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DOE OSTI2024

RUBISCO Soil Moisture Working Group (SMWG) Mini-Workshop Report

The RUBISCO Soil Moisture Working Group (SMWG), an initiative bolstered by the DOE RUBISCO Science Focus Area project, is committed to unraveling the intricacies of soil moisture and its profound impact on global hydrology, energy, and biogeochemical cycles. Confronted with the complex challenges in multi-scale SM data development, feedback analysis, and model benchmarking, the SMWG aims to catalyze advancements in the field through a synergistic approach that engages leaders in soil moisture research and encompasses diverse datasets and novel analytical techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations

Abstract. The vertical structure of clouds has a profound effect on the global energy budget, the global circulation, and the atmospheric hydrological cycle. The CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) missions have taken complementary, colocated observations of cloud vertical structure for over a decade. However, no globally gridded dataset is available to the public for the full length of this unique combined data record. Here we present the 3S-GEOPROF-COMB product (Bertrand et al. 2023, https://doi.org/10.5281/zenodo.8057791), a globally gridded (level 3S) community data product summarizing geometrical profiles (GEOPROF) of hydrometeor occurrence from combined (COMB) CloudSat and CALIPSO data. Our product is calculated from the latest release (R05) of per-orbit (level-2) combined cloud mask profiles. We process a set of cloud cover, vertical cloud fraction, and sampling variables at 2.5, 5, and 10° spatial resolutions and monthly and seasonal temporal resolutions. We address the 2011 reduction in CloudSat data collection with Daylight-Only Operations (DO-Op) mode by subsampling pre-2011 data to mimic DO-Op collection patterns, thereby allowing users to evaluate the impact of the reduced sampling on their analyses. We evaluate our data product against CloudSat-only and CALIPSO-only global-gridded data products as well as four comparable surface-based sites, underscoring the added value of the combined product. Interest in the product is anticipated for the study of cloud processes, cloud–climate interactions, and as a candidate baseline climate data record for comparison to follow-up satellite missions, among other uses.

Bertrand, Leah (ORCID:0009000001607558)↗
DOE OSTI2024

Environmental controls on isolated convection during the Amazonian wet season

The Amazon rainforest is a vital component of the global climate system, influencing the hydrological cycle and tropical circulation. However, understanding and modeling the evolution of convection in this region remain a scientific challenge. Here, we assess the environmental conditions associated with shallow, congestus, and isolated deep convection days during the wet season (December to April), employing measurements from the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment and large-scale wind fields from the constrained variational analysis. Composites of deep days show moister than average conditions below 3 km early in the morning. Analyzing the water budget at the surface through observations only, we estimated the water vapor convergence term as a residual of the water balance closure. Convergence remains nearly zero during the deep days until early afternoon (13:00 LST), when it becomes a dominant factor in the water budget. At 14:00 LST, the deep days experience a robust upward large-scale vertical velocity, especially above 4 km, which supports the shallow-to-deep convective transition occurring around 16:00–17:00 LST. In contrast, shallow and congestus days exhibit drier pre-convective conditions, along with diurnal water vapor divergence and large-scale subsidence that extend from the surface to the lower free troposphere. Moreover, afternoon precipitation exhibits the strongest linear correlation (0.6) with large-scale vertical velocity, nearly double the magnitude observed for other environmental factors, even moisture, at different levels and periods of the day. Precipitation also exhibits a moderate increase with low-level wind shear, while upper-level shear has a relatively minor negative impact on convection.

