Abstract
Hot Flow anomalies (HFAs), one of the most well-analyzed transient phenomena in the Earth's foreshock, are known as kinetic structures driven by tangential discontinuities (TDs). Recently, a 2-dimensional (2D) magnetohydrodynamics (MHD) model reproduced HFAs with either a high- or low-density core. Further investigation of an HFA with two cores observed by the Magnetospheric Multiscale (MMS) mission is reported. The observation via the Acceleration, Reconnection, Turbulence and Electrodynamics of the Moon's Interaction with the Sun (ARTEMIS) mission suggests this MHD HFA is associated with a foreshock density hole-like structure. The trailing flux tube in simulation may propagate with a TD in the foreshock. Our work suggests that HFAs with two low-density cores can also be achieved in MHD process. Results show the total ram pressure can be an excellent diagnostic for the presence of transient structures, such as HFAs, at the bow shock.
Abstract
The impact of the Madden-Julian oscillation (MJO) on the intraseasonal PNA (ISPNA) was investigated and was found to be modulated by the El Niño-Southern Oscillation (ENSO), which reasonably explains the skewness of the ISPNA during El Niño and La Niña winters. It was shown that the intensity and periodicity of the ISPNA was much stronger and slightly longer in La Niña winters than in the El Niño winters. The phase-locked association between the ISPNA and MJO indicate that this skewness was controlled by the MJO. The northward Rossby wave activities derived from the tropics associated with the MJO to the subtropical Pacific sector of the ISPNA clarified that the stronger intensity of the MJO convection in the western Pacific during the La Niña winters, as well as the slower eastward propagation of the MJO, led to the asymmetric intensity and period of the ISPNA in the two ENSO phases.
Abstract
Existing approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real-time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision-makers to improve current drought and water resource management.
Abstract
Dust records extracted from ice cores can facilitate the reconstruction of historical atmospheric dust levels and climate change. However, interpreting dust variations in ice cores is intricate because of the compounded influence of emission, transport, and deposition processes. This study investigated dust records retrieved from the Guliya ice cap drilled in 2015 on the West Tibetan Plateau using a mean trajectory transport and deposition model. Results showed that the Guliya dust concentration has exhibited a declining trend since the 1960s (−751 μg kg−1 yr−1). Applying an attribution approach, we discovered that low dust emission (80.3%) was the main cause of the drop in dust concentration, with changes related to transportation (5.2%) and deposition (14.5%) making only minor contributions. The weakening of surface wind speed in the desert and increasing precipitation in both the desert and glacier were the primary factors driving the decrease in Guliya dust concentration.
Abstract
The mid-latitude extreme weather disasters are often associated with explosively developing cyclones (ECs). Based on different vertical development characteristics, 4,608 ECs identified over the North Pacific in the cold season of 44 years of NCEP-CFSR reanalyzes are divided into four types of upward development and four types of downward development categories. ECs with vertical upward (downward) development follow a northeastward (nearly eastward) path, mainly explosively developing over the Northwest Pacific (Asia continent and Pacific). Furthermore, utilizing the piecewise potential vorticity inversion method reveals the synergetic forcing of the turbulent heat transport and baroclinicity in the lower troposphere, the latent heat release in the middle levels, the upper-level jet stream, and the downward intrusion of stratospheric potential vorticity on the ECs. Different configurations of these influences from the troposphere to the stratosphere result in the occurrences of eight types of ECs in the cold season over the North Pacific.
Abstract
We compare Fourier Amplitude Spectra of Fault Normal (FN) and Fault Parallel (FP) seismograms at near-fault sites for seven strike-slip earthquakes with moment magnitudes M
w ≥ 6. For all events we find large FN/FP ratios at low frequencies consistent with near-fault S-wave radiation patterns for strike-slip earthquakes. However, the difference diminishes with increasing frequency and FN/FP is about 1 above a transition frequency. The results may reflect small tensile/isotropic components in the earthquake rupture zones that homogenize the high-frequency radiation in different directions at near-fault sites. The FN/FP ratios at low frequencies and transition frequencies above which FN ∼ FP vary among the analyzed earthquakes and have no clear correlation with the magnitudes. The lack of correlation may signify a characteristic scale (e.g., process zone size, duration of source time function) controlling the isotropic radiation, and/or wave propagation and other effects that mask the source effects.
