Surveys in Geophysics

The risk of earthquakes and their effects on both nature and infrastructure in seismically active regions of India require adaptable and scalable earthquake early warning (EEW) systems. Developing a robust EEW system is crucial to mitigate earthquake risks in the region, but it is a challenging task. Various institutes have attempted to develop EEW systems using different methods. Still, there is no common consensus, and issues remain with response time and reliability of disseminated information to the public. Efforts by institutions like the Indian Institute of Technology, Roorkee, have advanced EEW technologies, focusing on dense seismic sensor networks, real-time data processing algorithms, and effective dissemination mechanisms. Recent initiatives aim to improve sensor sensitivity and accuracy through fast communication systems for quicker earthquake detection. However, challenges persist in making EEW accessible and affordable, particularly in remote areas, due to the lack of a nationwide system. The National Centre for Seismology (NCS), under the Ministry of Earth Sciences (MoES), is piloting an EEW system in the NW Himalayas, which could lead to a nationwide implementation. Developing region-specific algorithms for rapid data analysis and nurturing collaboration between academic institutions, government agencies, and international partners are crucial steps. Public awareness campaigns and educational programs are essential for community resilience and timely response to earthquake alerts. Establishing a robust EEW system in India could significantly enhance earthquake risk mitigation efforts in earthquake-prone zones of the country and should be viewed within the context of a holistic risk reduction framework. EEW systems can enhance mitigation efforts, but they must be complemented by other essential measures, such as improving building resilience and promoting public awareness.

Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised features. However, their accuracy and training cost restrict their applicability. To address these issues, we propose a novel initialization for PINNs based on meta-learning to enhance their performance. In our framework, we first utilize meta-learning to train a common network initialization for a distribution of medium parameters (i.e., velocity models). This phase employs a unique training data container, comprising a support set and a query set. We use a dual-loop approach, optimizing network parameters through a bidirectional gradient update from the support set to the query set. Following this, we use the meta-trained PINN model as the initial model for a regular PINN training for a new velocity model, where the optimization of the network is jointly constrained by the physical and regularization losses. Numerical results demonstrate that, compared to the vanilla PINN with random initialization, our method achieves a much faster convergence speed, and also obtains a significant improvement in the results accuracy. Meanwhile, we showcase that our method can be integrated with existing optimal techniques to further enhance its performance.

In this paper, we review the theoretical studies of the electromagnetic and other non-seismic phenomena accompanying earthquakes. This field of geophysical research is at the interception of several sciences: electrodynamics, solid-state physics, fracture mechanics, seismology, acoustic-gravity waves, magnetohydrodynamics, ionospheric plasma, etc. In order to make physics of these phenomena as transparent as possible, we use a simplified way of deriving some theoretical results and restrict our analysis to order-of-magnitude estimates. The main emphasis is on those theoretical models which give not only a qualitative, but also a quantitative, description of the observed phenomena. After some introductory material, the review is begun with an analysis of the causes of local changes in the rock conductivity occasionally observed before earthquake occurrence. The mechanisms of electrical conductivity in dry and wet rocks, including the electrokinetic effect, are discussed here. In the next section, the theories explaining the generation of low-frequency electromagnetic perturbations resulting from the rock fracture are covered. Two possible mechanisms of the coseismic electromagnetic response to the propagation of seismic waves are studied theoretically. Hereafter, we deal with atmospheric phenomena, which can be related to seismic events. Here we discuss models describing the effect of pre-seismic changes in radon activity on atmospheric conductivity and examine hypotheses explaining abnormal changes in the atmospheric electric field and in infrared radiation from the Earth, which are occasionally observed on Earth and from space over seismically active regions. In the next section, we review several physical mechanisms of ionospheric perturbations associated with seismic activity. Among them are acoustic-gravity waves resulting from the propagation of seismic waves and tsunamis and ionospheric perturbations caused by vertical acoustic resonance in the atmosphere. In the remainder of this paper, we discuss whether variations in radon activity and vertical seismogenic currents in the atmosphere can affect the ionosphere.