Environmental Sciences & Ecology↗
DOE OSTI2026

Iron as a driver of organic carbon fate in permafrost regions

Iron (Fe) and its biogeochemical interactions with organic carbon (OC) exert critical controls over carbon storage and water quality across Arctic and sub-Arctic landscapes. Permafrost thaw affects Fe–organic carbon interactions by exposing thawed material and driving shifts in hydrologic regimes. Here we propose a conceptual framework describing how Fe–OC interactions respond to permafrost thaw and the implications for global carbon cycling. The framework builds on current understanding of hydrologically driven processes that are altered by permafrost thaw, including microbial Fe reduction that influences OC decomposition and carbon emissions; Fe cycling through redox transformations and sulfide oxidation; dissolution and reprecipitation of Fe–OC assemblages; and Fe export to river networks. We put forward four testable hypotheses—on topics from Fe supply to its role in soil OC storage and transfer to rivers—to advance understanding of the coupled environmental controls on pore-scale reactions (that is, microscale reactions at mineral–water–microorganism interfaces) that are central to global Fe–OC cycling. Our framework highlights how soil-profile- and landscape-scale processes, such as hydrologic connectivity and redox dynamics, regulate pore-scale Fe–OC reactions.

Herndon, Elizabeth [ORNL] (ORCID:0000000291945493)↗
DOE OSTI2022

Organizational Principles of Hyporheic Exchange Flow and Biogeochemical Cycling in River Networks Across Scales

Hyporheic zones increase freshwater ecosystem resilience to hydrological extremes and global environmental change. However, current conceptualizations of hyporheic exchange, residence time distributions, and the associated biogeochemical cycling in streambed sediments do not always accurately explain the hydrological and biogeochemical complexity observed in streams and rivers. Specifically, existing conceptual models insufficiently represent the coupled transport and reactivity along groundwater and surface water flow paths, the role of autochthonous organic matter in streambed biogeochemical functioning, and the feedbacks between surface-subsurface ecological processes, both within and across spatial and temporal scales. While simplified approaches to these issues are justifiable and necessary for transferability, the exclusion of important hyporheic processes from our conceptualizations can lead to erroneous conclusions and inadequate understanding and management of interconnected surface water and groundwater environments. This is particularly true at the landscape scale, where the organizational principles of spatio-temporal dynamics of hyporheic exchange flow (HEF) and biogeochemical processes remain largely uncharacterized. This article seeks to identify the most important drivers and controls of HEF and biogeochemical cycling based on a comprehensive synthesis of findings from a wide range of river systems. We use these observations to test current paradigms and conceptual models, discussing the interactions of local-to-regional hydrological, geomorphological, and ecological controls of hyporheic zone functioning. This improved conceptualization of the landscape organizational principles of drivers of HEF and biogeochemical processes from reach to catchment scales will inform future river research directions and watershed management strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Global High‐Resolution Mesoscale Convective System Database Using Satellite‐Derived Cloud Tops, Surface Precipitation, and Tracking

Abstract A new methodology is developed to construct a global (60°S–60°N) long‐term (2000–2019) high‐resolution (∼10‐km h) mesoscale convective system (MCS) database by tracking MCS jointly using geostationary satellite infrared brightness temperature ( T b ) and precipitation feature (PF) characteristics from the Integrated Multi‐satellitE Retrievals for GPM precipitation data sets. Independent validation shows that the satellite‐based MCS data set is able to reproduce important MCS statistics derived from ground‐based radar network observations in the United States and China. We show that by carefully considering key PF characteristics in addition to T b signatures, the new method significantly improves upon previous T b ‐only methods in detecting MCSs in the midlatitudes for all seasons. Results show that MCSs account for over 50% of annual total rainfall across most of the tropical belt and in selected regions of the midlatitudes, with a strong seasonality over many regions of the globe. The tracking database allows Lagrangian aspects such as MCS lifetime and translational speed and direction to be analyzed. The longest‐lived MCSs preferentially occur over the subtropical oceans. The land MCSs have higher cloud‐tops associated with more intense convection, and oceanic MCSs have much higher rainfall production. While MCSs are observed in many regions of the globe, there are fundamental differences in their dynamic and thermodynamic structures that warrant a better understanding of processes that control their evolution. This global database provides significant opportunities for observational and modeling studies of MCSs, their characteristics, and roles in regional and global water and energy cycles, as well as their hydrologic and other impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Adaptive Sampling for In Situ Cloud Probe (Final Report)

