Subsurface sequestration of carbon dioxide (CO2) requires long-term monitoring of the injected CO2 plume to prevent CO2 leakage along the wellbore or across the caprock. Accurate knowledge of the location and movement of the injected CO2 is crucial for risk management at a geological CO2-storage complex. Conventional methods for locating/assessing the injected CO2 plume in the subsurface assume a geophysical model, which is specific and may not be applicable to all types of CO2-injection reservoirs and scenarios. We developed an unsupervised-learning-based visualization of the subsurface CO2 plume that adapts and scales based on the data without requiring an assumption of the geophysical model. The data-processing workflow was applied to the cross-well tomography data from the SECARB Cranfield carbon geo-sequestration project. A multi-level clustering approach was developed to account for data imbalance due to the absence of CO2 in the large portion of the imaged reservoir. The first level of clustering differentiated CO2-bearing regions from the non-CO2 bearing regions and achieved a silhouette score of 0.85, a Calinski-Harabasz index of 160666, and a Davies-Bouldin index of 0.43, which are indicative of high quality, reliable clustering. The second level of clustering further differentiated the CO2-bearing regions into regions containing low, medium, and high CO2 content. Overall, the multi-level clustering achieved a silhouette score, Calinski-Harabasz index, and Davies-Bouldin index of 0.74, 59656, and 0.32, which confirm the high quality and reliability of the newly proposed unsupervised-learning-based visualization. Three distinct clustering techniques, namely k-means, mean-shift, and agglomerative, generated similar visualizations. In terms of the adjusted Rand index, the similarity of clusters identified by the three distinct clustering techniques is around 0.98, which indicates the robustness of the cluster labels assigned to various regions of the CO2-injection reservoir. Further, we find certain geophysical signatures, such as Fourier transform and wavelet transform, to be highly relevant and informative indicators of the spatial distribution of CO2 content.

An ocean iodine cycling model is presented, which predicts upper ocean iodine speciation. The model comprises a three-layer advective and diffusive ocean circulation model of the upper ocean, and an iodine cycling model embedded within this circulation. The two primary reservoirs of iodine are represented, iodide and iodate. Iodate is reduced to iodide in the mixed layer in association with primary production, linked by an iodine to carbon (I:C) ratio. A satisfactory model fit with observations cannot be obtained with a globally constant I:C ratio, and the best fit is obtained when the I:C ratio is dependent on sea surface temperature, increasing at low temperatures. Comparisons with observed iodide distributions show that the best model fit is obtained when oxidation of iodide back to iodate is associated with mixed layer nitrification. Sensitivity tests, where model parameters and processes are perturbed, reveal that primary productivity, mixed layer depth, oxidation, advection, surface fresh water flux and the I:C ratio all have a role in determining surface iodide concentrations, and the timescale of iodide in the mixed layer is sufficiently long for non-local processes to be important. Comparisons of the modelled iodide surface field with parameterisations by other authors shows good agreement in regions where observations exist, but significant differences in regions without observations. This raises the question of whether the existing parameterisations are capturing the full range of processes involved in determining surface iodide, and shows the urgent need for observations in regions where there are currently none.

The Antarctic Ice Sheet (AIS) response to past warming consistent with the 1.5–2°C ‘safe limit’ of the United Nations Paris Agreement is currently not well known. Empirical evidence from the most recent comparable period, the Last Interglaciation, is sparse, and transient ice-sheet model simulations are few and inconsistent. Here we present new results from transiently-forced ice-sheet modelling experiments. We evaluate our results against near and far-field proxy reconstructions and find good agreement. Our simulations indicate that the AIS contributed approximately 4 m to global mean sea level, peaking at 126 ka BP, with ice lost primarily from the Amundsen but not Ross or Weddell Sea sectors. The AIS thinned in the area of the Wilkes Subglacial Basin but did not retreat. Continuing beyond present day our model predicts that the West Antarctic Ice Sheet may already be predisposed to collapse even in the absence of further environmental change.

