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Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean., 58 Pages, 5 Figures

Stratocumulus clouds cover about a fifth of Earths surface, and due to their albedo and low-latitude location, they have a strong effect on Earths radiation budget. Previous studies using Large Eddy Simulations have shown that multiple equilibria (both cloud-covered and cloud-free states) exist as a function of fixed-SST, with relevance to equatorward advected air masses. Multiple equilibria have also been found as a function of atmospheric CO2, with a subtropical SST nearly 10 K higher in the cloud-free state, and with suggested relevance to warm-climate dynamics. In this study, we use a mixed-layer model with an added surface energy balance and the ability to simulate both the cloud-covered and cloud-free states to study both types of multiple equilibria and the corresponding hysteresis. The model's simplicity and computational efficiency allow us to explore the mechanisms critical to the stratocumulus cloud instability and hysteresis as well as isolate key processes that allow for multiple equilibria via mechanism denial experiments not possible with a full-complexity model. For the hysteresis in fixed-SST, we find that decoupling can occur due to either enhanced entrainment warming or a reduction in cloud-top longwave cooling. The critical SST at which decoupling occurs is highly sensitive to precipitation and entrainment parameterizations. In the CO2 hysteresis, decoupling occurs in the simple model used even without the inclusion of an active SST and SST-cloud cover feedbacks, and the width of the hysteresis displays the same sensitivities as the fixed-SST case. Overall, the simple model analysis and results motivate further studies using higher complexity models., Submitted to Journal of Climate 06-12-2022

In this note, we apply Transition Path Theory (TPT) from Markov chains to shed light on the problem of Iceland-Scotland Overflow Water (ISOW) equatorward export. A recent analysis of observed trajectories of submerged floats demanded revision of the traditional abyssal circulation theory, which postulates that ISOW should steadily flow along a deep boundary current (DBC) around the subpolar North Atlantic prior to exiting it. The TPT analyses carried out here allow to focus the attention on the portions of flow from the origin of ISOW to the region where ISOW exits the subpolar North Atlantic and suggest that insufficient sampling may be biasing the aforementioned demand. The analyses, appropriately adapted to represent a continuous input of ISOW, are carried out on three time-homogeneous Markov chains modeling the ISOW flow. One is constructed using a high number of simulated trajectories homogeneously covering the flow domain. The other two use much fewer trajectories which heterogeneously cover the domain. The trajectories in the latter two chains are observed trajectories or simulated trajectories subsampled at the observed frequency. While the densely sampled chain supports a well-defined DBC, the more heterogeneously sampled chains do not, irrespective of whether observed or simulated trajectories are used. Studying the sampling sensitivity of the Markov chains, we can give recommendations for enlarging the existing float dataset to improve the significance of conclusions about time-asymptotic aspects of the ISOW circulation., Submitted to JPO. Comments welcomed!

The impacts of a tropical cyclone after landfall depend not only on storm intensity but also on the size and structure of the wind field. Hence, a simple predictive model for the wind field after landfall has significant potential value. This work tests existing theory for wind structure and size over the ocean against idealized axisymmetric landfall experiments in which the surface beneath a mature storm is instantaneously dried and roughened individually or simultaneously. Structure theory captures the response of the low-level wind field to different types of idealized landfalls, given the intensity and size response. Storm size, modeled to follow the ratio of simulated time-dependent storm intensity to the Coriolis parameter , can generally predict the transient response of the storm gale wind radii r34kt to inland surface forcings, particularly for at least moderate surface roughening regardless of the level of drying. Given knowledge of the intensity evolution, the above results combine to yield a theoretical model that can predict the full tangential wind field response to idealized landfalls. Significance Statement A theoretical model that can predict the time-dependent wind field structure of landfalling tropical cyclones (TCs) with a small number of physical, observable input parameters is essential for mitigating hazards and allocating public resources. This work provides a first-order prediction of storm size and structure after landfall, which can be combined with existing intensity predictions to form a simple model describing the inland wind field evolution. Results show its potential utility for modeling idealized inland TC wind fields.

