地球科学最新文献

Accurate predictions of monthly extremes assume paramount importance in enabling proactive decision-making, which however are lacked in skills even for state-of-the-art dynamical models. Taking the extreme precipitation prediction over the mid-to-lower reaches of the Yangtze River, China, as an instance, a multi-predictor U-Net deep learning approach is designed to enhance the prediction over the European Center for Medium-Range Weather Forecasts (ECMWF) model, with the single-predictor U-Net parallelly examined as the benchmark. Focusing on the precipitation extremes, an extreme associated component is incorporated into the model loss function for optimization. Besides, predictions composed by daily outputs with multiple lead times are imported as a comprehensive set in the training phase to augment the deep learning sample size and to emphasize enhancements in predictions at the monthly timescale as a whole. Results indicate that the multi-predictor U-Net effectively improves predictions of extreme summer precipitation frequency, showing distinct superiority to the raw ECMWF and the single-predictor U-Net. Multiple evaluation metrics indicate that the model shows a significant positive improvement ratio ranging from 65.1% to 80.0% across all grids compared to the raw ECMWF prediction, which has also been validated through applications in the two extreme summer precipitation cases in 2016 and 2020. Besides, a ranking analysis of feature importance reveals that factors such as humidity and temperature play even more crucial roles than precipitation itself in the multi-predictor extreme precipitation prediction model at the monthly timescale. That is, in such a deep learning approach, the monthly prediction on extreme precipitation benefits significantly from the inclusion of multiple associated predictors.

The impacts of extreme events are seldom caused by a single climatic variable but rather arise from the interaction of multiple climate drivers. This study employs observational data sets with high spatiotemporal resolution to analyze the risk of occurrence of compound dry-hot events in China over the past 120 years (i.e., 1901–2020). Simultaneously, attribution analysis based on distribution functions explores whether and to what extent human activities influence the occurrence of compound events. The results indicate that over the historical 120-year period, the frequency of compound dry-hot events in China has gradually increased, with the highest frequency observed in the most recent 40 years (i.e., 1981–2020). The frequency of compound dry-hot events during this period is approximately four times that of 1901–1940 and about twice that of 1941–1980. The analysis of the relative importance of different factors reveals that temperature changes contribute more (56%) to the occurrence of compound events than precipitation (23%), and also exceed the interaction between them (21%). The substantial increase in compound dry-hot events is largely attributed to the influence of human activities. Across seven sub-regions, human activities have led to an increase in the probability of compound events occurring, ranging from 7.9% to 31.6%. The findings of this study indicate that human activities have significant implications for explaining the observed increase in compound hot and dry events over the past 40 years.

Mineral carbon storage in mafic and ultramafic rock masses has the potential to be an effective and permanent mechanism to reduce anthropogenic CO₂. Several successful pilot-scale projects have been carried out in basaltic rock (e.g., CarbFix, Wallula), demonstrating the potential for rapid CO₂ sequestration. However, these tests have been limited to the injection of small quantities of CO₂. Thus, the longevity and feasibility of long-term, large-scale mineralization operations to store the levels of CO₂ needed to address the present climate crisis is unknown. Moreover, CO₂ mineralization in ultramafic rocks, which tend to be more reactive but less permeable, has not yet been quantified. In these systems, fractures are expected to play a crucial role in the flow and reaction of CO₂ within the rock mass and will influence the CO₂ storage potential of the system. Therefore, consideration of fractures is imperative to the prediction of CO₂ mineralization at a specific storage site. In this review, we highlight key takeaways, successes, and shortcomings of CO₂ mineralization pilot tests that have been completed and are currently underway. Laboratory experiments, directed toward understanding the complex geochemical and geomechanical reactions that occur during CO₂ mineralization in fractures, are also discussed. Experimental studies and their applicability to field sites are limited in time and scale. Many modeling techniques can be applied to bridge these limitations. We highlight current modeling advances and their potential applications for predicting CO₂ mineralization in mafic and ultramafic rocks.

