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At least 181 records · Page 10

Accounting for hydrological and microbial processes on greenhouse gas budgets from river systems (Final Report)

River systems have a disproportionately large impact on carbon and nitrogen cycling on this planet. For instance, despite their much smaller size in area, it is estimated that global carbon dioxide emission from river systems exceed the emission from lakes and reservoirs, and are on par with the estimated oceanic and land carbon sinks. Closing carbon and nitrogen budgets in river systems requires knowledge of end member greenhouse gas (GHG) products (e.g. carbon dioxide, nitrous oxide, methane), which typically have not been measured in river systems. Moreover, while the production of these gasses is thought to be microbially derived, little is known about microbial activities in sediments of large rivers, despite their potentially high influence on biogeochemical budgets. Typically, when we think of rivers we visualize the channels that carve through the earth, but this only represents a small part of the hydrologic exchange that a river participates in. Rivers are more than conduits; they are dynamically exchanging water and materials with surrounding terrestrial and subsurface environments. In fact, 96% of the carbon dioxide produced within river systems occurs in hyporheic zones, or sediment regions beneath and alongside a river where groundwater and river water mix. In fact, it is widely recognized that microbial processes in hyporheic zones, also known as the river’s liver, strongly influence the fate of carbon, nutrients, and contaminants transported through watersheds. Despite this general knowledge, observational studies linking nitrogen and carbon gas production to microbial activity, and studies of overall gas emissions, are limited across river systems, but especially absent from larger rivers. To identify microbial processes responsible for GHG emissions from large rivers, and to quantify their environmental drivers and dependencies, we selected the Columbia River as a representative field site. This is the largest river west of the North American Continental Divide, and produces a drainage and discharge roughly double of the Nile in Egypt. This river is also an important source for agricultural irrigation and generates more hydroelectric power than any other North American river. Dams manage the release of water in the river, with these changes in river discharge patterns strongly impacting microbial activities in hyporheic zones. Specifically, we target a region down-river from the Priest Rapids Dam (WA) associated with the PNNL SFA, where water discharge from the dam is highly controlled, resulting in 1.5 m fluctuations in river stage daily. This dynamic river stage creates an ideal research opportunity for investigating hydrological perturbations in GHG production, uptake, and emission from large river systems. In this exploratory 1-year project, we conducted data-driven field research to provide observations needed to support predictions about GHG flux from river ecosystems. Using porewater dialysis and surface flux measurements, we observed where and when GHGs were produced and is the emission impacted by river stage. Using meta-omics tools, we determined what microbial processes control production and consumption of GHG in these systems. Finally, we provided key observations to parameterize models that could predict the effect of environmental conditions on GHG emission in the Columbia River near shore environment, at the scale that the modeled processes operate. Closing knowledge gaps in the linkages between river stage, microbial processes, and GHG production and emission, as well as the spatial distribution and ecosystem dependencies of these processes, is necessary to better parameterize land surface models. We provide some of the first, detailed GHG measurements paired microbial activity from large river systems. Our findings have direct relevance to many other large rivers with flows altered by hydroelectric energy, reservoirs, snowmelt, tides, or agriculture.

54 ENVIRONMENTAL SCIENCES↗

Modelling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California

ABSTRACT Coastal wetlands, some of the most productive ecosystems on Earth, provide critical ecosystem services, including support of biodiversity, carbon sequestration and flood protection. In recent decades, these ecosystems have experienced extensive coastal wetland loss. Coastal wetland restoration provides a beacon of hope, offering a chance to reclaim these important habitats. However, even with billions of dollars invested worldwide in restoring coastal wetlands, we still lack comprehensive knowledge about the effectiveness of these restoration efforts in recovering wetland ecosystem functions and how future climate change may affect these efforts. The ability to evaluate how these ecosystems will function in the future is vital for examining current investments and developing future protection and management plans. We selected Elkhorn Slough, a tidal estuary, in California, to investigate the impact of wetland restoration and sea level rise (SLR) on coastal hydrology using the process‐based coastal hydrologic model, Advanced Terrestrial Simulator (ATS), informed by site‐specific data. We designed a novel modelling workflow for incorporating wetland restoration features into land cover and soil properties for the model parameterization. The validation results demonstrate a strong agreement between modelled and observed data. We studied the characteristics of coastal watershed hydrology, then focused on the surface water dynamics at two wetland sites within Elkhorn Slough, a reference site and a restored site. Our simulation results indicate that the restored site successfully maintains surface elevation, resulting in reduced surface inundation. We also examined the impact of wetland restoration under expected SLR over the next few decades. The low‐lying Yampah Marsh, the reference site, is likely to be inundated due to future SLR when highest tides arrive, while a higher percentage of Hester Marsh, the restored site, would retain marsh vegetation in coming decades, regardless of tidal conditions. Our study provides important information for examining the outcome of restoration practices that include surface elevation in tidal wetlands under climate changes.

