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Burrows, Susannah M.

Publications and source records attributed to Burrows, Susannah M..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enrichment of Phosphates, Lead, and Mixed Soil–Organic Particles in INPs at the Southern Great Plains Site

Ice nucleating particles (INPs) are rare particles that initiate primary ice formation, a critical step required for subsequent important cloud microphysical processes that ultimately govern cloud phase and cloud radiative properties. Laboratory studies have found that organic-rich dusts, such as those found in soils, are more efficient INPs compared to mineral dust. However, the atmospheric relevance of these organic-rich dusts are not well understood, particularly in regions with significant agricultural activity. The Agricultural Ice nuclei at the Southern Great Plains field campaign (AGINSGP) was conducted in rural Oklahoma to investigate how soil dusts contribute to INP populations in the Great Plains. We present chemical characterization of ambient and ice crystal residual particles from a single day of sampling, using single particle mass spectrometry (SPMS) and scanning microscopy. Ambient particles were primarily carbonaceous or secondary aerosol, while the fraction of dust particles was higher in the residual particles. We also observed an unusual particle type consisting of a carbonaceous core mixed with dust fragments on the surface, which was found in higher proportion in residuals. Dust particles measured during residual sampling contained greater proportions of phosphate $(^{63}\text{PO}^-_2$ and $^{79}\text{PO}^-_3)$ and lead ( 206 Pb + ). Strong sulfate signals were not seen in the residual dust particles measured by the SPMS, while nitrate was slightly depleted relative to ambient dust. This study shows that organic-rich soils may be important contributors to the ambient INP population in agricultural regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Present-day correlations are insufficient to predict cloud albedo change by anthropogenic aerosols in E3SM v2

Cloud albedo susceptibility to droplet number perturbation remains a source of uncertainty in understanding aerosol–cloud interactions and thus both past and present climate states. Through the Energy Exascale Earth System Model (E3SM) v2 experiments, we probe the effects of competing processes on cloud albedo susceptibility of low-lying marine stratocumulus in the northeast Pacific. In present-day conditions, we find that increasing precipitation suppression by aerosols increases cloud albedo susceptibility, whereas increasing cloud sedimentation decreases it. By constructing a hypothetical model configuration exhibiting negative susceptibility under all conditions, we conclude that cloud albedo change due to aerosol perturbation cannot be predicted by present-day co-variabilities in E3SM v2. As such, our null result herein challenges the assumption that present-day climate observations are sufficient to constrain past states, at least in the context of cloud albedo changes to aerosol perturbation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bioaerosols are the dominant source of warm-temperature immersion-mode INPs and drive uncertainties in INP predictability

Ice-nucleating particles (INPs) are rare atmospheric aerosols that initiate primary ice formation, but accurately simulating their concentrations and variability in large-scale climate models remains a challenge. Doing so requires both simulating major particle sources and parameterizing their ice nucleation (IN) efficiency. Validating and improving model predictions of INP concentrations requires measuring their concentrations delineated by particle type. We present a method to speciate INP concentrations into contributions from dust, sea spray aerosol (SSA), and bioaerosol. Field campaign data from Bodega Bay, California, showed that bioaerosols were the primary source of INPs between –12° and –20°C, while dust was a minor source and SSA had little impact. We found that recent parameterizations for dust and SSA accurately predicted ambient INP concentrations. However, the model did not skillfully simulate bioaerosol INPs, suggesting a need for further research to identify major factors controlling their emissions and INP efficiency for improved representation in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Contrasting the Biophysical and Radiative Effects of Rising CO 2 Concentrations on Ozone Dry Deposition Fluxes

Abstract The dry deposition of ozone from the atmosphere to ecosystems is an important coupling mechanism between atmospheric chemistry and terrestrial biogeochemical processes. In most Earth system models, dry deposition is simulated using a resistor‐in‐series approach that aims to parameterize the governing biological, chemical, and physical processes through a series of functional approximations. Here, we evaluate the influence of carbon cycle‐climate responses on this parameterization using the results of the Energy Exascale Earth System Model v1.1 Biogeochemistry simulation campaign. This simulation campaign was designed in part to explore the biophysical and radiative effects of rising historical CO 2 concentrations on the Earth system. We find that while the global annual ozone dry deposition is relatively insensitive to these effects, regionally the influence can be up to 10%. The strongest regional sensitivities in ozone dry deposition are predominantly in higher latitudes over land in the northern hemisphere and are dominated by the radiative effect of CO 2 , with little net influence of biophysical responses. Of all the impacts of the radiative effect of CO 2 , we point to the potential importance of accurately representing ozone deposition to snow in Earth System Models and provide recommendations for future simulation campaigns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Agricultural Ice Nuclei at Southern Great Plains Supplemental Sampling (AGINSGP-SUPP) Field Campaign Report