Clouds play a leading role in the Earth's global energy and solar radiation balance and hydrological cycle. Improving cloud models requires detailed information on the cloud microphysical properties, such as droplet size distribution and number density, liquid water content and cloud composition (droplets, ice particles), which can only be provided by aerial in situ measurements. However, for many atmospheric measurement instruments, the lack of flexibility in selecting the operational mode during operation can lead to uncertainties in sampling and measurement characteristics under continuously varying atmospheric conditions. This SBIR project is developing an advanced, compact optical imaging technology for in situ characterization of cloud hydrometeors. The development involves a deep modification of the existing Mesa Photonics’ Cloud Droplet Measurement System (CDMS) in order to implement real-time automatic adaptive sampling based on the acquired in situ data and environmental parameters. The new system, CDMS-2, implements two measurement modes: side-scatter imaging for smaller hydrometeors and direct bright-field-illumination imaging for larger hydrometeors in a significantly larger sample volume. The system measures the droplet size distribution (DSD) and number density with an added capability of discriminating between liquid water and ice hydrometeors (based on polarization-resolved side-scatter imaging). The instrument will implement automatic switching or alternating between the regular side-scatter imaging mode and sparse/large hydrometeor mode (based on the acquired data). Other adaptive sampling capabilities include variable sample volume and dynamic range (based on the measured DSD). The preferred deployment platforms are uncrewed aircraft systems (UAS) and tethered balloon/kite systems (TBS). The Phase I project achieved (or exceeded) the goals listed in the Work Plan. A CDMS-2 laboratory prototype implementing the polarization-resolved side-scatter imaging mode and direct bright-field-illumination imaging mode was designed and built. Additional capabilities included the variable illumination pulse energy and sample volume. The smallest detectable droplet diameter was improved to 3–4 μm (from the nominal 10 μm value specified for the original CDMS). Discrimination between water droplets and ice particles was experimentally demonstrated. The Phase I prototype was extensively tested and calibrated in the laboratory and also tested in the Pi Cloud Chamber at Michigan Technological University (MTU). The two intensive experimental campaigns at MTU provided unique opportunities of testing the CDMS-2 laboratory prototype under realistic warm and mixed-phase cloud conditions (stable for long periods of time), testing different sampling modes and intercomparing the CDMS-2 prototype to other co-located cloud characterization instruments. The Phase I project successfully demonstrated the feasibility of the proposed technology and identified the engineering challenges of designing a field deployable prototype instrument in Phase II. The Phase I study provides a solid basis for development, characterization and field-testing of the proposed advanced cloud probe with adaptive sampling in Phase II followed by commercialization of the technology in Phase III.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

An Evaluation of Dynamical Downscaling Methods Used to Project Regional Climate Change

In the past decade, dynamical downscaling using “pseudo-global-warming” (PGW) techniques has been applied frequently to project regional climate change. Such techniques generate signals by adding mean global climate model (GCM)-simulated climate change signals in temperature, moisture, and circulation to lateral and surface boundary conditions derived from reanalysis. An alternative to PGW is to downscale GCM data directly. This technique should be advantageous, especially for simulation of extremes, since it incorporates the GCM's full spectrum of changing synoptic-scale dynamics in the regional solution. Here, we test this assumption, by comparing simulations in Europe and Western North America. We find that for warming and changes in temperature extremes, PGW often produces similar results to direct downscaling in both regions. For mean and extreme precipitation changes, PGW generally also performs surprisingly well in many cases. Moisture budget analysis in the Western North America domain reveals why. Large fractions of the downscaled hydroclimate changes arise from mean changes in large-scale thermodynamics and circulation, that is, increases in temperature, moisture, and winds, included in PGW by design. The one component PGW may have difficulty with is the contribution from changes in synoptic-scale variability. When this component is large, PGW performance could be degraded. Global analysis of GCM data shows there are regions where it is large or dominant. Hence, our results provide a road map to identify, through GCM analyses, the circumstances when PGW would not be expected to accurately regionalize GCM climate signals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality

