Abstract
Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1- and 2-day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1-day (2-day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day-to-day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near-surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1-day R
2 = 0.39–0.48, 2-day R
2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models.
Abstract
The last deglaciation experienced the retreat of massive ice sheets and a transition from the cold Last Glacial Maximum to the warmer Holocene. Key simulation challenges for this period include the timing and extent of ice sheet decay and meltwater input into the oceans. Here, major uncertainties and forcing factors for the last deglaciation are evaluated. Two sets of transient simulations are performed based on the novel ice-sheet reconstruction PaleoMist and the more established GLAC1D. The simulations reveal that the proximity of the Atlantic meridional overturning circulation (AMOC) to a bifurcation point, where it can switch between on- and off-modes, is primarily determined by the interplay of greenhouse gas concentrations, orbital forcing and freshwater forcing. The PaleoMist simulation qualitatively replicates the Bølling-Allerød (BA)/Younger Dryas (YD) sequence: a warming in Greenland and Antarctica during the BA, followed by a cooling northern North Atlantic and an Antarctic warming during the YD.
Abstract
During the 3 month long eruption of Kı̄lauea volcano, Hawaii in 2018, the pre-existing summit caldera collapsed in over 60 quasi-periodic failure events. The last 40 of these events, which generated Mw > 5 very long period (VLP) earthquakes, had inter-event times between 0.8 and 2.2 days. These failure events offer a unique data set for testing methods for predicting earthquake recurrence based on locally recorded GPS, tilt, and seismicity data. In this work, we train a deep learning graph neural network (GNN) to predict the time-to-failure of the caldera collapse events using only a fraction of the data recorded at the start of each cycle. We find that the GNN generalizes to unseen data and can predict the time-to-failure to within a few hours using only 0.5 days of data, substantially improving upon a null model based only on inter-event statistics. Predictions improve with increasing input data length, and are most accurate when using high-SNR tilt-meter data. Applying the trained GNN to synthetic data with different magma-chamber pressure decay times predicts failure at a nearly constant stress threshold, revealing that the GNN is sensing the underling physics of caldera collapse. These findings demonstrate the predictability of caldera collapse sequences under well monitored conditions, and highlight the potential of machine learning methods for forecasting real world catastrophic events with limited training data.
Abstract
Increases in temperature and pressure caused by rapid burial of sediments seaward of the Sumatra subduction zone have been hypothesized to trigger dehydration reactions that diagenetically strengthen sediments and contribute to the formation of an over-pressured pre-décollement, which together facilitate the occurrence of large shallow earthquakes. We present paleomagnetic, rock magnetic, and electron microscopic analyses from drill cores collected offshore Sumatra at Site U1480 during IODP Expedition 362 that support this hypothesis. The older pre-fan units (Late Cretaceous to early Paleocene) were deposited when Site U1480 was moving rapidly northward with the Indian plate from a paleolatitude of 50° to 30°S, which would equate to expected absolute paleomagnetic inclinations of 70°–43°. Most of the older pre-fan sediments, however, have shallow observed inclinations (shallower than ±20°), indicating that the sediments were overprinted when Site U1480 was located near the paleoequator, as it has been since the early Oligocene. Electron microscopic observations reveal that the pre-existing detrital magnetite grains have undergone pervasive dissolution and alteration by hydrothermal fluids. The diagenesis observed is consistent with mineral dehydration, possibly driven by rapid burial of pelagic sediments by the ∼1250 m thick Nicobar Fan sequence. In addition, the elevated burial temperature also facilitated the smectite to illite conversion reaction. We hypothesize that chemical reactions resulted in the formation of fine-grained magnetite that records a chemical remanent magnetization overprint. This overprint is consistent with the alteration occurring after burial by the thick Nicobar Fan sequence sometime in the past few million years.