The Agricultural Ice Nuclei at Southern Great Plains Supplemental Sampling (AGINSGP-SUPP) field campaign took place from April 8 to April 28, 2022. It included sample collection and analysis of filter samples for ice-nucleating particle (INP) concentrations. By enabling quantification of INPs at warmer temperatures (i.e., T > 258 K), these samples complemented the real-time observations of INPs that were performed as part of the AGINSGP campaign during the same period. In addition to collecting ground-based samples, the campaign collected vertical samples of INPs aboard the Atmospheric Radiation Measurement (ARM) tethered balloon system (TBS) as shown in Figure 1. These vertical samples complemented the vertical profiling of aerosol properties that was supported under a related Facilities Integrating Collaborations for User Science (FICUS) proposal. The overall aim of these three campaigns was to improve understanding of the sources and variability in observed INP concentrations at ARM’s SGP observatory. For example, warm-temperature INPs in this agricultural region are thought to originate in part from regional emissions of fertile, agricultural soils containing high organic content. Laboratory experiments have shown that soil particles with high organic content are more efficient INPs than inorganic soils (e.g., Tobo et al. 2014). Quantification of warm-temperature INPs under ambient conditions currently can only be achieved via offline methods such as the ARM ice-nucleation spectrometer (INS). Therefore, offline sampling for the INS was required to complement the real-time INP measurements conducted during the AGINSGP campaign.

, tethered balloon system↗
DOE OSTI2022

gdess: A framework for evaluating simulated atmospheric CO 2 in Earth System Models

Atmospheric carbon dioxide (CO 2 ) plays a key role in the global carbon cycle and global warming. Climate-carbon feedbacks are often studied and estimated using Earth System Models (ESMs), which couple together multiple model components—including the atmosphere, ocean, terrestrial biosphere, and cryosphere—to jointly simulate mass and energy exchanges within and between these components. Despite tremendous advances, model intercomparisons and benchmarking are aspects of ESMs that warrant further improvement (Fer et al., 2021; Smith et al., 2014). Such benchmarking is critical because comparing the value of state variables in these simulations against observed values provides evidence for appropriately refining model components; moreover, researchers can learn much about Earth system dynamics in the process (Randall et al., 2019). We introduce `gdess` (a.k.a., Greenhouse gas Diagnostics for Earth System Simulations), which parses observational datasets and ESM simulation output, combines them to be in a consistent structure, computes statistical metrics, and generates diagnostic visualizations. In its current incarnation, `gdess` facilitates evaluating a model's ability to reproduce observed temporal and spatial variations of atmospheric CO 2 . The diagnostics implemented modularly in `gdess` support more rapid assessment and improvement of model-simulated global CO 2 sources and sinks associated with land and ocean ecosystem processes. We intend for this set of automated diagnostics to form an extensible, open source framework for future comparisons of simulated and observed concentrations of various greenhouse gases across Earth system models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Ice‐Nucleating Particles That Impact Clouds and Climate: Observational and Modeling Research Needs

Abstract Atmospheric ice‐nucleating particles (INPs) play a critical role in cloud freezing processes, with important implications for precipitation formation and cloud radiative properties, and thus for weather and climate. Additionally, INP emissions respond to changes in the Earth System and climate, for example, desertification, agricultural practices, and fires, and therefore may introduce climate feedbacks that are still poorly understood. As knowledge of the nature and origins of INPs has advanced, regional and global weather, climate, and Earth system models have increasingly begun to link cloud ice processes to model‐simulated aerosol abundance and types. While these recent advances are exciting, coupling cloud processes to simulated aerosol also makes cloud physics simulations increasingly susceptible to uncertainties in simulation of INPs, which are still poorly constrained by observations. Advancing the predictability of INP abundance with reasonable spatiotemporal resolution will require an increased focus on research that bridges the measurement and modeling communities. This review summarizes the current state of knowledge and identifies critical knowledge gaps from both observational and modeling perspectives. In particular, we emphasize needs in two key areas: (a) observational closure between aerosol and INP quantities and (b) skillful simulation of INPs within existing weather and climate models. We discuss the state of knowledge on various INP particle types and briefly discuss the challenges faced in understanding the cloud impacts of INPs with present‐day models. Finally, we identify priority research directions for both observations and models to improve understanding of INPs and their interactions with the Earth System.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Diurnal rainfall response to the physiological and radiative effects of CO 2 in tropical forests in the Energy exascale earth system model v1

