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DOE OSTI2015

Protected areas’ role in climate-change mitigation

Around the worl, 15.5 million km 2 of land are currently identified as protected areas, which provide society with many ecosystem services including climate-change mitigation. Combining a global database of protected areas, a reconstruction of global land-use history, and a global biogeochemistry model, we believe that protected areas currently sequester 0.5 Pg C annually, which is about one fifth of the carbon sequestered by all land ecosystems annually. Utilizing an integrated earth systems model to generate climate and land-use scenarios for the twenty-first century, we project that rapid climate change, similar to high-end projections in IPCC’s Fifth Assessment Report, would cause the annual carbon sequestration rate in protected areas to drop to about 0.3 Pg C by 2100. For the scenario with both rapid climate change and extensive land-use change driven by population and economic pressures, 5.6 million km 2 of protected areas would be converted to other uses, and carbon sequestration in the remaining protected areas would drop to near zero by 2100.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Phosphorus Speciation in Atmospherically Deposited Particulate Matter and Implications for Terrestrial Ecosystem Productivity

Chemical forms of P in airborne particulate matter (PM) are poorly known and do not correlate with solubility or extraction measurements commonly used to infer speciation. We used P X-ray absorption near-edge structure (XANES) and 31-P nuclear magnetic resonance (NMR) to determine P species in PM collected at four mountain sites (Colorado and California, USA). Organic P species dominated samples from high elevations, with organic P estimated at 65–100% of total P in bulk samples by XANES and 79–88% in extracted fractions (62–84% of total P) by NMR regardless of particle size (≥ 10 µm or 1–10 µm). Phosphorus monoester and diester organic species were dominant and present in about equal proportions, with low fractions of inorganic P species. By comparison, PM from low elevation contained mixtures of organic and inorganic P, with organic P estimated at 30-60% of total P. Intercontinental PM transport determined from radiogenic lead (Pb) isotopes varied from 0-59% (mean 37%) Asian-sourced Pb at high elevation, whereas stronger regional PM inputs were found at low elevation. Airborne flux of bioavailable P to high elevation ecosystems may be twice as high as estimated by global models, which will disproportionately affect net primary productivity.

31-P NMR↗

DOE OSTI2025

JAX-CanVeg: A Differentiable Land Surface Model

Land surface models consider the exchange of water, energy, and carbon along the soil-canopy-atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process-based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process-based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX-CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX-CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball-Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes (LE) and net ecosystem exchange (NEE), (b) improving the optimization trade-off when learning observations of both LE and NEE, and (c) benefiting a multi-layer canopy model setup. Our results show that the proposed hybrid model improved the simulations of LE and NEE at all sites, with an improved optimization trade-off over the process-based model. Additionally, the multi-layer canopy set benefited hybrid modeling at some sites. Anchored in differentiable modeling, our study provides a new avenue for modeling land-atmosphere interactions by leveraging the benefits of both data-driven learning and process-based modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Eddy Covariance Theory: A Review

Eddy covariance (EC), the gold standard for measuring ecosystem scale gas and heat exchanges, has transformed our understanding of the breathing of the biosphere, and thus global change biology. Despite numerous methodological improvements and insights gained from the technique, the community faces persistent challenges that have been present since the first EC measurements. Here, we review the theoretical developments underpinning EC. We present theoretical developments in four important areas that have relevance to EC measurements of the net ecosystem exchanges (NEE) of gases and heat from a single tower: (i) measuring the total vertical flux density, (ii) flux attenuation, (iii) coordinate rotations, and (iv) energy balance closure. Persistent problems with EC measurements, such as the inability to close the energy budget, led us to identify two priorities for revisiting the theory underlying: (i) sensible heat flux calculations, and (ii) constraining the mean vertical wind velocity. We present a framework for improved calculation of sensible heat flux derived from first principles of fluid mechanics and thermodynamics that considers coupled heat and mass transfer so that conservation of both is obeyed. These refinements are motivated by the need for unbiased measurements of energy and mass transfer between the land surface and atmosphere for ecosystem research and to validate satellite observations and land surface models.

