On October 2020, a Mw 7.6 earthquake struck to the south of the Shumagin Islands in Alaska, nearly 3 months after the Mw 7.8 Simeonof megathrust event. The initial models of the earthquake indicated a largely strike-slip rupture; however, the observed tsunami was much larger and widespread than expected for the focal mechanism. We investigate what sea surface deformation is necessary to recreate the tsunami waveforms using water-level inversion techniques. We find that the sea surface deformation does not resemble that expected from a purely strike-slip earthquake. We then carry out slip inversions with water level and static GNSS data as input. We explore the likelihood of megathrust co-seismic slip aiding tsunamigenesis. We propose that, concurrently with strike-slip faulting, it is likely that a considerable slip occurred on the megathrust westward and updip from the previous July 2020 event. We also propose that a smaller submarine landslide is likely to have occurred in an area prone to them. The Sand Point earthquake potentially released ~2 meters of accumulated slip in the western Shumagin Gap, but likely did not slip updip of ~15 km depth.

The role of zooplanktivorous mesopelagic fish in the ocean carbon cycle is attracting increasing attention. However, little information is available regarding the carbon budget of marine zooplanktivorous fish, let alone that of mesopelagic fish. Here, we propose a carbon release model that divides fish-released carbon into two parts (i.e., food carbon release and body carbon release, based on the source (ingested food and the fish body, respectively)) and three forms (dissolved organic carbon (DOC), CO2, and particulate carbon (PC)). By feeding a model marine zooplanktivorous fish, marine medaka (Oryzias melastigma), a radiocarbon-labeled living rotifer, Brachionus plicatilis, we quantified a detailed carbon budget for the fish that includes the allocation of released food and body carbon to DOC, CO2, and PC. The results indicate that 53%–75% of the ingested food carbon was not assimilated but was released mainly as DOC (48%–59%), followed by CO2 (30%–40%) and PC (11%–13%). The release (/efflux) rates of fish body carbon changed from 0.12 to 0.053 d-1 when daily food rations shifted from 2.2% to 4.3% of the fish biomass. DOC, CO2, and PC accounted for 39%–42%, 40%–45%, and 16%–18% of the carbon released from the fish body, respectively. By using the carbon release model and the parameters derived from the model fish and from the literature, we estimate that mesopelagic fish in the global open ocean produce 1.34–15.2, 0.95–10.8, and 0.35–3.97 Pg C/y of DOC, CO2, and PC, respectively. Our results show that marine zooplanktivorous fish can transform substantial fractions of their daily ingested food and released body carbon into DOC and that mesopelagic fish may be important sources of DOC and fast-sinking PC in the ocean.

Use of Real-Time Control (RTC) technology in Rainwater Harvesting Systems (RWH) can improve performance across water supply, flood protection, and environmental flow provision. Such systems make the most of rainfall forecast information, to release water prior to storm events and thus minimise uncontrolled overflows. To date, most advanced applications have adopted 24-hr forecast information, leaving longer-term forecasts largely untested. In this study, we aimed to predict the performance of four different RTC strategies, based on different forecast lead-time and preferred objectives. RTC systems were predicted to yield comparatively slightly less harvested rainwater than conventional passive systems, but delivered superior performance in terms of flood mitigation and delivery of environmental water for streamflow restoration. More importantly, using a 7-day rainfall forecast, the longest commercially available prediction window, was shown to enhance the ability of RTC in mitigating flood risks and delivering an outflow regime that is close to the natural (reference) streamflow. Such a finding suggests that RTC combined with 7-day forecast can enhance the functionality of rainwater harvesting systems to restore and even mimick the entire natural flow regimes in receiving streams. This also opens up a new opportunity for practitioners to implement smart technology in managing urban stormwater in a range of contexts and for a range of stream health objectives.

Investigating watershed hydrology from a data-driven causal perspective is an attractive opportunity to characterize and understand relationships between water storages and fluxes. Here we assess integrally how the water balance components interact with themselves, aiming to find relevant time-lags or dependency patterns. Granger’s causality test and time-lagged mutual information were used in a pairwise approach to examine cause-effect relationships between precipitation, streamflow, groundwater levels under different land-covers, and evapotranspiration data (daily timescale) from 2009 to 2019 in a Brazilian watershed (52 km²), located in a recharge area of the Guarani Aquifer System. A verification assessment using synthetic datasets shows that the methods are effective to identify the underlying generating mechanisms. Statistically significant causal connections were confirmed in practically all pairs of observed data. Granger’s causality indicates that groundwater and streamflow responses are influenced by precipitation even with a lag of 1-day, while evapotranspiration can take more than 200 days to influence groundwater responses, depending on the water table depth and surrounding land-cover. Mutual information curves show dependency patterns between hydrological processes that are different from the ones obtained by cross-correlation functions. The causal analysis provides a complementary view of the hydrological system’s functioning and may lead us to develop predictive models that reproduce not only the target variables but also the diverse time-lagged dependencies observed in environmental data.

