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Abstract

Habitability at the surface of a planet depends on having an atmosphere long enough for life to develop. The loss of atmosphere to space is an important component in assessing planetary surface habitability. Current models of atmospheric escape from exoplanets are not well constrained by observations. Atmospheric escape observations from the terrestrial planets are available in public data archives. We recast oxygen escape rates from Earth derived from an instrument on Dynamics Explorer-1 as function of solar wind and compare them to similar data from Mars. Analysis demonstrates that oxygen escape rates from Mars are not as sensitive to variations in solar power components as those from Earth. Available data from Venus can confirm or refute the assertion that oxygen escape from magnetized planets is more sensitive than that from unmagnetized planets.

Abstract

On New Year's Day 2024, a magnitude 7.6 event struck the Noto Peninsula in central Japan. Prior to this event, an intense earthquake swarm had persisted beneath the northeastern peninsula for more than five years. Geophysical evidence provides insight into the upwelling of deep fluids from the uppermost mantle that triggers the seismic swarm activity. The noble gases and their isotopes have been used as geochemical indicators to determine the origin of the fluids associated with the swarms and their upwelling. Gas samples collected from boreholes around the seismic source region are characterized by anomalously high 3He/4He ratios (∼3.9 RAcor), indicating infiltration of mantle fluids from the subcrustal lithosphere. Using a steady-state advection model, we calculated mantle helium fluxes of 1.1–2.4 × 10−15 mol cm−2 a−1, similar to those estimated for other representative fault zones, such as the San Andreas and North Anatolian faults.

Abstract

Saline lakes contributions to the carbon cycle is crucial to the Qinghai-Tibetan Plateau (QTP) carbon budget. Here, based on the 8-year direct measurement of CO2 flux over the Qinghai Lake (QHL) and 83 collected CO2 flux data estimated by pCO2 sampling from 45 lakes over the QTP, we identified the interannual variations of CO2 flux and its response to the extreme climate events. Results showed: (a) the QHL CO2 absorption weakened in the spring, autumn and winter and turn to CO2 emissions in the summer during 2013–2020; (b) with higher Ts and less precipitation, coupling of positive Pacific Decadal Oscillation (PDO) and El Niño enhanced the summer CO2 emissions; and (c) the PDO and ENSO had obvious superposition effect on the decrease of CO2 absorption in autumn. Our results show the potential mechanism of lake CO2 flux responses to extreme climate and further defines the significance of the QTP carbon budget and cycling.

A modular approach to volatile organic compound samplers for tethered balloon and drone platforms
Meghan Guagenti, Darielle Dexheimer, Alexandra Ulinksi, Paul Walter, James H. Flynn III, and Sascha Usenko
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-96,2024
Preprint under review for AMT (discussion: open, 0 comments)
A robust, automatic VOC collection system was developed for vertical volatile organic compounds (VOCs) sampling associated with the 2022 DOE ARM program-led TRACER in Houston, TX.  This modular sampler has been developed to measure vertical profiles of VOCs to improve near-surface characterization. This article helps fill the current lack of commercially available options for aerial VOC sampling and serves to support and encourage researchers to build and develop custom samplers. 

EMADDC: high quality, quickly available and high volume wind and temperature observations from aircraft using the Mode-S EHS infrastructure
Siebren de Haan, Paul de Jong, Michal Koutek, and Jan Sondij
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-110,2024
Preprint under review for AMT (discussion: open, 0 comments)
This manscript describes the operational method of extracting meteorological information from all airborne aircraft in the European airspace every 20 seconds to 1 minute. The methodology is described and the quality of the wind and temperature observations are evaluated against radiosonde observations.

Hydrothermal vents can be found around the world at the junctions of drifting tectonic plates. But there are many hydrothermal fields still to be discovered. During a 2022 expedition of the MARIA S. MERIAN, the first field of hydrothermal vents on the 500-kilometer-long Knipovich Ridge off the coast of Svalbard was discovered.

