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At least 109 records · Page 6

Data from: Nitrous oxide emissions during establishment of eight alternative cellulosic bioenergy cropping systems in the North Central United States

Greenhouse gas (GHG) emissions from soils are a key sustainability metric of cropping systems. During crop establishment, disruptive land-use change is known to be a critical, but under reported period, for determining GHG emissions. We measured soil N2O emissions and potential environmental drivers of these fluxes from a three-year establishment-phase bioenergy cropping systems experiment replicated in southcentral Wisconsin (ARL) and southwestern Michigan (KBS). Cropping systems treatments were annual monocultures (continuous corn, corn–soybean–canola rotation), perennial monocultures (switchgrass, miscanthus, and poplar), and perennial polycultures (native grass mixture, early successional community, and restored prairie) all grown using best management practices specific to the system. Cumulative three-year N2O emissions from annuals were 142% higher than from perennials, with fertilized perennials 190% higher than unfertilized perennials. Emissions ranged from 3.1 to 19.1 kg N2O-N ha−1 yr−1 for the annuals with continuous corn > corn–soybean–canola rotation and 1.1 to 6.3 kg N2O-N ha−1 yr−1 for perennials. Nitrous oxide peak fluxes typically were associated with precipitation events that closely followed fertilization. Bayesian modeling of N2O fluxes based on measured environmental factors explained 33% of variability across all systems. Models trained on single systems performed well in most monocultures (e.g., R2 = 0.52 for poplar) but notably worse in polycultures (e.g., R2 = 0.17 for early successional, R2 = 0.06 for restored prairie), indicating that simulation models that include N2O emissions should be parameterized specific to particular plant communities. Our results indicate that perennial bioenergy crops in their establishment phase emit less N2O than annual crops, especially when not fertilized. These findings should be considered further alongside yield and other metrics contributing to important ecosystem services.

09 BIOMASS FUELS↗

100 Years of Research on Mesoscale Convective Systems

Abstract When cumulonimbus clouds aggregate, developing into a single entity with precipitation covering a horizontal scale of hundreds of kilometers, they are called mesoscale convective systems (MCSs). They account for much of Earth’s precipitation, generate severe weather events and flooding, produce prodigious cirriform anvil clouds, and affect the evolution of the larger-scale circulation. Understanding the inner workings of MCSs has resulted from developments in observational technology and modeling. Time–space conversion of ordinary surface and upper-air observations provided early insight into MCSs, but deeper understanding has followed field campaigns using increasingly sophisticated radars, better aircraft instrumentation, and an ever-widening range of satellite instruments, especially satellite-borne radars. High-resolution modeling and theoretical insights have shown that aggregated cumulonimbus clouds induce a mesoscale circulation consisting of air overturning on a scale larger than the scale of individual convective up- and downdrafts. These layers can be kilometers deep and decoupled from the boundary layer in elevated MCSs. Cooling in the lower troposphere and heating aloft characterize the stratiform regions of MCSs. As a result, long-lived MCSs with large stratiform regions have a top-heavy heating profile that generates potential vorticity in midlevels, thus influencing the larger-scale circulation within which the MCSs occur. Global satellite data show MCSs varying in structure, depending on the prevailing large-scale circulation and topography. These patterns are likely to change with global warming. In addition, environmental pollution affects MCS structure and dynamics subtly. Feedbacks of MCSs therefore need to be included or parameterized in climate models.

Houze, Jr., Robert A.↗

Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modelling

Climate simulation with more accurate process-level representation at finer resolutions is a pressing necessity in order to provide actionable information to policy-makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building, and running high-resolution (HR, here 0.25° average grid spacing at the equator) models (HRM). A more affordable path to HRM is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low-resolution (LR, 1°). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations, as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces both well- and poorly simulated behaviours of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behaviour of physical parameterizations, especially for variables influencing sub-grid scale physical processes. RRM can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a testbed, and as such represents an efficient and more robust physics testbed capability. Our conclusions provide additional confirmatory evidence that the RRM is an efficient and effective approach for HRM development and hydrologic research.

54 ENVIRONMENTAL SCIENCES↗

A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment

This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R 2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system’s variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems.

