Feed aggregator

This study derives polarimetric radar vertical profiles and microphysical retrievals for 25 Synoptic Snow (SS) and 23 Lake Effect Snow (LES) cases using the Range-Defined Quasi-Vertical Profiles (RD-QVP), Columnar Vertical Profiles (CVP), and Process-oriented Vertical Profiles (POVP) methods. For all vertical profile techniques, SS cases exhibit a near-linear increase in reflectivity from −30 to 0°C whereas Z DR and K dp locally peak in the dendritic growth layer. LES cases universally exhibit negative Z DR , rather high Z, negligible K dp , and near-unity ρ hv . Ground measurements from the past OWLeS campaign provide direct evidence that conical graupel may strongly affect these polarimetric measurements in LES bands. Aggregation efficiencies for SS cases are estimated by optimizing the theoretical number concentration (N t ) and mean volume diameter (D m ) steady-state vertical profiles against radar-retrieved profiles derived from 20 of the 25 synoptic storm RD-QVPs. The median estimated aggregation efficiency is approximately 0.15 with a relatively narrow interquartile range that spans from 0.1 to just over 0.2. Values of optimized aggregation efficiencies are nearly independent of the assumed gamma distribution shape parameter. These results are used to derive temperature-dependent, climatological steady-state relations for vertical profiles of N t , D m , and liquid-equivalent snowfall rates. These results can be used in numerical weather prediction model aggregation parameterizations and can also provide climatologically representative vertical profiles of radar and microphysical quantities.

The Makassar Strait throughflow (MST) is the major component of the Indonesian Throughflow (ITF), transferring Pacific water into the Indian Ocean. In our previous study, we identified a new zonal pathway, a. k.a. the North Equatorial Subsurface Current (NESC), which carried equatorial water into the MST sub-thermocline (>300 m) in the summer 2016 following the 2015/16 El Niño. We now show continued strong southward MST in the sub-thermocline during the winter of 2016–2017, with salinity higher than that in the summer 2016, due to direct South Pacific water intrusion into the Sulawesi Sea. The origin of the intrusion is identified from the New Guinea Coastal Undercurrent (NGCUC) and from an anomalous westward flow along 3°N in the western equatorial Pacific. The identified interannual variability of the western Pacific Ocean circulation is particularly strong in the winter following super El Niño events.

The balance between particulate inorganic carbon (PIC) and particulate organic carbon (POC) holds significant importance in carbon storage within the ocean. A recent investigation delved into the spatial distribution of phytoplankton and the physiological mechanisms governing their growth. Employing random forests, a machine learning technique, this study unveiled apparent relationships between POC and 10 environmental fields. In this work, we extend the use of random forests to compare how observed PIC and POC respond to environmental conditions. PIC and POC exhibit similar responses to certain environmental drivers, suggesting that these do not explain differences in their distribution. However, PIC is less sensitive to iron and more sensitive to light and mixed layer depth. Intriguingly, both PIC and POC display weak sensitivity to CO2, contrary to previous studies, possibly due to the elevated pCO2 in our data set. This research sheds light on the underlying processes influencing carbon sequestration and ocean productivity.

Climate models struggle to produce sea surface temperature (SST) gradient trends in the tropical Pacific comparable to those seen recently in nature. Here, we find that the magnitude of the cloud-SST feedback in the subtropical Southeast Pacific is correlated across models with the magnitude of Eastern Pacific multi-decadal SST variability. A heat-budget analysis reveals coupling between cloud-radiative effects, circulation, and SST gradients in driving multi-decadal variability in the Eastern Pacific. Using this relationship and observed feedback estimates, we find that internal Eastern Pacific SST variability is underestimated in most models. Adjusting for model bias increases the likelihood of generating a cooling trend at least as large as observations in preindustrial control simulations by ∼ ${\sim} $56% on average. If models underestimate climate “noise,” as our results suggest, this bias should be accounted for when attributing the relative importance of forced versus unforced changes in the climate.

The depth of the Los Angeles (LA) basin is a critical factor for seismic hazard assessment and active tectonic studies. By analyzing S-waves generated by earthquakes below the basin that convert to P-waves at the sediment-bedrock interface, we estimate the maximum depth of the LA basin to be 9 km. This estimate depends on the velocities within and below the basin, and the depth presented here is based on the latest community velocity model. To map the basin depth, we use two dense arrays: the Community Seismic Network, a dense network of low-cost accelerometers in schools across LA region, and a basin-wide node survey conducted in 2022, consisting of about 300 geophones deployed for a month. Utilizing differential travel times between direct S and Sp converted phases of local earthquakes, we produce a detailed depth map of the LA basin.

