Showing papers by "Pacific Northwest National Laboratory published in 2023"

Physics‐Informed Neural Networks of the Saint‐Venant Equations for Downscaling a Large‐Scale River Model

Abstract: Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics especially near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one-dimensional (1-D) Saint-Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN-based downscaling can produce more reasonable subgrid solutions of the along-channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path towards improving emulation capabilities in large-scale models to characterize fine-scale coastal processes.

Mesoscale Convective Systems in DYAMOND Global Convection‐Permitting Simulations

Abstract: This study examines the deep convection populations and mesoscale convective systems (MCSs) simulated in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains) winter project. A storm tracking algorithm is applied to six DYAMOND simulations and a global high-resolution satellite cloud and precipitation data set for comparison. The simulated frequencies of tropical deep convection and organized convective systems vary widely among models and regions, although robust MCSs are generally underestimated. The diurnal cycles of MCS initiation and mature stages are well simulated, but the amplitudes are exaggerated over land. Most models capture the observed MCS lifetime, cloud shield area, rainfall volume and movement speed. However, cloud-top height and convective rainfall intensity are consistently overestimated, and stratiform rainfall area and amount are consistently underestimated. Possible causes for the model differences compared to observations and implications for future model developments are discussed.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Abstract: Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2)

Abstract: Perennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. Few Earth System Models (ESMs) represent such crops, however. In this study, we expand the Energy Exascale Earth System Land Model to include perennial bioenergy crops with a high potential for mitigating climate change. We focus on high-productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies five parameters associated with phenology, carbon/nitrogen allocation, stomatal conductance, and maintenance respiration as the most sensitive parameters for carbon and energy fluxes. We calibrated and validated the model against observations and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The validated model represents the latent heat flux fairly well, but sensible heat flux for miscanthus is not well captured. Finally, we validated the model against observed leaf area index (LAI) and harvest amount and found modeled LAI captured observed seasonality, although the model underestimates LAI and harvest amount. This work provides a foundation for future ESM analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger Earth system, and sets the stage for studying the impact of future biofuel expansion on climate and terrestrial systems.

Emission trends of air pollutants and CO₂ in China from 2005 to 2021

Abstract: Abstract. China is facing the challenge of synergistic reducing air pollutants and CO2 emissions in the coming decades. The coupled emission inventory of air pollutants and CO2 is a prerequisite to designing the synergetic emission reduction strategy. This study compiled China’s emission inventory of air pollutants and CO2 during 2005–2021 (ABaCAS-EI v2.0 dataset) based on a unified emission source framework and uniformed activity. The mitigation policies have decoupled the emissions of air pollutants and CO2 with economic development in China since 2013. In the context of growing activity levels, energy structure adjustment and energy & material saving brought a 7 % drop in the average annual growth rate of CO2 emissions after 2011; on the basis, end-of-pipe control contributed 51 %–98 % of air pollutants emission reductions after 2013. Sectors of industrial boilers and residential fossil combustion, and seven provinces (including Beijing, Tianjin, Shanghai, Jilin, Henan, Sichuan, and Qinghai) have achieved emission reductions of both air pollutants and CO2 during 2013–2021. The declining trends in both sectoral and regional emission ratios of air pollutants to CO2 indicated that the potential for synergistic emission reduction in China has declined from 2013 to 2021. The emission ratios in 2021 show that the residential fossil fuel combustion, iron and steel industry, and transportation have relatively higher co-benefits of SO2, PM2.5, NOx, and VOCs emission reductions when reducing CO2 emissions. Most of the cities with higher potential to synergistically reduce NOx, VOCs, and CO2 emissions are located within the Yangtze River Economic Belt; those with higher potential to co-control SO2 and CO2, and PM2.5 and CO2 are in southern and northeast China, respectively. What’s more, a further deconstruction of sectoral emissions in 2021 has suggested future reduction measures. For example, controlling coal consumption in the energy field; promoting innovative technologies with low air pollutant emission intensities and coal-saving effects in the iron and steel industry; combining coal and carbonate replacing technologies with separated particle control measures in the cement industry; controlling light-duty passenger vehicle, heavy-duty truck, agricultural machinery, and inland water transport in the transport field. Our dataset and analysis can provide insights into future co-control of air pollutants and CO2 emissions for China and other countries with the same demand worldwide. Our ABaCAS-EI v2.0 dataset can be accessed from https://doi.org/10.6084/m9.figshare.21777005.v1 (Li et al., 2022) by species, sector, and province.

