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Abstract Tropical cyclone (TC) impacts along the western Atlantic and Caribbean margin are not spatially uniform. Proxy based reconstructions of Common Era TC activity highlight this non‐uniform distribution at centennial‐millennial timescales. However, the sparse geographic scope of these reconstructions impedes our assessment of TC landfalls across broader spatial domains. This work presents a compilation of new and existing TC reconstructions from the Yucatan Peninsula for comparison with a contemporaneous compilation from New England, showing that these regions occupy distal nodes of a low‐frequency TC dipole. Increased Yucatan (New England) storminess is closely linked to intervals of Northern Hemisphere warming (cooling) and the expansion (contraction) of the Intertropical Convergence Zone, suggesting that secular shifts in the mean climate state mediate dipole orientation. 

Abstract New York City (NYC) faces many challenges in the coming decades due to climate change and its interactions with social vulnerabilities and uneven urban development patterns and processes. This New York City Panel on Climate Change (NPCC) report contributes to the Panel's mandate to advise the city on climate change and provide timely climate risk information that can inform flexible and equitable adaptation pathways that enhance resilience to climate change. This report presents up‐to‐date scientific information as well as updated sea level rise projections of record. We also present a new methodology related to climate extremes and describe new methods for developing the next generation of climate projections for the New York metropolitan region. Future work by the Panel should compare the temperature and precipitation projections presented in this report with a subset of models to determine the potential impact and relevance of the “hot model” problem. NPCC4 expects to establish new projections‐of‐record for precipitation and temperature in 2024 based on this comparison and additional analysis. Nevertheless, the temperature and precipitation projections presented in this report may be useful for NYC stakeholders in the interim as they rely on the newest generation of global climate models. 

Abstract The collapse of the Maya civilization in the late 1st/early 2nd millennium CE has been attributed to multiple internal and external causes including overpopulation, increased warfare, and environmental deterioration. Yet the role hurricanes may have played in the fracturing of Maya socio-political networks, site abandonment, and cultural reconfiguration remains unexplored. Here we present a 2200 yearlong hurricane record developed from sediment recovered from a flooded cenote on the northeastern Yucatan peninsula. The sediment archive contains fine grain autogenic carbonate interspersed with anomalous deposits of coarse carbonate material that we interpret as evidence of local hurricane activity. This interpretation is supported by the correlation between the multi-decadal distribution of recent coarse beds and the temporal distribution of modern regional landfalling storms. In total, this record allows us to reconstruct the variable hurricane conditions impacting the northern lowland Maya during the Late Preclassic, Classic, and Postclassic Periods. Strikingly, persistent above-average hurricane frequency between ~ 700 and 1450 CE encompasses the Maya Terminal Classic Phase, the declines of Chichén Itza, Cobá, and subsequent rise and fall of the Mayapán Confederacy. This suggests that hurricanes may have posed an additional environmental stressor necessary of consideration when examining the Postclassic transformation of northern Maya polities. 

Abstract Extreme heat events are becoming more frequent and intense. In cities, the urban heat island (UHI) can often intensify extreme heat exposure, presenting a public health challenge across vulnerable populations without access to adaptive measures. Here, we explore the impacts of increasing residential air-conditioning (AC) adoption as one such adaptive measure to extreme heat, with New York City (NYC) as a case study. This study uses AC adoption data from NYC Housing and Vacancy Surveys to study impacts to indoor heat exposure, energy demand, and UHI. The Weather Research and Forecasting (WRF) model, coupled with a multilayer building environment parameterization and building energy model (BEP–BEM), is used to perform this analysis. The BEP–BEM schemes are modified to account for partial AC use and used to analyze current and full AC adoption scenarios. A city-scale case study is performed over the summer months of June–August 2018, which includes three different extreme heat events. Simulation results show good agreement with surface weather stations. We show that increasing AC systems to 100% usage across NYC results in a peak energy demand increase of 20%, while increasing UHI on average by 0.42 °C. Results highlight potential trade-offs in extreme heat adaptation strategies for cities, which may be necessary in the context of increasing extreme heat events. 