54 ENVIRONMENTAL SCIENCES↗

An updated estimate of the Mu2e experiment sensitivity

The Mu2e experiment at Fermilab will search for the conversion of a negative muon into an electron inside the field of a nucleus. This process does not conserve charged-lepton flavour and is heavily suppressed in the Standard Model (SM), with a branching ratio < 10-50. Any evidence of it would be an undeniable evidence of new physics beyond the SM. The project sets out to achieve a single event sensitivity of $\sim 3 × 10^{-17}$ on the ratio between the probability for a conversion of a negative muon into an electron and the one for a muon capture by the nucleus. Such a sensitivity would represent a 4 orders of magnitude improvement on the previous upper limit for the process, making possible to test predictions of different extensions of the SM. Mu2e uses three superconducting solenoids to produce and measure the muon conversions. In the first solenoid, the Production Solenoid, pions and kaons are produced, together with other secondary products, by 8 GeV kinetic energy proton interactions in a tungsten target. A gradient magnetic field is specifically designed to direct low momentum particles into the Transport Solenoid, an S-shaped magnet that filters out particles with unwanted charge and momentum. Muons, produced by pion and kaon decays, finally reach the aluminum Stopping Target in the Detector Solenoid, where they eventually stop and convert. The result of the conversion process is a monochromatic electron of ~105 MeV/c momentum. The Detector Solenoid also hosts the two main detectors: a straw tube tracker and two CsI calorimeter disks, both providing measurement of event kinematics. A germanium detector and a LaBr crystal are located downstream of the Detector Solenoid to measure the X and gamma rays produced by the muon captures in the Stopping Target. A veto system of scintillators covering the Detector Solenoid and half of the Transport Solenoid is used to identify and reject cosmic rays interactions. With respect to the initial project, the Mu2e running plan has evolved to a staged configuration with 2 years at reduced intensity before the 2025 accelerator shutdown for the neutrino beam upgrade and 2 or 3 years at full intensity after that. This, together with geometry changes and a better knowledge of detector performances obtained by the first slice tests, has required a full revision of the signal over background selection that is the subject of this thesis. In order to achieve a new estimate of Mu2e sensitivity, the simulation of the data corresponding to the first 2 years of data acquisition has been performed. This includes both conversion electrons (CE) and the main sources of background: cosmics, decay-in orbits (DIO), radiative pion captures (RPC) and antiprotons. The characteristics of the signal and of the main backgrounds have been studied to define the best selection variables for CE. A special effort has been devoted to the evaluation of the antiproton background. The lack of experimental data for antiproton production cross section makes the systematic uncertainty on this background significant. A new parameterization of the cross section has been developed to fit the existing data and to provide a more reliable estimate of the systematic uncertainty by comparing the results of the old and the new model. A special effort has been devoted to the optimization of the antiprotons Monte Carlo generator. This study has also revealed that the dominant component within this background is represented by antiprotons produced in the opposite direction with respect to the Transport Solenoid entrance and then redirected to it by back-scattering processes in the Production Target. This ultimately highlights the sensitivity of the background estimate to the G4 handling of antiproton interactions in the 1 to 3 GeV/c momentum range. Finally, the final experimental sensitivity has been studied. The momentum and time selection have been optimized to obtain the 5-sigma discovery reach or, in case of no signal, the upper limit on conversion probability. The results confirm that, in the firs t two years of data taking, Mu2e will be able to improve the current experimental sensitivity for muon-to-electron conversion in an atomic field by more than 3 order of magnitudes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Evaluating Large‐Storm Dominance in High‐Resolution GCMs and Observations Across the Western Contiguous United States

Abstract Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance.

Environmental Sciences & Ecology↗

Building heights and urban canopy parameters for urban modeling

GLObal Building heights for Urban Studies (UT-GLOBUS) is a random forest model based framework that provides a level-of-detail-1 (LoD-1) building height dataset. The primary objective of UT-GLOBUS is not to precisely predict the height and footprint of individual buildings, but rather to offer a functional framework for generating building level information using open-source datasets for modeling applications. Specifically, UT-GLOBUS is tailored to meet the requirements of deriving urban canopy parameters (UCPs) for the multi-layer model within the Weather Research and Forecasting (WRF) model and building heights for the SOLWEIG and SUEWS model. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). The vector files employ the Universal Transverse Mercator (UTM) projection. The vector files are compatible with GIS platforms like QGIS and ArcGIS, and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the multi-layer urban model in the urban WRF in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These binary files can be directly incorporated into the WRF pre-processing system (WPS).

