Crustal magnetic fields influence a range of plasma processes at Mars, guiding the flow of energy from the solar wind into the planet’s atmosphere at some locations while shielding it at others. In this study we investigate how these crustal fields vary with changes in the direction of the incoming interplanetary magnetic field (IMF). Using spacecraft measurements of electron flux and magnetic field, we analyze magnetic topology throughout the Martian ionosphere for different IMF configurations. We find that the topology of crustal field cusp regions is dependent on IMF direction, and that cusps transition between open and closed topology regularly as they rotate through the nightside of Mars. Finally, we determine that cusps often become topologically closed due to reconnection with open magnetic fields in the Martian magnetotail, resulting in large day-to-night closed loops that could represent a source of energy input into nightside crustal field regions.

We investigate the synoptic precursors to the Harmattan wind and dust frontogenesis during the high impact Saharan dust outbreak over the Cape Verde Islands on 13 November 2017. We employ multi-scale observations including ship data and Weather Research and Forecasting model Coupled with Chemistry simulations. The analyses indicate that the dust storm was initiated on the leeside of the Saharan Atlas Mountains (SAM) in Algeria on the 10. This dust storm was associated with a double Rossby Wave Break (RWB) linked through non-linear wave reflection. Two successive RWB contributed to the wave amplification over the Eastern North Atlantic Ocean which transported large magnitude potential vorticity air into the North African continent. The resulting coupled pressure surge was associated with cold air advected equatorward over the SAM which organized the strong near-surface wind that ablated the dust. The simulation results indicate that the dust front was initially related to a density current which formed due to the cold airflow over the SAM. The density current then triggered undular bores on the leeside. Each bore perturbed the dust loading and then the subsequent diurnal heating generated differential planetary boundary layer (PBL) turbulence kinetic energy strengthening the dust frontogenesis. Dust became confined behind the cold surge and interacted with the daytime Saharan PBL leading to increased dust loading while the dust front propagated equatorward. Two distinct dust plumes arrived successively at low-levels at Mindelo, Cape Verde Islands; (1) from the coasts of Mauritania and Senegal and (2) from the SAM southern flank.

We present a method for predicting wave dissipation by sea ice that is based on the dimensional analysis of data with a scaling defined by ice thickness. Applying the method to an extensive dataset from the measurements during the “Polynyas, Ice Production, and seasonal Evolution in the Ross Sea” (PIPERS) cruise in 2017, we derive a new model of wave dissipation which not only describes a nonlinear dependence on ice thickness but also reveals its relation with the dependence on frequency. This nonlinear dependence on ice thickness can have more implications on predicting low-frequency waves. The root-mean-square error of the prediction is significantly reduced using the new model, compared with other existing parametric models that are also calibrated for the PIPERS dataset. The new model also explicitly describes a condition of similarity between large- and small-scale observations, which is shown to exist when various laboratory datasets collapse onto the prediction.

Subtle elastic rock deformation during aquifer testing may bear hydraulic parameter (permeability and compressibility) information owing to the poroelastic hydromechanical coupling effect. Here we report that such in situ rock deformations (~50 µε) during an aquifer pumping test are successfully measured along a vertical well by a high-resolution fiber optic distributed strain sensing (DSS) tool with an accuracy of 0.5 µε. We investigate the feasibility of hydraulic parameter estimation at meter scale using DSS data through a coupled hydromechanical model. Both synthetic and field cases are tested with sensitivity analysis. The results indicate that the simultaneous estimation of permeability and compressibility using DSS data is possible at low noise levels. However, only non-global near-optimal solutions can be obtained using the applied gradient-based inversion algorithm, because of parameter crosstalk and sensitivity problems when the data contain large noise. In particular, estimation is difficult for zones with relatively low permeability due to the low sensitivity to the strain changes. The estimated permeability/compressibility structures for the field test are largely consistent with other geological information from well logs. Our study suggests that DSS data can be quite useful in aquifer characterization and fluid flow profiling in addition to geomechanical monitoring. The obtained hydraulic information is beneficial for the optimized reservoir management of water and oil/gas storage.