advanced terrestrial simulator↗

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗

Numerical modeling of the effects of wave energy converter characteristics on nearshore wave conditions

Modeled nearshore wave propagation was investigated downstream of simulated wave energy converters (WECs) to evaluate overall near- and far-field effects of WEC arrays. Model sensitivity to WEC characteristics and WEC array deployment scenarios was evaluated using a modified version of an industry standard wave model, Simulating WAves Nearshore (SWAN), which allows the incorporation of device-specific WEC characteristics to specify obstacle transmission. The sensitivity study illustrated that WEC device type and subsequently its size directly resulted in wave height variations in the lee of the WEC array. Wave heights decreased up to 30% between modeled scenarios with and without WECs for large arrays (100 devices) of relatively sizable devices (26 m in diameter) with peak power generation near to the modeled incident wave height. Other WEC types resulted in less than 15% differences in modeled wave height with and without WECs, with lesser influence for WECs less than 10 m in diameter. Wave directions and periods were largely insensitive to changes in parameters. Furthermore, additional model parameterization and analysis are required to fully explore the model sensitivity of peak wave period and mean wave direction to the varying of the parameters.

16 TIDAL AND WAVE POWER↗

A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models

Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

25 ENERGY STORAGE↗

Data-model files associated with the manuscript "Modeling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California"

This package contains the data, simulation setups, notebooks and figures used in “Modeling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California” (Xu et al., 2025). In this study, we selected Elkhorn Slough, a tidal estuary, in California, to investigate the impact of wetland restoration and sea level rise on coastal hydrology using the process-based coastal hydrologic model, Advanced Terrestrial Simulator (ATS), informed by site-specific data. We designed a novel modeling workflow for incorporating wetland restoration features into land cover and soil properties for the model parameterization. The validation results demonstrate a strong agreement between modeled and observed data. We studied the characteristics of coastal watershed hydrology, then focused on the surface water dynamics at two wetland sites within Elkhorn Slough, a reference site and a restored site. Our simulation results indicate that the restored site successfully maintains surface elevation, resulting in reduced surface inundation. We also examined the impact of wetland restoration under expected sea level rise over the next few decades. The low-lying Yampah Marsh, the reference site, is likely to be inundated due to future sea level rise when highest tides arrive; while a higher percentage of Hester Marsh, the restored site, would retain marsh vegetation in coming decades, regardless of tidal conditions. Our study provides important information for examining the outcome of restoration practices that include surface elevation in tidal wetlands under climate changes.Several files can be found from this data package.1. README.md: This file describes the title, journal, co-authors, abstract, repository structure and model version.2. Simulation_Setups.zip: The file contains the model configuration files (XML format) for ATS. 3. Notebooks.zip: The file contains the Jupyter notebooks for generating the pre- and post-restoration meshes and the meshes of future scenarios. 4. Figures.zip: The file contains the figures used in the manuscript.5. Data.zip: The file contains the data used to drive the model simulations, including watershed and wetlands boundaries, mesh files and references to additional datasets (e.g., meteorological forcing, tidal dataset, DEMs, land cover, soil properties). Also, it contains water level observations at the restored wetland.

54 ENVIRONMENTAL SCIENCES↗

Improving Predictability of Mixed-Phase Clouds and Aerosol Interactions in the Community Earth System Model (CESM) with ARM Measurements

