Many boulders on (101955) Bennu, a near-Earth rubble pile asteroid, show signs of in situ disaggregation and exfoliation, indicating that thermal fatigue plays an important role in its landscape evolution. Observations of particle ejections from its surface also show it to be an active asteroid, though the driving mechanism of these events is yet to be determined. Exfoliation has been shown to mobilize disaggregated particles in terrestrial environments, suggesting that it may be capable of ejecting material from Bennu’s surface. We investigate the nature of thermal fatigue on the asteroid, and the efficacy of fatigue-driven exfoliation as a mechanism for generating asteroid activity, by performing finite element modeling of stress fields induced in boulders from diurnal cycling. We develop a model to predict the spacing of exfoliation fractures, and the number and speed of particles that may be ejected during exfoliation events. We find that crack spacing ranges from ~1 mm to 10 cm and disaggregated particles have ejection speeds up to ~2 m/s. Exfoliation events are most likely to occur in the late afternoon. These predictions are consistent with observed ejection events at Bennu and indicate that thermal fatigue is a viable mechanism for driving asteroid activity. Crack propagation rates and ejection speeds are greatest at perihelion when the diurnal temperature variation is largest, suggesting that events should be more energetic and more frequent when closer to the Sun. Annual thermal stresses that arise in large boulders may influence the spacing of exfoliation cracks or frequency of ejection events.

Forest cover and streamflow are generally expected to vary inversely because reduced forest cover typically leads to less transpiration and interception. However, recent studies in the western US have found no change or even decreased streamflow following forest disturbance due to drought and insect epidemics. We investigated streamflow response to forest cover change using hydrologic, climatic, and forest data for 159 watersheds in the western US from the CAMELS dataset for the period 2000-2019. Forest change and disturbance were quantified in terms of net tree growth (total growth volume minus mortality volume) and mean annual mortality rates, respectively, from the US Forest Service’s Forest Inventory and Analysis database. Annual streamflow was analyzed using multiple methods: Mann-Kendall trend analysis, time trend analysis to quantify change not attributable to annual precipitation and temperature, and multiple regression to quantify contributions of climate, mortality, and aridity. Many watersheds exhibited decreased annual streamflow even as forest cover decreased. Time trend analysis identified decreased streamflow not attributable to precipitation and temperature changes in many disturbed watersheds, yet streamflow change was not consistently related to disturbance, suggesting drivers other than disturbance, precipitation, and temperature. Multiple regression analysis indicated that although change in streamflow is significantly related to tree mortality, the direction of this effect depends on aridity. Specifically, forest disturbances in wet, energy-limited watersheds (i.e., where annual potential evapotranspiration is less than annual precipitation) tended to increase streamflow, while post-disturbance streamflow more frequently decreased in dry water-limited watersheds (where the potential evapotranspiration to precipitation ratio exceeds 2.35).

Momentum transport by boundary-layer turbulence causes a weak synoptic-scale vertical motion. The classical textbook solution for the strength of this Ekman pumping depends on the curl of the surface momentum flux. A new solution for Ekman pumping is derived in terms of the curl of the geostrophic wind and a term that depends in a non-trivial way on the vertical profile of the turbulent momentum flux. The solution is confined to a boundary-layer regime that is vertically well mixed and horizontally homogeneous. The momentum flux is computed from a commonly used bulk surface drag formula and a flux-jump relation to capture the entrainment flux of momentum at the top of the boundary layer. It is found that the strength of Ekman pumping is bounded. The weakening of Ekman pumping for enhanced turbulent surface friction can be explained from the fact that it will reduce the magnitude of the horizontal wind. It is demonstrated that entrainment of momentum across the top of the boundary layer tends to diminish the large-scale divergence of the wind. As momentum transport is parameterized in large-scale models, the analysis is relevant for the understanding and interpretation of the evolution of synoptic-scale vertical motions as predicted by such models.

