Union of Concerned Scientists

Abstract Understanding the weather and climate of Mesoamerica and the Caribbean remains challenging due to complex hydroclimate interactions, limited observations, and poor representation of regional processes in global models. We introduce the Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative that fosters research collaboration to advance weather and climate science, develop convection-permitting datasets, and promote knowledge exchange. MAAG’s first major contribution is a 2-week convection-permitting simulation of Hurricane Maria (2017) using Model for Prediction Across Scales–Atmosphere (MPAS-A), featuring a novel regional 15- to 3-km variable-resolution mesh over the region. Initial evaluation shows that MPAS-A captures key features like precipitation patterns, the intertropical convergence zone, and low-level jets. Some biases remain, particularly in enhanced land convection and slight deviations in Maria’s track. This novel dataset, now publicly available through NCAR’s Data Archive, supports studies of other extreme events and mesoscale convective systems active during the same period. It offers a valuable resource for the research community. MAAG is a new but rapidly growing initiative achieving notable milestones in a short time. It serves as a collaborative platform for codesigning high-resolution modeling experiments aimed at producing actionable weather and climate information. We invite the community to join MAAG, explore this initial dataset, and advance regional weather and climate research. Significance Statement The Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative, aims at addressing the complex challenges of understanding weather and climate in Mesoamerica and the Caribbean. MAAG’s first major achievement is a 2-week convection-permitting simulation of Hurricane Maria (2017) using a novel 15- to 3-km variable-resolution mesh. This dataset accurately captures key regional features and is publicly available through NCAR’s Data Archive. By fostering collaboration through data production and sharing, monthly group meetings that serve as a platform for networking and knowledge exchange, and the development of advanced high-resolution datasets, MAAG provides a vital resource for advancing regional weather and climate science. The initiative is rapidly growing, serving as a platform for codesigned modeling experiments aimed at producing actionable climate information for academia and different sectors. We invite the scientific community to join MAAG and advance research in this critical region.

The SARS-Cov-2 (COVID-19) pandemic has had enormous psychological and economic impacts around the globe. Early responses to the pandemic, including different forms of quarantine and social/physical distancing led to new workforce, educational, and mobility patterns. Such changes placed many people at greater risk of poor mental health. Maintenance of behavioural routines may buffer the impacts of stress on mental health. However, relationships between disruptions to routines and mental health outcomes during the pandemic – or how these relationships are moderated by changes in workforce participation – have been understudied. This article describes the development and implementation of an index of disruptions to routine behaviours during the COVID-19 pandemic in a national, probabilistic sample in the United States. We hypothesised and found that reported experience of the COVID-19-related disruptions to routines was associated with higher levels of psychological distress. We also hypothesised and found that individuals’ work status affected their ability to weather their experiences of the pandemic. In practical terms, the findings suggest that different types of support – from mental health support, to financial support, to help (re)establishing a routine – might be needed for different people, depending on the types of disruptions experienced.

ABSTRACT While existing US strategic missile defence systems are not designed for and cannot defend against peer and near-peer adversary threats, both Russia and China appear concerned about eventual technical breakthroughs and US overconfidence. Both Russia and China are developing long-range nuclear delivery systems that are designed to counter ballistic missile defences to assure their nuclear retaliatory capabilities. Advances in sensors, materials, and machine learning will afford improvements to missile defence in future decades. How will the offense/defence competition play out? This paper examines these technical trends and concludes that while technical advances are likely to improve the performance and cost-effectiveness of ballistic missile defences, peer and near-peer adversaries will also improve their ability to avoid, overwhelm, or defeat such defences and they will not play a decisive role in limiting nuclear retaliation by a responsive adversary.

Recent promotion of new reactor technologies appears to disregard decades-old concerns about nuclear proliferation

Abstract Coupled global climate models (GCMs) generally fail to reproduce the observed sea‐surface temperature (SST) trend pattern since the 1980s. The model‐observation discrepancies may arise in part from the lack of realistic Antarctic ice‐sheet meltwater input in GCMs. Here we employ two sets of CESM1‐CAM5 simulations forced by anomalous Antarctic meltwater fluxes over 1980–2013 and through the 21st century. Both show a reduced global warming rate and an SST trend pattern that better resembles observations. The meltwater drives surface cooling in the Southern Ocean and the tropical southeast Pacific, in turn increasing low‐cloud cover and driving radiative feedbacks to become more stabilizing (corresponding to a lower effective climate sensitivity). These feedback changes can contribute as substantially as ocean heat uptake efficiency changes in reducing the global warming rate. Accurately projecting historical and future warming thus requires improved representation of Antarctic meltwater and its impacts.

Outdoor workers perform critical societal functions, often despite higher-than-average on-the-job risks and below-average pay. Climate change is expected to increase the frequency of days when it is too hot to safely work outdoors, compounding risks to workers and placing new stressors on the personal, local, state, and federal economies that depend on them. After quantifying the number of outdoor workers in the contiguous United States and their median earnings, we couple heat-based work reduction recommendations from the U.S. Centers for Disease Control and Prevention with an analysis of hourly weather station data to develop novel algorithms for calculating the annual number of unsafe workdays due to extreme heat. We apply these algorithms to projections of the frequency of extreme heat days to quantify the exposure of the outdoor workforce to extreme heat and the associated earnings at risk under different emissions scenarios and, for the first time, different adaptation measures. With a trajectory of modest greenhouse gas emissions reductions, outdoor worker exposure to extreme heat would triple that of the late 20th-century baseline by mid-century, and earnings at risk would reach an estimated $39.3 billion annually. By the late century with that same trajectory, exposure would increase four-fold compared to the baseline with an estimated $49.2 billion in annual earnings at risk. Losses are considerably higher with a limited-mitigation trajectory. While universal adoption of 2 specific adaptation measures in conjunction could reduce mid-century and late-century economic risks by roughly 90% and 93%, respectively, practical limitations to their adoption suggest that emissions mitigation policies will be critical for ensuring the well-being and livelihoods of outdoor workers in a warming climate.

Perennial vegetables are a neglected and underutilized class of crops with potential to address 21st century challenges. They represent 33–56% of cultivated vegetable species, and occupy 6% of world vegetable cropland. Despite their distinct relevance to climate change mitigation and nutritional security, perennial vegetables receive little attention in the scientific literature. Compared to widely grown and marketed vegetable crops, many perennial vegetables show higher levels of key nutrients needed to address deficiencies. Trees with edible leaves are the group of vegetables with the highest levels of these key nutrients. Individual “multi-nutrient” species are identified with very high levels of multiple nutrients for addressing deficiencies. This paper reports on the synthesis and meta-analysis of a heretofore fragmented global literature on 613 cultivated perennial vegetables, representing 107 botanical families from every inhabited continent, in order to characterize the extent and potential of this class of crops. Carbon sequestration potential from new adoption of perennial vegetables is estimated at 22.7–280.6 MMT CO2-eq/yr on 4.6–26.4 Mha by 2050.