Abstract
Fault activity and structure are important factors for the assessment of seismic hazards. The Anninghe fault is one of the most active strike-slip faults in southwestern China but has been experiencing seismic quiescence for M > 4 earthquakes since the 1970s. To understand better the characteristics of its highly locked southern segment, we investigate seismicity and ground motion variability using recently deployed multi-scale dense arrays. Assisted by machine learning (ML) seismic phase picking and event discrimination models, we first compile a high-resolution catalog of local seismic events. We find limited earthquakes that occurred on the Anninghe fault, consistent with its generally acknowledged high locking degree. Whereas, most newly detected events appear within off-fault clusters, among which four are closely related to anthropogenic activities (e.g., mining blasts), and two neighboring faults host the remaining ones. We further apply an ML-based first-motion polarity (FMP) classifier and successfully obtain a reliable small earthquake focal mechanism, which agrees well with the geologically inferred north-south trending and eastward dipping of the Anninghe fault. Analyses of ground motion variations along two across-fault linear arrays show abrupt changes in FMPs and obvious frequency-dependent site amplifications near the mapped fault traces. It further suggests that, at finer scales, the damaged Anninghe fault zone may have split into two smaller damaged zones at shallower depths, resulting in a typical “flower-type” fault structure. The efficient workflow developed in this study can be well applied for the longer-term monitoring and better characterization of the southern Anninghe fault, or other similar regions.
Abstract
Understanding preferential flow in porous media holds substantial theoretical significance on the design and optimization of hydrocarbon exploitation in shale reservoir. Previous researches discussed the competition of imbibition front in layered porous media while the underlining mechanism for interfacial dynamics and induced displacement efficiency of multiphase flow remains ambiguous. In this paper, we investigate the spontaneous imbibition in dual permeable media and analyze the flux exchange between the neighboring porous zones with permeability contrast using dynamic pore network model. The impact of fluid viscosity ratio and permeability contrast on the spontaneous imbibition preference have been addressed, and finally a phase diagram for displacement efficiency has been obtained. The results reveal that the dual permeable structure enhances the invasion rate of wetting fluid in the low-permeable zone and induces unstable displacement patterns, leading to reduction of the long-term displacement efficiency. The interfacial pattern transition from stable displacement to unstable pattern in dual permeable media could be ascribed into the flux exchange between dual permeable zones, which shows a contrary impact on the fluid flow within the low-permeable zone under favorable and unfavorable viscosity ratios. The permeability contrast in dual permeable media intensifies this impact during spontaneous imbibition. These results help us to understand the occurrence and mutual interaction of multiphase flow in layered porous media, and provide a theoretical guidance for the hydrocarbon exploitation in shale reservoir.
Abstract
Open-vent volcanoes continuously emit magmatic products and frequently feature multiple adjacent craters. Temporal shifts of thermal emissions between craters are especially detectable by InfraRed satellites. Here, SENTINEL-2 and LANDSAT-8/9 Short Wave InfraRed (SWIR) high-spatial resolution satellite data, are combined to investigate 10 years (2013–2023) of thermal activity at Stromboli volcano (Italy). The correlation between Volcanic Radiative Power (VRP, in Watts) and Volcanic Radiative Energy (VRE, in Joules), retrieved by moderate MODIS and VIIRS Middle InfraRed (MIR) data, with the Thermal Index SWIR (TISWIR) data, allows us to quantify long-term series of heat fluxes (VRPSWIR) and energy (VRESWIR). Combining moderate and higher spatial resolution data and fitting cumulative trends of TISWIR with VREMIR allows to measure thermal activity sourced by single craters during Strombolian activity. Long-term results highlight that thermal emissions are clustered in the northern and southern parts of the crater terrace, with total energy emitted (∼12 × 1014 J) equally distributed. The thermal increase since April 2017 marked a reactivation of shallow magma transportation and an intensification of the activity after the 2014 eruption. Distinct thermal behaviors are shown by the NE, C, and SW craters, related to mechanisms of explosions. We found that short-term thermal variations match well those resolved by ground-based signals, and the NE crater as the most sensitive to the transition to higher-intensity activity. Our multispatial/multisensory investigation allows, for the first time, the long-term quantification of heat flux from Stromboli's craters, with an improved understanding of open-vent dynamics and a new approach to monitor multiple active craters.
