Crustal-stored magma reservoirs contain exsolved volatiles which accumulate in the reservoir roof, exerting a buoyancy force on the crust. This produces surface uplift and sudden loss of volatiles through eruption results in syn-eruptive subsidence. Here, we present three-dimensional, visco-elasto-plastic, numerical modeling results which quantify the ground deformation arising from the growth and release of a volatile reservoir. Deformation is independent of crustal thermal distribution and volatile reservoir shape, but is a function of volatile volume, density and depth and crustal rigidity. We present a scaling law for the volatiles’ contribution to syn-eruptive subsidence and show this contributes ~20% of the observed subsidence associated with the 2015 Calbuco eruption. Our results highlight the key role that volatile-driven buoyancy can have in volcano deformation, show a new link between syn-eruptive degassing and deflation, and highlight that shallow gas accumulation and release may have a major impact on ground deformation of volcanoes.

Parameterizations of unresolved turbulent processes often compromise the fidelity of large-scale ocean models. In this work, we argue for a Bayesian approach to the refinement and evaluation of turbulence parameterizations. Using an ensemble of large eddy simulations of turbulent penetrative convection in the surface boundary layer, we demonstrate the method by estimating the uncertainty of parameters in the convective limit of the popular ‘K-Profile Parameterization’. We uncover structural deficiencies and propose an alternative scaling that overcomes them.

The impact of horizontal resolution on meridional Ocean Heat Transport (OHT) and sea ice in the Arctic is investigated using the GFDL CM2-O climate model suite (1°, 1/4°, and 1/10°) in both preindustrial control and climate change simulations. Results show an increase in OHT associated to a decrease in sea ice extent (SIE) in the Arctic on inter-annual and decadal time scales. This link, however, is not monotonic with spatial resolution. While OHT increases and SIE decreases from the Low to the Medium resolution, the reverse is true from the Medium to the High resolution. Differences in OHT and SIE between the three model configurations mostly arise from the preindustrial state. As the spatial resolution increases, the Irminger Current is favored at the expense of the North Atlantic Drift. This rerouting of water to the Western side of Greenland results in less heat delivered to the Arctic in the High resolution configuration than in its Medium counterpart. As a result, the Medium resolution configuration is in best agreement with observed SIE and Atlantic OHT. Concurrent with the change in the partitioning in volume is a change in deep convection centers from the Greenland-Irminger-Norwegian Seas in the Low resolution to the Labrador Sea in the Medium and High resolutions. Results suggest a coupling between OHT into the Arctic and deep convection in the North Atlantic.

We present a significantly-improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework include an offline volume-conservative mapping to a cubed-sphere grid, improvements to the CNN architecture, and the minimization of the loss function over multiple steps in a prediction sequence. The cubed-sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN. Our improved model produces weather forecasts that are indefinitely stable and produce realistic weather patterns at lead times of several weeks and longer. For short- to medium-range forecasting, our model significantly outperforms persistence, climatology, and a coarse-resolution dynamical numerical weather prediction (NWP) model. Unsurprisingly, our forecasts are worse than those from a high-resolution state-of-the-art operational NWP system. Our data-driven model is able to learn to forecast complex surface temperature patterns from few input atmospheric state variables. On annual time scales, our model produces a realistic seasonal cycle driven solely by the prescribed variation in top-of-atmosphere solar forcing. Although it currently does not compete with operational weather forecasting models, our data-driven CNN executes much faster than those models, suggesting that machine learning could prove to be a valuable tool for large-ensemble forecasting.

Temporal changes in the subsurface seismic velocity structure reflect the physical processes that modulate the properties of the media through which seismic waves propagate. These processes, such as healing of the surface damage zone and deep crustal deformation, are described by similar functional forms and operate on similar timescales, making it difficult to determine which process drives the observed changes. We examine earthquake-induced velocity changes using the measured lag-time time series τ(t) of the repeating earthquake sequences since the 2004 Mw 9.2 Sumatra and 2005 Mw 8.6 Nias earthquakes. The S-coda velocity changes (δVS, equivalent to -τS) recover steadily during the 2005−2015 period. The Rayleigh wave velocity changes (δVLR, or -τLR) undergo transient recovery, followed by a strong δVLR reduction in late 2007. δVS recovery is most likely driven by deep processes, whereas the temporal breaks in δVLR recovery in 2007 mostly reflect surface damage and healing induced by the strong ground motions of the 2004 Mw 9.2, 2005 Mw 8.6, 2007 Mw 8.4 and Mw 7.9 Bengkulu, and 2008 Mw 7.3 Simeulue earthquakes. The observed differences between the temporal variations in δVS and δVLR can distinguish deep processes from healing of the surface damage zone.

