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

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps

We present a preliminary investigation of the use of Multi-Layer Perceptrons (MLP) and Recurrent Neural Networks (RNNs) as surrogates of parameter-to-prediction maps of computational expensive dynamical models. In particular, we target the approximation of Quantities of Interest (QoIs) derived from the solution of a Partial Differential Equations (PDEs) at different time instants. In order to limit the scope of our study while targeting a relevant application, we focus on the problem of computing variations in the ice sheets mass (our QoI), which is a proxy for global mean sea-level changes. We present a number of neural network formulations and compare their performance with that of Polynomial Chaos Expansions (PCE) constructed on the same data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A Simple Computational Proxy for Screening Magnetocaloric Compounds

Alternating cycles of isothermal magnetization and adiabatic demagnetization applied to a magnetocaloric material can drive refrigeration in very much the same manner as cycles of gas compression and expansion. The material property of interest in finding candidate magnetocaloric materials is ΔS M , their gravimetric entropy change upon application of a magnetic field under isothermal conditions. There is, however, no general method for screening materials for such an entropy change without actually performing the relevant, time- and effort-intensive magnetic measurements. Here we propose a simple computational proxy based on performing nonmagnetic and magnetic density functional theory calculations on magnetic materials. This proxy, which we term the magnetic deformation Σ M , is a measure of how much the unit cell deforms comparing with the relaxed structures with and without the inclusion of spin polarization. Σ M appears to correlate very well with experimentally measured magnetic entropy change values. Here, the proxy has been tested against 33 ferromagnetic materials with known ΔS M , including nine materials newly measured for this study. It has then been used to screen 134 ferromagnetic materials for which the magnetic entropy has not yet been reported, identifying 30 compounds as being promising for further study. As a demonstration of the effectiveness of our approach, we have prepared one of these compounds and measured its isothermal entropy change. MnCoP, with a T C of 575 K, shows a maximal ΔS M of –6.0 J kg –1 K –1 for an H = 5 T applied field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Disruption of Commercial Solar Inverter System by TLS Proxy Man-in-the-Middle Attack

Transport Layer Security (TLS) is a cryptographic protocol that encrypts communication data, providing end-to-end communication encryption and authentication. Currently, TLS is widely adopted for securing communication between servers and end devices, including solar inverter systems. Therefore, users/operators can securely access the solar inverters through a web user interface (WebUI) application programmable interface (API) on a PC or server over TLS-enabled Wi-Fi or Ethernet. However, the security of the TLS-based network becomes compromised if it is breached by a TLS proxy man-in-the-middle (MITM) exploit. This report explores potential vulnerabilities in a commercial solar inverter system that leverages a TLS proxy MITM and discusses the impacts through assume-breached penetration testing. Furthermore, the paper explores recommended mitigation methods against the TLS proxy MITM exploit in solar inverters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Datasets for DOE 2023 Communities LEAP

This data is aligned to eligibility criteria outlined in the United States Department of Energy (DOE) 2023 Communities LEAP (Local Energy Action Program). Please visit the LEAP website (https://www.energy.gov/communitiesLEAP/communities-leap) to learn more about LEAP and gain additional contextual information for how these data may be used. The data provided approximates how the eligibility criteria apply at the census tract level across the United States. This EDX submission provides access to information pertaining to each of the four eligibility criteria outlined (average energy burden, percent low income, communities with a historic economic dependence on fossil fuel industrial facilities, and disadvantaged communities) for all census tracts within the 50 U.S. States, the District of Columbia (D.C.), and Puerto Rico. This information can be access in a detailed excel spreadsheet or through the linked interactive web application (https://arcgis.netl.doe.gov/portal/apps/experiencebuilder/experience/?id=2a77f443d72b4a4d82474b3ffe33b8cd). Please note that while these data are provided at the census tract level, census tracts do not necessarily have the same physical boundaries as a community but were used as they provide the closest proxy based on publicly available information collected using an empirically robust method. U.S. territories are not listed but are eligible to apply to Communities LEAP. As stated in the Opportunity Announcement, applying communities should describe how they meet the eligibility criteria in their application even if these data do not specifically show that they are eligible.