Abstract
Megathrust geometric properties exhibit some of the strongest correlations with maximum earthquake magnitude in global surveys of large subduction zone earthquakes, but the mechanisms through which fault geometry influences subduction earthquake cycle dynamics remain unresolved. Here, we develop 39 models of sequences of earthquakes and aseismic slip (SEAS) on variably-dipping planar and variably-curved nonplanar megathrusts using the volumetric, high-order accurate code tandem to account for fault curvature. We vary the dip, downdip curvature and width of the seismogenic zone to examine how slab geometry mechanically influences megathrust seismic cycles, including the size, variability, and interevent timing of earthquakes. Dip and curvature control characteristic slip styles primarily through their influence on seismogenic zone width: wider seismogenic zones allow shallowly-dipping megathrusts to host larger earthquakes than steeply-dipping ones. Under elevated pore pressure and less strongly velocity-weakening friction, all modeled fault geometries host uniform periodic ruptures. In contrast, shallowly-dipping and sharply-curved megathrusts host multi-period supercycles of slow-to-fast, small-to-large slip events under higher effective stresses and more strongly velocity-weakening friction. We discuss how subduction zones' maximum earthquake magnitudes may be primarily controlled by the dip and dimensions of the seismogenic zone, while second-order effects from structurally-derived mechanical heterogeneity modulate the recurrence frequency and timing of these events. Our results suggest that enhanced co- and interseismic strength and stress variability along the megathrust, such as induced near areas of high or heterogeneous fault curvature, limits how frequently large ruptures occur and may explain curved faults' tendency to host more frequent, smaller earthquakes than flat faults.
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, 2024
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Abstract
The 2024 Mw 7.5 Noto Peninsula, Japan, earthquake was initiated within the source region of intense swarm activity. To reveal the mainshock early process, we relocated the earthquake hypocenters and found that many key phenomena, including the mainshock initiation, foreshocks, swarm earthquakes, and deep aseismic slip, occurred at parts of a previously unrecognized fault in intricate fault network. This fault is subparallel (several kilometers deeper) to a known active fault, and the mainshock initiation and foreshocks occurred at the front of a 2-year westward swarm migration. The initiation location coincides with the destination of the upward migration of a deeper earthquake cluster via several smaller faults. Fluid supply, small earthquakes, and aseismic slip on the fault likely triggered the mainshock, leading to the first major rupture at the western region, propagating further to the west and east sides, resulting in an Mw7.5 event, exceeding 100 km in length.
Abstract
As a practical reflection of the opportunity window of Madden-Julian Oscillation (MJO), there are intermittent periods of relatively high forecasting skills, namely the forecast skill windows. Robust forecast skill windows are identified based on the subseasonal-seasonal reforecast database, during which the majority of models show high forecast skills. A total of 15 MJO forecast skill windows during 1993–2020 have been identified. Most of the forecast skill windows are closely associated with active MJO events with high amplitude. Whether a high-skill forecast window appears significantly depends on the magnitude of MJO intensity during the same period. The maintenance of active strong MJO events is potentially related with the warmer surface sea temperature anomalies in the western Pacific. Further research into such processes may unveil the MJO development mechanism and improve the MJO forecast skill.
Partition between supercooled liquid droplets and ice crystals in mixed-phase clouds based on airborne in situ observations
Flor Vanessa Maciel, Minghui Diao, and Ching An Yang
Atmos. Meas. Tech., 17, 4843–4861, https://doi.org/10.5194/amt-17-4843-2024, 2024
The partition between supercooled liquid water and ice crystals in mixed-phase clouds is investigated using aircraft-based in situ observations over the Southern Ocean. A novel method is developed to define four phases of mixed-phase clouds. Relationships between cloud macrophysical and microphysical properties are quantified. Effects of aerosols and thermodynamic and dynamical conditions on ice nucleation and phase partitioning are examined.
Atmospheric H2 observations from the NOAA Cooperative Global Air Sampling Network
Gabrielle Pétron, Andrew M. Crotwell, John Mund, Molly Crotwell, Thomas Mefford, Kirk Thoning, Bradley Hall, Duane Kitzis, Monica Madronich, Eric Moglia, Donald Neff, Sonja Wolter, Armin Jordan, Paul Krummel, Ray Langenfelds, and John Patterson
Atmos. Meas. Tech., 17, 4803–4823, https://doi.org/10.5194/amt-17-4803-2024, 2024
Hydrogen (H2) is a gas in trace amounts in the Earth’s atmosphere with indirect impacts on climate and air quality. Renewed interest in H2 as a low- or zero-carbon source of energy may lead to increased production, uses, and supply chain emissions. NOAA measurements of weekly air samples collected between 2009 and 2021 at over 50 sites in mostly remote locations are now available, and they complement other datasets to study the H2 global budget.
