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Abstract

From 1979 to 2022, the summer monsoon precipitation has increased by a substantial 40% over Northwest India compared to the 1980s. This wetting trend aligns with the future projections of the Coupled Model Intercomparison Project 6 (CMIP6). The observationally constrained reanalysis data indicates that significant sea surface warming in the western equatorial Indian Ocean and the Arabian Sea is likely driving this increase in rainfall by enhancing the cross-equatorial monsoonal flow and associated evaporation. We demonstrate that the strengthening of the cross-equatorial monsoon winds is due to the rapid warming of the Indian Ocean and the enhanced Pacific Ocean trade winds, which result from the poleward shift and expansion of the Hadley cell. These strengthened winds boost the latent heat flux (evaporation), leading to increased moisture transport to Northwest India.

Abstract

Earlier proxy-observational studies, and a sole modeling study, suggest that the Indian Ocean Dipole (IOD), an important global climate driver, exhibited multi-scale temporal variability during the Last Millennium (LM; CE 0851–1849, with relatively high number of strong positive IOD events during the Little Ice Age (LIA; CE 1550–1749), and strong negative IOD events during the Medieval Warm Period (MWP; CE 1000–1199). Using nine model simulations from the PMIP3, we study the IOD variability during the LM after due validation of the simulated current day (CE 1850–2005) IOD variability. Majority of the models simulate relatively higher number of positive IOD events during the MWP, and negative IOD events in the LIA, commensurate with simulated background conditions. However, higher number of strong positive IOD events are simulated relative to the negative IODs during the LIA, in agreement with proxy-observations, apparently owing to increased coupled feedback during positive IODs.

Abstract

We investigate the role of the subpolar North Atlantic (SPNA) for downstream predictability, using two decadal climate prediction systems. We use the subpolar extreme cold and fresh anomaly event developing in winter 2013/2014 as initial conditions and evaluate ensemble predictions of the two systems in the following decade. In addition, we perform ensemble pacemaker experiments where the models are forced toward observed ocean temperature and salinity anomalies in the SPNA from November 2014 through December 2019. The pacemaker experiments show improved skill along the Atlantic Water pathway, compared with the standard decadal predictions, and we therefore conclude that the correct description of the ocean in the SPNA is the key. The enhanced skill is most prominent in subsurface salinity in the form of propagating anomalies.

Air pollution is increasing the severity of summertime thunderstorms, according to a recent study conducted by researchers at James Madison University and published in the journal Atmospheric Research.

The role of time-varying external factors in the intensification of tropical cyclones
Samuel Watson and Courtney Quinn
Nonlin. Processes Geophys., 31, 381–394, https://doi.org/10.5194/npg-31-381-2024, 2024
The intensification of tropical cyclones (TCs) is explored through a conceptual model derived from geophysical principals. Focus is put on the behaviour of the model with parameters which change in time. The rates of change cause the model to either tip to an alternative stable state or recover the original state. This represents intensification, dissipation, or eyewall replacement cycles (ERCs). A case study which emulates the rapid intensification events of Hurricane Irma (2017) is explored.

Iterative Placement of Decoupling Capacitors using Optimization Algorithms and Machine Learning
Zouhair Nezhi, Nima Ghafarian Shoaee, and Marcus Stiemer
Adv. Radio Sci., 21, 123–132, https://doi.org/10.5194/ars-21-123-2024, 2024
An optimum placement and dimensioning of decaps on a printed circuit board is determined by a Genetic Algorithm (GA). The use of an artificial neural network as surrogate model to compute fitness values for the GA significantly reduces computation time. With the optimization framework at hand, the risk of a redesign that would take several weeks can be significantly reduced by a computation that just needs a few minutes.

