Rapid progress in investigation of zircon records for U-Th-Pb ages and O and Hf isotopes in igneous rocks require understanding how magma bodies are formed and evolve in the crust. We here present a 2D model of magma bodies formation in granitic crust by injection of rhyolitic or andesitic dikes and sills. We combine this model with our zircon crystallization/dissolution software and compute zircon survival histories in individual batches of magma and country rocks. Simulations reproduces incremental accumulation of intruded magma into magma chambers generating eruptible and interconnected magma batches with melt fraction >50 vol% that form in clusters. The rate of melt production is highly variable in space and time. The volume of eruptible melt strongly depends on the input rates of magma Q and the width W of the dike injection region. For example, dikes injection with Q=0.25 m3/s with W=500 m during 4 ka generate 20 km3 of melt while no significant melt forms if W=4 km. Injection of andesitic dikes produces only slightly more melt than rhyolite to granite injection despite of much larger thermal input. Due to rock melting most of zircons loose significant portion of their old cores and, thus, average age. Magmatic zircons in the periphery of the intrusion form very quickly while in its central part crystals contain old cores and young rims and can grow during several hundreds of ka. We observe diverse proportions of crustal melt/newly intruded magma, which translates into diverse O and Hf isotope distribution in zircons.

The Arctic climate is changing dramatically, especially in terms of sea ice loss, with potentially large downstream impacts on the Nordic Seas and the North Atlantic Ocean. The East Greenland Current (EGC) transports substantial amounts of freshwater (in liquid and solid states) southward along the east Greenland continental slope. To increase our understanding of the drivers of surface salinity changes in the interior Nordic Seas, we investigate the diversion of freshwater from the EGC into the Nordic Seas. To this end, we analyse the outcomes of an ocean model hindcast for the period 1973-2004 with a horizontal resolution of 0.25 degree. We find that sea ice contributes large amounts of freshwater to the interior Nordic Seas. On an interannual time scale, this sea ice diversion has a high and significant correlation with surface salinity in the Greenland and Iceland Seas (correlation < -0.7). On a seasonal time scale, the model hindcast and observations demonstrate a clear signal in surface salinity: a lateral migration of the Polar Front position occurring along all of east Greenland. In the model hindcast, these lateral shifts in the front are consistent with seasonal changes in the westward wind-driven Ekman transport. Thus, this climate model study indicates that there are two main causes of seasonal and interannual surface salinity changes; wind-driven Ekman transport and sea ice diversion from the EGC, respectively.

Tropical rivers constitute a major portion of the global aquatic C flux entering the ocean, and the Rio Negro is one of the largest single C exporters with a particularly high export of terrestrial C. We investigated the role of whitesand ecosystems (WSEs) in blackwater formation in the Rio Negro basin to develop novel constraints for the terrestrial carbon export from land to the aquatic continuum. To this end, we used ultrahigh resolution mass spectrometry (FT-MS, Orbitrap) to identify markers in dissolved organic carbon (DOC) from ground- and surface waters of two contrasting WSEs feeding Rio Negro tributaries, and compared them with known Rio Negro marker from two openly available FT-MS datasets. Tributaries were fed by a whitesand riparian valley connected to terra firme plateau, and a typical upland whitesand Campina. WSE-DOC molecular composition differed by 80% from plateau DOC, which was characterized by reworked, highly unsaturated N- and S-containing molecules. WSE-DOC contained mainly condensed aromatics and polyphenols. WSE samples differed by 10% in molecular DOC composition and also by their isotopic content (14C, 18O, 2H). Upland WSE-DOC was exported by fresh precipitation and had maximum age of 13 years, being five years older than riparian valley WSE-DOC. Unexpectedly, only markers from the upland WSE, which cover a small proportion of the landscape, were identical to Negro markers. Markers of the riparian valley WSE, which are widespread and known for high DOC export, surprisingly showed lower coverage with Negro markers. Analysis of robust matching WSE markers between FT-MS datasets by Pubchem suggested well-known plant metabolites (chromenes and benzofurans) as promising candidates for targeted approaches and calibration. Our results suggest that terrestrial DOC from upland WSEs is a main source of specific blackwater molecules missing in the regional ecosystem C balance, whereas C export from the riparian valley and especially from terra firme plateaus represents mainly recycled and transformed carbon not directly affecting the ecosystem C balance. Our study highlights the potential of high-resolution techniques to constrain carbon balances of ecosystems and landscapes. Comparisons of FT-MS datasets and complementary isotopic information shows high potential to identify robust molecular markers that link forests, soils, aquifers and aquatic systems, and are needed for a deeper understanding of the regional C cycle in tropical blackwater catchments.

