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DOE OSTI2021

Assessing the drivers of Isoprene SOA: laboratory studies, field observations and modeling (Final Technical Report)

This report describes the research results and products stemming from the funding award DE-SC0018221. The main goals of this project were to develop a quantitative understanding of the factors driving secondary organic aerosol (SOA) formation from biogenic volatile organic compounds, namely isoprene and monoterpenes, using a combination of laboratory measurements, field data analysis, and modeling. Specifically, we aimed to: 1) develop an observationally constrained volatility distribution of trace gases produced from the oxidation of biogenic hydrocarbons that can explain the formation of SOA from in situ observations made during DOE ASR/ARM field campaigns such as HI-SCALE and BAECC 2) use new and existing (e.g. DOE ASR funded SOAFFEE experiments) laboratory chamber experiments to develop detailed parameterizations of isoprene-derived epxoy diol (IEPOX) reactive uptake, including product branching, product volatility, and solubility constants, as well as SOA formation generally from the formation of highly oxygenated organic molecules (HOM) formed from isoprene and monoterpene photo-oxidation that can be directly incorporated into models. 3) collaborate with PNNL modeling teams to incorporate new parameterizations into detailed box models, such as MOSAIC, and regional or Earth System models such as WRF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models

Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here, in this work, the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light-to-moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large-scale environment in convective triggering processes helps resolve the phase-locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light-to-moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low-resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes.

58 GEOSCIENCES↗
DOE OSTI2020

Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework

Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Assessing the Skill of the Improved Treatment of Riverine Freshwater in the Community Earth System Model (CESM) Relative to a New Salinity Climatology

Abstract Recent studies have explored the sensitivity of global ocean model simulations to the treatment of riverine freshwater and the representation of estuarine processes via an estuary box model applied within Community Earth System Model (CESM). This study builds on these efforts by assessing the model skill score relative to a new salinity climatology. The new climatology averages the original observational data of the World Ocean Database directly onto the CESM ocean component tracer grid cells without spatial interpolation, smoothing, or other gap‐filling techniques to mitigate coastal ocean salinity bias present in the World Ocean Atlas. The mean square error for coastal upper ocean salinity relative to climatology is reduced by up to 14%, and the mean square error of near‐surface salinity stratification is reduced by up to 28% near major river mouths in the simulations with improved treatments of river runoff. The improvement in upper ocean bulk salinity is attributed primarily to focusing runoff as point sources thereby avoiding the artificial horizontal spreading of the control run and to applying a locally varying instead of a global constant reference salinity for riverine virtual salt fluxes. The improvements in near‐surface salinity stratification are primarily attributed to adding parameterized estuarine mixing with the estuary box model. Salinity and salinity stratification skill improvements are achieved not just near large rivers but also along the global coast and skill improvements extend far offshore. Despite these improvements, many other sources of model‐climatology mismatch in coastal salinity and stratification remain and merit further attention.

58 GEOSCIENCES↗
DOE OSTI2023

Deciphering the sensitivity of urban canopy air temperature to anthropogenic heat flux with a forcing-feedback framework

Abstract The sensitivity of urban canopy air temperature ( T a ) to anthropogenic heat flux ( Q A H ) is known to vary with space and time, but the key factors controlling such spatiotemporal variabilities remain elusive. To quantify the contributions of different physical processes to the magnitude and variability of Δ T a / Δ Q A H (where Δ represents a change), we develop a forcing-feedback framework based on the energy budget of air within the urban canopy layer and apply it to diagnosing Δ T a / Δ Q A H simulated by the Community Land Model Urban over the contiguous United States (CONUS). In summer, the median Δ T a / Δ Q A H is around 0.01 K W m − 2 − 1 over the CONUS. Besides the direct effect of Q A H on T a , there are important feedbacks through changes in the surface temperature, the atmosphere–canopy air heat conductance ( c a ), and the surface–canopy air heat conductance. The positive and negative feedbacks nearly cancel each other out and Δ T a / Δ Q A H is mostly controlled by the direct effect in summer. In winter, Δ T a / Δ Q A H becomes stronger, with the median value increased by about 20% due to weakened negative feedback associated with c a . The spatial and temporal (both seasonal and diurnal) variability of Δ T a / Δ Q A H as well as the nonlinear response of Δ T a to Δ Q A H are strongly related to the variability of c a , highlighting the importance of correctly parameterizing convective heat transfer in urban canopy models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Phase-field simulations of fission gas bubble growth and interconnection in U-(Pu)-Zr nuclear fuel

