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Resolving atomistic structure and oxygen evolution activity in nickel antimonates

The oxygen evolution reaction (OER) requires electrodes that are not only catalytically active, but also stable under harsh electrochemical environments to enable efficient, durable technologies. Our recent report of a stable amorphous Ni 0.5 Sb 0.5 O z OER photoanode established Ni–Sb–O as an important system for computational understanding of both the structural and catalytic behavior of these complex oxides. In the present work we show that Ni x Sb 1-x O z with x > 0.33 crystallizes into a previously unknown phase. Guided by experimental X-ray diffraction, here, we use density functional theory calculations to perform a prototype phase search to identify a broad family of stable and metastable mixed rutile and hexagonal-like phases for x = 0.33, 0.50, and 0.66 compositions. For the identified phases, we predict favorable oxygen vacancy formation energies for Ni-rich compositions under the reducing synthesis conditions which match measured Ni K-edge X-ray absorption spectra. The calculated overpotential for the most active site decreases with increasing Ni content, from 0.91 V (x = 0.33) to 0.49 V (x = 0.66), which captures the experimentally observed trend. We find the active site changes from the Ni–O–Sb bridge to a Ni–O–Ni bridge at increasing Ni concentrations, rather than the commonly studied singly under-coordinated sites. Finally, detailed Pourbaix analysis of the identified phases show excellent electrochemical stability, consistent with experimentally measured low metal ion concentrations in the electrolyte of photoelectrochemical cells. Collectively, our consideration of an ensemble of structures enables identification of the most catalytically prolific structural motifs, aiding the understanding of crystalline and amorphous catalysts and elucidating the co-optimization of activity and durability in nickel antimonates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Understanding and tuning organocatalysts for versatile condensation polymer deconstruction

Plastics are widely used for their durability and versatility, but recycling remains a major challenge, especially for mixed or contaminated waste. Mechanical recycling works well for clean, single-polymer streams like PET but has limited efficiency for complex waste streams. Chemical recycling, particularly glycolysis, is often employed to selectively deconstruct condensation polymers under mild conditions. This study explores catalyst design for glycolysis using linear free energy (Hammett) analysis to evaluate how catalyst structure influences polymer deconstruction. Polycaprolactone (PCL) is used as a model polyester due to its solubility and low deconstruction temperature. Triazabicyclodecene (TBD) paired with benzoic acid derivatives depicts a clear linear trend in depolymerization rates with Hammett values. TBD with p-aminobenzoic acid (PABA) stands out for its catalytic efficiency, thermal stability, and scalability, along with PABA's commercial availability as vitamin B-10. The TBD : PABA catalyst not only effectively breaks down PCL but also enables sequential deconstruction of polycarbonate, PET, and Nylon in mixed waste streams. These results highlight the value of Hammett-guided catalyst design and establish TBD : PABA as a promising, scalable organocatalyst for mixed plastic recycling, enabling recovery of individual polymer building blocks from blended waste and offering a practical route toward circular plastics.

Zheng, Jackie [Univ. of Tennessee, Knoxville, TN (↗

Big Adaptive Rotor Phase I Final Report

The Big Adaptive Rotor (BAR) project was initiated by the U.S. Department of Energy (DOE) in 2018 with the goal of identifying novel technologies that can enable large (>100 meter [m]) blades for low-specific-power wind turbines. Five distinct tasks were completed to achieve this goal: 1. Assessed the trends, impacts, and value of low-specific-power wind turbines; 2. Developed a wind turbine blade cost-reduction road map study; 3. Completed research-and-development opportunity screening; 4. Performed detailed design and analysis; and, 5. Assessed low-cost carbon fiber. These tasks were completed by the national laboratory team consisting of Sandia National Laboratories (Sandia), the National Renewable Energy Laboratory (NREL), and Lawrence Berkeley National Laboratory.