The objective of this project is to improve the simulation and predictability of mixed-phase clouds and aerosol interactions in the Community Earth System Model (CESM) through comparisons with the DOE ARM observations. There are four major goals of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Develop a long-term multi-sensor mixed-phase cloud dataset; (3) Test the performance of improved ice microphysics in CESM-CAM5 with the ARM data; and (4) Examine mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds that include ice nucleation, ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process, feedbacks between cloud microphysics, dynamics and radiation, and provides links to aerosol properties in the Community Earth System Model (CESM)-Community Atmosphere Model version 5 (CAM5); (2) Developed a long-term multi-sensor mixed-phase cloud dataset. The mixed-phase cloud retrieval algorithms are improved by refining the phase determinations and ice number concentration retrievals. A multi-year mixed-phase cloud observation dataset (1999-2004; October 2013-February 2017) is regenerated at the ARM Barrow site with the improved algorithms. The dataset has been used in the global model evaluation to improve the model parameterizations; (3) Tested the performance of improved ice microphysics in CESM-CAM5 with the ARM data. CESM-CAM5 is run in the DOE Cloud-Associated Parameterizations Testbed (CAPT) and in the single-column model (SCM) mode to facilitate comparison with ARM observations. Multi-year CAPT simulations of seasonal variations of cloud microphysical properties, cloud liquid water path (LWP) and ice water path (IWP), cloud longwave and shortwave forcings, cloud occurrence frequency are evaluated against the ARM multi-sensor mixed-phase cloud retrievals; and (4) Examined mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5 that include the impacts of different ice nucleation mechanisms, sensitivity to dust IN concentration, treatment of the WBF process on mixed-phase cloud properties. Aerosol effect on mixed-phase clouds through the liquid phase (droplet activation) and ice phase processes (e.g., the glaciation indirect effect) is investigated.

54 ENVIRONMENTAL SCIENCES↗

Gas Hydrate Stability at Low Temperatures and High Pressures with Applications to Mars and Europa

Gas hydrates are implicated in the geochemical evolution of both Mars and Europa [1- 3]. Most models developed for gas hydrate chemistry are based on the statistical thermodynamic model of van der Waals and Platteeuw [4] with subsequent modifications [5-8]. None of these models are, however, state-of-the-art with respect to gas hydrate/electrolyte interactions, which is particularly important for planetary applications where solution chemistry may be very different from terrestrial seawater. The objectives of this work were to add gas (carbon dioxide and methane) hydrate chemistries into an electrolyte model parameterized for low temperatures and high pressures (the FREZCHEM model) and use the model to examine controls on gas hydrate chemistries for Mars and Europa.

Marion, G. M.↗

Atmospheric Dust Modeling from Meso to Global Scales with the Online NMMB/BSC-Dust Model Part 2: Experimental Campaigns in Northern Africa

The new NMMB/BSC-Dust model is intended to provide short to medium-range weather and dust forecasts from regional to global scales. It is an online model in which the dust aerosol dynamics and physics are solved at each model time step. The companion paper (Perez et al., 2011) develops the dust model parameterizations and provides daily to annual evaluations of the model for its global and regional configurations. Modeled aerosol optical depth (AOD) was evaluated against AERONET Sun photometers over Northern Africa, Middle East and Europe with correlations around 0.6-0.7 on average without dust data assimilation. In this paper we analyze in detail the behavior of the model using data from the Saharan Mineral dUst experiment (SAMUM-1) in 2006 and the Bodele Dust Experiment (BoDEx) in 2005. AOD from satellites and Sun photometers, vertically resolved extinction coefficients from lidars and particle size distributions at the ground and in the troposphere are used, complemented by wind profile data and surface meteorological measurements. All simulations were performed at the regional scale for the Northern African domain at the expected operational horizontal resolution of 25 km. Model results for SAMUM-1 generally show good agreement with satellite data over the most active Saharan dust sources. The model reproduces the AOD from Sun photometers close to sources and after long-range transport, and the dust size spectra at different height levels. At this resolution, the model is not able to reproduce a large haboob that occurred during the campaign. Some deficiencies are found concerning the vertical dust distribution related to the representation of the mixing height in the atmospheric part of the model. For the BoDEx episode, we found the diurnal temperature cycle to be strongly dependant on the soil moisture, which is underestimated in the NCEP analysis used for model initialization. The low level jet (LLJ) and the dust AOD over the Bodélé are well reproduced. The remaining negative AOD bias (due to underestimated surface wind speeds) can be substantially reduced by decreasing the threshold friction velocity in the model.

photometers↗

CERES Fast Longwave And SHortwave Radiative Flux (FLASHFlux): Research to Operation