The Cuo E (CE) core contains the entire Quaternary stratigraphy of the Cuo E Lake (31°24′ - 31°.32′ N, 91°28′ - 91°33′E, Tibetan Plateau, China). Here, we study the magnetic properties of 1748 samples from the CE core, combined with scanning electron microscopy and X-ray energy dispersive spectrometer analysis on selected samples. The main magnetic minerals appear to be magnetite and greigite. To further quantify the presence of greigite in CE core, 84 samples were rock magnetically analyzed in more detail including: susceptibility (χ) vs. temperature, coercivity component analysis of acquisition curves of the isothermal remanent magnetization (IRM), and Principal component analysis (PCA) of the first-order reversal curve (FORC) diagrams. The greigite content and saturation IRM over susceptibility (SIRM/χ) appear to be exponentially related. SIRM/χ can be used as greigite concentration indicator. We also propose a rapid way to identify greigite in the CE core: When the SIRM/χ value increases and anhysteretic remanent susceptibility (χARM) increases only slowly, the sample contains greigite. Samples with SIRM/χ < 15kAm-1 also obey this relation. This method has a lower limit of detection than the traditionally used SIRM/χ parameter for greigite detection. It is also faster than FORC analysis and thus particularly suitable for analysis of large sample collections. Twenty greigite-bearing layers were identified in the CE core using this method. The location of the Jaramillo subchron in the CE core is controversial in the currently available magnetostratigraphy, possibly related to the presence of greigite in this core interval.

Sediments composed of mixed cohesive clay and non-cohesive sand are widespread in a range of aquatic environments. The dynamics of ripples in mixed sand–clay substrates have been studied under pure current and pure wave conditions. However, the effect of cohesive clay on ripple development under combined currents and waves has not been examined, even though combined flows are common in estuaries, particularly during storms. Based on a series of large flume experiments, we identified robust inverse relationships between initial bed clay content, C0, and wave–current ripple growth rates. The experimental results also revealed two distinct types of equilibrium combined–flow ripples on mixed sand–clay beds: (a) large asymmetrical ripples with dimensions and plan geometries comparable to clean-sand counterparts for C0 ≤ 10.6%; and (b) small, flat ripples for C0 > 11%. The increase in bed cohesion contributed to this discontinuity, expressed most clearly in a sharp reduction in equilibrium ripple height, and thus a significant reduction in bed roughness, which implies that the performance of existing ripple predictors can be improved by the incorporation of this physical cohesive effect. For C0 ≤ 10.6%, strong clay winnowing efficiency under combined flows resulted in the formation of equilibrium clean-sand ripples and clay loss at depths far below the ripple base. In natural environments, this ‘deep cleaning’ of bed clay may cause a concurrent sudden release of a large amount of pollutants during storms, leading to a sudden reduction in post-storm resistance to erosion of mixed sand–clay substrates.

Geomagnetic storms, which are governed by the plasma magnetohydrodynamics of the solar-interplanetary-magnetosphere system, entail a formidable challenge for physical forward modeling. Yet, the abundance of high quality observational data has been amenable for the application of data-hungry neural networks to geomagnetic storm forecasting. Previous applications of neural networks to storm forecasting have utilized solar wind observations from the Earth-Sun first Lagrangian point (L1) or closer and have all generated deterministic output without uncertainty estimates. Furthermore, forecasting work has focused on indices that are also sensitive to induced internal magnetic fields, complicating the forecasting problem with another layer of non-linearity. We address these points, presenting neural networks trained on observations from both the solar disk and the L1 point. Our architecture generates reliable probabilistic forecasts over Est, the external component of the disturbance storm time index, showing that neural networks can gauge confidence in their output.

Utilizing atmospheric temperature observed from Mars Years 33 to 36 by the Imaging Ultraviolet Spectrograph (IUVS) onboard the Mars Atmosphere and Volatile Evolution (MAVEN), we derive the diurnal and semidiurnal thermal tides from 90 to 160 km. The seasonal variations of diurnal (DW1) and semidiurnal (SW2) tides in the thermosphere and mesosphere, observed by the Mars Climate Sounder (MCS), along with vertical phase velocities, indicate different sources for the migrating tide in the lower and upper atmosphere. The seasonal variation of diurnal eastward wavenumber 2 (DE2) tide in the thermosphere corresponds well to its counterpart in the lower atmosphere. Vertical phase velocities indicate that the DE2 propagates upward from the lower atmosphere to ~150 km, except near the perihelion (solar longitude 210° to 270°). The upward propagation of this DE2 tide could potentially impact the vertical coupling between the Martian lower and upper atmosphere.