Surface fluxes and states can recur and remain consistent across various spatial and temporal scales, forming space-time patterns. Quantifying and understanding the observed patterns is desirable, as they provide information about the dynamics of the processes involved. This study introduces the empirical spatio-temporal covariance function and a corresponding parametric covariance function as tools to identify and characterize spatio-temporal patterns in remotely sensed fields. The method is demonstrated using 2 km hourly GOES-16/17 land surface temperature (LST) data over the Contiguous United States by splitting the area into 1.0° × 1.0° domains. The summer day-time LST ESTCFs for 2018 to 2022 are derived for each domain, and a parametric covariance model is fitted. Clustering analysis is applied to detect areas with similar spatio-temporal LST patterns. Six main zones within CONUS are identified and characterized based on their variance and temporal and spatial characteristic length scales (i.e., scales for which the temperature variations are temporally and spatially related), respectively: (a) Eastern plains with 3 K2, ∼6 hr, and 0.15°, (b) Gulf of California with 60 K2, ∼8 hr, and 0.34°, (c) mountains and coasts transition 1 with 16 K2, ∼11 hr, and 0.25°, (d) central US, Midwest, and South cities with 5.5 K2, ∼8 hr, and ∼0.2°, (e) mountains and coasts transition 2 with ∼10 K2, ∼8 hr, and 0.2°, and (f) largest mountains and coastlines with ∼19 K2, ∼13 hr, and 0.3°. The tools introduced provide a pathway to formally identify and summarize the spatio-temporal patterns observed in remotely sensed fields and relate those to more complex processes within the Soil-Vegetation-Atmosphere System.

On 18 December 2022, Hawaiian Airlines flight HA35 encountered severe turbulence without warning in a cloud-free height. We reproduced this incident using the Weather Research and Forecasting Model (WRF) at a convection-permitting resolution. We found that this case of tropical upper-level turbulence occurred primarily due to the fast-growing convective tower in the unstable layer created by gravity wave breaking. At lower altitudes, a mesoscale convective system (MCS) caused a decrease in wind speed in both upstream and downstream regions. At upper levels, a large-scale jet descended and accelerated after flowing over the top of the MCS, which acted like a barrier and produced a situation similar to a downslope windstorm due to mountain terrain. Upper-level turbulence is 2–3 km higher than the top of the MCS. The critical level above the jet and the locally self-induced critical level created the locally enhanced descending jet stream, which destabilized the flow through Kelvin–Helmholtz instabilities. The convective tower existed near the flight route and played an important role in triggering turbulence in the unstable environment through its convective gravity waves.

Low latitude ionosphere experiences complex dynamical and electrodynamical processes, which make the spatiotemporal variations of the corresponding electron density complicated and therefore influence trans-ionosphere radio communications. The monitoring of low latitude dynamical drivers, such as neutral wind and ionospheric electric field, is essential for both dynamic mechanism investigations and applications. The Sanya Incoherent Scatter Radar Tristatic System (SYISR-TS) was proposed with the main objective of low latitude ionospheric monitoring and investigation and has been successfully developed over the past decade. The system consists of the Sanya (18.3°N, 109.6°E) trans-receiving main station with key parameters of ∼1,600 m2 antenna aperture, >4 MW peak power, <120 K system noise temperature, and ∼46 dBi normal gain, and Danzhou (19.5°N, 109.1°E) and Wenchang (19.6°N, 110.8°E) receiving only stations with key parameters of ∼790 m2 antenna aperture, <130 K system noise temperature, and ∼43 dBi normal gain. Three stations form a quasi-equilateral triangle at Hainan Island and use Global Navigation Satellite System satellite common view technique to achieve the time synchronization with the uncertainty of the timing and time synchronization less than 50 and 10 ns, respectively. Initial collaborative satellite tracking and ionospheric common volume experiments among three stations have confirmed the detection ability of SYISR-TS and the feasibility of achieving its scientific goals in the future.

