Oceanic transient tracers, such as chlorofluorocarbons (CFCs) and sulfur-hexafluoride (SF6), trace the propagation of intermediate-to-abyssal water masses in the ocean interior. Their temporal and spatial sparsity, however, has limited their utility in quantifying the global ocean circulation and its decadal variability. The Time-Correction Method presented here is a new approach to leverage the available CFCs and SF6 observations to solve for the Green’s functions describing the steady-state transport from the surface to the ocean interior. From the Green’s functions, we reconstruct global tracer concentrations (and associated uncertainties) in the ocean interior at annual resolution (1940 to 2021). The spatial resolution includes 50 neutral density levels that span the water column along WOCE/GO-SHIP lines. The reconstructed tracer concentrations return a global view of CFCs and SF6 spreading into new regions of the interior ocean, such as the deep north-western Pacific. For example, they capture the southward spreading and equatorial recirculation of distinct NADW components, and the spreading of CFC-rich AABW out of the Southern Ocean and into the North Pacific, East Indian, and West Atlantic. The reconstructed tracer concentrations fit the data in most locations (~75%), indicating that a steady-state circulation holds for the most part. Discrepancies between the reconstructed and observed concentrations offer insight into ventilation rate changes on decadal timescales. As an example, we infer decadal changes in Subantartic Mode Water (SAMW) and find an increase in SAMW ventilation from 1992 to 2014, highlighting the skill of the time-correction method in leveraging the sparse tracer observations.

A document by Zhenguang Huang. Click on the document to view its contents.

1. Noble gas tracers can infer the rate of sea ice production in polynyas. 2. Frazil ice in polynyas appears to block air-sea gas exchange mechanisms. 3. The solubility pump is influenced by glacial ice melt and sea ice formation.

A document by Cheryl L.B. Manning. Click on the document to view its contents.

The most recent generation of climate models exhibits an alarming increase in high climate sensitivity models compared to previous generations. Because the calculation of equilibrium climate sensitivity (ECS) requires simulations of a thousand years or more, most studies estimate ECS using shorter model integrations. However, the most widely used method for estimating ECS from shorter simulations underestimates ECS. Previous studies attributed this underestimate to the time-dependence of climate feedbacks. Here we demonstrate that it actually arises from an underestimate of the radiative forcing. We present a modified method that corrects for this underestimate and is shown to better agree with ECS calculated from “long run”, millennium-scale simulations. This method reveals that the actual number of “too hot” models is roughly double that previously diagnosed, with one out of every three CMIP6 climate models having an ECS greater than 5K - the “very likely” upper bound on ECS.

We explore how neural differential equations (NDEs) may be trained on highly resolved fluid-dynamical models of unresolved scales providing an ideal framework for data-driven parameterizations in climate models. NDEs overcome some of the limitations of traditional neural networks (NNs) in fluid dynamical applications in that they can readily incorporate conservation laws and boundary conditions and are stable when integrated over time. We advocate a method that employs a ‘residual’ approach, in which the NN is used to improve upon an existing parameterization through the representation of residual fluxes which are not captured by the base parameterization. This reduces the amount of training required and providing a method for capturing up-gradient and nonlocal fluxes. As an illustrative example, we consider the parameterization of free convection of the oceanic boundary layer triggered by buoyancy loss at the surface. We demonstrate that a simple parameterization of the process — convective adjustment — can be improved upon by training a NDE against highly resolved explicit models, to capture entrainment fluxes at the base of the well-mixed layer, fluxes that convective adjustment itself cannot represent. The augmented parameterization outperforms existing commonly used parameterizations such as the K-Profile Parameterization (KPP). We showcase that the NDE performs well independent of the time-stepper and that an online training approach using differentiable simulation via the Julia scientific machine learning software stack improves accuracy by an order-of-magnitude. We conclude that NDEs provide an exciting route forward to the development of representations of sub-grid-scale processes for climate science, opening up myriad new opportunities.

Two-phase flow of CO2/brine in porous media is critical to the capacity and safety of carbon sequestration into the brine aquifer. In order to provide valuable information and important theoretical basis for site selection and CO2 injection, the microscopic visualization technology was employed in this study to conduct displacement experiments of CO2/brine at the pore scale. Four micromodels with different sizes and structures, five injection rates of CO2 and six salinities of brine were used to study the effects of micromodel’s structure and displacement pattern on two-phase flow. Several parameters including the differential pressure, contact angle, permeability, velocity field and force field were obtained by experimental measurement, image post-processing and theoretical analysis, and then these parameters’ variation was investigated. Phenomena such as thin film, corner flow and Haines jump were also found during the displacement. Although brine could be completely displaced by CO2 in the capillary duct, the backflow of wetting phase would occur at low injection rate. Phenomena different from the theoretical analysis also occurred in pore doublet models: some brine was residual in the homogeneous pore doublet model at low injection rate, while the heterogeneous pore doublet model was fully occupied by CO2 at high injection rate. These phenomena are very useful for two-phase flow, and multiple factors need to be comprehensively considered to determine the operating conditions of CO2 storage into the brine aquifer.

