The Calhoun Critical Zone Observatory (CCZO) has been collecting above and below canopy water, energy and carbon fluxes and other hydro-meteorological processes since 2016. The observations provide a unique opportunity to investigate the coupling between aboveground environmental conditions and belowground fluxes of mass and energy in general and the fate of fixed carbon in forested ecosystems in particular. The simultaneous measurements of soil-canopy-atmosphere states and fluxes reveal the role of pine forest in diurnal and seasonal dynamics of the CCZ ecosystem.

The Salton Sea Geothermal Field (SSGF) is one of the most seismically active and geothermally productive fields in Southern California. Previous studies showed that local seismicity and seismic velocity in SSGF was disturbed by the 2010 M7.2 El Mayor-Cucapah earthquake. Here we present a systematic investigation of triggered seismicity and the spatial-temporal change of seismic velocity in SSGF following some regional and remote earthquakes. The continuous waveform from 2007 to 2014 in the Calenergy Borehole Network (EN) is used for this study. We apply a GPU-based waveform matched-filter technique (WMFT) to 60 days before and after the selected earthquakes, and find possible triggered seismicity following several regional earthquakes, such as the 2009 Mw6.9 Baja California and 2010 Mw5.7 Ocotillo Earthquakes. We also utilize an ambient noise cross-correlation method to quantify the temporal and spatial velocity changes in SSGF. Our result shows clear velocity reduction following the 2010 M7.2 El-Mayor earthquake, and the co-seismic reductions are larger for ray paths outside the geothermal regions. We also find some possible triggered events in SSGF following the 2019 M7.1 Ridgecrest earthquake. Our next step is to conduct a detailed investigation on the possible correlation between triggered seismicity and seismic velocity changes associated with the occurrence of the 2010 M7.2 earthquake, and other regional earthquakes.

Large volcanic eruptions drive significant climate perturbations through major anomalies in radiative fluxes and the resulting widespread cooling of the surface and upper ocean. Recent studies suggest that these eruptions also drive important variability in air-sea carbon and oxygen fluxes. By simulating the Earth system using two initial-condition large ensembles, with and without the aerosol forcing associated with the Mt. Pinatubo eruption in June 1991, we isolate the impact of this event on ocean physical and biogeochemical properties. The Mt. Pinatubo eruption generated significant anomalies in surface fluxes and the ocean interior inventories of heat, oxygen, and carbon. Pinatubo-driven changes persist for multiple years in the upper ocean and permanently modify the ocean’s heat, oxygen, and carbon inventories. Positive anomalies in oxygen concentrations emerge immediately post-eruption and penetrate into the deep ocean. In contrast, carbon anomalies intensify in the upper ocean over several years post-eruption, and are largely confined to the upper 150 m. In the tropics and northern high latitudes, the change in oxygen is dominated by surface cooling and subsequent ventilation to mid-depths, while the carbon anomaly is associated with solubility changes and eruption-generated ENSO variability. Our results indicate that Pinatubo does not substantially impact oxygen or carbon in the Southern Ocean; forced signals do not emerge from the large internal variability in this region.

We present a Bayesian geospatial modeling framework developed for the synthesis of point prevalence and health facility catchment data of mixed types: Plasmodium parasite prevalence and malaria febrile incidence. Since the clinical case definition for health facility record keeping is less strict than that used in cohort studies used to construct previous parasite prevalence to clinical incidence relationships (the latter usually incorporating a parasite density threshold to remove background fevers accompanied by coincidence asymptomatic parasite infections) our model learns a smooth prevalence-to-incidence conversion during posterior sampling. Also jointly fitted are a catchment model based on the relative travel times between each pixel location and its nearby health facilities, as well as a distribution regression-based covariance structure for explaining the residual errors at health facility level based on the similarity between the ‘bags’ of covariate values in their respective catchments.

Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. (Hodyss et al 2013, 2014) demonstrated that a correction term to various deterministic numerical schemes, known in stochastic analysis as the Itô correction, can help improve solution accuracy and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the Itô correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the Itô correction is derived for noises of any color. It is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized Itô correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized Itô correction allows for the use of significantly longer time steps and hence helps to reduce the computational cost of the numerical simulation.