Understanding how the connection between rainfall and tropical forests will respond to increasing CO$_2$ concentrations is a key element in understanding how the tropical water cycle will respond to increasing CO$_2$. The plant physiological and radiative impacts of CO$_2$ on rainfall patterns over tropical forest regions are examined in the Energy Exascale Earth System Model version 1.1 (E3SMv1.1-BGC) biogeochemistry experiments. Composite analysis reveals a dampening of the diurnal cycle of rainfall over the Amazon, Congo, and Maritime Continent in response to rising CO$_2$ levels, regardless of the sign of total rainfall change. A full factorial model experiment confirms that the CO$_2$ radiative and CO$_2$ plant physiological effects can individually or jointly reduce the magnitude of the rainfall diurnal cycle, though the physical pathway giving rise to the reduction differs between the two effects. For the physiological response, stomatal closure reduces evapotranspiration, which dries the boundary layer and raises the lifting condensation level. These effects combine to reduce deep convective rainfall during its peak occurrence in the late daytime to early nighttime period. For the radiative response, a relative reduction in daytime CAPE (consistent with a reduction in the diurnal temperature range) leads to less frequent triggering of deep convection and a reduction of rainfall diurnal amplitude. These diurnal rainfall changes are structurally similar across seasons, and show little sensitivity to representation of nutrient coupling for the land biogeochemistry. In agreement with previous findings, the physiological response has only minor impact on extreme rainfall relative to the radiative response.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds

Sea spray aerosol is one of the major sources of atmospheric particulate matter globally. It has increasingly been recognized that organic matter derived from ocean biological precursors contributes significantly to the composition of submicron sea spray and may modify sea spray aerosol impacts on clouds and climate. This paper describes the implementation of the OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) parameterization for sea spray organic aerosol emissions in a global Earth system model, the Energy Exascale Earth System Model (E3SM). OCEANFILMS is a physically based model that links sea spray chemistry with ocean biogeochemistry using a Langmuir partitioning approach. We describe the implementation details of OCEANFILMS within E3SM, compare simulated aerosol fields with observations, and investigate impacts on simulated clouds and climate. Four sensitivity cases are tested, in which organic emissions either strictly add to or strictly replace sea salt emissions (in mass and number) and are either fully internally or fully externally mixed with sea salt. The simulation with internally mixed, added organics agrees reasonably well with observed seasonal cycles of organic matter in marine aerosol and has been selected as the default configuration of the E3SM. In this configuration, sea spray organic aerosol contributes an additional source of cloud condensation nuclei, adding up to 30 cm -3 to Southern Ocean boundary-layer cloud condensation nuclei concentrations (supersaturation = 0.1 %). The addition of this new aerosol source strengthens shortwave radiative cooling by clouds by -0.36 W m -2 in the global annual mean and contributes more than -3.5 W m -2 to summertime zonal mean cloud forcing in the Southern Ocean, with maximum zonal mean impacts of about -4 W m -2 around 50–60°S. This is consistent with a previous top-down, satellite-based empirical estimate of the radiative forcing by sea spray organic aerosol over the Southern Ocean. Through its mechanistic approach, OCEANFILMS offers a path towards improved understanding of the feedbacks between ocean biology, sea spray organic matter, and climate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Development of Heterogeneous Ice Nucleation Rate Coefficient Parameterizations From Ambient Measurements

Ice nucleating particles (INPs) are a rare subset of particles that can have an outsized impact relative to their prevalence. To simulate INP variability, models require parameterizations for the most important sources of INPs. Most parameterizations in the literature were developed from laboratory experiments that used proxies for ambient particles. Whether these laboratory models effectively represent ambient aerosol is still not well understood and parameterizations developed from field measurements may be more atmospherically relevant. Expanding on previous work in which we used an ice chamber and a single-particle mass spectrometer (SPMS) to characterize INP composition, we calculate heterogeneous ice nucleation rate coefficients for ambient particles. We find good agreement between our dust and previous measurements of airborne dust. We also use a Monte Carlo approach to assess the relative contribution of each measurement to the uncertainty and find that the biggest source is the sampling efficiency of the SPMS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Earth System Model Development - Energy Exascale Earth System Model (2020 Principal Investigator Meeting Report)

