Constraining the volatile budget of the lunar interior has important ramifications for models of Moon formation. While many early and previous measurements of samples acquired from the Luna and Apollo missions suggested the lunar interior is depleted in highly volatile elements like H, a number of high-precision analytical studies over the past decade have argued that it may be more enriched in water than previously thought. Here, we integrate recent remotely sensed near-infrared reflectance measurements of several Dark-Mantle-Deposits (DMDs), interpreted to represent pyroclastic deposits, and physics-based eruption models to better constrain the pre-eruptive water content of lunar volcanic glasses. We model the trajectory and water loss of pyroclasts from eruption to deposition, coupling eruption dynamics with a volatile diffusion model for each pyroclast. Modeled pyroclast sizes and final water contents are then used to predict spectral reflectance properties for comparison with the observed orbital near-infrared data. We develop an inversion scheme based on the Markov-Chain Monte-Carlo (MCMC) method to retrieve constraints between governing parameters such as the initial volatile content of the melt and the pyroclast size distribution (which influences the remotely measured water absorption strengths). The MCMC inversion allows us to estimate the primordial (pre-eruption) water content for different DMDs and test whether their source is volatile-rich. Our results suggest that the parts of the lunar interior sampled by the source material of the DMDs investigated in this study range in water content from 400 to 800 ppm.

Sea level and its horizontal gradient are an expression of oceanic volume, heat content, and currents. Large-scale currents have historically been viewed as mostly “baroclinic’, and tides as “barotropic’, respectively, in the sense of being strongly related to the oceanic density distribution or not. The purpose of this note is to give dynamical precision to this distinction and, in the particular case of the tides, demonstrate the breadth of their combined barotropic-baroclinic interactions with a realistically forced, high-resolution simulation of the Pacific Ocean circulation. While the different tidal sea-level contributions manifest a horizontal scale separation (\eg more barotropic at larger scales; more baroclinic surface pressure-gradient force at smaller scales), there are cross-mode corrections in both at the level of tens of percent.

Turbidity currents transport vast quantities of sediment across the seafloor and form the largest sediment accumulations on Earth. These flows pose a hazard to strategically important seafloor infrastructure and are important agents for the transport of organic carbon and nutrients that support deep-sea ecosystems. Therefore, it is important to quantify the scale of these flows, the amount of sediment they transport, and the evolution of their discharge over time and space along their flow path. Two modes of flow evolution have been proposed based on experimental and numerical models. The first is termed ignition, where flows entrain seafloor sediment, becoming more voluminous and powerful and increasing their discharge. In the second mode of evolution, called dissipation, sediment falls out of suspension, so flows decelerate and lose discharge. Thus far, field-scale turbidity currents have only been measured at a handful of sites worldwide, and never in detail at multiple locations along their full course. Therefore, it has not yet been possible to determine when, where, and why flows diverge into these two modes in the deep sea, or how flow discharge varies. The ambitious multi-institution Coordinated Canyon Experiment measured turbidity currents at seven instrumented moorings along the Monterey Canyon, offshore California. Fifteen flows were recorded, including the fastest events yet measured at high resolution (>8 m/s). This remarkable dataset provides the first opportunity to quantify down-channel sediment and flow discharge evolution of turbidity currents in the deep sea. To understand whether flows ignite or dissipate, we derive total and sediment discharges for each of the flows at all seven mooring locations down the canyon. Discharges are calculated from measured velocities, and sediment concentrations are derived using a novel inversion method. We observe two distinct flow modes, as most flows rapidly dissipated in the upper reaches of the canyon, while three ran out for the full 50 km array length. We then explore why only these three flows ignited and discuss the implications for canyon and channel capacity and evolution.

Pyrrhotites are a class of geologically important nonstoichiometric iron sulfides with the general composition of Fe1-xS (0 ≤ x ≤ 0.125) and are found in a variety of intergrown polytypes, conventionally separated into the antiferromagnetic hexagonal and the ferrimagnetic monoclinic (4C) varieties. Both structures undergo magnetic phase transitions, where antiferromagnetic polytypes display the λ-transition at ~490K and the 4C polytype shows the Besnus transition at ~30K. However, recent studies have shown the relationship between pyrrhotite polytypes and their magnetic behavior to be more complex and new non-monoclinic polytypes (e.g., 3C) have been described that are also capable of retaining a spontaneous magnetization at room temperature. These advances raise the level of detail needed for the characterization of pyrrhotite in rock magnetic and paleomagnetic studies. This study demonstrates the utility of combining X-ray diffraction data collected as a function of temperature with low- and high-temperature magnetic measurements to characterize natural samples. We analyze two natural samples that contain mixtures of 4C, 5C, and 6C polytypes and describe how their polytypes and magnetic properties vary as a function of temperature across the λ-transition and how and when pyrite and greigite form. We also report the effect on natural samples of an annealing protocol commonly used to elevate the concentration of the 4C polytype in synthetic samples and found that annealing instead transformed some antiferromagnetic pyrrhotite into a form whose diffraction pattern most closely resembles the 4C polytype and displays room temperature spontaneous magnetization, but lacks the characteristic Besnus transition.