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, 2024
Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Abstract
This study analyzes hydrometeor evolution during rapid intensification (RI) and tropical cyclone (TC) intensity dependence using satellite data. Previous studies have suggested ice cloud water or non-convective precipitation as a predictor of RI from different perspectives. However, few studies have focused on the impact of TC intensity or comprehensive comparisons to identify better indicators. During RI, hydrometeor contents in weak TCs increase over the entire region, whereas they increase mainly in the inner-core region and decrease in advance in the outer-core region for strong TCs. Hydrometeor contents in the inner-core are higher in RI than in slow intensification, and their maxima location is related to TC intensity and intensification rate. Cloud water path (CWP) in the inner-core region is most correlated with the intensification rate, especially in weak TCs. Therefore, the CWP can serve as a predictor of RI and can be applied to all TC intensities.
Abstract
In this study, prominent dust source areas are identified along with their plume extent using high temporal frequency satellite observations. Hourly dust plume observations of the Dust Belt from geostationary-orbit satellites are analyzed for the 2017-12–2022-11 period. To identify dust source areas and their extents, we back-track plumes to their source, assessing source areas in terms of emission frequency, contribution, and plume extent patterns. This method advances over traditional source allocation techniques that rely on polar-orbiting satellites based on a few daily passes and meteorological wind fields for backtracking. Our findings indicate that Boreal summer is the most intense season for most sources, except in the Southern Sahara, which experiences winterly winds. Our analysis also reveals significant contributions from regions within the Sahara that experience expansive but infrequent dust storms, highlighting the importance of considering both frequency and magnitude in understanding dust emissions.
Abstract
Radio-echo sounding (RES) shows large-scale englacial stratigraphic folds are ubiquitous in Greenland's ice sheet. However, there is no consensus yet on how these folds form. Here, we use the full-Stokes code Underworld2 to simulate ice movements in three-dimensional convergent flow, mainly considering ice anisotropy due to a crystallographic preferred orientation, vertical viscosity and density gradients in ice layers, and bedrock topography. Our simulated folds show complex patterns and are classified into: large-scale folds (>100 m amplitude), small-scale folds (<<100 m) and basal-shear folds. The amplitudes of large-scale folds tend to be at their maximum in the middle of the ice column or just below, in accordance with observations in RES data. We conclude that ice anisotropy amplifies the perturbations in ice layers (mainly due to bedrock topography) into large-scale folds during flow. Density differences between the warm deep ice and cold ice above may enhance fold amplification.
Abstract
Ion-acoustic waves (IAWs) commonly occur near interplanetary (IP) shocks. These waves are important because of their potential role in the dissipation required for collisionless shocks to exist. We study IAW occurrence statistically at different heliocentric distances using Solar Orbiter to identify the processes responsible for IAW generation near IP shocks. We show that close to IP shocks the occurrence rate of IAW increases and peaks at the ramp. In the upstream region, the IAW activity is highly variable among different shocks and increases with decreasing distance from the Sun. We show that the observed currents near IP shocks are insufficient to reach the threshold for the current-driven instability. We argue that two-stream proton distributions and suprathermal electrons are likely sources of the waves.
Abstract
The Central Asian Orogenic Belt (CAOB) was formed by the aggregation and collage of numerous Paleozoic subduction-accretion assemblages and Precambrian microcontinental blocks. However, the tectonic nature of the southeastern CAOB remains controversial, which complicates the reconstruction of the Paleo-Asian Ocean. To address this issue, a deep seismic reflection survey was initiated across the southeastern CAOB and reveals broad gentle sub-horizontal reflectors in the middle-lower crust and a relatively transparent zone in the upper crust. Combining with the Precambrian geological outcrops and other geophysical features, we support a microcontinental block, the Xilinhot Block, existed in the Paleo-Asian domain. Thus, the Paleo-Asian Ocean was separated into two branches that underwent north-dipping and double-dipping oceanic plate subduction, respectively, to form the Hegenshan-Heihe and Solonker sutures. Multiple relics beneath Hegenshan-Heihe Suture indicate that multiple sets of unidirectional oceanic subduction-accretion and magmatism were important mechanisms of continental growth.