Plasma sheet convection is a key element of storm-time plasma dynamics in the magnetosphere. While decades of observations have advanced our understanding of convection in general, specifically storm-time convection remains poorly understood. Using data from ISAS/NASA's Geotail and NASA's MMS, this study characterizes plasma sheet magnetic flux transport across the magnetotail during numerous storms (both recovery and main phases) and contrasts these observations with those from quiet times. Our findings confirm the well-documented enhancement of the convection electric field during geomagnetic storms. Beyond that, our results reveal a significant dawn-dusk asymmetry. At dawn, the elevated convection is realized via relatively faster flows while at dusk, through a stronger northward magnetic field. These findings suggest a complex feedback loop between plasma sheet convection and ring current buildup, whereby the latter asymmetrically inflates the magnetotail on the dusk side, shifting the reconnection site and subsequently enhanced earthward flows toward dawn.

While the Cretaceous Normal Polarity Superchron has documented instances of brief reversed polarity intervals, the absence of accurate age determinations for such abrupt shifts poses a challenge in leveraging them as reliable reference tiepoints. This study presents a cyclostratigraphic analysis of gamma-ray data from the DSDP Site 402A. The identification of Milankovitch cycles allowed us to construct a 405-kyr astronomically-tuned age model for two reversed events that occurred in the Aptian. Our results estimate an age of 117.03 ± 0.14 Ma for the Chron M”-1r” or ISEA and 116.17 ± 0.14 Ma for reversal “2,” with timespans of ∼20 and ∼10 kyr, respectively. Sedimentation patterns are influenced by orbital eccentricity cycles with an average sedimentation rate of 5 cm/kyr. The short time intervals related to these reversals exposes the difficulty in their detection in cores through paleomagnetic analysis as well as in deep-tow surveys.

The interannual variation of the South China Sea (SCS) summer monsoon onset (SMO) may bring extreme weather and climate disasters in East Asia. However, its skillful forecast still remains challenging. This study investigates the intraseasonal ocean-atmosphere interaction that affects the SCSSMO through diagnostic analysis and numerical experiments. It reveals that the cold sea surface temperature in the Southern SCS during winter (referred as cold tongue, CT) is the key pathway controlling the propagation of the 30–60 days intraseasonal oscillation (ISO) convective system from the Bay of Bengal (BOB) to the SCS. The CT variations affect the interannual variation of the SCSSMO. Specifically, the strong (weak) CT after the peak of La Niña (El Niño) years suppresses (enhances) the propagating ISO from the BOB to the SCS, resulting in a delayed (advanced) SCSSMO. This finding offers the new scientific insights for improving the forecasting of the SCSSMO.

The Arase satellite observed the precipitation of monoenergetic electrons accelerated from a very high altitude above 32,000 km altitude on 16 September 2017. The event was selected in the period when the high-angular resolution channel of the electron detector looked at pitch angles within ∼5° from the ambient magnetic field direction, and thereby was the first to examine the detailed distribution of electron flux near the energy-dependent loss cone at such high altitudes. The potential energy below the satellite estimated from the observed energy-dependence of the loss cone was consistent with the energy of the upgoing ion beams, indicating that ionospheric ions were accelerated by a lower-altitude acceleration region. The accelerated electrons inside the loss cone carried a significant net field-aligned current (FAC) density corresponding to ionospheric-altitude FAC of up to ∼3μA/m2. Based on the anisotropy of the accelerated electrons, we estimated the height of the upper boundary of the acceleration region to be >∼2 R E above the satellite. The height distribution of the acceleration region below the satellite, estimated from the frequency of auroral kilometric radiation, was ∼4,000–13,000 km altitude, suggesting that the very-high-altitude acceleration region was separated from the lower acceleration region. Additionally, we observed time domain structure (TDS) electric fields on a subsecond time scale with a thin FAC indicated by magnetic deflections. Such a TDS may be generated by the formation of double layers in the magnetotail, and its potential drop could significantly contribute (∼40%–60%) to the parallel energization of precipitating auroral electrons.

We use conjugate observations of magnetospheric whistler-mode waves at frequencies up to 16 kHz by the DEMETER spacecraft (at an altitude of approximately 660 km) and the ground-based Kannuslehto station in Finland (L≈5.38) $(L\approx 5.38)$ to investigate the wave propagation to the ground and their characteristic spatial scales. For this purpose, we evaluate correlations between the wave intensities measured by the spacecraft and the ground-based station at various frequencies as a function of their longitudinal and L-shell separations. Two different approaches are used: (a) direct correlation of wave intensities measured at the same times and (b) correlation of wave intensities within corresponding frequency-time windows, focusing on the identification of the same frequency-time wave signatures. We show that the characteristic longitudinal scales of the investigated waves are between about 60° $60{}^{\circ}$ and 90° $90{}^{\circ}$. We further demonstrate that, while the wave intensities measured by DEMETER are generally larger during periods of enhanced geomagnetic activity, wave intensities measured on the ground during increased activity are only slightly larger during the daytime and decrease during the nighttime.

