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DOE OSTI2021

Southern Ocean latitudinal gradients of cloud condensation nuclei

Abstract. The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. While remoteness from anthropogenic and continental sources is responsible for its clean atmosphere, this also results in the dearth of atmospheric observations in the region. Here we present a statistical summary of the latitudinal gradient of aerosol (condensation nuclei larger than 10 nm, CN10) and cloud condensation nuclei (CCN at various supersaturations) concentrations obtained from five voyages spanning the Southern Ocean between Australia and Antarctica from late spring to early autumn (October to March) of the 2017/18 austral seasons. Three main regions of influence were identified: the northern sector (40–45∘ S), where continental and anthropogenic sources coexisted with background marine aerosol populations; the mid-latitude sector (45–65∘ S), where the aerosol populations reflected a mixture of biogenic and sea-salt aerosol; and the southern sector (65–70∘ S), south of the atmospheric polar front, where sea-salt aerosol concentrations were greatly reduced and aerosol populations were primarily biologically derived sulfur species with a significant history in the Antarctic free troposphere. The northern sector showed the highest number concentrations with median (25th to 75th percentiles) CN10 and CCN0.5 concentrations of 681 (388–839) cm−3 and 322 (105–443) cm−3, respectively. Concentrations in the mid-latitudes were typically around 350 cm−3 and 160 cm−3 for CN10 and CCN0.5, respectively. In the southern sector, concentrations rose markedly, reaching 447 (298–446) cm−3 and 232 (186–271) cm−3 for CN10 and CCN0.5, respectively. The aerosol composition in this sector was marked by a distinct drop in sea salt and increase in both sulfate fraction and absolute concentrations, resulting in a substantially higher CCN0.5/CN10 activation ratio of 0.8 compared to around 0.4 for mid-latitudes. Long-term measurements at land-based research stations surrounding the Southern Ocean were found to be good representations at their respective latitudes; however this study highlighted the need for more long-term measurements in the region. CCN observations at Cape Grim (40∘39′ S) corresponded with CCN measurements from northern and mid-latitude sectors, while CN10 observations only corresponded with observations from the northern sector. Measurements from a simultaneous 2-year campaign at Macquarie Island (54∘30′ S) were found to represent all aerosol species well. The southernmost latitudes differed significantly from both of these stations, and previous work suggests that Antarctic stations on the East Antarctic coastline do not represent the East Antarctic sea-ice latitudes well. Further measurements are needed to capture the long-term, seasonal and longitudinal variability in aerosol processes across the Southern Ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses

Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wind-Driven and Seasonal Effects on Marine Aerosol Production in the Bellingshausen Sea, Antarctica

We assessed satellite-retrieved marine aerosol in the western Antarctic Peninsula (WAP) across a 12-year period from coarse-mode aerosol optical depth (AOD C ), often used as a proxy for sea spray aerosol (SSA), and marine aerosol optical depth (MAOD), a newly developed proxy for tropospheric marine aerosol. Across open ocean to coastal regions, daily fluctuations in nighttime and daytime winds, respectively, drove increasing MAOD and AOD C . MAOD depicted strong correlations with wind speed across open ocean and weak correlations in coastal regions. In the open ocean, AOD C exhibited a very weak significant correlation to wind speed and a weak significant correlation to sea surface temperature (SST). We thus observed that warmer SST enhanced the production of SSA, supporting prior studies. This is the first study to assess patterns of AOD C in the WAP. In contrast to the tropical Pacific, seasonal patterns showed that biological activity likely contributed toward MAOD and AOD C magnitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Collaborative Research: Advancing Arctic Climate Projection Capability at Seasonal to Decadal Scales (Final Technical Report)