Gravity forward modeling provides important high-resolution information for the development of global gravity models, and can also be applied in many studies, e.g., topographic/isostatic effects computation and Bouguer anomaly maps compilation. In this paper, we present efficient spectral forward modeling approaches in the spheroidal harmonic domain, based on a single layer with constant density or volumetric layers with laterally varying density. With the binomial series expansion applied in spheroidal harmonic gravity forward modeling, the computational cost of these approaches is much lower than similar approaches. In both layering cases, we derive topographic potential models up to degree and order (d/o) 2190 by applying the approaches proposed here. Our methodology is evaluated by comparing these outcome models with other similar topographic potential models derived from spherical harmonic solutions. We find that topographic potentials from spheroidal and spherical harmonic approaches are in great agreement. Finally, the model named EHFM_Earth_7200 with a maximum degree of 7200 was derived by a layer-based approach. The evaluations by ground-truth data show that EHFM_Earth_7200 improves GO_CONS_GCF_2_DIR_R6 by 4% over Antarctica, and improves EGM2008 by ~ 34% over northern Canada. A global map of Bouguer gravity anomaly was also compiled with EHFM_Earth_7200 and EGM2008. As the main conclusion of this work, the new model EHFM_Earth_7200 is beneficial for investigating and modeling the Earth’s external gravity field, the new approaches have comparable accuracy to spherical harmonic approaches and are more suitable for practical use with guaranteed convergence regions because they are performed in the spheroidal harmonic domain.

Previous surveys using the remote sensing (RS) method revealed significant structures in the area of the Western Carpathians. It has not yet been possible to verify and explain the results of these surveys, even though all the phenomena are regional in nature and show many morphological features that clearly indicate recent activity and deformations, including current earthquake foci. The aim of the article was to verify these phenomena and compare them with new findings. A method of combining geomorphological data with satellite image analysis and verification using Global Navigation Satellite Systems (GNSS) and geophysics data was used. In this work, results are presented confirming the existence of a previously identified nonlinear structure—the "gravity nappe" in the western part of the Low Tatras, and the largest tectonic system Muráň—Malcov is analyzed in detail. Similar structures and tectonic zones, on a smaller scale, can also be found in other areas of the Carpathians. For example, the gravity structure in the Lesser Carpathians and the Ukrainian flysch Carpathians or the linear boundaries interpreted as tectonic systems—the Myjava-Subtatrans, Hron and Transgemerian tectonic zones. Recent movement trends have been confirmed by newly unified data from EUREF Permanent Network (EPN) stations and GNSS campaigns carried out in the last two decades in the given area. Both types of analyzed structures are directly related to the occurring foci of earthquakes.

Accurate diagnosis of regional atmospheric and surface energy budgets is critical for understanding the spatial distribution of heat uptake associated with the Earth’s energy imbalance (EEI). This contribution discusses frameworks and methods for consistent evaluation of key quantities of those budgets using observationally constrained data sets. It thereby touches upon assumptions made in data products which have implications for these evaluations. We evaluate 2001–2020 average regional total (TE) and dry static energy (DSE) budgets using satellite-based and reanalysis data. For the first time, a consistent framework is applied to the ensemble of the 5th generation European Reanalysis (ERA5), version 2 of modern-era retrospective analysis for research and applications (MERRA-2), and the Japanese 55-year Reanalysis (JRA55). Uncertainties of the computed budgets are assessed through inter-product spread and evaluation of physical constraints. Furthermore, we use the TE budget to infer fields of net surface energy flux. Results indicate biases < 1 W/m2 on the global, < 5 W/m2 on the continental, and ~ 15 W/m2 on the regional scale. Inferred net surface energy fluxes exhibit reduced large-scale biases compared to surface flux data based on remote sensing and models. We use the DSE budget to infer atmospheric diabatic heating from condensational processes. Comparison to observation-based precipitation data indicates larger uncertainties (10–15 Wm−2 globally) in the DSE budget compared to the TE budget, which is reflected by increased spread in reanalysis-based fields. Continued validation efforts of atmospheric energy budgets are needed to document progress in new and upcoming observational products, and to understand their limitations when performing EEI research.