Abstract

Specification and forecast of ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionospheric research. Neural networks (NNs) emerge as a powerful modeling tool for Ne prediction. However, heavy manual adjustments are time consuming to determine the optimal NN structures. In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to mitigate this problem. NAS aims to find the optimal network structure through the alternate optimization of the hyperparameters and the corresponding network parameters within a pre-defined hyperparameter search space. A total of 16-year data from Millstone Hill incoherent scatter radar (ISR) are used for the NN models. One single-layer NN (SLNN) model and one deep NN (DNN) model are both trained with NAS, namely SLNN-NAS and DNN-NAS, for Ne prediction and compared with their manually tuned counterparts (SLNN and DNN) based on previous studies. Our results show that SLNN-NAS and DNN-NAS outperformed SLNN and DNN, respectively. These NN predictions of Ne daily variation patterns reveal a 27-day mid-latitude topside Ne variation, which cannot be reasonably represented by traditional empirical models developed using monthly averages. DNN-NAS yields the best prediction accuracy measured by quantitative metrics and rankings of daily pattern prediction, especially with an improvement in mean absolute error more than 10% compared to the SLNN model. The limited improvement of NAS is likely due to the network complexity and the limitation of fully connected NN without the time histories of input parameters.

Abstract

To gain deeper insights into radiation belt loss into the atmosphere, a statistical study of MeV electron precipitation during radiation belt dropout events is undertaken. During these events, electron intensities often drop by an order of magnitude or more within just a few hours. For this study, dropouts are defined as a decrease by at least a factor of five in less than 8 hours. Van Allen probe measurements are employed to identify dropouts across various parameters, complemented by precipitation data from the CALorimetric Electron Telescope instrument on the International Space Station. A temporal analysis unveils a notable increase in precipitation occurrence and intensity during dropout onset, correlating with the decline of SYM-H, the north-south component of the interplanetary magnetic field, and the peak of the solar wind dynamic pressure. Moreover, dropout occurrences show correlations with the solar cycle, exhibiting maxima at the spring and autumn equinoxes. This increase during equinoxes reflects the correlation between equinoxes and the SYM-H index, which itself exhibits a correlation with precipitation during dropouts. Spatial analysis reveals that dropouts with precipitation penetrate into lower L-star regions, mostly reaching L-star <4, while most dropouts without precipitation don't penetrate deeper than L-star 5. This is consistent with the larger average dimensions of dropouts associated with precipitation. During dropouts, precipitation is predominantly observed in the dusk-midnight sector, coinciding with the most intense precipitation events. The results of this study provide insight into the contribution of precipitation to radiation belt dropouts by deciphering when and where precipitation occurred.

Abstract

The aurora and associated high energy particles and currents pose a space weather hazard to communication networks and ground-based infrastructure. Forecasting the location of the auroral oval forms an integral component of daily space weather operations. We evaluate a version of the OVATION-Prime 2013 auroral forecast model that was implemented for operational use at the UK Met Office Space Weather Operations Cent. Building on our earlier studies, we evaluate the ability of the OVATION-Prime 2013 model to predict the location of the auroral oval in all latitude and local time sectors under different levels of geomagnetic activity, defined by Kp. We compare the model predictions against auroral boundaries determined from IMAGE FUV data. Our analysis shows that the model performs well at predicting the equatorward extent of the auroral oval, particularly as the equatorward auroral boundary expands to lower latitudes for increasing Kp levels. The model performance is reduced in the high latitude region near the poleward auroral boundary, particularly in the nightside sectors where the model does not accurately capture the expansion and contraction of the polar cap as the open flux content of the magnetosphere changes. For increasing levels of geomagnetic activity (Kp ≥ 3), the performance of the model decreases, with the poleward edge of the auroral oval typically observed at lower latitudes than forecast. As such, the forecast poleward edge of the auroral oval is less reliable during more active and hazardous intervals.

