Explore the vast range of titles available.

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R2=0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

The development of any atmosphere or ocean model warrants a suite of test cases (TCs) to verify its spatial and temporal discretizations, order of accuracy, stability, reproducibility, portability, scalability, etc. In this paper, we present a suite of shallow water TCs designed to verify the barotropic solver of atmosphere and ocean models. These include the non‐dispersive coastal Kelvin wave; the dispersive inertia‐gravity wave; the dispersive planetary and topographic Rossby waves; the barotropic tide; and a non‐linear manufactured solution. These TCs check the implementation of the linear pressure gradient term; the linear constant or variable‐coefficient Coriolis and bathymetry terms; and the non‐linear advection terms. Simulation results are presented for a variety of time‐stepping methods as well as two spatial discretizations: a mimetic finite volume method based on the TRiSK scheme, and a high‐order discontinuous Galerkin spectral element method. The experimental procedure for conducting these numerical experiments is detailed. It underscores several key considerations that vary depending on the chosen spatial discretization method. Finally, convergence studies of every TC are conducted with refinement in both space and time, only in space, and only in time. The convergence slopes match the expected theoretical predictions. Plain Language Summary Before running an atmosphere, ocean, or a coupled climate simulation, every model developer should ensure the correct implementation of each term in the governing equations that drive the models forward in time. This motivates the development of idealized test cases (TCs), each of which verifies a subset of terms in the governing equations with different initial and boundary conditions. Here we present a suite of six TCs for the momentum equation and sea surface height equation for ocean models in a single‐layer configuration. The computed results from the ocean model can be compared to exact solutions. The computed solution always has a small error, but is said to converge to the exact solution with reduction in grid cell size and time step. If the model converges at the expected rate, then we know that it is solving the governing equations correctly. We show results of convergence tests from two models, and share the full specifications of these TCs so that other ocean modelers may reproduce them. Key Points A suite of test cases is presented for the verification of barotropic dynamics of ocean models, with exact and manufactured solutions Specifications are provided for coastal Kelvin wave, inertia‐gravity wave, planetary and topographic Rossby waves, barotropic tide, and non‐linear cases Results are presented for a variety of time‐stepping methods and two types of spatial discretizations: TRiSK and discontinuous Galerkin spectral element method

Climate variability and change in the Southern Hemisphere (SH) are influenced by the Southern Annular Mode (SAM) and are closely related to changes in the kinematic properties of the SH surface zonal winds. The SAM and SH surface zonal winds have strong effects on the atmospheric and oceanic circulation system. In this study we investigate the variability and trend in the SAM and position and strength of the surface zonal wind stress (TAUX), using two ensembles of simulations covering the historical record from the Energy Exascale Earth System Model (E3SM‐HIST and Atmospheric Model Intercomparison Project) for 1979–2014. In addition, performance of two CO 2 forcing simulations from the E3SM (E3SM‐1pctCO2 and 4xCO2) is assessed to examine the sensitivity of the variability and changes in the SAM and SH surface TAUX to climate forcing. In general, all E3SM simulations tend to capture the dominant feature of the SAM pattern reasonably well. The annual SAM index in the E3SM‐HIST simulation shows a significant increasing trend. These features are similar to the trends in the strength (along with poleward shift in the position) of the annual surface TAUX. For the climatological surface TAUX position and strength, the two CO 2 forcing simulations show slightly poleward movement and stronger intensity, while the E3SM‐HIST is equatorward and weaker than observations. In the relationship between the SAM and surface TAUX, we show that the SAM index exhibits a positive (negative) relationship with the strength (position) of the surface TAUX in the variability for all seasons and annual mean. Key Points Climate variability and trend in the SAM and Southern Hemisphere surface zonal wind stress in E3SM simulations are assessed Increasing CO2 leads to poleward shift and strengthening of maximum surface zonal wind stress The SAM index variability is closely associated with the variability in the position and strength of surface zonal wind stress

We report the results of a computational investigation of two blow-up criteria for the 3D incompressible Euler equations. One criterion was proven in a previous work, and a related criterion is proved here. These criteria are based on an inviscid regularization of the Euler equations known as the 3D Euler–Voigt equations, which are known to be globally well-posed. Moreover, simulations of the 3D Euler–Voigt equations also require less resolution than simulations of the 3D Euler equations for fixed values of the regularization parameter α>0. Therefore, the new blow-up criteria allow one to gain information about possible singularity formation in the 3D Euler equations indirectly, namely by simulating the better-behaved 3D Euler–Voigt equations. The new criteria are only known to be sufficient criterion for blow-up. Therefore, to test the robustness of the inviscid-regularization approach, we also investigate analogous criteria for blow-up of the 1D Burgers equation, where blow-up is well known to occur.