We are studying the generation and propagation of whistler-mode waves from the upper ionosphere into the magnetosphere. We are using a ray-tracing approach to solve the wave propagation problem, which enables low computational-cost, rapid parameter scans. This approach also gives insights into the physics of wave propagation, through the use of simplified models. We have developed a new python framework called Maestro. It is designed to orchestrate the ray tracing simulations, making them easier to setup, run, and analyze once complete. Maestro ray tracing results will be shown for whistler waves in simplified plasma geometries, illustrating the physics of whistler propagation, ionospheric ducting of VLF waves, and magnetospheric reflection. Results will also be shown for rays propagated in a more realistic ionospheric model, as computed by the SAMI2/3 code. This research is supported by the DARPA Defense Science Office

To characterize how moderately large impactors might alter the differentiation and internal structure of Mars, we examine the fractional crystallization of intermediate depth magma oceans, and document that residual liquids ultimately become denser than normal Martian mantle, establishing unstable density gradients and inducing extensive magma descent within an evolving Mars. Fractional crystallization of intermediate depth magma oceans on Mars is likely to produce liquids that are dense enough to descend to the core-mantle boundary (CMB) and thus form a stably stratified thermochemical boundary layer at the Martian CMB. If this layer cooled sufficiently to crystallize, its mineralogy would be dominated by garnet and ferropericlase, or stishovite and ringwoodite, with changes in the descending liquid’s bulk composition having relatively minor effects on the resulting phase assemblage. While the size of Mars’ core remains uncertain, the addition of such a thermal boundary layer would impede the stabilization of (Mg, Fe)SiO3-perovskite at depth in Mars, although it would contain modest amounts of CaSiO3-perovskite. Such a compositionally distinct thermal boundary layer at the base of the Martian mantle would substantially elevate the inferred temperature of the Martian core, and also produce markedly lowered heat flow at the top of its core, with a potentially causal relation with the current lack of an internally generated Martian magnetic field. We calculate the seismic velocity anomalies that would be expected to be associated with such a layer, and find that the shift in mineralogy at depth should produce a seismic discontinuity that could prospectively be detectable by Martian seismic deployments.

Streamers are millimeter-diameter cold plasma discharges that can extend, branch, and interact with complex collective dynamics to form meter-scale systems. Such streamer systems can be expected to play an important role in lightning. Small-scale streamer systems may grow to provide sufficient ionization and intensification of field to support initiation of hot plasma channel development. Streamer systems developing ahead of the lightning channel may guide channel extension and help explain the observed step-wise extension process. Rapidly-developing streamer systems may also help explain recent observations of fast positive and negative breakdown. We present simulations of streamer system development based on approximate particle-like treatment of individual streamer behavior but including large-scale system dynamics including interaction, collision/connection, and secondary streamer initiation. Here we focus on the observable effects of such streamer system development, including electrostatic field change and electromagnetic wave emissions with frequencies up to 500 MHz. Preliminary results suggest high-frequency (HF) emissions due to streamer acceleration and interaction in isolated streamer systems are comparable in scale to observations of HF emission from lightning and develop before detectable static field change occurs, but these results depend on simulation parameters.

We present an intercomparison and consistency study of aerosol optical depth (AOD) retrievals from the 1625 nm channel that has recently been added to the SURFRAD’s Multi-Filter Rotating Shadowband Radiometer (MFRSR) instruments. In addition, a Multi-Filter Radiometer (MFR) has been installed at the SURFRAD sites to measure upwelling radiation in the same wavelength bands. SURFRAD (Surface Radiation Budget Monitoring) is a NOAA measurement network with the prime directive to determine the surface radiative budget. It is composed of seven sites across the United States and two mobile facilities. Each site is equipped with an MFRSR which measures global and diffuse solar radiation in one broadband and six narrow band spectral channels. MFRSR retrivals include several aerosol optical properties including the spectrally resolved AOD at 415, 500, 673, 870, 940, and 1625 nm. With particular focus on the AOD from the 1625 nm channel we discuss measurement uncertainties based on instrument limitations, uncertainties from the retrieval algorithm, and statistical considerations. Some of the SURFRAD sites have a colocated CIMEL sunphotometer that in some instances is equipped with a 1625 nm channel. While the two datasets are highly correlated we observe various deviations. We furthermore present an aerosol particle size distribution retrieval based on the AOD from 5 narrow band MFRSR channels. Here we put particular emphasis on the 1625 nm channel and its potential to improve the retrieval of the coarse mode fraction of the size distribution.