The Raton Basin is known as an area of injection induced seismicity for the past two decades, but the reactivated fault zone structures and spatiotemporal response of seismicity to evolving injection have been poorly constrained in the past due to scarce public monitoring. Application of a machine-learning phase picker to four years of continuous data from a local array enables the detection and location of ~38,000 earthquakes. The events between 2016-2020 are ~2.5-6 km below sea level and range from ML<-1 to 4.2. Most earthquakes occur within previously identified ~N-S zones of seismicity, however our new catalog illuminates these zones are composed of many short faults with variable orientations. The two most active zones, the Vermejo Park and Tercio, are potentially linked by small intermediate faults. In total, we find ~60 short (<3 km) basement faults with strikes from WNW to slightly east of N. Faulting mechanisms are predominantly normal but some variability, including reverse dip-slip and oblique-slip, is observed. The Trinidad fault zone that hosted the 2011 Mw 5.3 earthquake is quiescent during 2016-2020, likely in response to decreased wastewater injection after 2012 and the shut-in of two nearby wells in 2015. Unlike some induced seismicity regions with higher injection rates, Raton Basin frequency-magnitude and spatiotemporal statistics are not distinguishable from tectonic seismicity. The similarity suggests that induced earthquakes in the Raton Basin are dominantly releasing tectonic stress.

Rocks around the InSight lander were measured in lander orthoimages of the near field (<10 m), in panoramas of the far field (<40 m), and in a high-resolution orbital image around the lander (1 km2). The cumulative fractional area versus diameter size-frequency distributions for four areas in the near field fall on exponential model curves used for estimating hazards for landing spacecraft. The rock abundance varies in the near field from 0.6% for the sand and pebble rich area to the east within Homestead hollow, to ~3-5% for the progressively rockier areas to the south, north and west. The rock abundance of the entire near field is just over 3%, which falls between that at the Phoenix (2%) and Spirit (5%) landing sites. Rocks in the far field (<40 m) that could be identified in both the surface panorama and a high-resolution orbital image fall on the same exponential model curve as the average near field rocks. Rocks measured in a high-resolution orbital image (27.5 cm/pixel) within ~500 m of the lander that includes several rocky ejecta craters fall on 4-5% exponential model curves, similar to the northern and western near field areas. As a result, the rock abundances observed from orbit falls on the same exponential model rock abundance curves as those viewed from the surface. These rock abundance measurements around the lander are consistent with thermal imaging estimates over larger pixel areas as well as expectations from fragmentation theory of an impacted Amazonian/Hesperian lava flow.

The mixing of tracers by mesoscale eddies, parameterized in many ocean general circulation models (OGCMs) as a diffusive-advective process, contributes significantly to the distribution of tracers in the ocean. In the ocean interior, diffusive contribution occurs mostly along the direction parallel to local neutral density surfaces. However, near the surface of the ocean, small-scale turbulence and the presence of the boundary itself break this constraint and the mesoscale transport occurs mostly along a plane parallel to the ocean surface (horizontal). Although this process is easily represented in OGCMs with geopotential vertical coordinates, the representation is more challenging in OGCMs that use a general vertical coordinate, where surfaces can be tilted with respect to the horizontal. We propose a method for representing the diffusive horizontal mesoscale fluxes within the surface boundary layer of general vertical coordinate OGCMs. The method relies on regridding/remapping techniques to represent tracers in a geopotential grid. Horizontal fluxes are calculated on this grid and then remapped back to the native grid, where fluxes are applied. The algorithm is implemented in an ocean model and tested in idealized and realistic settings. Horizontal diffusion can account for up to 10\% of the total northward heat transport in the Southern Ocean and Western boundary current regions of the Northern Hemisphere. It also reduces the vertical stratification of the upper ocean, which results in an overall deepening of the surface boundary layer depth. Lastly, enabling horizontal diffusion leads to meaningful reductions in the near-surface global bias of potential temperature and salinity.