As part of the U.S. Department of Energy (DOE) Office of Biological and Environmental Research (BER), the Earth System Model Development (ESMD) program area supports significant research in Earth system model development, including major efforts in the Energy Exascale Earth System Model (E3SM) project. Convened virtually on October 26–29, the 2020 ESMD-E3SM Principal Investigator (PI) Meeting provided a forum to survey ESMD sponsored research, recent progress made by the ESMD science team and the broader Earth system modeling community within and outside of DOE, foster interactions across ESMD projects, and discuss research gaps and opportunities for future advancements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Simulated Dust Transport in the Convective Boundary Layer

Soil dusts are an important source of aerosol in agricultural regions and can affect the Earth's radiation budget through the modification of cloud properties, and in particular, through their ability to act as ice nucleating particles. In order to impact cloud properties, agricultural soil dusts need to be transported from the point of emission to cloud–relevant altitudes. Vertical transport within the planetary boundary layer is strongly controlled by turbulence and is challenging to represent accurately in regional and global models. Large–eddy simulations (LES) are run at resolutions capable of resolving most of the turbulent energy directly and can thus better simulate vertical transport. In this study, we leverage the LES ARM Symbiotic Simulation and Observation (LASSO) large–eddy simulations to simulate vertical transport of agricultural dust within the turbulent boundary layer using a modified version of the stochastic dispersion model FLEXPART–WRF. We find that the modified model is better capable of simulating particle transport due to turbulence, and that particle size was the greatest factor in determining particle lifetime. Individual meteorology and particle density had intermediate effects upon particle transport, while release height had little effect upon simulation results. Finally, we utilize a quasi–single column model (QSCM) approach to determine how our results compare to a parameterized treatment of turbulence. The QSCM simulations led to greater tracer transport out of the boundary layer, with ramifications for any studies utilizing a Lagrangian stochastic model to understand tracer dispersion. Furthermore, these results highlight the importance of accurately simulating turbulence for understanding particle transport.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Atmospheric Aerosol Short Course for Early Career Scientists

The first Department of Energy (DOE) Environmental and Molecular Science Laboratory (EMSL)/Atmospheric Radiation Measurement (ARM) aerosol summer school was held July 15-19, 2019 at the EMSL user facility located at Pacific Northwest National Laboratory. The school set out to provide graduate students and early-career scientists an overview of atmospheric aerosol science and an introduction to the measurement techniques available through the EMSL and ARM user facilities to address current aerosol science issues. Instructors for the course were drawn from scientists from the EMSL (Laskin et al., 2016; China et al., 2018) and ARM facilities (McComiskey and Ferrare, 2016) as well as from the DOE Atmospheric System Research (ASR) program. Student support for the summer school was also jointly provided by EMSL, ARM, and ASR. The school spanned five days with each day covering a particular theme. 25 students from 21 institutions attended morning lectures on aerosol composition, properties, theory, measurements, and modeling. During afternoons, the students explored instruments that collect and analyze aerosol samples at EMSL and PNNL’s Atmospheric Measurements Laboratory. They also tested relevant computer modeling simulations and learned about NWChem, EMSL’s open-source computational chemistry software suite.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

EBSD seed LDRD project: Does Corona Virus – 2019 (COVID-19) and Seasonal Flu have similar meteorology and air quality controls driving their spread?

Seasonal influenza and Influenza like Illnesses (ILI) pose a serious public health risk and in turn affect the economy. Various factors affect ILI cases and mortality, including the pathogen and its interaction with the host, as well as environmental and socioeconomic factors such as meteorology, household structure, air pollution, urbanization, and population. Despite the growing number of studies on influenza and ILI, challenges remain in forecasting the timing of seasonal onset, outbreak patterns, and key factors affecting transmission. In particular, the impacts of meteorology and air quality on ILI have been challenging to understand, with linear regression studies focused on different geographic regions producing contradictory results. For example, influenza seasonality has been associated with cold-dry conditions in temperate mid-latitudes, but with humid-rainy conditions in tropical climates. These apparently contradictory results imply that the relationships between influenza cases and atmospheric variables may be too complex to be captured by linear regression models. In this seed project, we analyze meteorology and air quality variables from numerical models to determine which atmospheric variables are most helpful in predicting weekly changes in recorded flu cases. In contrast to most previous studies that relied on linear regression analysis to predict the timing of the flu onset or peak, we employ a robust machine learning algorithm to evaluate the contribution of atmospheric variables to weekly changes in recorded ILI cases. These results may also be relevant to the spread of other respiratory illnesses such as Corona Virus Disease – 2019 (COVID-19).

60 APPLIED LIFE SCIENCES↗