei-001388#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Gridded Meteorological Ensemble Tool (GMET) addresses the challenge of sparse and unevenly distributed meteorological stations by creating continuous gridded products that incorporate data and prediction errors into the analysis.","ai_generated":true,"unit":"sei-001388#insight-1","date_modified":"2026-10-23"},{"text":"In its first application outside the US, GMET used a database of more than 400 stations to produce daily climate grids for Bolivia and parts of Argentina, Brazil, Chile, Paraguay, and Peru for the period 1980–2016.","ai_generated":true,"unit":"sei-001388#insight-2","date_modified":"2026-10-23"},{"text":"The application of GMET in Bolivia showed that while the tool realistically represents spatial patterns and quantity in areas with sufficient station density, satellite-based data is still required to fill gaps in data-sparse regions to complete water balance calculations.","ai_generated":true,"unit":"sei-001388#insight-3","date_modified":"2026-10-23"},{"text":"The project focused on local capacity building and technology transfer, training Bolivian experts via the Laboratory of Atmospheric Physics (LFA) to use R software and Linux to run and update the open-source GMET tool.","ai_generated":true,"unit":"sei-001388#insight-4","date_modified":"2026-10-23"}],"climate_tags":["precipitation","temperature"],"geography_discussed":["United States","Peru","Brazil","Paraguay","Argentina","Chile"],"frame_perspective":[],"analysis_type":["case study","technology transfer"]},{"id":"wwatt-000074","url":"https://climateinsightsdirectory.org/document/wwatt-000074","title":"Kenya Drought 2016","publisher":{"name":"World Weather Attribution","organization":"world-weather-attribution"},"document_type":"research paper","language":"en","date_published":"2017-03-23","source_url":"https://www.worldweatherattribution.org/kenya-drought-2016/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The 2016 drought in Kenya was primarily driven by sea-surface temperature patterns, specifically La Niña, rather than human-induced climate change. While climate change did not significantly increase the likelihood of the rainfall deficit, it did lead to hotter temperatures that exacerbated the drought's impact. The event was less severe than the 2010-11 drought but still triggered a national emergency, affecting millions of people in arid and semi-arid lands.","ai_generated":true,"unit":"wwatt-000074#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The 2016 drought was primarily driven by sea-surface temperature patterns, with La Niña playing a significant role in creating drier conditions. In the northwest, La Niña explained 16 percent of the variance in October-November-December (OND) precipitation, and in the southeast, it explained 17 percent of September-November precipitation patterns.","ai_generated":true,"unit":"wwatt-000074#insight-1","date_modified":"2026-10-23"},{"text":"Human-induced climate change did not increase the likelihood of the 2016 rainfall deficit; in fact, simulations suggest the drought would have been more likely in a 'Natural' scenario without human influence. In the current climate, the drought was rendered 19–29 percent less likely in the northwest and 29–39 percent less likely in the southeast.","ai_generated":true,"unit":"wwatt-000074#insight-2","date_modified":"2026-10-23"},{"text":"While climate change did not drive the lack of rain, it increased the temperatures during the event, which likely worsened the drought's effects by accelerating the depletion of water and forage.","ai_generated":true,"unit":"wwatt-000074#insight-3","date_modified":"2026-10-23"},{"text":"The 2016 drought was an extreme event but was less severe than the 2010-11 drought. In the northwest, the 2016 OND rains had an expected return time of three years, compared to 60 years for the 2010-11 event; in the southeast, the 2016 overall lack of rainfall had a return time of five years, whereas the 2010-11 drought was expected to occur less frequently than once every 50 years.","ai_generated":true,"unit":"wwatt-000074#insight-4","date_modified":"2026-10-23"},{"text":"The drought caused a severe humanitarian crisis, leading President Uhuru Kenyatta to declare a national emergency on February 10, 2017. Approximately 2.7 million people were estimated to need food aid, and UNICEF estimated that 175,000 children were unable to attend school.","ai_generated":true,"unit":"wwatt-000074#insight-5","date_modified":"2026-10-23"}],"climate_tags":["drought","climate change","La Niña","precipitation","temperature","food security","extreme event attribution","sea surface temperature","climate variability"],"geography_discussed":["Somalia","Ethiopia","Nairobi"],"frame_perspective":[],"analysis_type":["real-time attribution analysis"]},{"id":"cdkn-000391","url":"https://climateinsightsdirectory.org/document/cdkn-000391","title":"Dengue Fever in the Belize District","publisher":{"name":"Climate and Development Knowledge Network","organization":"climate-and-development-knowledge-network"},"document_type":"case study","language":"en","date_published":"2015-08-10","source_url":"http://cdkn.org/wp-content/uploads/2013/01/Cariwig-Case-Study-Health-Sector-DF-Report-Final.