This paper presents the first practical application of local time‐stepping (LTS) schemes in the Model for Prediction Across Scales‐Ocean (MPAS‐O). We use LTS schemes in a single‐layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable‐resolution meshes used are of unprecedentedly high resolution in MPAS‐O, containing cells as small as 125 m wide in Delaware Bay. It is shown that a particular, third‐order LTS scheme (LTS3) produces sea‐surface height solutions that are of comparable quality to solutions produced by the classical four‐stage, fourth‐order Runge‐Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases considered, where the number of cells using the coarse time‐step relative to those using the fine time‐step is as low as 1:1. This shows that LTS schemes are viable for use in MPAS‐O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design and configuration of LTS regions for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time‐step relative to those using the fine time‐step, typically at least 1:5 to see an increase in performance. Plain Language Summary In many applications of modern ocean models, it is useful to partition the globe into cells of different sizes, depending on the level of accuracy desired in a given region. One uses smaller cells in regions where more accuracy is desired, and larger cells elsewhere in order to save on computational costs; such partitions are generally called variable‐resolution meshes. A well known limitation of explicit time‐stepping methods, used to advance the temporal state of the model, is that the largest step forward in time the model can take is limited by the size of the smallest cell in the mesh. Global time‐stepping schemes use a given time‐step, whose size is determined by the size of the smallest cell in the mesh, everywhere on the mesh. Local time‐stepping (LTS) methods, allow us to select multiple time‐steps based on the size of cells in a localized region. Here, we use LTS schemes to model the storm surge around the eastern US coast during Hurricane Sandy in 2012. We show that a particular LTS scheme produces sea‐surface height predictions of comparable quality to those produced by a state‐of‐the‐art global time‐stepping method and that LTS is up to 35% faster in the best cases. Key Points Storm surge modeling of Hurricane Sandy around Delaware Bay is used to demonstrate variable‐resolution ocean time‐stepping methods Local time‐stepping schemes are up to 35% faster, and produce solutions of comparable quality to higher‐order globally uniform schemes

The Energy Exascale Earth System Model (E3SM) is a new coupled Earth system model sponsored by the U.S Department of Energy. Here we present E3SM global simulations using active ocean and sea ice that are driven by the Coordinated Ocean‐ice Reference Experiments II (CORE‐II) interannual atmospheric forcing data set. The E3SM ocean and sea ice components are MPAS‐Ocean and MPAS‐Seaice, which use the Model for Prediction Across Scales (MPAS) framework and run on unstructured horizontal meshes. For this study, grid cells vary from 30 to 60 km for the low‐resolution mesh and 6 to 18 km at high resolution. The vertical grid is a structured z‐star coordinate and uses 60 and 80 layers for low and high resolution, respectively. The lower‐resolution simulation was run for five CORE cycles (310 years) with little drift in sea surface temperature (SST) or heat content. The meridional heat transport (MHT) is within observational range, while the meridional overturning circulation at 26.5°N is low compared to observations. The largest temperature biases occur in the Labrador Sea and western boundary currents (WBCs), and the mixed layer is deeper than observations at northern high latitudes in the winter months. In the Antarctic, maximum mixed layer depths (MLD) compare well with observations, but the spatial MLD pattern is shifted relative to observations. Sea ice extent, volume, and concentration agree well with observations. At high resolution, the sea surface height compares well with satellite observations in mean and variability. Key Points The Energy Exascale Earth System Model (E3SM) is a new climate model by the U.S. Department of Energy E3SM ocean and ice components use unstructured horizontal meshes for variable‐resolution simulations The 310‐year E3SM simulations agree well with observations in ocean currents and sea ice coverage

We examine ocean tides in the barotropic version of the Model for Prediction Across Scales (MPAS‐Ocean), the ocean component of the Department of Energy Earth system model. We focus on four factors that affect tidal accuracy: self‐attraction and loading (SAL), model resolution, details of the underlying bathymetry, and parameterized topographic wave drag. The SAL term accounts for the tidal loading of Earth's crust and the self‐gravitation of the ocean and the load‐deformed Earth. A common method for calculating SAL is to decompose mass anomalies into their spherical harmonic constituents. Here, we compare a scalar SAL approximation versus an inline SAL using a fast spherical harmonic transform package. Wave drag accounts for energy lost by breaking internal tides that are produced by barotropic tidal flow over topographic features. We compare a series of successively finer quasi‐uniform resolution meshes (62.9, 31.5, 15.7, and 7.87 km) to a variable resolution (45 to 5 km) configuration. We ran MPAS‐Ocean in a single‐layer barotropic mode forced by five tidal constituents. The 45 to 5 km variable resolution mesh obtained the best total root‐mean‐square error (5.4 cm) for the deep ocean (>$ > $ 1,000 m) M2${\\mathrm{M}}_{2}$tide compared to TPXO8 and ran twice as fast as the quasi‐uniform 8 km mesh, which had an error of 5.8 cm. This error is comparable to those found in other forward (non‐assimilative) ocean tide models. In future work, we plan to use MPAS‐Ocean to study tidal interactions with other Earth system components, and the tidal response to climate change. Plain Language Summary Over the next century, climate change impacts on coastal regions will include floods, droughts, erosion, and severe weather events. The Department of Energy (DoE) is funding the Integrated Coastal Modeling Project to understand these potential risks better. In this paper, we implement tides in the DoE ocean model. Tides themselves respond to climate change, altering coastal flooding risk assessments. We explore the sensitivity of tides to model resolution (the spacing of model gridpoints), ocean‐floor topography, and the so‐called “self‐attraction and loading” (SAL) effect. Self‐attraction and loading occurs as the mass of water in a location fluctuates, causing a deformation of the Earth's crust and changes in the gravitational potential, which must be accounted for when modeling tides. We present a computationally efficient method of calculating the SAL effects and show that it is more accurate than other commonly used approximations. In future work we will examine interactions of tides with other components of the climate system, including sea ice, floating ice shelves, rivers, and current systems. Key Points We calculate the full self‐attraction and loading (SAL) term inline, in a barotropic configuration of Model for Prediction Across Scales (MPAS‐Ocean) Inclusion of the inline SAL and higher resolution meshes yield improved tidal accuracy in stand‐alone barotropic MPAS‐Ocean configurations A 45 to 5‐km variable‐resolution mesh provides reduced tidal errors with better computational performance than a quasi‐uniform 8‐km mesh