Non-linear turbidity-discharge relationships are explored in the context of sediment sourcing and event-driven hysteresis using long-term (≥12 year) turbidity observations from the tidal freshwater and saline estuary of the Hudson River. At four locations spanning 175 km, turbidity generally increased with discharge but did not follow a constant log-log dependence, in part due to event-driven adjustments in sediment availability. Following major sediment inputs from extreme precipitation and discharge events in 2011, turbidity in the tidal river increased by 20-50% for a given discharge. The coherent shifts in the turbidity-discharge relationship along the tidal river over the subsequent 2 years suggest that the 2011 events increased sediment availability for resuspension. In the saline estuary, changes in the sediment-discharge relationship were less apparent after the high discharge events, indicating that greater background turbidity due to internal sources make event-driven inputs less important in the saline estuary at interannual time scales.

A variety of watershed responses to climate change are expected due to non-linear interactions between various hydrologic processes acting at different timescales that are modulated by watershed properties. Changes in statistical structure (spectral properties) of streamflow in the USA due to climate change were studied for water years 1980-2013. The Fractionally differenced Autoregressive Integrated Moving Average (FARIMA) model was fit to the deseasonalized streamflow time-series to model its statistical structure. FARIMA allows the separation of streamflow into low frequency (slowly varying) and high frequency (fast varying) components. Results show that in snow dominated watersheds, the contribution of low frequency components to total streamflow variance has decreased over the study period, and the contribution of high frequency components has increased. The change in snow dominated watersheds was primarily driven by changes in rainfall statistics and changes in snow water equivalent but also by changes in seasonal temperature statistics. Among rain-driven watersheds, the contribution of high frequency components generally increased in arid regions but decreased in humid regions. In both humid and arid rain-driven watersheds, increasing winter temperature was responsible for the change in streamflow regimes. These results have consequences for predictability of streamflow in the presence of climate change. We expect that changes in the high frequency component will result in poorer predictability of streamflow.

With a unique biogeophysical signature relative to other freshwater sources, meltwater from glaciers plays a crucial role in the hydrological and ecological regime of high latitude coastal areas. Today, as glaciers worldwide exhibit persistent negative mass balance, glacier runoff is changing in both magnitude and timing, with potential downstream impacts on infrastructure, ecosystems, and ecosystem resources. However, runoff trends may be difficult to detect in coastal systems with large precipitation variability. Here, we use the coupled energy balance and water routing model SnowModel-HydroFlow to examine changes in timing and magnitude of runoff from the western Juneau Icefield in Southeast Alaska between 1980 to 2016. We find that under sustained glacier mass loss (-0.57 +/-0.12 m w.e. a-1), several hydrological variables related to runoff show increasing trends. This includes annual and spring glacier ice melt volumes (+10% and +16% decade-1) which, because of higher proportions of precipitation, translate to smaller increases in glacier runoff (+3% and +7% decade-1) and total watershed runoff (+1.4% and +3% decade-1). These results suggest that the western Juneau Icefield watersheds are still in an increasing glacier runoff period prior to reaching ‘peak water.’ In terms of timing, we find that maximum glacier ice melt is occurring earlier (2.5 days decade-1), indicating a change in the source and quality of freshwater being delivered downstream in the early summer. Our findings highlight that even in maritime climates with large precipitation variability, high latitude coastal watersheds are experiencing hydrological regime change driven by ongoing glacier mass loss.

The rate of land ice loss due to iceberg calving is a key source of variability among model projections of 21st century sea-level rise. It is especially challenging to account for mass loss due to iceberg calving in Greenland, where ice drains to the ocean through hundreds of outlet glaciers, many smaller than typical model grid scale. Here, we apply a numerically efficient network flowline model (SERMeQ) forced by surface mass balance to simulate an upper bound on decadal calving retreat of 155 grounded outlet glaciers of the Greenland Ice Sheet—resolving five times as many outlets as was previously possible. We show that the upper bound holds for 91\% of glaciers examined and that simulated changes in terminus position correlate with observed changes. SERMeQ can provide a physically consistent constraint on forward projections of the dynamic mass loss from the Greenland Ice Sheet associated with different climate projections.