Understanding of Active Sites and Interconversion of Pd and PdO during CH4 Oxidation

Abstract: Pd-based catalysts are widely used in the oxidation of CH4 and have a significant impact on global warming. However, understanding their active sites remains controversial, because interconversion between Pd and PdO occurs consecutively during the reaction. Understanding the intrinsic active sites under reaction conditions is critical for developing highly active and selective catalysts. In this study, we demonstrated that partially oxidized palladium (PdOx) on the surface plays an important role for CH4 oxidation. Regardless of whether the initial state of Pd corresponds to oxides or metallic clusters, the topmost surface is PdOx, which is formed during CH4 oxidation. A quantitative analysis using CO titration, diffuse reflectance infrared Fourier-transform spectroscopy, X-ray diffraction, and scanning transmission electron microscopy demonstrated that a surface PdO layer was formed on top of the metallic Pd clusters during the CH4 oxidation reaction. Furthermore, the time-on-stream test of CH4 oxidation revealed that the presence of the PdO layer on top of the metallic Pd clusters improves the catalytic activity. Our periodic density functional theory (DFT) calculations with a PdOx slab and nanoparticle models aided the elucidation of the structure of the experimental PdO particles, as well as the experimental C-O bands. The DFT results also revealed the formation of a PdO layer on the metallic Pd clusters. This study helps achieve a fundamental understanding of the active sites of Pd and PdO for CH4 oxidation and provides insights into the development of active and durable Pd-based catalysts through molecular-level design.

Evaluation of Liquid Cloud Albedo Susceptibility in E3SM Using Coupled Eastern North Atlantic Surface and Satellite Retrievals

Abstract: Abstract. The impact of aerosol number concentration on cloud albedo is a persistent source of spread in global climate predictions due to multi-scale, interactive atmospheric processes that remain difficult to quantify. We use 5 years of geostationary satellite and surface retrievals at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site in the Azores to evaluate the representation of liquid cloud albedo susceptibility for overcast cloud scenes in DOE Energy Exascale Earth System Model version 1 (E3SMv1) and provide possible reasons for model-observation discrepancies. The overall distribution of surface 0.2 % CCN concentration values is reasonably simulated but simulated liquid water path (LWP) is lower than observed and layer-mean droplet concentration (Nd) comparisons are highly variable depending on the Nd retrieval technique. E3SMv1’s cloud albedo is greater than observed for given LWP and Nd values due to a lesser cloud effective radius than observed. However, the simulated albedo response to Nd is suppressed due to a solar zenith angle (SZA)-Nd correlation created by the seasonal cycle that is not observed. Controlling for this effect by examining the cloud optical depth (COD) shows that E3SMv1’s COD response to CCN concentration is greater than observed. For surface-based retrievals, this is only true after controlling for cloud adiabaticity because E3SMv1’s adiabaticities are much lower than observed. Assuming a constant adiabaticity in surface retrievals as done in TOA retrievals narrows the retrieved lnNd distribution, which increases the cloud albedo sensitivity to lnNd to match the TOA sensitivity. The greater sensitivity of COD to CCN is caused by a greater Twomey effect in which the sensitivity of Nd to CCN is greater than observed for TOA-retrieved Nd, and once model-observation adiabaticity differences are removed, this is also true for surface-retrieved Nd. The LWP response to Nd in E3SMv1 is overall negative as observed. Despite reproducing the observed LWP-Nd relationship, observed clouds become much more adiabatic as Nd increases while E3SMv1 clouds do not, associated with more heavily precipitating clouds that are partially but not completely caused by deeper clouds and weaker inversions in E3SMv1. These cloud property differences indicate that the negative LWP-Nd relationship is likely not caused by the same mechanisms in E3SMv1 and observations. The negative simulated LWP response also fails to mute the excessively strong Twomey effect, highlighting potentially important confounding factor effects that likely render the LWP-Nd relationship non-causal. Nd retrieval scales and assumptions, particularly related to cloud adiabaticity, contribute to substantial spreads in the model-observation comparisons, though enough consistency exists to suggest that aerosol activation and convective drizzle processes are critical areas to focus E3SMv1 development for improving the fidelity of aerosol-cloud interactions in E3SM.

