An earthquake-induced stress drop on a megathrust instigates different responses on the upper plate and slab. We mimic homogenous and heterogeneous megathrust interfaces at the laboratory scale to monitor the strain relaxation on two elastically bi-material plates by establishing analog velocity weakening and neutral materials. A sequential elastic rebound follows the coseismic shear-stress drop in our elastoplastic-frictional models: a fast rebound of the upper plate and the delayed and smaller rebound on the elastic belt (model slab). A combination of the rebound of the slab and the rapid relaxation (i.e., elastic restoration) of the upper plate after an elastic overshooting may accelerate the relocking of the megathrust. This acceleration triggers/antedates the failure of a nearby asperity and enhances the early slip reversal in the rupture area. Hence, the trench-normal landward displacement in the upper plate may reach a significant amount of the entire interseismic slip reversal and speeds up the stress build-up on the upper plate backthrust that emerges self-consistently at the downdip end of the seismogenic zones. Moreover, the backthrust switches its kinematic mode from a normal to reverse mechanism during the coseismic and postseismic stages, reflecting the sense of shear on the interface.

Unusually long duration and heavy rainfall on 5-6 February 2021 causes devastating floods in Semarang. The heavy rainfall is produced by two mesoscale convective systems (MCSs). The first MCS develops at 13Z on 5 February 2021 over the southern coast of Sumatra and propagates towards Semarang. The second MCS develops over the north coast of Semarang at 18Z on 5 February 2021, which later led to the first peak of precipitation at 21Z on 5 February 2021. These two MCSs eventually merge into single MCS, producing the second peak of precipitation at 00Z on 6 February 2021. Analysis of moisture transport indicates that the strong and persistent northwesterly wind near the surface induced by CENS prior to and during the event, creates an intensive meridional (southward) tropospheric moisture transport from the South China Sea towards Semarang. In addition, the westerly flow induced by low-frequency variability associated with La-Nina and the tropical depression associated with tropical cyclone formation over the North of Australia, produces an intensive zonal (eastward) tropospheric moisture transport from the Indian Ocean towards Semarang. The combined effects of the zonal and meridional moisture transports provide favorable conditions for the development of MCSs, and hence, extreme rainfall over Semarang.

The cryptocurrency sector is increasingly integrated into the global financial system. The world’s transition to a digital economy, facilitated by major technological breakthroughs, has several benefits. But as the demand for exchanging and investing in digital currencies is growing , the world must pay careful attention to the hidden and overlooked environmental impacts of this growth. The dramatic increase in the price of Bitcoin (BTC) over the last year and the resulting global race for BTC mining is turning the cryptocurrency market turning into one of the world’s leading polluting sectors. Yet, our knowledge about the environmental footprints of mining BTC is very limited. To address this hap, this study provides the first estimates of the carbon, water and land footprints of BTC mining around the world.

In its three-dimensional (3-D) characterization, drought is approached as an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time, i.e., drought dynamics. Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small ‘artifact’ droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor (LAFDM) for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.

Downwelling longwave radiation (DLR) is an important part of the surface energy budget. Spectral trends in the DLR provide insight into the radiative drivers of climate change. In this research, we process and analyze a 23-year downwelling longwave radiance record measured by the Atmospheric Emitted Radiance Interferometers (AERI) at the Southern Great Plains (SGP) site of the Atmospheric Radiation Program. Two AERIs were deployed at SGP with an overlapping observation period of about 10 years, which allows us to examine the consistency and accuracy of the measurements and to characterize discrepancies between them due to undetected instrumentation errors. Using the 23-year record, we analyze the all-sky radiance trends in DLR, which reflects the associated surface warming trend at SGP during this same period and also the complex changes in meteorological conditions. For instance, the observed radiance in the CO2 absorption band follows closely the near-surface air temperature variations. The changes in the sky fraction of clear-sky and thick cloudy-sky scenes offset the radiance changes in the window band. Our analysis shows that the radiance trend uncertainty in the DLR record to date mainly results from the climate internal variability rather than the measurement error, which highlights the importance of continuing the DLR spectral measurements to unambiguously detect and attribute climate change.