Digisonde data showed a peculiar behavior in the nighttime lower ionosphere over Cachoeira Paulista (CXP, 22.7°S, 45°W, dip ∼35°), a low-latitude station located inside the South American Magnetic Anomaly (SAMA) during the main phase of the extreme magnetic storm on 11 May 2024. The E region appeared in observational data at high altitudes after sunset, which is unexpected. In sequence, it performed an unusual descending movement due to the disturbed electric field. The extra ionization responsible for forming the nocturnal E layer is due to the precipitation (EPP) of low energic (<30 keV) particles. Moreover, a diurnal cusp-type Es layer (Es_c) appeared simultaneously, which has never been reported in the literature at such hours. Thus, the results further suggest that the EPP may have caused an oscillation in the thermosphere, forming the Es_c usually seen in the daytime. Therefore, this study shows the different mechanisms acting together during this magnetic storm, creating a daytime ionosphere after sunset over the SAMA region, as confirmed by observational data and simulations.

This study utilizes the wealth of observational data collected during the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift experiment to constrain and evaluate close to two-hundred daily Large-Eddy Simulations (LES) of Arctic boundary layers and clouds at high resolutions. A standardized approach is adopted to tightly integrate field measurements into the experimental configuration. Covering the full drift represents a step forward from single-case LES studies, and allows for a robust assessment of model performance against independent data under a range of atmospheric conditions. A homogeneously forced domain is simulated in a Lagrangian frame of reference, initialized with radiosonde and value-added cloud profiles. Prescribed boundary conditions include various measured surface characteristics. Time-constant composite forcing is applied, primarily consisting of subsidence rates sampled from reanalysis data. The simulations run for 3 hours, allowing turbulence and clouds to spin up while still facilitating direct comparison to MOSAiC data. Key aspects such as the vertical thermodynamic structure, cloud properties, and surface energy fluxes are well reproduced and maintained. The model captures the bimodal distribution of atmospheric states that is typical of Arctic climate. Selected days are investigated more closely to assess the model's skill in maintaining the observed boundary layer structure. The sensitivity to various aspects of the experimental configuration and model physics is tested. The model input and output are available to the scientific community, supplementing the MOSAiC data archive. The close agreement with observed meteorology justifies the use of LES for gaining further insight into Arctic boundary layer processes and their role in Arctic climate change.

Recent seismic tomography models suggest large-radius primary plumes originating from the core-mantle boundary, with grain size variations potentially explaining these observations. Additionally, grain size variations are thought to enhance the long-term stability of Large Low Shear Velocity Provinces (LLSVPs), identified as thermochemical piles near the core-mantle boundary. Nevertheless, geodynamic models investigating these hypotheses remain limited. To address this gap, we constructed a series of geodynamic numerical models incorporating grain size evolution, plate tectonics, and the spontaneous generation of deep mantle plumes above LLSVPs. Our results reveal that grain size evolution does not significantly affect the plume width, primarily because the increased strain rate in the mantle plume suppresses both its grain size and viscosity. The region adjacent to the plumes, characterized by the accumulation of mantle materials with larger grain size and low-temperature remnants of subducted slabs, displays a higher viscosity compared to the area near the subducted slabs. Furthermore, grain size evolution plays a crucial role in enhancing the stability of LLSVPs by increasing the viscosity ratio between LLSVPs and the ambient mantle. These findings underscore the need for incorporating grain size evolution in geodynamic models to gain a better understanding of the dynamics of plumes and lower mantle.

The Southern Ocean (SO) plays a crucial role in the process of sequestering heat and carbon dioxide from the atmosphere and transferring them to the deep ocean. This process is intricately linked to the formation of Antarctic Intermediate Water (AAIW) and Subantarctic Mode Water (SAMW), which are pivotal components of the Meridional Overturning Circulation (MOC) and have a substantial impact on the global climate balance. AAIW and SAMW take shape in specific regions of the Southern Ocean due to the influence of strong winds, buoyancy fluxes, and their effects, such as convection, the development of thick mixed layers, and wind-driven subduction. These water masses subsequently flow northward, contributing to the ventilation of the intermediate layers within the subtropical gyres. In this study, our focus lies on investigating the regional aspects of AAIW and SAMW transformation in CMIP6 models. We accomplish this by analyzing the relationship between the meridional transport of these water masses and air-sea fluxes, particularly Ekman pumping, freshwater fluxes, and heat fluxes. Our findings reveal that the highest transformation rates occur in the Indian sector of the Southern Ocean, with notable values also observed in the southeast Pacific and south of Africa. Additionally, we assess the potential changes in these formation regions under future scenarios projected for the end of the 21st century. Although the patterns of formation regions remain consistent, there is a significant decrease in the transformation process.