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Exposure to climate hazards is increasing, and the experiences of frontline communities warrant meaningful and urgent attention towards how to mitigate, manage, and adapt to hazards. We report results from a community-engaged pilot (November 2021–June 2022) of N = 30 participants in four frontline communities of the San Francisco Bay Area, California, USA. The study region is an area where low-income, non-English-speaking residents are inequitably exposed and vulnerable to wildfire smoke, extreme heat, and other climate hazards. Building from a yearslong partnership of researchers, community organizations, and community members, we report the feasibility of a project piloting (1) instruments to monitor indoor air quality, temperature, and participant sleep health, and (2) interventions to improve indoor air quality and support protective behaviors. Data collection included experience-based survey data (via in-person administered surveys and a smartphone application) and interviews about heat and air quality, as well as data from an air monitoring protocol. Results cover the prevalence of hazard exposure and protective actions among participants. We discuss throughout methods for conducting and evaluating a community-engaged pilot, particularly by using a community ambassador program. Implications include the feasibility of community-engaged research projects, including discussion of resources required to accomplish this work.

In the Malay Archipelago (Indonesia and Malaysia), forest is lost on large scales to cash-crop plantation (oil palm, rubber, and acacia, including fallow lands) and urban expansion. Deforestation changes land surface properties and fluxes, thereby modifying wind and rainfall. Despite the expansive land-cover change over a climatically sensitive region of the tropics, the resulting impact on the Asian summer monsoon has not been studied. Here we study the atmospheric response caused by the island surface change due to deforestation into cash-crop plantations and urban expansion. Using a large ensemble of atmospheric model experiments with observed and idealized land-cover-change specifications, we show that the deforestation warms the Malay Archipelago, caused by an increase in soil warming due to decreased evapotranspirative cooling. The island warming agrees well with in situ and satellite observations; it causes moisture to converge from the surrounding seas into Sumatra and Malaya, and updrafts, rainfall, and cyclonic circulations to spread northwestward into southern India and the Arabian Sea, as well as a drying anticyclonic circulation over the Indo-Gangetic plains, Indochina, and the South China Sea, weakening the Asian summer monsoon. The modeled monsoon weakening agrees well with, and tends to enhance, the observed long-term trend, suggesting the potential for continued weakening with protracted cash-crop plantation and urban expansion.

In response to increasing wildfire risks, California plans to expand the use of prescribed fire. We characterized the anticipated change in health impacts from exposure to smoke under a future fire‐management scenario relative to a historical period (2008–2016). Using dispersion models, we estimated daily fine particulate matter (PM2.5) emissions from hypothetical future prescribed fires on 500,000‐acres classified as high priority. To evaluate health impacts, we calculated excess daily cardiorespiratory emergency department visit rates attributed to all‐source PM2.5, distinguishing the portion of the burden attributed to prescribed fire. The total burden was differentiated by fire type and by smoke strata‐specific days to calculate strata‐specific burden rates, which were then applied to estimate the burden in the future scenario. This analysis suggests that the exposure to prescribed fire smoke, measured as the number of persons exposed per year, would be 15 times greater in the future. However, these exposures were associated with lower concentrations compared to the historical period. The increased number of exposure days led to an overall increase in the future health burden. Specifically, the northern, central, and southern regions experienced the largest burden increase. This study introduces an approach that integrates spatiotemporal exposure differences, baseline morbidity, and population size to assess the impacts of prescribed fire under a future scenario. The findings highlight the need to consider both the level and frequency of exposure to guide strategies to safeguard public health as well as aid forest management agencies in making informed decisions to protect communities while mitigating wildfire risks. Plain Language Summary Prescribed fire is a forest management strategy for reducing the risks of wildfires. While some fires are ecologically beneficial, smoke from fires is a major source of airborne particle pollution, which is harmful to human health. This study examined the change in health impacts resulting from an expected increase in the use of prescribed fire within California's high‐priority wildfire risk areas. We used daily counts of cardiorespiratory emergency department visits attributed to air quality combined with model‐generated measures of smoke pollution to estimate health impacts. We compared exposures and the associated health burden on days impacted by wildfire or prescribed fire smoke in the past to the impacts in the hypothetical future scenario with increased prescribed fire. Projections of future prescribed burning in high priority areas suggest that more people would experience smoke more often, although exposures would occur at lower concentrations. With more frequent lower‐level exposure days near populated areas, the health burden would increase relative to past prescribed fire. Understanding the potential impact of prescribed fire may simultaneously help protect public health and increase safety from wildfires. Key Points A California‐based model of future prescribed burning in high‐priority wildfire risk areas suggested more people will experience smoke An increased number of exposure days in the future scenario led to an overall increase in the future health burden The excess future health burden was due to the cumulative impact of lower exposure days and high population density in high‐priority areas

Climate change factors and expanded population growth in the Wildland Urban Interface (transition zone between human structures and undeveloped wildland) contribute to a projected increase in wildfire frequency and smoke exposure. As an unregulated source of air pollution, reducing smoke exposure represents a difficult challenge for health risk communicators. The target audience is broad with unpredictable health impacts due to spatial and temporal variability in exposure. Beyond providing information, agencies face challenges reaching affected populations, motivating behavior change, and overcoming barriers between intentions and actions (recommended health protection). The Smoke Sense citizen science project developed a smartphone app to provide an engagement, learning, and information-sharing platform. Here we draw upon previous trends in behavioral patterns and propose a synergistic approach of citizen and behavioral science that can be applied to increase understanding of health risk and motivate new habits to reduce exposure among impacted individuals. Presentation of the approach proceeds as follows: (1) we identify several core factors that contribute to an intention-action gap, (2) identify applicable social and behavioral science principles that can bridge the gap, (3) propose explicit examples focused on theoretical principles, (4) describe small-scale user preliminary feedback and examples for monitoring and evaluating impact, and (5) provide a look to the future for collaborative citizen engagement. Current health risk communication strategies often lack consideration of behavioral factors that may enhance motivation and encourage behavior change. The proposed approach aims to leverage the strengths of citizen and social science and seeks to encourage a focused ‘digital community’ to implement new habits in the face of unpredictable and dynamic environmental threats.

