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NASA NTRS2012

Forecasting the Solar Drivers of Solar Energetic Particle Events

Large flares and fast CMEs are the drivers of the most severe space weather including Solar Energetic Particle Events (SEP Events). Large flares and their co-produced CMEs are powered by the explosive release of free magnetic energy stored in non-potential magnetic fields of sunspot active regions. The free energy is stored in and released from the low-beta regime of the active region's magnetic field above the photosphere, in the chromosphere and low corona. From our work over the past decade and from similar work of several other groups, it is now well established that (1) a proxy of the free magnetic energy stored above the photosphere can be measured from photospheric magnetograms, maps of the measured field in the photosphere, and (2) an active region's rate of production of major CME/flare eruptions in the coming day or so is strongly correlated with its present measured value of the free-energy proxy. These results have led us to use the large database of SOHO/MDI full-disk magnetograms spanning Solar Cycle 23 to obtain empirical forecasting curves that from an active region's present measured value of the free-energy proxy give the active region's expected rates of production of major flares, CMEs, fast CMEs, and SEP Events in the coming day or so (Falconer et al 2011, Space Weather, 9, S04003). We will present these forecasting curves and demonstrate the accuracy of their forecasts. In addition, we will show that the forecasts for major flares and fast CMEs can be made significantly more accurate by taking into account not only the value of the free energy proxy but also the active region's recent productivity of major flares; specifically, whether the active region has produced a major flare (GOES class M or X) during the past 24 hours before the time of the measured magnetogram.

Falconer, David A.↗

NASA NTRS2012

Forecasting Space Weather from Magnetograms

Large flares and fast CMEs are the drivers of the most severe space weather including Solar Energetic Particle Events (SEP Events). Large flares and their co-produced CMEs are powered by the explosive release of free magnetic energy stored in non-potential magnetic fields of sunspot active regions. The free energy is stored in and released from the low-beta regime of the active region s magnetic field above the photosphere, in the chromosphere and low corona. From our work over the past decade and from similar work of several other groups, it is now well established that (1) a proxy of the free magnetic energy stored above the photosphere can be measured from photospheric magnetograms, maps of the measured field in the photosphere, and (2) an active region s rate of production of major CME/flare eruptions in the coming day or so is strongly correlated with its present measured value of the free-energy proxy. These results have led us to use the large database of SOHO/MDI full-disk magnetograms spanning Solar Cycle 23 to obtain empirical forecasting curves that from an active region s present measured value of the free-energy proxy give the active region s expected rates of production of major flares, CMEs, fast CMEs, and SEP Events in the coming day or so (Falconer et al 2011, Space Weather, 9, S04003). For each type of event, the expected rate is readily converted to the chance that the active region will produce such an event in any given forward time window of a day or so. If the chance is small enough (e.g. <5%), the forecast is All Clear for that type of event. We will present these forecasting curves and demonstrate the accuracy of their forecasts. In addition, we will show that the forecasts for major flares and fast CMEs can be made significantly more accurate by taking into account not only the value of the free energy proxy but also the active region s recent productivity of major flares; specifically, whether the active region has produced a major flare (GOES class M or X) during the past 24 hours before the time of the measured magnetogram.

