Earth and Space Science最新文献

The dissipation of elastic strain energy, or attenuation, in Earth materials contributes to a range of geophysical phenomena, such as the damping of seismic waves and tidal heating of planetary bodies. We present a new method for measuring attenuation in single crystals of minerals and in reference materials over a frequency range of 1–10⁻⁴ Hz via nanoindentation. In the experiments, we measure the phase lag between a sinusoidal load applied to the nanoindenter tip and the sinusoidal displacement of the tip into and out of the tested sample, which provides a measure of the inverse quality factor Q⁻¹, or attenuation, of the sample. Experiments were conducted on polymethyl methacrylate (PMMA), indium, halite, olivine and quartz. Attenuation spectra from our tests on PMMA and indium are in excellent agreement with reported values from previous studies. We quantified the natural damping of the nanoindenter and showed that it becomes comparable to that of the samples only at frequencies greater than 0.1 Hz, and is much less than that of the samples at frequencies below 0.1 Hz.

The phase lag between an applied forcing and a response to that forcing is a fundamental parameter in geophysical signal processing. For solid deforming materials, the phase lag between an oscillatory applied stress and the resulting strain response encapsulates information about the dynamical behavior of materials. The phase lag is not directly measured and must be extracted through multiple steps by carefully comparing two time-series signals. The extracted value of the phase lag is highly sensitive to the nature of the signals and the analysis method. Here, we propose a method for extracting the phase lag between two signals when either one or both include an underlying nonlinear trend, which is very common when measuring attenuation in creeping materials. We demonstrate the robustness of the method by analyzing artificial signals and quantifying their absolute and relative errors. We apply the method to two experimental datasets and compare our results with previous studies.

In this paper, we compute a new hybrid mean sea surface (MSS) model by merging three recent models, CNES_CLS22, SCRIPPS_CLS22, and DTU21, and taking advantage of their respective features. The errors associated with these models were assessed using sea level anomalies for wavelengths ranging from 15 to 100 km from Sentinel-3A (S3A), SWOT KaRIn during its calibration phase and ICESat-2 in the Arctic ice-covered regions. The variance of the error associated with this new Hybrid23 MSS is estimated at 0.15 ± 0.04 cm² with S3A. The greatest improvements observed on S3A sea level anomalies are mainly located in coastal regions and along geodetic structures: on average, the error is reduced by 23% within 200 km along the coast and by 35% in the Indonesian region compared with SCRIPPS_CLS22. Despite these improvements, the MSS error still impacts significantly sea level anomalies computed from altimetry: it explains 15% and 18% of the S3A and SWOT KaRIn respective global variance. It becomes predominant (>30%) if we consider the shorter wavelengths ([15, 30 km]). CNES_CLS15 (Pujol et al., 2018, https://doi.org/10.1029/2017jc013503), older, explains up to 88% of the variance of SWOT KaRIn at these wavelengths. MSS errors have become a major limiting factor to the accuracy of sea level anomalies, and hybridization even adds sub-mesoscale errors. SCRIPPS_CLS22 and DTU21 also remain better in certain regions of the North Atlantic above 60°N and in Arctic coastal areas. Finally, many efforts are still required to develop the MSS to a new level of precision, which we could soon achieve with SWOT KaRIn during the scientific phase.

On 21 June 2022, during the annual Geospace Environment Modeling (GEM) workshop, a panel discussion titled “Radiation Belt Loss: The Long-Standing Debate Part II” was organized by the focus group “System Understanding of Radiation Belt Particle Dynamics.” The panel focused on unresolved questions regarding the mechanisms driving electron loss in Earth's radiation belts, discussing topics including magnetopause shadowing, outward radial transport, and wave-particle interactions driving particle precipitation. In this commentary, we provide an overview of the outcomes of this discussion and highlight future needs to better resolve outstanding questions.

The rapid advancement of data-driven machine learning (ML) models has improved typhoon track forecasts, but challenges remain, such as underestimating typhoon intensity and lacking interpretability. This study introduces an ML-driven hybrid typhoon model, where Pangu forecasts constrain the Weather Research and Forecasting (WRF) model using spectral nudging. The results indicate that track forecasts from the WRF simulation nudged by Pangu forecasts significantly outperform those from the WRF simulation using the NCEP GFS initial field and those from the ECMWF IFS for Typhoon Doksuri (2023). Besides, the typhoon intensity forecasts from Pangu-nudging are notably stronger than those from the ECMWF IFS, demonstrating that the hybrid model effectively leverages the strengths of both ML and physical models. Furthermore, this study is the first to explore the significance of data assimilation in ML-driven hybrid typhoon model. The findings reveal that after assimilating water vapor channels from the FY-4B AGRI, the errors in typhoon intensity forecasts are significantly reduced.

