Earth and Space Science最新文献

We leverage ambient seismic noise to implement a novel geometric phase sensing method for investigating the effects of environmental conditions on near-surface ground properties. The geometric phase, derived from topological acoustics, characterizes the geometry of a wavefield by incorporating cross-correlation information between seismic sensors. Changes in geometric phase, $��\Delta �\eta �$, are expressed as changes in vectorial orientation, describing the wavefield evolution over time. To demonstrate the method, we designed an end-to-end workflow by applying an open access temporal high-resolution data from a seismic array in southwest Iceland and measured $��\Delta �\eta �$ over a 2-year period. We observe that the seasonal fluctuations of $��\Delta �\eta �$ are highly correlated with surface air temperature, reflecting changes in ground properties during the freeze-thaw cycle. We assess the seasonal stability of the noise source distribution and conduct a numerical test to verify that the seasonal pattern in $��\Delta �\eta �$ is minimally affected by shifts in noise source direction. Several advantages of geometric phase measurements, including the elimination of lag window selection and reduced computational costs, suggest their strong effectiveness in monitoring changes in ground properties with time. We suggest that the geometric phase can play a significant role in the future of environmental monitoring.

The simulation of discrete karst networks presents a significant challenge due to the complexity of the physicochemical processes at their origin, occurring within various geological and hydrogeological contexts over extended periods. This complex interplay leads to a wide variety of karst network patterns, each intricately linked to specific hydrogeological conditions. We explore a novel approach that represents karst networks as graphs and applies graph generative models (deep learning techniques) to capture the intricate nature of karst environments. In this representation, nodes retain spatial information and properties, while edges signify connections between nodes. Our generative process consists of two main steps. First, we utilize graph recurrent neural networks (GraphRNN) to learn the topological distribution of karst networks. GraphRNN decomposes the graph simulation into a sequential generation of nodes and edges, informed by previously generated structures. Second, we employ denoising diffusion probabilistic models on graphs (G-DDPM) to learn node features (spatial coordinates and other properties). G-DDPMs enable the generation of nodes features on the graphs produced by the GraphRNN that adhere to the learned statistical properties by sampling from the derived probability distribution, ensuring that the generated graphs are realistic and capture the essential features of the original data. We test our approach using real-world karst networks and compare generated subgraphs with actual subgraphs from the database, by using geometry and topology metrics. Our methodology allows stochastic simulation of discrete karst networks across various types of formations, a useful tool for studying the behavior of physical processes such as flow and transport.

NASA's ICESat-2 (Ice, Cloud and land Elevation Satellite-2) satellite launched in 2018, carrying a single instrument, the Advanced Topographic Laser Altimeter System (ATLAS). The Level 1 science objectives of the mission focus primarily on the cryosphere, with specific interest in monitoring changes in polar ice sheets, glaciers and sea ice. However, in addition to planned observations and data products for polar, land, vegetation, ocean and the atmosphere, ATLAS's photon-counting, green-wavelength instrumentation enables impressive bathymetric measurement capability. Most of the ICESat-2 along-track data products were developed during pre-launch studies, without a dedicated effort focused on bathymetry. The absence of a dedicated bathymetry product has required the scientific community to develop independent, individual algorithms for bathymetric signal extraction, most often tailored to local or regional studies. No existing approaches have been proven applicable to global application. Over the last 3 years, the ICESat-2 Project Science Office has sought to address the need for coastal and nearshore bathymetry through the development of a Level 3a, along-track data product for global shallow-water bathymetry (ATL24). The ATL24 workflow embraces several independent signal extraction algorithms in a machine learning ensemble to provide robust signal extraction of the sea floor and sea surface heights in variable environmental conditions and water quality. This paper explains the approach to the algorithms and an assessment of the algorithm performance to evaluate the usefulness for high-priority science and application use cases.