Climate change threatens the sustainable use of groundwater resources worldwide by affecting future recharge rates. However, assessments of global warming’s impact on groundwater recharge at local scales are lacking. This study provides a continental-scale assessment of groundwater recharge changes in Europe, past, present, and future, at a (5 x 5) km2 resolution under different global warming levels (1.5 K, 2.0 K, and 3.0 K). Utilizing multi-model ensemble simulations from four hydrologic and land-surface models (HMs), our analysis incorporates E-OBS observational forcing data (1970-2015) and five bias-corrected and downscale climate model (GCMs) datasets covering the near-past to future climate conditions (1970-2100). Results reveal a north-south polarization in projected groundwater recharge change: declines over 25-50% in the Mediterranean and increases over 25% in North Scandinavia at high warming levels (2.0-3.0 K). Central Europe shows minimal changes (±5%) with larger uncertainty at lower warming levels. The southeastern Balkan and Mediterranean region exhibited high sensitivity to warming, with changes nearly doubling between 1.5 K and 3.0 K. We identify greater uncertainty from differences among GCMs, though significant uncertainties due to HMs exist in regions like the Mediterranean, Nordic, and Balkan areas. The findings highlight the importance of using multi-model ensembles to assess future groundwater recharge changes in Europe and emphasize the need to mitigate impacts in higher warming scenarios.

Spacecraft equipped with magnetometers provide useful magnetic field data for a variety of applications such as monitoring the Earth’s magnetic field. However, spacecraft electrical systems generate magnetic noise that interfere with geomagnetic field data captured by magnetometers. Traditional solutions to this problem utilize mechanical booms to extend magnetometers away from noise sources. This solution can increase design complexity, cost, and introduce boom deployment risk. If a spacecraft is equipped with multiple magnetometers, signal processing algorithms can be used to compare magnetometer measurements and remove stray magnetic noise signals. We propose the use of density-based cluster analysis to identify spacecraft noise signals and compressive sensing to separate spacecraft noise from geomagnetic field data. This method assumes no prior knowledge of the number, location, or amplitude of noise signals, but assumes that they are independent and have minimal overlapping spectral properties. We demonstrate the validity of this algorithm by separating high latitude magnetic perturbations recorded by SWARM from noise signals in simulation and in a laboratory experiment using a mock CubeSat apparatus. In the case of more noise sources than magnetometers, this problem is an instance of Underdetermined Blind Source Separation (UBSS). This work presents a UBSS signal processing algorithm to remove spacecraft noise and eliminate the need for a mechanical boom.

As the ShakeAlert Earthquake Early Warning System is launched along the West Coast, how will this change the situational awareness and preparation actions of individuals who live and work in this region? This presentation describes the preliminary findings from a qualitative research study investigating the processes individuals who live and work near coastal Oregon experience as they develop earthquake and tsunami readiness and preparedness. This study provides a current snapshot before a local EWS is available. Getting ready and preparing to respond to natural hazards involves many layers of society from federal to individual efforts. When the natural hazard is a sudden-onset event with a high potential impact, such as an earthquake and tsunamis, many interoperating social, governmental, technical, and scientific systems are needed before the event occurs to prepare and create resilient communities and individuals who can thrive post-event. For individuals, very different types of actions are needed to become prepared before, to respond during, and to survive long-term from an earthquake and subsequent tsunamis. The Oregon Office of Emergency Management currently recommends that everyone should be prepared for two weeks if a major 8.5+ magnitude earthquake hits Oregon; for coastal communities, local agencies recommend closer to four weeks of supplies. This study has been investigating the processes jobholders and residents of coastal Oregon describe as they’ve actively prepared for earthquake and tsunami hazards. During these discussions, they are providing insight into what has encouraged and created obstacles to actively preparing for the eventual subduction earthquake and subsequent tsunamis. This presentation will share the progress of this project and suggestions for future research after ShakeAlert is implemented.