Abstract The growth and interconnection of fission gas bubbles in the hotter central regions of U-(Pu)-Zr nuclear fuel has been simulated with a phase-field model. The Cahn-Hilliard equation was used to represent the two-phase microstructure, with a single defect species. The volume fraction of the bubble phase and surface area of the bubble-matrix interface were determined during growth and interconnection. Surface area increased rapidly during the initial stages of growth, then slowed and finally decreased as bubble interconnection began and coarsening acted to reduce surface area. The fraction of the bubbles vented to a simulation domain boundary, f V , was quantified as a measure of the microstructure’s interconnectivity and plotted as a function of porosity p . The defect species diffusivity was varied; although changes in diffusivity significantly affected the microstructure, the plots of f V vs. p did not change significantly. The percolation threshold p c was calculated to be approximately 0.26, depending on the assumed diffusivity and using an initial bubble number density based on experimental observations. This is slightly smaller than the percolation threshold for continuum percolation of overlapping 3D spheres. The simulation results were used to parameterize two different engineering-scale swelling models for U-(Pu)-Zr in the nuclear fuel performance code BISON.

Aagesen, Larry K. (ORCID:000000034936676X)↗
DOE OSTI2023

Robustness of the Stochastic Parameterization of Subgrid-Scale Wind Variability in Sea Surface Fluxes

Abstract High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.

Endo, Kota↗
DOE OSTI2025

Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah‐MP Land Surface Model: Model Development and Evaluation

Abstract Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C ( V cmax,25 ) and the leaf respiration rate at 25°C ( R 25 )) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R 25 and V cmax,25 vary substantially both temporally and spatially within the same PFT ( C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in V cmax,25 (73% and 54% of the variations in R 25 ). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO 2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.

Ren, Yanghang [Department of Earth System Science ↗
DOE OSTI2019

Coupled Experiment and Theory to Explore the Limits of Material Strength at High Strain Rates

Critical components such as detonators in Sandia's stockpile are heterogeneous materials that rely on accurate constitutive models for each material used in the component. However, experiments to study materials at high pressure are challenging and time consumptive, therefore we turn to modeling tools to refine and predict outcomes beforehand. Efficient models balance absolute physical accuracy against approximate but computationally lightweight constitutive inputs. By using a relatively small number of high fidelity simulations and experiments we have be able to broaden the predictive power of the shock response in metals and polymers. We have tailored the analysis of these simulations to determine a size dependent material strength, which can be used as constitutive model inputs for continuum hydrodynamics codes. For the shocked Cu, MD simulations show a yield strength from RMI jet growth of approximately 450MPa that depends on the details of the free surface geometry. This value is close to the yield strength of 500MPa parameterized for an elastic-perfectly-plastic strength model from experiments at the Dynamic Compression Sector at Argonne National Lab. The same analysis applied to MD simulations of PMMA jetting resulted in no clear determination of yield strength, implying a more complex RMI process in polymeric materials. Atomistic simulations of both materials are shown to be valuable training metrics and show the need for explicit strain rate dependent strength for future improvements in strength models used in continuum codes.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Turbulence-driven coronal heating and improvements to empirical forecasting of the solar wind

Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvén waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Metrics for the Diurnal Cycle of Precipitation: Toward Routine Benchmarks for Climate Models

In this paper, metrics are proposed—that is, a few summary statistics that condense large amounts of data from observations or model simulations—encapsulating the diurnal cycle of precipitation. Vector area averaging of Fourier amplitude and phase produces useful information in a reasonably small number of harmonic dial plots, a procedure familiar from atmospheric tide research. The metrics cover most of the globe but down-weight high-latitude wintertime ocean areas where baroclinic waves are most prominent. This enables intercomparison of a large number of climate models with observations and with each other. The diurnal cycle of precipitation has features not encountered in typical climate model intercomparisons, notably the absence of meaningful “average model” results that can be displayed in a single two-dimensional map. Displaying one map per model guides development of the metrics proposed here by making it clear that land and ocean areas must be averaged separately, but interpreting maps from all models becomes problematic as the size of a multimodel ensemble increases. Global diurnal metrics provide quick comparisons with observations and among models, using the most recent version of the Coupled Model Intercomparison Project (CMIP). This includes, for the first time in CMIP, spatial resolutions comparable to global satellite observations. Finally, consistent with earlier studies of resolution versus parameterization of the diurnal cycle, the longstanding tendency of models to produce rainfall too early in the day persists in the high-resolution simulations, as expected if the error is due to subgrid-scale physics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On the Parameterization of Convective Downdrafts for Marine Stratocumulus Clouds