Global groundwater volumes in the upper 2 km of the Earth’s continental crust – critical for water security – are well estimated. Beyond these depths, a vast body of largely saline and non-potable groundwater exists down to at least 10 km —a volume that has not yet been quantified reliably at the global scale. Here, we estimate the amount of groundwater present in the upper 10 km of the Earth’s continental crust by examining the distribution of sedimentary and cratonic rocks with depth and applying porosity-depth relationships. We demonstrate that groundwater in the 2-10 km zone (what we call ‘deep groundwater’) has a volume comparable to that of groundwater in the upper 2 km of the Earth’s crust. These new estimates make groundwater the largest continental reservoir of water, ahead of ice sheets, provide a basis to quantify geochemical cycles, and constrain the potential for large-scale isolation of waste fluids.

A volcanic eruption is usually preceded by seismic precursors, but their interpretation and use for forecasting the eruption onset time remain a challenge. Eruption processes in geysers are similar to volcanoes, but occur more frequently. Therefore, geysers are useful sites for testing new forecasting methods. We tested the application of Permutation Entropy (PE) as a robust method to assess the complexity in seismic recordings of the Strokkur geyser, Iceland. Strokkur features several minute-long eruptive cycles, enabling us to verify in 63 recorded cycles whether PE behaves consistently from one eruption to the next one. We performed synthetic tests to understand the effect of different parameter settings in the PE calculation. Our application to Strokkur shows a distinct, repeating PE pattern consistent with previously identified phases in the eruptive cycle. We find a systematic increase in PE within the last 15s before the eruption, indicating that an eruption will occur. We quantified the predictive power of PE, showing that PE performs better than seismic signal strength or quiescence when it comes to forecasting eruptions.

Seismic anisotropy produced by aligned olivine in oceanic lithosphere offers a window into mid-ocean ridge dynamics and the state of Earth’s upper mantle. Yet, interpreting anisotropy in the context of grain-scale deformation processes observed in laboratory and natural olivine samples has proven challenging due to the vast length scale differences. We bridge this observational gap by estimating the first in situ elastic tensor of oceanic lithosphere using compressional- and shear-wavespeed anisotropy observations, with fast azimuth parallel to the fossil-spreading direction. This observation is compared with a database of 123 petrofabrics from the literature to infer olivine crystallographic orientations and shear strain accumulated within the lithosphere. Findings indicate D-type olivine lattice-preferred orientation (LPO) with girdled [010] and [001] crystallographic axes and strain accumulation of 300–400%, challenging the prevailing assumption of A-type LPO. This LPO is consistent with olivine deformation during seafloor spreading via grain-size sensitive dislocation-accommodated grain boundary sliding (disGBS), rather than grain-size insensitive dislocation creep. Such deformation implies in situ grain sizes of 0.3–15 mm, smaller on average than steady-state predictions for pure olivine from laboratory calibrations, which may be attributed to grain-boundary pinning by secondary phases or an underestimate of the importance of disGBS by standard flow laws. This work demonstrates the ability to integrate laboratory- with seismological-scale observations of seismic anisotropy and provides new constraints on in situ grain size and associated rheology during near-ridge mantle deformation.

Quantifying the anthropogenic fluxes of CO2 is important to understand the evolution of carbon sink capacities, on which the required strength of our mitigation efforts directly depends. For the historical period, the global carbon budget (GCB) can be compiled from observations and model simulations as is done annually in the Global Carbon Project’s (GCP) carbon budgets. However, the historical budget only considers a single realization of the Earth system and cannot account for internal climate variability. Understanding the distribution of internal climate variability is critical for predicting the future carbon budget terms and uncertainties. We present here a decomposition of the GCB for the historical period and the RCP4.5 scenario using single model large ensemble simulations from the Max Planck Institute Grand Ensemble (MPI-GE) to capture internal variability. We calculate uncertainty ranges for the natural sinks and anthropogenic emissions that arise from internal climate variability, and by using this distribution, we investigate the likelihood of historical fluxes with respect to plausible climate states. Our results show these likelihoods have substantial fluctuations due to internal variability, which are partially related to ENSO. We find that the largest internal variability in the MPI-GE stems from the natural land sink and its increasing carbon stocks over time. The allowable fossil fuel emissions consistent with 3°C warming may be between 9–18 PgCyr-1. The MPI-GE is generally consistent with GCP’s global budgets with the notable exception of land-use change emissions in recent decades, highlighting that human action is inconsistent with climate mitigation goals.