Abstract

Normal mode analysis is a Laplace-transform method for calculating the surface-loading response of laterally homogeneous spherical Earth models with linear viscoelasticity which delivers modal decay times and amplitudes. It can locally fail owing to numerical singularities arising from the viscoelastic parameters, leading to an incomplete accounting of the surface-loading response. Collocation methods were developed to circumvent this issue. The mixed collocation method includes least-squares fitting to the Laplace-transformed Earth response to determine amplitudes assuming the normal mode decay times are known, while the pure collocation method assumes a series of logarithmically regularly spaced inverse decay times for which amplitudes are determined numerically. Both collocation methods may determine amplitudes that are physically unrealistic and all three methods produce crustal motion predictions that differ significantly. The hybrid normal mode-collocation method presented here applies the normal mode analysis, and then applies the pure collocation to the resulting residuals. This retains the modal structure, while providing an improved fit. Our implementation avoids numerical singularities that may arise from Rayleigh-Taylor instabilities occurring at large times and can be automated. Vertical crustal motions predicted by the hybrid method for North America with the ICE-6G_C loading model and the VM5a viscosity structure have a root mean square (RMS) of 4.49 mm/yr and RMS differences with the normal mode, pure, and mixed collocation method of 0.06, 0.23, and 0.25 mm/yr, respectively. Maximum differences reach 0.20, 0.87, and 0.63 mm/yr. The differences increase for a viscosity profile with a greater viscosity increase with depth that exhibits stronger singularity issues.

Author(s): Giovanni Catania, Aurélien Decelle, and Beatriz Seoane

We characterize the equilibrium properties of a model of y coupled binary perceptrons in the teacher-student scenario, subject to a suitable cost function, with an explicit ferromagnetic coupling proportional to the Hamming distance between the students' weights. In contrast to recent works, we anal…

[Phys. Rev. E 109, 065313] Published Fri Jun 28, 2024

A study led by a UC Riverside atmospheric scientist predicts that unchecked carbon emissions will force tropical rains to shift northward in the coming decades, which would profoundly impact agriculture and economies near the Earth's equator.

Abstract

We present an analysis of Antarctic polar winters from 2005 to 2023 as observed by the Atmospheric Chemistry Experiment (ACE). The unique broad band infrared spectral features in ACE “residual” spectra are used to classify the spectra of polar aerosols by composition into polar stratospheric clouds (PSCs) and sulfate aerosols. The spectra of PSCs are further classified into nitric acid trihydrate, supercooled ternary solutions, supercooled nitric acid, ice-mix, and mixtures of PSCs. A breakdown of PSC composition is presented for each year. Antarctic winter seasons with unusual compositions are: 2011, in which volcanic ash mixed with PSCs was observed from July to August; 2019, which experienced a stratospheric warming event; 2020, the PSC season following the Australian Black Summer pyrocumulonimbus event; and 2023, which had unusually large sulfate aerosols following the Honga-Tonga Honga Ha'apai eruption of 2022.

Abstract

ANCHOR is a novel assimilative model developed at the U.S. Naval Research Laboratory, which was designed for rapid assimilative runs. ANCHOR uses recently developed PyIRI model for the background and for the formation of the background covariance matrix. It only takes a few minutes for ANCHOR to complete the data assimilation (DA) for one day, including data pre-processing and model set up. ANCHOR extracts ionospheric parameters from radio occultation (RO) and ionosonde data using PyIRI formalism and assimilates them as point measurements into maps of the background parameters using a Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment with synthetic data simulated using the SAMI3 model. ANCHOR reduces the root mean square errors in the analysis by more than a half for all of the ionospheric parameters in comparison to the background. Finally, this paper discusses advantages and limitations of the parametrized ionospheric DA, highlighting the avenues for its future improvement.

Science, Volume 384, Issue 6703, June 2024.

Science, Volume 384, Issue 6703, June 2024.

Science, Volume 384, Issue 6703, June 2024.

Science, Volume 384, Issue 6703, June 2024.

Abstract

In this paper the meteorological drivers of North American Monsoon (NAM) extreme precipitation events (EPEs) are identified and analyzed. First, the NAM area and its subregions are distinguished using self-organizing maps applied to the Climate Prediction Center global precipitation data set. This reveals distinct subregions, shaped by the inhomogeneous geographic features of the NAM area, with distinct extreme precipitation character and drivers. Next, defining EPEs as days when subregion-mean precipitation exceeds the 95th percentile of rainy days, five synoptic features and one mesoscale feature are investigated as potential drivers of EPEs. Essentially all EPEs can be associated with at least one selected driver, with only one event remaining unclassified. This analysis shows the dominant role of Gulf of California moisture surges, mesoscale convective systems and frontal systems in generating NAM extreme precipitation. Finally, a frequency and probability analysis is conducted to contrast precipitation distributions conditioned on the associated meteorological drivers. The findings demonstrate that the co-occurrence of multiple features does not necessarily enhance the EPE probability.