ecosystem fluxes↗

DOE OSTI2018

Methane emissions from tree stems: a new frontier in the global carbon cycle

Tree stems from wetland, floodplain and upland forests can produce and emit methane (CH 4 ). Tree CH 4 stem emissions have high spatial and temporal variability, but there is no consensus on the biophysical mechanisms that drive stem CH 4 production and emissions. Here, we summarize up to 30 opportunities and challenges for stem CH 4 emissions research, which when addressed will improve estimates of magnitudes, patterns, drivers and trace the potential origin of CH 4 emissions. We identified the need (i) for both long–term high frequency measurements of stem CH 4 emissions to understand the fine scale processes, alongside rapid large–scale measurements designed to understand variability across individuals, species and ecosystems; (ii) to identify microorganisms and biogeochemical pathways associated with CH 4 production; and (iii) to develop a mechanistic model including passive and active transport of CH 4 from the soil–tree–atmosphere continuum. Addressing these challenges would help to constrain magnitudes and patterns of CH 4 emissions, and would allow for the integration of pathways and mechanisms of CH 4 production and emissions into process–based models. As a result, these advances will facilitate upscaling of stem CH 4 emissions to the ecosystem level and quantify the role of stem CH 4 emissions for the local–to–global CH 4 budget.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Pre-Cambrian roots of novel Antarctic cryptoendolithic bacterial lineages

Cryptoendolithic communities are microbial ecosystems dwelling inside porous rocks that are able to persist at the edge of the biological potential for life in the ice-free areas of the Antarctic desert. These regions include the McMurdo Dry Valleys, often accounted as the closest terrestrial counterpart of the Martian environment and thought to be devoid of life until the discovery of these cryptic life-forms. Despite their interest as a model for the early colonization by living organisms of terrestrial ecosystems and for adaptation to extreme conditions of stress, little is known about the evolution, diversity, and genetic makeup of bacterial species that reside in these environments. Using the Illumina Novaseq platform, we generated the first metagenomes from rocks collected in Continental Antarctica over a distance of about 350 km along an altitudinal transect from 834 up to 3100 m above sea level (a.s.l.). A total of 497 draft bacterial genome sequences were assembled and clustered into 269 candidate species that lack a representative genome in public databases. Actinobacteria represent the most abundant phylum, followed by Chloroflexi and Proteobacteria. The “Candidatus Jiangella antarctica” has been recorded across all samples, suggesting a high adaptation and specialization of this species to the harshest Antarctic desert environment. The majority of these new species belong to monophyletic bacterial clades that diverged from related taxa in a range from 1.2 billion to 410 Ma and are functionally distinct from known related taxa. Our findings significantly increase the repertoire of genomic data for several taxa and, to date, represent the first example of bacterial genomes recovered from endolithic communities. Their ancient origin seems to not be related to the geological history of the continent, rather they may represent evolutionary remnants of pristine clades that evolved across the Tonian glaciation. These unique genomic resources will underpin future studies on the structure, evolution, and function of these ecosystems at the edge of life.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

NASA 5.2%-Scale Semispan High Lift Common Research Model Wind Tunnel Test at the 14- by 22-Foot Subsonic Tunnel

A wind tunnel test of the NASA 5.2%-scale Semispan High Lift Common Research Model (CRM-HL) was conducted at the 14- by 22-Foot Subsonic Tunnel (14x22) in the fall of 2022. The main purpose of the test was a model checkout before shipping it overseas. The model was shipped to Germany in the spring of 2023, and the team wanted to be sure that instrumentation in the model was working, all of the model parts fit together well, and model changes could be done smoothly, as designed while installed in a wind tunnel. Force and moment and pressure data were collected during the test, and while these data were a secondary objective, they can be used for for tunnel-to-tunnel verification of the model, and for database supplementation for the NASA CRM-HL ecosystem. The test was run from August 15, 2022 through September 8, 2022. Eight configurations were tested at Mach 0.2 and 0.26, over an alpha range from -4deg to +24deg. All of the configurations that were tested accomplished testing of all model parts. This test campaign was a unique opportunity to test a complicated model design in a wind tunnel that is less expensive to test in and has easier model access than the tunnel that it was designed to run in. The test was invaluable in providing lessons learned about building up and running the model, and the model was returned to the model manufacturer from December 2022 to February 2023 for updates to make model changes easier, to make design fixes on the model, and to do clean up work on the model.

CRM-HL↗

NASA NTRSDate not supplied

NASA 5.2%-Scale Semispan High Lift Common Research Model Wind Tunnel Test at the 14- by 22-Foot Subsonic Tunnel

Semispan↗

DOE OSTI2024

Solar Farm’s Microclimates Effects: Predicting Abundance and Visiting Frequency of Pollinators

The solar farm is garnering attention for its contribution to ecosystem services, particularly in supporting pollinators. Although numerous studies have focused on the distinct microclimates of solar farms, there has been limited effort to validate and model the impact of these conditions on insects [1]. To effectively evaluate and leverage the ecosystem services from solar farms in policymaking, understanding how insects react to microclimate variations in diverse designs and management methods is essential. This study aims to validate and model the impact of temperature and shade at solar farms on pollinator populations and their frequency of pollination as well as compare the potential for increased pollination under different types of panels within a solar grazing setup.