Current inference techniques for processing multi-needle Langmuir Probe (m-NLP) data are often based on the Orbital Motion-Limited (OML) theory which relies on several simplifying assumptions. Some of these assumptions, however, are typically not well satisfied in actual experimental conditions, thus leading to uncontrolled uncertainties in inferred plasma parameters. In order to remedy this difficulty, three-dimensional kinetic particle in cell simulations are used to construct synthetic data sets, which are then used to train and validate regression-based models capable of inferring electron density and satellite potentials from 4-tuples of currents collected with fixed-bias needle probes similar to those on the NorSat-1 satellite. Based on our synthetic data, the techniques presented enable excellent inferences of the plasma density, and floating potentials, while the generally accepted OML inferred densities are approximately three times too high. The new inference techniques that we propose, are applied to NorSat-1 data, and compared with OML inferences. While both regression and OML based inferences of floating potentials agree well with synthetic data, only regression inferred potentials are consistent with satellite measured currents, indicating that the regression based inference models are more robust and accurate when applied to satellite data.

Agricultural producers have many incentives to clear small natural areas from their fields, as this can expand their cultivated land base. However, natural areas can play a role in delivering ecosystem services that improve crop productivity (e.g., by providing habitat for beneficial arthropods, that deliver pollination or pest control). We assessed the impact of landscape complexity on adjacent canola (Brassica napus) yield at both the field- and subfield-level using remotely sensed products. Fields with higher landscape complexity generally had higher mean yields. However, fields surrounded mostly by either crop or non-crop covers had lower yields, possibly due to a lack of ecosystem services (i.e., pollination or natural pest control services) or a strong yield-reducing edge effect. At the subfield-level, we found evidence of a boost in yield between 30 and 100 m from the field edge towards its center, as well as a potential yield-stabilizing effect at the same range.

A Large Eddy Simulation (LES) model that simulates the aerosol lifecycle, including aerosol sources and sinks, was used to study the stratocumulus to cumulus transition (SCT). To initialize, force, and evaluate the LES, we used a combination of reanalysis, satellite, and aircraft data from the Cloud System Evolution in the Trades field campaign in summer 2015 over the Northeast Pacific. The simulations follow two Lagrangian trajectories from initially overcast stratocumulus to the tropical shallow cumulus region near Hawaii. The first trajectory is characterized by an initially clean, well-mixed stratocumulus-topped marine boundary layer (MBL), then continuous MBL deepening and precipitation onset followed by a clear SCT and a consistent reduction of aerosols that ultimately leads to an ultra-clean layer in the upper MBL. The second trajectory is characterized by an initially polluted and decoupled MBL, weak precipitation, and a late SCT. Overall, the LES simulates the general MBL features seen in observations. Sensitivity studies with different aerosol initial and boundary conditions reveal aerosol-induced changes in the transition, and albedo changes are decomposed into the Twomey effect and adjustments of cloud liquid water path and cloud fraction. Impacts on precipitation play a key role in the sensitivity to aerosols: for the first case, runs with enhanced aerosols exhibit distinct changes in microphysics and macrophysics such as enhanced cloud droplet number concentration, reduced precipitation, and delayed SCT. Cloud adjustments are dominant in this case. For the second case, enhancing aerosols does not affect cloud macrophysical properties significantly, and the Twomey effect dominates.

We investigate ionospheric flow patterns occurring on 28 January 2002 associated with the development of the nightside distorted end of a J-shaped transpolar arc (nightside distorted TPA). Based on the nightside ionospheric flows near to the TPA, detected by the SuperDARN (Super Dual Auroral Radar Network) radars, we discuss how the distortion of the nightside end toward the pre-midnight sector is produced. The J-shaped TPA was seen under southward interplanetary magnetic field (IMF) conditions, in the presence of a dominant dawnward IMF-By component. At the onset time of the nightside distorted TPA, particular equatorward plasma flows at the TPA growth point were observed in the post-midnight sector, flowing out of the polar cap and then turning toward the pre-midnight sector of the main auroral oval along the distorted nightside part of the TPA. We suggest that these plasma flows play a key role in causing the nightside distortion of the TPA. SuperDARN also found ionospheric flows typically associated with Tail Reconnection during IMF Northward Non-substorm Intervals (TRINNIs) on the nightside main auroral oval, before and during the TPA interval, indicating that nightside magnetic reconnection is an integral process to the formation of the nightside distorted TPA. During the TPA growth, SuperDARN also detected anti-sunward flows across the open–closed field line boundary on the dayside that indicate the occurrence of low-latitude dayside reconnection and ongoing Dungey cycle driving. This suggests that nightside distorted TPA can grow even in Dungey-cycle-driven plasma flow patterns.

