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DOE OSTI2021

Biomass burning aerosols in most climate models are too absorbing

Uncertainty in the representation of biomass burning (BB) aerosol composition and optical properties in climate models contributes to a range in modeled aerosol effects on incoming solar radiation. Depending on the model, the top-of-the-atmosphere BB aerosol effect can range from cooling to warming. By relating aerosol absorption relative to extinction and carbonaceous aerosol composition from 12 observational datasets to nine state-of-the-art Earth system models/chemical transport models, we identify varying degrees of overestimation in BB aerosol absorptivity by these models. Modifications to BB aerosol refractive index, size, and mixing state improve the Community Atmosphere Model version 5 (CAM5) agreement with observations, leading to a global change in BB direct radiative effect of -0.07 W m -2 , and regional changes of -2 W m -2 (Africa) and -0.5 W m -2 (South America/Temperate). Our findings suggest that current modeled BB contributes less to warming than previously thought, largely due to treatments of aerosol mixing state.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

58 GEOSCIENCES↗

NASA NTRS2001

Evaluation of Tropical Transport in a Global Chemistry and Transport Model

Observations of constituents from satellite, aircraft and sondes can be utilized to develop diagnostics of various aspects of tropical transport. These include tropical mid-latitude isolation, the seasonal transport from the upper tropical troposphere to the mid-latitude lowermost stratosphere, the seasonal cycle of the tropical total ozone and its variability. These diagnostics will be applied to constituent fields from an off-line chemistry and transport model (CTM) driven by winds from two sources. These are the Finite Volume Community Climate Model (FV-CCM), a general circulation model that uses the NCAR CCM physics and the Lin and Rood dynamical core, and an assimilation system developed by the Data Assimilation Office at the Goddard Space Flight Center that uses the FV-CCM at its core. Signatures of the quasi-biennial oscillation present in the observations will be emphasized to understand differences between the two model transports and the transport inferred from the observations.

Douglass, Anne R.↗

NASA NTRS2014

Assessment of the Accuracy of the Conventional Ray-Tracing Technique: Implications in Remote Sensing and Radiative Transfer Involving Ice Clouds.

A fundamental problem in remote sensing and radiative transfer simulations involving ice clouds is the ability to compute accurate optical properties for individual ice particles. While relatively simple and intuitively appealing, the conventional geometric-optics method (CGOM) is used frequently for the solution of light scattering by ice crystals. Due to the approximations in the ray-tracing technique, the CGOM accuracy is not well quantified. The result is that the uncertainties are introduced that can impact many applications. Improvements in the Invariant Imbedding T-matrix method (II-TM) and the Improved Geometric-Optics Method (IGOM) provide a mechanism to assess the aforementioned uncertainties. The results computed by the II-TMþIGOM are considered as a benchmark because the IITM solves Maxwell's equations from first principles and is applicable to particle size parameters ranging into the domain at which the IGOM has reasonable accuracy. To assess the uncertainties with the CGOM in remote sensing and radiative transfer simulations, two independent optical property datasets of hexagonal columns are developed for sensitivity studies by using the CGOM and the II-TMþIGOM, respectively. Ice cloud bulk optical properties obtained from the two datasets are compared and subsequently applied to retrieve the optical thickness and effective diameter from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Additionally, the bulk optical properties are tested in broadband radiative transfer (RT) simulations using the general circulation model (GCM) version of the Rapid Radiative Transfer Model (RRTMG) that is adopted in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM, version 5.1). For MODIS retrievals, the mean bias of uncertainties of applying the CGOM in shortwave bands (0.86 and 2.13 micrometers) can be up to 5% in the optical thickness and as high as 20% in the effective diameter, depending on cloud optical thickness and effective diameter. In the MODIS infrared window bands centered at 8.5, 11, and 12 micrometers biases in the optical thickness and effective diameter are up to 12% and 10%, respectively. The CGOM-based simulation errors in ice cloud radiative forcing calculations are on the order of 10Wm(exp 2).

remote sensing↗

NASA NTRS2016

The Viability of Trajectory Analysis for Diagnosing Dynamical and Chemical Influences on Ozone Concentrations in the UTLS