17 WIND ENERGY↗

A Comprehensive Economic Coal Transition in South Asia

Many countries are considering accelerating their coal transition. A coal transition refers to an energy sector’s shift from a reliance on coal toward an energy mix largely based on cleaner fuels and renewable energy sources. Such a transition is not just related to greenhouse gas emissions, but also encompasses a range of benefits, recognizing that global energy costs and options are changing. Since 2015, proposed new coal power capacity has dropped by three-quarters globally, leaving only a few countries that develop coal-fired power plants at scale (Littlecott et al., 2021). Historic steps were taken at the United Nations Climate Change 26th Conference of Parties (COP26) in Glasgow, as countries pledged to stop new coal builds, end international coal financing, phase down and phase out unabated coal use, and transition to clean energy. In South Asia, there have been several indicators suggesting that countries may be open to moving toward a coal transition. For example, the number of coal power plants under development across South Asia has decreased by 87% since 2015 (Littlecott et al., 2021). However, the challenges of assuring a just transition are substantial. Because coal plays a critical role in the energy and economic systems in South Asia, especially India, moving away from coal means realizing a broader country-wide economic and social transition. A comprehensive, integrated transition strategy for each state is thus needed urgently. This report briefly reviews the current trends and policies on coal in South Asian countries, develops a framework for a comprehensive economic coal transition, and assesses the opportunities and challenges of the transition in key countries. Several important findings emerge from the analysis. First, a coal transition can support overall economic growth and stability. Financial advantages to a well-planned coal transition include mitigating the risk of stranded assets and taking advantage of low-cost renewables. As a global coal transition proceeds, funds are being diverted from new unabated coal power plants, and utilization rates are declining. The likelihood that coal assets will become stranded is increasing, and the potential for future losses therefore increases as well. Second, coal imports in South Asia are rising. Of the coal consumed in Bangladesh, India, Nepal, and Sri Lanka, 32% is imported; this number increases to 94% when excluding India (International Energy Agency [IEA], 2021d). This illustrates a serious energy security risk. One example is the recent increase in coal prices in South Asia, to be discussed in Section 2.2.1. A diverse energy portfolio that incorporates local renewable energy can provide resilience in the face of changing commodity prices and availability. Third, the social benefits of a coal transition include positive health impacts and broader economic improvements in job creation, although assuring a just transition may be a challenge. Phasing out or phasing down coal can significantly reduce air pollutant emissions and therefore minimize associated premature mortality and improve life expectancy. Additional societal benefits of a coal transition include the high economy-wide potential for job creation, although it creates challenges in terms of reintegration and resettlement for coal miners and their communities.

01 COAL, LIGNITE, AND PEAT↗

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

60 APPLIED LIFE SCIENCES↗

The Effect of Environment on Galaxy Spiral Arms, Bars, Concentration, and Quenching

For a sample of 4378 nearby spiral and S0 galaxies, Yu & Ho used Fourier analysis of Sloan Digital Sky Survey images to show that the strengths of the spiral arms and the pitch angles of the arms are inversely correlated with central concentration. In the current study, we search for trends in the Yu & Ho spiral arm parameters with environment and specific star formation rate (sSFR). When comparing galaxies with similar concentrations, we do not find a significant difference in the arm strengths or pitch angles of spiral galaxies in clusters compared to field galaxies. When differences in concentration are taken into account, we also find no significant difference in the parameter f3 for cluster spirals compared to field spirals, where f3 is the normalized m = 3 Fourier amplitude. When concentration is held fixed, both arm strength and pitch angle are correlated with sSFR, but f3 is not. These relations support the suggestion by Davis et al. of a "fundamental plane" of spiral structure involving pitch angle, bulge stellar mass, and gas surface density. We discuss these results in terms of theories of spiral arm production and quenching in galaxies. To aid comparison with earlier studies based on Galaxy Zoo, we explore how the Yu & Ho parameters relate to similar parameters measured by Galaxy Zoo (i.e., f3 versus the number of arms, pitch angle versus winding parameter, and concentration versus bulge class).