One major challenge of solar flare prediction with machine learning methods is the scarcity of large flares. This issue of low positive sample size is even more severe for data observed in the relatively weak Solar Cycle 24, for example, the SHARPs data product. This partly hampers the successful application of deep learning methods, especially those dealing with high-dimensional spatial and/or temporal data. By joining SHARPs with Space-Weather MDI Active Region Patches (SMARPs), a new data product derived from observations in Solar Cycle 23, we are able to obtain a fused dataset with nearly tripled positive samples. We evaluated two deep learning methods, LSTM and CNN, using the selected parameter sequences and image snapshots in the fused dataset. Experiment results show that the two models trained on the fused dataset achieve better or equivalent test set performance than those trained on a single solar cycle. In addition, we demonstrate the improvement of the performance of the stacking ensemble that combines LSTM and CNN. We provided interpretation to CNN using modern visual attribution methods in computer vision. The results show that CNN is able to identify flare-related signatures in magnetograms.

As coastal areas become more vulnerable to climatic impacts, the need for understanding estuarine carbon budgets with sufficient spatiotemporal resolution arises. Under various hydrologic extremes ranging from drought to hurricane-induced flooding, a mass balance model has been constructed for carbon fluxes and their variabilities in four estuaries along the northwestern Gulf of Mexico (nwGOM) coast over a four-year period (2014 – 2018). Loading of total organic carbon (TOC) and dissolved inorganic carbon (DIC) to estuaries include riverine discharge and lateral exchange from tidal wetland. The lateral exchanges of TOC and DIC reach 4.5 ± 5.7 and 8.9 ± 1.4 mol·C·m-2·yr-1, accounting for 86.5% and 62.7% of total TOC and DIC inputs into these estuaries, respectively. A relatively high regional CO2 efflux (4.0 ± 0.7 mol·C·m-2·yr-1) has been found, which is two times of the average value in North American coastal estuaries based on a previous study. Meanwhile, oceanic export is the major pathway for TOC (5.6 ± 1.7 mol·C·m-2·yr-1, 81.2% of total) and DIC (9.9 ± 2.9 mol·C·m-2·yr-1, 69.7% of total) loss. Yet uncertainties remain due to a varying extent of remineralization coastwide. In addition, the carbon budget exhibits high variability in response to the hydrologic changes. For example, storm or hurricane induced flooding can elevate CO2 efflux by 2 – 10 times in short periods of time. Flood following a drought state also increases lateral TOC exchange (from -3.5 ± 4.7 to 67.8 ± 17.6 mmol·C·m-2·d-1) but decreases lateral DIC exchange (from 28.9 ± 3.5 to -7.1 ± 7.6 mmol·C·m-2·d-1). The large variability of carbon budgets highlights the importance of high-resolution spatiotemporal coverage under different hydrologic conditions, as well as the significance of carbon contribution from tidal wetland on coastal carbon cycling.

For decades, the Arctic has been warming at least twice as fast as the rest of the globe. As a first step towards quantifying parametric uncertainty in Arctic feedbacks, we perform a variance-based global sensitivity analysis (GSA) using a fully-coupled, ultra-low resolution (ULR) configuration of version 1 of the Department of Energy’s Energy Exascale Earth System Model (E3SMv1). The study randomly draws 139 realizations of ten model parameters spanning three E3SMv1 components (sea ice, atmosphere and ocean), which are used to generate 75 year long projections of future climate using a fixed pre-industrial forcing. We quantify the sensitivity of six Arctic-focused quantities of interest (QOIs) to these parameters using main effect, total effect and Sobol sensitivity indices computed with a Gaussian process emulator. A sensitivity index-based ranking of model parameters shows that the atmospheric parameters in the CLUBB (Cloud Layers Unified by Binormals) scheme have significant impact on sea ice status and the larger Arctic climate. We also use the Gaussian process emulator to predict the response of varying each variable when the impact of other parameters are averaged out. These results allow one to assess the non-linearity of a parameter’s impact on a QOI and investigate the presence of local minima encountered during the spin-up tuning process. Our study confirms the necessity of performing global analyses involving fully-coupled climate models, and motivates follow-on investigations in which the ULR model is compared rigorously to higher resolution configurations to confirm its viability as a lower-cost surrogate in fully-coupled climate uncertainty analyses.