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of how Earth’s energy flows are varying in time and space and how clouds and aerosols are affecting the Earth’s radiation budget. Nominally, CERES data products require months of validation and calibration before releasing a climate quality data. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed as to provide key data for applied science research involving the renewable energy and agricultural sectors within a week of observations. FLASHFlux achieves this by using simplified calibration, an operation meteorology product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Aqua and Terra observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1ox 1ogridded data that combines Aqua and Terra observations. We inter-compared of FLASHFlux Version4A calibration to the climate quality CERES Edition4A data product over time. In addition, we also inter-compared the impact of the fluxes by using the near real-time GMAO GEOS FP-IT instead of GMAO G5-CERES meteorology data. We also compare both SSF overpass and TISA radiative flux products to a set of surface measurement sites that are globally distributed and assess agreement.

P. C. Sawaengphokhai↗

Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations

Here, this work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to parameterized systems of nonlinear equations. These approximate solutions may arise from early termination of an iterative method, a lower-fidelity model, or a projection-based reduced-order model, for example. The proposed statistical model comprises the sum of a deterministic regression-function model and a stochastic noise model. The method constructs the regression-function model by applying regression techniques from machine learning (e.g., support vector regression, artificial neural networks) to map features (i.e., error indicators such as sampled elements of the residual) to a prediction of the approximate-solution error. The method constructs the noise model as a mean-zero Gaussian random variable whose variance is computed as the sample variance of the approximate-solution error on a test set; this variance can be interpreted as the epistemic uncertainty introduced by the approximate solution. This work considers a wide range of feature-engineering methods, data-set-construction techniques, and regression techniques that aim to ensure that (1) the features are cheaply computable, (2) the noise model exhibits low variance (i.e., low epistemic uncertainty introduced), and (3) the regression model generalizes to independent test data. Finally, numerical experiments performed on several computational-mechanics problems and types of approximate solutions demonstrate the ability of the method to generate statistical models of the error that satisfy these criteria and significantly outperform more commonly adopted approaches for error modeling.

97 MATHEMATICS AND COMPUTING↗

Advancing quantitative models of soil microbiology, ecology, and biochemistry

Rapid advances in soil biology are increasingly reflected in mathematical models. From molecular to global scales, models contribute to fundamental understanding and prediction of critical soil processes, such as carbon sequestration. Recent models explicitly incorporate microbial control over soil carbon and nutrient cycling, an approach that offers greater biological realism but raises new challenges in model parameterization and stability. Although they have proliferated at community and ecosystem scales, microbial-explicit models have yet to be incorporated into most Earth system models. Applying soil models across scales requires integration with data to support parameterization and validation using approaches such as Bayesian data assimilation. In conclusion, to catalyze future progress, we recommend breaking down barriers between modeling and empirical disciplines while broadening access to the computational infrastructure that supports soil biological modeling.

Xie, Wally↗

A Parameterized Neutrino Emission Model to Study Mass Ejection in Failed Core-collapse Supernovae

Some massive stars end their lives as failed core-collapse supernovae (CCSNe) and become black holes (BHs). Although in this class of phenomena the stalled supernova (SN) shock is not revived, the outer stellar envelope can still be partially ejected. This occurs because the hydrodynamic equilibrium of the star is disrupted by the gravitational mass loss of the protoneutron star (PNS) due to neutrino emission. We develop a simple parameterized model that emulates PNS evolution and its neutrino emission and use it to simulate failed CCSNe in spherical symmetry for a wide range of progenitor stars. Our model allows us to study mass ejection of failed CCSNe where the PNS collapses into a BH within ~100 ms and up to ~10 6 s. We perform failed CCSNe simulations for 262 different pre-SN progenitors and determine how the energy and mass of the ejecta depend on progenitor properties and the equation of state (EOS) of dense matter. In the case of a future failed CCSN observation, the trends obtained in our simulations can be used to place constraints on the pre-SN progenitor characteristics, the EOS, and on PNS properties at BH formation time.