79 ASTRONOMY AND ASTROPHYSICS↗

Deterministic Discrete Fracture Network (DFN) Model for the EGS Collab Project on the 4850 Level of the Sanford Underground Research Facility (SURF)

The EGS Collab is conducting hydraulic fracture stimulation and fluid circulation experiments in the Sanford Underground Research Facility (SURF) located in Lead, South Dakota. A total of eight ~60m-long subhorizontal boreholes were drilled from the 4850 Level (~1.5 km below the ground surface) into the crystalline rock of this former mine. Six of these holes are used for geophysical monitoring, one is used for hydraulic fracture stimulation, and the remaining hole was designed as a production borehole that receives water from the injection well via the induced and natural fracture system. The primary goal of creating the discrete fracture network model is to show that these modeling methods are critical for the development of enhanced geothermal systems (EGS). This includes the prediction of rock behavior during fracturing and during an extended period of water flow between the parallel injection and production boreholes. Understanding the results from the induced fracturing and flow is complicated by the presence of significant natural fractures that interact with the stimulation and/or flow pathways. The delineation and characterization of natural fractures is thus an important part of the project, and therefore a model of the Discrete Fracture Network (DFN) was developed on a deterministic basis. The DFN was populated using observations and interpretations integrated from drift (horizontal passageways that allow access in the underground) fracture mapping, analysis of core recovered from the eight boreholes, borehole televiewer logs and videos, and observations of flow between and within boreholes and in the drift. The natural fracture system is dominated by a pervasive northwest-trending, steeply dipping shear system that is identifiable in the drifts and the core. Hydraulic fracture stimulation, flow/tracer circulation tests, and geophysical monitoring revealed that the behavior of the injected water, and perhaps the growth of induced fractures, has been significantly influenced by the existing fractures identified in the DFN.

40 EE - Geothermal Technologies Office (EE-4G)↗

An Experimental Investigation of Students’ Learning Effects When Using a Simplified Nuclear Simulator

Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.

99 GENERAL AND MISCELLANEOUS↗

Evolution of intermittent filaments in the scrape-off layer of NSTX

Filamentary structures naturally arise from background turbulence in the scrape-off layer (SOL) of plasmas, leading to significant particle and heat transport that can degrade overall plasma confinement. This enhanced transport can contribute to unacceptably high heat loads on plasma-facing components. As such, understanding the physics of SOL plasma filaments is critical for predicting and mitigating their effects in future fusion devices. On the National Spherical Torus Experiment (NSTX), plasma filaments—commonly referred to as “blobs”—were investigated using the gas-puff imaging (GPI) diagnostic in the edge and SOL regions. The analysis involved identifying, segmenting, and tracking the characteristic contours of the blobs in each frame of the GPI video sequences. Their evolution was characterized through shape descriptors, velocity, and angular velocity derived from their contour coordinates. The results indicate that as the blob area increases, their shapes become more concave and less circular, suggesting reduced structural stability in larger blobs. This result aligns with previous theoretical results where it was shown that larger blobs are more susceptible to instabilities [Krasheninnikov et al., J. Plasma Phys. 74, 679–717 (2008) and D'Ippolito et al., Phys. Plasmas 18, 060501 (2011)]. A positive correlation was observed between radial velocity and radial position, suggesting radially outward acceleration of the filaments, potentially driven by decreasing viscous drag toward the far SOL. Interestingly, blobs in background SOL turbulence exhibited minimal spinning in contrast to filaments originating from edge localized modes, which show substantial rotation during their paths [Lampert et al., Phys. Plasmas 29, 102502 (2022)]. Statistical analysis of the solidity and total curvature shape descriptors, along with their temporal evolution, revealed relatively broad, near-Gaussian distributions. This suggests that blob morphology is strongly influenced by stochastic turbulent processes in the surrounding plasma environment. Blob parameters were also compared with bulk plasma and radial profile measurements. Notable trends were found between blob rotation and poloidal velocity with collisionality and line-integrated density. These findings contribute to a deeper understanding of blob dynamics and provide valuable insights for refining SOL turbulence models.