In the tropics, deep convection, which is often organized into convective systems, plays a crucial role in the water and energy cycles by significantly contributing to surface precipitation and forming upper-level ice clouds. The arrangement of these deep convective systems, as well as their individual properties, has recently been recognized as a key feature of the tropical climate. Using data from Africa and the tropical Atlantic Ocean as a case study, recent shifts in convective organization have been analyzed through a well-curated, unique record of METEOSAT observations spanning four decades. The findings indicate a significant shift in the occurrence of deep convective systems, characterized by a decrease in large, short-lived systems and an increase in smaller, longer-lived ones. This shift, combined with a nearly constant deep cloud fraction over the same period, highlights a notable change in convective organization. These new observational insights are valuable for refining emerging kilometer-scale climate models that accurately represent individual convective systems but struggle to realistically simulate their overall arrangement.

As observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions, global terrestrial water storage (TWS), excluding ice sheets and glaciers, declined rapidly between May 2014 and March 2016. By 2023, it had not yet recovered, with the upper end of its range remaining 1 cm equivalent height of water below the upper end of the earlier range. Beginning with a record-setting drought in northeastern South America, a series of droughts on five continents helped to prevent global TWS from rebounding. While back-to-back El Niño events are largely responsible for the South American drought and others in the 2014–2016 timeframe, the possibility exists that global warming has contributed to a net drying of the land since then, through enhanced evapotranspiration and increasing frequency and intensity of drought. Corollary to the decline in global TWS since 2015 has been a rise in barystatic sea level (i.e., global mean ocean mass). However, we find no evidence that it is anything other than a coincidence that, also in 2015, two estimates of barystatic sea level change, one from GRACE/FO and the other from a combination of satellite altimetry and Argo float ocean temperature measurements, began to diverge. Herein, we discuss both the mechanisms that account for the abrupt decline in terrestrial water storage and the possible explanations for the divergence of the barystatic sea level change estimates.

This study uses an oceanic energy budget to estimate the ocean heat transport convergence in the North Atlantic during 2005–2018. The horizontal convergence of the ocean heat transport is estimated using ocean heat content tendency primarily derived from satellite altimetry combined with space gravimetry. The net surface energy fluxes are inferred from mass-corrected divergence of atmospheric energy transport and tendency of the ECMWF ERA5 reanalysis combined with top-of-the-atmosphere radiative fluxes from the clouds and the Earth’s radiant energy system project. The indirectly estimated horizontal convergence of the ocean heat transport is integrated between the rapid climate change-meridional overturning circulation and heatflux array (RAPID) section at 26.5°N (operating since 2004) and the overturning in the subpolar north atlantic program (OSNAP) section, situated at 53°–60°N (operating since 2014). This is to validate the ocean heat transport convergence estimate against an independent estimate derived from RAPID and OSNAP in-situ measurements. The mean ocean energy budget of the North Atlantic is closed to within ± 0.25 PW between RAPID and OSNAP sections. The mean oceanic heat transport convergence between these sections is 0.58 ± 0.25 PW, which agrees well with observed section transports. Interannual variability of the inferred oceanic heat transport convergence is also in reasonable agreement with the interannual variability observed at RAPID and OSNAP, with a correlation of 0.54 between annual time series. The correlation increases to 0.67 for biannual time series. Other estimates of the ocean energy budget based on ocean heat content tendency derived from various methods give similar results. Despite a large spread, the correlation is always significant meaning the results are robust against the method to estimate the ocean heat content tendency.