Faults in carbonate rocks show both seismic and aseismic deformation processes, leading to a wide range of slip velocities. We deformed two centimeter-scale cores of Carrara marble at 25°C, under in-situ conditions of stress of 2-3 km depth, and imaged the nucleation and growth of creeping faults using dynamic synchrotron X-ray microtomography with micrometer spatial resolution. The first sample was under a constant confinement of 30 MPa and no pore fluid. The second sample was under a confinement in the range 35-23 MPa, with 10 MPa pore fluid pressure. We increased the axial stress by steps until creep deformation occurred and imaged deformation in 4D during creep. The samples deformed with a steady-state strain rate when the differential stress was constant, a process called creep. However, for both samples, we also observed transient events that include the acceleration of creep, i.e., creep bursts, phenomena similar to slow slip events that occur in continental active faults. During these transient creep events, strain rates increase and correlate in time with strain localization and the development of system-spanning fault networks. In both samples, the acceleration of opening and shearing of microfractures accommodated creep bursts. Using high-resolution time-lapse X-ray micro-tomography imaging, and digital image correlation, during triaxial deformation allowed quantifying creep in laboratory faults at sub-grain spatial resolution, and demonstrates that transient creep events (creep bursts) correlate with the nucleation and growth of faults.

Current soil C inventories focus on surface layers although over half of soil C is found below 20 cm. Recent and ongoing changes in agricultural management, crop productivity, and climate in Midwest US cropland may influence subsoil C stocks. The objectives of this study were to determine how surface soil and subsoil organic C stocks have changed in croplands of Iowa and Illinois and to evaluate mechanisms to explain the observed subsoil organic C changes. Using resampling studies from Iowa and Illinois, we found that subsoil (20-80 cm) organic C increased at a rate of 0.31 Mg C ha-1 yr-1 between the 1950s and early 2000s despite C losses of similar magnitude in the top 20 cm (0.26 Mg C ha-1 yr-1). Based on this analysis, we estimated a subsoil C storage rate of up to 11.8 Tg C yr-1 for Iowa and Illinois, which equates to 12% of annual US greenhouse gas emissions from crop cultivation if surface C losses and non-CO2 greenhouse gases are controlled. We also measured changes in soil organic C stocks from two long-term cropping systems experiments located in Iowa, which demonstrated similar rates of subsoil C changes for both historical and contemporary crop rotations. Using publicly available crop yield data, we determined that changes in crop productivity likely contributed minorly to observed changes in subsoil organic C. The accumulation of organic C in subsoils may be attributed to regional climate change, which has led to greater precipitation and wetter subsoils that inhibit transformation of soil organic C to CO2. Because farmers may respond to increasing soil wetness by expanding and intensifying artificial drainage infrastructure, there is an urgent need to further assess subsoil C stocks and their vulnerability to drainage system changes.

The process of evapotranspiration transfers water vapour from vegetation and soil surfaces to the atmosphere, the so-called latent heat flux (𝑄 LE), and thus crucially modulates Earth’s energy, water, and carbon cycles. Vegetation controls 𝑄 LE through regulating the leaf stomata (i.e., surface resistance 𝑟 s) and through altering surface roughness (aerodynamic resistance 𝑟 a). Estimating 𝑟 s and 𝑟 a across different vegetation types proves to be a key challenge in predicting 𝑄 LE. Here, we propose a hybrid modeling approach (i.e., combining mechanistic modeling and machine learning) for 𝑄 LE where neural networks independently learn the resistances from observations as intermediate variables. In our hybrid modeling setup, we make use of the Penman-Monteith equation based on the Big Leaf theory in conjunction with multi-year flux measurements across different forest and grassland sites from the FLUXNET database. We follow two conceptually different strategies to constrain the hybrid model to control for equifinality arising when estimating the two resistances simultaneously. One strategy is to impose an a priori constraint on 𝑟 a based on our mechanistic understanding (theory-driven strategy), while the other strategy makes use of more observational data and adds a constraint in predicting 𝑟 a through multi-task learning of the latent as well as the sensible heat flux (𝑄 H ; data-driven strategy). Our results show that all hybrid models exhibit a fairly high predictive skill for the target variables with 𝑅 2 = 0.82-0.89 for grasslands and 𝑅 2 = 0.70-0.80 for forests sites at the mean diurnal scale. The predictions of 𝑟 s and 𝑟 a show physical consistency across the two regularized hybrid models, but are physically implausible in the under-constrained hybrid model. The hybrid models are robust in reproducing consistent results for energy fluxes and resistances across different scales (diurnal, seasonal, interannual), reflecting their ability to learn the physical dependence of the target variables on the meteorological inputs. As a next step, we propose to test these heavily observation-informed parameterizations derived through hybrid modeling as a substitute for overly simple ad hoc formulations in Earth system models.