The Mars2020 Perseverance Rover landed successfully on the Martian surface on the Jezero Crater floor (18.44°N, 77.45°E) at Martian solar longitude, $L_s$, $\sim$5 in February 2021. Since then it has produced highly valuable environmental measurements with a versatile scientific payload including the MEDA (Mars Environmental Dynamics Analyzer) suite of environmental sensors. One of the MEDA systems is the PS pressure sensor system which weighs 40 grams and has an estimated absolute accuracy of better than 3.5 Pa and a resolution of 0.13 Pa. We present initial results from the first 414 sols of Martian atmospheric surface pressure observations by the PS whose performance was found to meet its specifications. Observed sol-averaged atmospheric pressures follow an anticipated pattern of pressure variation in the course of the advancing season and are consistent with data from other landing missions. The observed diurnal pressure amplitude varies by $\sim$2-5 \% of the sol-averaged pressure, with absolute amplitude 10-35 Pa in an approximately direct relationship with airborne dust. During a regional dust storm, which began at $L_s~135^\circ$ the diurnal pressure amplitude roughly doubles. The diurnal pressure variations were found to be remarkably sensitive to the seasonal evolution of the atmosphere. In particular analysis of the diurnal pressure signature revealed diagnostic information likely related to the regional scale structure of the atmosphere. Comparison of Perseverance pressure observations to data from other landers reveals the global scale seasonal behaviour of Mars’ atmosphere.

Deep learning techniques are being increasingly used in earth science applications - from climate change modelling to feature extraction from remote sensing imagery, given their advantage of increased contextual and hierarchical feature representation. However, deep learning comes at an expense of extensive computational resources and long training time to achieve benchmark results. This study suggests time-optimized deep learning techniques for training deep convolutional networks for one of the most sought after feature extraction subsets – building extraction from satellite/aerial imagery. Building extraction is one of the most important tasks in the dynamic pipeline of urban applications such as urban planning and management, disaster management, urban mapping etc. among other geospatial applications. Automatically extracting buildings from remotely sensed imagery has always been a challenging task, given the spectral homogeneity of buildings with the non-building features as well as the complex structural diversity within the image. With the availability of high resolution open-source satellite and UAV data, deep learning techniques have greatly improved building extraction. However, training on such high resolution data requires the networks to be significantly deeper, resulting in long model training and inference times. This study proposes a combination of two time efficient methods to train a Dynamic Res-U-Net for building extraction in less time without decreasing the training parameters: 1) Using Cyclical Learning and SuperConvergence concepts by dynamically changing the learning rate while training the network to achieve very high accuracy in very less time and 2) Using a specific order to train the layers of the network(s) to specially have the last layers of the networks perform better, leading to an overall improved network performance in lesser time. Building extraction results are gauged using the metrics of Accuracy, Dice Score and Intersection over Union (IoU) and F1-Score. The metrics comparison of training the Res-U-Net in the conventional way vs the proposed techniques shows an evident optimisation in terms of time. Better results are achieved in lesser training epochs using the proposed time-optimised training techniques.

Warm, subsurface ocean waters that access ice shelves in the Amundsen Sea are likely to be a key driver of high meltrates and ice shelf thinning. Numerical models of the ocean circulation have been essential for gaining understanding of the mechanisms responsible for heat delivery and meltrate response, but a number of challenges remain for simulations that incorporate this region. Here, we develop a suite of numerical experiments to explore how sub ice shelf cavity circulation and meltrate patterns are impacted by parameterization schemes for (1) subgrid-scale ocean turbulence, and (2) ice-ocean interactions. To provide a realistic context, our experiments are developed to simulate the ocean circulation underneath the Pine Island ice shelf, and validated against mooring observations and satellite derived meltrate estimates. Each experiment is forced with data-informed open boundary conditions that bear the imprint of the gyre in Pine Island Bay. We find that even at a ~600 m grid resolution, flow aware ocean parameterizations for subgrid-scale momentum and tracer transfer are crucial for representing the circulation and meltrate pattern accurately. Our simulations show that enhanced meltwater diffusion near the ice-ocean interface intensifies near wall velocities via thermal wind, which subsequently increases meltrates near the grounding line. Incorporating a velocity dependent ice-ocean transfer coefficient together with a flow aware ocean turbulence parameterization therefore seems to be necessary for modelling the ocean circulation underneath ice shelves in the Amundsen Sea at this resolution.

