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The current status of cloud parameterization research is reviewed. It is emphasized that the upper tropospheric stratiform clouds associated with deep convection are both physically important and poorly parameterized in current models. Emerging parameterizations are described in general terms, with emphasis on prognostic cloud water and fractional cloudiness, and how these relate to the problem just mentioned.
Here we established a comprehensive modeling approach for investigating the microstructure-aware effective thermal conductivity ($κ_{eff}$) for porous microstructures containing solid particles and gaseous pores. Our approach combines the mesoscale computational modeling framework and the semi-analytical method, allowing for efficient prediction of $κ_{eff}$ for realistic porous microstructures, while considering complicated microstructural thermal conduction pathways effectively in the prediction. We used the diffuse-interface mesoscale computational model to generate extensive simulated $κ_{eff}$ data for realistic digital representations of microstructures with wide ranges of porosity ($f_p$), thermal conductivity of the gas phase ($κ_g$), and thermal conductivity of the solid phase ($κ_s$). From the simulated data, we identified two property variation regimes for $κ_{eff}$: (1) a slow $κ_{eff}$ increase for $κ_s ~ κ_g$; and (2) a faster $κ_{eff}$ increase for $κ_s \gg κ_g$. To capture the key features of the relationship between the microstructure and $κ_{eff}$, we derived a semi-analytical model by introducing structure and intensification factors. The two new factors incorporate the calibrated effective contribution of the solid volume with $κ_s$ and additional interfacial effects into the prediction of $κ_{eff}$, respectively, allowing for consideration of parallel, serial, and interfacial conduction mechanisms effectively. Using the selected simulation data, we quantified key model parameters within the semi-analytical model and verified that the parameterized model exhibits excellent agreement with simulated $κ_{eff}$ for the entire range of the parameter space.
Watersheds play a critical role in supplying water resources needed for human use and ecosystem health. Understanding and predicting how, when, and where changes in the quantity and quality of water resources occur under different environmental stresses including extreme events is crucial for sustainable management of water resources under a changing environment. However, few studies have attempted to quantify or identify the factors and process interactions controlling the impact of extreme events across water-shed systems. Only few large-scale studies include coordinated monitoring and modeling efforts, which limits our ability to assess the large-scale impact of extreme events on water supply and quality. Methods are lacking to propagate uncertainty in process understanding through an integrated hydro-biogeochemical model framework and evaluate its importance, thus failing to take full advantage of the information potentially available through transformative advances in characterization technologies from high-resolution mass spectrometry to airborne and satellite-based remote sensing. There are consequent risks to our nations water security and to human and ecosystem health that may become exacerbated with the increasing frequency of extreme events that is projected for the coming decades. This paper reviews the current status of watershed science for both water quantity and quality and identifies critical gaps in our current knowledge and modeling capability in addressing the emergent needs in predicting watershed hydrologic and biogeochemical responses (i.e., water quantity and quality) under natural and anthropogenic perturbations. We highlight the need to (1) understand how environmental perturbations including extreme events like floods and droughts propagate through watershed systems and assess their short- and long-term impacts on watershed biogeochemistry, water quality and their recovery pathways; (2) develop and improve a watershed water quality model that reflects the state of scientific understanding gained from observations; and (3) construct a data-model fusion system for watershed characterization, process identification, and mechanistic model parameterization. A large base of modeling, monitoring and data capabilities have been built by various federal government agencies given the relevance of water to their critical missions. An emerging need is to build an integrated national capability for watershed water availability and quality that can address water-related missions across multiple federal agencies.
This memo derives a particular use of a rate-dependent, perfectly plastic Von Mises Plasticity constitutive model that represents a Newtonian (or Bingham) fluid. Only two parameters are needed: the viscosity and bulk modulus to complete the model parameterization. Under certain deformations, the model exactly represents a Newtonian fluid. Under others, the model shear thins. A Newtonian fluid representation may be used in confined flow applications in which a fluid-solid interaction is desired but code coupling is not.