In 1979, Herbert Riehl and Joanne Simpson (Malkus) analytically estimated that 1600–2400 undilute convective cores vertically transport energy to the tropopause at any given time within a region where upper-tropospheric energy is only exported from the tropics. The focus of this paper is to update this estimate using modern satellite observations, compare hot tower frequency and intensity characteristics to all deep convective cores that reach the upper troposphere, and document hot tower spatiotemporal variability in relation to precipitation and high cloud properties within the tropical trough zone (between 13 °S and 19 °N). Cloud vertical profiles from CloudSat and CALIPSO measurements supply convective core diameters and proxies for intensity and convective activity, and these proxies are augmented with brightness temperature data from geostationary satellite observations, precipitation information from IMERG, and cloud radiative properties from CERES. Less than 35% of all deep cores are classified as hot towers, and we estimate that 800–1700 hot towers occur at any given time over the course of a day, with the mean maximum core and hot tower frequency occurring at the time of year when peak convective intensity and precipitation occur. Convective objects that contain hot towers frequently contain multiple cores, and the largest systems with five or more distinct cores most frequently occur in regions where organized mesoscale convective systems and the highest climatological mean rain rates are known to occur. Analysis of co-located radar and infrared brightness temperatures reveals that passive observations alone are not sufficient to unambiguously distinguish hot towers using simple brightness temperature thresholds.

This study aims to revisit the classic “hot tower” hypothesis proposed by Riehl and Simpson (Malkus) in 1958 and revisited in 1979. Our investigation centers on the convective mass flux of hot towers within the tropical trough zone, using geostationary (GEO) satellite data and an innovative analysis technique, known as ML16, which integrates various data sources, including hot tower heights, ambient profiles, and a plume model, to determine convective mass flux. The GEO-based ML16 approach is evaluated against collocated ground-based radar wind profiler observations, showing broad agreement. Our GEO-based estimate of hot tower convective mass flux, 2.8 × 1011–3.4 × 1011 kg s−1, is similar to the revisited estimate in Riehl and Simpson (1979), 2.6–3.0 × 1011 kg s−1. Additionally, our analysis gives a median count of around 550 hot towers with a median size of about 11 km, in contrast to the previous estimates of 1600–2400 hot towers, each characterized by a fixed size of 5 km. We discuss the causes of these discrepancies, emphasizing the fundamental differences between the two approaches in characterizing tropical hot towers. While both approaches have various uncertainties, the evidence suggests that greater credibility should be placed on results derived from direct satellite observations. Finally, we identify future opportunities in Earth Observations that will provide more accurate measurements, enabling further evaluation of the role played by tropical hot towers in mass transport.

The global seasonal cycle of energy in Earth’s climate system is quantified using observations and reanalyses. After removing long-term trends, net energy entering and exiting the climate system at the top of the atmosphere (TOA) should agree with the sum of energy entering and exiting the ocean, atmosphere, land, and ice over the course of an average year. Achieving such a balanced budget with observations has been challenging. Disagreements have been attributed previously to sparse observations in the high-latitude oceans. However, limiting the local vertical integration of new global ocean heat content estimates to the depth to which seasonal heat energy is stored, rather than integrating to 2000 m everywhere as done previously, allows closure of the global seasonal energy budget within statistical uncertainties. The seasonal cycle of energy storage is largest in the ocean, peaking in April because ocean area is largest in the Southern Hemisphere and the ocean’s thermal inertia causes a lag with respect to the austral summer solstice. Seasonal cycles in energy storage in the atmosphere and land are smaller, but peak in July and September, respectively, because there is more land in the Northern Hemisphere, and the land has more thermal inertia than the atmosphere. Global seasonal energy storage by ice is small, so the atmosphere and land partially offset ocean energy storage in the global integral, with their sum matching time-integrated net global TOA energy fluxes over the seasonal cycle within uncertainties, and both peaking in April.

Since the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies.

