The Martian surface composition appears mainly mafic but recent observations have revealed the presence of differentiated rocks, only in the Highlands. Here, we demonstrate that differentiated melts can form during the construction of thick crustal regions on Mars by fractional crystallisation of a mafic protolith, without plate tectonics. On a stagnant-lid planet, regions of thicker crusts contain more heat-producing elements and are associated to thinner lithospheres and to higher mantle melt fractions. This induces larger crustal extraction rates where the crust is thicker. This positive feedback mechanism is favoured at large wavelengths and can explain the formation of the Martian dichotomy. We further develop an asymmetric parameterised thermal evolution model accounting for crustal extraction, where the well-mixed convective mantle is topped by two lithospheres (North/South) characterised by specific thermal and crustal structures. We use this model in a Bayesian inversion to investigate the conditions that allow crustal temperatures to be maintained above the basalt solidus during crustal growth, resulting in the formation of evolved melts. Among the thermal evolution models matching constraints on the structure of the Martian crust and mantle provided by the InSight NASA mission, a non-negligible fraction allows partial melting and differentiation of the crust in the south, which can occur very early (<100 Myr) as well as during the Hesperian ; partial melting in the north appears unlikely. Although crustal differentiation may occur on a hemispheric scale on Mars, its vertical extent is limited to less than a third of the crustal thickness.

Experimental investigations of silica under high pressure and temperature offer crucial insights into modeling of Earth and super-Earth’s interiors. Despite extensive studies on Hugoniots of silica polymorphs like fused-silica (2.20 g/cm3), quartz (2.65 g/cm3) and stishovite (4.29 g/cm3) up to tera-pascal, the initial density dependent Hugoniots leave an unexplored region of melting and liquid of silica at high pressures. This emphasizes the urgence to supplementing the phase diagram to constrain silica properties under extreme conditions. Here, the Hugoniot and shock temperature of coesite (2.92 g/cm3), were studied by laser shock compressions up to 950 GPa. Our findings confirm the shock-induced superheating in coesite, reveals a higher Grüneisen parameter and lower electrical conductivity compared to those of fused-silica and quartz along an isothermal line (< 2×104 K). These results suggest unique properties of shocked coesite, and implys a warmer and longer survival silica magma ocean in earlier rocky-planetary interior.

There has been increasing interest in the possibility of abrupt climatic changes in the North Atlantic and their potential impacts on the downstream regions of northwestern Europe. Here, we investigate whether this system undergoes abrupt change in a large ensemble of CMIP6 climate models. We define two metrics based on subpolar sea surface temperatures (SSTs) or mixed layer depths (MLDs) to explore the link between surface temperatures and convection collapse. These metrics yield similar numbers of abrupt events but represent seemingly unrelated phenomena. Abrupt MLD changes appear related to ongoing warming. Abrupt SST changes follow a pattern of decadal timescale cooling and warming related to coupled dynamics involving the NAO. Constraining the models by their representation of NAO variability increases the estimated likelihood of an abrupt SST event from 15% to 29%. Both the cooling and warming phases have important implications for impacts and adaptation, particularly over the British Isles.