54 ENVIRONMENTAL SCIENCES↗

Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5

This paper evaluates the impact of sub-grid variability of surface wind on sea salt and dust emissions in the Community Atmosphere Model version 5 (CAM5). The basic strategy is to calculate emission fluxes multiple times, using different wind speed samples of a Weibull probability distribution derived from model-predicted grid-box mean quantities. In order to derive the Weibull distribution, the sub-grid standard deviation of surface wind speed is estimated by taking into account four mechanisms: turbulence under neutral and stable conditions, dry convective eddies, moist convective eddies over the ocean, and air motions induced by mesoscale systems and fine-scale topography over land. The contributions of turbulence and dry convective eddy are parameterized using schemes from the literature. Wind variabilities caused by moist convective eddies and fine-scale topography are estimated using empirical relationships derived from an operational weather analysis data set at 15 km resolution. The estimated sub-grid standard deviations of surface wind speed agree well with reference results derived from 1 year of global weather analysis at 15 km resolution and from two regional model simulations with 3 km grid spacing. The wind-distribution-based emission calculations are implemented in CAM5. In terms of computational cost, the increase in total simulation time turns out to be less than 3 %. Simulations at 2° resolution indicate that sub-grid wind variability has relatively small impacts (about 7 % increase) on the global annual mean emission of sea salt aerosols, but considerable influence on the emission of dust. Among the considered mechanisms, dry convective eddies and mesoscale flows associated with topography are major causes of dust emission enhancement. With all the four mechanisms included and without additional adjustment of uncertain parameters in the model, the simulated global and annual mean dust emission increase by about 50 % compared to the default model. By tuning the globally constant dust emission scale factor, the global annual mean dust emission, aerosol optical depth, and top-of-atmosphere radiative fluxes can be adjusted to the level of the default model, but the frequency distribution of dust emission changes, with more contribution from weaker wind events and less contribution from stronger wind events. Lastly, in Africa and Asia, the overall frequencies of occurrence of dust emissions increase, and the seasonal variations are enhanced, while the geographical patterns of the emission frequency show little change.

58 GEOSCIENCES↗

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗

AI-assisted detector design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

97 MATHEMATICS AND COMPUTING↗

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Abstract Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model‐based phenology representations fail to capture local‐ to regional‐scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground‐based observations to estimate models that better represent how community‐level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing‐based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species‐specific models in combination with species composition information to ‘upscale’ model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species‐specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.

Melaas, Eli K.↗

Test of Weak Equivalence Principle with the Multi-band Timing of the Crab Pulsar

The Weak Equivalent Principle (WEP) can be tested through the parameterized post-Newtonian parameter γ, representing the space curvature produced by unit rest mass. The parameter γ in turn has been constrained by comparing the arrival times of photons originating in distant transient events, such as gamma-ray bursts, fast radio bursts, and giant pulses of pulsars. Those measurements normally correspond to an individual burst event with very limited energy bands and signal-to-noise ratios (S/Ns). In this paper, the discrepancy in the pulse arrival times of the Crab Pulsar between different energy bands is obtained by the phase difference between corresponding pulse profiles. This allows us to compare the pulse arrival times at the largest energy band differences, between radio and optical, radio and X-ray, and radio and gamma-ray respectively. Because the pulse profiles are generated by phase-folding thousands of individual pulses, the time discrepancies between two energy bands are actually measured from thousands of events at each energy band, which corresponds to a much higher S/N. The upper limit of the γ discrepancy set by such an extensively observed and well-modeled source is as follows: γ{sub radio}--γ{sub γ-ray}<3.28×10{sup −9} at the energy difference of E{sub γ-ray}/E{sub radio}∼10{sup 13}, γ{sub radio}--γ{sub X-ray}<4.01×10{sup −9} at the energy difference of E{sub X-ray}/E{sub radio}∼10{sup 9}, γ{sub radio}--γ{sub optical}<2.63×10{sup −9} at E{sub optical}/E{sub radio}∼10{sup 5}, and γ{sub optical}--γ{sub γ-ray}<3.03×10{sup −10} at E{sub γ-ray}/E{sub optical}∼10{sup 8}. This actually measures the arrival times of freely falling photons in the gravitational field of the Milky Way with the largest amount of events and with data of the highest S/N, which tests WEP at energy band differences that have never been reached before.