The Regional Arctic System Model (RASM) at process resolving configurations has been used to (i) advance understanding of physical processes and feedbacks involved in Arctic amplification and (ii) understand and potentially reduce uncertainty in prediction of arctic climate change at seasonal to decadal scales. RASM consists the atmosphere (Weather and Research Forecasting model, WRF), ocean (Parallel Ocean Program, POP), sea ice (CICE), land hydrology (Variable Infiltration Capacity model, VIC), river routing scheme (RVIC), marine biogeochemistry components and the coupling framework (CPL7). Its domain is pan-Arctic, with the atmosphere and land components configured on a 50-km or 25-km grid and four configurations of the ocean and sea ice components: 1/12°(~9.3km) or 1/48°(~2.4km) and 45 or 60 vertical layers. These RASM configurations have been motivated by the emerging exascale capability for high performance computing to improve model fidelity. The dynamical downscaling of reanalysis allows comparison of RASM results with observations in place and time to: (i) advance system level understanding of physical processes and coupling involved in an event, (ii) optimize model parameter space, (iii) diagnose and reduce model biases and (iv) produce realistic and consistent across all the components initial conditions for predictions and predictability studies, which are all unique capabilities not available in global Earth System Models (ESMs). An evaluation of RASM 1.0 (Cassano et al. 2017) revealed that it had a cold bias over the oceans and a warm bias over land areas due largely to cloud and radiation biases in the model, with too little cloud cover simulated over land and too much cloud cover simulated over sub-polar oceans. This study has motivated an upgrade to WRF version 3.7.1 in RASM and allowed for the inclusion of the radiative impact of convective clouds. A variety of atmospheric physics parameterizations were evaluated against observations (e.g. data from the Arctic Clouds in Summer Experiment (ACSE); Sedlar et al. 2020) to identify an optimal suite of WRF physics options in RASM. The RASM with the optimized WRF physics were used to study the impact of strong mesoscale winds over the ocean around the southern tip of Greenland (DuVivier and Cassano 2016) and their impact on oceanic convection (DuVivier et al. 2017a). Data from the PolarWinds field campaign were used to evaluate WRF boundary layer physics and resolution impacts on the simulation of a Greenland barrier wind event (DuVivier et al. 2017b). The RVIC streamflow routing model has been implemented in RASM to realistically represent high-resolution streamflow processes (Hamman et al. 2017) and to couple the land buoyancy fluxes to the ocean. The RASM-RVIC high-resolution data set of all coastal freshwater fluxes for the Arctic drainage basin and surrounding areas for 1979-2014 was published as a separate product (https://doi.org/10.5281/zenodo.293037). The fidelity of atmospheric momentum transfer to and the response of polar marine Ekman layer in RASM and Community Earth System Model (CESM) was investigated by Roberts et al. (2015). The increased frequency of oceanic flux exchange in CESM, following the RASM guidance, caused a considerable increase in the median inertial ice speed across the Southern Ocean and parts of the Arctic. A comprehensive evaluation of the RASM1.0 atmosphere-ocean-sea ice-land interface was completed by Brunke et al. (2018). RASM was also demonstrated for its capability to simulate extreme events in agreement with observations in space and time (Lee et al. submitted). In particular, the development of three open water events, known as polynyas, have been simulated north of Greenland in February of 2011, 2017 and 2018, in agreement with satellite observations for the past four decades. The optimized RASM sea ice results have been favorably evaluated against satellite observations and a subset of eleven CMIP6 models (Watts et al. submitted). In a complementary project, Jin et al. (2018) have shown that RASM with higher-resolution and new sea-ice processes contributed to lower model errors in sea-ice conditions, concentrations of nutrients and ice algae, in comparison to results from the coarse-resolution (1°) CESM. In two other complementary studies, RASM results were used (i) to explain areas of concentrated use by bowhead whales, the seasonal progression in the use, and the physical environment within those areas (Citta et al. 2015) and (ii) for a synthesis of fall bowhead whales distribution and migration in the Bering-Chukchi-Beaufort (BCB) Sea to investigate whale movements and feeding to the local ocean hydrography and currents (Citta et al. 2018). However, the multi-decadal output from the CESM Large Ensemble yielded unrealistic forcing. Instead, the shorter NCEP CFSv2 9-month forecasts were successfully tested and afforded an increased ensemble size (~30) to demonstrate gains of dynamical downscaling at sub-seasonal to intra-annual time scales. The improved model physics and coupling among RASM model components have yielded more realistic representation of the sea ice cover and consistent across all model components initial conditions. Consequently, RASM demonstrates significant gains compared to simulation of sea ice in the NCEP reanalysis. In addition, RASM 6-month ensemble forecasts yield very realistic sea ice distribution, which demonstrates both significant gains of dynamical downscaling and the continued impact of the initial conditions on forecasts out to 6 months (https://nps.edu/web/rasm/predictions). A follow up study using RASM for dynamical downscaling of the more realistic CESM initialized Decadal Prediction Large Ensemble output is currently ongoing as part of the DOE RGMA HiLAT-RASM project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improved Predictability of the Indian Ocean Dipole Using a Stochastic Dynamical Model Compared to the North American Multimodel Ensemble Forecast