A primary goal of the Climate and Environmental Sciences Division (CESD) is to improve the predictive ability of regional and global climate models (GCMs). One set of tools used for evaluating and improving model predictions and projections are testbeds that combine data derived from observations and model simulations. In these testbeds, outputs of model simulations can be compared to observations of the climate system in order to identify errors and/or biases in the model simulations and determine the specific model processes that need improvements. Testbeds can also be used to provide scientific insights into dominant processes and process interactions as well as increase our understanding of the role of various physical processes involved in a particular case study or meteorological event. CESD currently funds multiple activities, which are developing testbeds to evaluate and improve atmospheric model parameterizations across a range of models, scales, and processes. For each of these testbeds, the Atmospheric Radiation Measurement (ARM) Climate Research Facility represents a primary source of observational data for evaluating the models. The Atmospheric System Research (ASR), Earth System Modeling (ESM), Regional and Global Climate Modeling (RGCM), and ARM program managers co-sponsored a workshop on atmospheric testbed activities across CESD. The workshop was held August 5-6, 2013, at the U. S. Department of Energy (DOE) Germantown facility. The principal objectives of the workshop were to survey the CESD testbed portfolios; identify the distinctive capabilities of each testbed as well as commonalities between the testbeds; identify areas for coordination and collaboration among testbeds; improve linkages between testbed activities and the ARM facility; identify gaps in testbed activities; and identify ways to improve coordination and communication among the testbeds and between the testbeds and the broader scientific community.
Development of a defensible source-term model (STM), usually a thermodynamical model for radionuclide solubility calculations, is critical to a performance assessment (PA) of a geologic repository for nuclear waste disposal. Such a model is generally subjected to rigorous regulatory scrutiny. In this article, we highlight key guiding principles for STM model development and validation in nuclear waste management. We illustrate these principles by closely examining three recently developed thermodynamic models with the Pitzer formulism for aqueous H + —Nd 3+ —NO 3 - (—oxalate) systems in a reverse alphabetical order of the authors: the XW model developed by Xiong and Wang, the OWC model developed by Oakes et al., and the GLC model developed by Guignot et al., among which the XW model deals with trace activity coefficients for Nd(III), while the OWC and GLC models are for concentrated Nd(NO 3 ) 3 electrolyte solutions. The principles highlighted include the following: (1) Principle 1. Validation against independent experimental data: A model should be validated against experimental data or field observations that have not been used in the original model parameterization. We tested the XW model against multiple independent experimental data sets including electromotive force (EMF), solubility, water vapor, and water activity measurements. The results show that the XW model is accurate and valid for its intended use for predicting trace activity coefficients and therefore Nd solubility in repository environments. (2) Principle 2. Testing for relevant and sensitive variables: Solution pH is such a variable for an STM and easily acquirable. All three models are checked for their ability to predict pH conditions in Nd(NO 3 ) 3 electrolyte solutions. The OWC model fails to provide a reasonable estimate for solution pH conditions, thus casting serious doubt on its validity for a source-term calculation. In contrast, both the XW and GLC models predict close-to-neutral pH values, in agreement with experimental measurements. (3) Principle 3. Honoring physical constraints: Upon close examination, it is found that the Nd(III)-NO 3 association schema in the OWC model suffers from two shortcomings. Firstly, its second stepwise stability constant for Nd(NO 3 ) 2+ (log K 2 ) is much higher than the first stepwise stability constant for NdNO 3 2+ (log K 1 ), thus violating the general rule of (log K 2 –log K 1 ) < 0, or $\frac{K1}{K2}$>1. Secondly, the OWC model predicts abnormally high activity coefficients for Nd(NO 3 ) 2 + (up to ~900) as the concentration increases. (4) Principle 4. Minimizing degrees of freedom for model fitting: The OWC model with nine fitted parameters is compared with the GLC model with five fitted parameters, as both models apply to the concentrated region for Nd(NO 3 ) 3 electrolyte solutions. The latter appears superior to the former because the latter can fit osmotic coefficient data equally well with fewer model parameters. The work presented here thus illustrates the salient points of geochemical model development, selection, and validation in nuclear waste management.
Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple “big-leaf” approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1 x 1°. While the photosynthetic capacity parameter (V c,max25 )is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity.
Inferring mathematical relationships with quantified uncertainty from measurement data is common to computational science and metrology. Sufficient knowledge of measurement process noise enables Bayesian inference. Otherwise, an alternative approach is required, here termed compartmentalized inference, because collection of uncertain data and model inference occur independently. Bayesian parameterized model inference is compared to a Bayesian-compatible compartmentalized approach for ISO-GUM compliant calibration problems in renewable energy metrology. In either approach, model evidence can help reduce model discrepancy.
Oxidation modeling importance ASME consideration Effect on graphite properties Model Overview Numerical Model Oxidation Diffusion Reaction Kinetics Model Parameterization Example problems Editing the model Simple problem example Conclusions Coupling to stress analysis Oxidation result analysis
Abstract In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.