The precipitation of magnesium oxide (MgO) from the Earth’s core has been proposed as a potential energy source to power the geodynamo prior to the inner core solidification. Yet, the stable phase and exact amount of MgO exsolution remain elusive. Here we utilize an iterative learning scheme to develop a unified deep learning interatomic potential for the Mg-Fe-O system valid over a wide pressure-temperature range. This potential enables direct, large-scale simulations of MgO exsolution processes at the Earth’s core-mantle boundary. Our results suggest that Mg exsolves in the form of crystalline Fe-poor ferropericlase as opposed to a liquid MgO component presumed previously. The solubility of Mg in the core is limited, and the present-day core is nearly Mg-free. The resulting exsolution rate is small yet nonnegligible, suggesting that MgO exsolution can provide a potentially important energy source, although it alone may be difficult to drive an early geodynamo.

Plant phenology regulates the carbon cycle and land-atmosphere coupling. Currently, climate models often disagree with observations on the seasonal cycle of vegetation growth, partially due to how spring onset is measured and simulated. Here we use both thermal and leaf area index (LAI) based indicators to characterize spring onset in CMIP6 models. Although the historical timing varies considerably across models, most agree that spring has advanced in recent decades and will continue to arrive earlier with future warming. Across the Northern Hemisphere for the periods 1950-2014, 1981-2014, and 2015-2099, thermal-based indicators estimate spring advances of -0.7±0.2, -1.4±0.4, and -2.4±0.7 days/decade, while LAI-based indicators estimate -0.4±0.3, -0.1±0.3, and -1±1.1 days/decade. Thereby, LAI-based indicators exhibit later spring and weaker trends toward earlier onset, leading to index-related uncertainty being as large or larger than model uncertainty for a given index. Reconciling index-related discrepancies is therefore critical for understanding future changes in spring onset.

Compound event research has gained significant momentum over the past few years. Traditionally risk assessment studies considered either one climatic driver or process at a time. However, it is now being recognised that it is the combination of multiple drivers and their statistical dependencies that lead to aggravated, non-linear impacts. We aim to identify hotspots for SM-P coupling over India from 2004 to 2020 using Event Coincidence Analysis (ECA) and an extremal tail dependence measure. We further characterise how these complex interconnected interactions can lead to more significant flash floods and landslide risk. The analysis is done at different temporal scales to pinpoint a location prone to floods during the year. High precursor coincidence rates (>60%) were obtained for traditional flash flood-prone areas over India, indicating the robustness of the approach. ECA results were compared with the probabilistic extreme value approach, and a similar pattern was observed in both. The increase in hotspots from 2004 to 2020 matches the observed increase in flood-prone districts reported by earlier studies. We also used the trigger coincidence rate to identify areas where soil moisture anomalies can trigger extreme precipitation. The seasonal variations in precursor coincidence rates are observed to be the same as those usually expected due to changing atmospheric circulation patterns. Our results will complement the traditional flood risk assessment studies and have implications for better understanding the dynamic, ever-evolving nature of compound preconditioned flooding events worldwide.

We compute stress drops from P and S phase spectra for 534 earthquakes in the source region of the 2014 MW 8.1 Iquique megathrust earthquake in the northern Chilean subduction zone. An empirical Green’s function based method is applied to suitable event pairs selected by template matching of eight years of continuous waveform data. We evaluate the parameters involved in the stress drop estimation, consider the effect of the local velocity structure and apply an empirical linear relation between P and S phase related geometry factors (k values). Data redundancy produced by multiple EGFs and the combination of P and S phase spectra leads to a substantial reduction of uncertainty and robust stress drop estimates. The resulting stress drop values show a well-defined log-normal distribution with a median value of 4.36 MPa; most values range between 0.1-100 MPa. There is no evidence for systematic large scale lateral variations of stress drop. A detailed analysis reveals several regions of increased median stress drop, an increase with distance to the interface, but no consistent increase with depth. This suggests that fault regime and fault strength have a stronger impact on the stress drop behavior than absolute stresses. Interestingly, we find a weak time-dependence of the median stress drop, with an increase immediately before the April 1, 2014 MW 8.1 Iquique mainshock, a continuous reduction thereafter and a subsequent recovery to average values. Additionally, the data set indicates a relatively strong dependence of stress drop on magnitude which extends over the entire analyzed magnitude range.

