A document by Ankit Ghanghas. Click on the document to view its contents.

A document by Matthew Kenneth Brown. Click on the document to view its contents.

Stratospheric Sudden Warmings (SSWs) predominantly occur in the Northern Hemisphere with only 1 major event recorded in the Southern Hemisphere in the satellite era. Investigating factors that contribute to this asymmetry can help to reveal the cause of SSWs and lead to improved forecasts. Here we use climate model simulations to investigate the impact of boundary conditions (topography and ocean circulation) on the asymmetry. Flattening topography eliminates Northern Hemisphere SSWs, while removing the ocean meridional overturning circulation reduces their frequency by half. The SSW response to boundary conditions is controlled by decrease in hemispheric asymmetry of eddy heat flux. The reduction is driven by a decrease in amplitude of both eddy meridional wind and eddy temperature, as well as an increase in the difference between their phases. The results suggest boundary conditions play an important role in shaping SSWs, especially topographic forcing, but that the boundary condition interactions are nonlinear.

This study investigates the physical processes behind extreme precipitation events (EPEs) in the Himalayas, notorious for causing frequent floods and significant loss of life and property. Due to the presence of complex terrain, understanding the driving factors behind these EPEs has proven challenging. Here, we decipher the driving conditions responsible for the occurrence of EPEs in the western Himalayas (WH) for the period 1979 to 2020. Our findings provide compelling evidence for the role of large-scale circulation patterns and their associated dynamics and thermodynamics in instigating EPEs. The presence of distinct upper-tropospheric gyres flanking the WH, alongside a prominent zonal wave pattern, underscores the conducive atmospheric configuration during EPEs. This configuration promotes a southward extension of the trough, intensifying the convergence of moisture-laden winds from the adjoining seas, leading to substantial moisture availability for the EPEs. Moreover, the southward advancement of cyclonic vorticity further aids in northward moisture advection towards the region. At a regional scale, using moisture budget analysis, we find that vertical moisture advection plays a significant role, emphasizing the dominance of local dynamics driving these EPEs. Furthermore, the intensifying diabatic heating structure over the WH leads to intensified convection through stronger vertical motions, facilitating the development of deep convection. Our results also pinpoint the role of the shifting of the Intertropical Convergence Zone (ITCZ), strongly linked to the dynamics of convective clouds, resulting in changes in the intensity of EPEs over the WH. Additionally, Quasi-Resonance Amplification is linked with the most intensified/persistent EPEs over the Himalayas.

IntroductionAtmospheric River (AR) is a long, narrow stream of water vapor that moves in the earth’s atmosphere and carries abound moisture from the mid-latitude, engendering extreme rainfall upon landfall (Ralph et al., 2006). According to Zhu & Newell, (1998), around 90% of the largest rainfall events in the world have been followed by ARs. Furthermore, recent research shows the relevance of AR in occurring extreme precipitation in South East Asia, especially in the Bay of Bengal Region (Yang et al., 2018). Hence, the role of AR in generating extreme rainfall in Meghalaya is necessary for a better understanding of flash floods in the North Eastern (NE) region of Bangladesh. The NE region of Bangladesh is exposed to regular flash floods because of the rapid rainfall-runoff response from the extreme precipitation in Meghalaya. On June 2022, this region experienced one of the most devastating floods, more than 70% of the Sylhet and Sunamganj district went under water, as the aftermath of the heaviest rainfall in the last 102 years in Mawsynram, Meghalaya in conjunction with the existing water in the floodplain due to the flash flood in the previous month.  Therefore, this study illustrates the contribution of AR in generating the extreme precipitation in the Meghalaya region on June 2022 that created enormous flooding in the NE region of Bangladesh. MethodologyTo analyze the contribution of AR in the extreme rainfall event in Meghalaya on June 2022, the Vertically integrated Northward and Eastward Water Vapor Flux or Integrated Vapor Transport (IVT) in Meridian and Zonal direction data are collected from the European Center of Medium Range Weather Forecasts (ECMWF) ERA-5 Reanalysis data of June 2022 with an interval of 6 hours. Secondly, the extreme rainfall days have been identified from gauge station rainfall data from the Indian Meteorological Department (IMD). After that, the presence of AR will be detected by calculating the magnitude of IVT and identifying the geometric Criteria of AR (Length and area of AR, Length to Width ratio, and the direction of the IVT front)(Xu et al., 2020) from the collected data. Finally, the identified AR for June 2022 will be compared with the extreme rainfall timeline to determine the contribution of AR to this extreme rainfall. The conceptual framework of the study is provided in Figure 1.

