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A bstract Two-dimensional conformal field theories exhibit a universal free energy in the high temperature limit T → ∞, and a universal spectrum in the Cardy regime, Δ → ∞. We show that a much stronger form of universality holds in theories with a large central charge c and a sparse light spectrum. In these theories, the free energy is universal at all values of the temperature, and the microscopic spectrum matches the Cardy entropy for all Δ ≥ c 6 . The same is true of three-dimensional quantum gravity; therefore our results provide simple necessary and sufficient criteria for 2d CFTs to behave holographically in terms of the leading spectrum and thermodynamics. We also discuss several applications to CFT and gravity, including operator dimension bounds derived from the modular bootstrap, universality in symmetric orbifolds, and the role of non-universal ‘enigma’ saddlepoints in the thermodynamics of 3d gravity.

A bstract The Gepner model (2) 4 describes the sigma model of the Fermat quartic K3 surface. Moving through the nearby moduli space using conformal perturbation theory, we investigate how the conformal weights of its fields change at first and second order and find approximate minima. This serves as a toy model for a mechanism that could produce new chiral fields and possibly new nearby rational CFTs.

A bstract We analyze how deforming symmetric product orbifolds of two-dimensional N = 2 conformal field theories by an exactly marginal operator lifts higher spin currents present at the orbifold point. We find on the one hand that these currents are universally lifted regardless of the underlying CFT. On the other hand the details of the lifting are surprisingly non-universal, with dependence on the central charge of the underlying CFT and the specific marginal operator in use. In the context of the AdS/CFT correspondence, our results illustrate the mechanism by which the stringy spectrum turns into a supergravity spectrum when moving through the moduli space. They also provide further evidence that symmetric product orbifolds of N = 2 minimal models are holographic.

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change.We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2).We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) andR2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. ModelledNOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and<0.1, respectively.This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

Social distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business-as-usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO2) and ozone (O3) at 5756 observation sites in 46 countries from January through June 2020. Reductions in NO2 coincide with the timing and intensity of COVID-19 restrictions, ranging from 60 % in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO2 concentrations were 18 (13–23) % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5 % of the business-as-usual estimate. NO2 reductions in Europe and the US have been more gradual, with a halting recovery starting in late March. We estimate that the global NOx (NO + NO2) emission reduction during the first 6 months of 2020 amounted to 3.1 (2.6–3.6) TgN, equivalent to 5.5 (4.7–6.4) % of the annual anthropogenic total. The response of surface O3 is complicated by competing influences of nonlinear atmospheric chemistry. While surface O3 increased by up to 50 % in some locations, we find the overall net impact on daily average O3 between February–June 2020 to be small. However, our analysis indicates a flattening of the O3 diurnal cycle with an increase in nighttime ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production. The O3 response is dependent on season, timescale, and environment, with declines in surface O3 forecasted if NOx emission reductions continue.

A bstract We investigate the growth of coefficients in the elliptic genus of symmetric product orbifolds at large central charge. We find that this landscape decomposes into two regions. In one region, the growth of the low energy states is Hagedorn, which indicates a stringy dual. In the other, the growth is much slower, and compatible with the spectrum of a supergravity theory on AdS 3 . We provide a simple diagnostic which places any symmetric product orbifold in either region. We construct a class of elliptic genera with such supergravity-like growth, indicating the possible existence of new realizations of AdS 3 /CFT 2 where the bulk is a semi-classical supergravity theory. In such cases, we give exact expressions for the BPS degeneracies, which could be matched with the spectrum of perturbative states in a dual supergravity description.

Computational models of the Earth System are critical tools for modern scientific inquiry. Effortstoward evaluating and improving errors in representations of physical and chemical processes inthese large computational systems are commonly stymied by highly nonlinear and complexerror behavior. Recent work has shown that these errors can be effectively predicted usingmodern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previousstudies to apply an interpretable A.I. technique to not only predict model errors but also movetoward understanding the underlying reasons for successful error prediction. We use XGBoostclassification trees and SHapley Additive exPlanations (SHAP) analysis to explore the errors inthe prediction of lightning occurrence in the NASA GEOS model, a widely used Earth SystemModel. This explainable error prediction system can effectively predict the model error andindicates that the errors are strongly related to convective processes and the characteristics ofthe land surface.

Tropospheric ozone is an air pollutant and a greenhouse gas whose anthropogenic production is limited principally by the supply of nitrogen oxides (NOx) from combustion. Tropospheric ozone in the northern hemisphere has been rising despite the flattening of NOx emissions in recent decades. Here we propose that this sustained increase could result from the photolysis of nitrate particles (pNO3−) to regenerate NOx. Including pNO3− photolysis in the GEOS‐Chem atmospheric chemistry model improves the consistency with ozone observations. Our simulations show that pNO3− concentrations have increased since the 1960s because of rising ammonia and falling SO2 emissions, augmenting the increase in ozone in the northern extratropics by about 50% to better match the observed ozone trend. pNO3− will likely continue to increase through 2050, which would drive a continued increase in ozone even as NOx emissions decrease. More work is needed to better understand the mechanism and rates of pNO3− photolysis. Plain Language Summary In the troposphere, ozone is an air pollutant and a greenhouse gas. Tropospheric ozone forms from reactions involving carbon monoxide and volatile organic compounds in the presence of nitrogen oxides. Global emissions of nitrogen oxides have been leveling off in the past few decades, yet tropospheric ozone levels have kept on rising. We propose that this rise in ozone could be driven by a growing source of nitrogen oxides from the photolysis of nitrate particles, which have become more abundant due to falling sulfur dioxide and rising ammonia emissions. We find that including nitrate particle photolysis in an atmospheric chemistry model improves its consistency with the observed ozone distribution and trends. Our results point to the importance of considering nitrate particle photolysis for future projections of climate forcing from tropospheric ozone, and the need for further work to reduce the uncertainty in the mechanism and rates of the process. Key Points Particulate nitrate photolysis improves the consistency of tropospheric ozone in the GEOS‐Chem model with observations Increase in particulate nitrate due to falling SO2 and rising NH3 emissions could augment the long‐term increase in tropospheric ozone Better characterization of the mechanism and rates of particulate nitrate photolysis is needed

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25 degree) global constituent prediction system from NASA’s Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA’s broad range of space-based and in-situ observation sand to support flight campaign planning, support of satellite observations, and air quality research. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide analyses and 5-day forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community.Evaluation of GEOS-CF against satellite, ozone sonde and surface observations show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to -0.3, normalized root mean square errors (NRMSE) between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants under a variety of meteorological conditions, yet also highlight current limitations, such as an over prediction of summertime ozone over the Southeast United States. GEOS-CFv1.0 generally overestimates aerosols by 20-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions.The 5-day hourly forecasts have skill scores comparable to the analysis. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

A bstract We discuss the application of Siegel paramodular forms to the counting of polar states in symmetric product orbifold CFTs. We present five special examples and provide exact analytic counting formulas for their polar states. The first example reproduces the known result for type IIB supergravity on AdS 3 × S 3 × K 3, whereas the other four examples give new counting formulas. Their crucial feature is that the low energy spectrum is very sparse, which suggests the existence of a suitable dual supergravity theory. These examples open a path to novel realizations of AdS 3 /CFT 2 .