A model-data inversion is applied to an extensive observational dataset collected in the Southern Ocean north of the Ross Sea during late autumn to early winter, producing estimates of the frequency-dependent rate of dissipation by sea ice. The modeling platform is WAVEWATCH III® which accounts for non-stationarity, advection, wave generation, and other relevant processes. The resulting 9477 dissipation profiles are co-located with other variables such as ice thickness to quantify correlations which might be exploited in later studies to improve predictions. An average of dissipation profiles from cases of thinner ice near the ice edge is fitted to a simple binomial. The binomial shows remarkable qualitative similarity to prior observation-based estimates of dissipation, and the power dependence is consistent with at least three theoretical models, one of which assumes that dissipation is dominated by turbulence generated by shear at the ice-water interface. Estimated dissipation is lower closer to the ice edge, where ice is thinner, and waveheight is larger. The quantified correlation with ice thickness may be exploited to develop new parametric predictions of dissipation.

Global water use for food production needs to be reduced to remain within planetary boundaries, yet the financial feasibility of crucial measures to reduce water use is poorly quantified. Here, we introduce a novel method to compare the costs of water conservation measures with the added value that reallocation of water savings might generate if used for expansion of irrigation. Based on detailed water accounting through the use of a high-resolution hydrology-crop model, we modify the traditional cost curve approach with an improved estimation of demand, increasing marginal cost per water conservation measure combination and add a correction to control for impacts on downstream water availability. We apply the method to three major river basins in the Indo-Gangetic plain (Indus, Ganges and Brahmaputra), a major global food producing region but increasingly water stressed. Our analysis shows that at basin level only about 10% (Brahmaputra) to just over 20% (Indus and Ganges) of potential water savings would be realised; the equilibrium price for water is too low to make the majority of water conservation measures cost effective. The associated expansion of irrigated area is moderate, about 7% in the Indus basin, 5% in the Ganges and negligible in the Brahmaputra, but farmers’ gross profit increases more substantially, by 11%. Increasing the volumetric cost of irrigation water influences supply and demand in a similar way and has little influence on water reallocation. Controlling for the impact on return flows is important and more than halves the amount of water available for reallocation.

Well-dated lacustrine records are essential to establish the timing and drivers of regional hydroclimate change. Searles Basin, California records the depositional history of a fluctuating saline-alkaline lake in the terminal basin of the Owens River system draining the eastern Sierra Nevada. Here we establish a U-Th chronology for the ~76-m-long SLAPP-SLRS17 core collected in 2017 based on dating of evaporite minerals. 98 dated samples comprising 9 different minerals were evaluated based on stratigraphic, mineralogic, textural, chemical and reproducibility criteria. After application of these criteria, a total of 37 dated samples remained as constraints for the age model. A lack of dateable minerals between 145-110 ka left the age model unconstrained over the penultimate glacial termination (Termination II). We thus established a tie point between plant wax δD values in the core and a nearby speleothem δ18O record at the beginning of the Last Interglacial. We construct a Bayesian age model allowing stratigraphy to inform sedimentation rate inflections. We find the >210 ka SLAPP-SRLS17 record contains five major units that correspond with prior work. The new dating is broadly consistent with previous efforts but provides more precise age estimates and a detailed evaluation of evaporite depositional history. We also offer a substantial revision of the age of the Bottom Mud-Mixed Layer contact, shifting it from ~130 ka to 178±3 ka. The new U-Th chronology documents the timing of mud and salt layers and lays the foundation for climate reconstructions.

Coastal flooding associated with hurricanes and other major storm events along the U.S. Coast results from complex interactions between freshwater flows, tides, storm surge, and wave effects. We have developed a two-way coupled model consisting of the National Water Model (NWM), the Advanced Circulation Ocean Model (ADCIRC), and WAVEWATCH III (WWIII) to quantify these interactions and compute total water levels in the coastal zone after significant riverine and coastal flooding events. This coupled continental coastal model covers the US Gulf and Atlantic Coasts, extending from the US-Canada border to the US-Mexico border. The Delft3D FM, D-Flow Flexible Mesh (D-Flow FM) model simulates coastal flooding on a 2D unstructured mesh within the National Water Model (NWM)/ADCIRC/WWIII coupled system. We developed a high quality 2D unstructured mesh using a sizing function that assigns element sizes based on proximities of coastal features at given spatial locations. Data sources used to identify relevant coastal features included NWM streamlines, the National Hydrography Dataset (NHD), and United States Army Corps of Engineers (USACE) data, allowing integration of D-Flow FM with the NWM and optimization of the number of computational points. The system obtains freshwater inflow boundary conditions to D-Flow FM from the NWM channel network. Offshore water levels boundary conditions for D-Flow FM come from the coupled ADCIRC-WWIII model. Domain sub-setting keeps runtimes within reasonable limits, as it does execution of the detailed hydrodynamic model within a user-defined area enclosing the storm landfall site. The advantage of this approach comes from the fact that the same coupled model setup allows simulation of coastal flooding for different storm events; only the sub-setting enclosure and the atmospheric forcing require updating from case to case. Model validation, consisting of water level comparisons against observations from simulations using the coupled system for historical storm events. The model simulations satisfactorily reproduced observed spatial and temporal variations of total water levels. In conclusion, this study presents performance of the sub-setting approach in reducing runtime considerably without compromising the accuracy of the coupled modeling system solution.