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The study identifies a positive association between dengue fever occurrence in the Belize District and both precipitation and minimum temperature. Projections for the 2050s suggest a general warming trend with an increase in the frequency of days where minimum temperatures exceed 18°C, which may increase DF occurrence. Conversely, a projected increase in days where maximum temperatures exceed 32°C may create less favourable conditions for the disease. The report emphasizes that non-climate factors, including changes in health reporting structures and socio-economic drivers, also influence DF trends.","ai_generated":true,"unit":"cdkn-000391#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Statistical analysis of data from 2007-2014 indicates that rainfall and minimum temperature (Tmin) are major factors contributing to dengue fever (DF) in the Belize District, showing positive associations during the Sept-Nov and Dec-Feb periods.","ai_generated":true,"unit":"cdkn-000391#insight-1","date_modified":"2026-10-23"},{"text":"Dengue fever occurrence is most prevalent in urban areas of the Belize District, with the highest rates per 100 people found in Belize City, followed by San Pedro Town on Ambergris Caye.","ai_generated":true,"unit":"cdkn-000391#insight-2","date_modified":"2026-10-23"},{"text":"Climate projections for the 2050s indicate a general warming trend, with an expected increase in both maximum (Tmax) and minimum (Tmin) temperatures across all months.","ai_generated":true,"unit":"cdkn-000391#insight-3","date_modified":"2026-10-23"},{"text":"In the 2050s, the number of days where Tmin exceeds 18°C is projected to increase considerably, particularly from January to March and November to December, which may lead to increased DF occurrence.","ai_generated":true,"unit":"cdkn-000391#insight-4","date_modified":"2026-10-23"},{"text":"Projections for the 2050s show a decrease in the number of days where Tmax is between 20°C and 32°C, primarily because there is a major increase in days exceeding 32°C, which is associated with a decline in DF incidence.","ai_generated":true,"unit":"cdkn-000391#insight-5","date_modified":"2026-10-23"},{"text":"Precipitation patterns in the 2050s are projected to change, with a decrease in mean wet-day precipitation from May to September and an increase in heavy rainfall and variability during October and November.","ai_generated":true,"unit":"cdkn-000391#insight-6","date_modified":"2026-10-23"},{"text":"Observed increases in DF may be influenced by non-climate factors, such as population growth, socio-economic drivers, and significant changes in how the Ministry of Health classified and reported DF cases between 2007 and 2014.","ai_generated":true,"unit":"cdkn-000391#insight-7","date_modified":"2026-10-23"}],"climate_tags":["precipitation","temperature","climate variability","climate change","public health","urbanization"],"geography_discussed":["Belize City","San Pedro Town","Ambergris Caye","Mexico"],"frame_perspective":[],"analysis_type":["case study","statistical analysis","climate projection"]},{"id":"climcent-000087","url":"https://climateinsightsdirectory.org/document/climcent-000087","title":"Superbowl XLVIII & Groundhog Day","publisher":{"name":"Climate Central","organization":"climate-central"},"document_type":"briefing","language":"en","date_published":"2014-01-29","source_url":"https://www.climatecentral.org/climate-matters/superbowl-xlviii-groundhog-day","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The briefing compares the climate of East Rutherford, New Jersey, with other NFL cities, noting that while it is a cold-weather venue for Super Bowl XLVIII, it is not the coldest in the league. It highlights historical Super Bowl temperature extremes and records the highest one-day snowfall in NFL cities, specifically noting Foxborough, MA. Additionally, it cites MIT research indicating that temperatures below 50°F negatively impact field goal accuracy more than wind or precipitation.","ai_generated":true,"unit":"climcent-000087#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Green Bay's Lambeau Field is the coldest stadium in the NFL, with an average high temperature of 25.6°F during the first week of February.","ai_generated":true,"unit":"climcent-000087#insight-1","date_modified":"2026-10-23"},{"text":"The coldest Super Bowl on record was