79 ASTRONOMY AND ASTROPHYSICS↗

A laboratory study to estimate pore geometric parameters of sandstones using complex conductivity and nuclear magnetic resonance for permeability prediction

Here, we estimate parameters from the Katz and Thompson permeability model using laboratory complex electrical conductivity (CC) and nuclear magnetic resonance (NMR) data to build permeability models parameterized with geophysical measurements. We use the Katz and Thompson model based on the characteristic hydraulic length scale, determined from mercury injection capillary pressure estimates of pore throat size, and the intrinsic formation factor, determined from multisalinity conductivity measurements, for this purpose. Two new permeability models are tested, one based on CC data and another that incorporates CC and NMR data. From measurements made on forty-five sandstone cores collected from fifteen different formations, we evaluate how well the CC relaxation time and the NMR transverse relaxation times compare to the characteristic hydraulic length scale and how well the formation factor estimated from CC parameters compares to the intrinsic formation factor. We find: (1) the NMR transverse relaxation time models the characteristic hydraulic length scale more accurately than the CC relaxation time (R 2 of 0.69 and 0.33 and normalized root mean square errors (NRMSE) of 0.16 and 0.21, respectively); (2) the CC estimated formation factor is well correlated with the intrinsic formation factor (NRMSE50.23). We demonstrate that that permeability estimates from the joint-NMR-CC model (NRMSE50.13) compare favorably to estimates from the Katz and Thompson model (NRMSE50.074). Lastly, this model advances the capability of the Katz and Thompson model by employing parameters measureable in the field giving it the potential to more accurately estimate permeability using geophysical measurements than are currently possible.

36 MATERIALS SCIENCE↗

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

54 ENVIRONMENTAL SCIENCES↗

An open-source framework for balancing computational speed and fidelity in production cost models

Studies of bulk power system operations need to incorporate uncertainty and sensitivity analyses, especially around exposure to weather and climate variability and extremes, but this remains a computational modeling challenge. Commercial production cost models (PCMs) have shorter runtimes, but also important limitations (opacity, license restrictions) that do not fully support stochastic simulation. Open-source PCMs represent a potential solution. They allow for multiple, simultaneous runs in high-performance computing environments and offer flexibility in model parameterization. Yet, developers must balance computational speed (i.e. runtime) with model fidelity (i.e. accuracy). In this paper, we present Grid Operations (GO), a framework for instantiating open-source, scale-adaptive PCMs. GO allows users to search across parameter spaces to identify model versions that appropriately balance computational speed and fidelity based on experimental needs and resource limits. Results provide generalizable insights on how to navigate the fidelity and computational speed tradeoff through parameter selection. We show that models with coarser network topologies can accurately mimic market operations, sometimes better than higher-resolution models. It is thus possible to conduct large simulation experiments that characterize operational risks related to climate and weather extremes while maintaining sufficient model accuracy.

42 ENGINEERING↗

Improving Process Level Understanding of Boundary Layer Winds over the Northeast U.S. Shelf: The Third Wind Forecast Improvement Project (WFIP3)

The third Wind Forecast Improvement Project (WFIP3), a U.S. Department of Energy and National Oceanic and Atmospheric Administration sponsored investigation, sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the marine atmospheric boundary layer. WFIP3 focused on mesoscale and submesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high-use coastal zone, using a 3D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the marine atmospheric boundary layer over the ocean was done from an air-sea interaction flux tower and extended deployments of a large autonomous barge platform. Numerous critical forecasting phenomena were observed that are being evaluated within regional coupled and uncoupled modeling systems, including the National Oceanic and Atmospheric Administration's foundational High-Resolution Rapid Refresh forecast model.

Kirincich, Anthony↗

Accelerated Climate Modeling for Energy (ACME) – Atmospheric Radiation Measurement (ARM) Climate Research Facility – Atmospheric System Research (ASR) Coordination Workshop, October 21–22, 2015

The Climate and Environmental Sciences Division (CESD) of the U.S. Department of Energy’s Office of Biological and Environmental Research focuses on advancing a robust predictive understanding of Earth’s climate and environmental systems to inform the development of sustainable solutions to the Nation’s energy and environmental challenges. CESD climate activities related to atmospheric processes include: observations through the Atmospheric Radiation Measurement (ARM) Climate Research Facility; atmospheric research and model parameterization development through the Atmospheric System Research (ASR) program; climate model development and climate research through the Accelerated Climate Modeling for Energy (ACME) project along with other computationally focused model-development activities within the CESD Earth System Modeling (ESM) program, and development of robust model analytical and testing frameworks at multiple scales through the Regional and Global Climate Modeling program. This workshop focused primarily on improving model treatment of atmospheric processes as a means to focus discussion and did not address research on terrestrial ecosystem and subsurface processes, which are also part of the CESD research portfolio.

54 ENVIRONMENTAL SCIENCES↗