The hydrological behavior and freshwater availability in any typical river basin are highly dependent upon precipitation making it the most crucial input variable for hydrological modelling. Precipitation as an input variable to hydrological models is available in gridded form with various spatiotemporal resolution. The variations in the model inputs could be subjected to uncertainties in the hydrological model simulation, which further affect the estimation of blue water flow (BWF) and green water flow (GWF) of a river basin. In this study, we investigated the effects of three gridded precipitation datasets [Watch forcing data ERA-Interim (WFDEI); Princeton datasets; Indian Meteorological Department (IMD)] on streamflow pattern, BWF, and GWF using a semi-distributed hydrological model [Soil and Water Assessment Tool (SWAT)] and a lumped rainfall-runoff model [Hydrological Simulation model (HYSIM)] in the Damodar river basin situated in eastern India. Both the models are simulated at daily time steps with the calibration of ten years (1994 – 2004) and validation of five years (2005 – 2010) at catchment outlet (Durgapur barrage) using three precipitation datasets. The performance of all the three precipitation products is evaluated on the basis of streamflow simulation for both HYSIM and SWAT model at the basin outlet using the performance indicators viz., Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2) and percent bias (PBIAS). The seasonal and annual variation in precipitation values of the WFDEI, Princeton, and IMD dataset could attribute to the significant variations in the performance indicators. Subsequently, the best performance in streamflow simulation is achieved by HYSIM model compared to SWAT with IMD precipitation input. Both models showed remarkable differences in BWF and GWF estimation due to changes in precipitation inputs. The results also indicate that BWF is more sensitive to precipitation than GWF as BWF is directly generated from precipitation. All the above observations suggest that the choice of appropriate precipitation datasets is essential to examine the catchment hydrological behavior, and it further helps policymakers to make critical water management decisions.

Pillar et al.

We discover a remarkable correlation between the inter-tremor time interval and the slenderness ratio of the overriding plate in subduction zones all over the world. In order to understand this phenomenon better, we perform numerical simulations of 3D deformation. The numerical bending studies show that critical bending load and slenderness ratio indeed have an inverse nonlinear relation between them – identical to the classical Euler’s critical load relation, and closely resembling the relationship observed between the inter-tremor time interval and the slenderness ratio of the overriding plate. From the above analysis, we conclude that the observed relation is the result of bending of the overriding continental plate. In addition, we analyze the surficial 3D spatio-temporal displacements of the overriding plates in Cascadia and Alaska using 3-component GPS data. We find that these deformations are consistent with the bending of the overriding continental crust. Based on these novel observations and guided by numerous existing scientific observations and findings, we propose an Episodic Bending and Collapse model of subduction zones, wherein periodic geodetic changes and tectonic-tremor activity, result from the episodic bending of the overriding continental crust and its rapid collapse on the subducting oceanic slab. According to this model, geodetic measurements, previously inferred as slow slip, are the surficial expressions of slowly-evolving bending and rapid collapse of the overriding plate, while tremor swarms result from the striking of the collapsing overriding plate on the subducting slab (as opposed to slipping or shearing).

A volcanic eruption is usually preceded by seismic precursors, but their interpretation and use for forecasting the eruption onset time remain a challenge. A part of the eruptive processes in open conduits of volcanoes may be similar to those encountered in geysers. Since geysers erupt more often, they 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.