Falconer, David A.↗

NASA NTRS2016

A Model-Model and Data-Model Comparison for the Early Eocene Hydrological Cycle

A range of proxy observations have recently provided constraints on how Earth's hydrological cycle responded to early Eocene climatic changes. However, comparisons of proxy data to general circulation model (GCM) simulated hydrology are limited and inter-model variability remains poorly characterised. In this work, we undertake an intercomparison of GCM-derived precipitation and P - E distributions within the extended EoMIP ensemble (Eocene Modelling Intercomparison Project; Lunt et al., 2012), which includes previously published early Eocene simulations performed using five GCMs differing in boundary conditions, model structure, and precipitation-relevant parameterisation schemes. We show that an intensified hydrological cycle, manifested in enhanced global precipitation and evaporation rates, is simulated for all Eocene simulations relative to the preindustrial conditions. This is primarily due to elevated atmospheric paleo-CO2, resulting in elevated temperatures, although the effects of differences in paleogeography and ice sheets are also important in some models. For a given CO2 level, globally averaged precipitation rates vary widely between models, largely arising from different simulated surface air temperatures. Models with a similar global sensitivity of precipitation rate to temperature (dP=dT ) display different regional precipitation responses for a given temperature change. Regions that are particularly sensitive to model choice include the South Pacific, tropical Africa, and the Peri-Tethys, which may represent targets for future proxy acquisition. A comparison of early and middle Eocene leaf-fossil-derived precipitation estimates with the GCM output illustrates that GCMs generally underestimate precipitation rates at high latitudes, although a possible seasonal bias of the proxies cannot be excluded. Models which warm these regions, either via elevated CO2 or by varying poorly constrained model parameter values, are most successful in simulating a match with geologic data. Further data from low-latitude regions and better constraints on early Eocene CO2 are now required to discriminate between these model simulations given the large error bars on paleoprecipitation estimates. Given the clear differences between simulated precipitation distributions within the ensemble, our results suggest that paleohydrological data offer an independent means by which to evaluate model skill for warm climates.

Boundary conditions↗

NASA NTRS2017

Meteoroid Bulk Density and Ceplecha Types

Determination of asteroid bulk density is an important aspect of NEO characterization, yet difficult to measure. As a fraction of meteoroids originate from asteroids (including some NEOs), a study of meteoroid bulk densities can potentially provide useful insights into the densities of NEOs and PHOs in lieu of mutual perturbations, satellite, or expensive spacecraft missions. NASA's Meteoroid Environment Office characterizes the meteoroid environment for the purpose of spacecraft risk and operations. To accurately determine the risk, a distribution of meteoroid bulk densities are needed. This is not trivial to determine. If the particle survives to the ground the bulk density can be directly measured, however only the most dense particles land on the Earth. The next best approach is to model the meteor's ablation, which is not straightforward. Clear deceleration is necessary to do this and there are discrepancies in results between models. One approach to a distribution of bulk density is to use a measured proxy for the densities, then calibrate the proxy with known densities from meteorite falls, ablation modelling, and other sources. An obvious proxy choice is the Ceplecha type, K(sub B), thought to indicate the strength of a meteoroid. KB is frequented cited as a good proxy for meteoroid densities, but we find it is poorly correlated with density. However, a distinct split by dynamical type was seen with Jovian Tisserand parameter, T(sub J), with meteoroids from Halley Type comets (T(sub J less than 2 ) exhibiting much lower densities than those originating from Jupiter and asteroids (T(sub J greater than 2).

Blaauw, R. C.↗

NASA NTRS2017

Meteoroid Bulk Density and Ceplecha Types

The determination of asteroid bulk density is an important aspect of Near Earth Object (NEO) characterization. A fraction of meteoroids originate from asteroids (including some NEOs), thus in lieu of mutual perturbations, satellites, or expensive spacecraft missions, a study of meteoroid bulk densities can potentially provide useful insights into the densities of NEOs and PHOs (Potentially Hazardous Objects). Meteoroid bulk density is still inherently difficult to measure, and is most often determined by modeling the ablation of the meteoroid. One approach towards determining a meteoroid density distribution entails using a more easily measured proxy for the densities, then calibrating the proxy with known densities from meteorite falls, ablation modelling, and other sources. An obvious proxy choice is the Ceplecha type, KB (Ceplecha, 1958), which is thought to indicate the strength of a meteoroid and often correlated to different bulk densities in literature. KB is calculated using the air density at the beginning height of the meteor, the initial velocity, and the zenith angle of the radiant; quantities more readily determined than meteoroid bulk density itself. Numerical values of K(sub B) are sorted into groups (A, B, C, etc.), which have been matched to meteorite falls or meteor showers with known composition such as the porous Draconids. An extensive survey was conducted to establish the strength of the relationship between bulk density and K(sub B), specifically looking at those that additionally determined K(sub B) for the meteors. In examining the modeling of high-resolution meteor data from Kikwaya et al. (2011), the correlation between K(sub B) and bulk density was not as strong as hoped. However, a distinct split by dynamical type was seen with Jovian Tisserand parameter (T(sub J)), with meteoroids from Halley Type comets (T(sub J) < 2) exhibiting much lower bulk densities than those originating from Jupiter Family comets and asteroids (T(sub J) > 2). Therefore, this work indicates that the dynamical classification of a meteoroid is a better indicator of the density than the strength proxy, a somewhat surprising result.

