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Abstract

Electromagnetic ion cyclotron (EMIC) waves and fast magnetosonic (MS) waves were previously reported to be simultaneously generated by ring current protons (10s keV) within the magnetic dip. In this work, we present a distinct physical scenario of concurrent high-frequency EMIC (HFEMIC) and MS waves within a magnetic dip where low-energy (10s–100s eV) and hot (10s keV) protons facilitate the local growth of HFEMIC and MS waves, respectively. Moreover, the low-energy protons exhibit remarkable perpendicular flux enhancements, which are well modulated by MS waves as evidenced by their significant correlation coefficient (∼0.78). Consequently, the concurrent two wave modes should arise from the complicated coupling between HFEMIC and MS waves, marking a departure from previous studies. Our observations demonstrate that the magnetic dip can provide favorable conditions for such intricate coupling processes, offering novel insights into its impact on magnetospheric dynamics.

Abstract

The physical processes governing a tropical cyclone's lifecycle are largely understood, but key processes occur at scales below those resolved by global climate models. Increased resolution may help simulate realistic tropical cyclone intensification. We examined fully coupled, global storm-resolving models run at resolutions in the range 28–2.8 km in the atmosphere and 28–5 km in the ocean. Simulated tropical cyclone activity, peak intensity, intensification rate, and horizontal wind structure are all more realistic at a resolution of ∼5 km compared with coarser resolutions. Rapid intensification, which is absent at typical climate model resolutions, is also captured, and exhibits sensitivity to how, and if, deep convection is parameterized. Additionally, the observed decrease in inner-core horizontal size with increasing intensification rate is captured at storm-resolving resolution. These findings highlight the importance of global storm-resolving models for quantifying risk and understanding the role of intense tropical cyclones in the climate system.

Abstract

Decadal variations in near-surface wind speed (NSWS) and their causes are poorly understood. We found that the decadal transition of winter NSWS in northern China (NC) was 10 years earlier than in southern China (SC), which could be linked to the changes in intensities of the eastward wave-activity flux and Siberian High (SH) induced by the Warm Arctic-Cold Eurasia (WACE) dipole pattern. From 1973 to 1990, the WACE pattern from positive to negative phases confined the eastward wave trains to high latitudes with a decreasing SH, inducing an NSWS reduction. From 1991 to 2000, the WACE strengthened from negative to positive phases, causing a decadal transition in NSWS first in NC. After 2000, accompanied by the strengthening of the positive WACE, the eastward wave trains propagated downstream to lower latitudes, the SH and the meridional pressure gradient enhanced. Therefore, the transition of decadal NSWS occurred in SC until 2000.

SummaryUnderstanding the crustal seismic characteristics of tectonically active regions is crucial for seismic hazard assessment. The study conducted in NW Iran utilized surface wave tomography, radial anisotropy, and density information to analyze the complex crustal structure of the region, which is outstanding because of diverse tectonic features, sedimentary basins, and volcanic formations. By selecting a dataset of 1243 events out of over 3,500 earthquakes with M>4, and employing strict data selection criteria (such as SNR, M, Δ), the researchers calculated Rayleigh and Love wave group velocity dispersion curves using Gaussian multiple filters and phase-matched filtering. The tomographic procedure was initiated by excluding data with residuals > 2σ for enhanced stability. Individual inversions were then carried out for local Rayleigh and Love wave dispersion measurements to obtain 1D VSV and VSH models. Radial anisotropy and VS iso were determined through a discrepancy and averaging of the obtained VSH and VSV, respectively. Gravity modeling was also employed alongside surface wave analysis to understand the region's complex geology, revealing insights into upper-middle-lower crust boundaries, subsurface structures, and Moho depths. The study's velocity maps reveal significant findings related to geological units and tectonic features in various regions based on the provided results. Low velocities in the South Caspian Basin (SCB) and Kura Depression (KD) regions are attributed to substantial sedimentary layers, while low velocities, and depth of VS in NW Iran and Eastern Anatolian Accretionary Complex (EAAC) regions suggest the presence of partially molten materials in the upper and middle crust. The Sanandaj-Sirjan Zone (SSZ) region shows a low velocity anomaly in longer periods and greater depths of VS, surrounded by normal to high velocities, indicating a thick middle crust. Analyzing radial anisotropy and VS iso profiles offers insights into upper-middle-lower crust boundaries, subsurface structures, and Moho depths, highlighting middle crust thickening and lower crust thinning beneath the SSZ. The study confirms the gentle subduction of the SCB oceanic-like lower crust beneath NW Iran in the Talesh (TAL) region, with a rigid middle crust. Additionally, cross-sections reveal igneous laccoliths underplate with a VS iso of 3.7 km/s in the volcanic region. The difference observed by subtracting the velocity models at two adjacent depths, combined with parametric test results, indicates that the Sahand volcanic system is clearly identifiable, while the influence of subtle subduction on the Sabalan volcano at depths up to 30 km remains less distinct. The magma chamber beneath Sahand is situated at depths ranging from 18 to 25 km.