Understanding the consequences of different management practices on vegetation phenology, forage production and quality, plant and microbial species composition, greenhouse gas emissions, and water budgets in tallgrass prairie systems is vital to identify best management practices. As part of the Southern Plains Long-Term Agroecosystem Research (SP-LTAR) grassland study, a long-term integrated Grassland-LivestOck Burning Experiment (iGLOBE) has been established with a cluster of six eddy covariance (EC) systems on differently managed (i.e., different burning and grazing regimes) native tallgrass prairie systems located in different landscape positions. The purpose of this paper is to describe this long-term experiment, report preliminary results on the responses of differently managed tallgrass prairies under variable climates using satellite remote sensing and EC data, and present future research directions. In general, vegetation greened-up and peaked early, and produced greater forage yields in burned years. However, drought impacts were greater in burned sites due to reductions in soil water availability by burning. The impact of grazing on vegetation phenology was confounded by several factors (e.g., cattle size, stocking rate, precipitation). Moreover, prairie systems located in different landscapes responded differently, especially in dry years due to differences in water availability. The strong correspondence between vegetation phenology and eddy fluxes was evidenced by strong linear relationships of greenness index (i.e., enhanced vegetation index) with evapotranspiration and gross primary production. Results indicate that impacts of climate and management practices on vegetation phenology may profoundly impact carbon and water budgets of tallgrass prairie. Interacting effects of multiple management practices and inter-annual climatic variability on the responses of tallgrass prairie highlight the necessity of establishing an innovative and comprehensive long-term experiment to address inconsistent responses of tallgrass prairie to different intensities, frequencies, timing, and duration of management practices, and to identify best management practices.

This study exploits the high-density zonal sampling at the turnaround latitude of the High Resolution Dynamics Limb Sounder (HIRDLS) in the Southern Hemisphere to investigate the missing drag in chemistry-climate models near 60 S. Gravity wave (GW) properties including amplitude, zonal wavenumber, vertical wavelength, and momentum flux are estimated with a wavelet analysis method based on the S-transform. Monthly means of GW properties compare well with estimates from previous studies. We further investigate the contribution to GW momentum flux above orographic and nonorographic regions and find that while fluxes are much larger locally over orographic regions, the contribution to the zonal mean is roughly 3 times smaller than the contribution over nonorographic regions. We also investigate the relationship with the zonal wind and find that GW momentum flux is highly correlated with the near surface winds over orographic regions. In addition to momentum flux, we also provide estimates of the zonal drag and use these estimates to evaluate the current GW parameterizations and resolved wave forcing in models participating in phase 1 of the Chemistry-Climate Model Initiative (CCMI-1). The HIRDLS zonal drag estimates suggest that the CCMI-1 models have insufficient zonal drag, especially in June, July, and August, and that the majority of the missing drag is over nonorographic regions. Our discussion includes implications for the Brewer-Dobson Circulation and ozone hole.

In recent years, the Southern Ocean has experienced unprecedented surface warming and sea ice loss—a stark reversal of sea ice expansion and surface cooling trends that prevailed over preceding decades. The most dramatic changes occurred in the austral spring of 2016 when Antarctic sea-ice extent (SIE) reached a record minimum as sea surface temperatures (SST) climbed to a near-record high. In late 2019, another circumpolar surface warming event spanned the Southern Ocean, albeit with no appreciable decline in Antarctic SIE. A mixed layer heat budget analysis reveals that these recent circumpolar surface warming events were triggered by a weakening of the circumpolar westerlies, which decreased northward Ekman transport and accelerated the seasonal shoaling of the mixed layer. The latter effect amplified the surface warming effect of air-sea heat fluxes during months of peak solar insolation. More generally, summertime SST across the Southern Ocean is sensitive to the timing of the springtime shoaling of the mixed layer, which is controlled by the strength and temporal variance of the circumpolar westerlies. An examination of the CESM1 large ensemble demonstrates that these recent circumpolar warming events are consistent with the internal variability associated with the Southern Annual Mode (SAM), whereby negative SAM in austral spring favors shallower mixed layers and anomalously high summertime SST. Thus, future Southern Ocean surface warming extremes will depend on the evolution of regional mixed layer depths and interannual SAM variability.