Water level variations influence the biochemical and hydrological processes within river networks. Through river cameras, obtaining reliable water segmentation from image data can practically support the monitoring of water level. However, limited annotated data and tedious local deployment restrict the applicability of current deep learning water segmentation models in new river scenarios. To pursue transferability, this study proposes a novel framework that combines domain-specific models with General AI for water segmentation. The framework utilizes a ResUnet model pre-trained on a non-local dataset to identify the pixel with the highest probability of being water from the image. The Segment Anything Model (SAM), a promptable foundational computer vision model developed by Meta AI, is then adopted to use the pixel as prompt for generating water masks. When prompted, different modes of SAM are used for comparison. We applied the framework to image sequences acquired from river cameras stationed at four locations in Tewkesbury, UK. The framework significantly improved segmentation performance, with an increase of over 15% in Intersection over Union (IoU) over the single ResUnet model. Meanwhile, the results substantiated point prompt as the optimal mode for feeding prior knowledge on water to SAM. The static observer flooding index (SOFI) time series calculated based on the framework’s segmented masks under point prompt mode exhibit an average correlation of 0.90 with real water level fluctuations, significantly surpassing the correlation of 0.54 attained by ResUnet. Our study thus represents a step toward implementing river cameras for robust water level trend monitoring.

The paucity of fine particulate matter (PM2.5) measurements limits estimates of air pollution mortality in Sub-Saharan Africa. If well calibrated, low-cost sensors can provide reliable data especially where reference monitors are unavailable. We evaluate the performance of Clarity Node-S PM monitors against a Tapered element oscillating microbalance (TEOM) 1400a and develop a calibration model in Mombasa, Kenya’s second largest city. As-reported Clarity Node-S data from January 2023 through April 2023 was moderately correlated with the TEOM-1400a measurements (R2 = 0.61) and exhibited a mean absolute error (MAE) of approximately 7.03 µg m–3. Employing three calibration models, namely, multiple linear regression (MLR), gaussian mixture regression (GMR) and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40 µg m–3 respectively. The R2 value improved to 0.63 for the MLR model but all other models registered a decrease (R2 = 0.44 and 0.60 respectively). Applying the correction factor to a 5-sensor network in Mombasa that was operated between July 2021 and July 2022 gave insights to the air quality in the city. The average daily concentrations of PM2.5 within the city ranged from 12 to 18 µg m–3. The concentrations exceeded the WHO daily PM2.5 limits more than 50% of the time, in particular at the sites nearby frequent industrial activity. Higher averages were observed during the dry and cold seasons and during early morning and evening periods of high activity. These results represent some of the first air quality monitoring measurements in Mombasa and highlight the need for more study.

Barchans are dunes of crescent shape found on Earth, Mars and other celestial bodies. Among the different types of barchan-barchan interaction, there is one, known as chasing, in which the dunes remain close but without touching each other. In this paper, we investigate the origins of this barchan-barchan repulsion by carrying out grain-scale numerical computations in which a pair of granular heaps is deformed by the fluid flow into barchan dunes that interact with each other. In our simulations, data such as position, velocity and resultant force are computed for each individual particle at each time step, allowing us to measure details of both the fluid and grains that explain the repulsion. We show the trajectories of grains, time-average resultant forces, and mass balances for each dune, and that the downstream barchan shrinks faster than the upstream one, keeping, thus, a relatively high velocity although in the wake of the upstream barchan. In its turn, this fast shrinkage is caused by the flow disturbance, which induces higher erosion on the downstream barchan and its circumvention by grains leaving the upstream dune. Our results help explaining the mechanisms behind the distribution of barchans in dune fields found on Earth and Mars.

The most detrimental geomagnetically induced currents (GICs) documented to date have all taken place during geomagnetic storms. Yet, the probability of GICs throughout geomagnetic storms driven by different solar wind transients, such as high-speed streams/stream interaction regions (HSS/SIR) or interplanetary coronal mass ejection (ICME) sheaths and magnetic clouds (MC), is poorly understood. We present an algorithm to detect geomagnetic storms and storm phases, resulting in a catalog of 755 geomagnetic storms from January 1996 to June 2023 with the solar wind drivers. Using these storms and the IMAGE magnetometer network, we study the temporal and spatial evolution of spikes in the external dH\textsubscript{ext}/dt greater than 0.5 nT/s during geomagnetic storms driven by HSS/SIR, sheaths and MCs. Spikes occur more often toward the end of the storm main phase for HSS/SIR and MC-driven storms, while sheaths have spikes throughout the entire main phase. During the main phase most spikes occur in the morning sector around 05 magnetic local time (MLT) and the extent in MLT is narrowest for MCs and widest for sheaths. However, spikes in the pre-midnight sector during the main and recovery phases are most prominent for HSS/SIR-driven storms. During the storm sudden commencement (SSC), three MLT hotspots exist, the post-midnight at 04 MLT, pre-noon at 09 MLT and afternoon at 15 MLT. The pre-noon hotspot has the highest probability of spikes and the widest extent in magnetic latitude.