Abstract. This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. The WBM was first published in 1989; here, we describe the first fully open-source WBM version (v.1.0.0). Earlier descriptions of WBM methods provide the foundation for the most recent model version that is detailed here. We present an overview of the model functionality, utility, and evaluation of simulated global river discharge and irrigation water use. This new version adds a novel suite of water source tracking modules that enable the analysis of flow-path histories on water supply. A key feature of WBM v.1.0.0 is the ability to identify the partitioning of sources for each stock or flux within the model. Three different categories of tracking are available: (1) primary inputs of water to the surface of the terrestrial hydrologic cycle (liquid precipitation, snowmelt, glacier melt, and unsustainable groundwater); (2) water that has been extracted for human use and returned to the terrestrial hydrologic system; and (3) runoff originating from user-defined spatial land units. Such component tracking provides a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior. We find that WBM v.1.0.0 simulates global river discharge and irrigation water withdrawals well, even with default parameter settings, and for the first time, we are able to show how the simulation arrives at these fluxes by using the novel tracking functions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

How Moist and Dry Intrusions Control the Local Hydrologic Cycle in Present and Future Climates

Models disagree on the magnitude of the intensification of hydrologic cycle extremes under climate change. Changes in these extremes are expected to scale with the Clausius-Clapeyron relation but may locally diverge due to the uncertain response of the atmospheric general circulation. Recent work has identified that the circulation contribution (the “dynamic” component) is the largest contributor to uncertainty in changes in hydrologic cycle extremes. In this work, we develop an exact budget for changes in wet and dry extremes based on changes in the associated moist and dry intrusions behind those extremes. We decompose changes into four contributors: thermodynamic, low-level convergence, anomalous meridional moisture advection, and zonal wave activity convergence. In analyzing these changes, we show that low-level convergence is the main dynamic contributor to the tropical and subtropical annual response, consistent with weakening of the Hadley circulation, and we show that anomalous meridional moisture advection is the primary dynamic contributor annually for the extratropics, consistent with storm track changes. We achieve a clean separation between dynamic and thermodynamic contributions through an empirical scaling between extremes and their associated wave activities, showing again that shifting storm tracks and expanding subtropics are critical to the dynamic response of the transient hydrologic cycle. We infer that changes in the large-scale circulation are predominantly responsible for the annual, dynamic response of hydrologic cycle extremes and therefore an important contributor to uncertainty in future projections.

Hydrological cycle, hydrological extremes, dynamic↗
DOE OSTI2023

Understanding and Predictability of Integrated Mountain Hydroclimate (Workshop Report)