Forest biodiversity has been declining across the globe due to anthropogenic activities. Losses of biodiversity have led to reduced forest health and ecosystem services. Therefore, it has become necessary to monitor changes in biodiversity over wide geographic areas. Remote sensing has the potential to monitor biodiversity changes, but the accuracy in which it can be estimated is under debate. In this study, we tested 1) the relationship among distinct metrics of biodiversity, 2) the role topographic measures have on determining biodiversity, and 3) the ability of hyperspectral remote sensing to estimate biodiversity in temperate forests of the Northeastern United States. We characterized biodiversity according to four different metrics: species, functional, structural, and phylogenetic diversity. All four metrics were quantified using species inventory data as well as Light Detecting and Ranging (LiDAR) to calculate additional indices of structural diversity. A digital elevation model was used to obtain measures of slope, aspect, and other topographic indices such as topographic wetness. Hyperspectral imagery was used to obtain reflectance, entropy, and several vegetation indices. In our analyses, species, functional, and phylogenetic diversity were shown to be moderately correlated suggesting similarities between the metrics while correlations between structural diversity and the other metrics were weak. The calculated topographic indices also showed weak correlations with the biodiversity metrics suggesting that topography does not influence measures of biodiversity at the plot level. Depending on the biodiversity metric, relationships between the hyperspectral analyses and biodiversity were weak to moderately strong. These findings suggest that hyperspectral imagery holds some potential for estimating multiple metrics of biodiversity.

Numerous recent studies found significant correlations between weakening of the Gulf Stream (GS) and rising coastal sea level (CSL) along the U.S. East Coast. Based on monthly altimeter data and Florida Current transport, Chi et al. (2023; here, CH23) argued that geostrophic adjustment of the GS is unlikely to drive variations in CSL in the Mid-Atlantic Bight (MAB). It is argued here that this conclusion cannot be universally applicable to all cases, since the monthly data disregard correlations previously found for short time scales based on hourly and daily data; the impact of GS variability on time scales of decades and longer as well as potential time lags between the GS and CSL variability were also not considered by CH23. Examples are given here to demonstrate the important role of the GS in post hurricane coastal flooding.

The primary data sources for reconstructing the geomagnetic field of the past millennia are archaeomagnetic and sedimentary paleomagnetic data. Sediment records, in particular, are crucial in extending the temporal and spatial coverage of global geomagnetic field models, especially when archaeomagnetic data is sparse. However, the post-depositional detrital remanent magnetization (pDRM) process is still poorly understood and can cause smoothing of the magnetic signal and offsets with respect to the sediment age. To make effective use of sedimentary data, it is essential to understand the lock-in process and its impact on the magnetic signal. In this study, we investigate the lock-in process theoretically and derive a parameterized lock-in function that can approximate possible lock-in behaviors. Additionally, we demonstrate that a lock-in function that is independent of absolute parameters can only be applied to the magnetic direction components (declination and inclination), but not to the relative intensity. Integrating this lock-in function into the ArchKalmag14k modeling procedure \cite{schanner2022archkalmag14k} allows including data from sediment records. The parameters of the lock-in function are estimated by the maximum likelihood method using archaeomagnetic data as a reference. The effectiveness of the proposed method is evaluated through synthetic tests. Additionally, we apply our technique to sediment records from two lakes in Sweden (Kälksjön and Gyltigesjön) as first case studies. Our results demonstrate that the proposed method is capable of effectively correcting the distortion caused by the lock-in process, making data from sedimentary records a more reliable and informative source for geomagnetic field reconstructions.