We constructed a catalog of volcanic deep low-frequency (DLF) earthquakes across 52 regions in Japan to investigate their seismicity based on three analyses: relocation, classification, and detection. Relocation and classification analyses are based on waveform correlation, and detection analysis is conducted using the matched filter technique. We found that DLF earthquakes in many regions are spatially clustered at approximately 5 km intervals between the lower limit of crustal earthquakes and Moho discontinuity. Based on temporal seismicity patterns, DLF earthquake groups in each region can be classified into episodic and non-episodic types. Episodic groups consist of seismic swarms and quiescence. In some episodic groups, DLF earthquakes have constant recurrence interevent times or increasing interevent times as a function of time. Swarms of DLF earthquakes sometimes are associated with volcanic activity at the surface, such as eruptions or crustal deformations in some regions. The spatiotemporal characteristics of DLF earthquake groups may be linked to the movement of magmatic fluids. The discrete vertical separation of DLF earthquake groups may reflect small-scale heterogeneities, such as injected magma. Periodic activity patterns may be caused by volcanic mechanisms, such as second boiling. The variety of DLF earthquake patterns may suggest that multiple mechanisms may trigger DLF earthquakes, such as complex underground structures and volcanic processes, rather than a single mechanism.

Wetlands are an important land type – they provide vital ecosystem services such as regulating floods, storing carbon, and providing wildlife habitat. The ability to simulate their spatial extent and hydrological processes is important for valuing wetlands’ function. The purpose of this study is to dynamically simulate wetlands’ hydrological processes and their feedback to the regional climate in the Prairie Pothole Region (PPR) of North America, where a large number of wetlands exist. In this study, we incorporated a wetland scheme into the Noah-MP Land Surface Model with two major modifications: (1) modifying the sub-grid saturation fraction for spatial wetland extent; (2) incorporating a dynamic water storage to simulate hydrological processes. This scheme was tested at a fen site in central Saskatchewan, Canada and applied regionally in the PPR with 13-year climate forcing produced by a high-resolution convection-permitting model. The differences between wetland and no-wetland simulations are significant, with increasing latent heat and evapotranspiration while decreasing sensible heat and runoff. Finally, the dynamic wetland scheme was tested using the coupled WRF model, showing an evident cooling effect of 1~3℃ in summer where wetlands are abundant. In particular, the wetland simulation shows reduction in the number of hot days for more than 10 days over the summer of 2006, when a long-lasting heatwave occurred. This research has great implications for land surface/regional climate modeling, as well as wetland conservation, for valuing wetlands in providing a moisture source and mitigating extreme heatwaves, especially under climate change.

Carbonate clumped isotope thermometry (Δ_47) is a temperature proxy that is becoming more widely used in the geosciences. Most calibration studies have used ordinary least squares linear regressions or York models to describe the relationship between Δ_47 and temperature. However, Bayesian models have not yet been explored for clumped isotopes. There also has not yet been a comprehensive study assessing the performance of commonly used regression models in the field. Here, we use simulated datasets to compare the performance of seven regression models, three of which are new and fit using a Bayesian framework. While Bayesian and non-Bayesian ordinary least squares linear regression models show the best overall accuracy for calibrations, Bayesian models outperform other models in terms of precision, especially if datasets are sufficiently large (>50 data points). For temperature reconstructions where a given regression model is applied to predict temperature from Δ_47), Bayesian and non-Bayesian models show variable performance advantages depending on the the structure of errors in the calibration dataset. Overall, our analyses suggest that the advantages of using Bayesian models for calibrating and reconstructing temperatures using clumped isotope paleothermometry are realized through the use of large calibration datasets (>50 data points). When used with large datasets, Bayesian regressions are expected to substantially improve the accuracy and precision of (i) calibration parameter estimates and (ii) temperature reconstructions (e.g., typically improving precision by at least a factor of two). We implement our comparative framework into a new web-based interface, BayClump. This data tool should increase reproducibility by enabling access to the different Bayesian and non-Bayesian regression models. Finally, we utilize BayClump with three published datasets to examine precision and accuracy in regression parameters and reconstructed temperatures. We show that BayClump yields similarly accurate results to published studies. However, the use of BayClump generally produces temperature reconstructions with meaningful reductions in temperature uncertainty, as demonstrated through reanalysis of data from a Late Miocene hominoids site in Yunnan, China.