Understanding the potential risk of stress corrosion cracking of spent nuclear fuel dry storage canisters has been identified as a knowledge gap for determining the safety of long-term interim storage of spent nuclear fuel. To address this, the DOE is funding a multi-lab DOE effort to understand the timing, occurrence, and consequences of potential canister SCC. Sandia National Laboratories has developed a probabilistic model for canister penetration by SCC. This model has been continuously updated at SNL since 2014. Model uncertainties are treated using a nested loop structure, where the outer loop accounts for uncertainties due to lack of data and the inner aleatoric loop accounts for uncertainties due to variation in nature. By separating uncertainties into these categories, it is possible to focus future work on reducing the most influential epistemic uncertainties. Several experimental studies have already been performed to improve the modeling approach through expanded process understanding and improved model parameterization. The resulting code is physics-based and intended to inform future work by identifying (1) important modeling assumptions, (2) experimental data needs, and (3) necessary model developments. In this document, several of the sub-models in the probabilistic SCC model have been exercised, and the intermediate results, as the model progresses from one sub-model to the next, are presented. Evaluating the sub-models in this manner provides a better understanding of sub-model outputs and has identified several unintended consequences of model assumptions or parameterizations, requiring updates to the modeling approach. The following updates have been made, and future updates have been identified.
The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms. Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (MOYD04). TheMOYD04 product is of course normally produced from MOYD021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a MOYD021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source. We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.
Life cycle assessment (LCA) is a method for assessing all environmental impacts associated with provisioning a good or service. A life cycle encompasses the system which directly produces a good, as well as all systems and processes on which that system is dependent. The objective of this work is to develop a flexible, parameterized model representing a superstructure of life cycles for advanced biofuel production. Parameterizing the model allows users to define a variety of biofuel production scenarios and assess key sustainability metrics, without needing to acquire additional data or perform separate life cycle assessments. Within the model, parameters will include feedstock species and phenotype, a range of farming and harvest practices, feedstock pre-processing, and operating conditions within the biomass conversion process. The model will be used to identify major impact drivers within bioenergy life cycles and aid CBI researchers and leadership in targeting areas where additional R&D efforts will have the most beneficial impacts.
Convection‐permitting models (CPMs) explicitly resolve deep convection yet under‐resolve the organized lateral exchanges among drafts and their environment that control entrainment/detrainment, precipitation efficiency, and mesoscale structure. In this work, we introduce the Optimized Advection Scheme (OAS), which introduces a small rotation of the Cartesian frame of reference for the horizontal winds relative to other variables used in advection that induces cross‐gradient transport to mimic under‐resolved convective mixing. The rotation angle is selected to minimize the Kullback–Leibler divergence between the simulated and satellite observed precipitation intensity distributions, yielding a physically consistent perturbation that is computationally inexpensive and portable. Optimized Advection Scheme is implemented in WRF and evaluated over Amazon (April 2014). It shifts precipitation–precipitable‐water joint distributions toward lighter rain, reduces overly intense rates, and improves mesoscale convective system (MCS) lifetime and propagation. Mechanistically, the added cross‐gradient transport promotes convective detrainment and environmental mixing, which cools and moistens the mid‐troposphere, weakens downward momentum transport, alleviates excessive downwelling shortwave biases, and warms the surface temperature. The optimized rotation angle yields comparable improvements at 4‐km and 1‐km grid spacing, demonstrating resolution‐independent benefits across the CPM gray zone. By targeting the dynamical root of under‐mixed convective circulations, rather than tuning model microphysics or closures, OAS delivers robust, scale‐aware improvements in precipitation statistics, cloud vertical structure, and characteristics of MCS (MCSs), offering a practical pathway to more reliable CPM simulations for weather and climate applications.
The NASA Global Modeling and Assimilation Office is exploring the assimilation of boundary layer height (PBLH) derived from a variety of observations, including radiosondes, GNSS-RO, space- and ground-based lidar, and radar wind profilers. Previous work using the Goddard Earth Observing System (GEOS) has shown that within the lower troposphere, the representation of thermodynamic structure is strongly affected by model parameterizations. In assimilation experiments, parameterizations can compensate for analysis thermodynamic increments and reduce the observation information carried forward in time. To mitigate this effect, the present study explores a dynamic parameter adjustment based on the PBLH analysis field. The PBLH analysis increment is used to adjust model parameters that influence the depth of parameterized boundary layer mixing in both stable and convective regimes. Replay (nudging) experiments and cycled data assimilation experiments are used to evaluate the approach. The parameter adjustment is shown to reduce model compensation of the analysis thermodynamic tendencies and bring model parameterized mixing depths into closer agreement with observed PBLH.