SummaryA new automated algorithm for picking the arrival times of the global P-, SH- and SV-wave phases from multi-component seismic waveform data is presented. This picker is based on a sequential approach using autoregressive prediction of the filtered waveform in a sliding time window, the Akaike-Information-Criterion and the Hilbert transform of the original waveform. The quality of the individual picks is computed by combining signal-to-noise ratios and higher order statistics into a single measure. Synthetic tests are used to find values for high and low quality thresholds. The algorithm is applied to a global data set of waveforms from teleseismic events with magnitude 6 or higher that occurred between 1990 and 2019. This resulted in approximately 4 million P-phase arrival times as well as approximately 3 million SH- and SV-phase arrival times each. These automatic picks are compared to approximately 830 000 manual P-picks as well as approximately 70 000 manual S-picks from the ISC-EHB catalogue. An upper bound for the picking errors of the automatic picks is estimated by using high quality picks of neighbouring stations. This upper bound is found to be 0.55s for the P-picks and 4.3s for the S-picks. If only high quality picks are considered, this represents 50 per cent of the P-picks and 25 per cent of the S-picks, then these errors decrease to 0.35s for the P-picks, and 1.5s for the S-picks, respectively. As a by-product of the picking, the dominant periods of the arriving signals are determined as well.

Abstract

Real-time service (RTS) products are an important guarantee for real-time precise point positioning (RT-PPP), and the RTS outages caused by loss of network connection are a concern. In this paper, a multivariate CNN-LSTM model is proposed for short-term BDS satellite clock offset prediction during the discontinuity in receiving RTS clock offsets, which utilizes the superior feature of convolution neural network (CNN) and long short-term memory (LSTM) for simultaneous prediction of multiple satellite clock offsets by considering the inter-satellite correlation. First, the correlation between satellite clock offsets was analyzed to identify satellites suitable for parallel prediction. Then, to preserve the sequential structure of the features extracted from multiple parallel satellite clock offsets, remove the pooling layer of traditional CNN, and use the convolution layer to learn the relationships and dependencies between clock offsets of different satellites and the LSTM layer to model the temporal dependencies in satellite clock offsets. The experiment results show that the computational efficiency of the proposed model is significantly better than that of autoregressive integrated moving average (ARIMA), wavelet neural network (WNN), and LSTM models. Compared with the linear polynomial (LP), quadratic polynomial model (QP), ARIMA, WNN, and the LSTM models, the prediction accuracy of the multivariate CNN-LSTM model for 5 min, 15 min, 30 min, and 1 h is improved by approximately (84.0, 76.6, 1.5, 8.3, 8.3)%, (72.0, 62.6, 6.0, 15.3, 18.7)%, (57.1, 48.5, 11.3, 18.4, 23.3)%, and (34.9, 35.1, 27.3, 21.8, 26.3)%, respectively.

Nature Geoscience, Published online: 29 August 2024; doi:10.1038/s41561-024-01532-z

The upwelling mantle beneath Iceland underwent melt depletion at least 1 billion years ago and is therefore compositionally buoyant, according to a study of neodymium and hafnium isotope ratios in peridotites from the Charlie Gibbs Transform Zone.

Nature Geoscience, Published online: 29 August 2024; doi:10.1038/s41561-024-01496-0

Enhanced chemical weathering following continental breakup may have driven a succession of Mesozoic oceanic anoxic events, according to tectonic and biogeochemical modelling.

Nature Geoscience, Published online: 29 August 2024; doi:10.1038/s41561-024-01517-y

About 6% of the total uptake of carbon dioxide by the ocean is due to rainfall, according to an analysis of satellite observations and ERA5 reanalysis data from 2008 to 2018.