The viability of trajectory analysis for diagnosing the interplay between chemistry and dynamics is investigated by comparing ozone mixing ratios modelled using air-parcel pathways to values observed along flight tracks during ATTREX (Airborne Tropical TRopopause EXperiment). Trajectories are initiated at the locations of ozone observations and tracked backward in time to their sources at termini of backward trajectories. The modelled values of ozone utilize 3-dimensional analysis fields from WACCM (Whole Atmosphere Community Climate Model) (a chemical-climate model with dynamical fields nudged towards MERRA (Modern-Era Retrospective Analysis and Research Applications) reanalysis) and ERA-interim (product of ECMWF - the European Centre for Medium-Range Weather Forecasts) to determine source mixing ratios with chemical production and loss terms derived from the ozone chemistry used in WACCM. A statistical base of modelled ozone is constructed with 6 trajectory platforms (adiabatic, diabatic, and kinematic forced by ERA-interim and MERRA), two chemical models (WACCM chemistry and no chemistry), and 4 trajectory lengths (5, 10, 20, and 30 days). Linear regression is employed to separate systematic errors from random errors and to characterize the impact of source mixing ratios, path length, vertical motion, and chemistry on modelled ozone errors. Errors in the analysis ozone fields are large, if not dominant, contributors to model error. Random errors are particularly large for point-by-point comparisons, however averaging over 800 km (75 minutes) flight segments substantially reduces random error and exposes systematic errors. Of the two analysis ozone data sets, WACCM, which incorporates detailed chemistry, provides the smaller systematic errors while ERA-interim, which has crude chemistry but assimilates observational data, has the smaller random errors. Of the different trajectory platforms, adiabatic calculations produce the smaller random errors (irrespective of the use of chemistry) but both vertical motion and chemistry are required to optimally reduce systematic errors. These results suggest that meaningful analysis of dynamical and chemical interactions that control ozone mixing ratios are viable on spatial scales larger than a few reanalysis grid spaces, that errors in the analyzed ozone data sets are large but not prohibitively so, and that vertical velocities and heating rates from reanalysis data, while problematic, contain useful information [on the ozone concentrations in the UTLS (Upper Troposphere/Lower Stratosphere)].

ozone↗

NASA NTRSDate not supplied

Analyzing Space Weather at Mars with MAVEN

To understand the processes driving Martian atmospheric escape to space, the MAVEN mission has been monitoring solar inputs at Mars since the spacecraft went into orbit in September 2014. MAVEN’s payload provides in situ measurements of solar wind ions and electrons, solar energetic particles, extreme ultraviolet radiation, and magnetic fields. With this comprehensive suite of instruments, the MAVEN team has monitored space weather at Mars for nearly a decade, over various parts of Solar Cycles 24 and 25. MAVEN observations have determined that space weather activity can deposit enough energy into the Martian system to enhance atmospheric escape by an order of magnitude. As the mission continues, monitoring space weather at Mars has become increasingly important to understand the Sun’s influence on the Martian environment. The MAVEN team provides regular reports of space weather activity, such as the occurrence and arrival time of coronal mass ejections as well as flare magnitude and peak times, to the Mars community when predictions suggest that a space weather-impacting event may be directed towards Mars. MAVEN provides observations of solar activity on the ‘far side’ of the Sun when Earth and Mars are near solar conjunction to augment measurements of solar observatories (e.g., SDO, STEREO, GOES, PSP, Solar Orbiter), providing a more global coverage of solar activity. Additionally, MAVEN is working closely with the Community Coordinated Modeling Center (CCMC) and the Moon to Mars (M2M) Space Weather Analysis Office, both located at NASA GSFC, to form the Mars Space Weather Collaboration. This collaboration utilizes MAVEN observations of space weather activity to facilitate model and prediction validation while creating tools that are available to the science community. These activities are in support of future human and robotic exploration as we identify needs to enhance current capabilities for analyzing space weather at Mars.

Gina A DiBraccio↗

DOE OSTI2022

Community Energy Planning: Best Practices and Lessons Learned in NREL's Work with Communities

The clean energy transition is accelerating due to local, state, and national actions combined with external market forces. Regardless of where goals are set and decisions are made, clean energy deployment occurs on the ground in communities. As a result, communities increasingly need technical expertise and assistance with planning for and managing the energy transition. Building on decades of work with state, local, and tribal jurisdictions, the National Renewable Energy Laboratory (NREL) increasingly provides community-driven modeling, analysis, and technical assistance to enable more data-driven and equitable community energy planning. To inform and enhance NREL's capabilities in community energy planning and provide a resource for others working in this space, the Joint Institute for Strategic Energy Analysis (JISEA) Sustainable Communities Catalyzer supported this best-practices presentation based on interviews with seasoned NREL practitioners and a literature review on equitable community energy planning. Findings include five best practices for community energy planning that NREL practitioners and others can apply to strengthen their impact: 1) do your homework in preparation for community interactions; 2) be humble, authentic, and honest in your interactions with community members; 3) respect community agency in every step of the process; 4) meet the community where they are; 5) democratize participation.

