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1. On the Sensitivity of Large-Eddy Simulations of the Atmospheric Boundary Layer Coupled with Realistic Large-Scale Dynamics

   https://doi.org/10.1175/MWR-D-23-0101.1  

   Giani, Paolo ; Crippa, Paola ( April 2024 , Monthly Weather Review)

   We present a new ensemble of 36 numerical experiments aimed at comprehensively gauging the sensitivity of nested large-eddy simulations (LES) driven by large-scale dynamics. Specifically, we explore 36 multiscale configurations of the Weather Research and Forecasting (WRF) Model to simulate the boundary layer flow over the complex topography at the Perdigão field site, with five nested domains discretized at horizontal resolutions ranging from 11.25 km to 30 m. Each ensemble member has a unique combination of the following input factors: (i) large-scale initial and boundary conditions, (ii) subgrid turbulence modeling in thegray zoneof turbulence, (iii) subgrid-scale (SGS) models in LES, and (iv) topography and land-cover datasets. We probe their relative importance for LES calculations of velocity, temperature, and moisture fields. Variance decomposition analysis unravels large sensitivities to topography and land-use datasets and very weak sensitivity to the LES SGS model. Discrepancies within ensemble members can be as large as 2.5 m s⁻¹for the time-averaged near-surface wind speed on the ridge and as large as 10 m s⁻¹without time averaging. At specific time points, a large fraction of this sensitivity can be explained by the different turbulence models in the gray zone domains. We implement a horizontal momentum and moisture budget routine in WRF to further elucidate the mechanisms behind the observed sensitivity, paving the way for an increased understanding of the tangible effects of the gray zone of turbulence problem.

   Several science and engineering applications, including wind turbine siting and operations, weather prediction, and downscaling of climate projections, call for high-resolution numerical simulations of the lowest part of the atmosphere. Recent studies have highlighted that such high-resolution simulations, coupled with large-scale models, are challenging and require several important assumptions. With a new set of numerical experiments, we evaluate and compare the significance of different assumptions and outstanding challenges in multiscale modeling (i.e., coupling large-scale models and high-resolution atmospheric simulations). The ultimate goal of this analysis is to put each individual assumption into the wider perspective of a realistic problem and quantify its relative importance compared to other important modeling choices.

    

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2. Politics of attributing extreme events and disasters to climate change

   https://doi.org/10.1002/wcc.750  

   Lahsen, Myanna ; Ribot, Jesse ( December 2021 , WIREs Climate Change)

   Climate change certainly shapes weather events. However, describing climate and weather as the cause of disasters can be misleading, since disasters are caused by pre‐existing fragilities and inequalities on the ground. Analytic frames that attribute disaster to climate can divert attention from these place‐based vulnerabilities and their socio‐political causes. Thus, while politicians may want to blame crises on climate change, members of the public may prefer to hold government accountable for inadequate investments in flood or drought prevention and precarious living conditions. To be both strategic and moral, framing choices must therefore be sensitive to context‐dependent political meanings and particularities, and to how the values implicit within analytic frames about the causes of disasters shape policy responses. Such sensitivity requires multicausal analysis of weather‐linked disasters to illuminate a broader range of means to reduce the damages associated with climate change and weather extremes. Through examples from around the world, and especially Brazil, we discuss how and why climate‐centric disaster framing can erase from view—and, thus, from policy agendas—the very socio‐economic and political factors that most centrally cause vulnerability and suffering in weather extremes and disasters. We also offer a theoretical discussion of why attribution is not neutral. Analytic frameworks always embed choices about factors that matter, and thus are inherently normative and consequential for understandings of responsibility and action.

   This article is categorized under:

   Social Status of Climate Change Knowledge > Climate Science and Decision Making

   Attributing crises only to climate is inadequate from a mechanical, moral, and strategic policy points of view.