A document by Xianwei Wang. Click on the document to view its contents.

Tropical cyclones (TCs) simulated at gray-zone resolution (9km) in the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) are performed and evaluated over the Western North Pacific (WNP). Two sets of experiments are carried out to test the effect the cumulus parameterization (CPS), driven by the European Center for Medium-Range Weather Forecasts (ECMWF) fifth-generation global atmospheric reanalysis (ERA5), over 11-year (2008-2018) TC seasons (June–November). Through comparisons with the observation and reanalysis, it is shown that the ICPS (involving CPS) experiment yields good skills in simulating TC frequency due to more realistic large-scale mean states, including stronger low-tropospheric circulation, wetter mid-tropospheric environment and more active ascending motion over TC main development region (MDR). On the other hand, the NICPS (not involving CPS) experiment is found to better reproduce intense TCs (Saffir-Simpson hurricane categories 3, 4 and 5) in terms of intensity, accumulated cyclone energy (ACE) and inner-core size. Although NICPS can capture TC inner-core structure well with stronger radial inflow in the boundary layer, rising motion around the eyewall and outflow in the mid to upper troposphere, NICPS significantly underestimates TC outer size due to rapidly decay of TC outer wind field, suggesting limited severe convective activities in the outer region.

Quantifying and characterizing suspended sediment is essential to successful monitoring and management of estuaries and coastal environments. To quantify suspended sediment, optical and acoustic backscatter instruments are often used. Optical backscatter systems are more sensitive to fine particles ($<63 \mu m$) and flocs, whereas acoustic backscatter systems are more responsive to larger sand grains ($>63 \mu m$). It is thus challenging to estimate the relative proportion of sand or mud in environments where both types of sediment are present. The suspended sediment concentration measured by these devices depends on the composition of that sediment, so it is also difficult to measure concentration with a single instrument when the composition varies. The objective of this paper is to develop a methodology for characterizing the relative proportions of sand and mud in mixed sediment suspensions by comparing the response of simultaneous optical and acoustic measurements. We derive a sediment composition index (SCI) that can be used to directly predict the relative fraction of sand in suspension. Here we verify the theoretical response of these optical and acoustic instruments in laboratory experiments, and successfully apply this approach to field measurements on the ebb-tidal delta of Ameland Inlet in the Netherlands. Increasing sand content decreases SCI, which was verified in laboratory experiments. A reduction in SCI is seen under more energetic conditions when sand resuspension is expected. Conversely, the SCI increases in calmer conditions when sand settles out, leaving behind finer sediment. This approach provides crucial knowledge of suspended sediment composition in mixed sediment environments.

Integration of multiple geophysical methods in combined data analysis is a key practice to reduce model uncertainties and enhance geological interpretations. Electrical resistivity models resulting from inversion of marine magnetotelluric (MT) data, often lack depth resolution of lithological boundaries, and distinct information for shallow model parts. This is due to the nature of the physics i.e. diffusive method, model regularization during inversion, and survey setup i.e. large station spacing and missing high frequency data. Thus, integrating data or models to constrain layer thicknesses or structural boundaries is an effective approach to derive better constrained, more detailed resistivity models. We investigate the different impacts of three cross-gradient coupled constraints on 3D MT inversion of data from the Namibian passive continental margin. The three constraints are a) coupling with a fixed structural density model; b) coupling with satellite gravity data; c) coupling with a fixed gradient velocity model. Here we show that coupling with a fixed model (a and c) improves the resistivity model most. Shallow conductors imaging sediment cover are confined to a thinner layer in the resulting resistivity models compared to the MT-only model. Additionally these constraints help to suppress vertical smearing of a conductive anomaly attributed to a fracture zone, and clearly show that the seismically imaged Moho is not accompanied by a change in electrical resistivity. All of these observations aid interpretation of an Earth model indicating involvement of a plume impact in continental break-up during the early Cretaceous.