Land surface temperature (LST) monitoring via Earth observation constellation will become optimized and consistent with spatiotemporal-explicit characteristics. Besides, scientific evidence for the interaction between LST and vegetation biophysical variables remains limited through spatial large-scale assessment and seamless long-term tracking. This study addresses this gap by utilizing gap-filled fine spatial resolution LST products in understanding the dynamic over the period 2000–2023 and the spatiotemporal relationship with leaf area index (LAI). Firstly, Moderate Resolution Imaging Spectroradiometer (MODIS) LST 1,000 m of both daytime and nighttime were downscaled to a finer resolution of 250 m using the Random Forest algorithm. The Whittaker algorithm was then applied to obtain gap-free LST products due to the typical cloud cover under tropical monsoon climate. Time series decomposition of gap-filled fine resolution LST revealed slight warming trends in daytime (0.005°C year⁻¹), nighttime (0.036°C year⁻¹), and mean of all-day time (0.02°C year⁻¹) over recent 24 years, while seasonal amplitude in daytime (−3.7°C–4.8°C) is more fluctuated than in nighttime (−2.5°C–1.9°C). Spatial correlations of monthly LSTs and LAI indicated a consistent negative correlation (R ranging from −0.717 to −0.45). These findings shed light on the quantitative relationship between vegetation LAI and LST, contributing to a more unified theoretical framework for understanding functional vegetation responses under diverse climatic conditions.

Extreme Space Weather events can negatively affect ground-based infrastructure and satellite communications. European Space Agency plans to launch a new operational mission, Vigil, to monitor space weather activity and provide timely warnings about immediate danger. In this work, we have identified 24 instruments that have already acquired data on 8 space missions and are similar to instruments planned for mission Vigil. We then selected the 39 most extreme space weather events that affected the Earth in the past 30 years and gathered Vigil-like data for them. The objective of this work and our main motivation was to address the following question: “How would Vigil have observed extreme space weather events if it had been operational during those events?” For this reason, we prepared a pipeline for the community to obtain images and in-situ measurements for these specific periods, allowing straightforward applications for the follow-up data-driven studies. This effort could maximize Vigil's potential. Additionally, we studied the sources of extreme space weather events and the time it took for solar plasma to reach Earth's magnetosphere. This analysis demonstrates the utilization of the gathered data set and provides interesting insights into the most hazardous space events that influenced society in recent decades.

This study investigates land use, land cover (LULC) changes, vegetation health, and drought severity in Rajasthan, India, from 1985 to 2020 using remote sensing techniques. By analyzing satellite imagery with the normalized difference vegetation index (NDVI), temperature condition index (TCI), vegetation condition index (VCI), and NDVI deviation (Dev_NDVI), we assess the spatial and temporal dynamics of the region's landscape and drought conditions. Our findings indicate significant LULC changes, including a decrease in water bodies from 6412.87 to 2248.51 km² and dense forests by 61.37%, while built-up areas expanded by 890.50%, reflecting substantial human impact and environmental change. Drought analysis revealed that nearly 49% of the study area experienced moderate to severe drought conditions, with VCI levels below 40%, indicating widespread drought impact across different regions and time periods. The study employs weighted sum analysis of Dev_NDVI, VCI, and TCI to create a detailed drought severity map, revealing areas of severe and extreme drought that necessitate immediate action for sustainable management. The novelty of this approach lies in its integrated multi-index method for assessing drought over a 35 year period, providing a robust framework for analyzing environmental dynamics and the resilience of ecosystems to climatic stresses. This research emphasizes the value of remote sensing for continuous environmental monitoring and highlights future implications for integrating advanced satellite technologies to enhance drought management strategies, ultimately informing policy decisions for sustainable land and water resource management in Rajasthan and similar semi-arid regions globally.

Supraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland-wide supraglacial lakes during 2018 and 2019; low and high melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: (a) Refreezing, (b) rapidly draining, (c) slowly draining, and (d) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes, and a particular dominance of slowly draining lakes, in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. We further observe rapidly draining lakes at higher elevations than the previously hypothesized upper-elevation hydrofracture limit (1,600 m), and that non-draining lakes are generally deeper during the lower melt 2018 season. Our automatic classification approach and the resulting 2-year ice-sheet-wide data set provide new insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research.

The Arctic region is experiencing significant changes due to climate change, and the resulting decline in sea ice concentration and extent is already impacting ocean dynamics and exacerbating coastal hazards in the region. In this context, numerical models play a crucial role in simulating the interactions between the ocean, land, sea ice, and atmosphere, thus supporting scientific studies in the region. This research aims to evaluate how different sea ice products with spatial resolutions varying from 2 to 25 km influence a phase averaged spectral wave model results in the Alaskan Arctic under storm conditions. Four events throughout the Fall to Winter seasons in 2019 were utilized to assess the accuracy of wave simulations generated under the dynamic sea ice conditions found in the Arctic. The selected sea ice products used to parameterize the numerical wave model include the National Snow and Ice Data Center (NSIDC) sea ice concentration, the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), the HYbrid Coordinate Ocean Model-Community Ice CodE (HYCOM-CICE) system assimilated with Navy Coupled Ocean Data Assimilation (NCODA), and the High-resolution Ice-Ocean Modeling and Assimilation System (HIOMAS). The Simulating WAves Nearshore (SWAN) model's accuracy in simulating waves using these sea ice products was evaluated against Sea State Daily Multisensor L3 satellite observations. Results show wave simulations using ERA5 consistently exhibited high correlation with observations, maintaining an accuracy above 0.83 to the observations across all events. Conversely, HIOMAS demonstrated the weakest performance, particularly during the Winter, with the lowest correlation of 0.40 to the observations. Remarkably, ERA5 surpassed all other products by up to 30% in accuracy during the selected storm events, and even when an ensemble was assessed by combining the selected sea ice products, ERA5's individual performance remained unmatched. Our study provides insights for selecting sea ice products under different sea ice conditions for accurately simulating waves and coastal hazards in high latitudes.