Abstract
Abduallah et al. (2024b, https://doi.org/10.1029/2023sw003824) proposed a novel approach using a deep neural network model, which includes a graph neural network and a bidirectional LSTM layer, named SYMHnet, to forecast the SYM-H index one and 2 hr in advance. Additionally, the network also provides an uncertainty quantification of the predictions. While the approach is innovative, there are some areas where the model's design and implementation may not align with best practices in uncertainty quantification and predictive modeling. We focus on discrepancies in the input and output of the model, which can limit the applicability in real-world forecasting scenarios. This comment aims to clarify these issues, offering detailed insights into how such discrepancies could compromise the model's interpretability and reliability, thereby contributing to the advancement of predictive modeling in space weather research.
Always on my mind: indications of post-traumatic stress disorder among those affected by the 2021 flood event in the Ahr valley, Germany
Marie-Luise Zenker, Philip Bubeck, and Annegret H. Thieken
Nat. Hazards Earth Syst. Sci., 24, 2837–2856, https://doi.org/10.5194/nhess-24-2837-2024, 2024
Despite the visible flood damage, mental health is a growing concern. Yet, there is limited data in Germany on mental health impacts after floods. A survey in a heavily affected region revealed that 28 % of respondents showed signs of post-traumatic stress disorder 1 year later. Risk factors include gender, serious injury or illness due to flooding, and feeling left alone to cope with impacts. The study highlights the need for tailored mental health support for flood-affected populations.
Abstract
This study presents an innovative approach to improving the accuracy and reducing the error convergence time of static Precise Point Positioning (PPP) in Global Positioning System (GPS) navigation. The research focuses on the impact of the high spatial and temporal resolution of a regional ionospheric data assimilation model on PPP over Taiwan. The study further evaluates the performance of both static PPP with the ionospheric information using commonly used models such as Klobuchar and International Reference Ionosphere (IRI), as well as a global ionospheric data assimilation model. Compared to the default IRI, the data assimilated IRI model can improve the overall ionospheric total electron content by approximately 83%. Additionally, it can significantly reduce horizontal positioning errors and shorten the error convergence time more than 52% for static PPP, even during geomagnetic storm events. The study concludes that the high resolution of a regional ionospheric data assimilation model can enhance the accuracy and reduce the error convergence time of PPP navigation and positioning. This research provides valuable insights for future studies in this field, especially in the development of more precise and efficient models for correcting ionospheric delay in GPS navigation.
Abstract
Fundamental processes in plasmas act to convert energies into different forms, for example, electromagnetic, kinetic and thermal. Direct derivation from the Vlasov-Maxwell equation yields sets of equations that describe the temporal evolution of magnetic, kinetic and internal energies in either the monofluid or multifluid frameworks. In this work, we focus on the main terms affecting the changes in kinetic energy. These are pressure-gradient-related terms and electromagnetic terms. The former account for plasma acceleration/deceleration from a pressure gradient, while the latter from an electric field. Although limited spatial and temporal deviations are expected, a statistical balance between these terms is fundamental to ensure the overall conservation of energy and momentum. We use in-situ observations from the Magnetospheric MultiScale (MMS) mission to study the relationship between these terms. We perform a statistical analysis of those parameters in the context of magnetic reconnection by focusing on small-scale Electron Diffusion Regions and large-scale Flux Transfer Events. The analysis reveals a correlation between the two terms in the monofluid force balance, and in the ion force and energy balance. However, the expected relationship cannot be verified from electron measurements. Generally, the pressure-gradient-related terms are smaller than their electromagnetic counterparts. We perform an error analysis to quantify the expected underestimation of gradient values as a function of the spacecraft separation compared to the gradient scale. Our findings highlight that MMS is capable of capturing energy and force balance for the ion fluid, but that care should be taken for energy conversion terms based on electron pressure gradients.