79 ASTRONOMY AND ASTROPHYSICS↗

Characterization of 3D Cirrus Cloud and Radiation Fields Using ARS/AIRS/MODIS data and its Application to Climate Model

During the report period, we have made the following research accomplishments. First, we performed analysis for a number of MODIS scenes comprising of heavy dust events and ice clouds, covering regions of frequent dust outbreaks in East Asia, Middle East, and West Africa, as well as areas associated with long-range dust transports over the Equatorial Tropical Atlantic Ocean. These scenes contain both dust/aerosols and clouds. We collected suitable aerosol/ice-cloud data, correlated ice cloud and aerosol parameters by means of statistical analysis, and interpreted resulting correlation trends based on the physical principles governing cloud microphysics. Aerosol and cloud optical depths and cloud effective particle size inferred from MODIS for selected domains were analyzed from which the parameters including dust aerosol number concentration, ice cloud water path, and ice particle number concentration were subsequently derived. We illustrated that the Twomey (solar albedo) effect can be statistically quantified based on the slope of best-fit straight lines in the correlation study. Analysis of aerosol and cloud retrieval products revealed that for all cases, the region with a larger dust aerosol optical depth is always characterized by a smaller cloud particle size, consistent with the Twomey hypothesis for aerosol-cloud interactions. Second, we developed mean correlation curves with uncertainties associated with small ice-crystal concentration observations for the mean effective ice crystal size (De) and ice water content (IWC) by dividing the atmosphere into three characteristic regions: Tropics cirrus, Midlatitude cirrus, including a temperature classification to improve correlation, and Arctic ice clouds. We illustrated that De has a high correlation with IWC based on theoretical consideration and analysis of thousands of observed ice crystal data obtained from a number of ARM-DOE field campaigns and other experiments. The correlation has the form: ln(De) = a + b ln(IWC) + c ((ln(IWC))2, where a, b, and c are fitting coefficients and are functions of three regions. We demonstrated that this correlation can be effectively incorporated in GCMs and climate models that predict IWC - a significant advance in ice microphysics parameterization for interactive cloud-radiation analysis and feedback. Substantial July mean differences are shown in the OLR (W/m2) and precipitation (mm/day) patterns between UCLA GCM simulations based on Des determined from the De-IWC correlations and the control run using a fixed ice crystal size. Third, in order to improve the computation of spectral radiative transfer processes in the WRF model, we developed a consistent and efficient radiation scheme that can better resolve the spectral bands, determine the cloud optical properties, and provide more reliable and accurate radiative heating fields. In the newly developed radiation module, we have implemented in WRF a modified and improved version referred to as the Fu-Liou-Gu scheme, which includes a combination of delta-four-stream and delta-two-stream approximations for solar and IR flux calculations, respectively. This combination has been proven to be computationally efficient and at the same time to produce a high degree of accuracy. The incorporation of nongray gaseous absorption in multiple scattering atmospheres was based on the correlated k-distribution method. The solar and IR spectra are divided into 6 and 12 bands, respectively, according to the location of absorption bands of H2O, CO2, O3, CH4, N2O, and CFCs. We further included absorption by the water vapor continuum and a number of minor absorbers in the solar spectrum leading to an additional absorption of solar flux in a clear atmosphere on the order of 1-3 W/m2. Additionally, we incorporated the ice microphysics parameterization that includes an interactive mean effective ice crystal size in association with radiation parameterizations. The Fu-Liou-Gu scheme is an ideal tool for the simulation of radiative transfer and ice microphysics within the domain of WRF. It is particularly useful for studying direct and indirect aerosol radiative effects associated with ice cloud formation. The newly implemented radiation module has been demonstrated to work well in WRF and can be effectively used for studies related to cirrus cloud formation and evolution as well as aerosol-cloud-radiation interactions. With the newly implemented radiation scheme, the simulations of cloud cover and ice water path have been improved for cirrus clouds, with a more consistent comparison with the corresponding MODIS observations, especially for optically thin cirrus with an improvement of about 20% in the simulated mean ice water path.