Covariance and correlation↗

Validation and Independent Uncertainty Analysis of the MIX-SOL-THERM-003 ICSBEP Benchmark

The International Criticality Safety Benchmark Evaluation Project (ICSBEP) was started in 1992 by the United States Department of Energy and later in 1995 became an international project with contributions from 22 countries. The project is now organized by the OECD (Organisation for Economic Co-operation and Development) Nuclear Energy Agency (NEA). In its most recent iteration, the ICSBEP handbook contains over five thousand evaluations of critical, near-critical, and subcritical experiments conducted in facilities all around the world. These benchmarks serve as valuable information for criticality safety engineers who can use them to validate calculation techniques and establish minimum subcritical margins for operations with fissionable materials. The benchmarks in the handbook are categorized by their fissile material composition, material form (oxide, solution, or metal), and fission energy spectra. This is especially useful for those looking for benchmarks similar to a system they are working on to compare methods and identify trends. The ICSBEP Handbook Uncertainty Guide is document outlining recommended practices and methods for determining uncertainties in these benchmarks. Quantifying these uncertainties thoroughly is crucial as it allows a higher degree of confidence that data used from them is valid and relevant. The guide stresses the importance of a thorough and well documented uncertainty analysis when evaluating an experiment. All measured values of a system, whether they be dimensions or material compositions, have a certain amount of uncertainty associated with them and can be analyzed one by one to determine their effects on the system. Many evaluated benchmarks in the handbook present this in detail, however some do not, mostly earlier evaluations performed in the 1990’s and early 2000’s. Recently at Los Alamos National Laboratory (LANL), the Nuclear Criticality Safety Division (NCSD) of LANL has been validating MCNP6.2 ® input files of criticality benchmarks for use by Whisper, a criticality safety code developed at LANL. This effort is also part of the OECD NEA Working Party on International Nuclear Data Evaluation Co-operation (WPEC) Subgroup 45, also known as Validation of Nuclear Data Libraries (VaNDaL). The goal of VaNDaL is to compile a set of validated simulation inputs for use in validating nuclear data and simulation codes. As part of these efforts, one of the benchmarks reviewed was the MIX SOL-THERM-003 ICSBEP benchmark. This paper provides an independent uncertainty analysis of this benchmark experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

The Pantheon+ Analysis: Forward Modeling the Dust and Intrinsic Color Distributions of Type Ia Supernovae, and Quantifying Their Impact on Cosmological Inferences

Abstract Recent studies have shown that the observed color distributions of Type Ia supernovae (SNe Ia) can be well described by a combination of a dust distribution and an intrinsic color distribution. Using the Pantheon+ sample of 1701 SN Ia, we apply a new forward-modeling fitting method (Dust2Dust) to measure the parent dust and color distributions, including their dependence on host-galaxy mass. At each fit step, the SN Ia selection efficiency is determined from a large simulated sample that is reweighted to reflect the proposed distributions. We use five separate metrics to describe the goodness of fit: distribution of fitted light-curve color c , cosmological residual trends with c , cosmological residual scatter with c , fitted color–luminosity relationship β SALT2 , and intrinsic scatter σ int . We present the results and the uncertainty in 12-dimensional space. Furthermore, we measure that the uncertainty on this modeling propagates to an upper threshold uncertainty in the equation of state of dark energy w of 0.014(1) for the Pantheon+ cosmology analysis and contributes negligible uncertainty to the Hubble constant H 0 . The Dust2Dust code is made publicly available at https://github.com/djbrout/dustdriver .

79 ASTRONOMY AND ASTROPHYSICS↗

The future evolution of energy-water-agriculture interconnectivity across the US

Abstract Energy, water, and agricultural resources across the globe are highly interconnected. This interconnectivity poses science challenges, such as understanding and modeling interconnections, as well as practical challenges, such as efficiently managing interdependent resource systems. Using the US as an example, this study seeks to define and explore how interconnectivity evolves over space and time under a range of influences. Concepts from graph theory and input–output analysis are used to visualize and quantify key intersectoral linkages using two new indices: the ‘Interconnectivity Magnitude Index’ and the ‘Interconnectivity Spread Index’. Using the Global Change Analysis Model (GCAM-USA), we explore the future evolution of these indices under four scenarios that explore a range of forces, including socioeconomic and technological change. Analysis is conducted at both national and state level spatial scales from 2015 to 2100. Results from a Reference scenario show that resource interconnectivity in the US is primarily driven by water use amongst different sectors, while changes in interconnectivity are driven by a decoupling of the water and electricity systems, as power plants become more water-efficient over time. High population and GDP growth results in relatively more decoupling of sectors, as a larger share of water and energy is used outside of interconnected sector feedback loops. Lower socioeconomic growth results in the opposite trend. Transitioning to a low-carbon economy increases interconnectivity because of the expansion of purpose-grown biomass, which strengthens the connections between water and energy. The results highlight that while some regions may experience similar sectoral stress projections, the composition of the intersectoral connectivity leading to that sectoral stress may call for distinctly different multi-sector co-management strategies. The methodology we introduce here can be applied in diverse geographical and sectoral contexts to enable better understanding of where, when, and how coupling or decoupling between sectors could evolve and be better managed.