Blaauw, R. C.↗

NASA NTRSDate not supplied

TPSAS-NF1676L-10574-DND

The Infrared Atmospheric Sounding Interferometer (IASI) aboard of MetOP-A was successfully launched on 19 October 2006 and has been providing excellent data for more than 3 years. The IASI spectra can be converted into proxy data for the NPOESS Crosstrack Infrared Sounder (CrIS) through a rigorous mathematical transformation by taking into account of instrument characteristics of both the IASI and the CrIS. The 15-channel AMSU temperature sounder, and the 5-channel and Microwave Humidity Sounder (MHS) aboard of MetOP-A can be used to generate proxy data for the NPOESS Advanced Technology Mircrowave Sounder (ATMS). The CrIS and ATMS make up the Cross-track Infrared and Microwave Sounder Suite (CrIMSS). CrIMSS will primarily provide global temperature, moisture, and pressure profiles. We will describe the method used in generating the proxy data and show results of using the proxy data for evaluation of the NPOESS CrIMSS Environmental Data Record (EDR) retrieval algorithm.

Xu Liu↗

NASA NTRSDate not supplied

TPSAS-NF1676L-10898-DND

Xu Liu↗

NASA NTRSDate not supplied

Evaluating Retrieval Algorithm Climate Stability: Estimating 3D Optical Thickness Bias Distributions by Cloud Type

Detecting climate trends on large spatiotemporal scales requires accurate, stable measurements and stable retrieval algorithms. We strive to estimate how time-variant retrieval algorithm biases may impact trend detection. Here we focus on the 3D cloud optical thickness (τc) bias, which is among the largest in passive cloud retrieval algorithms. If this bias is time dependent, a possibility with potential decadal changes in cloud morphology, it may obscure genuine trends in τc. Although previous studies have evaluated the cloud- and sun-view geometry-dependent 3D τc bias on small spatial scales, before our current study none have evaluated the stability of this well-known bias on climate-relevant large spatiotemporal scales. These studies must estimate large scale distributions of the 3D τc bias by cloud type and estimate how cloud type amount may change between two climate states. We employ a novel approach to estimate large scale distributions of 3D τc using a proxy of the bias that quantifies the departure of clouds from satisfying the 1D radiative transfer assumption used in passive τc retrievals. This existing globally-distributed proxy is an angular consistency metric that was developed using fused Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) measurements. Calculating the 3D τc bias and the proxy, for known cloud fields enables us to establish statistical relationships between these two quantities, which can be used to calculate large-scale distributions of the 3D τc bias. This approach limits the number of 3D radiative transfer simulations required to only those needed to estimate a statistical relationship between the 3D τc bias for known cloud fields and a proxy of the bias. It is likely that future studies will be needed to evaluate retrieval algorithm bias stability for other geophysical variables as the community develops climate data records from satellite observations and their retrievals. This must be done in addition to monitoring and correcting measurement errors and uncertainties and understanding their impact on retrieved essential climate variables.

Yolanda Shea↗

NASA NTRS2021

A Testbed for Geomagnetic Data Assimilation

Geomagnetic data assimilation merges past and present-day observations of the Earth’s mag-netic field with numerical geodynamo models and the results are used to initialize forecasts.We present a new ‘proxy model’ that can be used to test, or rapidly prototype, numerical tech-niques for geomagnetic data assimilation. The basic idea for constructing a proxy is to capturethe conceptual difficulties one encounters when assimilating observations into high-resolution,3-D geodynamo simulations, but at a much lower computational cost. The framework of usingproxy models as ‘gate-keepers’ for numerical methods that could/should be considered formore extensive testing on operational models has proven useful in numerical weather predic-tion, where advances in data assimilation and, hence, improved forecast skill, are at least inpart enabled by the common use of a wide range of proxy models. We also present a largeset of systematic data assimilation experiments with the proxy to reveal the importance oflocalization and inflation in geomagnetic data assimilation.