The subauroral ion drift (SAID) denotes a latitudinally narrow channel of fast westward ion drift in the subauroral region, often observed during geomagnetically disturbed intervals. The recently recognized subauroral optical phenomena, the Strong Thermal Emission Velocity Enhancement (STEVE) and the Picket Fence, are both related to intense SAIDs. In this study, we present a 2D time-dependent model simulation of the self-consistent variations of the elec-tron/ion temperature, density, and FAC, under strong SAID, with more focus in the lower ionosphere. Our simulation reproduces many key features of SAID, such as the anomalous electron heating in the E-region, the strong electron temperature enhancement in the upper F-region, the intense ion frictional heating, and the plasma density depletion. Most importantly, the ion Pedersen drifts is found to play a crucial role in the density variations and FAC dynamics in the lower ionosphere. The transport effect of ion Pedersen drifts leads to strong density depletion in the lower ionosphere in a large portion of SAID. The FAC inside SAID is mainly downward with magnitude ï¿¿ ~1 ï¿¿A/m 2. At the poleward edge of SAID, the ion Pedersen drift leads to a pileup of the plasma density and an upward FAC. Our simulation results also corroborate the presence of strong gradients of plasma density, temperature, and flows, at the edge of SAID, which may be conducive to certain plasma instabilities. Our model provides a useful tool for the future exploration of the generation mechanisms of STEVE and Picket Fence.

Exploration of Venus in the 1970–1990’s revealed that the geology of Venus, the most Earth-like of the terrestrial planets, was decidedly un-Earth-like, with no plate tectonics, and no record of the first 80% of its history. A major outstanding question is whether Venus is still volcanically active today. We find that regions of Ganis Chasma have low radar emissivity values, due to low volumes of high dielectric minerals formed by surface – atmosphere weathering on the timescales of around 10s Ma. This confirms the presence of geologically recent volcanism in association with this major tectonic rift zone. The spatial correspondence of this emissivity signature with transient thermal anomalies suggests that Venus has been volcanically active at this site for at least the last few decades, a prediction that can be tested with space missions to Venus in the coming decade.

A dissection of the physics of seasonal cycle of oceanic upper layer stratification is necessary to improve climate predictions of biogeochemical cycles. We present a time series of vertical profiles of ε, the dissipation rate of turbulent kinetic energy, obtained from a microstructure profiler during the destratification period (summer-to-winter) at a mid-latitude 75m-deep coastal site. Significant correlation is obtained in the mixed layer depth (MLD) with a model combining effects of wind, wave, and buoyancy forcings, estimated from bulk parameters ~10 hours before observations, and used to identify the dominant forcings leading to MLD deepening. Intermittency at surface is correlated with seasonal storminess, and we observe a quadratic relation between kurtosis and skewness for ε statistics. By splitting the time series into layers, we observe the co-location of patches of higher ε with the shear maxima of the two first baroclinic modes, and significant correlations with surface wind stress in the transitional layer the past 24 hours, and at longer scale (4.25 days) in the baroclinic layer, suggesting that internal waves activity influences the setup of mixing intensity despite the lack of tidal forcing. The low-passed microstructure shear distribution seems to support this hypothesis despite possible signal contamination. In the highly stratified layers associated to salt-fingering (MLD’s basis and below), the buoyancy Reynolds number indicates a buoyancy regime control with low mixing value (0.2 x 10e-5 m²/s). More turbulent flows are identified in both surface and bottom layers (0.6 - 0.8 x 10e-5 m²/s), suggesting a seasonal erosion of the stratification by the boundary processes.

We assess the potential implementation of earthquake early warning (EEW) across Europe, where there is a clear need for measures that mitigate seismic risk. EEW systems consist of seismic networks and mathematical models/algorithms capable of real-time data telemetry that alert stakeholders (e.g., civil-protection authorities, the public), to an earthquake’s nucleation seconds before shaking occurs at target sites. During this time, actions can be taken that might decrease detrimental impacts. We investigate distributions of EEW lead times available across various parts of the Euro-Mediterranean region, based on seismicity models and seismic network density. We then determine the potential usefulness of these times for EEW purposes, by defining their spatial relationship with population exposure, seismic hazard, and an alert accuracy proxy, using well-established earthquake-engineering tools for measuring the impacts of earthquakes. The mapped feasibility results demonstrate that, under certain conditions, EEW could be effective for some parts of Europe.