In West Africa, lakes and reservoirs play a vital role as they are critical resources for drinking water, livestock, irrigation and fisheries. Given the scarcity of in situ data, satellite remote sensing is an important tool for monitoring lake volume changes in this region. Several methods have been developed to do this using water height and area relationships, but few publications have compared their performance over small and medium-sized lakes. In this work we compare four methods based on recent data from the Pleiades, Sentinel-2 and -3, ICESat-2 and GEDI missions over 16 lakes in the Central Sahel, ranging in area from 0.22 km² to 21 km². All methods show consistent results and are generally in good agreement with in situ data (height RMSE and volume NRMSE mostly below 0.30m and 11% respectively). The obtained height-area relationships show very little noise (fit RMSD mostly below 0.10m), except for the Sentinel-3-based method which tends to produce higher dispersion. The precision of the estimated water height is about 0.20m for Pleiades Digital Surface Models (DSMs) and less than 0.13m for the other methods. In addition, fine shape patterns are consistently observed over small height amplitudes, highlighting the ability to monitor shallow lakes with non-linear bathymetric behavior. Inherent limitations such as DSM quality, temporal coverage of DSM and lidar data, and spatial coverage of radar altimetry data are identified. Finally, we show that the combination of lidar and radar altimetry-based methods has great potential for estimating water volume changes in this region.

Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.

Hubble Space Telescope Wide-Field Camera 3 (HST/WFC3) observations spanning 2015 to 2021 confirm a brightening of Uranus’ north polar hood feature with time. The vertical aerosol model of Irwin et al. (2023) (IRW23), consisting of a deep haze layer based at ~5 bar, a 1 - 2 bar haze layer, and an extended haze rising up from the 1 - 2 bar layer, was applied to retrievals on HST Space Telescope Imaging Spectrograph (HST/STIS) observations (Sromovsky et al., 2014, 2019} revealing a reduction in cloud-top CH4 by an average of 0.19 {plus minus} 0.03% between 40 - 80{degree sign}N between 2012 and 2015. A combination of latitudinal retrievals on the HST/WFC3 & HST/STIS datasets, again employing the IRW23 model, reveal a temporal thickening of the 1 - 2 bar haze layer to be the main cause of the polar hood brightening, finding an average increase of 1.09 {plus minus} 0.08 at 0.8 μm north of ~45{degree sign}N, concurrent with a decrease in the imaginary refractive index spectrum of the 1 - 2 bar haze layer north of ~40{degree sign}N and longwards of ~0.7 μm, and between 60{degree sign}N and 80{degree sign}N at ~0.5 μm. Small contributions to the brightening were found from a thickening of the deep aerosol layer, with an average increase in integrated opacity of 0.6 {plus minus} 0.1 north of 45{degree sign}N between 2012 and 2015, and from the aforementioned decrease in cloud-top CH4 abundance. Our results are consistent with the slowing of a meridional circulation, exhibiting strong subsidence at the poles.

A document by Terence John O'Kane. Click on the document to view its contents.

Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML-based rainfall maps despite the great extension of networks across the world. We present CML based rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. For homogenization we apply three increasingly effective algorithms of filtering and time series interpolation. We show that straightforward algorithms used for homogenisation strongly reduce anomalies in the results. Furthermore, we find that after the homogenization and joint processing, CML-based rainfall maps can be generated which seamlessly visualize a rainfall event traversing the German-Czech border. This displays the potential for large scale (e.g. continental) CML-based rainfall estimation.

This study presents a workflow to predict the upscaled absolute permeability of the rock core direct from CT images whose resolution are not sufficient to allow direct pore-scale permeability computation. This workflow exploits the deep learning technique with the data of raw CT images of rocks and their corresponding permeability value obtained by performing flow simulation on high resolution CT images. The permeability map of a much larger region in the rock core is predicted by the trained neural network. Finally, the upscaled permeability of the entire rock core is calculated by the Darcy flow solver, and the results showed a good agreement with the experiment data. This proposed deep-learning based upscaling method allows estimating the permeability of large-scale core samples while preserving the effects of fine-scale pore structure variations due to the local heterogeneity.

A document by Collin Bode. Click on the document to view its contents.