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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25

Numerical simulation of asteroid geometry variance on airburst threat

For an atmospheric airburst the primary source of concern when assessing uncertainty is the size and velocity. Determining these properties provides the basis for threat assessment, as the total energy of the asteroid may then be estimated, and the threat investigated thoroughly. Even with clarity as to how much energy an asteroid may deposit, a great deal of uncertainty still exists for the actual energy deposition process. One such source of uncertainty is the geometry of the incoming asteroid. The geometry of an asteroid will alter the stress distribution during entry, which adds uncertainty to when fracture will occur. Here, in this study, we use Smoothed Particle Hydrodynamics to model the atmospheric airburst of Tunguska-scale asteroids with varying geometric profiles, including a sphere, ellipsoid, binary and superellipsoid. Each asteroid is modeled as a homogenous structure with strength. We assess uncertainty through a series of planar 2D simulation cases for each geometry, comparing the source of stochasticity across geometries. A single 3D airburst simulation for each geometry is also analyzed. Additionally, the 3D cases are compared to the highly uncertain Tunguska event, predicting variance in burst height across geometries, but all bounded by theoretical burst heights proposed for Tunguska.

Airburst↗

A synthetic co-culture for bioproduction of ammonia from methane and air

Abstract Fixed nitrogen fertilizers feed 50% of the global population, but most fixed nitrogen production occurs using energy-intensive Haber–Bosch-based chemistry combining nitrogen (N2) from air with gaseous hydrogen (H2) from methane (CH4) at high temperatures and pressures in large-scale facilities sensitive to supply chain disruptions. This work demonstrates the biological transformation of atmospheric N2 into ammonia (NH3) using CH4 as the sole carbon and energy source in a single vessel at ambient pressure and temperature, representing a biological “room-pressure and room-temperature” route to NH3 that could ultimately be developed to support compact, remote, NH3 production facilities amenable to distributed biomanufacturing. The synthetic microbial co-culture of engineered methanotroph Methylomicrobium buryatense (now Methylotuvimicrobium buryatense) and diazotroph Azotobacter vinelandii converted three CH4 molecules to l-lactate (C3H6O3) and powered gaseous N2 conversion to NH3. The design used division of labor and mutualistic metabolism strategies to address the oxygen sensitivity of nitrogenase and maximize CH4 oxidation efficiency. Media pH and salinity were central variables supporting co-cultivation. Carbon concentration heavily influenced NH3 production. Smaller-scale NH3 production near dispersed, abundant, and renewable CH4 sources could reduce disruption risks and capitalize on untapped energy resources. One-Sentence Summary Co-culture of engineered microorganisms Methylomicrobium buryatense and Azotobacter vinelandii facilitated the use of methane gas as a sole carbon feedstock to produce ammonia in an ambient temperature, atmospheric pressure, single-vessel system.

Biotechnology & Applied Microbiology↗

Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model

Abstract Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting) and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective “average event.” We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT’s capacities on a wave–mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass, which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also backward in time to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the reactive current . The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions.

Meteorology & Atmospheric Sciences↗

Compiled TBS data from Oliktok Point from ICARUS, AVPOP, and POPEYE

The dataset encompasses QA/QC TBS data, including TBS altitude, atmospheric pressure, dew point, POPs number concentrations and mean aerosol size, relative humidity, temperature, equivalent potential temperature, equivalent temperature, and date and time. These data are provided per day for at least one TBS flight. Supporting data from the AMF3 aligned with the timestamps of the TBS data include cloud base distance from TBS, if the time corresponds to cloud base transit of the TBS, surface-based pressure, surface-based precipitation rate, surface-based UHSAS data, ceilometer cloud base height, KAZR cloud top height, and MWR liquid water content. Both POPS and UHSAS aerosol number concentrations are provided as: (1) absolute (i.e. concentrations for each instrument's total size range) and (2) the overlapping size range (i.e. the same size bins for both instruments).