Observations of Offshore Internal Boundary Layers

Abstract: The growth of the marine internal boundary layer (MIBL, height hi) with the shore-normal distance x, is a topic of continuing interest because of its applications in coastal pollution dispersion, offshore wind farm siting, coastal air-sea fluxes and in evaporative ducting. Available data on MIBL are scarce, given the difficulty of measuring the variability of coastal winds. During the Coupled Air-Sea Processes and Electromagnetic Research campaigns, an array of instrumentation was deployed to measure offshore spatial variability and its effect on electromagnetic (EM) wave propagation. Meteorological sensors (flux towers and remote sensing) deployed along the coast of Point Mugu, California, on a research vessel and FLoating Instrument Platform provided surface layer and boundary layer observations. Measurements from multiple remote sensors such as synchronized triple Doppler lidars, small boat operations with tethered lifting system, and radiosondes provided a holistic view of the MIBL growth and its spatial variability in coastal areas. Convective and stable MIBL observed during two intensive operating period days showed distinct growth characteristics off the coast of Point-Mugu. During stable stratified atmospheric conditions, an MIBL was observed to develop least as far as 47 km from the coast. The growth of MIBL within the nearshore adjustment zone was influenced by surrounding atmospheric, oceanographic, and topographic conditions. A parameterization scheme is developed based on advection-diffusion balance equations, accounting for upstream turbulence, and compared with hi observations from a Doppler lidar and profiles taken from a small boat. An evaluation of existing IBL theories is also conducted.

The Effect of Climate Change on Global Wildfire Activity

Abstract: Recent long and intensive wildfire seasons in many regions have highlighted the urgency to understand the shift in worldwide fire regimes, raising the question if human induced climate change has played a role therein. However, attributing changes in fire to anthropogenic climate change is difficult, since possible signals are confounded by multiple drivers including fire weather, fuel availability and sources of ignition. Therefore, fire indices or individual input variables are often used as proxies. There have been some attempts to model drivers of recent trends in fire, though assessment of overall anthropogenic climate change is still lacking. Recent integration of fire models into ISIMIP now allow us to perform a fire impact attribution analysis using multiple coupled fire-vegetation models. Here, we combine both ISIMIP factual and counterfactual simulations with remote sensed observations to understand how burnt area has changed over the historical period due to a changing climate.

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

Abstract: In low-lying estuarine regions, compound flooding (CF) is caused by the co-occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial-coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial-coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid-Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial-coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

Reply on RC2

Abstract: Abstract. Seasonal snow has crucial impacts on climate, ecosystems and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM), mechanistically simulates snow processes from accumulation, canopy interception, compaction, snow aging to melt. Although high-quality field measurements, remote sensing snow products and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125° resolution during 2001–2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product, the spatially and temporally complete (STC) Snow-Covered Area and Grain Size (MODSCAG) and MODIS Dust and Radiative Forcing in Snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the Snow Property Inversion from Remote Sensing (SPIReS) product) and two data assimilation products of snow water equivalent and snow depth (i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS)). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability and elevation gradients for different snow properties including snow cover fraction (f_sno), surface albedo (𝛼_sur) over snow cover regions, snow water equivalent (SWE) and snow depth (D_sno). However, there are large biases of f_sno with dense forest cover and 𝛼_sur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size and light-absorbing particles induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of -20.7 mm (-35.9 %) and -20.4 mm (-35.5 %), respectively for spring, and -13.8 mm (-27.8 %) and -10.2 mm (-22.2 %), respectively for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69, but negative mean biases of -122.7 mm, respectively. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset date by 17.3 and 12.4 days, earlier snow end date by 35.5 and 26.8 days, and shorter snow duration by 52.9 and 39.5 days. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

Substantially positive contributions of new particle formation to Cloud Condensation Nuclei under low supersaturation in China based on numerical model improvements