Collision between the Pontides and Anatolide-Tauride Block along the İzmir-Ankara-Erzincan suture in Anatolia has been variously estimated from the Late Cretaceous to Eocene. It remains unclear whether this age range results from a protracted, multi-phase collision or differences between proxies of collision age and along strike. Here, we leverage the Cretaceous-Eocene evolution of the forearc-to-foreland Central Sakarya Basin system in western Anatolia to determine when and how collision progressed. New detrital zircon and sandstone petrography results indicate that the volcanic arc was the main source of sediment to the forearc basin in the Late Cretaceous. The first appearance of Pontide basement-aged detrital zircons, in concert with exhumation of the accretionary prism and a decrease in regional convergence rates indicates intercontinental collision initiated no later than 76 Ma. However, this first contractional phase does not produce thick-skinned deformation and basin partitioning until ca. 54 Ma, coeval to regional syn-collisional magmatism. We propose three non-exclusive and widely applicable mechanisms to reconcile the observed ~20 Myr delay between initial intercontinental collision and thick-skinned upper plate deformation: relict basin closure north and south of the İAES, gradual underthrusting of thicker lithosphere, and Paleocene slab breakoff. These mechanisms highlight the links between upper plate deformation and plate coupling during continental collision.

The spectra of earthquake waveforms can provide important insight into rupture processes, but the analysis and interpretation of these spectra is rarely straightforward. Here we develop a Bayesian framework that embraces the inherent data and modeling uncertainties of spectral analysis to infer key source properties. The method uses a spectral ratio approach to correct the observed waveform spectra of nearby earthquakes for path and site attenuation. The objective then is to solve for a joint posterior probability distribution of three source parameters -- seismic moment, corner frequency, and high-frequency falloff rate -- for each earthquake in the sequence, as well as a measure of rupture directivity for target events with good azimuthal station coverage. While computationally intensive, this technique provides a quantitative understanding of parameter tradeoffs and uncertainties and allows one to impose physical constraints through prior distributions on all source parameters, which guide the inversion when data is limited. We demonstrate the method by analyzing in detail the source properties of 14 different target events of magnitude M5 in southern California that span a wide range of tectonic regimes and fault systems. These prominent earthquakes, while comparable in size, exhibit marked diversity in their source properties and directivity, with clear spatial patterns, depth-dependent trends, and a preference for unilateral directivity. These coherent spatial variations source properties suggest that regional differences in tectonic setting, hypocentral depth or fault zone characteristics may drive variability in rupture processes, with important implications for our understanding of earthquake physics and its relation to hazard.

Recent Innovations in Precision Agriculture (PA) are driven by Computer Vision and Data Processing systems to quantify plant parameters. Quantitative analysis of Plant Phenotyping in PA and monitoring morphological traits is a protracting process, precluding the objective and phenotyping pipeline. Greenhouses growing Genetically Modified (GM) crops need to be maintained at constant environmental and simulated conditions. Multiple parameters have to be controlled and regulated inside a greenhouse for effective growth of crops and yield maximisation. Not at all times are these factors derived and so, yield maximisation in greenhouse is an experimental approach to new varieties. For deduced environmental parameters and conditions for certain crops, few other biotic and abiotic factors can hinder or affect growth in certain ways that are not always factored in during calculating parameters conducive for plant growth. Such factors may not always be affecting parametral calculations, but transpose visual cues on plant growth environment such as spectral change in soil values, or minute changes like leaf reflectance or visible changes in plant stimuli to biotic factors. Plant growth is inclusive of multiple environmental variables, and yield maximisation approaches are experimental to finding the optimum derived value for these variables. Computer Vision provides a catalytic approach to predicting optimum parameters for yield maximization in phenomics. Computer Vision and Generative Adversarial Networks (GAN)’s offer a catalytic approach to the time-consuming process, providing a solution to the phenotyping bottleneck. This research proposes a concept of curating data of plant growth over time to predict conditional growth and responsive stimuli of the plant under different situations and how this can affect crop yield. The method proposed here is a non-invasive approach to the existing destructive biomass estimation methods and Frameworks. This methodology of the research focuses on utilizing image parameters modelled using a time series Progressively Growing Generative Adversarial Networks PGGAN to map plant growth patterns and progressive variance in biomass of plant in the Spatio-Temporal Domain. These Generative networks evaluate and predict based on merely raw pixel input excluding dependence on further constraints, feature vectors or parameters influencing data.