Polarization and propagation characteristics of ultra-low frequency (ULF, ≃1−1000 $\simeq 1-1000$ mHz) waves are conventionally studied using arrays of ground-based magnetometers. However, the ground magnetometer observations are subject to distortions due to polarization rotation and spatial integration effects caused by the transition of the magnetohydrodynamic wave into an electromagnetic wave at the lower ionospheric boundary. In contrast, high-frequency (3–30 MHz) radars, like those comprising the Super Dual Auroral Radar Network (SuperDARN), are capable of direct observations of the ULF wave characteristics at ionospheric altitudes via measuring plasma drift velocity variations caused by the wave's electric field. In this work, we use multi-beam data from SuperDARN Hokkaido East, Hokkaido West, and Christmas Valley West radars to identify the dominant polarization modes as well as azimuthal wave numbers of evening-night-side-morning ULF waves in the Pc5 frequency band (1.67–6.67 mHz) propagating over sub-auroral and mid-latitude regions. The observed statistical characteristics of these waves point at the solar wind dynamic pressure variations and Kelvin-Helmholtz instability at the magnetopause as their potential principal sources, although the drift-bounce resonance with trapped energetic ions may contribute to the small-scale part of the observed Pc5 wave population.

Standard finite volume or finite difference methods may produce unphysical negative solutions of phase space density when applied to radiation belt diffusion equation with cross diffusion terms. In this work, we apply a recently proposed positivity-preserving finite volume (PPFV) method to a 2D diffusion problem of radiation belt electrons with both structured and unstructured meshes. Our test using a model problem shows that the new method does not produce unphysical negative solutions with both types of meshes even with strong cross-diffusion terms. By applying the method to the 2D pitch angle and energy diffusion problem, we demonstrate that the method achieves positivity of solutions without requiring excessive number of grid points and shows good agreement with previous results obtained using a layer method. The ability of preserving positivity of the solution with unstructured meshes allows the method to handle complex boundary configurations. Our results suggest that the new PPFV method could be useful in modeling radiation belt diffusion processes or in building a physics-based forecast model.

Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)-32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision-making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short-Term Average/Long-Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long-term, real-time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

Recently, more advanced synchronous global-scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO-2) solar-induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root-mean-squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (Ks) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi-arid regions. The wilting point and maximum Rubisco carboxylation rate (Vmax) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO-2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles.

Meteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land-atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under-sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under-sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station.

The magnetotail plasma sheet boundary layer (PSBL) is a dynamic boundary layer between the hot-denser plasma sheet and the cold-tenuous tail lobes. It plays an important role in exchanging mass and energy in the magnetotail. In previous studies, the local current carried by the electron beams has been well understood. The strong energy conversion (E ⋅ J, E is electric field and J is current density), however, is barely reported at the PSBL. Here, using magnetospheric multiscale mission, we find a strong dawn-dusk current with a magnitude of 80 nA/m2 at the magnetotail PSBL. The strong current appears during crossing the PSBL and is primarily contributed by the perpendicular electron currents. Cooperating with an intense fluctuating electric field (reaching ∼40 mV/m) carried by the lower-hybrid drift waves, this dawn-dusk current leads to a strong energy conversion with a magnitude larger than 3 nW/m3. This study enhances the understanding of local energy-conversion processes at the PSBL.

Ambient noise interferometry has become a common technique for monitoring slight changes in seismic velocity in a variety of contexts. However, the physical origin of the resolved small velocity fluctuations is not well established for long-term seasonal effects. Here we propose a physical forward model of scattered waves in a deformable medium that includes acousto-elastic effect, which refers to non-linear elasticity with third-order elastic constants. The model shows that small pressure perturbations of a few kPa due to seasonal variations in the water table can induce seismic velocity changes compatible with those measured at the surface by ambient noise interferometry. The results are consistent with field observations near the deep geothermal site of Rittershoffen (France). They illustrate the capability in modeling the diffuse wavefield from scattering synthetic waves to reproduce ambient noise signals for monitoring environmental and/or deep reservoir signals.