The Cedars is an area in Northern California with a chain of highly alkaline springs resulting from CO2-charged meteorological water interacting with a peridotite body. Serpentinization resulting from this interaction at depth leads to the sequestration of various carbonate minerals into veins accompanied by a release of Ca2+ and OH- enriched water to the surface, creating an environment which promotes rapid precipitation of CaCO3 at surface springs. This environment enables us to apply the recently developed Δ47-Δ48 dual clumped isotope analysis to probe kinetic isotope effects (KIEs) and timescales of CO2 transformation in a region with the potential for geological CO2 sequestration. We analyzed CaCO3 recovered from various localities and identified significant kinetic fractionations associated with CO2 absorption in a majority of samples, characterized by enrichment in Δ47 values and depletion in Δ48 values relative to equilibrium. Surface floes exhibited the largest KIEs (ΔΔ­47: 0.163‰, ΔΔ­48: -0.761‰). Surface floe samples begin to precipitate out of solution within the first hour of CO2 absorption, and the dissolved inorganic carbon (DIC) pool requires a residence time of >100 hours to achieve isotopic equilibria. The Δ48/Δ47 slope of samples from the Cedars (-3.223±0.519; 1 SE) is within the range of published theoretical values designed to constrain CO2 hydrolysis-related kinetic fractionation (-1.724 to -8.330). The Δ47/δ18O slope (-0.009±0.001) and Δ47/δ13C slope (0.009±0.001) are roughly consistent with literature values reported from a peridotite in Oman of -0.006±0.002 and -0.005±0.002, respectively. The consistency of slopes in the multi-isotope space suggests the Δ47-Δ48 dual carbonate clumped isotope framework can be applied to study CO2-absorption processes in applied systems, including sites of interest for geological sequestration.

Large Igneous Provinces (LIPs) are among the greatest magmatic events in Earth history withvolumes in excess of ∼500,000 km3 of predominantly basaltic lavas covering hugecontinental and ocean regions (>100,000 km2). Field observations suggest that lava flowfields in LIPs are made largely of sheet pāhoehoe lava lobes and the 10-100 m thick flows areformed by inflation. Understanding the emplacement history of these lava lobes can help usinfer the magnitude and temporal dynamics of past events.We use a phase-field model to describe solidification and re-melting of sequentially emplacedlava flows. We calibrate model parameters using field measurements at Makaopuhi lava lakeand perform extensive numerical simulations by varying the thickness of individual flow and thetime intervals between eruptions. These results help quantify the complex interplay betweenthermal evolution, flow thickness and emplacement frequency. If flows are thick enough andthe interval between emplacement short enough, reheating and re-melting may remove thetextural record of flow contacts – making flows appear thicker than they actually were. Guidedby field observations in Columbia River Basalt and Deccan Traps, we illustrate how the finalmorphology of sequentially emplaced lava is controlled by both the time scale of emplacementintervals and the time scale of cooling. We summarize our results to provide theoreticalconstraints on the thickness and emplacement intervals of individual LIP lava flows.

The increase in world population, added to socioeconomic development and climate change, have highlighted one of the biggest problems worldwide: the depletion of water resources. The La Ligua and Petorca river basins, in central Chile, are an example of this problem, as rainfall has decreased in recent years, while socio-economic activities, mainly agriculture have increase. This situation has led to a severe water stress, and the need for integrated and sustainable river basin management, aimed at understanding the behavior of basins, aquifers, and the exchange of flows between them. Therefore, the main objective of this research is to quantify the impacts of climate change, in terms of groundwater scarcity, in semi-arid basins using integrated modeling of water resources. For this purpose, groundwater/surface waters integrated models of La Ligua and Petorca basins were developed using WEAP and MODFLOW. Both basins present different hydrological, social, and geographical characteristics. Different scenarios were evaluated to quantify groundwater depletion. These scenarios depend on climatic forcings, such as precipitation and temperature, which were obtained from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Results forecast that annual precipitation will decrease, whereas average annual temperature will increase in these semi-arid regions. As a consequence, the aquifer’s recovery rate will reduce, decreasing the number of wells that provide drinking water in rural and agricultural areas. In conclusion, the coupling of hydrological and hydrogeological models is a tool that allows researchers and stakeholders to make opportune and appropriate decisions on the management of basins and aquifers, which is even more important in basins that are expected to be or are already under severe water stress.