Abstract

Measurements of Ar and N2 densities at 160–250 km altitude from the Mars Atmosphere and Volatile Evolution (MAVEN) Neutral Gas and Ion Mass Spectrometer (NGIMS) during February 2015–February 2023 are analyzed to provide a comprehensive analysis of their diurnal- and zonal-mean (DZM) structures, and ZM (solar-synchronous) diurnal (DW1) and semidiurnal (SW2) tides. After applying a solar flux trend correction, multi-year binning and averaging with respect to longitude, local solar time (LST), latitude and Ls at each height results in the first full global picture of these components of the ZM thermosphere for a single climatological Mars year. The following new observational insights into Mars thermosphere are obtained: The DZM N2 latitude versus Ls (latvsLs) structures contain a prominent latitudinally-symmetric annual component (∼±25%–35%) due to the eccentricity of Mars orbit around the Sun, and an antisymmetric component (∼±30%–45%) below about 190 km that is seasonally-symmetric and thus consistent with the tilt of Mars rotation axis. Aperiodic deviations from these symmetries increase with height and are tentatively attributed to dissipation of waves originating in the lower atmosphere. DW1 and SW2 maximize around 200–220 km altitude, suggesting existence of an unknown dissipation mechanism at higher altitudes. The DZM, DW1 and SW2 components of Ar generally exceed those of N2 by factors of 1.4–2.5. The scale heights of Ar and N2 between 205 and 245 km are also employed to derive DZM exosphere temperatures, which reflect aperiodic ∼±15K deviations from the annual-mean in the latvsLs frame.

Abstract

Pc5 ULF waves play an important role in transporting energy and particles in the coupled magnetospheric and ionospheric system. They are known to be initiated by dynamic pressure fluctuations upstream of the magnetopause, including those induced by hot flow anomalies (HFAs). However, the role of HFAs in generating magnetospheric and ground magnetic Pc5 ULF oscillations has not been investigated statistically yet. Thus, in this paper, we investigate the contribution of HFAs to ground magnetic Pc5 ULF oscillations and analyze how the characteristics of HFAs influence these oscillations, based on the coordinated observations between the THEMIS probes and the ground magnetometers at high latitudes during the years 2008, 2009 and 2019. We find that HFAs can serve as a notable source of ground magnetic Pc5 ULF oscillations, with about 18.9% of Interplanetary Magnetic Field (IMF) discontinuity-induced HFAs associated with discernible enhancements in Pc5 ULF wave power, whereas spontaneous HFAs play a comparatively minor role in generating these oscillations. Furthermore, we observe that the cores of HFAs are likely to contribute more significantly to modulating the induced ground magnetic Pc5 ULF oscillations than their compressed boundaries. More dynamic pressure reductions within HFA cores correspond to stronger ground magnetic Pc5 ULF oscillations. Additionally, HFAs can propagate with the IMF discontinuity along the bow shock, continuously generating ground magnetic Pc5 ULF oscillations during their propagation. This research sheds light on the mechanisms underlying Pc5 ULF wave generation and underscores the role of HFAs in driving magnetospheric-ionospheric interactions.

Nature Geoscience, Published online: 20 August 2024; doi:10.1038/s41561-024-01507-0

Humid heatwaves are often limited by the onset of convective rain, such as thunderstorms. Observational reanalysis data and climate models indicate that dry air 1–3 km above the Earth’s surface can curtail convective storms, allowing humid heatwaves to intensify on the ground. This effect is likely to be exacerbated by increasing global temperatures.

Nature Geoscience, Published online: 20 August 2024; doi:10.1038/s41561-024-01519-w

Graphitic carbon was hydrothermally cycled through shear zones during late-stage orogenesis associated with Nuna supercontinent assembly, according to a coupled rhenium–osmium and uranium–lead dating study.

Nature Geoscience, Published online: 20 August 2024; doi:10.1038/s41561-024-01512-3

Soil moisture greatly affects the response of soil respiration to warming, according to 13 years of warming experiments in a boreal forest.