A new algorithm is proposed for estimating time-evolving global forcing in climate models. The method is a further development of the work of Forster et al. (2013), taking into account the non-constancy of the global feedbacks. We assume that the non-constancy of this global feedback can be explained as a time-scale dependence, associated with linear temperature responses to the forcing on different time scales. With this method we obtain stronger forcing estimates than previously assumed for the representative concentration pathway experiments in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The reason for the higher future forcing is that the global feedback parameter is more negative at shorter time scales than at longer time scales, consistent with the equilibrium climate sensitivity increasing with equilibration time. Our definition of forcing provides a clean separation of forcing and response, and we find that linear temperature response functions estimated from experiments with abrupt quadrupling of CO$_2$ can be used to predict responses also for future scenarios. In particular, we demonstrate that for most models, the response to our new forcing estimate applied on the 21st century scenarios provides a global surface temperature up to year 2100 consistent with the output of coupled model versions of the respective model.

The 2019 Ridgecrest, California, earthquake sequence represents a complex pattern of seismicity that is characterized by the occurrence of a well defined foreshock sequence followed by a mainshock and subsequent aftershocks. In this work, a detailed statistical analysis of the sequence is performed. Particularly, the parametric modelling of the frequency-magnitude statistics and the earthquake occurrence rate is carried out. It is shown that the clustering of earthquakes plays an important role during the evolution of this sequence. In addition, the problem of constraining the magnitude of the largest expected aftershocks to occur during the evolution of the sequence is addressed. In order to do this, two approaches are considered. The first one is based on the extreme value theory, whereas the second one uses the Bayesian predictive framework. The latter approach has allowed to incorporate the complex earthquake clustering through the Epidemic Type Aftershock Sequence (ETAS) process and the uncertainties associated with the model parameters into the computation of the corresponding probabilities. The results indicate that the inclusion of the foreshock sequence into the analysis produces higher probabilities for the occurrence of the largest expected aftershocks after the M7.1 mainshock compared to the approach based on the extreme value distribution combined with the Omori-Utsu formula for the earthquake rate. Several statistical tests are applied to verify the forecast.

Radiation schemes are physically important but computationally expensive components of weather and climate models. This has spurred efforts to replace them with a cheap emulator based on neural networks (NN), obtaining large speed-ups, but at the expense of accuracy, energy conservation and generalization. An alternative approach which is slower but more robust than full emulation is to use NNs to predict optical properties, without abandoning the radiative transfer equations. Recently, NNs were developed to replace the RRTMGP gas optics scheme, and shown to be accurate while improving speed.However, the evaluations were based solely on offline radiation computations. In this paper, we describe the implementation and prognostic evaluation of RRTMGP-NN in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new gas optics scheme was incorporated into ecRad, the modular ECMWF radiation scheme. Using a hybrid loss function designed to reduce radiative forcing errors, and an early stopping method based on monitoring fluxes and heating rates with respect to a line-by-line benchmark, new NN models were trained on RRTMGP k-distributions with reduced spectral resolutions. Offline evaluation shows a very high level of accuracy for clear-sky fluxes and heating rates; for instance the RMSE in shortwave surface downwelling flux is 0.78 W m−2 for RRTMGP and 0.80 W m−2 for RRTMGP-NN in a present-day scenario, while upwelling flux errors are actually smaller for the NN. Because our approach does not affect the treatment of clouds, no additional errors will be introduced for cloudy profiles. RRTMGP-NN closely reproduces radiative forcings for 5 important greenhouse gases across a wide range of concentrations such as 8x CO2. To assess the impact of different gas optics schemes in the IFS, four 1-year coupled ocean-atmosphere simulations were performed for each configuration. The results show that RRTMGP-NN and RRTMGP produce very similar model climates, with the differences being smaller than those between existing schemes, and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas optics being almost 3 times faster), and is also faster than the older and less accurate RRTMG which is used in the current operational cycle of the IFS

