Reviews of Geophysics最新文献

Synthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high-resolution, all-weather, and day-night imaging has revolutionized our understanding of various geophysical processes. Recent advancements in SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, and integrating machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize SAR's comprehensive applications for geosciences, especially emphasizing recent advancements in SAR technologies and applications. Moreover, current SAR-related review papers have primarily focused on SAR technology or SAR imaging and data processing techniques. Hence, a review that integrates SAR technology with geophysical features is needed to highlight the significance of SAR in addressing challenges in geosciences, as well as to explore SAR's potential in solving complex geoscience problems. Spurred by these requirements, this review comprehensively and in-depth reviews SAR applications for geosciences, broadly including various aspects in air-sea dynamics, oceanography, geography, disaster and hazard monitoring, climate change, and geosciences data fusion. For each applied field, the scientific advancements produced because of SAR are demonstrated by combining the SAR techniques with characteristics of geophysical phenomena and processes. Further outlooks are also explored, such as integrating SAR data with other geophysical data and conducting interdisciplinary research to offer comprehensive insights into geosciences. With the support of deep learning, this synergy will enhance the capability to model, simulate, and forecast geophysical phenomena with greater accuracy and reliability.

While deterministically predicting the time and location of earthquakes remains impossible, earthquake forecasting models can provide estimates of the probabilities of earthquakes occurring within some region over time. To enable informed decision-making of civil protection, governmental agencies, or the public, Operational Earthquake Forecasting (OEF) systems aim to provide authoritative earthquake forecasts based on current earthquake activity in near-real time. Establishing OEF systems involves several nontrivial choices. This review captures the current state of OEF worldwide and analyzes expert recommendations on the development, testing, and communication of earthquake forecasts. An introductory summary of OEF-related research is followed by a description of OEF systems in Italy, New Zealand, and the United States. Combined, these two parts provide an informative and transparent snapshot of today's OEF landscape. In Section 4, we analyze the results of an expert elicitation that was conducted to seek guidance for the establishment of OEF systems. The elicitation identifies consensus and dissent on OEF issues among a non-representative group of 20 international earthquake forecasting experts. While the experts agree that communication products should be developed in collaboration with the forecast user groups, they disagree on whether forecasting models and testing methods should be user-dependent. No recommendations of strict model requirements could be elicited, but benchmark comparisons, prospective testing, reproducibility, and transparency are encouraged. Section 5 gives an outlook on the future of OEF. Besides covering recent research on earthquake forecasting model development and testing, upcoming OEF initiatives are described in the context of the expert elicitation findings.

A holistic review is given of the Southern Ocean dynamic system, in the context of the crucial role it plays in the global climate and the profound changes it is experiencing. The review focuses on connections between different components of the Southern Ocean dynamic system, drawing together contemporary perspectives from different research communities, with the objective of closing loops in our understanding of the complex network of feedbacks in the overall system. The review is targeted at researchers in Southern Ocean physical science with the ambition of broadening their knowledge beyond their specific field, and aims at facilitating better-informed interdisciplinary collaborations. For the purposes of this review, the Southern Ocean dynamic system is divided into four main components: large-scale circulation; cryosphere; turbulence; and gravity waves. Overviews are given of the key dynamical phenomena for each component, before describing the linkages between the components. The reviews are complemented by an overview of observed Southern Ocean trends and future climate projections. Priority research areas are identified to close remaining loops in our understanding of the Southern Ocean system.

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil-plant-atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth-dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM.

Convective flow in the deep mantle controls Earth's dynamic evolution, influences plate tectonics, and has shaped Earth's current surface features. Present and past convection-induced deformation manifests itself in seismic anisotropy, which is particularly strong in the mantle's uppermost and lowermost portions. While the general patterns of seismic anisotropy have been mapped for the upper mantle, anisotropy in the lowermost mantle (called D′′) is at an earlier stage of exploration. Here we review recent progress in methods to measure and interpret D′′ anisotropy. Our understanding of the limitations of existing methods and the development of new measurement strategies have been aided enormously by the availability of high-performance computing resources. We give an overview of how measurements of seismic anisotropy can help constrain the mineralogy and fabric of the deep mantle. Specifically, new and creative strategies that combine multiple types of observations provide much tighter constraints on the geometry of anisotropy than have previously been possible. We also discuss how deep mantle seismic anisotropy provides insights into lowermost mantle dynamics. We summarize what we have learned so far from measurements of D′′ anisotropy, how inferences of lowermost mantle flow from measurements of seismic anisotropy relate to geodynamic models of mantle flow, and what challenges we face going forward. Finally, we discuss some of the important unsolved problems related to the dynamics of the lowermost mantle that can be elucidated in the future by combining observations of seismic anisotropy with geodynamic predictions of lowermost mantle flow.

On behalf of the authors and readers of Reviews of Geophysics (RoG), the American Geophysical Union, and the broader scientific community, the editors wish to wholeheartedly thank those who reviewed manuscripts for RoG in 2023.

Data assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in-depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment-scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory-data land processes studies.

Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process-based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process-based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

River damming has brought great benefits to flood mitigation, energy and food production, and will continue to play a significant role in global energy supply, particularly in Asia, Africa, and South America. However, dams have extensively altered global river dynamics, including riverine connectivity, hydrological, thermal, sediment and solute regimes, and the channel morphology. These alterations have detrimental effects on the quality and quantity of fish habitat and associated impacts on aquatic life. Indeed, dams have been implicated in the decline of numerous fishes, emphasizing the need for effective conservation measures. Here, we present a global synthesis of critical issues concerning the impacts of river damming on physical fish habitats, with a particular focus on key fish species across continents. We also consider current fish conservation measures and their applicability in different contexts. Finally, we identify future research needs. The information presented herein will help support sustainable dam operation under the constraints of future climate change and human needs.

Atmospheric aerosols affect the Earth's climate in many ways, including acting as the seeds on which cloud droplets form. Since a large fraction of these particles is anthropogenic, the clouds' microphysical and radiative characteristics are influenced by human activity on a global scale leading to important climatic effects. The respective change in the energy budget at the top of the atmosphere is defined as the effective radiative forcing due to aerosol-cloud interaction (ERF_aci). It is estimated that the ERF_aci offsets presently nearly 1/4 of the greenhouse-induced warming, but the uncertainty is within a factor of two. A common method to calculate the ERF_aci is by the multiplication of the susceptibility of the cloud radiative effect to changes in aerosols by the anthropogenic change of the aerosol concentration. This has to be done by integrating it over all cloud regimes. Here we review the various methods of the ERF_aci estimation. Global measurements require satellites' global coverage. The challenge of quantifying aerosol amounts in cloudy atmospheres are met with the rapid development of novel methodologies reviewed here. The aerosol characteristics can be retrieved from space based on their optical properties, including polarization. The concentrations of the aerosols that serve as cloud drop condensation nuclei can be also estimated from their impact on the satellite-retrieved cloud drop number concentrations. These observations are critical for reducing the uncertainty in the ERF_aci calculated from global climate models (GCMs), but further development is required to allow GCMs to properly simulate and benefit these novel observables.