   The threat to human health posed by Volatile Organic Compounds- carcinogenic substances for which the EPA has set no safe threshold exposure levels- is profound[1]. Despite international[2], national and state air quality standards, and greatly improved air quality monitoring, exposure remains a health hazard.     Open panel adsorption, an open-source technical solution to mitigate ambient VOCs, uses small screened panels of commodity granulated activated carbon placed in locations polluted with VOC aerosols. After pollutants are adsorbed, the panels are desorbed by pyrolysis or thermal oxidation and contaminants incinerated, and the material is reactivated. Alternative methods such as electrochemical treatment or microwave desorption are under development.    A simple adsorption system for ambient air is faced with atmospheric humidity, the erratic mixing ratio of ambient air, and diffusion in the low partial pressure of pollutants in ambient air. Adsorption systems use closed reactor beds and a closely controlled and specified cubic feet per minute inlet to hasten mass transfer to breakthrough (saturation and dynamic equilibrium).     OPA relies on 1) repeated flows to the adsorbent material, achieving saturation over a much longer duration; 2) deployment locations carefully chosen in order to maximize persistent and episodic acute air pollutant concentrations; and 3) increasing the adsorbent surface area by designing panels open to the air.  Adsorption panels should be placed in locations where pollutants are dense, preferably where air flow is laminar with low friction (because of impermeable adjacent vertical walls, for instance) to facilitate adsorption. This design thus alternately utilizes the relatively passive diffusion of batch adsorption systems, and effusion under pressure used by the more prevalent tubular or plug flow or column reactor adsorption system.    Timed jar tests utilize AC sorbent material in VOC-saturated air, with a handheld Ion Science Cub Photo-Ionization Detector unit placed into a 2.3 cubic Liter Rubbermaid Brilliance container. These tests have indicated favorable adsorption with repeated introductions of TVOC (nail polish remover), as well as rapid adsorbance under varying light, RH, and air turbulence conditions. Another initial experiment used screened panels of 4x8 mesh coconut-shell AC, adjacent to a six-lane arterial urban route. Tested by TD-GC-MS analysis using a modified EPA Method TO-17 protocol, the panels evidenced significant hydrocarbon species. Crucially, the AC was not blinded by PM 2.5, nor did competition from adsorbed H20 hinder adsorption of VOCs.    We calculated cost for the first TVOC removal test (See Attachment A, 11.30.2022, and Appendix 3).  The rate equation of 1969.8 ppm removed over 135 minutes is 14.5911, that is 14.5911 ppm removed per minute.  Alternatively, one could say that the 50 grams of AC with a delta of 1969.8 ppm, removes 39.396 ppm of TVOC per gram of Activated Carbon, over two and one-quarter hours.  Since GAC cost $22.99 for five pounds, the 50 grams of AC, at 0.11.231 lb., cost 50 cents (0.506842). At one penny per gram, or about 40 cents to remove 39.393ppm TVOC, this is modest in price. This would be more emphatically the case with the most rapid rate equations- those for the first round of adsorption, in sunlight, from the maximum reading to 5.0ppm. In such cases, with a higher rate equation of per-minute adsorption, the cost would be less than one gram, or one penny, per minute as 14.5911ppm TVOC is removed. The cost per-ppm adsorbance metric is significant for the case for OPA, which is intended for circumstances in which and constituencies for whom time is less dear than money. The cost of the complementary processes of desorption and decomposition, by any one of several methods, is being explored.      These preliminary findings affirm the working hypothesis: ambient diffusion will do the job of adsorption, and a columnar reactor with no PSA or TSA mechanism exerted. Ambient adsorption using only diffusion may not complete its work optimally, but well enough for the circumstances for which OPA is intended. Panels attached to buildings and hardscape, regularly desorbed, could offer public health co-benefits in an environmental justice context. [1] Propper, Ralph1 [email protected] Wong, Patrick et al. “Ambient and Emission Trends of Toxic Air Contaminants in California. Environmental Science & Technology. 10/6/2015, Vol. 49 Issue 19, p11329-11339.[2] Air quality guidelines for Europe: second edition. WHO Regional publications. European series No 91.

A document by Tomohiro Takagawa. Click on the document to view its contents.

A document by Julia G Mason. Click on the document to view its contents.

Nitric Oxide (NO) significantly impacts energy distribution and chemical processes in the mesosphere and lower thermosphere (MLT). During geomagnetic storms, a substantial influx of energy in the thermosphere leads to an increase in NO infrared emissions. Accurately predicting the radiative flux of Nitric Oxide is crucial for understanding the thermospheric energy budget, particularly during extreme space weather events. With advancements in computational techniques, machine learning (ML) has become a highly effective tool for space weather forecasting. This effort becomes even more worthwhile considering the availability of two decades of continuous NO infrared emissions measurement by TIMED/SABER, along with several other key thermospheric variables. We present the scheme of development of an ML-based predictive model for Nitric Oxide Infrared Radiative Flux (NOIRF). Various ML algorithms have been tested for better predictive ability, and an optimized model (NOEMLM) has been developed for the study of NOIRF. This model is able to extract the underlying relationships between the input features and effectively predict the NOIRF. The NOEMLM predictions have very good agreements with SABER observation during quiet time as well as geomagnetic storms. In comparison with the existing TIEGCM model, NOEMLM has very good performance, especially during extreme space weather conditions. The results of this study suggest that utilizing geomagnetic and space weather indices with ML/AI can serve as superior parameters for studying the upper atmosphere, as compared to focusing on specific species having complex chemical processes and associated uncertainties in constituents. ML techniques can effectively carry out the analysis with greater ease than traditional chemical studies.

Full waveform inversion (FWI) creates high resolution models of the Earth’s subsurface structures from seismic waveform data. Due to the non-linearity and non-uniqueness of FWI problems, finding globally best-fitting model solutions is not necessarily desirable since they fit noise as well as signal in the data. Bayesian FWI calculates a so-called posterior probability distribution function, which describes all possible model solutions and their uncertainties. In this paper, we solve Bayesian FWI using variational inference and propose a new methodology called physically structured variational inference, in which a physics-based structure is imposed on the variational distribution. In a simple example motivated by prior information from past FWI solutions, we include parameter correlations between pairs of spatial locations within a dominant wavelength of each other, and set other correlations to zero. This makes the method far more efficient in terms of both memory requirements and computation, at the cost of some loss of generality in the solution found. We demonstrate the proposed method with a 2D acoustic FWI scenario, and compare the results with those obtained using other methods. This verifies that the method can produce accurate statistical information about the posterior distribution with hugely improved efficiency (in our FWI example, 1 order of magnitude in computation). We further demonstrate that despite the possible reduction in generality of the solution, the posterior uncertainties can be used to solve post-inversion interrogation problems connected to estimating volumes of subsurface reservoirs and of stored CO2, with minimal bias, creating a highly efficient FWI-based decision-making workflow.