Abstract: Abstract. New particle formation (NPF) and subsequent particle growth are important sources of condensation nuclei (CN) and cloud condensation nuclei (CCN). While a number of observations have shown positive contributions of NPF to CCN at low supersaturation, negative NPF contributions were often simulated. Using the observations in a typical coastal city of Qingdao, we thoroughly evaluate the simulated number concentrations of CN and CCN using a NPF-explicit parameterization embedded in WRF-Chem model. In terms of CN, the initial simulation shows large biases of particle number concentrations at 10–40 nm (CN10–40) and 40–100 nm (CN40–100). By adjusting the process of gas-particle partitioning, including mass accommodation coefficient of sulfuric acid, the phase changes of primary organic aerosol emissions and the condensational amount of nitric acid, the concomitant improvement of the particle growth process yields a substantial reduction of overestimates of CN10–40 and CN40–100. Regarding CCN, SOA formed from the oxidation of semi-volatile and intermediate volatility organic vapors (SI-SOA) yield is an important contributor. In the original WRF-Chem model with 20 size bins setting, the yield of SI-SOA is too high without considering the differences in oxidation rates of the precursors. Lowering the SI-SOA yield results in much improved simulations of the observed CCN concentrations. On the basis of the bias-corrected model, we find substantial positive contributions of NPF to CCN at low supersaturation (~0.2 %) in Qingdao and over the broad areas of China, primarily due to the competing effects of increasing particle hygroscopicity surpassing that of particle size decrease. This study highlights the potentially much larger NPF contributions to CCN on a regional and even global basis.

Collaboration and Negotiation

Abstract: Collaboration and Negotiation is a critical high-level function of an Autonomous Intelligent Cyber-Defense Agent (AICA) that enables communication among agents, central cyber command-and-control (C2), and human operators. Maintaining the Confidentiality, Integrity, and Availability (CIA) triad while achieving mission goals requires stealthy AICA agents to exercise: (1) minimal communication as needed for avoiding detection, (2) verification of information received with limited resources, and (3) active learning during operations to address dynamic conditions. Moreover, negotiations to jointly identify and execute a Course of Action (COA) will require building consensus under distributed and/or decentralized multi-agent settings with information uncertainties. This chapter presents algorithmic approaches for enabling the collaboration and negotiation function. Strengths and limitations of potential techniques are identified, and a representative example is illustrated. Specifically, a two-tier Multi-Agent Reinforcement Learning (MARL) algorithm was implemented to learn joint strategies among agents in a navigation and communication simulation environment. Based on simulation experiments, emergent collaborative team behaviors among agents were observed under information uncertainties. Recommendations for future development are also discussed.

Rainbows and Climate Change: A tutorial on climate model diagnostics and parameterization

Abstract: Abstract. Earth System Models (ESMs) must represent processes below the grid scale of a model using representations (parameterizations) of physical and chemical processes. As a tutorial exercise to understand diagnostics and parameterization, this work presents a representation of rainbows for an ESM: the Community Earth System Model version 2 (CESM2). Using the 'state' of the model, basic physical laws, and some assumptions, we generate a representation of this unique optical phenomena as a diagnostic output. Rainbow occurrence and it's possible changes are related to cloud occurrence and rain formation which are critical uncertainties for climate change prediction. The work highlights issues which are typical of many diagnostics parameterizations such as assumptions, uncertain parameters and the difficulty of evaluation against uncertain observations. Results agree qualitatively with limited available global 'observations' of Rainbows. Rainbows are seen in expected locations in the sub-tropics over the ocean where broken clouds and frequent precipitation occurs. The diurnal peak is in the morning over ocean and in the evening over land. The representation of rainbows is found to be quantitatively sensitive to the assumed amount of cloudiness and the amount of stratiform rain. Rainbows are projected to have decreased, mostly in the Northern Hemisphere, due to aerosol pollution effects increasing cloud coverage since 1850. In the future, continued climate change is projected to decrease cloud cover, associated with a positive cloud feedback. As a result the rainbow diagnostic projects that rainbows will increase in the future, with the largest changes at mid-latitudes. The diagnostic may be useful for assessing cloud parameterizations, and is an exercise in how to build and test parameterizations of atmospheric phenomena.

Nutrient pollution impact reduction assessment in a deep estuary, euphotic zone avoidance/bypass considerations 