We investigate the spatiotemporal patterns of strain accommodation during multiphase rift evolution in the Shire Rift Zone (SRZ), East Africa. The NW-trending SRZ records a transition from magma-rich rifting phases (Permian-Early Jurassic: Rift-Phase 1 (RP1), and Late Jurassic-Cretaceous: Rift-Phase 2 (RP2)) to a magma-poor phase in the Cenozoic (ongoing: Rift-Phase 3 (RP3)). Our observations show that although the rift border faults largely mimic the pre-rift basement metamorphic fabrics, the rift termination zones occur near crustal-scale rift-orthogonal basement shear zones (Sanangoe (SSZ) and the Lurio shear zones) during RP1-RP2. In RP3, the RP1-RP2 sub-basins were largely abandoned, and the rift axes migrated northeastward (rift-orthogonally) into the RP1-RP2 basin margin, and northwestward (strike-parallel) ahead of the RP2 rift-tip. The northwestern RP3 rift-axis side-steps across the SSZ, with a rotation of border faults across the shear zone and terminates further northwest at another regional-scale shear zone. We suggest that over the multiple pulses of tectonic extension and strain migration in the SRZ, pre-rift basement fabrics acted as: 1) zones of mechanical strength contrast that localized the large rift faults, and 2) mechanical ‘barriers’ that refracted and possibly, temporarily halted the propagation of the rift zone. Further, the cooled RP1-RP2 mafic dikes facilitated later-phase deformation in the form of border fault hard-linking transverse faults that exploited mechanical anisotropies within the dike clusters and served as mechanically-strong zones that arrested some of the RP3 fault-tips. Overall, we argue that during pulsed rift propagation, inherited strength anisotropies can serve as both strain-localizing, refracting, and transient strain-inhibiting tectonic structures.

Vegetation is an important component of terrestrial ecosystem as it supports other biological activities through the photosynthetic production. The biophysical and biochemical parameters of vegetation retrieved from satellite observations have been used extensively in studying the physiological states and growing conditions of vegetation that enabling global vegetation monitoring. Most of vegetation remote sensing applications using data from MODIS, Landsat, and Sentinel, though it would be beneficial, from the user perspective, to have an even more diverse data sources that not only secure data sustainability in case satellite retirement or sensor failure, but also enables research opportunities such as multi-sensor data fusion/integration and multi-angle remote sensing that can take advantage of observations acquired from different spaceborne sensors. In this regard, it would be worth to explore the potential of the large number of Chinese Earth Observation Satellites (CEOS) that have been put into orbit over past decade. Here we summarized the recent advances in applying CEOS remote sensing of vegetation and its associated applications. We focused on the uncertainty and limitations for retrieving several commonly-used vegetation parameters by critically examining the case studies conducted over different vegetation types. Suggestions for research opportunities that can benefit from the additional data from CEOS are also provided. The hope is to provide the community an overview of what could be useful to their specific ecological, environmental and global change studies by leveraging the growing data volume from the orbiting CEOS sensors.

The January 2022 Hunga Tonga–Hunga Haʻapai eruption was one of the most explosive volcanic events of the modern era, producing a vertical plume which peaked > 50km above the Earth. The initial explosion and subsequent plume triggered atmospheric waves which propagated around the world multiple times. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves5,6 propagating at phase speeds of 318.2+/-6 ms-1 at surface level and between 308+/-5 to 319+/-4 ms-1 in the stratosphere, and gravity waves propagating at 238+/-3 to 269+/-3 ms-1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for >12 hours, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.