This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management

Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources worldwide. Tracking and mitigating GHG emissions require a pivot to operational monitoring of regional carbon flux and stock changes. The current GHG observing system is addressing needs at two distinct scales: 1) Local scale (< 1 km), related to anthropogenic point source emissions, and 2) global scales (> 1000 km), related to land and ocean carbon sinks. More focus on intermediate (10–1000 km) scales is needed to more effectively monitor progress in reducing carbon emissions, enhancing removals, and maintaining sinks. Representatives from carbon cycle biomass and flux communities across United States government agencies and academic institutions met in September 2024 to discuss the rationale and scientific context for more effectively implementing an operational system for GHG monitoring in support of urban and national carbon management needs. To guide development of this system, we propose a multi-tiered global spaceborne observing framework for carbon flux and stock, prioritizing: 1) frequent GHG partial columns for carbon emissions and removals; 2) continuous time series and data fusion of biomass from Lidar and Synthetic Aperture Radar (SAR) for carbon stocks, and 3) expanded coverage of tropical, high latitude, and oceanic regions to monitor carbon cycle tipping points and feedbacks. This system should be complemented by expanded surface and airborne networks for oceanic and terrestrial/aquatic ecosystems for calibration, ground truthing, and study of under-sampled regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

U–Pb Geochronology and Stable Isotope Geochemistry of Terrestrial Carbonates, Lower Cretaceous Cedar Mountain Formation, Utah: Implications for Synchronicity of Terrestrial and Marine Carbon Isotope Excursions

The terrestrial Lower Cretaceous Cedar Mountain Formation, Utah, is a critical archive of paleoclimate, tectonics, and vertebrate ecology and evolution. Early Cretaceous carbon cycle perturbations associated with ocean anoxia have been interpreted from this succession, as expressed in stable carbon isotopes. However, refining the timing of the observed stable isotope excursions remains a key challenge in understanding how marine anoxia affects the Earth system, and is ultimately recorded in the terrestrial realm. The geochronology and geochemistry of a terrestrial carbonate near the base of this succession, which potentially records the Ap7 global carbon isotope excursion, is studied here. Petrographic and geochemical analyses are used to test plausible mechanisms for U incorporation into the calcite lattice in this sample. Using these methods, the hypothesis that the incorporation of U was at or close to the timing of carbonate precipitation is evaluated. U–Pb geochronology of calcite indicates a plausible Early Cretaceous age. However, comparison of the new U–Pb ages of calcite with detrital zircon maximum depositional ages immediately beneath the studied sample indicates a disparity in the apparent sedimentation rates if both types of geochronologic information are interpreted as reflecting the timing of sediment deposition. The totality of data supports an early, and high-temperature, diagenetic timing of U incorporation, with potential for minor leaching of U in subsequent fluid–rock interaction. The most likely mechanism for U transport and immobilization in these samples is hydrothermal fluid–rock interaction. Therefore, the radiometric ages, and corresponding stable isotope composition of U-bearing carbonate domains in this sample, indicate early subsurface fluid–rock interactions and not a record of atmosphere–soil geochemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Plankton energy flows using a global size-structured and trait-based model

Plankton community models are critical tools for understanding the processes that shape marine plankton communities, how plankton communities impact biogeochemical cycles, and the feedbacks between community structure and function. Here, using the flexible Marine Biogeochemistry Library (MARBL), we present the Size-based Plankton ECological TRAits (MARBL-SPECTRA) model, which is designed to represent a diverse plankton community while remaining computationally tractable. MARBL-SPECTRA is composed of nine phytoplankton and six zooplankton size classes represented using allometric scaling relationships for physiological traits and interactions within multiple functional types. MARBL-SPECTRA is embedded within the global ocean component of the Community Earth System Model (CESM) and simulates large-scale, emergent patterns in phytoplankton growth limitation, plankton phenology, plankton generation time, and trophic transfer efficiency. The model qualitatively reproduces observed global patterns of surface nutrients, chlorophyll biomass, net primary production, and the biogeographies of a range of plankton size classes. In addition, the model simulates how predator:prey dynamics and trophic efficiency vary across gradients in total ecosystem productivity. Shorter food chains that export proportionally more carbon from the surface to the ocean interior occur in productive, eutrophic regions, whereas in oligotrophic regions, the food chains are relatively long and export less organic matter from the surface. The union of functional type modeling with size-resolved, trait-based modeling approaches allows MARBL-SPECTRA to capture both large-scale elemental cycles and the structure of planktonic food webs affecting trophic transfer efficiency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Influence of Land‐Surface Conditions on the 2020–2021 Western US Drought