State-of-the-art astrogeodetic measurement systems—Digital Zenith Camera Systems (DZCSs) and the tachymeter-based QDaedalus systems—are currently used to obtain astrogeodetic deflection of the vertical (DoV) components. In this study, we use the CODIAC DZCS (accuracy ~0.05″) and two QDaedalus systems (accuracy ~0.20″) developed by ETH Zurich to collect DoV data in the Surses Region (Canton of Grisons), Switzerland. Based on these data, we assess the quality of the DoV predicted by the Global Gravity Model GGMplus and the Swiss Geoid model CHGeo2004. DoV data previously observed with the QDaedalus system in coastal (Istanbul) and smooth (Munich region) terrain areas were compared with GGMplus to determine the accuracy of the model for both types of terrain. The Surses Region was chosen for these observations because of its mountainous nature to examine the quality of the GGMplus model in mountainous terrain, as well as the GGMplus’ quality with the CHGeo2004. Astrogeodetic observations were conducted at 15 benchmarks in the astrogeodetic profile, at elevations ranging from 1185-1800 m and a station spacing of ~1.5 km. At one benchmark (BM12), observations were done with all three systems (eccentric BMs), while at 13 benchmarks, observations were only completed with the QDaedalus systems. Observations at the final benchmark were completed only with the CODIAC (0⁰ zenith angle) due to line of sight obstacles. The standard deviations of the QDaedalus data for each session are 0.04″-0.22″ and 0.01″-0.20″ for the N-S and E-W components, respectively. These high-quality data were compared to DoV data derived from GGMplus and CHGeo2004. The residuals from GGMplus are: The N-S component exhibit large residuals ranging from -2.31″ to 1.75″, while the E-W component residuals are -0.27″ to 1.80″. The residuals from CHGeo2004 range from -0.60 to 1.21 for the N-S components, and -1.01 to 0.32 for the E-W components. The results show that the predicted DoV data from CHGeo2004 are closer to the observed DoV, and more accurate than GGMplus. We conclude from these results that GGMplus is of significantly lower quality in mountainous terrain than in the smooth terrain of the Munich region (~0.2″, and reaching a maximum of 0.3″ and 0.4″ for the N-S and E-W components).

It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence (AI) algorithms, however, is challenged with its vagueness, non-quantitiveness, computational inefficiency, etc. During the past 18 years, these challenges have been essentially resolved, with the establishment of a rigorous formalism of causality analysis initially motivated from atmospheric predictability. This not only opens a new field in the atmosphere-ocean science, namely, information flow, but also has led to scientific discoveries in other disciplines, such as quantum mechanics, neuroscience, financial economics, etc., through various applications. This note provides a brief review of the decade-long effort, including a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications in geoscience pertaining to this journal, such as those on the anthropogenic cause of global warming, the decadal prediction of El Niño Modoki, the forecasting of an extreme drought in China, among others.

Synoptic weather systems are a major driver of spatial gradients in atmospheric CO2 mole fractions. During frontal passages, air masses from different regions meet at the frontal boundary creating significant gradients in CO2 mole fractions. We quantitatively describe the atmospheric transport of CO2 mole fractions during a mid-latitude cold front passage and explore the impact of various sources of CO2. We focus here on a cold front passage over Lincoln, Nebraska on August 4th, 2016 observed by aircraft during the Atmospheric Carbon and Transport (ACT)-America campaign. A band of air with elevated CO2was located along the frontal boundary. Observed and simulated differences in CO2 across the front were as high as 25 ppm. Numerical simulations using WRF-Chem at cloud resolving resolutions (3km), coupled with CO2 surface fluxes and boundary conditions from CarbonTracker (CT-NRTv2017x), were performed to explore atmospheric transport at the front. Model results demonstrate that the frontal CO2 difference in the upper troposphere can be explained largely by inflow from outside of North America. This difference is modified in the atmospheric boundary layer and lower troposphere by continental surface fluxes, dominated in this case by biogenic and fossil fuel fluxes. Horizontal and vertical advection are found to be responsible for the transport of CO2 mole fractions along the frontal boundary. We show that cold front passages lead to large CO2 transport events including a significant contribution from vertical advection, and that mid-continent frontal boundaries are formed from a complex mixture of CO2 sources.