K Gwirtz↗

DOE OSTI2023

Hardware assisted fine-grained data movement

A processor includes a task scheduling unit and a compute unit coupled to the task scheduling unit. The task scheduling unit performs a task dependency assessment of a task dependency graph and task data requirements that correspond to each task of the plurality of tasks. Based on the task dependency assessment, the task scheduling unit schedules a first task of the plurality of tasks and a second proxy object of a plurality of proxy objects specified by the task data requirements such that a memory transfer of the second proxy object of the plurality of proxy objects occurs while the first task is being executed.

Hassaan, Muhammad Amber↗

DOE OSTI2024

Hardware assisted fine-grained data movement

Hassaan, Muhammad Amber↗

DOE OSTI2026

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.

Zheng, Jake [Chicago U.] (ORCID:0009000218901379)↗

DOE OSTI2025

Clarifying the trophic state concept to advance macroscale freshwater science and management

For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use‐specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to classify TS based on both autotrophic and heterotrophic production. Both classification approaches aim to characterize an ecosystem's function broadly, but with varying degrees of autotrophic and heterotrophic processes considered in those characterizations. Moreover, differing classification schemes can create inconsistent interpretations of ecosystem integrity. For example, the US Clean Water Act focuses exclusively on algal threats to water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non‐algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations. The usefulness of distilling complex information into a TS index is substantial such that usage inconsistencies should be explicitly addressed and resolved. To emphasize the consequences of diverging TS classification schemes, we present three case studies for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables, ecosystem type, as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re‐evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science.

classification↗

DOE OSTI2020

Mercury linked to Deccan Traps volcanism, climate change and the end-Cretaceous mass extinction

Mercury (Hg) anomalies linked to Large Igneous Provinces (LIP) volcanism have been identified in sediments across all five major mass extinctions in Earth's history. This study tests whether Hg in marine sediments is a reliable proxy linking Deccan Traps volcanic eruptions to late Maastrichtian global climate warming and the mass extinction at the Cretaceous-Paleogene boundary (KPB). Our primary test site is the Elles section in Tunisia, the auxiliary Global Stratotype Section and Point (GSSP) to El Kef. Elles has the most complete marine sedimentary record and a high average sedimentation rate of ~4.7 cm/ky. Here, we chose the Hor Hahar section in Israel to corroborate the geographic distribution of Hg fallout from Deccan volcanism. Reliability of the Hg proxy over the last 550 ky of the Maastrichtian to early Danian was evaluated based on high-resolution age control (orbital cyclostratigraphy), stable isotope climate record, Hg concentrations, biotic turnover and mass extinction. These results were correlated with the pulsed Deccan eruptive history constrained previously by U-Pb zircon geochronology. Our results support Hg as robust proxy for Deccan volcanism with large Hg spikes marking “extreme event” (EE) pulsed eruptions correlative with climate warming peaks separated by steady, less intense eruptions. Long-term global climate warming began near ~350 ky pre-KPB, reached maximum warming (3–4 °C) between 285 and 200 ky pre-KPB, followed by gradual cooling and rapid temperature drop between 45 and 25 ky pre-KPB. During the last 25 ky before the KPB, multiple Hg EE eruptions correlate with hyperthermal warming that culminated in the rapid mass extinction at Elles during ≤1000 years of the Cretaceous. These latest Cretaceous Hg peaks may correlate with massive, distal, Deccan-sourced lava flows (>1000 km long) that traversed the Indian subcontinent and flowed into the Bay of Bengal, bracketing the mass extinction. These results support Deccan volcanism as a primary driver of the end-Cretaceous mass extinction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A mechanistic understanding of oxygen isotopic changes in the Western United States at the Last Glacial Maximum