Vegetation restoration in arid and semi-arid regions holds significant promise for climate change mitigation and ecosystem enhancement. However, limited water availability poses fundamental constraints. Here, we assess restoration potential and its hydrological impacts across global drylands using three Eco-Evolutionary Optimality (EEO) models—the Eagleson model, Yang-Medlyn model, and P model—under both historical (2001–2020) and future (2021–2100) climate conditions. Restoration potential was evaluated by expanding existing vegetation types, with water availability impacts quantified using a modified Budyko framework incorporating atmospheric moisture feedback. Results show that restoration potential increases from 97.3 ± 3.4 million hectares historically to ∼603 ± 46 million hectares under the high-emissions SSP5-8.5 scenario by the end of the century. While increased vegetation cover significantly enhances evapotranspiration and reduces water availability—especially during dry seasons in the Northern Hemisphere—future water stress is partly mitigated by projected precipitation increases and enhanced plant water-use efficiency under elevated CO₂. These findings underscore the need for region-specific, adaptive restoration strategies that balance ecological gains with water sustainability in a changing climate.

The study of pre-seismic variations in the ionosphere due to 2021 Haiti earthquake (Mw = 7.2) is carried out using ground and space-based instruments. The day time ionospheric response is analyzed using Vertical Total Electron Content (VTEC) from IGS stations and electron density from Swarm satellite data. Results demonstrate that bandpass filtered VTEC reveals clear, pronounced, pre - seismic oscillations of peak magnitudes of ∼0.2 TECU on 05 August 2021, that is, 9 days before the earthquake for stations near and just outside the earthquake preparation zone. Similarly, enhanced wave-like oscillations are also evident in the filtered VTEC data near the conjugate stations on the same day. Another unique feature during 05 August 2021 is the anomalous enhancement of northern Equatorial Ionization Anomaly crest shown by Swarm electron density data. Such an enhancement is not observed for other days during August. This is also concurrent with the drop in Relative humidity occurred during the same day near the impending epicenter region. Hence the concomitant anomalies found in various atmospheric and ionospheric parameters suggest that the anomalies found on 05 August 2021 is plausibly related to the Haiti 2021 earthquake. This study also sheds some light into similarities with the Haiti 2010 event which occurred very close to the epicenter of 2021 event, hence emphasizing the need of detailed study of the Earthquake prone regions of Haiti using multiple precursor parameters.

High-precision Earth Rotation Parameter (ERP) products often experience delays that range from several days to weeks. The use of precise forecasting models can effectively compensate for the impact of such delays. Based on a systematic analysis of the forecasting capabilities of the traditional harmonic least squares fitting and autoregressive (AR) combined model, this study proposes a hybrid prediction model incorporating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), an AR model, and a Transformer-Long Short-Term Memory network for accurate polar motion (PM) time series prediction, termed M-CAL. Within this hybrid framework, we initially classify the decomposed signal components, using multiscale entropy obtained from CEEMDAN, into three categories: pseudo-white noise, low-frequency signals (reflecting long-term trends and seasonal variations), and high-frequency signals (indicating non-stationary fluctuations). These components are then forecasted, respectively, using white noise simulation, AR modeling, and deep learning approaches. Finally, the prediction results are generated through superposition. To evaluate the long-term effectiveness of the hybrid model, 80 experiments were conducted, each involving 30-day PM forecasts, which were then compared with the IERS Bulletin A products. The validation results indicate that, over the 30-day forecast horizon covering ultra-short- and short-term intervals, the X- and Y-components of PM were improved by approximately 53% and 61%, respectively, with maximum improvements reaching 90%. We therefore recommend the application of this model for practical implementation in ERP forecasting to further enhance prediction accuracy and reliability.

The Joint Total Solar Irradiance Monitor (JTSIM) onboard the Fengyun-3E meteorological satellite has been launched successfully on 4th of July 2021. It aims at measuring the Total Solar Irradiance (TSI) from the Low Earth Orbit. The instruments on the Fengyun-3E/JTSIM include the Digital Absolute Radiometer (DARA) from the Physikalisch Meteorologisches Observatorium, Davos and World Radiation Center (PMOD/WRC) and the Solar Irradiance Absolute Radiometer (SIAR) from the Changchun Institute of Optics, Fine Mechanics and Physics Chinese Academy of Sciences (CIOMP/CAS). The first light measurements and TSI value determined from DARA and SIAR are compared with other active missions (SOHO-VIRGO,TSIS-1).