International audience; We study an atmospheric column and its discretization. Because of numerical considerations, the column must be divided into two parts: (1) a surface layer, excluded from the computational domain and parameterized, and (2) the rest of the column, which reacts more slowly to variations in surface conditions. A usual practice in atmospheric models is to parameterize the surface layer without excluding it from the computational domain, leading to possible consistency issues. We propose here to unify the two representations in a Finite Volume discretization. In order to do so, the reconstruction inside the first grid cell is performed using the particular functions involved in the parameterizations and not only with polynomials. Using a consistency criterion, surface layer management strategies are compared in different physical situations.

The Australian continent contributes substantially to the year-to-year variability of the global terrestrial carbon dioxide (CO2) sink. However, the scarcity of in-situ observations in remote areas prevents deciphering the processes that force the CO2 flux variability. Here, examining atmospheric CO2 measurements from satellites in the period 2009-2018, we find recurrent end-of-dry-season CO2 pulses over the Australian continent. These pulses largely control the year-to-year variability of Australia's CO2 balance, due to 2-3 times higher seasonal variations compared to previous top-down inversions and bottom-up estimates. The CO2 pulses occur shortly after the onset of rainfall and are driven by enhanced soil respiration preceding photosynthetic uptake in Australia's semi-arid regions. The suggested continental-scale relevance of soil rewetting processes has large implications for our understanding and modelling of global climate-carbon cycle feedbacks., Comment: 28 pages (including supplementary materials), 3 main figures, 7 supplementary figures; v2 changes: Last name of first author changed

Over the past few years, quantum computers and quantum algorithms have attracted considerable interest and attention from numerous scientific disciplines. In this article, we aim to provide a nontechnical yet informative introduction to key aspects of quantum computing. We discuss whether quantum computers one day might become useful tools for numerical weather and climate prediction. Using a recently developed quantum algorithm for solving nonlinear differential equations, we integrate a simple nonlinear model. In addition to considering the advantages that quantum computers have to offer, we shall also discuss the challenges one faces when trying to use quantum computers for real-world problems involving “big data,” such as weather prediction.

Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting), and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective "average event." We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT's capacities on a wave-mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass (1976), which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also \emph{backward in time} to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the \emph{reactive current}. The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions., Comment: 18 pages, 7 figures (main text), 19 pages, 1 figure (supplement). Accepted for publication in the Journal of the Atmospheric Sciences

Many aspects of anthropogenic global change, such as land cover change, biodiversity loss and the intensification of agricultural production, threaten the natural biosphere. These aspects seem somewhat disjunct and specific so that it is hard to obtain a bigger picture of what these changes imply and to distinguish beneficial from detrimental human impacts. Here I describe a holistic approach that provides such a bigger picture and use it to understand how the terrestrial biosphere can be sustained in the presence of increased human activities. This approach focuses on the free energy generated by photosynthesis, energy needed to sustain either the dissipative metabolic activity of ecosystems or human activities, with the generation rate being set by the physical constraints of the environment. We can then distinguish two kinds of human impacts on the biosphere: detrimental effects caused by enhanced human consumption of this free energy, and empowering effects that allow for more photosynthetic activity and therefore more dissipative activity of the biosphere. I use examples from the terrestrial biosphere to illustrate this view as well as global datasets to show how this can be estimated. I then discuss how certain aspects of human-made technology can act to enhance the free energy generation of the terrestrial biosphere, which can then facilitate sustaining the biosphere in times at which human activity increasingly shapes the functioning of the Earth system., Comment: 21 pages, 7 figures