97 MATHEMATICS AND COMPUTING↗

Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression

Understanding future changes in extreme temperature events in a transient climate is inherently challenging. A single model simulation is generally insufficient to characterize the statistical properties of the evolving climate, but ensembles of repeated simulations with different initial conditions greatly expand the amount of data available. In this paper, we present a new approach for using ensembles to characterize changes in temperature distributions based on quantile regression that more flexibly characterizes seasonal changes. Specifically, our approach uses a continuous representation of seasonality rather than breaking the dataset into seasonal blocks; that is, we assume that temperature distributions evolve smoothly both day to day over an annual cycle and year to year over longer secular trends. To demonstrate our method’s utility, we analyze an ensemble of 50 simulations of the Community Earth System Model (CESM) under a scenario of increasing radiative forcing to 2100, focusing on North America. As previous studies have found, we see that daily temperature bulk variability generally decreases in wintertime in the continental mid- and high latitudes (>40°). A more subtle result that our approach uncovers is that differences in two low quantiles of wintertime temperatures do not shrink as much as the rest of the temperature distribution, producing a more negative skew in the overall distribution. Although the examples above concern temperature only, the technique is sufficiently general that it can be used to generate precise estimates of distributional changes in a broad range of climate variables by exploiting the power of ensembles.

54 ENVIRONMENTAL SCIENCES↗

Performance of Convection-Permitting and Convection-Parameterized Models in Reproducing the Extreme Precipitation Intensity Relationship with Surface Conditions

Here, this study investigates the warm-season extreme precipitation–temperature scaling relationship in CONUS404, a convection-permitting (4 km) Weather Research and Forecasting (WRF) Model simulation over the conterminous United States for the past four decades, and compares it with the WRF-Thermodynamic Global Warming (WRF-TGW) historical simulation at a coarser resolution (12 km) using parameterized convection. We also analyze the NCEP stage IV and NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) datasets as observational benchmarks. We examine how extreme precipitation intensity (EPI) varies with temperature and saturation deficit over representative regions based on hourly data. The stage IV and IMERG data show a similar pattern of EPI variation with temperature and saturation deficit, except that the EPI peak is lower in IMERG than in stage IV. Under dry and hot conditions, EPI decreases too rapidly with elevated saturation deficit in both CONUS404 and WRF-TGW compared to observations, but the performance of CONUS404 is superior to WRF-TGW. When the near-surface atmosphere is saturated or close to saturated, both CONUS404 and WRF-TGW produce higher peak values of EPI relative to the observational references; IMERG exhibits scaling rates close to the Clausius–Clapeyron (C–C) relationship, while CONUS404, WRF-TGW, and stage IV all demonstrate super-C–C scaling behaviors. Despite marked warming over the past four decades, in both CONUS404 and WRF-TGW, the scaling relationship between EPI and temperature in a saturated atmosphere remains stable and robust. This indicates a strong potential for the EPI–temperature scaling rate under saturation to be used as an emergent constraint in reducing uncertainties of future extreme precipitation projection.

Atmosphere↗

Evaluation of downscaled wind speeds and parameterised gusts for recent and historical windstorms in Switzerland

Assessments of local-scale windstorm hazard require highly resolved spatial information on wind speeds and gusts. In this study, maximum (peak) sustained wind speeds on a 3-km horizontal grid over Switzerland are obtained by dynamical downscaling from the Twentieth Century Reanalysis (20CR) employing the Weather Research and Forecasting (WRF) model. Subsequently, simulated peak gusts are derived using four wind gust parameterizations (WGPs). Evaluations against observations at 63 locations in complex terrain include four high-impact windstorms (occurring in 1919, 1935, 1990, and 1999) and 14 recent windstorms (occurring between 1993 and 2011). Peak sustained wind speeds and directions are generally well simulated, although wind speeds are mostly overestimated. In general, performance and skill measures are best for locations on the Swiss Plateau and inferior for Alpine mountain and valley locations. An independent ERA-Interim WRF downscaling configuration produces overall comparable results, implying that the 20CR ensemble mean is a reliable data set in dynamical downscaling exercises. The four evaluated WGPs largely reproduce the observed gustiness, although the timing and magnitude of the peak gusts are not regularly captured. None of the WGPs stands out as single best for the complex topography of Switzerland. Differences among the WGPs are small compared to the biases inherited from the sustained-wind part in the WGP formulations. All WGPs transform overestimated peak sustained winds into underestimated peak gusts, which points to an underrepresentation of the turbulent part in the WGP formulations. The range of simulated peak gusts from downscaling all 20CR ensemble members does not reliably include the observed peak gust, indicating limited benefit in applying an ensemble approach. Despite the limitations, we infer that with spatial optimisations of the simulation (e.g. by bias correction or adaptation of the WGP schemes), downscaling of 20CR input is an efficient option for high-resolution assessments of windstorm hazard and risk in Switzerland.