As an important grassland ecological function area on the Tibetan Plateau, the risk of grassland fires in Qinghai Province has gradually increased due to climate warming and human activities. To quantitatively assess changes in grassland fire susceptibility under future climate scenarios, this study used historical fire data and CMIP6 model data, combined with multiple regression and the MaxEnt model, to simulate the distribution and trend of NDVI and fire susceptibility. Results showed that NDVI decreased under the low emission scenario (SSP119), and NDVI of grassland with medium and low coverage increased under medium and high emission scenarios (SSP245 and SSP585), while that of high coverage grassland decreased slightly. Fire susceptibility was higher in the east and south, and lower in the Qaidam Basin and northwest, with wind speed, distance from settlements, NDVI, slope, and human footprint as main driving factors. Mann-Kendall and Theil-Sen slope analyses showed that future fire susceptibility areas under medium- and high-emission scenarios increased significantly and fluctuated, concentrating in the periphery of the Qaidam Basin and Southern Qinghai Plateau. Risk varied significantly among grasslands of different coverage. The study reveals the impact of global emission pathways on regional fire risk, emphasizing the need to strengthen adaptation, mitigation, and optimize grassland fire prevention to safeguard ecological security of the Qinghai-Tibetan Plateau.

Climate variability affects multiple processes on Earth, with significant system effects driving hydrometeorological, glaciological, atmospheric, and geophysical variability. Research into these fields is driven by acquisition and processing of voluminous amount of data at multiple spatial and temporal scales. Intersection of data and tools to work around this complexity, to extract a consistent and useful picture of the effects of climate change in the Earth System, requires handling of big data sets and their processing tools. This effort is generating novel approaches to the analysis of big data sets and new perspective on the predictive power of the tools used. For this reason, in March 2023, AGU launched the Special Collection Analyzing Big Data for Understanding Climate Variability, Natural Phenomena, and Rapid Environmental Changes, inviting contributions to showcase the latest advances and the role of machine learning and deep learning in climate data analysis. In this introduction, we outline the key findings and insights presented in 16 articles published in the special collection, and we highlight the emerging trends within this field of research. The following journals participated in the special collection: Journal of Geophysical Research: Solid Earth, Journal of Geophysical Research: Atmospheres, Geophysical Research Letters, and Earth and Space Science.

In southeastern Korea, various synoptic conditions are responsible for heavy rainfall events (HREs) exceeding 30 $��\text{mm}��h^{�-�1�}�$ during the Changma, that is, the Korean summer monsoon season. We objectively classify such synoptic patterns of HREs using a self-organizing map with sea level pressure and 850-hPa geopotential height fields as input variables. A total of eight synoptic clusters (SCs) are identified. The first three clusters (SC1$��-�$3), which explain about 18% of HREs, are influenced by tropical cyclones. Excluding minor cluster SC4, SC5$��-�$8 are dominated by midlatitude weather systems with a southeast-high or northwest-low pattern. When examining HREs not influenced by tropical cyclones, SC5 and SC8 show contrasting synoptic patterns. SC5 is influenced by an eastward-propagating surface low, whereas SC8 is associated with an expansion of the Western North Pacific Subtropical High (WNPSH). In quasi-geostrophic motion, SC5 exhibits the strongest upward motion, primarily driven by dynamic forcing and diabatic heating. SC8 shows the weakest ascent. Observations show that SC5 is accompanied by widespread nighttime HREs propagating eastward alongside the synoptic system. In contrast, SC8 is accompanied by a localized daytime HRE that propagates northeastward, governed by background southwesterlies along the WNPSH periphery. SC6$��-�$7 share the synoptic characteristics of SC5 and SC8 but also have distinct characteristics. For example, SC7, which produces intense localized daytime rainfall, is characterized by warm surface and strong upslope winds, indicating terrain-induced effect with surface heating as a major driver. These results suggest that HREs in southeastern Korea are organized by multiscale processes under various background conditions.