Survey evidence has indicated that a significant percentage of the population does not fully embrace the scientific consensus regarding climate change. This paper assesses whether the hourly temperature data support this denial. Specifically, this paper examines the relationship between hourly CO2 atmospheric concentration levels and temperature using hourly data from the NOAA-operated Barrow observatory in northern Alaska. At this observatory, the average annual temperature over the 2015-2020 period has been about 3.37 oC higher than in the 1985-1990 period. A time-series model to explain hourly temperature is formulated using the following explanatory variables: the hourly level of total downward solar irradiance, the hourly CO2 value lagged by one hour, proxies for the diurnal variation in temperature, proxies for the seasonal temperature variation, and proxies for possible non-anthropomorphic drivers of temperature. A time-series modeling specification is employed to capture the data’s heteroskedastic and autoregressive nature. The model is estimated using hourly data from 1 Jan 1985 through 31 Dec 2015. The results are consistent with the hypothesis that increases in CO2 concentration levels have nontrivial consequences for hourly temperature. The estimated annual contributions of factors exclusive of CO2 and downward total solar irradiance are very small. The model was evaluated using out-of-sample hourly data from 1 Jan 2016 through 31 Aug 2017. The model’s out-of-sample hourly temperature predictions are highly accurate, but this accuracy is significantly degraded if the estimated CO2 effects are ignored. In short, the results are consistent with the scientific consensus on climate change.

Lithospheric discontinuities, including the lithosphere-asthenosphere boundary (LAB) and the enigmatic mid-lithospheric discontinuities (MLDs), hold important clues about the structure and evolution of tectonic plates. However, P- and S-receiver-function techniques (PRF and SRF), two traditional techniques to image Earth’s deep discontinuities, have some shortcomings in imaging lithosphere discontinuities. Here, we propose a new method using reflections generated by teleseismic S waves (hereafter S reflections) to image lithospheric discontinuities, which is less affected by multiple phases than PRFs and has better depth resolution than SRFs. We apply this method to data collected by the Transportable Array and other regional seismic networks and obtain new high-resolution images of the lithosphere below the contiguous US. Beneath the tectonically active Western US, we observe a negative polarity reflector (NPR) in the depth range of 60–110 km, with greatly varying amplitude and depth, which correlates with active tectonic processes. We interpret this feature as the lithosphere-asthenosphere boundary below the Western US. Beneath the tectonically stable Central and Eastern US, we observe two NPRs in the depth ranges of 60–100 km and 100–150 km, whose amplitude and depth also vary significantly, and which appear to correlate with past tectonic processes. We interpret these features as mid-lithospheric discontinuities below the Central and Eastern US. Our results show reasonable agreement with results from PRFs, which have similar depth resolution, suggesting the possibility of joint inversion of S reflections and PRFs to constrain the properties of lithospheric discontinuities.

Understanding the behavior of the shallow portion of the subduction zone, which generates the largest earthquakes and devastating tsunamis, is a vital step forward in earthquake geoscience. Monitoring only a fraction of a single megathrust earthquake cycle and the offshore location of the source of these earthquakes are the foremost reasons for the insufficient understanding. The frictional-elastoplastic interaction between the interface and its overlying wedge causes variable surface strain signals such that the wedge strain patterns may reveal the mechanical state of the interface. We employ Seismotectonic Scale Modeling and simplify elastoplastic megathrust subduction, generate hundreds of analog seismic cycles at laboratory scale, and monitor the surface strain signals over the model’s forearc over high to low temporal resolutions. We establish two coseismically compressional and extensional wedge configurations to explore the mechanical and kinematic interaction between the shallow wedge and the interface. Our results demonstrate that this interaction can partition the wedge into different segments such that the anlastic extensional segment overlays the seismogenic zone at depth. Moreover, the different segments of the wedge may switch their state from compression/extension to extension/compression domains. We highlight that a more segmented upper plate represents megathrust subduction that generates more characteristic and periodic events. Additionally, the strain time series reveals that the strain state may remain quasi-stable over a few seismic cycles in the coastal zone and then switch to the opposite mode. These observations are crucial for evaluating earthquake-related morphotectonic markers (i.e., marine terraces) and short-term interseismic GPS time-series onshore (coastal region).

Simulation models of multi-sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi-sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single- and multi-sector systems. Here we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi-sector model and their interactions with policy design as they emerge at different stages of analysis: (1) inference and model calibration; (2) projecting future outcomes; and (3) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi-sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.