    

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3. Nonparametric Estimates of Demand in the California Health Insurance Exchange

   https://doi.org/10.3982/ECTA17215  

   Tebaldi, Pietro ; Torgovitsky, Alexander ; Yang, Hanbin ( January 2023 , Econometrica)

   We develop a new nonparametric approach for discrete choice and use it to analyze the demand for health insurance in the California Affordable Care Act marketplace. The model allows for endogenous prices and instrumental variables, while avoiding parametric functional form assumptions about the unobserved components of utility. We use the approach to estimate bounds on the effects of changing premiums or subsidies on coverage choices, consumer surplus, and government spending on subsidies. We find that a $10 decrease in monthly premium subsidies would cause a decline of between 1.8% and 6.7% in the proportion of subsidized adults with coverage. The reduction in total annual consumer surplus would be between $62 and $74 million, while the savings in yearly subsidy outlays would be between $207 and $602 million. We estimate the demand impacts of linking subsidies to age, finding that shifting subsidies from older to younger buyers would increase average consumer surplus, with potentially large impacts on enrollment. We also estimate the consumer surplus impact of removing the highly‐subsidized plans in the Silver metal tier, where we find that a nonparametric model is consistent with a wide range of possibilities. We find that comparable mixed logit models tend to yield price sensitivity estimates toward the lower end of the nonparametric bounds, while producing consumer surplus impacts that can be both higher and lower than the nonparametric bounds depending on the specification of random coefficients. 

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4. Unpriced climate risk and the potential consequences of overvaluation in US housing markets

   https://doi.org/10.1038/s41558-023-01594-8  

   Gourevitch, Jesse D. ; Kousky, Carolyn ; Liao, Yanjun ; Nolte, Christoph ; Pollack, Adam B. ; Porter, Jeremy R. ; Weill, Joakim A. ( February 2023 , Nature Climate Change)

   Climate change impacts threaten the stability of the US housing market. In response to growing concerns that increasing costs of flooding are not fully captured in property values, we quantify the magnitude of unpriced flood risk in the housing market by comparing the empirical and economically efficient prices for properties at risk. We find that residential properties exposed to flood risk are overvalued by US$121–US$237 billion, depending on the discount rate. In general, highly overvalued properties are concentrated in counties along the coast with no flood risk disclosure laws and where there is less concern about climate change. Low-income households are at greater risk of losing home equity from price deflation, and municipalities that are heavily reliant on property taxes for revenue are vulnerable to budgetary shortfalls. The consequences of these financial risks will depend on policy choices that influence who bears the costs of climate change.

    

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5. Context-Dependent Modulation of Early Visual Cortical Responses to Numerical and Nonnumerical Magnitudes

   https://doi.org/10.1162/jocn_a_01774  

   Park, Joonkoo ; Godbole, Sonia ; Woldorff, Marty G. ; Brannon, Elizabeth M. ( November 2021 , Journal of Cognitive Neuroscience)

   Abstract Whether and how the brain encodes discrete numerical magnitude differently from continuous nonnumerical magnitude is hotly debated. In a previous set of studies, we orthogonally varied numerical (numerosity) and nonnumerical (size and spacing) dimensions of dot arrays and demonstrated a strong modulation of early visual evoked potentials (VEPs) by numerosity and not by nonnumerical dimensions. Although very little is known about the brain's response to systematic changes in continuous dimensions of a dot array, some authors intuit that the visual processing stream must be more sensitive to continuous magnitude information than to numerosity. To address this possibility, we measured VEPs of participants viewing dot arrays that changed exclusively in one nonnumerical magnitude dimension at a time (size or spacing) while holding numerosity constant and compared this to a condition where numerosity was changed while holding size and spacing constant. We found reliable but small neural sensitivity to exclusive changes in size and spacing; however, exclusively changing numerosity elicited a much more robust modulation of the VEPs. Together with previous work, these findings suggest that sensitivity to magnitude dimensions in early visual cortex is context dependent: The brain is moderately sensitive to changes in size and spacing when numerosity is held constant, but sensitivity to these continuous variables diminishes to a negligible level when numerosity is allowed to vary at the same time. Neurophysiological explanations for the encoding and context dependency of numerical and nonnumerical magnitudes are proposed within the framework of neuronal normalization. 

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