Marine carbon dioxide removal (mCDR) and geological carbon storage in the marine environment (mCS) promise to contribute to the mitigation of global climate change in combination with drastic emission reductions. However, the implementable potential of mCDR and mCS depends, apart from technology readiness, also on site-specific conditions. In this paper, we explore different options for mCDR and mCS, using the German context as a case study. We challenge each option to remove 10 Mt CO2 yr-1, which accounts for 8-22% of projected hard-to-abate and residual emissions of Germany in 2045. We focus on the environmental, resource, and infrastructure requirements of individual mCDR and mCS options at a specific site, within the German jurisdiction when possible. Furthermore, we discuss main uncertainty factors and research needs, and, where possible, cost estimates, expected environmental effects, and monitoring approaches. In total, we describe ten mCDR and mCS options; four aim at enhancing the chemical carbon uptake of the ocean through alkalinity enhancement, four aim at enhancing blue carbon ecosystems’ sink capacity, and two employ geological off-shore storage. Our results indicate that five out of ten options would potentially be implementable within German jurisdiction, and three of them could potentially rise to the challenge. This exercise provides a basis for further studies to assess the socio-economic, ethical, political, and legal aspects for such implementations.

The characteristics and extent of forest damage and subsequent patterns of recovery reflect the intensity of an explosive volcanic eruption and have the potential to be a novel proxy for eruption magnitude and impact. Using vegetation damage and recovery patterns following the 2015 explosive eruption of Calbuco, Chile, we develop a satellite-based approach to assess impact on surrounding temperate forests. The Calbuco eruption resulted in tephra deposition over 100skm2, pyroclastic flows extending 6km and lahars extending 15km. We explore NDVI derived from optical imagery (June 2013 - May 2023) as well as radar backscatter and phase coherence (October 2014 - June 2023) through time series analysis, clustering and estimation of recovery timescales to find patterns in forest disturbance and recovery. We find that forest damage and recovery correspond primarily with deposit type, thickness and dispersal directions. The thickest tephra deposits (> 40cm) correlate with the most vegetation loss, so our vegetation impact maps allow us to refine the spatial mapping of tephra fall-deposit isopachs to give a revised eruption volume of 0.28km3. Vegetation recovery rates relate to initial impact intensity, but also local topography, aspect and altitude. Our results demonstrate the potential of vegetation disturbance as a novel proxy that can be used to determine eruption extents and magnitudes, especially in remote and densely vegetated environments, and to refine field-based analyses in inaccessible or intensely damaged zones.

We describe an ocean hydrostatic dynamical core implemented in Oceananigans optimized for Graphical Processing Unit (GPU) architectures. On 64 A100 GPUs, equivalent to 16 computational nodes in current state-of-the-art supercomputers, our dynamical core can simulate a decade of global ocean dynamics per wall-clock day at an 8-kilometer horizontal resolution; a resolution adequate to resolve the ocean’s mesoscale eddy field. Such efficiency, achieved with relatively modest hardware resources, suggests that climate simulations on GPUs can incorporate fully eddy-resolving ocean models. This removes a major source of systematic bias in current IPCC coupled model projections, the parameterization of ocean eddies, and represents a major advance in climate modeling. We discuss the computational strategies, focusing on GPU-specific optimization, and numerical implementation details that enable such high performance.

Coastal fast ice provides a protective barrier to continental land ice and an important biological habitat. Antarctic fast ice extent has been well quantified using satellite remote sensing but spaceborne measurement of sea ice thickness remains challenging, especially near the coast. Here, we present the first ICESat-2 laser altimeter fast ice freeboard measurements and thickness retrievals in Antarctica. In north Terra Nova Bay, ICESat-2 measured in situ validated fast ice freeboard with high accuracy, detected higher freeboard of thicker Ice Shelf Water-influenced fast ice with a sub-ice platelet layer beneath, and distinguished smooth-thermodynamic and rough-dynamic fast ice compositions. Estimates of sub-ice platelet layer thickness obtained by comparing ICESat-2 ice thicknesses with projected fast ice thicknesses and snow depths agreed with prior electromagnetic induction surveys. ICESat-2 has significant potential for large-scale assessment of fast ice thickness and for identifying regions of glacial meltwater outflow, provided snow depth is constrained.

A document by Timothy Scheibe. Click on the document to view its contents.