Abstract In summer 2021, 90% of the western United States (WUS) experienced drought, with over half of the region facing extreme or exceptional conditions, leading to water scarcity, crop loss, ecological degradation, and significant socio‐economic consequences. Beyond the established influence of oceanic forcing and internal atmospheric variability, this study highlights the importance of land‐surface conditions in the development of the 2020–2021 WUS drought, using observational data analysis and novel numerical simulations. Our results demonstrate that the soil moisture state preceding a meteorological drought, due to its intrinsic memory, is a critical factor in the development of soil droughts. Specifically, wet soil conditions can delay the transition from meteorological to soil droughts by several months or even nullify the effects of La Niña‐driven meteorological droughts, while drier conditions can exacerbate these impacts, leading to more severe soil droughts. For the same reason, soil droughts can persist well beyond the end of meteorological droughts. Our numerical experiments suggest a relatively weak soil moisture‐precipitation coupling during this drought period, corroborating the primary contributions of the ocean and atmosphere to this meteorological drought. Additionally, drought‐induced vegetation losses can mitigate soil droughts by reducing evapotranspiration and slowing the depletion of soil moisture. This study highlights the importance of soil moisture and vegetation conditions in seasonal‐to‐interannual drought predictions. Findings from this study have implications for regions like the WUS, which are experiencing anthropogenically‐driven soil aridification and vegetation greening, suggesting that future soil droughts in these areas may develop more rapidly, become more severe, and persist longer.

Jiang, Yelin [Lamont‐Doherty Earth Observatory Col↗
DOE OSTI2021

Predictability of Seawater DMS During the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES)

This work presents an overview of a unique set of surface ocean dimethylsulfide (DMS) measurements from four shipboard field campaigns conducted during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES) project. Variations in surface seawater DMS are discussed in relation to biological and physical observations. Results are considered at a range of timescales (seasons to days) and spatial scales (regional to sub-mesoscale). Elevated DMS concentrations are generally associated with greater biological productivity, although chlorophyll a (Chl) only explains a small fraction of the DMS variability (15%). Physical factors that determine the location of oceanic temperature fronts and depth of vertical mixing have an important influence on seawater DMS concentrations during all seasons. The interplay of biomass and physics influences DMS concentrations at regional/seasonal scales and at smaller spatial and shorter temporal scales. Seawater DMS measurements are compared with the global seawater DMS climatology and predictions made using a recently published algorithm and by a neural network model. The climatology is successful at capturing the seasonal progression in average seawater DMS, but does not reproduce the shorter spatial/temporal scale variability. The input terms common to the algorithm and neural network approaches are biological (Chl) and physical (mixed layer depth, photosynthetically active radiation, seawater temperature). Both models predict the seasonal North Atlantic average seawater DMS trends better than the climatology. However, DMS concentrations tend to be under-predicted and the episodic occurrence of higher DMS concentrations is poorly predicted. The choice of climatological seawater DMS product makes a substantial impact on the estimated DMS flux into the North Atlantic atmosphere. These results suggest that additional input terms are needed to improve the predictive capability of current state-of-the-art approaches to estimating seawater DMS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Aerosol hygroscopicity over the southeast Atlantic Ocean during the biomass burning season – Part 1: From the perspective of scattering enhancement​​​​​​​