Abstract: The feasibility of reducing nutrient pollution impacts by redirecting excess nutrient flux away from the photic zone is investigated. Alternate effluent discharge strategies to avoid or bypass the euphotic zone were tested under the hypothesis that in deep estuaries, depth of the surface exchange outflow layer may be greater than euphotic zone depth, providing opportunity for a fraction of the nutrient pollution to be exported out passively. We used the Salish Sea region in the Pacific Northwest as a test bed for this assessment. Euphotic zone depth in the Puget Sound basin of Salish Sea in U.S waters varies from 8 m to 25 m while the depth of outflow layer is approximately 60m. Sensitivity of biological response and water quality impact were quantified using an established biophysical model of the system, using exposure to low DO levels as the metric (< 2 mg/L hypoxia and < 5 mg/L impairment). Opportunity to reduce nutrient pollution impact was tested through outfall relocation strategies, applied to 99% of the anthropogenic loads currently delivered to the Puget Sound. The results show that relative to natural impairment levels, marine wastewater outfalls are responsible for 36% of increase, while loads from upstream watersheds that enter Puget Sound via river flows, are responsible for 70% of increase in impairment. Results were consistent with the hypothesis in that moving the outfalls to deeper waters resulted in reduced primary production. However, in some basins, the benefits of lower water column respiration were offset by reduced DO production and were accompanied by some loss in the strength of circulation. Puget Sound basin results indicate worsening of DO impairment hours (average +3.0%), while Whidbey Basin showed improvement in DO impairment hours (-6.8%) relative to existing conditions. The results indicate that presence of multiple sills and the associated reflux flows / circulation obstruct the export of nutrients out of the system. The efforts to relocate outfalls to achieve euphotic zone bypass and improve DO impairment were therefore not as effective as hypothesized.

Shift in Peaks of PAH‐Associated Health Risks From East Asia to South Asia and Africa in the Future

Abstract: Lung cancer risk from exposure to ambient polycyclic aromatic hydrocarbons (PAHs) is expected to change significantly by 2050 compared to 2008 due to changes in climate and emissions. Integrating a global atmospheric chemistry model, a lung cancer risk model, and plausible future emissions trajectories of PAHs, we assess how global PAHs and their associated lung cancer risk will likely change in the future. Benzo(a)pyrene (BaP) is used as an indicator of cancer risk from PAH mixtures. From 2008 to 2050, the population-weighted global average BaP concentrations under all RCPs consistently exceeded the WHO-recommended limits, primarily attributed to residential biofuel use. Peaks in PAH-associated incremental lifetime cancer risk shift from East Asia (4 × 10−5) in 2008 to South Asia (mostly India, 2–4 × 10−5) and Africa (1–2 × 10−5) by 2050. In the developing regions of Africa and South Asia, PAH-associated lung cancer risk increased by 30–64% from 2008 to 2050, due to increasing residential energy demand in households for cooking, heating, and lighting, the continued use of traditional biomass use, increases in agricultural waste burning, and forest fires, accompanied by rapid population growth in these regions. Due to more stringent air quality policies in developed countries, their PAH lung cancer risk substantially decreased by ∼80% from 2008 to 2050. Climate change is likely to have minor effects on PAH lung cancer risk compared to the impact of emissions. Future policies, therefore, need to consider efficient combustion technologies that reduce air pollutant emissions, including incomplete combustion products such as PAH.

Mapping the performance of a versatile water-based condensation particle counter (vWCPC) with COMSOL simulation and experimental study

Abstract: Abstract. Accurate airborne aerosol instrumentation is required to determine the spatial distribution of ambient aerosol particles, particularly when dealing with the complex vertical profiles and horizontal variations of atmospheric aerosols. A versatile water-based condensation particle counter (vWCPC) has been developed to provide aerosol concentration measurements under various environments with the advantage of reducing the health and safety concerns associated with using butanol or other chemicals as the working fluid. However, the airborne deployment of vWCPCs is relatively limited due to the lack of characterization of vWCPC performance at reduced pressures. Given the complex combinations of operating parameters in vWCPCs, modeling studies have advantages in mapping vWCPC performance. In this work, we thoroughly investigated the performance of a laminar flow vWCPC using COMSOL Multiphysics® simulation coupled with MATLAB. We compared it against a modified commercial vWCPC (vWCPC Model 3789, TSI, Shoreview, MN, USA). Our simulation determined the performance of particle activation and droplet growth in the vWCPC growth tube, including the supersaturation, Dp,kel,0 (smallest size of particle that can be activated), Dp,kel,50 (particle size activated with 50 % efficiency) profile, and final growth particle size Dd under wide operating temperatures, inlet pressures P (0.3–1 atm), and growth tube geometry (diameter D and initiator length Lini). The effect of inlet pressure and conditioner temperature on vWCPC 3789 performance was also examined and compared with laboratory experiments. The COMSOL simulation result showed that increasing the temperature difference (∆ T) between conditioner temperature Tcon and initiator Tini will reduce Dp,kel,0 and the cut-off size Dp,kel,50 of the vWCPC. In addition, lowering the temperature midpoint (Tmid = (Tcon + Tini) / 2) increases the supersaturation and slightly decreases the Dp,kel. The droplet size at the end of the growth tube is not significantly dependent on raising or lowering the temperature midpoint but significantly decreases at reduced inlet pressure, which indirectly alters the vWCPC empirical cut-off size. Our study shows that the current simulated growth tube geometry (D = 6.3 mm and Lini = 30 mm) is an optimized choice for current vWCPC flow and temperature settings. The current simulation can more realistically represent the Dp,kel for 7 nm vWCPC and also achieved a good agreement with the 2 nm setting. Using the new simulation approach, we provide an optimized operation setting for the 7 nm setting. This study will guide further vWCPC performance optimization for applications requiring precise particle detection and atmospheric aerosol monitoring.