54 ENVIRONMENTAL SCIENCES↗

Dependence of wind-farm-induced gravity waves and wind farm performance on non-dimensional atmospheric parameters and simulation configuration

This large-eddy simulation (LES) study examines how wind-farm-induced atmospheric gravity waves (AGWs) and wind farm performance depend on non-dimensional atmospheric parameters and simulation configuration. A hypothetical aligned wind farm of actuator disks is simulated under neutral surface conditions, with a stable capping inversion and a mildly stable free atmosphere, to assess the effects of stratification beyond the atmospheric boundary layer (ABL) on ABL flow. Simulation set-ups fully resolving AGWs are validated to minimize spurious wave generation and reflection from the domain boundaries. The validated set-up is then used to analyze AGW types and characteristics, as well as stratification impacts under conventionally neutral boundary layer (CNBL) conditions. These conditions are governed by four non-dimensional parameters: the Froude numbers of the free atmosphere and capping inversion (Fr, Fr i ), and the aspect ratios of the ABL and wind farm (H̃ i , S h ). Simulation configurations that fully resolve AGWs – capturing at least one wavelength both horizontally and vertically – yield the most realistic stratification effects on ABL flow, whereas partial or unresolved configurations produce non-physical, channel-like behavior. A coherent description of the AGW phenomena is provided, highlighting the central role of capping inversion displacement in linking ABL fluctuations with AGWs. Trapped waves are confined within the capping inversion, while interfacial and internal waves aloft are identified as the AGW types most relevant to wind farm performance. The wavy inversion, analogous to an interfacial wave, forms converging and diverging zones that drive power fluctuations across the farm. The interfacial wavelength, measured over the wind farm, corresponds to one diverging, one converging, and one mildly diverging zone. As the interfacial wavelength decreases with Fr i , multiple convergence–divergence zones develop under sub-critical conditions (Fr i <1.0), while for super-critical conditions (Fr i > 1.0), the wavelength approaches the farm length. Wave amplitude increases with decreasing H̃ i (i.e., shallower capping inversions). Wind farm performance is most sensitive to H̃ i : shallow boundary layers increase blockage and reduce efficiency, while deeper layers enhance efficiency. Increasing Fr and Fr i mitigates blockage, and increasing S h mainly improves wake recovery. Although local power fluctuations arise from AGWs, overall wind farm efficiency remains nearly constant with Fr and Fr i , improving primarily with larger H̃ i and S h .

17 WIND ENERGY↗

Experimental protocol for phase 1 of the APARC QUOCA (QUasibiennial oscillation and Ozone Chemistry interactions in the Atmosphere) working group

The quasi-biennial oscillation (QBO) is the main mode of variability in the tropical stratosphere, influencing the predictability of other regions in the atmosphere through its teleconnections to the stratospheric polar vortices and coupling to surface tropical and extratropical variability. However, climate and forecasting models consistently underestimate QBO amplitudes in the lower stratosphere, likely contributing to their failure to simulate these teleconnections. One underexplored contributor to model biases is missing representation of ozone-radiative feedbacks, which enhance temperature variability in the lower stratosphere, particularly at periods at and greater than the QBO (>28 months). While previous studies suggest that ozone-radiative feedbacks can impact QBO periods, amplitudes and the associated secondary circulation in the lower stratosphere, the reported impacts differ widely among models and are hard to interpret due to differences in methodology. To this end, here we propose a coordinated experimental protocol – held joint between the Atmospheric Processes and their Role in Climate (APARC) Quasi-Biennial Oscillation Initiative (QBOi) and Chemistry Climate Modeling Initiative (CCMI) activities – which is aimed at assessing the coupling between stratospheric ozone, temperature and the circulation. We use the proposed experiments to define the ozone feedback on the QBO in both present-day and idealized (abrupt quadrupling of carbon dioxide) climates. While primary focus is on the QBO, the proposed protocol also enables analysis of other aspects of ozone-radiative-dynamical coupling in the atmosphere, including impacts on the Brewer-Dobson Circulation and tropospheric eddy-driven jet responses to future climate change. Here we document the scientific rationale and design of the QUOCA Phase 1 experiments, summarize the data request, and give a brief overview of participating models. Preliminary results using the NASA Goddard Institute for Space Studies E2-2 climate model are used to illustrate sensitivities to certain methodological choices.