Super Bowl VI in 1972 at New Orleans' Tulane Stadium, which had a high of 43°F and an overnight low of 24°F.","ai_generated":true,"unit":"climcent-000087#insight-2","date_modified":"2026-10-23"},{"text":"The highest one-day snowfall recorded at a station closest to an NFL stadium during the first week of February was 29.3 inches in 1978 in Foxborough, MA.","ai_generated":true,"unit":"climcent-000087#insight-3","date_modified":"2026-10-23"},{"text":"An MIT analysis suggests that field goal accuracy decreases when temperatures drop below 50°F, and this temperature effect is more significant than the impact of wind or precipitation.","ai_generated":true,"unit":"climcent-000087#insight-4","date_modified":"2026-10-23"}],"climate_tags":["temperature","climate data"],"geography_discussed":["Green Bay","New Orleans","Los Angeles","San Diego","Buffalo","Foxborough","Philadelphia","Baltimore"],"frame_perspective":[],"analysis_type":["historical data analysis"]},{"id":"cdkn-000007","url":"https://climateinsightsdirectory.org/document/cdkn-000007","title":"Infrastructure and climate change: impacts and adaptations for the Zambezi River Valley","publisher":{"name":"Climate and Development Knowledge Network","organization":"climate-and-development-knowledge-network"},"document_type":"research paper","language":"en","date_published":"2013-04-19","source_url":"https://cdkn.org/resource/infrastructure-and-climate-change-impacts-and-adaptations-for-the-zambezi-river-valley","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The study estimates that Malawi, Mozambique, and Zambia face a potential US$596 million cost to maintain and repair road infrastructure due to climate-driven temperature and precipitation changes by 2050.","ai_generated":true,"unit":"cdkn-000007#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Using a stressor-response approach and 425 climate scenarios per road type and policy option, the study estimates that Malawi, Mozambique, and Zambia face a potential cost of US$596 million based on median scenarios to repair and maintain roads damaged by climate-related temperature and precipitation changes between 2010 and 2050.","ai_generated":true,"unit":"cdkn-000007#insight-1","date_modified":"2026-10-23"}],"climate_tags":["climate change adaptation","precipitation","temperature","flood"],"geography_discussed":["Africa"],"frame_perspective":[],"analysis_type":["quantitative","predictive"]},{"id":"prio-001735","url":"https://climateinsightsdirectory.org/document/prio-001735","title":"Sensitivity Analysis of Climate Variability and Civil War","publisher":{"name":"Peace Research Institute Oslo","organization":"peace-research-institute-oslo"},"document_type":"research paper","language":"en","date_published":"2010-11-17","source_url":"https://cdn.cloud.prio.org/files/3db295cf-fa98-42f8-abd4-8183ea5ae75b/Sensitivity Analysis of Climate Variability and Civil War PRIO Paper 2010 v1.pdf?inline=true","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The paper challenges the finding that warming is robustly linked to civil war in Sub-Saharan Africa by replicating and testing the model used by Burke et al. (2009). Through influence diagnostics, alternative operationalizations of civil war, and expanded sample periods, the authors find that the reported positive effect of temperature is tenuous and often disappears. The analysis suggests that the original results were driven by a few influential observations involving foreign intervention and that climate variables provide negligible predictive power compared to country-specific fixed effects and time trends.","ai_generated":true,"unit":"prio-001735#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The reported positive link between temperature and civil war is highly sensitive to a small number of influential observations. Removing six highly influential cases—including Guinea-Bissau (1998), Sierra Leone (1998), and Chad (1987, 1990)—causes the effect of temperature to decrease dramatically.","ai_generated":true,"unit":"prio-001735#insight-1","date_modified":"2026-10-23"},{"text":"A common characteristic of the most influential cases in the baseline model is the presence of foreign intervention, a variable omitted from the original analysis. The authors note a lack of references to climate anomalies or agricultural income loss in the narratives of these specific conflicts.","ai_generated":true,"unit":"prio-001735#insight-2","date_modified":"2026-10-23"},{"text":"The positive effect of temperature disappears when the definition of civil war is adjusted. While the result is reproduced using the PRIO Battle Deaths Dataset, it vanishes when the fatality threshold is shifted to 500 or 2,000 battle-related deaths, or when continuous measures of battle deaths are used.","ai_generated":true,"unit":"prio-001735#insight-3","date_modified":"2026-10-23"},{"text":"Expanding