Reply on RC1

Abstract: Turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability of TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models have not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. Sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution ’s Martha’s Vineyard Coastal Observatory. Results show while simulated TKE is generally smaller than lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimate TI from models. Due to notable uncertainty in accurately simulating those events, it suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.

Observationally constrained analysis of sulfur species in the marine troposphere

Abstract: The NASA Earth Venture Suborbital (EVS-2) Atmospheric Tomography Mission (ATom) provided rich gas and aerosol measurements over the global oceans. In this study, we investigate the sulfur species of dimethyl sulfide (DMS), sulfur dioxide (SO2), methane sulfonic acid (MSA), and sulfate (SO4) that were measured during the ATom aircraft campaigns and simulated by five AeroCom models. This study focuses on remote regions over the Pacific, Atlantic, and Southern Oceans from near the surface to ~12 km altitude and covers all four seasons. We examine the vertical and seasonal variations of these sulfur species over tropical, mid-, and high latitude regions in both hemispheres. We identify their origins from land versus ocean and from anthropogenic versus natural sources with sensitivity studies by applying tagged tracers linking to emission types and regions. Using the GEOS model, we also investigate impact of cloud simulation (i.e., one-moment bulk cloud module, 1MOM vs two-moment cloud microphysics module, 2MOM) on the sulfur cycle and identify critical mechanisms of cloud impact by performing process-level budget analyses. Generally, SO4 has a better model-observation agreement than DMS, SO2 and MSA, and there are much larger DMS simulated concentrations close to the sea surface than measured, indicating all model DMS emissions may be too high. Anthropogenic emissions are the dominant source (44-60% of the total amount) for atmospheric SO4 simulated along ATom flight tracks in almost every altitude, followed by volcanic eruptions (18-33%) and oceanic sources (16-28%). GEOS SO4 simulations differ significantly between the 1MOM and 2MOM cloud schemes, with the sulfate chemical production via aqueous phase reactions seeming to be the critical process.

Reply on RC2

Abstract: Abstract. Seasonal snow has crucial impacts on climate, ecosystems and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM), mechanistically simulates snow processes from accumulation, canopy interception, compaction, snow aging to melt. Although high-quality field measurements, remote sensing snow products and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125° resolution during 2001–2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product, the spatially and temporally complete (STC) Snow-Covered Area and Grain Size (MODSCAG) and MODIS Dust and Radiative Forcing in Snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the Snow Property Inversion from Remote Sensing (SPIReS) product) and two data assimilation products of snow water equivalent and snow depth (i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS)). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability and elevation gradients for different snow properties including snow cover fraction (f_sno), surface albedo (𝛼_sur) over snow cover regions, snow water equivalent (SWE) and snow depth (D_sno). However, there are large biases of f_sno with dense forest cover and 𝛼_sur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size and light-absorbing particles induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of -20.7 mm (-35.9 %) and -20.4 mm (-35.5 %), respectively for spring, and -13.8 mm (-27.8 %) and -10.2 mm (-22.2 %), respectively for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69, but negative mean biases of -122.7 mm, respectively. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset date by 17.3 and 12.4 days, earlier snow end date by 35.5 and 26.8 days, and shorter snow duration by 52.9 and 39.5 days. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

Quantifying Airborne Fraction Trends and the Ultimate Fate of Anthropogenic CO2 by Tracking Carbon Flows in a Simple Climate Model