To close the overturning circulation, dense bottom water must upwell via turbulent mixing. Recent studies have identified thin bottom boundary layers (BLs) as locations of intense upwelling, yet it remains unclear how they interact with and shape the large-scale circulation of the abyssal ocean. The current understanding of this BL–interior coupling is shaped by 1D theory, suggesting that variations in locally produced BL transport generate exchange with the interior and thus a global circulation. Until now, however, this picture has been based on a 1D theory that fails to capture the local evolution in even highly idealized 2D geometries. The present work applies BL theory to revised 1D dynamics, which more naturally generalizes to two and three dimensions. The BL is assumed to be in quasi-equilibrium between the upwelling of dense water and the convergence of downward buoyancy fluxes. The BL transport, for which explicit formulas are presented, exerts an influence on the interior by modifying the bottom boundary condition. In 1D, this BL transport is independent of the interior evolution, but in 2D the BL and interior are fully coupled. Once interior variables and the bottom slope are allowed to vary in the horizontal, the resulting convergences and divergences in the BL transport exchange mass with the interior. This framework allows for the analysis of previously inaccessible problems such as the BL–interior coupling in the presence of an exponential interior stratification, laying the foundation for developing a full theory for the abyssal circulation.

One hundred and one atmospheric nuclear weapon tests were conducted between 1945 and 1962 in the United States, resulting in widespread dispersion of radioactive fallout, and leading to environmental contamination and population exposures. Accurate assessment of the extent of fallout from nuclear weapon tests has been challenging in the United States and elsewhere, due to limited monitoring and data accessibility. Here we address this deficit by combining U.S. government data, high-resolution reanalyzed historical weather fields, and atmospheric transport modeling to reconstruct radionuclide deposition across the contiguous United States, with 10-kilometer spatial and one-hour temporal resolution for five days following detonation, from all 94 atmospheric tests detonated in New Mexico and Nevada with fission yields sufficient to generate mushroom clouds. Our analysis also includes deposition estimates for 10 days following the detonation of Trinity, the first ever nuclear weapon test, on July 16, 1945. We identify locations where radionuclide deposition significantly exceeded levels in areas covered by the U.S. Radiation Exposure Compensation Act (RECA). These findings include deposition in all 48 contiguous U.S. states. They provide an opportunity for re-evaluating the public health and environmental implications from atmospheric nuclear testing. Finally, our findings also speak to debates about marking the beginning of the Anthropocene with nuclear weapons fallout. Our deposition estimates indicate that direct fallout from Trinity, a plutonium device, reached Crawford Lake in Canada, the proposed "golden spike" site marking the beginning of the Anthropocene epoch, starting on July 20, 1945., 19 pages, 4 figures, 1 supplementary table, 3 supplementary figures

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the 'quiet revolution' of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable skill for both global metrics and extreme events, when verified against both the operational analysis and synoptic observations. Increasing forecast smoothness and bias drift with forecast lead time are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

Remote sensing observations of the Earth's surface are frequently stymied by clouds, water vapour, and aerosols in our atmosphere. These degrade or preclude the measurementof quantities critical to scientific and, hence, societal applications. In this study, we train a natural language processing (NLP) algorithm with high-fidelity ocean simulations in order to accurately reconstruct masked or missing data in sea surface temperature (SST)--i.e. one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the Enki model repeatedly outperforms previously adopted inpainting techniques by up to an order-of-magnitude in reconstruction error, while displaying high performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masking fractions of at least 40% show reconstruction errors of less than the known sensor uncertainty (RMSE < ~0.1K). We attribute Enki's success to the attentive nature of NLP combined with realistic SST model outputs, an approach that may be extended to other remote sensing variables. This study demonstrates that systems built upon Enki--or other advanced systems like it--may therefore yield the optimal solution to accurate estimates of otherwise missing or masked parameters in climate-critical datasets sampling a rapidly changing Earth., 21 pages, 6 main figure, 3 in Appendix; submitted