SummaryIntermediate-depth earthquakes, accommodating intra-slab deformation, typically occur within subduction zone settings at depths between 60-300 km. These events are in a unique position to inform us about the geodynamics of the subducting slab, specifically the geometry of the slab and the stress state of the host material. Improvements in the density and quality of recorded seismic data enhance our ability to determine precise locations of intermediate-depth earthquakes, in order to establish connections between event nucleation and the tectonic setting. Depth phases (near-source surface reflections, e.g. pP and sP) are crucial for the accurate determination of earthquake source depth using global seismic data. However, they suffer from poor signal-to-noise ratios in the P-wave coda. This reduces the ability to systematically measure differential travel times to the corresponding direct arrival, particularly for the frequent lower-magnitude seismicity which highlights considerable seismogenic regions of the subducted slabs. To address this limitation, we have developed an automated approach to group globally-distributed stations at teleseismic distances into ad-hoc arrays with apertures of 2.5○, before optimising and applying phase-weighted beamforming techniques to each array. Resultant vespagrams allow automated picking algorithms to determine differential arrival times between the depth phases (pP, sP) and their corresponding direct P arrival. These are subsequently used to invert for a new depth. These will allow new comparisons and insights into the governing controls on the distribution of earthquakes in subducted slabs. We demonstrate this method by relocating intermediate-depth events associated with northern Chile and the Peruvian flat slab regions of the subducting Nazca plate. The relocated Chilean catalogue contains comparable event depths to an established catalogue, calculated using a semi-automated global methodology, which serves to validate our new fully automatic methodology. Our new Peruvian catalogue indicates 3 broad zones of seismicity approximately between latitudes 1-7○S, 7-13○S and 13-19○S. These align with flat to steep slab dip transitions and the previously identified Pucallpa Nest. We also find a regionally deeper slab top than indicated by recent slab models, with intra-slab events concentrated at points where the slab bends, suggesting a link between slab flexure and intermediate-depth earthquake nucleation.

SummaryThe dam of Lampy (Black Mountain, Aude, France) is considered as one of the oldest dams in France. A geophysical survey is performed to better understand the pattern of groundwater flow downstream of this dam in the granitic substratum. Induced polarization is first used to image both electrical conductivity and normalized chargeability. 8 core samples of granite from this site are measured and analyzed in the laboratory. Their electrical conductivity and normalized chargeability are expressed as a function of the porosity and Cation Exchange Capacity (CEC). The field data and the petrophysical results are used to image the water content, the CEC, and the permeability distribution of the substratum. Then, self-potential is used as a complementary passive geophysical technique, which, in absence of metallic bodies, is directly sensitive to groundwater flow through the so-called streaming potential effect. Indeed, the excess of electrical charges in the vicinity of the solid grains, in the so-called double layer, is dragged by the ground water flow generating in turn an electrical (streaming) current and therefore an electrical field. A map of the resulting self-potential signals is done over the area covered by the induced polarization profiles. This map shows a large positive anomaly with an amplitude of ∼80 mV possibly associated with upwelling groundwater in an area where the soil is water-saturated. A groundwater flow simulation is performed to model this anomaly. This is done in two steps. A preliminary groundwater flow model is built using the permeability and water content distributions obtained from the induced polarization data. Then, this groundwater flow model is updated using the information contained in the self-potential data including the electrical conductivity distribution obtained through resistivity tomography. The algorithm for the inversion of the self-potential data is validated through a 2D numerical test. This analysis yields a groundwater flow model with the flow being focused through a high permeability zone. This study shows how three geoelectrical methods (self-potential, induced polarization and electrical resistivity) can be efficiently combined to image groundwater flow in the vicinity of a dam.