Mountainous systems cover approximately 23% of Earth’s land and are distributed across all continents. They can capture and store atmospheric moisture that is then cycled through the terrestrial surface and subsurface system, released to downstream communities, and cycled back to the atmosphere. Mountain hydroclimate—characterized by steep gradients, geological, ecological, and biogeochemical diversity—is influenced by topographic forcing and elevated warming and susceptible to large subseasonal to multidecadal variability and rapid changes. Terrestrial hydrological and biogeochemical cycles also experience cascading effects from global warming impacts, such as multidecadal declines in mountain snowpack, longer growing seasons, and increased frequency and severity of extreme events like droughts and wildfires. However, little is known about the effects of these impacts and their feedbacks on climate systems and surface-subsurface compartments. Also unknown are the full implications of changing hydroclimate and extreme events on hydro biogeochemical cycles across atmosphere, terrestrial, and human systems in mountain regions and beyond. This knowledge gap is critical, given human reliance on mountain systems for stable water supply and quality. Mountain systems’ increasing vulnerability to climate change and human perturbations motivates the need to improve understanding of integrated mountain hydroclimate (IMHC) systems and their feedbacks and impacts on humans across scales. However, due to large heterogeneity and strong gradients, coupled natural-human processes in mountain regions present significant challenges for observations, modeling, predictions, and projections. Motivated by gaps in mountain hydroclimate understanding, observations, and modeling and the need for credible projections of future changes, the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program organized a virtual workshop on “Understanding and Predictability of Integrated Mountain Hydroclimate.” Sponsored by BER’s Earth and Environmental Systems Sciences Division (EESSD), the workshop aimed to inform and catalyze EESSD’s growing interests in enhancing predictive understanding of IMHC. Organizers structured the workshop to identify (1) knowledge gaps, (2) observational and modeling challenges, (3) short-term (1 to 3 years) and long-term (10 years and beyond) research opportunities, and (4) strategies for fostering collaboration and coordination. To address the outstanding challenges of IMHC, the workshop included two sessions organized by disciplinary, cross-disciplinary, and crosscutting science topics. The disciplinary and cross-disciplinary topics focused on essential IMHC elements: atmosphere, terrestrial, and human systems and their interactions. Breakout sessions on disciplinary and cross-disciplinary topics facilitated identification of crosscutting topics and central emerging themes. Session 1 focused on connecting existing DOE investments to accelerate progress related to scientific challenges in understanding mountain hydroclimate. In Session 2, participants further explored key Session 1 takeaways through the lens of multiagency collaborations and coordination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO 2 , ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm 2 for the ARMP and 0.26 g/cm 2 for the AERONET datasets, with bias of 0.072 g/cm 2 and 0.096 g/cm 2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Land Surface Modeling 2.0 for agricultural climate change impact assessments

Focus Area: This white paper addresses DOE AI4ESP focus area #2 by providing a sketch blueprint for a next generation, hybrid AI/process-based global Land Surface Modeling (LSM) framework to improve projections of climate change impacts on the land surface system including agriculture and the hydrological cycle. Science Challenge: Climate change impacts on agriculture are highly uncertain: -50% to +150% global production changes for major grains under high-end climate change, for example. The core challenge in land surface system predictability is the large gap between the scale at which the relevant biological processes act and the scale at which the risks need to be assessed. Conventional empirical and process-based modeling approaches are insufficient. A new multi-scale modeling paradigm which employs AI/ML to learn from new streams of remote sensing data and targeted ’gene-to-global’ simulations is needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Future precipitation increase constrained by climatological pattern of cloud effect

The fractional increase in global mean precipitation (?P ¯/P ¯) is a first-order measure of the hydrological cycle intensification under anthropogenic warming. However, ?P ¯/P ¯ varies by a factor of more than three among model projections, hindering credible assessments of the associated climate impacts. The uncertainty in ?P ¯/P ¯ stems from uncertainty in both hydrological sensitivity (global mean precipitation increase per unit warming) and climate sensitivity (global mean temperature increase per forcing). Here, by investigating hydrological and climate sensitivities in a unified surface-energy-balance perspective, we find that both sensitivities are significantly correlated with surface shortwave cloud feedback, which is further linked to the climatological pattern of cloud shortwave effect. The observed pattern of cloud effect thus constrains both sensitivities and consequently constrains ?P ¯/P ¯. The 5%-95% uncertainty range of ?P ¯/P ¯ from 1979-2005 to 2080-2100 under the high-emission (moderate-emission) scenario is constrained from 6.34±3.53% (4.19±2.28%) in the raw ensemble-model projection to 7.03±2.59% (4.63±1.71%). The constraint thus suggests a higher most-likely ?P ¯/P ¯ and reduces the uncertainty by ~25%, providing valuable information for impact assessments.