Nature Geoscience, Published online: 29 August 2024; doi:10.1038/s41561-024-01510-5

Subtropical and extratropical sea surface temperature changes can explain recent observed Walker circulation strengthening, according to climate model experiments.

Abstract

Nighttime oxidation of monoterpenes (MT) via the nitrate radical (NO3) and ozone (O3) contributes to the formation of secondary organic aerosol (SOA). This study uses observations in Atlanta, Georgia from 2011 to 2022 to quantify trends in nighttime production of NO3 (PNO3) and O3 concentrations and compare to model outputs from the EPA's Air QUAlity TimE Series Project (EQUATES). We present urban-suburban gradients in nighttime NO3 and O3 concentrations and quantify their fractional importance (F) for MT oxidation. Both observations and EQUATES show a decline in PNO3, with modeled PNO3 declining faster than observations. Despite decreasing PNO3, we find that NO3 continues to dominate nocturnal boundary layer (NBL) MT oxidation (FNO3 = 60%) in 2017, 2021, and 2022, which is consistent with EQUATES (FNO3 = 80%) from 2013 to 2019. This contrasts an anticipated decline in FNO3 based on prior observations in the nighttime residual layer, where O3 is the dominant oxidant. Using two case studies of heatwaves in summer 2022, we show that extreme heat events can increase NO3 concentrations and FNO3, leading to short MT lifetimes (<1 hr) and high gas-phase organic nitrate production. Regardless of the presence of heatwaves, our findings suggest sustained organic nitrate aerosol formation in the urban SE US under declining NOx emissions, and highlight the need for improved representation of extreme heat events in chemistry-transport models and additional observations along urban to rural gradients.

Researchers investigating the historic stresses of the American West's water supply have identified a simple solution that could put parts of the Colorado River Basin on a more sustainable path.

More accurately predicting periods of increased hurricane activity weeks in advance may become possible due to new research published this month published in the journal Monthly Weather Review.

Publication date: Available online 15 August 2024

Source: Advances in Space Research

Author(s): Subbulakshmi M, Sachikanta Nanda

Publication date: Available online 14 August 2024

Source: Advances in Space Research

Author(s): Yury A. Nagovitsyn, Aleksandra A. Osipova, Sofia N. Fedoseeva

Abstract

Iron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long-term, near-source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub-sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under- or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting-related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long-term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates.

HTAP3 Fires: Towards a multi-model, multi-pollutant study of fire impacts
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Stephen R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christophe Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-126,2024
Preprint under review for GMD (discussion: open, 0 comments)
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model set up are discussed, and the official recommendations for the project are presented.

Development of an under-ice river discharge forecasting system in Delft-Flood Early Warning System (Delft-FEWS) for the Chaudière River based on a coupled hydrological-hydrodynamic modelling approach
Kh Rahat Usman, Rodolfo Alvarado Montero, Tadros Ghobrial, François Anctil, and Arnejan van Loenen
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-116,2024
Preprint under review for GMD (discussion: open, 0 comments)
Rivers in cold climate regions such as Canada undergo freeze up during winters which makes the estimation forecasting of under-ice discharge very challenging and uncertain since there is no reliable method other than direct measurements. The current study explored the potential of deploying a coupled modelling framework for the estimation and forecasting of this parameter. The framework showed promising potential in addressing the challenge of estimating and forecasting the under-ice discharge.

Stoked by Canada's warmest and driest conditions in decades, extreme forest fires in 2023 released about 640 million metric tons of carbon, NASA scientists have found. That's comparable in magnitude to the annual fossil fuel emissions of a large industrialized nation.

Are 2D shallow-water solvers fast enough for early flood warning? A comparative assessment on the 2021 Ahr valley flood event
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième
Nat. Hazards Earth Syst. Sci., 24, 2857–2874, https://doi.org/10.5194/nhess-24-2857-2024, 2024
Early warning is essential to minimise the impact of flash floods. We explore the use of highly detailed flood models to simulate the 2021 flood event in the lower Ahr valley (Germany). Using very high-resolution models resolving individual streets and buildings, we produce detailed, quantitative, and actionable information for early flood warning systems. Using state-of-the-art computational technology, these models can guarantee very fast forecasts which allow for sufficient time to respond.