We systematically searched USArray seismograms for intraplate tremor and earthquakes that were dynamically triggered by the 2012 M8.6 Sumatra earthquake. We confirm triggered seismic activity along the western boundary of the North America and note the absence of triggered seismicity east of the Rocky Mountains. We newly observed dynamically triggered tremor near the Yellowstone hotspot, Wyoming, and triggered earthquakes in the Raton Basin and in central Utah. We then identified additional triggered events for each of these three locations by investigating seismograms recorded between 2001 to 2017. To advance our understanding and identification of the conditions that lead to dynamic triggering of seismic events, we applied a machine-learning algorithm (decision tree) to these three data sets as well as a published data set of known instances of triggered seismic tremor in central California. The algorithm found that dynamic stress estimates from teleseismic surface waves indeed appear to be a deciding factor in triggering tremor, though may be secondary to the back azimuth from which the surface waves arrive. Our findings confirm that transient stresses generated by surface waves from strong earthquakes arriving from favorable directions can lead to triggered tectonic tremor in seismically active regions, such as central California and Yellowstone. These stresses do not appear to be deciding factors for the potentially dynamically triggered earthquakes in the Raton Basin and central Utah, while back azimuth does appear to be a deciding factor.

Data from the South Pole ice core (SPC14) are used to constrain climate conditions and ice-flow-induced layer thinning for the last 54,000 years. Empirical constraints are obtained from the SPC14 ice and gas timescales, used to calculate annual-layer thickness and the gas-ice age difference (Δage), and from high-resolution measurements of water isotopes, used to calculate the water-isotope diffusion length. Both Δage and diffusion length depend on firn properties and therefore contain information about past temperature and snow-accumulation rate. A statistical inverse approach is used to obtain an ensemble of reconstructions of temperature, accumulation-rate, and thinning of annual layers in the ice sheet at the SPC14 site. The traditional water-isotope/temperature relationship is not used as a constraint; the results therefore provide an independent calibration of that relationship. The temperature reconstruction yields a glacial-interglacial temperature change of 6.7 ± 1.0 °C at the South Pole. The sensitivity of δ180 to temperature is 0.99 ± 0.03 ‰/°C, significantly greater than the spatial slope of ~0.8 ‰/°C that has been used previously to determine temperature changes from East Antarctic ice core records. The reconstructions of accumulation rate and ice thinning show millennial-scale variations in the thinning function as well as decreased thinning at depth compared to the results of a 1-D ice flow model, suggesting influence of bedrock topography on ice flow.

In climate model simulations of future climate change, the Atlantic Meridional Overturning Circulation (AMOC) is projected to decline. However, the impacts of this decline, relative to other changes, remain to be identified. Here we address this problem by analyzing 30 idealized abrupt-4xCO2 climate model simulations. We find that in models with larger AMOC decline, there is a minimum warming in the North Atlantic, a southward displacement of the Inter-tropical Convergence Zone (ITCZ) and a poleward shift of the mid-latitude jet. The changes in the models with smaller AMOC decline are drastically different: there is a relatively larger warming in the North Atlantic, the precipitation response exhibits a wet-get-wetter, dry-get-drier pattern, and there are smaller displacements of the mid-latitude jet. Our study indicates that the AMOC is a major source of inter-model uncertainty, and continued observational efforts are needed to constrain the AMOC response in future climate change.