Polycyclic aromatic hydrocarbons (PAHs) are a group of environmental toxicants found ubiquitously as complex mixtures in human-impacted environments. Developmental zebrafish exposures have been used widely to study PAH toxicity, but most studies report nominal exposure concentrations. Nominal exposure concentrations can be unreliable dose metrics due to differences in toxicant bioavailability resulting from disparate exposure methodologies and chemical properties. Toxicokinetic modeling can predict toxicant tissue doses to facilitate comparison between exposures of different chemicals, methodologies, and biological models. We parameterize a biologically based dosimetry model for developmental zebrafish toxicity assays for 9 PAHs. The model was optimized with measurements from media, tissue, and plastic plate walls throughout a static developmental exposure to a mixture of 10 PAHs of high abundance within the Portland Harbor Superfund Site. Plate binding, volatilization, zebrafish permeability, and tissue—media partitioning coefficients vary widely between PAHs. Model predictions accounted for 83% and 54% of 48 hpf body burdens within a factor of 2 resulting from exposures to mixtures and individual PAHs, respectively. Accounting for solubility significantly improves model performance. Competition for active sites in metabolizing enzymes may change biotransformation kinetics between individual PAH and mixture exposures. Area under the curve estimations of concentrations in zebrafish resulted in altered hazard rankings from nominal exposure concentrations. Future work will be oriented to generalizing the model to other PAHs. This PAH dosimetry model improves the interpretability of developmental zebrafish toxicity assays by providing time-resolved body burdens from nominal exposure concentrations.
Abstract Burn area and the frequency of extreme fire events have been increasing during recent decades in North America, and this trend is expected to continue over the 21st century. While many aspects of the North American carbon budget have been intensively studied, the net contribution of fire disturbance to the overall net carbon flux at the continental scale remains uncertain. Based on national scale, spatially explicit and long‐term fire data, along with the improved model parameterization in a process‐based ecosystem model, we simulated the impact of fire disturbance on both direct carbon emissions and net terrestrial ecosystem carbon balance in North America. Fire‐caused direct carbon emissions were 106.55 ± 15.98 Tg C/yr during 1990–2012; however, the net ecosystem carbon balance associated with fire was −26.09 ± 5.22 Tg C/yr, indicating that most of the emitted carbon was resequestered by the terrestrial ecosystem. Direct carbon emissions showed an increase in Alaska and Canada during 1990–2012 as compared to prior periods due to more extreme fire events, resulting in a large carbon source from these two regions. Among biomes, the largest carbon source was found to be from the boreal forest, primarily due to large reductions in soil organic matter during, and with slower recovery after, fire events. The interactions between fire and environmental factors reduced the fire‐caused ecosystem carbon source. Fire disturbance only caused a weak carbon source as compared to the best estimate terrestrial carbon sink in North America owing to the long‐term legacy effects of historical burn area coupled with fast ecosystem recovery during 1990–2012.
Developing nations incur a greater risk to climate change than the developed world due to poorly managed human/natural resources, unreliable infrastructure and brittle governing/economic institutions. These vulnerabilities often give rise to a climate induced “domino effect” of reduced natural resource production-leading to economic hardship, social unrest, and humanitarian crises. Integral to this cascading set of events is increased human migration, leading to the “spillover” of impacts to adjoining areas with even broader impact on global markets and security. Given the complexity of factors influencing human migration and the resultant spill-over effect, quantitative tools are needed to aid policy analysis. Toward this need, a series of migration models were developed along with a system dynamics model of the spillover effect. The migration decision models were structured according to two interacting paths, one that captured long-term “chronic” impacts related to protracted deteriorating quality of life and a second focused on short-term “acute” impacts of disaster and/or conflict. Chronic migration dynamics were modeled for two different cases; one that looked only at emigration but at a national level for the entire world; and a second that looked at both emigration and immigration but focused on a single nation. Model parameterization for each of the migration models was accomplished through regression analysis using decadal data spanning the period 1960-2010. A similar approach was taken with acute migration dynamics except regression analysis utilized annual data sets limited to a shorter time horizon (2001-2013). The system dynamics spillover model was organized around two broad modules, one simulating the decision dynamics of migration and a second module that treats the changing environmental conditions that influence the migration decision. The environmental module informs the migration decision, endogenously simulating interactions/changes in the economy, labor, population, conflict, water, and food. A regional model focused on Mali in western Africa was used as a test case to demonstrate the efficacy of the model.