20CR↗

Topics in Theoretical Physics

This award supported a broadly based research effort in theoretical particle physics, including research aimed at uncovering the laws of nature at short (subatomic) and long (cosmological) distances. These theoretical developments apply to experiments in laboratories such as CERN, the facility that operates the Large Hadron Collider outside Geneva, as well as to cosmological investigations done using telescopes and satellites. The results reported here apply to physics beyond the so-called Standard Model of particle physics; physics of high energy collisions such as those observed at the Large Hadron Collider; theoretical and mathematical tools and frameworks for describing the laws of nature at short distances; cosmology and astrophysics; and analytic and computational methods to solve theories of short distance physics. Some specific research accomplishments include + Theories of the electroweak interactions, the forces that give rise to many forms of radioactive decay; + Physics of the recently discovered Higgs boson. + Models and phenomenology of dark matter, the mysterious component of the universe, that has so far been detected only by its gravitational effects. + High energy particles in astrophysics and cosmology. + Algorithmic research and Computational methods for physics of and beyond the Standard Model. + Theory and applications of relativity and its possible limitations. + Topological effects in field theory and cosmology. + Conformally invariant systems and AdS/CFT. This award also supported significant training of students and postdoctoral fellows to lead the research effort in particle theory for the coming decades. These students and fellows worked closely with other members of the group as well as theoretical and experimental colleagues throughout the physics community. Many of the research projects funded by this grant arose in response to recently obtained experimental results in the areas of particle physics and cosmology. We describe a few of these below. Relativity is founded on a symmetry property of nature called "Lorentz Invariance". Like all symmetry properties, it is essential to determine precisely how symmetric nature actually is; that is, do the laws of nature fully respect the symmetry or is there room for tiny symmetry violating effects? An important consequence of Lorentz invariance is the existence of a universal limiting velocity for all physical particles. Light travels at this limiting velocity so it is frequently referred to as simply "the speed of light", but relativity requires that ALL particles travel more slowly than this speed. Once the Higgs particle was discovered in 2012 a natural question was whether or not this particle's speed was consistent with relativity. Although the speed of the Higgs particle is not measurable directly, Cohen has shown that, if the maximal speed of the Higgs particle was not precisely the same as the speed of light, then the Higgs would have some unusual properties. In some cases the Higgs would be unstable to some unusual decay modes; in other cases the interactions of the Higgs with other particles would change the properties of these other particles in ways that could be observed in so-called cosmic rays, very energetic particles (such as photons, protons and other atomic nuclei) coming from space. Once these particles hit the upper atmosphere they produce a "shower" of particles that can be seen by ground-based instruments. If the Higgs has a maximal speed that differs even a tiny bit from the speed of light these showers would look quite different from what is observed. In this way Cohen was able to establish that the Higgs travels with a maximal speed that cannot differ from the speed of light by more than one part in a thousand-trillion. This is by far the most precisely determined property of the Higgs particle. Cohen and Schmaltz reviewed evidence from the Large Hadron Collider (LHC), a particle physics experiment operating at the CERN laboratory near Geneva, for a new particle sometimes called a W'. This evidence included certain unexpected by-products in collisions of protons at very high energy. While the evidence was not significant enough to claim a discovery, it was sufficiently intriguing that many particle theorists worked to construct explanations for this signal. Cohen and Schmaltz were able to determine that such explanations are highly constrained by previous experiments involving collisions of very energetic particles. Nevertheless they were able to construct a theory that adequately explains the LHC data and remain consistent with prior experiments. Their explanation predicts the existence of yet another new particle, called a Z', with a mass slightly greater than that of the W'. This additional particle, if it exists, should be seen as more data is collected from the LHC. Amusingly, there is one collision by-product that has already been seen by the CMS experiment at the LHC that supports the existence of this new particle; however, it is not unlikely that this single event is a so-called "background" event, that is a somewhat atypical by-product of a conventional Standard Model process. This theory for the anomalous LHC data will either be confirmed or excluded with further data-taking at the LHC. The ratio of the number of electrons produced in bottom quark decays over the number of muons produced has been measured at the LHC. This ratio is interesting because it can be predicted very precisely from a basic property of the Standard Model: lepton universality. If lepton universality is correct, the ratio of electrons to muons is predicted to be equal to 1. The first measurements of this ratio find a value different from 1 with a statistical significance of about 3 standard deviations. Schmaltz and collaborator proposed a new extension of the Standard Model which