Environmental sciences↗

The Oceanic Barrier Layer in the Eastern Indian Ocean as a Predictor for Rainfall Over Indonesia and Australia

Barrier layers in the tropics trap heat in a shallow and stable near-surface layer and limit entrainment of cooler water from below. Both processes act to increase sea surface temperature and enhance atmospheric convection. The high resolution fully coupled pre-industrial Energy Exascale Earth System Model version 0 (E3SMv0) is used to investigate the relationship between barrier layers in the eastern Indian Ocean during the wet season with local atmospheric convection and remote rainfall. A partial least squares regression reveals a significant relationship between Australasian rainfall and the barrier layer thickness (BLT) west of Sumatra, occurring one month earlier. Furthermore, the largest positive regression coefficients are over northern Australia. The region west of Sumatra is strategically located where the East-Asian monsoon moisture flows toward northern Australia. Thickening of the west Sumatra BLT intensifies evaporation and local convection and amplifies the moisture transported to Australia acting to increase the terrestrial rainfall.

58 GEOSCIENCES↗

Simulation of Compound Flooding Using River‐Ocean Two‐Way Coupled E3SM Ensemble on Variable‐Resolution Meshes

Abstract Coastal zone compound flooding (CF) can be caused by the interactive fluvial and oceanic processes, particularly when coastal backwater propagates upstream and interacts with high river discharge. The modeling of CF is limited in existing Earth System Models (ESMs) due to coarse mesh resolutions and one‐way coupled river‐ocean components. In this study, we present a novel multi‐scale coupling framework within the Energy Exascale Earth System Model (E3SM), integrating global atmosphere and land with interactively coupled river and ocean models using different meshes with refined resolutions near the coastline. To evaluate this framework, we conducted ensemble simulations of a CF event (Hurricane Irene in 2011) in a Mid‐Atlantic estuary. The results demonstrate that the novel E3SM configuration can reasonably reproduce river discharge and sea surface height variations. The two‐way river‐ocean coupling improves the representation of coastal backwater effects at the terrestrial‐aquatic interface that are caused by the combined actions of tide and storm surge during the CF event, thus providing a valuable modeling tool for better understanding the river‐estuary‐ocean dynamics in extreme events under climate change. Notably, our results show that the most significant CF impacts occur when the highest storm surge generated by a tropical cyclone meets with a moderate river discharge. This study highlights the state‐of‐the‐art advancements developed within E3SM for simulating multi‐scale coastal processes.

54 ENVIRONMENTAL SCIENCES↗

Molybdenum-99 from Molten Salt Reactor as a Source of Technetium-99m for Nuclear Medicine: Past, Current, and Future of Molybdenum-99

Technitium-99m ( 99m Tc), a widely used radioisotope, is used in tens of millions of medical diagnostic procedures annually. However, it is hard to store and must be immediately used upon production due to its short half-life (i.e., 6 h); thus, it is currently produced from 99 Mo, which itself is a result of 235 U fission. The majority of 99 Mo supplies to U.S. patients are currently provided by foreign producers and produced using highly enriched uranium (HEU). In order to minimize the proliferation risks of HEU-based medical isotope production, the U.S. Department of Energy’s National Nuclear Security Administration has funded a program to accelerate the development of technologies to produce 99 Mo without the use of HEU. Today, the global supply of 99 Mo depends on a limited number of nuclear reactors, and production has been interrupted unexpectedly since 2009 due to the fleet’s advanced age. Herein, alternative options for 99 Mo production are discussed, and one potential option is to obtain 99m Tc from molten salt reactors (MSRs). A MSR is a nuclear fission reactor that can operate at or close to atmospheric pressure with liquid fuel, which allows for producing isotopes in a timely manner. In this paper, the past and current production of 99 Mo via nuclear reactors is described, and the future of 99 Mo production by MSRs is discussed. The behavior and chemical properties of molybdenum in fluoride salts in MSRs and the possible extraction methods are also examined in addition to the limitation of current studies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Molecular level characterization of wildfire aerosol induced changes to plant health and value