Khan, Zarrar (ORCID:0000000281478553)↗

Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones

Unprecedented global vegetation greening during past decades is well known to affect annual and seasonal land surface temperatures (LST). However, the impact of observed vegetation cover change on diurnal LST across global climatic zones is not well understood. In this study, using global climatic time-series datasets, we investigated the long-term growing season daytime and nighttime LST changes globally and explored associated dominant contributors including vegetation and climate factors including air temperature, precipitation, and solar radiation. Results revealed asymmetric growing season mean daytime and nighttime LST warming (0.16 °C/10a and 0.30 °C/10a, respectively) globally from 2003 to 2020, as a result, the diurnal LST range (DLSTR) declined at 0.14 °C/10a. The sensitivity analysis indicated the LST response to changes in LAI, precipitation, and SSRD mainly concentrated during daytime instead of nighttime, however, which showed comparable sensitivities for air temperature. Combining the sensitivities results and the observed LAI and climate trends, we found rising air temperature contributes to 0.24 ± 0.11 °C/10a global daytime LST warming and 0.16 ± 0.07 °C/10a nighttime LST warming, turns to be the dominant contributor to the LST changes. Increased LAI cooled global daytime LST (–0.068 ± 0.096 °C/10a) while warmed nighttime LST (0.064 ± 0.046 °C/10a); hence LAI dominates declines in DLSTR trends (–0.12 ± 0.08 °C/10a), despite some daynight process variations across climate zones. In Boreal regions, reduced DLSTR was due to nighttime warming from LAI increases. In other climatic zones, daytime cooling, and DLSTR decline, was induced by increased LAI. Biophysically, the pathway from air temperature heats the surface through sensible heat and increased downward longwave radiation during day and night, while the pathway from LAI cools the surface by enhancing energy redistribution into latent heat rather than sensible heat during the daytime. These empirical findings of diverse asymmetric responses could help calibrate and improve biophysical models of diurnal surface temperature feedback in response to vegetation cover changes in different climate zones.

54 ENVIRONMENTAL SCIENCES↗

An AI-accelerated pathway for reproducible and stable halide perovskites

Halide perovskites (HPs) have remarkable optoelectronic properties, and in the last decade their photovoltaic power conversion efficiency and light-emitting diode efficiency have skyrocketed. Despite the surge in research on these burgeoning materials, two key challenges in the field remain: material irreproducibility and instability. Their behavior is especially dynamic in response to environmental stressors, due to complex interactions with the perovskite crystal lattice. Here, in this review, we survey the latest achievements in HP materials research accomplished with the assistance of artificial intelligence (AI), through the implementation of automated experimentation and machine learning (ML) data analysis. Automated synthesis and characterization tackle problems with material irreproducibility by systematically controlling parameters with very high precision, creating massive datasets, and allowing methodical comparisons from which unbiased conclusions can be drawn. AI can reveal otherwise unnoticed trends, inform future experiments with the highest potential information gain, and forecast future performance. The review concludes with a forward viewpoint of how human-assisted closed-loop laboratories and shared databases allow halide perovskite materials’ processing, properties, and performance to be potentially optimized with AI, accelerating the development of highly reproducible and stable optoelectronic devices.