2023↗
DOE OSTI2016

An evaluation of paired δ 18 O and ( 234 U/ 238 U) 0 in opal as a tool for paleoclimate reconstruction in semi-arid environments

Authigenic opal deposits are widespread in semi-arid and arid environments and often contain high uranium concentrations that facilitate high-precision dating by U-series and U-Pb geochronology. However, despite their ubiquity and utility for placing temporal constraints on surficial processes, the potential for authigenic opal deposits to be used as archives of past climate variability remains largely unexplored compared with more frequently used authigenic carbonate deposits. Here we investigate the potential for authigenic opal deposits to provide records of paleoclimate and paleohydrologic variability through the development of a combined record of uranium (( 234 U/ 238 U) 0 ) and oxygen (δ 18 O) isotope variability using in situ ion microprobe methods on opal deposits from Pinnacles National Park, California that precipitated from approximately 16 to 3 ka. Pinnacles opal ( 234 U/ 238 U) 0 suggests a moderate decrease in net infiltration from the early to middle Holocene, coincident with the timing of increasing aridity inferred from Sierra Nevada lake records, while Pinnacles opal δ18O indicates a relative increase in subtropical storms at the same time. A shift to more North Pacific sourced storms commenced after ~ 5 ka coincident with the easing of aridity in Northern California and increased storminess in the Sierra Nevada as indicated by lake sediment records. These results suggest that useful insights about past hydrologic change can be gleaned from authigenic opal deposits, and that these deposits have the potential to enhance the spatial and temporal coverage of paleoclimate reconstructions in regions where other archives might be scarce. However, further study of the influence of water in opal on δ 18 O variability and additional in situ analysis of δ 18 O variability on longer-lived opal deposits with uniform growth histories are necessary to verify the controls on this potential proxy. Similarly, further investigation into short-term and spatial variability in ( 234 U/ 238 U)sw will improve qualitative and quantitative applications of ( 234 U/ 238 U) 0 in authigenic opal deposits, enhancing our ability to use this promising new paleoclimate archive.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Exploring Causal Relationships and Adjustment Timescales of Aerosol-Cloud Interactions in Geostationary Satellite Observations and CAM6 Using Wavelet Phase Coherence Analysis

We present for the first time within the cloud physics context, the application of wavelet phase coherence analysis to disentangle counteracting physical processes associated with the lead-lag phase difference between cloud-proxy liquid water path (LWP) and aerosol-proxy cloud droplet number concentration ( N d ) in an Eulerian framework using satellite-based observations and climate model outputs. This approach allows us to identify the causality and dominant adjustment timescales governing the correlation between LWP and N d . Satellite observations indicate a more prevalent positive correlation between daytime LWP and N d regardless of whether LWP leads or lags N d . The positive cloud water response, associated with precipitation processes, typically occurs within 1 hr, while the negative response resulting from entrainment drying, usually takes 2–4 hr. CAM6 displays excessively rapid negative responses along with overly strong negative cloud water response and insufficient positive response, leading to a more negative correlation between LWP and N d compared to observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The environmental controls on efficiency of enhanced rock weathering in soils

Abstract Enhanced rock weathering (ERW) in soils is a promising carbon removal technology, but the realistically achievable efficiency, controlled primarily by in situ weathering rates of the applied rocks, is highly uncertain. Here we explored the impacts of coupled biogeochemical and transport processes and a set of primary environmental and operational controls, using forsterite as a proxy mineral in soils and a multiphase multi-component reactive transport model considering microbe-mediated reactions. For a onetime forsterite application of ~ 16 kg/m 2 , complete weathering within five years can be achieved, giving an equivalent carbon removal rate of ~ 2.3 kgCO 2 /m 2 /yr. However, the rate is highly variable based on site-specific conditions. We showed that the in situ weathering rate can be enhanced by conditions and operations that maintain high CO 2 availability via effective transport of atmospheric CO 2 (e.g. in well-drained soils) and/or sufficient biogenic CO 2 supply (e.g. stimulated plant–microbe processes). Our results further highlight that the effect of increasing surface area on weathering rate can be significant—so that the energy penalty of reducing the grain size may be justified—only when CO 2 supply is nonlimiting. Therefore, for ERW practices to be effective, siting and engineering design (e.g. optimal grain size) need to be co-optimized.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Smart Proxy Modeling of SACROC CO2-EOR

Large CO2-enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO2-EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology.