A new mission called GRACE Follow-On is now flying to continue the measurements started by the GRACE mission, and to test a laser interferometry system for making more accurate measurements of the satellite separation. In this paper we discuss the potential scientific benefit of strongly reducing the acceleration noise in a Next Generation Gravity Mission (NGGM), compared with that for GRACE and for GRACE Follow-On. A useful way of comparing the scientific benefits is from the view point of how well they can be used to test different procedures for estimating the changes in the geopotential based on sources of geophysical information other than satellite gravity results. In particular, changes in hydrology, the atmospheric density, and ocean conditions can make large and very non-uniform changes in the geopotential in short periods of time. To make the discussion as simple as possible, we consider mainly the variations in the geopotential at altitude along the satellite orbit for different ground tracks. For the NGGM, we initially assume laser interferometry between the two satellites but the same satellite acceleration noise level as for the GRACE-Follow-On mission. Then the total measurement noise level at long and medium wavelengths would be only moderately below the geopotential variation estimation uncertainty. However, if the acceleration noise level were sharply reduced by replacing the GRACE-type accelerometers by simplified gravitational reference sensors, it appears that considerably improved tests of different procedures for geophysical estimates of the geopotential variations could be made.

Antarctic Ice Sheet projections show the highest sensitivity to increased basal melting in the Amundsen Sea sector. However, little is known about the processes controlling future increase in melt rates. We build an ensemble of three ocean–sea-ice–ice-shelf simulations for both the recent decades and the late 21st century, constrained by regional atmosphere simulations and the multi-model mean climate change of the 5th Climate Model Intercomparison Project under the RCP8.5 scenario. The ice shelf melt rates are typically multiplied by 1.4 to 2.2 from present day to future, for a total basal mass loss increased by 347Gt/yr. This is approximately equally explained by advection of warmer water from remote locations and by regional changes in Ekman downwelling and in the ice-shelf melt-induced circulation, while increased iceberg melt plays no significant role. Our simulations suggest that high-end melt projections previously used to constrain recent sea level projections may have been significantly overestimated.

Projections of nonstationary climate risks can vary considerably from one source to another, posing considerable communication and decision-analytical challenges. One such challenge is how to present trade-offs under deep uncertainty in a salient and interpretable manner. Some common approaches include analyzing a small subset of projections or treating all considered projections as equally likely. These approaches can underestimate risks, hide deep uncertainties, and are mostly silent on which assumptions drive decision-relevant outcomes. Here we introduce and demonstrate a transparent Bayesian framework for synthesizing deep uncertainties to inform climate risk management. The first step of this workflow is to generate an ensemble of simulations representing possible futures and analyze them through standard exploratory modeling techniques. Next, a small set of probability distributions representing subjective beliefs about the likelihood of possible futures is used to weight the scenarios. Finally, these weights are used to compute and characterize trade-offs, conduct robustness checks, and reveal implicit assumptions. We demonstrate the framework through a didactic case study analyzing how high to elevate a house to manage coastal flood risks.

Generation of 2D meshes with reduced number of elements while yielding accurate results is a major challenge in coastal numerical models. High-quality 2D unstructured meshes were generated using sizing functions, which were computed from Euclidean distances to coastal features at given spatial locations and assigned element sizes based on calculated distances. The coastal features consist of National Water Model (NWM) streamlines, National Hydrography Dataset (NHD), NOAA Medium Resolution Shoreline and bathymetric features from the United States Army Corps of Engineers (USACE). This approach allows improved integration of the hydrodynamic D-Flow Flexible Mesh (D-Flow FM) model into the hydrological NWM and results in an optimum number of computational points. The method grants the user flexibility to control element sizes and avoids manual iterative procedures by determining an optimal element-sizing function that defines small element scales in regions where geometrical and physical characteristics exist, with larger scales elsewhere. Newly created continental-scale meshes on the Atlantic Ocean, Gulf of Mexico and Pacific Ocean coastlines demonstrate the application of the proposed method for automatic generation of unstructured, high-quality 2D meshes.