Hering, Abigail R. [Univ. of California, Davis, CA↗

Formation and optical properties of indium nanoparticle arrays for deep-UV plasmonics

We utilize a combined computational-experimental approach to examine the influence of indium nanoparticle (NP) array distributions on deep-ultraviolet (UV) plasmon resonances. For photon energies < 5.7 eV, analysis of ellipsometric spectra reveals an increase in silicon reflectance induced by indium NP arrays on silicon. For various energies in the range 5.7–7.0 eV, a decrease in reflectance is induced by the NP arrays. Similar trends in reflectance are predicted from finite-difference time-domain (FDTD) simulations using NP size distributions extracted from atomic-force micrographs as input. In addition, in the energy range of 7.4–9.2 eV, the FDTD simulations reveal reflectance minima, characteristic of localized surface plasmon resonances. Here, electron energy-loss spectroscopy collected from individual indium NPs reveals the presence of LSPR at ≈ 8 eV, further supporting the promise of indium NP arrays on silicon for deep-UV plasmonics.

36 MATERIALS SCIENCE↗

LiG Metrology, Correlated Error, and the Integrity of the Global Surface Air-Temperature Record

The published 95% uncertainty of the global surface air-temperature anomaly (GSATA) record through 1980 is impossibly less than the 2σ = ±0.25 °C lower limit of laboratory resolution of 1 °C/division liquid-in-glass (LiG) thermometers. The ~0.7 °C/century Joule-drift of lead- and soft-glass thermometer bulbs renders unreliable the entire historical air-temperature record through the 19th century. A circa 1900 Baudin meteorological spirit thermometer bulb exhibited intense Pb X-ray emission lines (10.55, 12.66, and 14.76 keV). Uncorrected LiG thermometer non-linearity leaves 1σ = ±0.27 °C uncertainty in land-surface air temperatures prior to 1981. The 2σ = ±0.43 °C from LiG resolution and non-linearity obscures most of the 20th century GSATA trend. Systematic sensor-measurement errors are highly pair-wise correlated, possibly across hundreds of km. Non-normal distributions of bucket and engine-intake difference SSTs disconfirm the assumption of random measurement error. Semivariogram analysis of ship SST measurements yields half the error difference mean, ±½Δε 1,2, not the error mean. Transfer-function adjustment following a change of land station air-temperature sensor eliminates measurement independence and forward-propagates the antecedent uncertainty. LiG resolution limits, non-linearity, and sensor field calibrations yield GSATA mean ±2σ RMS uncertainties of, 1900–1945, ±1.7 °C; 1946–1980, ±2.1 °C; 1981–2004, ±2.0 °C; and 2005–2010, ±1.6 °C. Finally, the 20th century (1900–1999) GSATA, 0.74 ± 1.94 °C, does not convey any information about rate or magnitude of temperature change.

54 ENVIRONMENTAL SCIENCES↗

Attribution of the Australian bushfire risk to anthropogenic climate change

Abstract. Disastrous bushfires during the last months of 2019 and January 2020 affected Australia, raising the question to what extent the risk of these fires was exacerbated by anthropogenic climate change. To answer the question for southeastern Australia, where fires were particularly severe, affecting people and ecosystems, we use a physically based index of fire weather, the Fire Weather Index; long-term observations of heat and drought; and 11 large ensembles of state-of-the-art climate models. We find large trends in the Fire Weather Index in the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) since 1979 and a smaller but significant increase by at least 30 % in the models. Therefore, we find that climate change has induced a higher weather-induced risk of such an extreme fire season. This trend is mainly driven by the increase of temperature extremes. In agreement with previous analyses we find that heat extremes have become more likely by at least a factor of 2 due to the long-term warming trend. However, current climate models overestimate variability and tend to underestimate the long-term trend in these extremes, so the true change in the likelihood of extreme heat could be larger, suggesting that the attribution of the increased fire weather risk is a conservative estimate. We do not find an attributable trend in either extreme annual drought or the driest month of the fire season, September–February. The observations, however, show a weak drying trend in the annual mean. For the 2019/20 season more than half of the July–December drought was driven by record excursions of the Indian Ocean Dipole and Southern Annular Mode, factors which are included in the analysis here. The study reveals the complexity of the 2019/20 bushfire event, with some but not all drivers showing an imprint of anthropogenic climate change. Finally, the study concludes with a qualitative review of various vulnerability and exposure factors that each play a role, along with the hazard in increasing or decreasing the overall impact of the bushfires.

van Oldenborgh, Geert Jan (ORCID:0000000268989535)↗