Chung, Hyun Yong↗

DOE OSTI2022

UAS remote sensing (DJI Phantom 4 RTK platform): RGB orthomosaic, digital surface and canopy height models, plant functional type map, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *hdr,), ENVI image file (*.dat), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing

Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions model uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

NGEE Arctic Integrated Modeling (IM3): Improved snow-vegetation interaction

This data product represents the integration of new code capability for arctic tundra snow-vegetation-terrain interactions into the Energy Exascale Earth System Model (E3SM), through the E3SM Land Model (ELM) component. This code integration is the result of collaborative effort between the NGEE Arctic project and the E3SM project. The NGEE Arctic project developed a total of six Integrated Modeling (IM) modules informed by observations and experiments. New ELM capability represented by this data product (IM3) falls into three categories: 1) Downscaling from gridcell to topographic unit level when working through the existing coupler bypass code. 2) Four new parameters (taper, stocking, bendresist, and vegshape) have been added to ELM to allow for flexible definition of snow-vegetation interactions. 3) Vegshape and bendresist parameters are used to calculate the fraction of leaf area and/or stem area buried by snow for a given snow depth. This data record consists of a single document (pdf format) that describes the theoretical basis for the snow-vegetation-terrain interactions added to ELM, and describes the modifications made to the ELM code. The Methods section of this metadata record includes a link to the public E3SM code repository where the exact code modifications as integrated in E3SM can be accessed. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Thornton, Peter E [ORNL] (ORCID:0000000247595158)↗

DOE OSTI2017

XVIS: Visualization for the Extreme-Scale Scientific-Computation Ecosystem Final Scientific/Technical Report

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. The XVis project brought together collaborators from predominant DOE projects for visualization on accelerators and combining their respective features into a new visualization toolkit called VTK-m.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling

A recent acceleration of model-data synthesis activities has leveraged many terrestrial carbon (C) datasets, but utilization of soil respiration (RS) data has not kept pace with other types such as eddy covariance (EC) fluxes and soil C stocks. Here we argue that RS data, including non-continuous measurements from survey sampling campaigns, have unrealized value and should be utilized more extensively and creatively in data synthesis and modeling activities. We identify three major challenges in interpreting RS data, and discuss opportunities to address them. The first challenge is that when RS is compared to ecosystem respiration (RECO) measured from EC towers, it is not uncommon to find substantial mismatch, indicating one or both flux methodologies are unreliable. We argue the most likely cause of mismatch is unreliable EC data, and there is an unrecognized opportunity to utilize RS for EC quality control. The second challenge is that RS integrates belowground heterotrophic (RH) and autotrophic (RA) activity, whereas modelers generally prefer partitioned fluxes, and few models include an explicit RS output. Opportunities exist to use the total RS flux for data assimilation and model benchmarking methods rather than less-certain partitioned fluxes. Pushing for more experiments that not only partition RS but also monitor the age of RA and RH, as well as for the development of belowground RA components in models, would allow for more direct comparison between measured and modeled values. The third challenge is that soil respiration is generally measured at a very different resolution than that needed for comparison to EC or ecosystem- to global-scale models. Measuring soil fluxes with finer spatial resolution and more extensive coverage, and downscaling EC fluxes to match the scale of RS, will improve chamber and tower comparisons. Opportunities also exist to estimate RH at regional scales by implementing decomposition functional types, akin to plant functional types. We conclude by discussing the benefits that wide use of RS data will bring to model development, and database developments that will make RS data more robust, useful, and broadly available to the research community.

carbon cycle↗

DOE OSTI2017

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Year-end report FY17.

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO 2 Flux and its Inter-Annual Variability

Drylands occupy ~40% of the land surface and are thought to dominate global carbon (C) cycle inter-annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO 2 fluxes. Here, for this work, we address this gap using a Bayesian data assimilation system and 89 site-years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long-term carbon-climate feedbacks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem (Mid-year report FY15 Q2)

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Year-end report FY15 Q4.

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

97 MATHEMATICS AND COMPUTING↗