The potential impacts of wildfire smoke on plant health and value are of increasing concern, due both to increasing awareness, and more frequent wildfire events. Wildfires produce substantial amounts of atmospheric pollution in smoke and particulate matter which can travel thousands of miles. These smoke events can blanket entire agricultural regions and cause impacts to plant development. One notable example of this is ‘smoke taint’ in wine, where vines, exposed to smoke, absorb many small volatile phenolic (VP) compounds. These VPs can be metabolized (glycosylated) and transported throughout the plant. Later, during downstream processing (fermentation of the grape musts), these glycosylated VPs can be hydrolyzed to re-release and volatilize the aroma-active phenolic compounds. The final product, thus, smells of smoke. Despite the obvious commercial and general research interests here, the nature of wildfire impacts on plant health are incompletely understood. Using established metabolomics and organic matter (aerosol) characterization methods, the overall aim of this proposal is thus to study wildfire smoke impacts on plant health and value, in the context of a model system (V. vinifera). This project closed early, and this report reflects some of the limited work conducted prior to conclusions. Specifically, this report discusses some sample preparation and initial MALDI-mass spectrometry analysis.

54 ENVIRONMENTAL SCIENCES↗

Emergence of a nocturnal low-level jet from a broad baroclinic zone

An analytical model is presented for the generation of a Blackadar-like nocturnal low-level jet in a broad baroclinic zone. The flow is forced from below (flat ground) by a surface buoyancy gradient and from above (free atmosphere) by a constant pressure gradient force. Diurnally-varying mixing coefficients are specified to increase abruptly at sunrise and decrease abruptly at sunset. With attention restricted to a surface buoyancy that varies linearly with a horizontal coordinate, the Boussinesq-approximated equations of motion, thermal energy, and mass conservation reduce to a system of one-dimensional equations that can be solved analytically. Sensitivity tests with southerly jets suggest that (i) stronger jets are associated with larger decreases of the eddy viscosity at sunset (as in Blackadar theory), (ii) the nighttime surface buoyancy gradient has little impact on jet strength, and (iii) for pure baroclinic forcing (no free-atmosphere geostrophic wind), the nighttime eddy diffusivity has little impact on jet strength, but the daytime eddy diffusivity is very important and has a larger impact than the daytime eddy viscosity. The model was applied to a jet that developed in fair weather conditions over the Great Plains from southern Texas to northern South Dakota on 1 May 2020. The ECMWF Reanalysis v5 (ERA5) for the afternoon prior to jet formation showed that a broad north-south-oriented baroclinic zone covered much of the region. The peak model-predicted winds were in good agreement with ERA5 winds and lidar data from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) central facility in north-central Oklahoma.

54 ENVIRONMENTAL SCIENCES↗

Quantifying the Impact of Vertical Resolution on the Representation of Marine Boundary Layer Physics for Global-Scale Models

Abstract While GCM horizontal resolution has received the majority of scale improvements in recent years, ample evidence suggests that a model’s vertical resolution exerts a strong control on its ability to accurately simulate the physics of the marine boundary layer. Here we show that, regardless of parameter tuning, the ability of a single-column model (SCM) to simulate the subtropical marine boundary layer improves when its vertical resolution is improved. We introduce a novel objective tuning technique to optimize the parameters of an SCM against profiles of temperature and moisture and their turbulent fluxes, horizontal winds, cloud water, and rainwater from large-eddy simulations (LES). We use this method to identify optimal parameters for simulating marine stratocumulus and shallow cumulus. The novel tuning method utilizes an objective performance metric that accounts for the uncertainty in the LES output, including the covariability between model variables. Optimization is performed independently for different vertical grid spacings and value of time step, ranging from coarse scales often used in current global models (120 m, 180 s) to fine scales often used in parameterization development and large-eddy simulations (10 m, 15 s). Uncertainty-weighted disagreement between the SCM and LES decreases by a factor of ∼5 when vertical grid spacing is improved from 120 to 10 m, with time step reductions being of secondary importance. Model performance is shown to converge at a vertical grid spacing of 20 m, with further refinements to 10 m leading to little further improvement. Significance Statement In successive generations of computer models that simulate Earth’s atmosphere, improvements have been mainly accomplished by reducing the horizontal sizes of discretized grid boxes, while the vertical grid spacing has seen comparatively lesser refinements. Here we advocate for additional attention to be paid to the number of vertical layers in these models, especially in the model layers closest to Earth’s surface where climatologically important marine stratocumulus and shallow cumulus clouds reside. Our experiments show that the ability of a one-dimensional model to represent physical processes important to these clouds is strongly dependent on the model’s vertical grid spacing.