This study reports on the upper atmospheric response over Thailand to the Hunga Tonga volcano’s eruption on January 15th, 2022. The eruption occurred during the geomagnetic storm recovery phase, providing a rare comparison between effects from outside (geomagnetic storm) and inside atmosphere (volcanic eruption). About nine hours later, we observed post-eruption fluctuations in the ionosphere total electron content (TEC). TEC was recorded in Thailand approximately ten times remarkably higher than typical levels from this large perturbation. The initial impact reached Thailand with speed of ~275 m/s. Detrended TEC (dTEC) revealed mixed wave packets at various intervals. Two significant traveling ionospheric disturbance (TID) waves reached Thailand at 13 UT and 14 UT, respectively. Equatorial plasma bubbles (EPBs) were observed between 12-15 UT and 17-18 UT over Thailand. Our findings could provide insight into how communication signals over Thailand are affected by both disturbances, particularly in the case of widespread volcanic eruptions.

Previous efforts to measure atmospheric iodine have focused on marine and coastal regions. We report the first ground-based tropospheric iodine monoxide (IO) radical observations over the central continental United States. Throughout April 2022, IO columns above Storm Peak Laboratory, Colorado (3220 m.a.s.l.) ranged from 0.7±0.5 to 3.6±0.5×1012 (average: 1.9×1012 molec cm-2). IO was consistently elevated in air masses transported from over the Pacific Ocean. The observed IO columns were up to three times higher and the range was larger than predicted by a global model, which warrants further investigation into iodine sources, sinks, ozone loss, and particle formation. IO mixing ratios increased with altitude. At the observed levels, iodine may be competitive with bromine as an oxidant of elemental mercury at cold temperatures typical of the free troposphere (4–12km; <260K). Iodine-induced mercury oxidation is missing in atmospheric models, understudied, and helps explain model underestimation of oxidized mercury measurements.

Recent seismic tomography studies have shown that distinct low-velocity anomalies exist below subducting slabs in many subduction zones and these anomalies are interpreted as a hot plume from the lower mantle. However, how high the temperatures in the sub-slab low-velocity anomalies are still unclear. Here, we conduct receiver function analysis and estimate the horizontal temperature variation in the mantle transition zone by determining the depth variation of 410 and 660 discontinuities beneath northeastern Japan. The obtained results show that the depth of the two discontinuities changes little, which suggests no distinct thermal heterogenies over the study area. Therefore, we infer that the major cause of the sub-slab low-velocity anomaly is attributable not to high-temperature anomaly but to the presence of small amount (~0.2 wt%) of hydrous minerals, which can explain the sub-slab low-velocity anomalies and the flat 410 and 660 discontinuities.

The Kuroshio Loop Current (KLC) is an important form of Kuroshio intrusion into the northeastern South China Sea (NESCS), which has significant influences on dynamical and biogeochemical processes in the NESCS. Recent studies suggested that the KLC is a hot spot of submesoscale processes (submesoscales) with spatiotemporal scales of O(1–10) km and O(1–10) days, but submesoscales’ roles in energy cascade and salt and heat transports remain obscure. Here, we investigate this issue through analyzing outputs from a 1/48° simulation. The kinetic energy exchange rate between submesoscale and larger-scale processes (KER) is overall positive in the KLC region, which suggests the dominance of forward cascade. The magnitude of KER is comparable with the temporal change rate of larger-scale kinetic energy in the upper 200 m. We also find that magnitude and direction of KER are closely associated with strain rate and horizontal divergence of background flows, respectively. In addition, the KLC region shows elevated submesoscale salinity and heat diffusivities with magnitudes reaching O(102) m2s-1. During the KLC period, horizontal mixing by submesoscales can transport 0.90×1013 kg salt and 0.71×1020 J heat westward into the NESCS interior, which are an order of magnitude larger than those caused by the KLC eddy shedding. These results suggest that submesoscales play important roles not only in energy cascade but also in salt and heat transports in the KLC region. Therefore, the roles of submesoscales should be taken into account when studying energy, salt, and heat budgets in the NESCS.

Advancements in large language models (LLMs) provide opportunities to accelerate progress towards the attainment of the Sustainable Development Goals (SDGs). Current research largely overlooks the nuanced benefits and dangers LLMs introduce to sustainability research and communication, as well as broader challenges that need to be addressed in the longer term. This paper overcomes these shortcomings by introducing and discussing a framework that highlights how LLMs can benefit knowledge production, mobilization, and communication in the sustainability sciences, as well as any associated dangers. In addition, it outlines potential long-term challenges that must be acknowledged and addressed to ensure the responsible use of LLMs in advancing sustainability science. A key to the development and use of LLMs for sustainability science is the development of regulatory measures. These measures should be guided by what is needed for expanding sustainability science on the one hand and a holistic view to ensure its responsible use on the other. Failure to reflect and act on this might result in unintended consequences or misuse, making the technology another roadblock to progress towards the SDGs.