Conceptual hydrological models imply a simplification of the complexity of the hydrological system; however, it lacks the flexibility in reproducing a wide range of the catchment responses. Usually, a trade-off is done to sacrifice the accuracy of a specific aspect of the system behavior in favor of the accuracy of other aspects. This study evaluates the benefit of using a modular approach, “The fuzzy committee model” of building specialized models (same structure associated with different parameter realization) to reproduce specific responses (high and low flow response) of the catchment. The study also assesses the applicability of using predicted runoff from both specialized models with certain weights based on a fuzzy membership function to form a fuzzy committee model. This research continues to explore the fuzzy committee models first presented by [Solomatine, 2006] and further developed by [Fenicia et al., 2007; Kayastha et al., 2013]. In this paper, weighting schemes with power parameter values are investigated. A thorough study is conducted on the relation between the fuzzy committee variables (the membership functions and the weighting schemes), and their effect on the model performance. Furthermore, the Fuzzy committee concept is applied on a conceptual distributed model with two cases, the first with lumped catchment parameters and the latter with distributed parameters. A comparison between different combinations of the fuzzy committee variables showed the superiority of all Fuzzy Committee models over single models. Fuzzy committee of distributed models performed well, especially in capturing the highest peak in the calibration data set; however, it needs further study of the effect of model parameterization on the model performance and uncertainty.

The projection of extreme convective precipitation by global climate models (GCM) exhibits significant uncertainty due to coarse resolutions. Direct dynamical downscaling (DDD) of regional climate at kilometer-scale resolutions provides valuable insight into extreme precipitation changes, but its computational expense is formidable. Here we document the effectiveness of machine learning to enable smart dynamical downscaling (SDD), which selects a small subset of GCM data to conduct downscaling. Trained with data for three subtropical/tropical regions, convolutional neural networks (CNNs) retained 92% to 98% of extreme precipitation events (rain intensity higher than the 99th percentile) while filtering out 88% to 95% of circulation data. When applied to reanalysis data sets differing from training data, the CNNs’ skill in retaining extremes decreases modestly in subtropical regions but sharply in the deep tropics. Nonetheless, one of the CNNs can still retain 62% of all extreme events in the deep tropical region in the worst case.

Heliophysics data analysis often involves combining diverse science measurements, many of them captured as time series. Although there are now only a few commonly used data file formats, the diversity in mechanisms for automated access to and aggregation of such data holdings can make analysis that requires inter-comparison of data from multiple data providers difficult. The Heliophysics Application Programmer’s Interface (HAPI) is a recently developed standard for accessing distributed time-series data to increase interoperability. The HAPI specification is based on the common elements of existing data services, and it standardizes the two main parts of a data service: the request interface and the response data structures. The interface is based on the REpresentational State Transfer (REST) or RESTful architecture style, and the HAPI specification defines five required REST endpoints. Data are returned via a streaming format that hides file boundaries; the metadata is detailed enough for the content to be scientifically useful, e.g., plotted with appropriate axes layout, units, and labels. Multiple mature HAPI-related open-source projects offer server-side implementation tools and client-side libraries for reading HAPI data in multiple languages (IDL, Java, MATLAB, and Python). Multiple data providers in the US and Europe have added HAPI access alongside their existing interfaces. Based on this experience, data can be served via HAPI with little or no information loss compared to similar existing web interfaces. Finally, HAPI has been recommended as a COSPAR standard for time series data delivery.

Clay material characterization is of importance for many geo-engineering and environmental applications, and geo-electrical methods are often used to detect them in the subsurface. Spectral induced polarization (SIP) is a geo-electric method that non-intrusively measures the frequency-dependent complex electrical conductivity of a material, in the mHz to the kHz range. We present a new SIP dataset of four different types of clay (a red montmorillonite sample, a green montmorillonite sample, a kaolinite sample, and an illite sample) at five different salinities (initially de-ionized water, ~10-3, ~10-2, ~10-1, and 1 mol/L of NaCl). We propose a new laboratory protocol that allows the repeatable characterization of clay samples. The complex conductivity spectra are interpreted with the widely used phenomenological double-Pelton model. We observe an increase of the real part of the conductivity with salinity for all types of clay, while the imaginary part presents a non monotonous behavior. The decrease of polarization over conduction with salinity is interpreted as evidence that conduction increases with salinity faster than polarization. We test the empirical petrophysical relationship between σ”surf and σ’surf and validate this approach based on our experimental data and two other datasets from the literature. With this dataset we can better understand the frequency-dependent electrical response of different types of clay. This unique dataset of complex conductivity spectra for different types of clay samples is a step forward toward better characterization of clay formations in situ.

Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording the total solar irradiance (TSI). Some of these measurements last for more than a decade. It is then mandatory to merge them to obtain a seamless record whose duration exceeds that of the individual instruments. Climate models can be better validated using such long TSI records which can also help provide stronger constraints on past climate reconstructions (e.g.,back to the Maunder minimum). We propose a 3-stepmethod based on data fusion, including a stochastic noise model to take into account short and long-term correlations. Compared with previous products, the difference in terms of mean value over the whole time series and at the various solar minima are below 0.2W/m2. Next, we model the frequency spectrum of this 41-year TSI composite time series with a Generalized Gauss-Markov model to help describing an observed flattening at high frequencies. It allows us to fit a linear trend into these TSI time series by joint inversion with the stochastic noise model via a maximum-likelihood estimator. Our results show that the amplitude of such trend is∼−0.009±0.010 W/(m2.yr) for the period 1980-2021. These results are compared with the difference of irradiance values estimated from two consecutive solar minima. We conclude that the trend in these com-posite time series is mostly an artefact due to the coloured noise.

Ensemble-based data assimilation of radar observations across inner-core regions of tropical cyclones (TCs) in tandem with satellite all-sky infrared radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all-sky microwave radiances using Hurricane Harvey (2017) as an example. Assimilating GPM constellation all-sky microwave radiances in addition to GOES-16 all-sky infrared radiances reduces the forecast errors in the TC track, rapid intensification, and peak intensity compared to assimilating all-sky infrared radiances alone, including a 24-hour increase in forecast lead-time for rapid intensification. Assimilating all-sky microwave radiances also improves Harvey’s hydrometeor fields, which leads to improved forecasts of rainfall after Harvey’s landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC-associated hazards in the future.

Future mission carrying seismometer payloads to icy ocean worlds will measure global and local seismicity to determine where the ice shell is seismically active. We use two locations, a seismically active site on Gulkana Glacier, Alaska, and a more seismically quiet site on the northwestern Greenland Ice Sheet as geophysical analogs. We compare the performance of a single-station seismometer against a small-aperture seismic array to detect both high (> 1 Hz) and low (< 0.1 Hz) frequency events at each site. We created catalogs of high frequency (HF) and low frequency (LF) seismicity at each location using the automated Short-Term Average/ Long-Term Average technique. We find that with a 2-meter small-aperture seismic array, our detection rate increased (9 % for Alaska, 46% for Greenland) over the single-station approach. At Gulkana, we recorded an order of magnitude greater HF events than the Greenland site. We ascribe the HF events sources to a combination of icequakes, rockfalls, and ice-water interactions, while very high frequency events are determined to result from bamboo poles that were used to secure gear. We further find that local environmental noise reduces the ability to detect low-frequency global tectonic events. Based upon this study, we recommend that future missions consider the value of the expanded capability of a small array compared to a single station, design detection algorithms that can accommodate variable environmental noise, and assess the potential landings sites for sources of local environmental noise that may limit detection of global events.

The extreme variability of the cold point tropopause temperature (TCPT) and height (HCPT) are examined over a tropical station, Gadanki (13.45N, 79.2E) using high-resolution radiosonde data during the period 2006-2014. The extreme variabilities such as the coldest (warmest) tropopause is defined if TCPT is lesser (greater) than the lower (upper) limit of its two-sigma level whereas the highest (lowest) tropopause is defined as the HCPTis greater (lesser) than the lower (upper) limit of its two-sigma level. In total 161 extreme cases such as the coldest (52) and warmest (30) TCPT and the highest (57) and lowest (22) HCPT are observed. The coldest (187.2±1.60 K, 17.3±0.52 km), warmest (194.2±1.78 K, 16.9±0.89 km), lowest (191.7±1.78 K, 18.2±0.55 km) and highest (191.8±2.11 K, 16.2±0.38 km) occurs without preference of season. These extreme tropopause cases occur independently. Thermal structure of the coldest tropopause cases reveals that they are often sharper whereas the warmest, highest and lowest tropopause is broader. Water vapor and ozone concentrations are found to be high for the warmest tropopause and low for the coldest tropopause. Under the shallow convection, extreme temperature profiles, in general, show prominent warming between 8-14 km while anomalous cooling (warming) just below (above) the CPT. The occurrence of the tropical cyclones, cirrus clouds and equatorial wave propagation are the possible candidates for the occurrence of the extreme tropopauses.

Barrier island overwash occurs when the elevation of wave runup exceeds the dune crest and induces landward transport of sediment across a barrier island and deposition of a washover deposit. Washover deposition is generally attributed to major storms, is important for the maintenance of barrier island resilience to sea-level rise and is used to extend hurricane records beyond historical accounts by reconstructing the frequency and extent of washover deposits preserved in the sedimentary record. Here, we present a high-fidelity three-year record of washover evolution and overwash at a transgressive barrier island site. During the first year after establishment, washover volume and area increased 1,595% and 197%, respectively, from at least monthly overwash. Most of the washover accretion resulted from the site morphology having a low resistance to overwash, as opposed to being directly impacted by major storms. Washover deposits can accrete over multi-year time scales; therefore, paleowashover deposits are more complex than simply event beds.