The role of nonlocal transport on the development and maintenance of marine stratocumulus (Sc) clouds in coarse-resolution models is investigated, with a special emphasis on the downdraft contribution. A new parameterization of cloud-top-triggered downdrafts is proposed and validated against large-eddy simulation (LES) for two Sc cases. The applied nonlocal mass-flux scheme is part of the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) framework decomposing the turbulent transport into local and nonlocal contributions. The complementary local turbulent transport is represented with the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme. This EDMF version has been implemented in the Weather Research and Forecasting (WRF) single-column model (SCM) and tested for three model versions: without mass flux, with updrafts only, and with both updrafts and downdrafts. In the LES, the downdraft and updraft contributions to the total heat and moisture transport are comparable and significant. The WRF SCM results show a good agreement between the parameterized downdraft turbulent transport and LES. While including updrafts greatly improves the modeling of Sc clouds over the simulation without mass flux, the addition of downdrafts is less significant, although it helps improve the moisture profile in the planetary boundary layer.

Wu, Elynn↗
DOE OSTI2016

Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Köhler theory to predict the effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid–liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. The model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Root Influences on Mobilization and Export of Mineral-bound Soil Organic Matter

Biogeochemical cycles within mountainous watersheds are key regulators of ecosystem carbon storage and downstream nutrient loadings, and they have shown to be particularly vulnerable to climate change impacts. Increasing temperature and persistent droughts have already dramatically changed vegetation cover across the mountainous western US, with unknown consequences for soil carbon and nutrient cycles belowground. What remains elusive is to what extent associated changes in root-soil interactions may mobilize the vast pool of organic matter (OM) that has been stabilized by associations with minerals for centuries or millennia. Although plant root-driven OM mobilization from minerals may be a central control on carbon loss and nutrient export, such mechanisms are currently missing from conceptual and numerical models. The overall objective of this Exploratory Project is to identify the biogeochemical mechanisms by which roots destabilize mineral-associated organic matter (MAOM) and the cumulative impact on carbon and nutrient fate. To accomplish this goal, we integrated well-controlled laboratory experiments with in-field measurements and a scalable modeling approach. First, we conducted model system experiments to assess the vulnerability of MAOM to exudate-mediated mechanisms. Our results show that common root exudates effectively destabilize MAOM not only through direct, ligand-driven mobilization mechanisms, but also indirect, microbially-mediated mechanisms relying on secondary metabolites and enzymes. We further found that OM bound to poorly crystalline Fe and Al (hydr)oxides is more vulnerable to exudate-induced destabilization than OM bound to more crystalline phases, particularly in response to direct, ligand-promoted mechanisms. These findings demonstrate that the stability of MAOM is not just a function of their inherent properties, but also will depend in large parts on the ability of plant roots and microbes to produce exudates capable of triggering suitable mobilization mechanisms. We further employed a well-controlled rhizobox approach, combining advanced microsensor and mass spectrometry techniques, to resolve spatiotemporal variations in the composition and availability of exudates along single growing roots of grasses. Our results show that the composition of functionally relevant exudate compounds varies at extremely short time scales, seemingly shifting from ligands such as aromatic acids around root tips to less reactive metabolites such as amino acids around mature root segments. These results suggest a prevalence of direct MAOM mobilization mechanisms around the root tip, while indirect MAOM mobilization strategies may dominate around more mature root segments. Additionally, we utilized microsensor measurements in rhizoboxes to parameterize a rhizosphere (hydro)biogeochemistry reactive transport model (eSTOMP-ROOTS). The resulting model was used to assess how root exudations affects the stability of MAOM. Model simulations show that diel pulses of root exudation are strong enough to cause oscillations in biogeochemical conditions—particularly in pH, oxygen concentrations, and microbial activity— that repeatedly disrupt MAOM. Finally, we assessed field-scale impacts of root-driven MAOM destabilization over short and long time scales. A combination of in-field incubations and field-based characterizations showed that MAOM may not only be vulnerable to seasonal variations in root activity, but also to root-driven weathering over pedogenic time scales. In sum, results from our Exploratory Award highlight the strong control plant roots exert on MAOM mobilization and, thus, on the potential for carbon and nutrient export from watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

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↗
DOE OSTI2022

Micro-spectroscopic and freezing characterization of ice-nucleating particles collected in the marine boundary layer in the eastern North Atlantic