The Rockall Trough is one of the main conduits for warm Atlantic Water to the Nordic Seas. Ocean heat anomalies, originating from the eastern subpolar gyre, are known to influence Arctic sea ice extent, marine ecosystems, and continental climate. Knowledge of the transport through this basin has previously been limited to estimates from hydrographic sections which cannot characterise the intra‐annual and multi‐annual variability. As part of the Overturning in the Subpolar North Atlantic Programme (OSNAP), a mooring array was deployed in the Rockall Trough in order to obtain the first continuous measurements of transport. Here, we define the methodology and the errors associated with estimating these transports. Results show a 4‐year mean northward transport of 6.6 Sv (1 Sv = 106 m3/s) by the North Atlantic Current (NAC) in the east and interior of the Rockall Trough (2014‐2018). A mean transport of ‐2.0 Sv (southward) is observed in the west of the basin, which could be part of a recirculation around the Rockall Plateau. The 90‐day low‐pass filtered transport shows large sub‐annual and inter‐annual variability (‐1.6 Sv to 9.1 Sv), mostly resulting from changes in the mid‐basin geostrophic transport. Satellite altimetry reveals the periods of low and high transport are associated with significant changes in the Rockall Trough circulation. There is a detectable seasonal signal, with the greatest transport in spring and autumn.

Clumped isotope thermometry can independently constrain the formation temperatures of carbonates, but a lack of precisely temperature-controlled calibration samples limits its application on aragonites. To address this issue, we present clumped isotope compositions of aragonitic bivalve shells grown under highly controlled temperatures (1‒18°C), which we combine with clumped isotope data from natural and synthetic aragonites from a wide range of temperatures (1‒850°C). We observe no discernible offset in clumped isotope values between aragonitic foraminifera, mollusks, and abiogenic aragonites or between aragonites and calcites, eliminating the need for a mineral-specific calibration or acid fractionation factor. However, due to non-linear behavior of the clumped isotope thermometer, including high-temperature (>100°C) datapoints in linear clumped isotope calibrations causes them to underestimate temperatures of cold (1‒18°C) carbonates by 2.7 ± 2.0°C (95% confidence level). Therefore, clumped isotope-based paleoclimate reconstructions should be calibrated using samples with well constrained formation temperatures close to those of the samples.

Temperature and density in the upper Martian atmosphere, above ~100 km, are key diagnostic parameters to study processes of the species’ escape, investigate the impact of solar activity, model the atmospheric circulation, and plan spacecraft descent or aerobraking maneuvers. In this paper, we report vertical profiling of carbon dioxide (CO2) density and temperature from the Atmospheric Chemistry Suite (ACS) solar occultations onboard the ExoMars Trace Gas Orbiter (TGO). A strong CO2 absorption band near 2.7 μm observed by the middle infrared spectrometric channel (ACS MIR) allows the retrieval of the atmospheric thermal structure in a large altitude range, from 20 to 180 km. We present the latitudinal and seasonal climatology of the thermal structure for 1.5 Martian years (MYs), from the middle of MY 34 to the end of MY 35. The results show the variability of distinct atmospheric layers, such as a mesopause (derived from 70 to 150 km) and homopause, changing from 80-90 km at aphelion to 100-110 km at perihelion. Some short-term homopause fluctuations are also observed depending on the dust activity.

X-Ray micro-computed tomography (micro-CT) is a standard method to perform three-dimensional analysis of the internal structure of a rock sample. 3D X-Ray microscopes, such as those from the XRadia Versa family, provide images of high resolution and contrast. Medical scanning machines can also be used for scanning rock samples to reduce operational cost and time, but they generally provide poorer spatial resolution and contrast compared to 3D X-Ray microscopes. Recent success in implementing deep learning algorithms to enhance image quality demonstrated that, in some cases, the application of convolutional neural network (CNN) models might significantly enhance the resolution of the micro-CT images. In this research, a super-resolution technique employing the U-Net 3D CNN architecture is applied to enhance the resolution of granodiorite rock sample images obtained by two different 3D scanning machines. The high-resolution dataset was obtained using the XRadia Versa XRM-500 microscope. It contained images with nominal resolutions of 10.3 and 5 microns. The low-resolution scanning was performed using a Scanco medical µCT 50 machine, and the images from this dataset had a nominal resolution of 10.3 microns. Several models were created to enhance the quality of the low-resolution images, and the results were analysed. It was observed that super-resolution processing could significantly improve the low-resolution micro-CT image quality and suppress noise that appeared on medical images. The results presented in this study are of particular interest and value to geoscientists that use medical scanners to study the structure of rock samples at large scale.