We examine the uneven social and spatial distributions of COVID-19 and their relationships with indicators of social vulnerability in the U.S. epicenter, New York City (NYC). As of July 17th, 2020, NYC, despite having only 2.5% of the U.S. population, has [Formula: see text]6% of all confirmed cases, and [Formula: see text]16% of all deaths, making it a key learning ground for the social dynamics of the disease. Our analysis focuses on the multiple potential social, economic, and demographic drivers of disproportionate impacts in COVID-19 cases and deaths, as well as population rates of testing. Findings show that immediate impacts of COVID-19 largely fall along lines of race and class. Indicators of poverty, race, disability, language isolation, rent burden, unemployment, lack of health insurance, and housing crowding all significantly drive spatial patterns in prevalence of COVID-19 testing, confirmed cases, death rates, and severity. Income in particular has a consistent negative relationship with rates of death and disease severity. The largest differences in social vulnerability indicators are also driven by populations of people of color, poverty, housing crowding, and rates of disability. Results highlight the need for targeted responses to address injustice of COVID-19 cases and deaths, importance of recovery strategies that account for differential vulnerability, and provide an analytical approach for advancing research to examine potential similar injustice of COVID-19 in other U.S. cities. Significance Statement Communities around the world have variable success in mitigating the social impacts of COVID-19, with many urban areas being hit particularly hard. Analysis of social vulnerability to COVID-19 in the NYC, the U.S. national epicenter, shows strongly disproportionate impacts of the pandemic on low income populations and communities of color. Results highlight the class and racial inequities of the coronavirus pandemic in NYC, and the need to unpack the drivers of social vulnerability. To that aim, we provide a replicable framework for examining patterns of uneven social vulnerability to COVID-19- using publicly available data which can be readily applied in other study regions, especially within the U.S.A. This study is important to inform public and policy debate over strategies for short- and long-term responses that address the injustice of disproportionate impacts of COVID-19. Although similar studies examining social vulnerability and equity dimensions of the COVID-19 outbreak in cities across the U.S. have been conducted (Cordes and Castro 2020, Kim and Bostwick 2002, Gaynor and Wilson 2020; Wang et al. 2020; Choi and Unwin 2020), this study provides a more comprehensive analysis in NYC that extends previous contributions to use the highest resolution spatial units for data aggregation (ZCTAs). We also include mortality and severity rates as key indicators and provide a replicable framework that draws from the Centers for Disease Control and Prevention’s Social Vulnerability indicators for communities in NYC. 

Almost all of the work in graphical models for game theory has mirrored previous work in probabilistic graphical models. Our work considers the opposite direction: Taking advantage of advances in equilibrium computation for probabilistic inference. In particular, we present formulations of inference problems in Markov random fields (MRFs) as computation of equilibria in a certain class of game-theoretic graphical models. While some previous work explores this direction, we still lack a more precise connection between variational probabilistic inference in MRFs and correlated equilibria. This paper sharpens the connection, which helps us exploit relatively more recent theoretical and empirical results from the literature on algorithmic and computational game theory on the tractable, polynomial-time computation of exact or approximate correlated equilibria in graphical games with arbitrary, loopy graph structure. Our work discusses how to design new algorithms with equally tractable guarantees for the computation of approximate variational inference in MRFs. In addition, inspired by a previously stated game-theoretic view of tree-reweighted message-passing techniques for belief inference as a zero-sum game, we propose a different, general-sum potential game to design approximate fictitious-play techniques. Empirical evaluations on synthetic experiments and on an application to soft de-noising on real-world image datasets illustrate the performance of our proposed approach and shed some light on the conditions under which the resulting belief inference algorithms may be most effective relative to standard state-of-the-art methods.