The Interagency Fuels Treatment Decision Support System (IFTDSS) is a web-based software and data integration framework that organizes fire and fuels software applications into a single online application. IFTDSS is designed to make fuels treatment planning and analysis more efficient and effective. In IFTDSS, users can simulate fire behavior and fire effects using the scientific algorithms and processes found in desktop applications including FlamMap, Behave, FOFEM, and Consume. Strategic-level goals of IF-TDSS are to simplify the fuels treatment planning decision-support process improve the overall quality of analysis and planning control long-term costs encourage scientific collaboration reduce agency information technology (IT) workload in-deploying and maintaining fuels applications and data; and promote interagency collaboration within the fire and fuels community. This paper discusses the tools and processes IFTDSS offers to fire, fuels, and resource managers responsible for planning fuels treatment within a framework of hazard analysis and risk assessment. We outline how fire and fuels treatment planners can use IFTDSS to identify areas of high hazard and risk, evaluate the potential burning risk and hazard level for valued resources (values at risk) within the area of interest, and simulate the effectiveness of fuels treatments in reducing the potential harm to values at risk.

Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression.Aims: We developed a new fire mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS and 2012–2020 VIIRS data) to develop a high-resolution wildfire growth dataset, including growth areas, perimeters, and cross-referenced fire information from agency reports.Methods: Satellite fire detections were buffered using a historical pixel-to-fire size relationship, then grouped spatiotemporally into individual fire events. Sub-daily and daily growth areas and perimeters were calculated for each fire event. After assembly, fire event characteristics including location, size, and date, were merged with agency records to create a cross-referenced dataset.Key results: Our satellite-based total fire size shows excellent agreement with agency records for MODIS (R2 = 0.95) and VIIRS (R2 = 0.97) in California. VIIRS-based estimates show improvement over MODIS for fires with areas less than 4047 ha (10 000 acres). To our knowledge, this is the finest resolution quality-assured fire growth dataset available.Conclusions and Implications: The novel spatiotemporal resolution and methodological consistency of our dataset can enable advances in fire behaviour and fire weather research and model development efforts, smoke modelling, and near real-time fire monitoring.

Low-frequency variations of typhoon paths are often attributed to changes in the North Pacific subtropical high and monsoon through influences on the steering wind. Evidence indicates however a strong imprint of the Kuroshio on the atmosphere. Here we show that the meridional oscillation of sea surface temperature (SST) front over the Kuroshio Extension east of Japan significantly correlates with the number of land-falling typhoons along East Asia from June to October, accounting for 70% of the low-frequency variance since 1980. We used observations and a simple model to show that when the SST front shifts poleward (equatorward), SST gradient south of the current and westerly tropospheric wind weaken (strengthen), steering more typhoons to veer toward (away from) the East Asian continent. Our analysis also indicates that long-term weakening of SST gradient and westerly wind appears to be concomitant with poleward shifting of the Kuroshio, attributed to global warming in some studies, and suggests the potential for more land-falling typhoons in East Asia in the coming decades.

Land managers rely on prescribed burning and naturally ignited wildfires for ecosystem management, and must balance trade-offs of air quality, carbon storage, and ecosystem health. A current challenge for land managers when using fire for ecosystem management is managing smoke production. Smoke emissions are a potential human health hazard due to the production of fine particulate matter (PM 2.5 ), carbon monoxide (CO), and ozone (O 3 ) precursors. In addition, smoke emissions can impact transportation safety and contribute to regional haze issues. Quantifying wildland fire emissions is a critical step for evaluating the impact of smoke on human health and welfare, and is also required for air quality modeling efforts and greenhouse gas reporting. Smoke emissions modeling is a complex process that requires the combination of multiple sources of data, the application of scientific knowledge from divergent scientific disciplines, and the linking of various scientific models in a logical, progressive sequence. Typically, estimates of fire size, available fuel loading (biomass available to burn), and fuel consumption (biomass consumed) are needed to calculate the quantities of pollutants produced by a fire. Here we examine the 2006 Tripod Fire Complex as a case study for comparing alternative data sets and combinations of scientific models available for calculating fire emissions. Specifically, we use five fire size information sources, seven fuel loading maps, and two consumption models (Consume 4.0 and FOFEM 5.7) that also include sets of emissions factors. We find that the choice of fuel loading is the most critical step in the modeling pathway, with different fuel loading maps varying by 108 %, while fire size and fuel consumption show smaller variations (36 % and 23 %, respectively). Moreover, we find that modeled fuel loading maps likely underestimate the amount of fuel burned during wildfires as field assessments of total woody fuel loading were consistently higher than modeled fuel loadings in all cases. The PM 2.5 emissions estimates from Consume and FOFEM vary by 37 %. In addition, comparisons with available observational data demonstrate the value of using local data sets where possible.