best practices↗

DOE OSTI2026

Importance of Spatially Continuous Urban Surface Properties in Urban‐Resolving Earth System Modeling

Accurate representation of urban properties and processes at higher resolutions in global modeling systems is essential for advancing our ability to capture the complexities of urban systems and informing effective resilience strategies. However, the prescription of coarse global-scale urban properties in most state-of-the-art Earth system models (ESMs) is limiting their potential for capturing urban signals as they advance toward kilometer-scale simulation capabilities. To bridge this gap in inadequate urban property representation and to advance urban-resolving Earth system modeling, this work integrates the newly-developed global 1 km-resolution facet-level urban surface property data set, U-Surf, into the land component of Community Earth System Model (CESM)—Community Terrestrial System Model (CTSM). The land-only CTSM simulations are validated against satellite measurements, ground-based urban weather stations, flux tower observations, and reanalysis data. Results demonstrate that the enhanced urban properties allow improved simulations of urban meteorology and surface energy fluxes compared to the default coarse-resolution categorical urban canopy parameters. Spatial scaling analysis reveals regime-dependent information loss during resolution aggregation, as well as substantial scale-dependent variations in urban surface energy flux representation. Furthermore, these findings have critical implications for coupled Earth system modeling when including the effect of land-atmosphere interaction. This work establishes a foundation for future urban-resolving kilometer-scale ESM development, which will enable systematic intra- and inter-city comparisons that inform urban adaptation strategies across diverse global urban environments.

Cheng, Yifan [University of Illinois Urbana-Champa↗

NASA NTRSDate not supplied

Retrospectives: Intersection of Spaceflight Stressors and Microbial Risk to Crew and Craft

OVERVIEW The spaceflight environment has several unique stressors that affect the health of both the crew and the spacecraft. An area of continued, albeit incomplete, study is the interaction of these stressors on microbial populations inherent to both astronauts and spacecraft surfaces and systems. A primary concern is the potential for the spaceflight environment to perturb the phenotype of these populations towards negative outcomes for crew and craft. In order to effectively mitigate these potential risks, they must first be characterized. We performed a retrospective literature analysis to assess the current state of knowledge regarding the affects of ionizing radiation and elevated CO2 on relevant microbial populations. The results of these retrospectives will guide next steps in the decisions of what (if any) further studies should be pursued and to guide decisions of the need for countermeasures. STRESSORS Ionizing radiation. The health risk involved with increased exposure to cosmic radiation has been studied in crew for 35+ years, with human health and cancer risk being the main focus. However, space radiation could also affect both the resident microorganisms aboard the ISS and the normal, healthy astronaut microbiomes that are of direct concern for crew health. A retrospective review of over 250 publications was accomplished looking at the impact of cumulative ionizing radiation doses lower than 3 Gy (chronic or acute) on microbial populations. Elevated CO2. The health risk involved with elevated atmospheric CO2 in spacecraft, primarily focusing on human toxicological risks, is understudied. The current Spaceflight Maximum Allowance Concentration for 24-hour average CO2 is 0.4% (3 mm Hg), which is significantly higher than terrestrial levels (0.04%). Whether these elevated ambient CO2 levels aboard spacecraft influence the diversity and phenotypic responses of the resident microbial communities from both the spacecraft environment (air, surface, water) and crew members (gut, nasal,skin microbiomes) is not known. A retrospective review was accomplished looking at the impact of chronic CO2 exposure up to 0.7% (5 mm Hg) for up to 6 months and acute exposure up to 2.6% (20 mm Hg) for up to 24 hours. CONCLUSIONS: MICROBIOME OF THE BUILT ENVIRONMENT The microbiome of the built spacecraft environment has been sampled consistently over the course of human spaceflight and significant advancements have been made in identifying microbial populations on the ISS. The dominant source of microbes on spacecraft surfaces are human-derived. Once in the spacecraft built environment,the extreme environment selects for features that enhance survival. While efforts to understand potential antibiotic resistance and pathogenicity of ISS isolates is robust, there is little to no understanding of which spaceflight environmental stressors, to include ionizing radiation or elevated CO2, drive the evolutionary trajectory of spacecraft-associated microbial populations. CONCLUSIONS: MICROBE-HOST INTERACTIONS The host-microbiome field has emerged as an important factor in human health on Earth as well in spaceflight. The field is struggling with the complexity of the system under investigation as there is substantial taxonomic and functional heterogeneity in these communities, making it difficult to establish clear stimulus-response dynamics.Taxonomic characterization is the norm; however, the functional role of each community member is key to linking environmental perturbations to potential dysbiosis. For both ionizing radiation and elevated CO2, the likely target of the perturbation is the host tissue, not the microbes themselves.. Any resulting changes to the microbial community composition and/or function is likely a result of adapting to those changes in the host physiology. RECOMMENDATIONS Emphasize functional characterization as opposed to taxonomic characterization of microbial communities.Increase the number of investigations using chronic, spaceflight-relevant doses of ionizing radiation. MoBE studies should move away from observational studies towards predictive modeling of community dynamics. Continue to develop scale-down models, such as tissues-on-a-chip & defined microbial communities. Focus on the crew response to elevated CO2 over MoBE considerations. Assess how direct contact with the hypercapnic environment affects skin microbiome dynamics.