This paper presents the first ever observations on aspect-sensitive characteristics of 205 MHz Stratosphere-troposphere radar located at a tropical station Cochin (10.04°N, 76.3°E) using volume scanning. The most significant and new observation is that the signal-to-noise ratio in zenith and off-zenith beams are nearly equal in some height region, indicating the presence of isotropic turbulence. Signal strength decreases by 0.75 dB per degree from 0 to 10 degree off-zenith, 0.9 dB per degree from 10 to 20 degree off-zenith and 0.3 dB per degree beyond 20 degree offzenith. Different causative mechanisms are discussed on the basis of various estimated parameters associated with aspect sensitivity. Maximum aspect sensitivity is observed between 12 and 17 km, indicating the presence of dynamic instability arising due to strong wind shear and atmospheric stability. When both the square of wind shear and stability parameters are above 0.25 × 10 −3 s −2 , the scatterers become mostly isotropic. The study also shows a power difference in the symmetric beams as well as azimuth angle dependency. Analysis suggests that this asymmetry is due to the tilting of layers by the action of atmospheric gravity waves generated through Kelvin-Helmholtz-instability. The

As a unique phenomenon occurring in the subsurface ocean, submesoscale coherent vortices (SCVs) are believed to have a pivotal role in the long-distance ocean tracer transports. SCVs have been widely observed in the global oceans, however, most of them are captured accidentally and their kinematic characteristics and water mass transports have only been studied in a limited number of regions. Here, we use 4-year observations of velocity, temperature, and salinity from five moorings in the northeastern South China Sea (NESCS) to examine dozens of newly discovered SCVs. A total of 34 SCVs were identified during the observational period, including 25 convex lens-like anticyclones and 9 concave lens-like cyclones. The maximum swirl velocity, mean radius, and vertical length scale of the anticyclones (cyclones) are 0.19±0.07 m s–1 (0.19±0.07 m s–1), 26.4±13.9 km (17.0±5.4 km), and 204±62 m (188±53 m), respectively. Vertically, the velocity structure of the observed SCVs conforms to a Gaussian function when the effect of stratification is removed. Water mass analyses suggest that 88% (30/34) of the SCVs carried Kuroshio water, which demonstrates the mechanism proposed by Zhang et al. (2022) that they are formed by Kuroshio-islands interactions within the Luzon Strait. This category of SCVs is therefore named Luzon Strait island wake eddies (Liddies). We further estimate that Liddies can result in an equivalent annual-mean volume transport of 0.20 Sv westward across the Luzon Strait, which suggests that they play a nonnegligible role in the subsurface water transports between the NESCS and the northwestern Pacific.

A document by Ran Holtzman. Click on the document to view its contents.

Continuous measurement and monitoring of river or creek surface water coverage is crucial for studying the exchange fluxes between the surface and subsurface water. These fluxes directly impact carbon and nitrogen exchange and cycles, which are related to organic matter transport and reactions. While satellite and related techniques have been widely used for large-scale monitoring, they may not be accurate, sensitive, or cost-efficient for monitoring and tracking of surface water at fine-scale spatial (i.e., sub-meter) and temporal (i.e., daily) variations. This is especially true for small creeks with large plant canopy coverage. On-site in-situ sensors monitoring methods primarily yield point data, often insufficient in capturing the entire spatial distribution. Wildlife cameras have proven a cost-efficient way to continuously monitor surface water coverage of rivers and creeks. To efficiently analyze the images and/or videos from the wildlife cameras, in this study, two machine learning approaches, YOLOv8 and Mask2Former, have been applied. Both models were trained by images obtained from the public dataset ADE20k along with a small dataset from wildlife camera photos collected at the current study area. Once surface water coverage is segmented, the width of the surface water in real world can be approximated according to the wildlife camera, lens, and positioning parameters. In this study, surface water was detected and monitored by applying the proposed approaches for the six wildlife cameras in the Yakima River Basin in 2023 to 2024 in United States of America. Though Mask2Former model provides slightly better transferability, both models can accurately capture the surface water from the wildlife cameras, which are installed in significantly different environments, such as the different brightness, contrast, and varying front scene object blockages. The proposed approach enables long-term continuous monitoring and quantification of river intermittency and water availability with high accuracy and low-cost, which will benefit river ecosystem research and management.