Surface crevasses on the Greenland Ice Sheet capture nearly half of the seasonal runoff, yet their role in transferring meltwater to the bed has received little attention compared to that of supraglacial lakes and moulins. Here, we present observations of crevasse ponding and investigate controls on their hydrological behaviour at a fast-moving, marine-terminating sector of the Greenland Ice Sheet. We map surface meltwater, crevasses, and surface-parallel stress across a ~2,700 km² region using satellite data and contemporaneous uncrewed aerial vehicle (UAV) surveys. From 2017-2019 an average of 26% of the crevassed area exhibited ponding at locations that remained persistent between years despite rapid advection rates. We find that the spatial distribution of ponded crevasses does not relate to previously proposed methods for predicting the distribution of supraglacial lakes (elevation and topography) or crevasses (von Mises stress thresholds), suggesting the operation of some other physical control(s). Ponded crevasse fields were preferentially located in regions of compressive surface-parallel mean stress, which we interpret to result from the hydraulic isolation of these systems, in contrast to unponded crevasse fields, which we suggest are able to drain into the wider supraglacial and englacial network. UAV observations show that ponded crevasses can drain episodically and rapidly, likely through hydrofracture. We therefore propose that the surface stress regime informs a spatially heterogeneous transfer of meltwater through crevasses to the bed of ice sheets, with potential consequences for processes such as subglacial drainage and the heating of ice via latent heat release by refreezing meltwater.

The Tatun volcano group (TVG) proximate to the 7-million-population metropolis Taipei has long been a central concern in volcanic hazard in the Taiwan. While the TVG has been previously considered an extinct volcano, recent evidences suggested a much younger age of the last eruption event (~6000 years) and the possible existence of magma reservoir beneath the TVG. To examine and unveil the TVG magmatic plumbing system in detail, the local P-wave travel time data and the teleseismic waveform data from a new island-wide Formosa Array Project are combined for a 3D joint inversion. The new model reveals a pronounced magma reservoir with high fraction of melt (~16.5%) beneath the TVG at 5-25 km and better illuminate the deep magmatic structures that may suggest delamination processes in the northern Taiwan. Plain Language Summary The Tatun volcano group (TVG) is close to the metropolis Taipei and poses a direct threat to the 7 million population nearby. While the TVG was thought to be extinct in the past, recent studies found much younger eruption records (~6000 years) and some indications of alive magma chamber underneath. To better understand whether the magma chamber exists and what its geometry is, the Formosa Array project deployed 120 broadband stations in the northern Taiwan well covering the TVG area. Integrating the Formosa Array dataset, a new 3-D P-wave velocity model is constructed and shows a clear image of the magma chamber beneath the TVG at the depth of 5-25 km. Deep magmatic structures are also imaged and suggest the crust in the northern Taiwan was thickened and has possibly peeled off during the mountain building processes.

The sciences struggle to integrate across disciplines, coordinate across data generation and modeling activities, produce connected open data, and build strong networks to engage stakeholders within and beyond the scientific community. The American Geophysical Union (AGU) is divided into 25 sections intended to encompass the breadth of the geosciences. Here, we introduce a special collection of commentary articles spanning 19 AGU sections on challenges and opportunities associated with the use of ICON science principles. These principles focus on research intentionally designed to be Integrated, Coordinated, Open, and Networked (ICON) with the goal of maximizing mutual benefit (among stakeholders) and cross-system transferability of science outcomes. This article 1) summarizes the ICON principles; 2) discusses the crowdsourced approach to creating the collection; 3) explores insights from across the articles; and 4) proposes steps forward. There were common themes among the commentary articles, including broad agreement that the benefits of using ICON principles outweigh the costs, but that using ICON principles has important risks that need to be understood and mitigated. It was also clear that the ICON principles are not monolithic or static, but should instead be considered a heuristic tool that can and should be modified to meet changing needs. As a whole, the collection is intended as a resource for scientists pursuing ICON science and represents an important inflection point in which the geosciences community has come together to offer insights into ICON principles as a unified approach for improving how science is done across the geosciences and beyond.