can explain the new data. In addition, Schmaltz and collaborators proposed several new measurements of ratios of decay rates which can confirm or rule out the surprising results from the earlier LHC data. The most recent and precise measurements of the cosmic microwave background from the Planck satellite, from a combination of measurements of the dark matter distribution in the universe, and from a measurement of the expansion rate of the universe today show some disagreement when interpreted in terms of the so-called LambdaCDM model. Schmaltz and collaborators proposed an alternative model to LambdaCDM in which the usual cold dark matter is replaced by a new ``dark sector". This sector consists of a cold dark matter particle which interacts with a newly postulated dark radiation component of the universe. The dark radiation can help explain the discrepancy in measurements of the expansion rate, and the dark matter interactions subtly modify the clumping of dark matter at large scales, thus potentially explaining both kinds of tensions in the data. In two publications Schmaltz described the new model and then performed a precision comparison of the predictions of the model with all currently available cosmological data. The results favor the new model at the level of three standard deviations with current data. Quantum Field Theory (QFT) is the language we use to describe quantum systems which are consistent with Einstein’s theory of Special Relativity. In particular, the requirement of Einstein’s theory that signals not travel faster than the speed of light constrains the types of interactions which particles can engage in. One consequence of relativity is that these interactions cannot preserve particle number. The stronger the interactions, the more severe the particle number violation in a given Relativistic QFT. When particle number violation is strong, it becomes very difficult to adequately parameterize the quantum wave function (which characterizes the state of a quantum system). For example, though we can formulate the QFT which describes the strong force as a set of interactions between quark and gluon particles, we have no clear idea how to express the proton state in terms of these quarks and gluons. This is because the proton, though a bound state of quarks and gluons, is not a state of a fixed number of particles due to strong interactions. Yet, understanding the proton state is very important in order to theoretically predict the reaction rates observed at the LHC in Geneva, which is a proton-proton collider. Katz has formulated a new approach to QFT, which among other things offers a way to adequately approximate the quantum wave function of a bound state at strong coupling. The approximation scheme is related to the fact that any sensible QFT (including that of the strong interactions) is at short distances approximately self-similar upon rescaling of space and time. It turns out that keeping track of the response upon this rescaling is important in efficiently parameterizing the state. Katz and collaborators have used this observation to approximate the state of the proton in toy versions of the strong force. In the late 60s Sheldon Glashow, Abdus Salam and Steven Weinberg (1979 Nobel Prize awardees) proposed a theory unifying weak and electromagnetic interaction which assumed the existence of new particles, the W and Z bosons. The W and Z bosons were eventually detected in high-energy collision in a particle accelerator at CERN, and the recent discovery of the Higgs meson at the Large Hadron Collider (LHC), always at CERN, completed the picture. However, deep theoretical considerations indicate that the theory by Glashow, Weinberg and Salam, often referred to as "the standard model" cannot be the whole story: the existence of new particles and new interactions at yet higher energies is widely anticipated. The experiments at the LHC are looking for these, while theorists, like Brower, Rebbi and collaborators, are investigating models for these new interactions. Working in a large national collaboration with access to the most powerful DOE computers Brower, Rebbi and colleagues have been using calculational techniques, similar to those successfully employed for many years to investigate the interactions among quarks in nucleons, to study theories that can describe the expected "beyond the standard model" (BSM) interactions. Their results, which include also a model for dark matter, have been published in several refereed papers in prestigious journals. Various ideas in topologically interesting field theories predict hypothetical objects such as fractional charges and Majorana excitations. However, such fascinating objects have not been seen in particle physics. Nevertheless, these objects demonstrate possible phenomena that quantum field theory can support. Pi used condensed matter physics as a laboratory to study possible realizations and observable effects of these objects predicted by quantum field theory. In recent times there has developed considerable interest among condensed matter field theorists in precisely the same geometrical and topological structures, which were first discovered in particle physics field theories. From particle physicists' point of view, this is an interesting development, since condensed matter provides an arena in which one can concretely realize particle physics ideas. Moreover, particle physicists can learn new ideas from condensed matter physics. Higgs phenomenon is precisely an important particle physics realization of condensed matter ideas. In contrast to the small distance characterizing condensed matter systems, field theory also describes large distance physics characterizing cosmology. Pi worked on various geometrical effects in the standard theory of cosmology, viz general relativity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