Viscosity is of great importance in governing the dynamics of volcanoes, including their eruptive style. The viscosity of a volcanic melt is dominated by temperature and chemical composition, both oxides and water content. The changes in melt structure resulting from the interactions between the various chemical components are complex, and the construction of a physical viscosity model that depends on composition has not yet been achieved. We therefore train an Artificial Neural Networks (ANN) on a large database of measured compositions, including water, and viscosities that spans virtually the entire chemical space of terrestrial magmas, as well as some technical and extraterrestrial silicate melts. The ANN uses composition, temperature, a structural parameter reflecting melt polymerisation and the alkaline ratio as input parameters. It successfully reproduces and predicts measurements in the database with significantly higher accuracy than previous global models for volcanic melt viscosities. A calculator based on our ANN model is available at https://share.streamlit.io/domlang/visc_calc/main/final_script.py. Viscosity measurements are restricted to low and high viscosity range, which exclude typical eruptive temperatures. Without training data at such conditions, the ANN cannot reliably predict viscosities for this important temperature range. To overcome this limitation, we use the ANN to create a synthetic viscosity data in the high and low viscosity regime and fit these points using a physically motivated, temperature-dependent viscosity model. An Excel file to calculate viscosities using these parameters and the MYEGA equation is supplied in the Supporting Information.

We perform a geomagnetic event simulation using a newly developed magnetohydrodynamic with adaptively embedded particle-in-cell (MHD-AEPIC) model. We have developed effective criteria to identify reconnection sites in the magnetotail and cover them with the PIC model. The MHD-AEPIC simulation results are compared with Hall MHD and ideal MHD simulations to study the impacts of kinetic reconnection at multiple physical scales. At the global scale, the three models produce very similar SYM-H and SuperMag Electrojet (SME) indexes, which indicates that the global magnetic field configurations from the three models are very close to each other. We also compare the ionospheric solver results and all three models generate similar polar cap potentials and field aligned currents. At the mesoscale we compare the simulations with in situ Geotail observations in the tail. All three models produce reasonable agreement with the Geotail observations. At the kinetic scales, the MHD-AEPIC simulation can produce a crescent shape distribution of the electron velocity space at the electron diffusion region which agrees very well with MMS observations near a tail reconnection site. These electron scale kinetic features are not available in either the Hall MHD or ideal MHD models. Overall, the MHD-AEPIC model compares well with observations at all scales, it works robustly, and the computational cost is acceptable due to the adaptive adjustment of the PIC domain. It remains to be determined whether kinetic physics can play a more significant role in other types of events, including but not limited to substorms.

Extreme precipitation events, including those associated with weather fronts, have wide-ranging impacts across the world. Here we use a deep learning algorithm to identify weather fronts in high resolution Community Earth System Model (CESM) simulations over the contiguous United States (CONUS), and evaluate the results using observational and reanalysis products. We further compare results between CESM simulations using present-day and future climate forcing, to study how these features might change with climate change. We find that detected front frequencies in CESM have seasonally varying spatial patterns and responses to climate change and are found to be associated with modeled changes in large scale circulation such as the jet stream. We also associate the detected fronts with precipitation and find that total and extreme frontal precipitation mostly decreases with climate change, with some seasonal and regional differences. Decreases in Northern Hemisphere summer frontal precipitation are largely driven by changes in the frequency of different front types, especially cold and stationary fronts. On the other hand, Northern Hemisphere winter exhibits some regional increases in frontal precipitation that are largely driven by changes in frontal precipitation intensity. While CONUS mean and extreme precipitation generally increase during all seasons in these climate change simulations, the likelihood of frontal extreme precipitation decreases, demonstrating that extreme precipitation has seasonally varying sources and mechanisms that will continue to evolve with climate change.