the sample period beyond 1981\u00132002 weakens the reported correlation. When the temporal domain is extended to 1981\u00132008, the regression coefficient for temperature drops by 40% and loses statistical significance; it is also non-significant for the full post-colonial sample (1960\u00132008).","ai_generated":true,"unit":"prio-001735#insight-4","date_modified":"2026-10-23"},{"text":"Climate variables have a trivial marginal impact on the prediction of civil war. A model containing only country fixed effects and time trends produces predictions nearly identical to the baseline model, with a correlation of r=0.999.","ai_generated":true,"unit":"prio-001735#insight-5","date_modified":"2026-10-23"},{"text":"Out-of-sample prediction tests for the period 1999\u00132002 show that climate variables do not improve the model's ability to correctly predict wars. The model with climate variables produced more false positives without increasing true positives compared to the reduced model.","ai_generated":true,"unit":"prio-001735#insight-6","date_modified":"2026-10-23"}],"climate_tags":["climate variability","temperature","precipitation","environmental degradation"],"geography_discussed":["Guinea-Bissau","Sierra Leone","Chad","Congo, Republic of","Sudan","Mozambique","Uganda","Ethiopia","Angola"],"frame_perspective":[],"analysis_type":["sensitivity analysis","replication study","econometric analysis"]},{"id":"cdkn-000389","url":"https://climateinsightsdirectory.org/document/cdkn-000389","title":"Drought Assessment & Projection for the Eastern Caribbean Using the CARiDRO Tool","publisher":{"name":"Climate and Development Knowledge Network","organization":"climate-and-development-knowledge-network"},"document_type":"case study","language":"en","date_published":null,"source_url":"http://cdkn.org/wp-content/uploads/2013/01/cariwig-case-study-final-report-eastern-caribbean.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The study uses the CARiDRO tool and the Standardized Precipitation Index (SPI) to analyze the 2009-2010 drought in the Eastern Caribbean and project future risks. It finds that the 2009-2010 event was a 1 in 40 years occurrence, but future droughts are projected to be more frequent, with an average recurrence of 1 in 5 years. The report suggests that the area affected by drought will increase after the middle of the 21st century, necessitating improved planning in water management and agriculture.","ai_generated":true,"unit":"cdkn-000389#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The 2009-2010 drought event in the Eastern Caribbean was characterized as a 1 in 40 years event in terms of its intensity and spatial extension.","ai_generated":true,"unit":"cdkn-000389#insight-1","date_modified":"2026-10-23"},{"text":"Projections based on seven regional climate model outputs indicate that future drought events will be more frequent, with an average recurrence time of 1 in 5 years, though individual models range from 1 in 3 to 1 in 12 years.","ai_generated":true,"unit":"cdkn-000389#insight-2","date_modified":"2026-10-23"},{"text":"Multi-model projections show an increase in the total area of the Caribbean affected by drought after the middle of the 21st century.","ai_generated":true,"unit":"cdkn-000389#insight-3","date_modified":"2026-10-23"}],"climate_tags":["drought","precipitation","temperature","water management","agriculture","early warning system"],"geography_discussed":["Caribbean"],"frame_perspective":[],"analysis_type":["case study","projection"]},{"id":"iai-000672","url":"https://climateinsightsdirectory.org/document/iai-000672","title":"FF_NEW","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"fact sheet","language":"es","date_published":null,"source_url":"https://iai.int/administrador/assets/images/ckfinder/files/FireForecast_ESP.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"This fact sheet from the Inter-American Institute for Global Change Research details the probability of burns and forest fires in South American protected areas from August to October 2020, based on a report by Cemaden, INPE, and the Met Office Hadley Center.","ai_generated":true,"unit":"iai-000672#summary","date_modified":"2026-08-17"},"key_insights":[{"text":"The number of heat spots in South America reached its highest level since 1998 during the current year, with August and September generally being the most critical months. A report by Cemaden, INPE, and the Met Office Hadley Center identified 117 protected areas facing a high probability of burns and forest fires.","ai_generated":true,"unit":"iai-000672#insight-1","date_modified":"2026-08-17"}],"climate_tags":["forest fires","heat spots","protected areas","temperature","precipitation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["forecast","risk assessment"]}]}