Abstract
This paper conducts a multi-instrument and data assimilation analysis of the three-dimensional ionospheric electron density responses to the total solar eclipse on 08 April 2024. The altitude-resolved electron density variations over the continental US and adjacent regions are analyzed using the Millstone Hill incoherent scatter radar data, ionosonde observations, Swarm in situ measurements, and a novel TEC-based ionospheric data assimilation system (TIDAS) with SAMI3 model as the background. The principal findings are summarized as follows: (a) The ionospheric hmF2 exhibited a slight enhancement in the initial phase of the eclipse, followed by a distinct reduction of 20–30 km in the recovery phase of the eclipse. The hmF2 in the umbra region showed a post-eclipse fluctuation, characterized by wavelike perturbations of 10–25 km in magnitude and a period of ∼ ${\sim} $30 min. (b) There was a substantial reduction in ionospheric electron density of 20%–50% during the eclipse, with the maximum depletion observed in the F-region around 200–250 km. The ionospheric electron density variation exhibited a significant altitude-dependent feature, wherein the response time gradually delayed with increasing altitude. (c) The bottomside ionospheric electron density displayed an immediate reduction after local eclipse began, reaching maximum depletion 5–10 min after the maximum obscuration. In contrast, the topside ionospheric electron density showed a significantly delayed response, with maximum depletion occurring 1–2.5 hr after the peak obscuration.
Abstract
Solar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space exploration on many fronts. We have recently introduced a novel probabilistic ensemble model called the Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence and physical properties of SEPs. The occurrence forecasting, thoroughly discussed in a preceding paper (MEMPSEP-I by Chatterjee et al., 2024a, https://doi.org/10.1029/2023sw003568), is complemented by the work presented here, which focuses on forecasting the physical properties of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural Networks, which leverage a multi-variate data set comprising full-disc magnetogram sequences and numerous derived and in-situ data from various sources (MEMPSEP-III by Moreland et al., 2024, https://doi.org/10.1029/2023SW003765). Skill scores demonstrate that MEMPSEP exhibits improved predictions on SEP properties for the test set data with SEP occurrence probability above 50%, compared to those with a probability below 50%. Results present a promising approach to address the challenging task of forecasting SEP physical properties, thus improving our forecasting capabilities and advancing our understanding of the dominant parameters and processes that govern SEP production.
Abstract
We introduce a new multivariate data set that utilizes multiple spacecraft collecting in-situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998–2013), we identify 252 solar events (>C-class flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory, Solar and Heliospheric Observatory, and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a data set created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our ML pipeline. The data set has been used to drive the newly-developed Multivariate Ensemble of Models for Probabilistic Forecast of SEPs (MEMPSEP; see MEMPSEP-I (Chatterjee et al., 2024, https://doi.org/10.1029/2023SW003568) and MEMPSEP-II (Dayeh et al., 2024, https://doi.org/10.1029/2023SW003697) for accompanying papers).