SummaryWe present a general framework for multiphysics joint inversion of any number of geophysical datasets. Its main feature is the use of the variable splitting approach: an auxiliary multiparameter model space is introduced in which minimization of the coupling and stabilizing functionals is carried out. The use of rediscretization and interpolation to map between this auxiliary space and the model spaces allows the coupled models to have completely different parameterizations. Joint inversion is decoupled into the individual inversion and the coupling-regularization subproblems, each of which can be solved by a different optimization algorithm. For each subproblem, the linking term controlling the distance between the model and the corresponding auxiliary variable takes the form of a quadratic regularization with a reference model. As a result, any existing inversion code supporting such regularization can be integrated without modifications into the developed framework. As a concrete example scheme, we consider an application of the framework to 3D joint inversion of magnetotelluric, seismic refraction and gravity data. We discuss different coupling functionals, mainly those corresponding to the more universal structural constraints: joint total variation, joint minimum support, cross-gradient, one-way cross-gradient and their combinations for a general multi-model case. The use of coupling based on explicit ”petrophysical” relationship between the properties is also considered. Performance of the developed framework is studied on three synthetic cases: a time-lapse joint inversion of full-tensor gravity gradiometry and seismic data, a joint inversion of magnetotelluric, seismic and gravity data and a joint inversion for electrical resistivity tomography and audio-magnetotellurics.

SummaryIn the past two decades or so, ambient noise tomography (ANT) has emerged as a powerful tool for investigating high-resolution crustal and upper-mantle structures. A crucial step in the ANT involves extracting phase velocities from cross-correlation functions (CCFs). However, obtaining precise phase velocities can be a formidable challenge, particularly when significant lateral velocity variations exist in shallow subsurface imaging that relies on short-period surface waves from ambient noise. To address this challenge, we propose an unwrapping correction method that enables the accurate extraction of short-period dispersion curves. Our method relies on the examination of the continuity of phase velocities extracted from CCFs between a common station and other neighboring stations along a linear array. We demonstrate the effectiveness of our approach by applying our method to both synthetic data and field data. Both applications suggest our unwrapping correction method can identify and correct unwrapping errors in phase velocity measurements, ensuring the extraction of accurate and reliable dispersion curves at short periods from ambient noise, which is essential for subsequent inversion for subsurface structures.

Abstract

As a first step in preparing for the return of samples from the Moon by the Artemis Program, NASA initiated the Apollo Next Generation Sample Analysis Program (ANGSA). ANGSA was designed to function as a low-cost sample return mission and involved the curation and analysis of samples previously returned by the Apollo 17 mission that remained unopened or stored under unique conditions for 50 years. These samples include the lower portion of a double drive tube previously sealed on the lunar surface, the upper portion of that drive tube that had remained unopened, and a variety of Apollo 17 samples that had remained stored at −27 °C for approximately 50 years. ANGSA constitutes the first preliminary examination phase of a lunar “sample return mission” in over 50 years. It also mimics that same phase of an Artemis surface exploration mission, its design included placing samples within the context of local and regional geology through new orbital observations collected since Apollo and additional new “boots-on-the-ground” observations, data synthesis, and interpretations provided by Apollo 17 astronaut Harrison Schmitt. ANGSA used new curation techniques to prepare, document, and allocate these new lunar samples, developed new tools to open and extract gases from their containers, and applied new analytical instrumentation previously unavailable during the Apollo Program to reveal new information about these samples. Most of the 90 scientists, engineers, and curators involved in this mission were not alive during the Apollo Program, and it had been 30 years since the last Apollo core sample was processed in the Apollo curation facility at NASA JSC. There are many firsts associated with ANGSA that have direct relevance to Artemis. ANGSA is the first to open a core sample previously sealed on the surface of the Moon, the first to extract and analyze lunar gases collected in situ, the first to examine a core that penetrated a lunar landslide deposit, and the first to process pristine Apollo samples in a glovebox at −20 °C. All the ANGSA activities have helped to prepare the Artemis generation for what is to come. The timing of this program, the composition of the team, and the preservation of unopened Apollo samples facilitated this generational handoff from Apollo to Artemis that sets up Artemis and the lunar sample science community for additional successes.

A recent study has pinpointed the top-performing solar-induced chlorophyll fluorescence (SIF) products for precise global monitoring of photosynthesis and vegetation dynamics. By thoroughly evaluating eight widely-used SIF datasets, the research team identified Global OCO-2 SIF (GOSIF) and Contiguous Solar-Induced Fluorescence (CSIF) as leading tools for estimating gross primary productivity (GPP) and forecasting key phenological stages.