Remotely sensed land surface temperature (LST) enables global modelling and monitoring carbon dioxide (CO2) fluxes from peatlands. We aimed to provide the first overview of the LST potential for monitoring ecosystem respiration (Reco) in disturbed (drained and extracted) peatlands. We used chamber measured data (2017–2020) from five disturbed and two intact northern peatlands and LST data from Landsat 7, 8, and MODIS missions. First, we studied the strength of relationships between fluxes and their in-situ drivers: thermal and moisture conditions. Second, we examined the association between LST and in-situ temperatures. Third, we compared chamber measured Reco with the modelled Reco based on (i) in-situ measured surface temperature and (ii) MODIS LST. In-situ temperatures were a stronger driver of CO2 fluxes in disturbed sites (Spearman correlation R=0.8–0.9) than in intact ones (R=0.5–0.7). LST had a higher association with in-situ measured temperatures (mean R=0.74 for MODIS) in disturbed sites and weaker in the intact peatlands (mean R=0.34 for Landsat and 0.36 for MODIS). Reco models driven by MODIS LST and in-situ surface temperature yielded similar accuracy: R-squared was 0.26, 0.64, 0.65 and 0.28, 0.68, 0.58 for intact, drained and extracted sites, correspondingly. Therefore, LST has a great potential to be utilized in Reco models as a proxy of thermal regime in disturbed and intact northern peatlands.

This study presents the development of a TKE-l parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large-scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and their number minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has also been paid to the numerical stability at typical time steps used in General Circulation Models. Test, parametric sensitivity assessment and preliminary tuning are performed on single-column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are also assessed through its implementation in the LMDZ General Circulation Model and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers.

Recently intensified forest fires in the Amazon region have led to large-scale forest losses, particularly in Brazil, after more than a decade of effective forest conservation policy. Analysis of the time course of fire impacts on water and carbon cycling is required for accurate measurement of changes in the forest-atmosphere interactions. Moreover, measurements must also account for natural variations associated with vegetation phenology, and generally direct and indirect effects of environmental changes at annual, seasonal and sub-annual time scales. Here, we study the recovery of two contrasting terra firme forests affected by fire in eastern (sub-montane ombrophile forests) and western (bamboo dominated forests) Amazonia in terms of water and carbon fluxes utilizing remote sensing (Moderate Resolution Imaging Spectroradiometer, MODIS) and climate reanalysis data (Global Land Data Assimilation System, GLDAS). Our results showed that fires significantly increased land surface temperature and air temperature in the forests over different time scales. However, the forests showed an ability to recover their original states in terms of coupling between the carbon and water cycles based on the comparison of the periods before and after the fires. Results from a wavelet analysis showed an intensification in annual and seasonal fluctuations, and in some cases (e.g., evapotranspiration) sub-annual fluctuations. Understanding the mechanisms controlling the forest-atmosphere interactions are essential for assessing how forest fires will influence the exchanges of water and carbon in the future. Improving data and theory about the impacts of fire and other disturbances on the energy balance is essential to improve earth systems models for forecasting the role of tropical forest fires in climate change. Within this context, our approach and, consequently, the results obtained here will help improve the understanding of how fires in terra firme Amazonian forests impact land-atmosphere coupling at different spatial and temporal scales.

The electromagnetic and electrostatic fields from powerful lightning heat and ionize the lower ionosphere. The disturbances appear as halos, sprites and elves, and are also observed as perturbations in crossing radio signals. The characteristic of the lightning discharges leading to the various types of perturbations is not fully understood. Here we present an analysis of 63 elves and corresponding VLF and MF signal perturbations from an almost stationary thunderstorm that allows us to untangle some of the dependencies of perturbations on the lightning characteristics. We characterize the perturbations to a VLF-transmitter signal as “long-recovery-early-events” (LOREs), “early” events, or “rapid-onset-rapid-decay” (RORD) events. We find that LOREs are related to high lightning current and bright elves, and their amplitude and sign depend on their location along the signal path. With observations in the ELF and MF band, we find that lightning with elves has three times the impulse charge moment change (iCMC) and ten times the power than lightning of similar peak current without elves. Attenuation in MF links appear in a higher proportion and longer duration observed with elves than with high peak current lightning without elves. The remaining types of VLF perturbations occur without TLEs but with sequences of lightning that produce slowly rising CMCs reaching high values (up to ~3500 C km within ~500 ms). Slower rise times lead to lower fields in the mesosphere that may not create significant ionization but instead drive dissociative attachment of free electrons. The depletions can result in perturbations to crossing VLF signals.