Meteorology & Atmospheric Sciences↗

ARM Aerial Facility (AAF) Merged Value-Added Product Report for Historical G-1 Field Campaigns

For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Grumman Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center (ADC) and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated data set was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated data set includes data collected from 236 flights (766.4 hours). Four of the seven field campaigns were based in the U.S. One campaign collected data from the wildfires in the U.S. Pacific Northwest and agricultural burns in the lower Mississippi River valley as part of the Biomass Burning Observation Project (BBOP) in 2013. In 2015, the ARM Cloud Aerosol Precipitation Experiment provided data on atmospheric rivers and associated aerosol-cloud interactions that produce heavy precipitation on the U.S. west coast during the early spring. Research data from Airborne Carbon Measurements-V (ACME-V), collected during the summer of 2015, gave scientists insight into trends and variability of trace gases in the atmosphere over the North Slope of Alaska to improve arctic climate models. In the early summer and autumn of 2016, the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign provided an extensive data set geared toward coupled processes that affect the life cycle of shallow clouds through the interaction among aerosol, cloud, land surface, and ecosystems. In 2014 (March and October), the airborne sampling moved outside of the U.S. to the city of Manaus in central Amazonia, Brazil, where residential and industrial emissions were extensively characterized by flights of the G-1. The GoAmazon2014/15 aircraft campaign data are being integrated with aquatic and terrestrial ecosystem measurements to quantify anthropogenic perturbations to a usually pristine tropical environment. Another international airborne mission was carried out in the Eastern North Atlantic region. The Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign saw the G-1 aircraft fly from Terceira Island in the Azores during the summer of 2017 and the winter of 2018. The campaign studied both seasons to measure key aerosol and cloud processes under various meteorological and cloud conditions with different aerosol sources. Then the G-1 deployed to the Sierras de Córdoba range in central Argentina from October to November 2018 for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign to study orographic convective cloud interactions with their surrounding environment. These comprehensive datastreams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states, and properties for addressing essential science questions in Earth system process studies.

54 ENVIRONMENTAL SCIENCES↗

ARM Aerial Facility (AAF) Merged Value-Added Product Report for Historical G-1 Field Campaigns

For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Grumman Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center (ADC) and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated data set was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated data set includes data collected from 236 flights (766.4 hours). Four of the seven field campaigns were based in the U.S. One campaign collected data from the wildfires in the U.S. Pacific Northwest and agricultural burns in the lower Mississippi River valley as part of the Biomass Burning Observation Project (BBOP) in 2013. In 2015, the ARM Cloud Aerosol Precipitation Experiment provided data on atmospheric rivers and associated aerosol-cloud interactions that produce heavy precipitation on the U.S. west coast during the early spring. Research data from Airborne Carbon Measurements-V (ACME-V), collected during the summer of 2015, gave scientists insight into trends and variability of trace gases in the atmosphere over the North Slope of Alaska to improve arctic climate models. In the early summer and autumn of 2016, the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign provided an extensive data set geared toward coupled processes that affect the life cycle of shallow clouds through the interaction among aerosol, cloud, land surface, and ecosystems. In 2014 (March and October), the airborne sampling moved outside of the U.S. to the city of Manaus in central Amazonia, Brazil, where residential and industrial emissions were extensively characterized by flights of the G-1. The GoAmazon2014/15 aircraft campaign data are being integrated with aquatic and terrestrial ecosystem measurements to quantify anthropogenic perturbations to a usually pristine tropical environment. Another international airborne mission was carried out in the Eastern North Atlantic region. The Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign saw the G-1 aircraft fly from Terceira Island in the Azores during the summer of 2017 and the winter of 2018. The campaign studied both seasons to measure key aerosol and cloud processes under various meteorological and cloud conditions with different aerosol sources. Then the G-1 deployed to the Sierras de Córdoba range in central Argentina from October to November 2018 for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign to study orographic convective cloud interactions with their surrounding environment. These comprehensive datastreams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states, and properties for addressing essential science questions in Earth system process studies.