Aerosol hygroscopicity plays a vital role in aerosol radiative forcing. One key parameter describing hygroscopicity is the scattering enhancement factor, f(RH), defined as the ratio of the scattering coefficient at humidified relative humidity (RH) to its dry value. Here, we utilize the f(80 %) from ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) 2016 and 2018 airborne measurements to investigate the hygroscopicity of aerosols, its vertical distribution, its relationship with chemical composition, and its sensitivity to organic aerosol (OA) hygroscopicity over the southeast Atlantic (SEA) Ocean during the biomass burning (BB) season. We found that aerosol hygroscopicity remains steady above 2 km, with a mean f(80 %) of 1.40 ± 0.17. Below 2 km, aerosol hygroscopicity increases with decreasing altitude, with a mean f(80 %) of 1.51 ± 0.22, consistent with higher values of BB aerosol hygroscopicity found in the literature. The hygroscopicity parameter of OA (κ OA ) is retrieved from the Mie model with a mean value of 0.11 ± 0.08, which is in the middle to upper range compared to the literature. Higher OA hygroscopicity is related to aerosols that are more aged, oxidized, and present at lower altitudes. The enhanced biomass burning aerosol (BBA) hygroscopicity at lower altitudes is mainly due to a lower OA fraction, increased sulfate fraction, and greater κ OA at lower altitudes. We propose a parameterization that quantifies f(RH) with chemical composition and κOA based on Mie simulation of internally mixed OA–(NH 4 ) 2 SO 4 –BC mixtures. The good agreement between the predictions and the ORACLES measurements implies that the aerosols in the SEA during the BB season can be largely represented by the OA–(NH 4 ) 2 SO 4 –BC internal mixture with respect to the f(RH) prediction. The sensitivity of f(RH) to κ OA indicates that applying a constant κ OA is only suitable when the OA fraction is low and κ OA shows limited variation. However, in situations deviating these two criteria, κ OA can notably impact scattering coefficients and aerosol radiative effect; therefore, accounting for κ OA variability is recommended.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Observed Effects of Cold Pools on Convection Triggering and Organization during DYNAMO/AMIE

Cold pools that form from existing convection can generate new clouds nearby, a process that has been suggested to contribute to cloud organization. However, some disagreement exists on the role of cold pools in cloud organization over tropical oceans, and much remains to be understood on how this role depends on the state of large-scale disturbances such as the Madden-Julian Oscillation (MJO). Here, this study addresses this question by examining the intraseasonal variability in the properties of cold pools and their effects on convection triggering using observations. The unique set of surface meteorology data and ground-based radars deployed during the DYNAMO/AMIE field campaign are used to identify cold pools and the associated spatiotemporal evolution of rainfall. The results suggest that cold pools enhance rainfall at their expected speed of 3–8 m s –1 , which includes the Doppler-shifting effect by the background wind. The cold pools also enhance rainfall mainly within a 20 km radius, especially in the direction of boundary-layer vertical wind shear. However, gravity waves appear to contribute predominantly to the propagation of convection over a wider range of distances due to their faster propagation speeds. The interface of convection triggering and propagation at different speeds of cold pools and gravity waves seems to contribute to cloud organization. The effectiveness of cold pools and gravity waves on cloud organization also depends highly on the MJO due to the combined effects of changes in environmental humidity and boundary to lower-tropospheric wind.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Response of salt intrusion in a tidal estuary to regional climatic forcing

Abstract Salinity distribution in a large tidal estuary is subject to estuarine adjustment under the influences of multiple physical drivers such as freshwater pulses and sea level rise, and is crucial to upstream water quality, aquaculture, and ecosystem functions of the estuary. To better understand the estuarine salinity response to climate change, the unstructured-grid Finite Volume Community Ocean Model was implemented to simulate the salt intrusion in the Delaware Bay Estuary. The model was first validated by multiple observational data sets and subsequently applied in an idealized setting to examine the response of salt front to freshwater pulses in high flow conditions, followed by a long-term drought condition supported by a multi-decadal streamflow drought analysis in the estuary. The model results showed that after the freshwater pulses the salt front location moved further upstream with sea level rise. Under the simulated long-term drought condition, the adjustment timescale of salt intrusion varies nonlinearly with sea level rise. With a significant increase in sea level rise, the adjustment timescale starts to decrease. This shift suggests a transition into a different regime where the estuary becomes more stratified, as indicated by an increasing bulk Simpson number with rising sea levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Novel fisheries investigations by Harald Dannevig: some parallels with Johan Hjort on the other side of the world