A document by Michael A. Chavez. Click on the document to view its contents.

This report highlights the complex interplay between cardiac func8on, metabolic disorders, and the impact of Oshtoran Syndrome on overall health. Par8cular emphasis is placed on the implica8ons of a le? ventricular ejec8on frac8on (LV-EF) decline, PDH cytopathy, and the resultant metabolic dysregula8on. Addi8onally, the cri8cal role of catecholamine suppression in preven8ng poten8ally life-threatening episodes is discussed. Introduc/on: Oshtoran Syndrome (H63D Syndrome Type-3) presents a mul8faceted clinical challenge, characterized by both cardiac and metabolic complica8ons. This report examines the deteriora8on in cardiac func8on, the metabolic impact of PDH cytopathy, and the systemic effects observed in a pa8ent with this syndrome, along with the essen8al role of catecholamine suppression. 1,4,5 Cardiac issues: Le? Ventricular Ejec8on Frac8on (LV-EF): The pa8ent's LV-EF has decreased from 63% to 49%, indica8ng a significant impairment in the heart's ability to pump blood and oxygen efficiently. This decline reflects a compromised cardiac func8on, likely contribu8ng to systemic hypoperfusion and metabolic stress. 9,10,11,12

Oceanic stratification plays a key role in climate by modulating the ocean’s resistance to be mixed vertically. It has been traditionally assumed that, beyond the deepest reach of winter mixing, stratification is relatively invariant on the time scales of contemporary climate evolution. Here, we test this view by performing a global investigation of the spatio-temporal variability of stratification from the surface to the main pycnocline, using 20 years (2003-2022) of data from the Argo float array. Both near-surface and main pycnocline stratifications are found to exhibit spatially-structured, vertically-coherent, global-scale variations on seasonal-to-decadal time scales, suggesting that both stratifications are a rapidly-evolving, readily-interactive component of the climate system. Variability in stratification is organized into well-defined patterns that replicate the spatial footprints and time scales of major climate modes, pointing to these modes of internal variability as important drivers of stratification changes. Our diagnosed patterns and forcings of stratification variability provide an important benchmark for advancing the climate models used to understand and predict the ongoing climate change

In this paper, based on the electric field data (EFD) of the China Seismo-Electromagnetic Satellite (CSES), which is divided into 4 frequency bands (ULF, ELF, VLF, HF) from DC to 3.5 MHz, we study the impact characteristics of the 4 November 2021 magnetic storm activity in different frequency bands. It was found that the electric field anomalies caused by magnetic storms were mainly concentrated below 18 kHz, and above 18 kHz the effects were weak and gradually diminished to negligible. In the ULF band, excluding the influence of Schumann waves, the electric field anomalies caused by magnetic storms are mainly below 5 Hz, with the most influential frequency point being 3.4 Hz. In the ELF band, the more obvious anomalies appear at 300 Hz – 900 Hz and above 1.8 kHz, with the most significant anomalies in the 300 Hz – 900 Hz band around 780 Hz. In the VLF band, electric field anomalies are concentrated in 2.5 - 10 kHz. Magnetic storms had essentially no effect on the HF band. Magnetic storms at low and middle latitudes have a weak effect on the ELF and VLF bands and are more pronounced in the ULF band. During the main phase of the magnetic storm, the absolute magnitude of variance change in the ELF and VLF bands is greater than that in the ULF band as a whole, but the relative magnitude of variance change in the ULF band is 10% greater than that in the ELF and VLF bands.

This study proposes a new global-scale lightning model, predicting lightning rates from large-scale climatic variables. Using satellite lightning records spanning a period of 29 years, we apply machine learning methods to derive a functional relationship between lightning and climate reanalysis data. In particular, we design a model tree, representing different lightning regimes with separate single hidden layer neural networks of low dimensionality. We apply multiple complexity constraints in the model development stages, which makes the lightning model straightforward to implement as a lightning scheme for global climate models (GCMs). We demonstrate that, for years not used for model training, our lightning model captures 70.6% of the daily global spatio-temporal lightning variability, which corresponds to a >42% relative improvement compared to well-established lightning schemes. Similarly, the model correlates well with lightning observations for the monthly climatology (r>0.92), inter-annual variability (r>0.90), and latitudinal and longitudinal distributions (r>0.86). Most notably, the model brings a critical improvement in representing lightning magnitude and variability in the three tropical lightning chimney regions: central Africa, the Amazon, and the Maritime Continent. We implement the lightning model in the Community Earth System Model to verify its stability and performance as a GCM component, and we provide detailed implementation guidelines. As an intermediate approach between high-dimensional machine learning models and first-order lightning parameterizations, our model offers GCMs a straightforward and efficient tool to improve lightning simulation, which is critical for representing atmospheric chemistry and naturally-ignited wildfires.