Countermeasures↗

NASA NTRS2016

Enhanced Representation of Soil NO Emissions in the Community Multiscale Air Quality (CMAQ) Model Version 5.0.2

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

remote sensing↗

NASA NTRS2014

Assimilation of Dual-Polarimetric Radar Observations with WRF GSI

Dual-polarimetric (dual-pol) radar typically transmits both horizontally and vertically polarized radio wave pulses. From the two different reflected power returns, more accurate estimate of liquid and solid cloud and precipitation can be provided. The upgrade of the traditional NWS WSR-88D radar to include dual-pol capabilities will soon be completed for the entire NEXRAD network. Therefore, the use of dual-pol radar network will have a broad impact in both research and operational communities. The assimilation of dual-pol radar data is especially challenging as few guidelines have been provided by previous research. It is our goal to examine how to best use dual-pol radar data to improve forecast of severe storm and forecast initialization. In recent years, the Development Testbed Center (DTC) has released the community Gridpoint Statistical Interpolation (GSI) DA system for the Weather Research and Forecasting (WRF) model. The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this study explores regional assimilation of the dual-pol radar variables from the WSR-88D radars for real case storms. Our presentation will highlight our recent effort on incorporating the horizontal reflectivity (ZH), differential reflectivity (ZDR), specific differential phase (KDP), and radial velocity (VR) data for initializing convective storms, with a significant focus being on an improved representation of hydrometeor fields. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to dual-pol variables. Beyond the dual-pol variable assimilation procedure developing within a GSI framework, highresolution (≤1 km) WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. Further details of the methodology of data assimilation, the impact of different dual-pol variables, the influence on precipitation forecast will be presented at the conference.

Li, Xuanli↗

NASA NTRS2014

Assimilation of Dual-Polarimetric Radar Observations with WRF GSI

Li, Xuanli↗

DOE OSTI2025

Demonstrating Hierarchical System Development With the Common Community Physics Package Single‐Column Model: A Case Study Over the Southern Great Plains

This study demonstrates a specific application of the hierarchical system development (HSD) approach to investigate, analyze, and attribute model issues within the Unified Forecast System (UFS), with a focus on process isolation. By evaluating a non‐precipitating, shallow cumulus case at the Atmospheric Radiation Measurement Southern Great Plains site in the UFS global forecast against the observation, the investigation identifies a warmer and deeper daytime convective planetary boundary layer (PBL) and misrepresented nocturnal PBL transition. Hypothesis testing, which employs the Common Community Physics Package (CCPP) single‐column model (SCM) and uses the same physics as the UFS global model, confirms that these issues are attributed to the model physics and initialization. Specifically, misrepresented PBL processes are linked to problematic surface condition and a lack of cloud formation, which may stem from deficiencies in PBL and cloud microphysics parameterizations and their interactions. The UFS initial condition contributes to an earlier, excessively collapsed daytime convective boundary layer and a lack of decoupling between the stable boundary layer and residual layer late in the afternoon. This work introduces an avenue for the community to engage with the application of HSD, along with the CCPP and CCPP SCM, to understand the interplay of model physics, disentangle the roles of model components, as well as facilitate model and forecast improvement.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluation of the CAM6 Climate Model Using Cloud Observations at McMurdo Station, Antarctica