The processes responsible for freshwater flux from the Antarctic Ice Sheet (AIS), ice‐shelf basal melting and iceberg calving, are generally poorly represented in current Earth System Models (ESMs). Here we document the cryosphere configuration of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) v1.2. This includes simulating Antarctic ice‐shelf basal melting, which has been implemented through simulating the ocean circulation within static Antarctic ice‐shelf cavities, allowing for the ability to calculate ice‐shelf basal melt rates from the associated heat and freshwater fluxes. In addition, we added the capability to prescribe forcing from iceberg melt, allowing for realistic representation of the other dominant mass loss process from the AIS. In standard resolution simulations (using a noneddying ocean) under preindustrial climate forcing, we find high sensitivity of modeled ocean/ice shelf interactions to the ocean state, which can result in a transition to a high basal melt regime under the Filchner‐Ronne Ice Shelf (FRIS), presenting a significant challenge to representing the ocean/ice shelf system in a coupled ESM. We show that inclusion of a spatially dependent parameterization of eddy‐induced transport reduces biases in water mass properties on the Antarctic continental shelf. With these improvements, E3SM produces realistic ice‐shelf basal melt rates across the continent that are generally within the range inferred from observations. The accurate representation of ice‐shelf basal melting within a coupled ESM is an important step toward reducing uncertainties in projections of the Antarctic response to climate change and Antarctica's contribution to global sea‐level rise. Plain Language Summary The future of the Antarctic Ice Sheet (AIS) has the potential to have broad impacts on global climate, perhaps most notably in contributing to sea‐level rise. The current generation of Earth System Models (ESMs) do not accurately represent the two primary means in which ice is lost from the AIS, through melting at the base of ice shelves floating on the ocean and the calving of icebergs. This limits our ability to make climate projections that incorporate the impacts of the AIS in a changing climate. Here, we demonstrate a novel capability to model one of those processes, ice‐shelf basal melting, in an ESM. We demonstrate the ability to simulate ice‐shelf basal melt rates across many Antarctic ice shelves that are in line with present day observations. We also find that, for certain ice shelves, modeled ice‐shelf basal melting can experience a rapid transition to high melting far above present‐day estimates, and this simulated high melting can be mitigated through improved ocean physics. Key Points Capabilities have been added to an Earth System Model to model realistic Antarctic ice‐shelf basal melt fluxes and prescribe iceberg forcing Simulated basal melt rates have a strong sensitivity to the ocean mesoscale eddy parameterization For one choice of the mesoscale eddy parameterization, the Filchner‐Ronne Ice Shelf transitions to a high melt regime

Climate model components utilizing unstructured meshes enable variableresolution, regionally enhanced simulations within global domains. Here we investigate the relationship between mesh quality and simulation statistics using the JIGSAW unstructured meshing library and the Model for Prediction Across ScalesOcean (MPASOcean) with a focus on Gulf Stream dynamics. In the base configuration, the refined region employs 8 km cells that extend 400 km from the coast of North America. This coastal refined region is embedded within a lowresolution global domain, with cell size varying latitudinally between 30 and 60 km. The resolution transition region between the refined region and background mesh is 600 km wide. Three sensitivity tests are conducted: 1) the quality of meshes is intentionally degraded so that horizontal cells are progressively more distorted; 2) the transition region from high to low resolution is steepened; and 3) resolution of the coastal refinement region is varied from 30 km to 8 km. Overall, the ocean simulations are shown to be robust to mesh resolution and quality alterations. Meshes that are substantially degraded still produce realistic currents, with Southern Ocean transports within 0.4% and Gulf Stream transports within 12% of highquality mesh results. The narrowest transition case of 100 km did not produce any spurious effects. Refined regions with high resolution produce eddy kinetic energy and sea surface height variability that are similar to the highresolution reference simulation. These results provide heuristics for the design criteria of variableresolution climate model domains.