54 ENVIRONMENTAL SCIENCES↗

The GFDL‐CM4X Climate Model Hierarchy, Part I: Model Description and Thermal Properties

We present the GFDL‐CM4X (Geophysical Fluid Dynamics Laboratory Climate Model version 4X) coupled climate model hierarchy. The primary application for CM4X is to investigate ocean and sea ice physics as part of a realistic coupled Earth climate model. CM4X utilizes an updated MOM6 (Modular Ocean Model version 6) ocean physics package relative to CM4.0, and there are two members of the hierarchy: one that uses a horizontal grid spacing of 0.25° (referred to as CM4X‐p25) and the other that uses a grid 0.125° (CM4X‐p125). CM4X also refines its atmospheric grid from the nominally 100 km (cubed sphere C96) of CM4.0–50 km (C192). Finally, CM4X simplifies the land model to allow for a more focused study of the role of ocean changes to global mean climate. CM4X‐p125 reaches a global ocean area mean heat flux imbalance of -0.02 W m -2 within $\mathcal{O}$ (150) years in a pre‐industrial simulation, and retains that thermally equilibrated state over the subsequent centuries. This 1850 thermal equilibrium is characterized by roughly 400 ZJ less ocean heat than present‐day, which corresponds to estimates for anthropogenic ocean heat uptake between 1870 and present‐day. CM4X‐p25 approaches its thermal equilibrium only after more than 1000 years, at which time its ocean has roughly 1100 ZJ more heat than its early 21st century ocean initial state. Furthermore, the root‐mean‐square sea surface temperature bias for historical simulations is roughly 20% smaller in CM4X‐p125 relative to CM4X‐p25 (and CM4.0). We offer the mesoscale dominance hypothesis for why CM4X‐p125 shows such favorable thermal equilibration properties.

54 ENVIRONMENTAL SCIENCES↗

Attribution of 2020 hurricane season extreme rainfall to human-induced climate change

Abstract The 2020 North Atlantic hurricane season was one of the most active on record, causing heavy rains, strong storm surges, and high winds. Human activities continue to increase the amount of greenhouse gases in the atmosphere, resulting in an increase of more than 1 °C in the global average surface temperature in 2020 compared to 1850. This increase in temperature led to increases in sea surface temperature in the North Atlantic basin of 0.4–0.9 °C during the 2020 hurricane season. Here we show that human-induced climate change increased the extreme 3-hourly storm rainfall rates and extreme 3-day accumulated rainfall amounts during the full 2020 hurricane season for observed storms that are at least tropical storm strength (>18 m/s) by 10 and 5%, respectively. When focusing on hurricane strength storms (>33 m/s), extreme 3-hourly rainfall rates and extreme 3-day accumulated rainfall amounts increase by 11 and 8%, respectively.

54 ENVIRONMENTAL SCIENCES↗

Comparing gas composition from fast pyrolysis of live foliage measured in bench-scale and fire-scale experiments

Background: Fire models have used pyrolysis data from oxidising and non-oxidising environments for flaming combustion. In wildland fires pyrolysis, flaming and smouldering combustion typically occur in an oxidising environment (the atmosphere). Aims: Using compositional data analysis methods, determine if the composition of pyrolysis gases measured in non-oxidising and ambient (oxidising) atmospheric conditions were similar. Methods: Permanent gases and tars were measured in a fuel-rich (non-oxidising) environment in a flat flame burner (FFB). Permanent and light hydrocarbon gases were measured for the same fuels heated by a fire flame in ambient atmospheric conditions (oxidising environment). Log-ratio balances of the measured gases common to both environments (CO, CO 2 , CH 4 , H 2 , C 6 H 6 O (phenol), and other gases) were examined by principal components analysis (PCA), canonical discriminant analysis (CDA) and permutational multivariate analysis of variance (PERMANOVA). Key results: Mean composition changed between the non-oxidising and ambient atmosphere samples. PCA showed that flat flame burner (FFB) samples were tightly clustered and distinct from the ambient atmosphere samples. CDA found that the difference between environments was defined by the CO-CO 2 log-ratio balance. PERMANOVA and pairwise comparisons found FFB samples differed from the ambient atmosphere samples which did not differ from each other. Conclusion: Relative composition of these pyrolysis gases differed between the oxidising and non-oxidising environments. This comparison was one of the first comparisons made between bench-scale and field scale pyrolysis measurements using compositional data analysis. Implications: These results indicate the need for more fundamental research on the early time-dependent pyrolysis of vegetation in the presence of oxygen.

54 ENVIRONMENTAL SCIENCES↗

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