Satellite observations from the Clouds and the Earth’s Radiant Energy System show that Earth’s energy imbalance has doubled from 0.5 ± 0.2 Wm−2 during the first 10 years of this century to 1.0 ± 0.2 Wm−2 during the past decade. The increase is the result of a 0.9 ± 0.3 Wm−2 increase absorbed solar radiation (ASR) that is partially offset by a 0.4 ± 0.25 Wm−2 increase in outgoing longwave radiation (OLR). Despite marked differences in ASR and OLR trends during the hiatus (2000–2010), transition-to-El Niño (2010–2016) and post-El Niño (2016–2022) periods, trends in net top-of-atmosphere flux (NET) remain within 0.1 Wm−2 per decade of one another, implying a steady acceleration of climate warming. Northern and southern hemisphere trends in NET are consistent to 0.06 ± 0.31 Wm−2 per decade due to a compensation between weak ASR and OLR hemispheric trend differences of opposite sign. We find that large decreases in stratocumulus and middle clouds over the sub-tropics and decreases in low and middle clouds at mid-latitudes are the primary reasons for increasing ASR trends in the northern hemisphere (NH). These changes are especially large over the eastern and northern Pacific Ocean, and coincide with large increases in sea-surface temperature (SST). The decrease in cloud fraction and higher SSTs over the NH sub-tropics lead to a significant increase in OLR from cloud-free regions, which partially compensate for the NH ASR increase. Decreases in middle cloud reflection and a weaker reduction in low-cloud reflection account for the increase in ASR in the southern hemisphere, while OLR changes are weak. Changes in cloud cover in response to SST increases imply a feedback to climate change yet a contribution from radiative forcing or internal variability cannot be ruled out.

This paper is concerned with how the diabatically-forced overturning circulations of the atmosphere, established by the deep convection within the tropical trough zone (TTZ), first introduced by Riehl and (Malkus) Simpson, in Contr Atmos Phys 52:287–305 (1979), fundamentally shape the distributions of tropical and subtropical cloudiness and the changes to cloudiness as Earth warms. The study first draws on an analysis of a range of observations to understand the connections between the energetics of the TTZ, convection and clouds. These observations reveal a tight coupling of the two main components of the diabatic heating, the cloud component of radiative heating, shaped mostly by high clouds formed by deep convection, and the latent heating associated with the precipitation. Interannual variability of the TTZ reveals a marked variation that connects the depth of the tropical troposphere, the depth of convection, the thickness of high clouds and the TOA radiative imbalance. The study examines connections between this convective zone and cloud changes further afield in the context of CMIP6 model experiments of climate warming. The warming realized in the CMIP6 SSP5-8.5 scenario multi-model experiments, for example, produces an enhanced Hadley circulation with increased heating in the zone of tropical deep convection and increased radiative cooling and subsidence in the subtropical regions. This impacts low cloud changes and in turn the model warming response through low cloud feedbacks. The pattern of warming produced by models, also influenced by convection in the tropical region, has a profound influence on the projected global warming.

In this study, we investigate the representation of the global mean energy balance components in 10 atmospheric reanalyses, and compare their magnitudes with recent reference estimates as well as the ones simulated by the latest generation of climate models from the 6th phase of the coupled model intercomparison project (CMIP6). Despite the assimilation of comprehensive observational data in reanalyses, the spread amongst the magnitudes of their global energy balance components generally remains substantial, up to more than 20 Wm−2 in some quantities, and their consistency is typically not higher than amongst the much less observationally constrained CMIP6 models. Relative spreads are particularly large in the reanalysis global mean latent heat fluxes (exceeding 20%) and associated intensity of the global water cycle, as well as in the energy imbalances at the top-of-atmosphere and surface. A comparison of reanalysis runs in full assimilation mode with corresponding runs constrained only by sea surface temperatures reveals marginal differences in their global mean energy balance components. This indicates that discrepancies in the global energy balance components caused by the different model formulations amongst the reanalyses are hardly alleviated by the imposed observational constraints from the assimilation process. Similar to climate models, reanalyses overestimate the global mean surface downward shortwave radiation and underestimate the surface downward longwave radiation by 3–7 Wm−2. While reanalyses are of tremendous value as references for many atmospheric parameters, they currently may not be suited to serve as references for the magnitudes of the global mean energy balance components.