The time scale of usual hydrological applications can vary from a few minutes to a few days. An accurate description of the precipitation probability distributions at the appropriate time scale is needed to compute meaningful summaries like return levels and periods. However few statistical parametric models can handle the full rainfall distribution at these different temporal scales. In this context, we propose and study a new meta-Gaussian model which leads to a parametric model with four parameters for the full distribution of precipitation. The main advantage of our model is that it can be applied to a wide range of accumulation periods. In particular, it still performs well below the hourly scale. In addition, each parameter is linked to a different part of the distribution: one of them describes the probability of rainfall occurrence, two parameters are related respectively to the shape of lower and upper tails of the distribution, and the last one is a multiplicative scale parameter. The building block of our model is the use of a latent Gaussian process that offers flexibility and simple inference algorithms. The model is fitted to rain gauge data recorded in Guipavas (France). It is shown that the proposed distribution handles accumulation periods from 6 minutes to several days. The model outperforms other meta-Gaussian models which have been proposed in the literature.

The Chenghai fault zone, an important part of the Dali fault system, is instrumental in comprehending the crustal deformation of the southeastern margin of the Tibetan Plateau. Detailed remote sensing interpretation and field mapping are used to study the geometry and kinematic characteristics of this fault. The results show that the Chenghai fault zone extends up to 200 km from Jinguan to the south end of the Midu basin, and it truncated and inherited the trace of the Red River fault on the east side of the Midu basin. Furthermore, it is an oblique-slip fault with both normal and sinistral strike-slip component, and the normal component is more significant. The transtensional activity of this fault may have started in the Early Pliocene (5–6 Ma). The average maximum dip-slip rate can be 0.37–0.57 mm/yr, and the maximum left-slip rate is 0.83–1.20 mm/yr. The clockwise rotation of the Dali block resulted in the Z-shaped Dali fault system and the Chenghai fault zone. Moreover, the difference of angular velocity between the inner and the outer arcuate belts divided by the Litang–Dali–Ruili fault system leads to the clockwise rotation of the Dali block.

Existing real-time coastal flooding guidance systems in the US tend to underestimate total water level (TWL) predictions in upstream tidal areas of the Chesapeake Bay rivers, impacting flood forecasts for highly vulnerable areas, such as the National Capital Region. These under-predictions are mostly due to missing physical processes, lack of integration between hydrological and hydrodynamic models, and an oversimplification of the model setups used to predict TWL. In this study, an integrated TWL forecast system was introduced, where a high-resolution two-dimensional coastal storm surge model (ADCIRC) was implemented to simulate the combined influence of various flood drivers (storm tide, river flows, urban runoff, and local wind forcing) in the Potomac River. In this framework, the downstream boundaries of storm tide predictions are provided by existing coastal guidance systems, whereas, streamflow forecasts at upstream rivers and local urban runoff are provided by the National Weather Service and the National Water Model. Additionally, high-resolution wind fields from the North American Mesoscale and the National Blend of Models are added to account for local wind effects on TWL. This model setup was successfully validated with a range of historical events and it also demonstrated improved forecast performance against the existing large-scale coastal guidance systems in a reforecast evaluation during 2020. Unlike other studies, we provided a comprehensive evaluation on the influence of individual flood drivers on TWL modeling and clearly demonstrated that the absence of one or more flood drivers in the model framework can underestimate simulated TWL in the National Capital Region.

Gulf Stream frontal eddies, cyclonic features that form in the trough of meanders in the South Atlantic Bight, provide a natural mesocosm for investigating questions around submesoscale spatial and temporal variability. These frontal eddies drive important nutrient fluxes into the euphotic zone which generate considerable productivity on the outer shelf of the Southeast US coastline. Modeling suggests these nutrient fluxes are partly due to submesoscale physics, but current observational approaches miss much of these dynamics and the biological response to them. As a result, current global biogeochemical models are not parameterized to account for potential submesoscale contributions to productivity and carbon export. We present work observing these frontal eddies from new satellite platforms such as ECOSTRESS which provides an order of magnitude higher resolution SST compared to MODIS, GOES-16 which provides an order of magnitude higher temporal cadence SST compared to MODIS, and VIIRS for ocean color. Characterizing spatial patterns and temporal variation of SST and chlorophyll-a within frontal eddies at finer spatiotemporal resolution provides a better understanding of whether their submesoscale variability is captured by current remote sensing products, and insight into their overall role in ocean productivity and biodiversity.