At the Last Glacial Maximum (LGM), records suggest drier conditions in the northwest United States and wetter conditions in the southwest United States relative to present-day as well as widespread changes in the isotopic composition of water. However, the mechanisms responsible for these changes remain ambiguous. Here, we explore differences in western United States hydroclimate between the LGM and preindustrial with a water isotope tracer enabled Earth System Model. We then use proxy forward models to compare simulated and recorded δ 18 O in speleothems. We find that the pattern of hydroclimate response in the western United States at the LGM relates to a combination of 1) increased frequency and southward shifted wintertime extratropical cyclones in the North Pacific, 2) greater rainout of moisture as it moves over the continent, and 3) reduced evaporation in the cooler LGM climate. The simulated lower δ 18 O of precipitation at the LGM relates predominantly to an increase in cool season moisture removal efficiency, with a secondary contribution from relatively more cool season precipitation. Both surface temperatures and North American ice sheets contribute to these hydroclimate changes at the LGM. Comparisons between δ 18 O from proxy forward models and speleothem records in the western United States show general agreement at the LGM, with increasing depletion moving towards the continental interior. Finally, this study highlights the similarities and differences between hydrologic and δ 18 O changes at the LGM and emphasizes the utility of model-proxy comparison for interpretation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Interrelationships among methods of estimating microbial biomass across multiple soil orders and biomes

Understanding the role of soil microbes is critical to ecosystem processes, and more thorough comparisons of measurement proxies for soil microbial biomass could broaden the inclusion of explicit microbial parameterization in soil carbon cycling and earth system models. We measured physical, chemical, and biological data from eight soil orders representing 11 major biomes and four climate regions. Four prominent methods to measure microbial abundance—chloroform fumigation extraction (CFE), total DNA yield, gene copy number by quantitative polymerase chain reaction (GCN), and phospholipid fatty acids (PLFA)—were compared to assess their relationships with each other and with soil characteristics. Correlations were observed when comparing methods, with CFE correlating strongly with total DNA yield, GCN, and PLFA; CFE with bacterial GCN and bacterial PLFA; and to a lesser extent, total PLFA and total DNA yield. Correlations improved with the removal of organic soils (Histosols, Gelisols). Comparisons involving extracted DNA were improved by correcting for clay content, due to DNA extraction inefficiencies in clay-rich soils. Correlations involving fungi (PLFA or GCN) were always less significant. These methods could serve as reliable, inter-relatable proxies for the estimation of total soil microbial biomass while recognizing that the proxies are less effective at parsing differences between bacteria and fungi. Here, we provide specific equations to relate measures of soil microbial biomass by these four different methods to enable microbial models to utilize a greater diversity of observed data sources in parameterizations and simulations. Caveats for the equations and their values are also discussed.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Diurnal and Seasonal Dynamics of Solar-Induced Chlorophyll Fluorescence, Vegetation Indices, and Gross Primary Productivity in the Boreal Forest

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides a powerful proxy for gross primary productivity (GPP). It is particularly promising in boreal ecosystems where seasonal downregulation of photosynthesis occurs without significant changes in canopy structure or chlorophyll content. The use of SIF as a proxy for GPP is complicated by inherent non-linearities due to both physical (illumination effects) and ecophysiological (light use efficiencies) controls at fine spatial (tower/leaf) and temporal (half-hourly) scales. Here, to study the SIF-GPP relationship, we investigated the diurnal and seasonal dynamics of continuous tower-based measurements of SIF, GPP, and common vegetation indices at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA over the course of two years. We find that SIF outperforms other vegetation indices as a proxy for GPP at all temporal scales but shows a non-linear relationship with GPP at a half-hourly resolution. At small temporal scales, SIF and GPP are predominantly driven by light and non-linearity between SIF and GPP is due to the light saturation of GPP. Averaged over daily and monthly scales, the relationship between SIF and GPP is linear due to a reduction in the observed PAR range. Seasonal changes in the light responses of SIF and GPP are driven by changes in light use efficiency which co-vary with changes in temperature, while illumination and canopy structure partially linearize the SIF-GPP relationship. Additionally, we find that the SIF-GPP relationship has a seasonal dependency. Our results help clarify the utility of SIF for estimating carbon assimilation in boreal forests.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Large Ensemble Particle Filter for Spatial Climate Reconstructions Using a Linear Inverse Model

Proxy records that document the last 2000 years of climate provide evidence for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) Particle filter (PF) that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamics of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR PF. Our results show that the LIMs allow for skillful ensemble forecasts at 1-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM-emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR PF.

54 ENVIRONMENTAL SCIENCES↗