Geoscience applications have been using sophisticated machine learning methods to model complex phenomena. These models are described as black boxes since it is unclear what relationships are learned. Models may exploit spurious associations that exist in the data. The lack of transparency may limit user’s trust, causing them to avoid high performance models since they cannot verify that it has learned realistic strategies. EXplainable Artificial Intelligence (XAI) is a developing research area for investigating how models make their decisions. However, XAI methods are sensitive to feature correlations. This makes XAI challenging for high-dimensional models whose input rasters may have extensive spatial-temporal autocorrelation. Since many geospatial applications rely on complex models for target performance, a recommendation is to combine raster elements into semantically meaningful feature groups. However, it is challenging to determine how best to combine raster elements. Here, we explore the explanation sensitivity to grouping scheme. Experiments are performed on FogNet, a complex deep learning model that uses 3D Convolutional Neural Networks (CNN) for coastal fog prediction. We demonstrate that explanations can be combined with domain knowledge to generate hypotheses about the model. Meteorological analysis of the XAI output reveal FogNet’s use of channels that capture relationships related to fog development, contributing to good overall model performance. However, analyses also reveal several deficiencies, including the reliance on channels and channel spatial patterns that correlate to the predominate fog type in the dataset, to make predictions of all fog types. Strategies to improve FogNet performance and trustworthiness are presented.

The field of MultiSector Dynamics (MSD) explores the dynamics and co-evolutionary pathways of human and Earth systems with a focus on critical goods, services, and amenities delivered to people through interdependent sectors. This commentary lays out core definitions and concepts, identifies MSD science questions in the context of the current state of knowledge, and describes ongoing activities to expand capacities for open science, leverage revolutions in data and computing, and grow and diversify the MSD workforce. Central to our vision is the ambition of advancing the next generation of complex adaptive human-Earth systems science to better address interconnected risks, increase resilience, and improve sustainability. This will require convergent research and the integration of ideas and methods from multiple disciplines. Understanding the tradeoffs, synergies, and complexities that exist in coupled human-Earth systems is particularly important in the context of energy transitions and increased future shocks.

For the first time in 2015, four dedicated hydrographic cruises – one in each season – took place around the Canary Islands to determinate the seasonality of the flows at the eastern boundary of the North Atlantic Subtropical Gyre. The Canary Current (CC) is the eastern boundary current of the North Atlantic Subtropical Gyre and links the Azores Current with the North Equatorial Current. The CC shows a seasonal behavior in its path and strength, flowing on its easternmost position in winter (3.4±0.3 Sv), through the Canary Islands in spring (2.1±0.7 Sv) and summer (2.0±0.6 Sv) and on its westernmost position in fall (3.2±0.4 Sv). At the Lanzarote Passage (LP), the dominant flow is southward except in fall, where a northward transport is observed at surface (1.1±0.3 Sv) and intermediate (1.3±0.2 Sv) layers. A historical composite observational seasonal cycle is built from all the available estimations on the area and fits the 2015 seasonal cycle. The LP seasonal cycle and seasonal amplitude match the seasonal cycle of the Atlantic Meridional Overturning Circulation (AMOC) measured by the RAPID-MOCHA data array.

Simulating magma propagation pathways requires both a well-calibrated model for the stress state of the volcano and models for dike advance within such a stress field. With the purpose of establishing a framework for calculating computationally efficient and flexible shallow magma propagation scenarios, we develop three-dimensional models for the stress state of volcanoes with complex topographies and edifice histories as well as a new simplified three-dimensional model of dike propagation using the stress state of the volcano as input. Next, we combine all these models to calculate shallow dike propagation scenarios for complex caldera settings. The resulting synthetic magma pathways and eruptive vent locations broadly reproduce the variability observed in natural calderas.

The 18 March 2020 M 5.7 Magna earthquake near Salt Lake City, Utah, offers a rare glimpse into the subsurface geometry of the Wasatch fault system—one of the world’s longest active normal faults and a major source of seismic hazard in northern Utah. We analyze the Magna earthquake sequence and resolve oblique-normal slip on a shallow (30–35º) west-dipping fault at ~9–12 km depth. Combined with near-surface geological observations of steep dip (~70º), our results support a curved, or listric, fault shape. High-precision aftershock locations show the activation of multiple, low-angle (<30º) structures, indicating the existence of a complicated fault system. Our observations provide the first direct evidence for the deep structure of the Wasatch fault system, and suggest that ground shaking in the Salt Lake City region in future Wasatch fault earthquakes may be higher than previously estimated.