Earth’s energy imbalance (EEI) is a fundamental metric of global Earth system change, quantifying the cumulative impact of natural and anthropogenic radiative forcings and feedback. To date, the most precise measurements of EEI change are obtained through radiometric observations at the top of the atmosphere (TOA), while the quantification of EEI absolute magnitude is facilitated through heat inventory analysis, where ~ 90% of heat uptake manifests as an increase in ocean heat content (OHC). Various international groups provide OHC datasets derived from in situ and satellite observations, as well as from reanalyses ingesting many available observations. The WCRP formed the GEWEX-EEI Assessment Working Group to better understand discrepancies, uncertainties and reconcile current knowledge of EEI magnitude, variability and trends. Here, 21 OHC datasets and ocean heat uptake (OHU) rates are intercompared, providing OHU estimates ranging between 0.40 ± 0.12 and 0.96 ± 0.08 W m−2 (2005–2019), a spread that is slightly reduced when unequal ocean sampling is accounted for, and that is largely attributable to differing source data, mapping methods and quality control procedures. The rate of increase in OHU varies substantially between − 0.03 ± 0.13 (reanalysis product) and 1.1 ± 0.6 W m−2 dec−1 (satellite product). Products that either more regularly observe (satellites) or fill in situ data-sparse regions based on additional physical knowledge (some reanalysis and hybrid products) tend to track radiometric EEI variability better than purely in situ-based OHC products. This paper also examines zonal trends in TOA radiative fluxes and the impact of data gaps on trend estimates. The GEWEX-EEI community aims to refine their assessment studies, to forge a path toward best practices, e.g., in uncertainty quantification, and to formulate recommendations for future activities.

Seismic and electromagnetic (EM) imaging are essential tools for characterizing velocity and conductivity. However, the separate inversion of seismic and EM data is challenging due to the noisy measurements, inadequate data collection, and reliance on prior information, consequently resulting in uncertainty and ambiguity of the solutions. Moreover, the two methods are different in sensitivity and spatial resolution, making it difficult to discover consistencies in the inverted models. Joint inversion of seismic and EM data takes advantage of both methods and significantly improves the imaging capability of subsurface structures. In this paper, we review various coupling strategies for the joint inversion of seismic and EM data and highlight the application advances from 1-D to 3-D inversion. Specifically, we investigate the integration of machine learning techniques to tackle ill-posed inverse problems and showcase their effectiveness in coupling. Following this, we construct a deep-learning-based joint inversion workflow and provide a synthetic test to demonstrate its superiority by applying an attention mechanism, which enhances the model’s capability to focus on specific features within the data. This study proves the potential of integrating artificial intelligence into joint inversion and understanding the deep Earth interior by incorporating multiple geophysical data.

This review article provides a synthesis and perspective on how weather and climate extreme events can play a role in influencing tipping elements and triggering tipping points in the Earth System. An example of a potential critical global tipping point, induced by climate extremes in an increasingly warmer climate, is Amazon rainforest dieback that could be driven by regional increases in droughts and exacerbated by fires, in addition to deforestation. A tipping element associated with the boreal forest might also be vulnerable to heat, drought and fire. An oceanic example is the potential collapse of the Atlantic meridional overturning circulation due to extreme variability in freshwater inputs, while marine heatwaves and high acidity extremes can lead to coral reef collapse. Extreme heat events may furthermore play an important role in ice sheet, glacier and permafrost stability. Regional severe extreme events could also lead to tipping in ecosystems, as well as in human systems, in response to climate drivers. However, substantial scientific uncertainty remains on mechanistic links between extreme events and tipping points. Earth observations are of high relevance to evaluate and constrain those links between extreme events and tipping elements, by determining conditions leading to delayed recovery with a potential for tipping in the atmosphere, on land, in vegetation, and in the ocean. In the subsurface ocean, there is a lack of consistent, synoptic and high frequency observations of changes in both ocean physics and biogeochemistry. This review article shows the importance of considering the interface between extreme events and tipping points, two topics usually addressed in isolation, and the need for continued monitoring to observe early warning signs and to evaluate Earth system response to extreme events as well as improving model skill in simulating extremes, compound extremes and tipping elements.