Abstract
The Sun continuously affects the interplanetary environment through a host of interconnected and dynamic physical processes. Solar flares, Coronal Mass Ejections (CMEs), and Solar Energetic Particles (SEPs) are among the key drivers of space weather in the near-Earth environment and beyond. While some CMEs and flares are associated with intense SEPs, some show little to no SEP association. To date, robust long-term (hours-days) forecasting of SEP occurrence and associated properties (e.g., onset, peak intensities) does not effectively exist and the search for such development continues. Through an Operations-2-Research support, we developed a self-contained model that utilizes a comprehensive data set and provides a probabilistic forecast for SEP event occurrence and its properties. The model is named Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive data set (MEMPSEP-III by Moreland et al. (2024, https://doi.org/10.1029/2023SW003765)) of full-disc magnetogram-sequences and in situ data from different sources to forecast the occurrence (MEMPSEP-I—this work) and properties (MEMPSEP-II by Dayeh et al. (2024, https://doi.org/10.1029/2023SW003697)) of a SEP event. This work focuses on estimating true SEP occurrence probabilities achieving a 2.5% improvement in reliability and a Brier score of 0.14. The outcome provides flexibility for the end-users to determine their own acceptable level of risk, rather than imposing a detection threshold that optimizes an arbitrary binary classification metric. Furthermore, the model-ensemble, trained to utilize the large class-imbalance between events and non-events, provides a clear measure of uncertainty in our forecast.
Abstract
Solar climate intervention refers to a group of methods for reducing climate risks associated with anthropogenic warming by reflecting sunlight. Marine cloud brightening (MCB), one such approach, proposes to inject sea-salt aerosol into one or more regional marine boundary layer to increase marine cloud reflectivity. Here, we assess the potential influence of various MCB experiments on Africa's climate using simulations from the Community Earth System Model (CESM2) with the Community Atmosphere Model (CAM6) as its atmospheric component. We analyzed four idealized MCB experiments under a medium-range background forcing scenario (SSP2-4.5), which brighten clouds over three subtropical ocean regions: (a) Northeast Pacific (MCBNEP); (b) Southeast Pacific (MCBSEP); (c) Southeast Atlantic (MCBSEA); and (d) these three regions simultaneously (MCBALL). Our results suggest that the climate impacts of MCB in Africa are highly sensitive to the deployment region. MCBSEP would produce the strongest global cooling effect and thus could be the most effective in decreasing temperatures, increasing precipitation, and reducing the intensity and frequency of temperature and precipitation extremes across most parts of Africa, especially West Africa, in the future (2035–2054) compared to the historical climate (1995–2014). MCB in other regions produces less cooling and wetting despite similar radiative forcings. While the projected changes under MCBALL are similar to those of MCBSEP, MCBNEP and MCBSEA could see more residual warming and induce a warmer future than under SSP2-4.5 in some regions across Africa. All MCB experiments are more effective in cooling maximum temperature and related extremes than minimum temperature and related extremes.
Closing the gap in the tropics: the added value of radio-occultation data for wind field monitoring across the Equator
Julia Danzer, Magdalena Pieler, and Gottfried Kirchengast
Atmos. Meas. Tech., 17, 4979–4995, https://doi.org/10.5194/amt-17-4979-2024, 2024
We investigated the potential of radio occultation (RO) data for climate-oriented wind field monitoring, focusing on the equatorial band within ±5° latitude. In this region, the geostrophic balance breaks down, and the equatorial balance approximation takes over. The study encourages the use of RO wind fields for mesoscale climate monitoring for the equatorial region, showing a small improvement in the troposphere when including the meridional wind in the zonal-mean total wind speed.
Shortwave Array Spectroradiometer-Hemispheric (SAS-He): design and evaluation
Evgueni Kassianov, Connor J. Flynn, James C. Barnard, Brian D. Ermold, and Jennifer M. Comstock
Atmos. Meas. Tech., 17, 4997–5013, https://doi.org/10.5194/amt-17-4997-2024, 2024
Conventional ground-based radiometers commonly measure solar radiation at a few wavelengths within a narrow spectral range. These limitations prevent improved retrievals of aerosol, cloud, and surface characteristics. To address these limitations, an advanced ground-based radiometer with expanded spectral coverage and hyperspectral capability is introduced. Its good performance is demonstrated using reference data collected over three coastal regions with diverse types of aerosols and clouds.
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, 2024
The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, 2024
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, 2024
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, 2024
Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