Abstract. Formation of atmospheric ice plays a crucial role in the microphysical evolution of mixed-phase and cirrus clouds and thus climate. How aerosol particles impact ice crystal formation by acting as ice-nucleating particles (INPs) is a subject of intense research activities. To improve understanding of atmospheric INPs, we examined daytime and nighttime particles collected during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign conducted in summer 2017. Collected particles, representative of a remote marine environment, were investigated for their propensity to serve as INPs in the immersion freezing (IMF) and deposition ice nucleation (DIN) modes. The particle population was characterized by chemical imaging techniques such as computer-controlled scanning electron microscopy with energy-dispersive X-ray analysis (CCSEM/EDX) and scanning transmission X-ray microscopy with near-edge X-ray absorption fine-structure spectroscopy (STXM/NEXAFS). Four major particle-type classes were identified where internally mixed inorganic–organic particles make up the majority of the analyzed particles. Following ice nucleation experiments, individual INPs were identified and characterized by SEM/EDX. The identified INP types belong to the major particle-type classes consisting of fresh sea salt with organics or processed sea salt containing dust and sulfur with organics. Ice nucleation experiments show IMF events at temperatures as low as 231 K, including the subsaturated regime. DIN events were observed at lower temperatures of 210 to 231 K. IMF and DIN observations were analyzed with regard to activated INP fraction, ice-nucleation active site (INAS) densities, and a water activity-based immersion freezing model (ABIFM) yielding heterogeneous ice nucleation rate coefficients. Observed IMF and DIN events of ice formation and corresponding derived freezing rates demonstrate that the marine boundary layer aerosol particles can serve as INPs under typical mixed-phase and cirrus cloud conditions. The derived IMF and DIN parameterizations allow for implementation in cloud and climate models to evaluate predictive effects of atmospheric ice crystal formation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Molecular thermodynamic modeling of aqueous Na + -K + -Mg 2+ -Ca 2+ -SO 4 2- quinary system

As part of the effort to develop a comprehensive molecular thermodynamic model for high salinity produced water based on the electrolyte nonrandom two-liquid theory, this work presents the model results for the aqueous Na + -K + -Mg 2+ -Ca 2+ -SO 4 2- quinary system. With the binary interaction parameters fully parameterized for the water-electrolyte pairs and the electrolyte-electrolyte pairs, the model satisfactorily describes the thermodynamic properties for the aqueous quinary system and its binary, ternary, and quaternary subsystems for the temperature range 273.15–473.15 K and the salt concentrations up to saturation. The model results are compared with the salt solubility and thermodynamic data available for the subsystems with special attention to systems involving (Ca 2+ SO 4 2- ).

Chemistry↗
DOE OSTI2020

Toward Understanding the Simulated Phase Partitioning of Arctic Single-Layer Mixed-Phase Clouds in E3SM

21 Mixed-phase clouds are widespread in the Arctic and have substantial impacts on the 22 surface energy budget, sea ice extent, and regional climate. Significant changes are found in the 23 modeled phase partitioning of Arctic mixed-phase clouds in the newly developed U.S. 24 Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model 25 version 1 (EAMv1) compared to its predecessor (CAM5/EAMv0). The mixed-phase clouds 26 simulated by EAMv1 is overly dominated by super-cool liquid which is often underestimated by 27 CAM5. Compared to CAM5, EAMv1 has made several important changes in its cloud physical 28 parameterizations, including using CLUBB to unify shallow convection, PBL turbulence, and 29 cloud macrophysics, as well as updating the MG two-moment cloud microphysics scheme and 30 using the CNT heterogeneous ice nucleation scheme to replace the Meyers scheme for mixed- 31 phase clouds. To understand which of the newly-implemented physical parameterizations is 32 responsible for the change of modeled mixed-phase cloud phase partitioning in EAMv1, 33 sensitivity tests are conducted by utilizing the short-term hindcast approach and results are 34 compared to the observations from the ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) 35 field campaign. 36 It is found that although each scheme contributes to the decrease of cloud ice water 37 content (IWC) and increase of cloud liquid water content (LWC), only the combined effect of 38 CLUBB and CNT can explain the significant underestimation of IWC. By examining the budgets 39 of cloud microphysical processes, CLUBB is found to play a dominant role due to the fact that 40 CLUBB does not diagnoses cloud ice phase water in its assumed joint PDF method and thus may 41 not provide sufficient cloud ice to initialize ice phase microphysical processes such as the growth 42 of ice crystals through vapor deposition. Although a more effective heterogeneous ice nucleation 43 may alleviate the underestimation of IWC, the ice phase process in CLUBB requires urgent 44 attention in the future model development.

Zhang, Meng↗