Zhou, Wenyu↗
DOE OSTI2022

Simulated Hydrological Dynamics and Coupled Iron Redox Cycling Impact Methane Production in an Arctic Soil

The fate of organic carbon (C) in permafrost soils is important to the climate system due to the large global stocks of permafrost C. Thawing permafrost can be subject to dynamic hydrology, making redox processes an important factor controlling soil organic matter (SOM) decomposition rates and greenhouse gas production. In iron (Fe)-rich permafrost soils, Fe(III) can serve as a terminal electron acceptor, promoting anaerobic respiration of SOM and increasing pH. Current large-scale models of Arctic C cycling do not include Fe cycling or pH interactions. Here, a geochemical reaction model was developed by coupling Fe redox reactions and C cycling to simulate SOM decomposition, Fe(III) reduction, pH dynamics, and greenhouse gas production in permafrost soils subject to dynamic hydrology. In this study we parameterized the model using measured CO 2 and CH 4 fluxes as well as changes in pH, Fe(II), and dissolved organic C concentrations from oxic and anoxic incubations of permafrost soils from polygonal permafrost sites in northern Alaska, United States. In simulations of repeated oxic-anoxic cycles, Fe(III) reduction during anoxic periods enhanced CO 2 production, while the net effect of Fe(III) reduction on cumulative CH 4 fluxes depended on substrate C availability. With lower substrate availability, Fe(III) reduction decreased total CH 4 production by further limiting available substrate. With higher substrate availability, Fe(III) reduction enhanced CH 4 production by increasing pH. Our results suggest that interactions among Fe-redox reactions, pH and methanogenesis are important factors in predicting CH 4 and CO 2 production as well as SOM decomposition rates in Fe-rich, frequently waterlogged Arctic soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

High–Resolution Maps of Near–Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter–scale maps of near–surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near–surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k–fold cross validation, and the modeled extents of near–surface permafrost were compared to the observed extents at each site. At–a–site near–surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at–a–site prediction (70%–83%), yet they had greater accuracy when transferred to the non–training site (62%–78%). These models demonstrate the potential for integrating high–resolution spatial data and machine learning models to develop maps of near–surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Boundary Layer Controls on the Shallow-to-Deep Cumulus Transition (Final Technical Report)

This project advanced our understanding of the processes governing cumulus cloud formation and provides an improved observational basis for validating earth system models. To be specific, the project used laser- and radar-remote sensors to examine the physical properties of updrafts that rise from earth’s surface and initiate clouds deeper in the atmosphere. These updrafts comprise “thermals” and “plumes” and occur at small spatial and temporal scales (e.g., 10s of minutes, 100s of meters). These small scales preclude explicit representation in most earth system and climate models, and thus necessitate “sub-grid-scale” parameterization of updraft processes. The innovation of this project was to directly measure the size, shape, strength and water vapor content of these updrafts with Doppler and Raman lidars, respectively, and to link these updraft properties to cloud processes using vertically pointed weather radars. The resulting data sets comprise 100s of thousands of updrafts and thousands of clouds, which far exceeds previous efforts, and thereby provides a robust statistical and physical representation of these processes. From these large datasets the project produced a sequence of scientific analyses that: (1) Elucidate how variations in the turbulent structure of the convective boundary layer control shallow cumulus convection, (2) Quantify the upward transport of water vapor to cloud base via thermals and plumes, (3) Validate large-eddy simulations of updrafts and shallow convective clouds, (4) Demonstrate a size-to-strength relationship between updraft width and updraft speed, and (5) Demonstrate how updrafts interact with the stability at the top of the convective boundary layer to modulate the depth and vigor of convective clouds. These results have been disseminated via several published journal articles, academic theses, and conference presentations. Collectively these results contribute to the Atmospheric System Research (ASR) program’s goal to “improve understanding of the key cloud, aerosol, precipitation, and radiation processes that affect the Earth’s radiative balance and hydrological cycle, particularly processes that limit the predictive ability of regional and global earth system models”.

54 ENVIRONMENTAL SCIENCES↗