There are different numerical models, such as the transmission-line matrix model or partially uniform knee model used to predict Schumann radiation. This report introduces a new idea, and reasoning to the previously stated idea of locating Schumann resonances on a single particle’s radiation pattern using a Golden ratio and their Octave, triad relationship. In addition, this different prediction method for Schumann resonances derived from the first principle fundamental physics combining both particle radiation patterns and the mathematical concept of the golden ratio spiral that expands at the rate of the golden ratio. The idea of golden ratio spiral allows locating Schumann resonant frequencies on particle’s radiation patterns. The Octaves allows us to predict the magnitude of other Schumann resonances on the radiation pattern of a single accelerated charged particle conveniently by knowing the value of the initial Schumann resonant frequency. In addition, it also allows us to find and match Schumann resonances that are on the same radiation lobe. Furthermore, it is important to find Schumann octaves as they propagate in the same direction and have a higher likelihood of wave interference. Method of Triads together with Octaves helps to predict magnitude and direction of Schumann resonant points without needing to refer to a radiation pattern plot. As the golden ratio seems to be part of the Schumann resonances, it is helpful in understanding to know why this is the case. The main method used in the reasoning of the existence of golden ratio in Schumann resonances is the eigenfrequency modes, sqrt{n(n+1)} in the spherical harmonic model. It has been found that eigenfrequency modes have two a start off points, n_0 = 0 or n_0 = frac{\sqrt{5}-1}{2} where the non-zero one is exactly the golden ratio. This allows to extend the existing eigenfrequency modes to \sqrt{(n_0+n)^2+(n_o+n)} in order to explain why golden ratio exist within Schumann resonances. As Fibonacci numbers increase, ratio of two consecutive Fibonacci number approaches to the value of Golden ratio. New equation describing the value of Fibonacci number can be used to re-write eigen-frequency orders of Schumann resonances.

The monthly depositional fluxes of three natural radionuclides ($^{7}$Be, $^{210}$Pb and $^{40}$K) were measured at a Mediterranean coastal station (Malaga, Southern Spain) over a 14-year period from 2005 to 2018, corresponding to 168 monthly samples. The study of these radionuclides provides valuable information on the atmospheric air circulation, transportation and erosion processes as well as a control of the environmental radioactivity. In this work, the depositional fluxes of these radionuclides are investigated and their relations with several atmospheric variables, such as air temperature, pressure or precipitations, have been studied by applying two popular machine learning methods: Random Forest and Neural Network algorithms. We extensively test different configurations of these algorithms and demonstrate their predictive ability for reproducing depositional fluxes of $^{7}$Be, $^{210}$Pb and $^{40}$K. We use the Pearson-R correlation coefficient and the mean average error to evaluate the predictions of the developed models, revealing that the models derived with Neural Networks achieve slightly better results, in average, although similar, having into account the uncertainties. The mean Pearson-R coefficients, evaluated with a k-fold cross-validation method, are around 0.85 for the three radionuclides using Neural Network models, while they go down to 0.83, 0.79 and 0.8 for $^{7}$Be, $^{210}$Pb and $^{40}$K, respectively, for the Random Forest models. Additionally, applying the Recursive Feature Elimination technique we determine the variables more correlated with the depositional fluxes of these radionuclides, which elucidates the main dependencies of their temporal variability.

Stories are an essential part of our everyday life, vehicles to understand how the world around us works, both physically and emotionally. They allow us to organise otherwise random facts and events into a cohesive and logical structure, making them easier to understand and remember. Science itself is also full of facts and processes, often seemingly disconnected but that, when put in context, pave the road for scientific discoveries. We propose that classical story-telling strategies can also be used to communicate science to a variety of audiences, specialist and non-specialist alike, and present a few practical examples of how this can be achieved. We focus on what we call the “story circle” narrative structure (see Fig. 1), a distillation of the “Hero’s Journey”(Campbell, 1949; Harmon, 2009). In this storytelling framework, the circle begins with a hero who, posed with a question, chooses to venture beyond their familiar space in a quest for answers. When the hero returns to familiar territory, they have been forever changed by their journey. Firstly, we discuss how this story circle can be directly mapped onto the structure of a research paper, enabling researchers to write up their work in a way that makes it easier to follow for the reader. Then we apply the story circle strategy to a real research example aimed at explaining large-scale mantle convection, in a story where silica is the “hero” who descends into the deep in a subducting slab to then rise back up to the surface in an upwelling plume. This approach to communicating science by exploiting its story-like qualities is key when explaining complex deep Earth processes to the non-academic public who, understandably, can struggle to grasp these concepts due to their abstract nature and detachment from everyday life. Ultimately, the scientific process is an expression of the most fundamental story of humanity – researchers look at the world as it is, see questions that need answering and go on a voyage of discovery to find the answers. When they return, the world has changed because of what was found on that journey. And so the cycle repeats, the circle keeps turning, and the ideas keep changing after every iteration. In science, however, we will never truly write “The End”.