The use of modeling tools for integrated water resources management is a complex task due to the large number of processes involved in a basin. Moreover, these modeling tools commonly require information that is not readily available, such as illegal water withdrawals, or other data difficult to obtain, which results in groundwater models that fail to capture the aquifer dynamics. In recent years, machine learning algorithms have shown outstanding performance as prediction tools. Despite being questioned for not having a physical basis, they have been used in areas such as hydrology and hydrogeology (e.g., for flow prediction, rain forecast). Thus, the objective of this research is to estimate groundwater withdrawals using machine learning algorithms and integrated water management models. To achieve this objective, ensembles of groundwater levels were generated with a previously calibrated groundwater/surface water integrated model. Then, these ensembles were used as input parameters for Gaussian process regression (GPR) and artificial neural network (ANN) models to construct time series of water withdrawals throughout a basin. This method was applied in the Petorca and La Ligua basins, in central Chile, as they exhibit a contrasting reality in terms of water availability even when they have geographical proximity. Also, these basins are within an effective extraction monitoring program lead by the Chilean water authority that can be used to validate the users’ water withdrawal. Our results show that the GPR model, compared to ANNs, adequately estimates the spatiotemporal distribution of groundwater withdrawals in the pilot basins. Thus, the use of machine learning algorithms improves the performance of integrated water resources management models.

Machine learning algorithm is applied to shear wave velocity (Vs) inversion in surface wave tomography, where a set of 1-D Vs profiles and the corresponding synthetic dispersion curves are used in network training. Previous studies showed that performances of a trained network depend on the input training dataset with limited diversity and therefore lack generalizability. Here, we present an improved semi-supervised algorithm-based network that takes both model-generated and observed surface wave dispersion data in the training process. The algorithm is termed Wasserstein cycle-consistent generative adversarial networks (Wcycle-GAN). Different from conventional supervised approaches, the GAN architecture extracts feature from the observed surface wave dispersion data that can compensate the limited diversity of the training dataset generated synthetically. The cycle-consistency enforces the reconstruction ability of input data from predicted model using a separate data generating network, while Wasserstein metric provides improved training stability and enhanced spatial smoothness of the output Vs model. We demonstrate improvements by applying the Wcycle-GAN method to 4076 pairs of fundamental mode Rayleigh wave phase and group velocity dispersion curves obtained in Southern California. The final 3-D Vs model from the best trained network shows large-scale features that are consistent with the surface geology. Our Vs model has smaller data misfits, yields better spatial smoothing, and provides sharper images of structures near faults in the top 15 km, suggesting the proposed Wcycle-GAN algorithm has stronger training stability and generalization abilities compared to conventional machine learning methods.

In observations and cloud-resolving model (CRM) simulations, large-scale domains where convection is more aggregated (clustered into a smaller number of clouds) are associated with a drier troposphere. What mechanisms explain this drying? Hypotheses involve dynamical and microphysical processes. The goal of this study is to quantify the relative importance of these processes. We use a series of CRM simulations with different dynamical regimes and different kinds of convective organization forced by external forcings (isolated cumulonimbi, tropical cyclones, squall lines). We interpret the simulation results in the light of a hierarchy of simpler models (last-saturation model, analytical model). In CRM simulations, the troposphere is drier in the environment of more aggregated convection (tropical cyclones and squall lines). A last-saturation model is able to reproduce the drier troposphere even in absence of any microphysical processes or horizontal motions. Cloud intermittence is the key factor explaining this drying: when clouds are more intermittent, subsiding air parcels are more likely to encounter a cloud. An analytical model highlights the key role of the duration of convective systems. Remoistening by microphysical processes contributes to the moister troposphere when convection is less aggregated, though its importance is secondary smaller than that of intermittence. We suggest that the observed anti-correlation between convective aggregation and relative humidity may, at least partially, be mediated by the duration of convective systems.