A comparative analysis between observational data from McMurdo Station, Antarctica and the Community Atmosphere Model version 6 (CAM6) simulation is performed focusing on cloud characteristics and their thermodynamic conditions. Ka-band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) retrievals are used as the basis of cloud fraction and cloud phase identifications. Radiosondes released at 12-hour increments provide atmospheric profiles for evaluating the simulated thermodynamic conditions. Our findings show that the CAM6 simulation consistently overestimates (underestimates) cloud fraction above (below) 3 km in four seasons of a year. Normalized by total in-cloud samples, ice and mixed phase occurrence frequencies are underestimated and liquid phase frequency is overestimated by the model at cloud fractions above 0.6, while at cloud fractions below 0.6 ice phase frequency is overestimated and liquid-containing phase frequency is underestimated by the model. The cloud fraction biases are closely associated with concurrent biases in relative humidity (RH), i.e., high (low) RH biases above (below) 2 km. Frequencies of correctly simulating ice and liquid-containing phase increase when the absolute biases of RH decrease. Cloud fraction biases also show a positive correlation with RH biases. Water vapor mixing ratio biases are the primary contributor to RH biases, and hence, likely a key factor controlling the cloud biases. Finally, this diagnosis of the evident shortfalls of representations of cloud characteristics in CAM6 simulation at McMurdo Station brings new insight in improving the governing model physics therein.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Improving Low‐Cloud Fraction Prediction Through Machine Learning

Abstract In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of “too few” low clouds and “too rapid” SCT in global climate models.

Geology↗

NASA NTRS2022

Evaluating the Impact of Chemical Complexity and Horizontal Resolution on Tropospheric Ozone Over the Conterminous US With a Global Variable Resolution Chemistry Model

A new configuration of the Community Earth System Model (CESM)/Community Atmosphere Model with full chemistry (CAM-chem) supporting the capability of horizontal mesh refinement through the use of the spectral element (SE) dynamical core is developed and called CESM/CAM-chem-SE. Horizontal mesh refinement in CESM/CAM-chem-SE is unique and novel in that pollutants such as ozone are accurately represented at human exposure relevant scales while also directly including global feedbacks. CESM/CAM-chem-SE with mesh refinement down to ∼14 km over the conterminous US (CONUS) is the beginning of the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICAv0). Here MUSICAv0 is evaluated and used to better understand how horizontal resolution and chemical complexity impact ozone and ozone precursors over CONUS as compared to measurements from five aircraft campaigns, which occurred in 2013. This field campaign analysis demonstrates the importance of using finer horizontal resolution to accurately simulate ozone precursors such as nitrogen oxides and carbon monoxide. In general, the impact of using more complex chemistry on ozone and other oxidation products is more pronounced when using finer horizontal resolution where a larger number of chemical regimes are resolved. Large model biases for ozone near the surface remain in the Southeast US as compared to the aircraft observations even with updated chemistry and finer horizontal resolution. This suggests a need for adding the capability of replacing sections of global emission inventories with regional inventories, increasing the vertical resolution in the planetary boundary layer, and reducing model biases in meteorological variables such as temperature and clouds.

MUSICA↗

DOE OSTI2023

Using Methyl Bromide for Interspecies Cell–Cell Signaling and As a Reporter in a Model Soil Consortium

Synthetic microbial communities are emerging as model systems for studying consortia-scale phenotypes. To establish synthetic biology tools for studying these communities in hard-to-image environmental materials, we evaluated whether a single member of a model soil consortium (MSC) can be programmed to report on gene expression without requiring matrix disruption. For these studies, we targeted a five member MSC that includes Dyadobacter fermentans, Ensifer adhaerens, Rhodococcus sp003130705, Streptomyces sp001905665, and Variovorax beijingensis. By coupling the expression of a methyl halide transferase to a constitutive promoter, we show that Variovorax beijingensis can be programmed to synthesize methyl halides that accumulate in the soil headspace at levels that are ≥24-fold higher than all other MSC members across a range of environmentally-relevant hydration conditions. We find that methyl halide production can report on a MSC promoter that is activated by changes in water potential, and we demonstrate that a synthetic gas signal can be read out directly using gas chromatography and indirectly using a soil-derived Methylorubrum that is programmed to produce a visual output in response to methyl halides. Finally, these tools will be useful for future studies that investigate how MSC respond to dynamic hydration conditions, such as drought and flood events induced by climate change, which can alter soil water potential and induce the release of stored carbon.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

DERiving Community Economic Development through Distributed Solar

This report explores how distributed solar stakeholders can plan and align economic development with existing community priorities while advancing new solar projects. Because the amount of local community influence drives community economic development, we note the need for investment in community ownership models, which are scarce today. This report seeks to outline the current barriers to community-led economic development through distributed solar, best practices for implementation, and recommendations for sector stakeholders.

14 SOLAR ENERGY↗