Abstract: Carbon dioxide (CO2) concentrations have increased in the atmosphere as a direct result of human activity and are at their highest level over the last 2-3 million years, with profound impacts on the Earth system. However, the magnitude and future dynamics of land and ocean carbon sinks are not well understood; therefore, the amount of anthropogenic fossil fuel emissions that remain in the atmosphere (the airborne fraction) is poorly constrained. This work aims to quantify the sources and controls of atmospheric CO2, the fate of anthropogenic CO2 over time, and the trend and robustness of the airborne fraction. We use Hector v3.0, a coupled simple climate and carbon cycle model, with the novel ability to explicitly track carbon as it flows through the Earth system. We use a priori probability distribution functions for key model parameters in a Monte Carlo analysis of 10,000 coupled carbon-climate model runs from 1750 to 2300. Results are filtered for physical realism against historical observations and CMIP6 projection data, and we calculate the relative importance of parameters controlling how much CO2 ends up in the atmosphere. Unsurprisingly, we find that anthropogenic emissions are the dominant source of near- and long-term atmospheric CO2, composing roughly 45% of the atmosphere, which is consistent with observational studies of the airborne fraction. The overwhelming majority of model runs exhibited a negative trend in the airborne fraction from 1960-2020, implying that current-day land and ocean sinks are proportionally taking up more carbon than the atmosphere. Furthermore, when looking at the destination of anthropogenic fossil fuel emissions, only a quarter ends up in the atmosphere while more than half of emissions are taken up by the land sink on centennial timescales. This study evaluates the likelihood of airborne fraction trends and provides insights into the dynamics and destination over time of anthropogenic CO2 in the Earth system.

Energy cascade for distribution and evolution of supermassive black holes

Abstract: Abstract Strong correlations exist between supermassive black holes (SMBHs) and their host galaxies. These correlations suggest a missing component in our current understanding: the role of energy cascade in SMBH-bulge coevolution. In this picture, energy is continuously cascaded from bulge scale $r_b$ down to the BH scale (Schwarzschild radius $r_s$). Energy cascade has a scale-independent, but decreasing rate $\varepsilon_b(t)\approx \sigma_b^3/r_b$ due to the cooling of baryonic component, where $\sigma_b$ is bulge velocity dispersion. The bulge mass-size ($M_b$-$r_b$) relation can be expressed as $M_b \propto \varepsilon_b^{2/3}r_b^{5/3}G^{-1}$, or a bulge density-size relation $\rho_b \propto \varepsilon_b^{2/3}r_b^{-4/3}G^{-1}$, with $\varepsilon_b \approx a^{-5/2}\times 10^{-4}m^2/s^3$, as confirmed by the galaxy survey, where $a$ is the scale factor and $G$ is the gravitational constant. Intermediate length scales can be defined based on the dominant physics on that scale, i.e. the BH sphere of influence $r_B$, radiation scale $r_p$, and dissipation scale $r_x$. For SMBH with a mass $M_B$, bolometric luminosity $L_B$, energy cascade leads to a "cascade" force that must be balanced by the BH radiation force in its early life, i.e. $L_B/c=M_B \varepsilon_b /\sigma_p$, where $c$ is light speed and $\sigma_p$ is the velocity dispersion on scale $r_p$. Since $\varepsilon_b$ is much larger in the early universe, BH accretion can be super-Eddington with $L_B$ exceeding the Eddington limit. In addition, the BH mass-dispersion relation ($M_B\propto \sigma_b^5$) is a natural result of the cascade theory. By introducing two dimensionless parameters $\gamma={L_B}/(M_B\varepsilon_b)$ and $\eta=({GL_B}/{c^5})^{{1}/{4}}$, the distribution and evolution of SMBHs can all be mapped onto the $\gamma$-$\eta$ plane. By setting $r_s \le r_p\le r_B$, the upper limit of distribution is found to be $L_B \propto (\varepsilon_b M_B)^{4/5}G^{-1/5}c$. The lower limit is found to be $L_B \propto (\varepsilon_b M_B)^{4/3}G^{1/3}c^{-5/3}$. Quasars tend to approach the upper limit, while dormant SMBHs (Sgr A* and M31) tend to approach the lower limit. A three stage mathematical model is proposed for SMBH evolution involving co-evolution, transitional, and dormant stages, respectively. Models are finally compared against the BH accretion history from quasar luminosity function from 2dF Redshift Survey, local galaxy and SMBHs data, and high redshift quasars from SDSS DR7 and CFHQS surveys.