This is a reply to a comment on the original research study with the title: ‘Unintentional unfairness when applying new greenhouse gas emissions metrics at country level’. This reply responds to some of the claims made in the comment and provides a scientific rebuttal.

Located in northern Dominican Republic, the Early Cretaceous Rio Boba mafic-ultramafic plutonic sequence constitutes a lower crust section of the Caribbean island arc, made up by gabbroic rocks and subordinate pyroxenite. Modal compositions, mineral chemistry, whole-rock compositions and thermobarometric calculations indicate that pyroxenites and gabbronorites represent a cumulate sequence formed by fractionation of tholeiitic magmas with initially very low H2O content in the lower crust of the arc (0.6-0.8 GPa). Melts evolved along a simplified crystallization sequence of olivine ® pyroxenes ® plagioclase ® Fe-Ti oxides. The magmatic evolution of the Rio Boba sequence and associated supra-crustal Puerca Gorda metavolcanic rocks is multi-stage and involves the generation of magmas from melting of different sources in a supra-subduction zone setting. The first stage included the formation of a highly depleted substrate as result of decompressional melting of a refractory mantle source, represented by a cumulate sequence of LREE-depleted IAT and boninitic gabbronorites and pyroxenites. The second stage involved volumetrically subordinate cumulate troctolites and gabbros, which are not penetratively deformed. The mantle source was refractory and enriched by a LILE-rich hydrous fluid derived from a subducting slab and/or overlying sediments, and possibly by a LREE-rich melt. The third stage is recorded in the upper crust of the arc by the Puerca Gorda ‘normal’ IAT protoliths, which are derived from an N-MORB mantle source enriched with a strong subduction component. This magmatic evolution has implications for unravelling the processes responsible for subduction initiation and subsequent building of the Caribbean island arc.

Modelling the spatial distribution of infrasound attenuation (or transmission loss, TL) is key to understanding and interpreting microbarometer data and observations. Such predictions enable the reliable assessment of infrasound source characteristics such as ground pressure levels associated with earthquakes, man-made or volcanic explosion properties, and ocean-generated microbarom wavefields. However, the computational cost inherent in full-waveform modelling tools, such as Parabolic Equation (PE) codes, often prevents the exploration of a large parameter space, i.e., variations in wind models, source frequency, and source location, when deriving reliable estimates of source or atmospheric properties – in particular for real-time and near-real-time applications. Therefore, many studies rely on analytical regression-based heuristic TL equations that neglect complex vertical wind variations and the range-dependent variation in the atmospheric properties. This introduces significant uncertainties in the predicted TL. In the current contribution, we propose a deep learning approach trained on a large set of simulated wavefields generated using PE simulations and realistic atmospheric winds to predict infrasound ground-level amplitudes up to 1000 km from a ground-based source. Realistic range dependent atmospheric winds are constructed by combining ERA5, NRLMSISE-00, and HWM-14 atmospheric models, and small-scale gravity-wave perturbations computed using the Gardner model. Given a set of wind profiles as input, our new modelling framework provides a fast (0.05 s runtime) and reliable (~5 dB error on average, compared to PE simulations) estimate of the infrasound TL.

The establishment of the High Sensitivity Seismograph Network (Hi-net) in Japan has led to the discovery of deep low-frequency tremors. Since such tremors are considered to be related to large earthquakes adjacent to tremors on the same subducting plate interface, it is important in seismology to investigate tremors before establishing modern seismograph networks that record seismic data digitally. We propose a deep learning method to detect evidence of tremors from seismogram images recorded on paper more than 50 years ago. In our previous study, we constructed a convolutional neural network (CNN) based on the Residual Network (ResNet) structure and verified its performance through learning with synthetic images generated based on past seismograms. In this study, we trained the CNN with seismogram images converted from real seismic data recorded by Hi-net. The CNN trained by fine-tuning achieved an accuracy of 98.64% for determining whether an input image contains tremors. The Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps to visualize model predictions indicate that the CNN successfully detects tremors without affections of a variety of noises, such as teleseisms. The trained CNN was applied to the past seismograms recorded at the Kumano observatory, Japan, operated by Earthquake Research Institute, The University of Tokyo. The CNN shows the potential to detect tremors from past seismogram images for broader applications, such as publishing a new tremor catalog.