We present a fast model for stratospheric ozone chemistry based on a neural network approach. The model is intended to replace the detailed chemistry schemes of chemistry and transport models (CTMs), general circulation models (GCMs) or Earth system models (ESMs), which are computationally very expensive. The neural network (NN) model estimates the rate of change of ozone in 24 hours at a grid point and is trained on data of the detailed full chemistry model of the ATLAS chemistry and transport model (CTM). The benefit of this surrogate models is a much lower computation time (minutes instead of days) while the same level of accuracy is achieved. This represents a necessary step from understanding the chemistry and building sophisticated CTMs towards the usage of this knowledge in climate models, which is only feasible if much lower computation times can be achieved. Modelling of the Earth system is a complex task and models usually contain a large number of sub-modules and parameterizations. This applies for example to the atmosphere, hydrosphere, solid earth and the ice sheets. Atmospheric chemistry is complex and usually involves dozens of species and hundreds of reactions with a wide range of concentrations and lifetimes. This project concentrates on the estimation of the rate of change of ozone in the extrapolar stratosphere. The dynamics from the polar regions and from other layers of the atmosphere regarding the ozone change are not treated within this work. The ATLAS model is a Lagrangian CTM for stratospheric chemistry. It solves a coupled differential equation system using a stiff solver and a variable time-step. The stratospheric chemistry scheme of ATLAS has 46 active species, 171 reactions and heterogeneous chemistry on polar stratospheric clouds. It is not using the concept of chemical families. The application of the ATLAS CTM has high requirements on computational power. This is the reason why the coupling of full chemical models to climate models is generally not feasible with respect to the computation time of a global climate model. However, the incorporation of detailed chemistry is often desirable, in order to account for various feed-backs between chemistry, atmosphere and ocean. These complex chemical models motivate the formulation of faster but still accurate surrogate models, that are tailored to the coupling into earth climate models. This project builds on the SWIFT project, which has a polar and an extrapolar surrogate model for the stratospheric ozone chemistry. We investigate an alternative approach to the polynomial approach used by extrapolar SWIFT by exploiting the improved approximation capability of NN with respect to nonlinear contexts.

The NEXUS area covers approximately 30% of the Brazilian territory. In order to assist preservation and sustainable development policies in that region, this study proposes to replicate the work done by Yeh et al in Africa , in which a convolutional neural network estimates indicators through satellite images, each covering a region of approximately 45 km². This work compares the size and distribution of Brazil’s census tracts with those in Africa to define if the scale of images can be maintained and to define the clusters that will be used. To avoid biasing the model, special care must be taken in selecting clusters, such as keeping a balance between urban and rural sectors and, most importantly, making sure that there is little to no overlap of clusters. To do so, two approaches were proposed. The first one samples tracts in each municipality as centroids for clusters, the second merges neighboring urban tracts into a single group and fits clusters to these groups.

The diversity of slip processes occurring in the megathrust indicates that stress is highly variable in space and time. Based on GNSS and InSAR data, we study in depth the evolution of the interplate slip-rate along the Oaxaca subduction zone, Mexico, from October 2016 through August 2020, including the pre-seismic, coseismic and post-seismic phases associated with the 2020 Mw 7.4 Huatulco earthquake, to understand how different slip processes contribute to the stress accumulation in the region. Our results show that continuous changes in both the aseismic stress-releasing slip and the coupling produced a high stress concentration over the main asperity of the Huatulco earthquake and a stress shadow zone in the adjacent updip region. These findings may explain both the downdip rupture propagation of the Huatulco earthquake and its rupture impediment to shallower, tsunamigenic interface regions, respectively. Time variations of the interplate coupling around the adjacent 1978 Puerto Escondido rupture zone clearly correlate with the occurrence of the last three Slow Slip Events (SSEs) in Oaxaca far downdip of this zone, suggesting that SSEs are systematically accompanied by interplate coupling counterparts in the shallower seismogenic zone. In the same period, the interface region of the 1978 event experienced a remarkably high CFS built-up, imparted by the co-seismic and early post-seismic slip of the Huatulco rupture, indicating large earthquake potential near Puerto Escondido. Continuous monitoring of the interplate slip-rate thus provides a better estimation of the stress accumulation in the seismogenic regions where future earthquakes are likely to occur.

We present new, densely sampled shear-wave splitting results from southern Minnesota and adjacent areas of neighboring states, sampling the southwestern limit of the Archean Superior Province and straddling the Proterozoic Mid-Continent Rift (MCR). The new measurements include data from the Earthscope Transportable Array (TA) as well as the Superior Province Rifting Earthscope Experiment (SPREE), yielding 99 new station-averaged measurements. The split times show a consistent decrease from 1.1 s in the NE to 0.2 s in the SW, with the lowest values being associated with the Minnesota River Valley Terrane (MRVT). From modelling and other geophysical constraints, we interpret the split time variations to represent variations in fabric strength within a thick lithosphere, rather than lithospheric thinning or a multi-layered effect, and propose that the weak fabric of the MRVT is associated with a different mechanism of formation than elsewhere in the Superior Province. The fast directions we measure range from NNE-SSW to E-W and vary on a shorter length scale than the split times, with a pattern of NE-SW splits that closely follows the axis of the MCR. We interpret this as a perturbation of the net fast direction due to anisotropy in an underplate along the rift.