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Contemporary food and agricultural systems degrade soils, pollute natural resources, and contribute to greenhouse gas emissions. The waste output from these systems, however, can be repurposed as an agricultural input, reducing emissions associated with organics disposal while actively sequestering atmospheric carbon in soils—thus transitioning the sector from a carbon source to a carbon sink. This research estimates the near-term technical and economic potential of utilizing composted organic feedstocks as a soil amendment to mitigate climate change and improve long-term soil quality, in line with California’s organics diversion policies, by connecting food scraps and organics residuals in California’s municipal solid waste to existing infrastructure and working lands in the state. The multi-objective spatial optimization results indicate considerable carbon sequestration benefits in the range of −1.9 ± 0.5 MMT CO₂eq annually, by applying compost to 6 million hectares of California rangelands at a price of approximately $200 per ton, presenting a cost-effective climate change mitigation strategy within proposed federal sequestration credits. Expanding composting capacity is predicted to increase the total amount of carbon sequestered while reducing the cost per ton and per hectare treated. This model aids decision makers in considering the technical, economic, and institutional potential of actively managing the State’s organic materials in municipal waste streams for climate change mitigation.

 

Downslope wind‐driven fires have resulted in many of the wildfire disasters in the western United States and represent a unique hazard to infrastructure and human life. We analyze the co‐occurrence of wildfires and downslope winds across the western United States (US) during 1992–2020. Downslope wind‐driven fires accounted for 13.4% of the wildfires and 11.9% of the burned area in the western US yet accounted for the majority of local burned area in portions of southern California, central Washington, and the front range of the Rockies. These fires were predominantly ignited by humans, occurred closer to population centers, and resulted in outsized impacts on human lives and infrastructure. Since 1999, downslope wind‐driven fires have accounted for 60.1% of structures and 52.4% of human lives lost in wildfires in the western US. Downslope wind‐driven fires occurred under anomalously dry fuels and exhibited a seasonality distinct from other fires—occurring primarily in the spring and fall. Over 1992–2020, we document a 25% increase in the annual number of downslope wind‐driven fires and a 140% increase in their respective annual burned area, which partially reflects trends toward drier fuels. These results advance our understanding of the importance of downslope winds in driving disastrous wildfires that threaten populated regions adjacent to mountain ranges in the western US. The unique characteristics of downslope wind‐driven fires require increased fire prevention and adaptation strategies to minimize losses and incorporation of changing human‐ignitions, fuel availability and dryness, and downslope wind occurrence to elucidate future fire risk.

 

In high energy physics (HEP), analysis metadata comes in many forms—from theoretical cross-sections, to calibration corrections, to details about file processing. Correctly applying metadata is a crucial and often time-consuming step in an analysis, but designing analysis metadata systems has historically received little direct attention. Among other considerations, an ideal metadata tool should be easy to use by new analysers, should scale to large data volumes and diverse processing paradigms, and should enable future analysis reinterpretation. This document, which is the product of community discussions organised by the HEP Software Foundation, categorises types of metadata by scope and format and gives examples of current metadata solutions. Important design considerations for metadata systems, including sociological factors, analysis preservation efforts, and technical factors, are discussed. A list of best practices and technical requirements for future analysis metadata systems is presented. These best practices could guide the development of a future cross-experimental effort for analysis metadata tools.

 

As marginalized communities continue to bear disproportionate impacts from environmental hazards, we urgently call for researchers and institutions to elevate the principles of Environmental Justice. The American Geophysical Union (AGU) GeoHealth section supports members' engagement in health‐related community‐engaged and community‐led transdisciplinary research. We highlight intersectional research that provides examples and actions for both individuals and organizations on community science and trust building, removing barriers created by scientific agency priorities and career expectations, and opportunities in education and policy. Justice does not start or end at one meeting; this is ongoing work that is active, evolving, and an ethical responsibility of AGU's membership.

 

ABSTRACT Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.