Abstract Harald Dannevig was Australia’s first Director of fisheries research and Director of Australia’s first ocean-going research vessel. Dannevig’s initial contributions concerned hatchery technology, freshwater fisheries, and impacts of estuarine prawn trawling. Later, he revealed the growth and migration of sea mullet, the spawning of pelagic eggs in the coastal ocean, and he was the first to demonstrate the effect of onshore winds on recruitment to estuarine fisheries. Using plans of the first Norwegian research trawler Michael Sars, he advised on the construction and commissioning of Endeavour. He organized 99 research voyages over 6 years to determine suitable trawling grounds over ∼7000 km, discovering 263 new species, including 96 new fish species and ∼5000 catalogued specimens. Harald Dannevig’s significant achievements in Australia were soon forgotten after his death with the loss of Endeavour in the Southern Ocean at the beginning of World War I. Both Johan Hjort and Dannevig were numerate, loved natural history, and were keenly observant on the deck. As these two scientists did not correspond, their innovative and parallel thinking stems from the shared university environment with G.O. Sars, and the rapport between Sars and Harald’s father Gunder Dannevig, concerning the fish hatchery and stocking of larval cod.

Suthers, Iain M.↗
DOE OSTI2023

A Finite-Time Ensemble Method for Mixed Layer Model Comparison

Here, this work evaluates the fidelity of various upper-ocean turbulence parameterizations subject to realistic monsoon forcing and presents a finite-time ensemble vector (EV) method to better manage the design and numerical principles of these parameterizations. The EV method emphasizes the dynamics of a turbulence closure multimodel ensemble and is applied to evaluate 10 different ocean surface boundary layer (OSBL) parameterizations within a single-column (SC) model against two boundary layer large-eddy simulations (LES). Both LES include realistic surface forcing, but one includes wind-driven shear turbulence only, while the other includes additional Stokes forcing through the wave-average equations that generate Langmuir turbulence. The finite-time EV framework focuses on what constitutes the local behavior of the mixed layer dynamical system and isolates the forcing and ocean state conditions where turbulence parameterizations most disagree. Identifying disagreement provides the potential to evaluate SC models comparatively against the LES. Observations collected during the 2018 monsoon onset in the Bay of Bengal provide a case study to evaluate models under realistic and variable forcing conditions. The case study results highlight two regimes where models disagree 1) during wind-driven deepening of the mixed layer and 2) under strong diurnal forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Freezing Processes in Southern Ocean Mixed Phase Clouds (Final Technical Report)

Southern Ocean (SO) low-level clouds remain a challenge for Earth system models to represent accurately. This project utilized DOE ARM observations and the Community Atmosphere Model (CAM) to improve process-level understanding of aerosol-cloud interactions and cloud microphysics for SO mixed-phase clouds. Specifically, our efforts include two main topics: 1) investigate the SO aerosol, cloud condensation nuclei (CCN), and ice nucleating particle (INP) population based on observations and determine predictive skill of simulating SO INPs, and 2) assess the ice formation pathways, including ice nucleation and secondary ice production, in SO mixed phase clouds utilizing a combination of observations and modeling tools. Studies focused on SO aerosol and INP resulted four manuscripts. Ice formation pathways in SO mixed phase clouds were investigated, revealing observed evidence of SIP active across all mixed phase temperatures. However, a key finding of this project is that despite significant progress in predicting SO INPs, the role of INP variability in SO cloud phase, precipitation, and radiative properties remain unknown due to challenges in microphysics parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Response Of Salt Intrusion In A Tidal Estuary To Regional Climatic Forcing (FVCOM Output for the baseline scenario)

Salinity distribution in a large tidal estuary is subject to estuarine adjustment under the influences of multiple physical drivers such as freshwater pulses and sea level rise, and is crucial to upstream water quality, aquaculture, and ecosystem functions of the estuary. To better understand the estuarine salinity response to climate change, the unstructured-grid Finite Volume Community Ocean Model was implemented to simulate the salt intrusion in the Delaware Bay Estuary. The model was first validated by multiple observational data sets and subsequently applied in an idealized setting to examine the response of salt front to freshwater pulses in high flow conditions, followed by a long-term drought condition supported by a multi-decadal streamflow drought analysis in the estuary. The model results showed that after the freshwater pulses the salt front location moved further upstream with sea level rise. Under the simulated long-term drought condition, the adjustment timescale of salt intrusion varies nonlinearly with sea level rise. With a significant increase in sea level rise, the adjustment timescale starts to decrease. This shift suggests a transition into a different regime where the estuary becomes more stratified, as indicated by an increasing bulk Simpson number with rising sea levels. These findings have been summarized into a journal manuscript. The data included in this information release request are FVCOM model output associated with the numerical simulations described in the journal manuscript.

Liu, Jinliang↗