Vida, Gholami↗
DOE OSTI2020

Angular scattering of protons through ultrathin graphene foils: Application for time-of-flight instrumentation

Space plasma instruments often rely on ultrathin carbon foils for incident ion detection, time-of-flight (TOF) mass spectrometry, and ionization of energetic neutral atoms. Angular scattering and energy loss of ions or neutral atoms in the foil can degrade instrument performance, including sensitivity and mass resolution; thus, there is an ongoing effort to manufacture thinner foils. Using new 3-layer graphene foils manufactured at the Los Alamos National Laboratory, we demonstrate that these are the thinnest foils reported to date and discuss future testing required for application in space instrumentation. We characterize the angular scattering distribution for 3–30 keV protons through the foils, which is used as a proxy for the foil thickness. We show that these foils are ~2.5–4.5 times thinner than the state-of-the-art carbon foils and ~1.6 times thinner than other graphene foils described in the literature. We find that the inverse relationship between angular scattering and energy no longer holds, reaffirming that this may indicate a new domain of beam–foil interactions for ultrathin (few-layer) graphene foils.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Global variations in critical drought thresholds that impact vegetation

ABSTRACT Identifying the thresholds of drought that, if crossed, suppress vegetation functioning is vital for accurate quantification of how land ecosystems respond to climate variability and change. We present a globally applicable framework to identify drought thresholds for vegetation responses to different levels of known soil-moisture deficits using four remotely sensed vegetation proxies spanning 2001–2018. The thresholds identified represent critical inflection points for changing vegetation responses from highly resistant to highly vulnerable in response to drought stress, and as a warning signal for substantial vegetation impacts. Drought thresholds varied geographically, with much lower percentiles of soil-moisture anomalies in vegetated areas covered by more forests, corresponding to a comparably stronger capacity to mitigate soil water deficit stress in forested ecosystems. Generally, those lower thresholds are detected in more humid climates. State-of-the-art land models, however, overestimated thresholds of soil moisture (i.e. overestimating drought impacts), especially in more humid areas with higher forest covers and arid areas with few forest covers. Based on climate model projections, we predict that the risk of vegetation damage will increase by the end of the twenty-first century in some hotspots like East Asia, Europe, Amazon, southern Australia and eastern and southern Africa. Our data-based results will inform projections on future drought impacts on terrestrial ecosystems and provide an effective tool for drought management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

ASC Tri-lab Co-design Level 2 Milestone Report 2015

In 2015, the three Department of Energy (DOE) National Laboratories that make up the Advanced Sci- enti c Computing (ASC) Program (Sandia, Lawrence Livermore, and Los Alamos) collaboratively explored performance portability programming environments in the context of several ASC co-design proxy applica- tions as part of a tri-lab L2 milestone executed by the co-design teams at each laboratory. The programming environments that were studied included Kokkos (developed at Sandia), RAJA (LLNL), and Legion (Stan- ford University). The proxy apps studied included: miniAero, LULESH, CoMD, Kripke, and SNAP. These programming models and proxy-apps are described herein. Each lab focused on a particular combination of abstractions and proxy apps, with the goal of assessing performance portability using those. Performance portability was determined by: a) the ability to run a single application source code on multiple advanced architectures, b) comparing runtime performance between \native" and \portable" implementations, and c) the degree to which these abstractions can improve programmer productivity by allowing non-portable implementation details to be hidden from the application developer. This report captures the work that was completed for this milestone, and outlines future co-design work to be performed by application developers, programming environment developers, compiler writers, and hardware vendors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Position-specific carbon isotope analysis of serine by gas chromatography/Orbitrap mass spectrometry, and an application to plant metabolism

Position-specific 13 C/ 12 C ratios within amino acids remain largely unexplored in environmental samples due to methodological limitations. We hypothesized that natural-abundance isotope patterns in serine may serve as a proxy for plant metabolic fluxes including photorespiration. Here we describe an Orbitrap method optimized for the position-specific carbon isotope analysis of serine to test our hypothesis and discuss the generalizability of this method to other amino acids. Position-specific carbon isotope ratios of serine were measured using a Thermo Scientific™ Q Exactive™ GC Orbitrap™. Amino acids were hydrolyzed from Arabidopsis biomass, purified from potential matrix interferences, and derivatized alongside standards. Derivatized serine (N,O-bis(trifluoroacetyl)methyl ester) was isolated using gas chromatography, trapped in a reservoir, and purged into the electron ionization source over tens of minutes, producing fragment ions containing different combinations of atoms from the serine-derivative molecule. The 13 C/ 12 C ratios of fragments with monoisotopic masses of 110.0217, 138.0166, and 165.0037 Da were monitored in the mass analyzer and used to calculate position-specific δ 13 C values relative to a working standard. This methodology constrains position-specific δ 13 C values for nanomole amounts of serine isolated from chemically complex mixtures. The δ 13 C values of fragment ions of serine were characterized with ≤1‰ precisions, leading to propagated standard errors of 0.7–5‰ for each carbon position. Position-specific δ 13 C values differed by up to ca 28 ± 5‰ between serine molecules hydrolyzed from plants grown under contrasting pCO 2 , selected to promote different fluxes through photosynthesis and photorespiration. The method was validated using pure serine standards characterized offline. Here this study presents the first Orbitrap-based measurements of natural-abundance, position-specific carbon isotope variation in an amino acid isolated from a biological matrix. We present a method for the precise characterization of isotope ratios in serine and propose applications probing metabolism in plants. We discuss the potential for extending these approaches to other amino acids, paving the way for novel applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling

The recent development of unconventional oil and gas (O&G) reservoirs has led to an abundant hydrocarbon supply, both domestically and globally. However, there is a continued push to develop new and innovative approaches to improve exploration and extraction efficiencies and overall well productivity moving forward. Substantial improvements in unconventional O&G development are expected through optimized well completion and stimulation strategies aimed at maximizing well productivity. Optimizing well designs will require tailoring to the distinctive geologic conditions present for any newly placed well. To better evaluate the impact of well design attributes and their associated interactions on productivity in a major unconventional play, multivariate machine learning-based models that use empirical datasets were developed. A gradient boosted regression tree (GBRT) algorithm was applied. GBRT has been narrowly investigated for O&G applications but enables straightforward parametric importance and influence evaluation, as well as assessment of parameter interaction effects. Models were trained on well design and locational parameters that serve as a proxy for variable geologic conditions to estimate two types of productivity indicator response variables strongly correlated to estimated ultimate recovery (EUR). The dataset utilized consists of over 7,000 well observations that cover the majority of the productive region of the Marcellus Shale. Model performance was evaluated and algorithm parameters tuned by analyzing the goodness-of-fit for simulated results against observed data in a cross-validation approach. Models were found capable of 73–79 percent prediction accuracy on held out testing data of gas equivalent production and can be used to inform future well design and placement decisions for increasing EUR per well and improving overall field-level recovery. Study results indicate that Marcellus well performance improves most with upscaling perforated interval lengths and water and proppant volumes per foot; but relative productivity improvements are spatially dependent across the play. Finally, optimal combinations of water and proppant on well performance were found to vary depending on well location, emphasizing the utility of data-driven models capable of broad application across a play of interest for informing tailored well design approaches prior to their field deployment.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2015

Function Proxies for Improved Resource Sharing in High Level Synthesis

The current generation of High Level Synthesis (HLS) tools usually generates hierarchical and modular designs, mimicking the structure of the call graph of the original high-level input specification. The standard approach is to progressively synthesize functions into modules by navigating the application call graph from the leaves up to the top function. In the synthesized architecture, function calls corresponds to the instantiation of the related module into the datapath generated for the caller. Our work introduces a methodology that enables sharing of (sub)modules across modules boundaries.

FPGA, High Level Synthesis, function proxies↗
DOE OSTI2024

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch-screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, in this work, we explore the application of an open-source pre-trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics-informed machine learning (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Reactivity of Shale to Supercritical CO 2 : Insights from Microstructural Characterization and Mineral Phase Evolution in Caney Shales for CCUS Applications

Understanding mineral–fluid interactions in shale under supercritical CO 2 (scCO 2 ) conditions is relevant for assessing long-term geochemical containment. This study characterizes mineralogical transformations and elemental redistribution in five Caney Shale samples serving as proxies for reservoir (R1, R2, R3) and caprock (D1, D2) facies, subjected to 30-day static exposure to pure scCO 2 at 60 °C and 17.23 MPa (2500 psi), with no brine or impurities introduced. SEM-EDS analyses were conducted before and after exposure, with mineral phases classified into silicates, carbonates, sulfides, and organic matter. Initial compositions were dominated by quartz (38–47 wt.%), illite (16–23 wt.%), carbonates (12–18 wt.%), and organic matter (8–11 wt.%). Post-exposure, carbonate loss ranged from 15 to 40% in reservoir samples and up to 20% in caprock samples. Illite and K-feldspar showed depletion of Fe 2+ , Mg 2+ , and K + at grain edges and cleavages, while pyrite underwent oxidation with Fe redistribution. Organic matter exhibited scCO 2 -induced surface alteration and apparent sorption effects, most pronounced in R2 and R3. Elemental mapping revealed Ca 2+ , Mg 2+ , Fe 2+ , and Si 4+ mobilization near reactive interfaces, though no secondary mineral precipitates formed. Reservoir samples developed localized porosity, whereas caprock samples retained more structural clay integrity. The results advance understanding of mineral reactivity and elemental fluxes in shale-based CO 2 sequestration.

geochemical CO2 sequestration↗
DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