Abstract

Classically, vertical reference frames were realized as national or continent-wide networks of geopotential differences derived from geodetic leveling, i.e., from the combination of spirit leveling and gravimetry. Those networks are affected by systematic errors in leveling, leading to tilts in the order of decimeter to meter in larger networks. Today, there opens the possibility to establish a worldwide unified vertical reference frame based on a conventional (quasi)geoid model. Such a frame would be accessible through GNSS measurements, i.e., physical heights would be derived by the method of GNSS-leveling. The question arises, whether existing geodetic leveling data are abolished completely for the realization of vertical reference frames, are used for validation purposes only, or whether existing or future geodetic leveling data can still be of use for the realization of vertical reference frames. The question is mainly driven by the high quality of leveled potential differences over short distances. In the following we investigate two approaches for the combination of geopotential numbers from GNSS-leveling and potential differences from geodetic leveling. In the first approach, both data sets are combined in a common network adjustment leading to potential values at the benchmarks of the leveling network. In the second approach, potential differences from geodetic leveling are used as observable for regional gravity field modeling. This leads to a grid of geoid heights based on classical observables like gravity anomalies and now also on leveled potential differences. Based on synthetic data and a realistic stochastic model, we show that incorporating leveled potential differences improves the quality of a continent-wide network of GNSS-heights (approach 1) by about 40% and that formal and empirical errors of a regional geoid model (approach 2) are reduced by about 20% at leveling benchmarks. While these numbers strongly depend on the chosen stochastic model, the results show the benefit of using leveled potential differences for the realization of a modern geoid-based reference frame. Independent of the specific numbers of the improvement, an additional benefit is the consistency (within the error bounds of each observation type) of leveling data with vertical coordinates from GNSS and a conventional geoid model. Even though we focus on geodetic leveling, the methods proposed are independent of the specific technique used to observe potential (or equivalently height) differences and can thus be applied also to other techniques like chronometric or hydrodynamic leveling.

Nature Geoscience, Published online: 28 June 2024; doi:10.1038/s41561-024-01481-7

Climate models and paleoclimate proxy records indicate that the absence of preserved eastern Mediterranean organic-rich layers preceding mid-Pliocene glaciation is linked to a pan-North African humid period caused by a more northerly African monsoon front relative to subsequent glacials. The vegetation expansion caused by this humid phase might have influenced early hominin dispersal.

SummaryMode conversion of P waves at the boundary between Earth's crust and upper mantle, when analyzed using receiver functions (RFs), allows characterization of Earth structure where seismic station density is high and earthquake sources are favorably distributed. We applied two ensemble decision tree algorithms – Random Forest (RanFor) and eXtreme Gradient Boost (XGBoost) – to synthetic and real RF data to assess these machine learning techniques' potential for crustal imaging when available data are sparse. The synthetic RFs, entailing both sharp increases in seismic velocity across the Moho and gradational Moho structures, calculated with and without added random noise, correspond to idealized crustal structures: a dipping Moho, Moho offset by crustal-scale faults, anti- and synform Moho structures and combinations of these. The RanFor/XGBoost algorithm recovers input structures well regardless of event-station distributions. Useful crustal and upper mantle seismic velocities can also be determined using RanFor and XGBoost, making it possible to image crustal thickness and P and S wave velocities simultaneously from receiver functions alone. We applied the trained RanFor/XGBoost to receiver functions determined from real seismic data recorded in the contiguous U.S., producing a map of the Moho and P and S wave velocities of the lowermost crust and uppermost mantle. Use of XGBoost, which evaluates residuals between input RFs and ground-truth to update the decision tree using the gradient of a penalty function, improves the crustal thickness estimates.

SummaryDuring the inversion of seafloor topography (ST) using the backpropagation neural network (BPNN), the random selection of parameters may decrease the accuracy. To address this issue and achieve a more efficient global search, this paper introduces a genetic algorithm-backpropagation (GA-BP) neural network. Benefiting from the global search and parallel computing capabilities of the GA, this study refines the seafloor topography of the South China Sea using multi-source gravity data. The results indicate that the GA-BP model, with a root mean square (RMS) value of 126.0 m concerning ship-measured water depths. It is noteworthy that when dealing with regions characterized by sparse survey line distributions, the GA-BP neural network stronger robustness compared to BPNN, showing less sensitivity to the distribution of survey data. Furthermore, the paper explores the influence of different data preprocessing methods on the neural network inversion of sea depths. This research introduces an optimization algorithm that reduces instability during BPNN initialization, resulting in a more accurate prediction of seafloor topography.