Here we propose an index to quantify and analyse the impact of climatological variability on the energy system at different timescales. We define the Climatological Renewable Energy Deviation Index (CREDI) as the cumulative anomaly of a renewable resource with respect to its climate over a specific time period of interest. We analyse the index at decadal, annual and (sub-)seasonal timescales using the forthcoming Pan-European Climate Database and consider the starting point and window of analysis for its use at those timescales. The CREDI is meant as an analytical tool for researchers and stakeholders to help them quantify, understand, and explain, the impact of the variability of weather on the energy system across timescales. Improved understanding translates to better assessments of how renewable resources, and the associated risks for energy security, may fare in current and future climatological settings. The practical use of the index is in resource planning. For example transmission system operators may be able to adjust short-term planning to reduce adequacy issues before they occur or combine the index with storyline event selection for improved assessments of climate change related risks.

High spatial resolution wind data are essential for a wide range of applications in climate, oceanographic and meteorological studies. Large-scale spatial interpolation or downscaling of bivariate wind fields having velocity in two dimensions is a challenging task because wind data tend to be non-Gaussian with high spatial variability and heterogeneity. In spatial statistics, cokriging is commonly used for predicting bivariate spatial fields. However, the cokriging predictor is not optimal except for Gaussian processes. Additionally, cokriging is computationally prohibitive for large datasets. In this paper, we propose a method, called bivariate DeepKriging, which is a spatially dependent deep neural network (DNN) with an embedding layer constructed by spatial radial basis functions for bivariate spatial data prediction. We then develop a distribution-free uncertainty quantification method based on bootstrap and ensemble DNN. Our proposed approach outperforms the traditional cokriging predictor with commonly used covariance functions, such as the linear model of co-regionalization and flexible bivariate Mat\'ern covariance. We demonstrate the computational efficiency and scalability of the proposed DNN model, with computations that are, on average, 20 times faster than those of conventional techniques. We apply the bivariate DeepKriging method to the wind data over the Middle East region at 506,771 locations. The prediction performance of the proposed method is superior over the cokriging predictors and dramatically reduces computation time.

Arabian Sea Mid-Tropospheric Cyclones (MTCs), which are responsible for extreme rainfall events in western India, often coincide with monsoon low-pressure systems (LPS) over the Bay of Bengal. However, the role of Bay of Bengal LPSs in the formation of Arabian Sea MTCs remains unclear. This study utilizes the Weather Research and Forecasting Model (WRF) to investigate the atmospheric connection between the two basins. By introducing a balanced bogus vortex over the Bay of Bengal, cyclonic systems are induced over the Arabian Sea in the majority of ensemble members, exhibiting characteristics consistent with observations. As the Bay of Bengal vortex moves westward, the middle tropospheric trough deepens, horizontal wind shear increases, the low-level Arabian Sea stable inversion layer weakens, and the middle troposphere moisture content increases over western India and the northeast Arabian Sea. Subsequently, MTC genesis occurs along the western edge of the trough within 2-4 days of model integration over the northeast Arabian Sea. Vorticity budget analysis highlights the critical role of vorticity advection and tilting during the initial 24 hours of MTC genesis, while vortex stretching becomes the dominant vorticity source during rapid intensification. To further substantiate these findings, a mechanism denial experiment is conducted using a real-world instance of coexistent Arabian Sea MTC and Bay of Bengal LPS, which was replicated in the model. In the experiment, conditions unfavorable for LPS genesis are created by cooling and drying the Bay of Bengal. The results demonstrate that when the Bay of Bengal LPS does not develop or intensify, the Arabian Sea MTC fails to form. This study presents compelling evidence for the significant influence of Bay of Bengal low-pressure systems on the formation of severe weather-inducing MTCs over the Arabian Sea and western India., 32 pages 18 figures

Humanity is close to characterizing the atmospheres of rocky exoplanets due to the advent of JWST. These astronomical observations motivate us to understand exoplanetary atmospheres to constrain habitability. We study the influence greenhouse gas supplement has on the atmosphere of TRAPPIST-1e, an Earth-like exoplanet, and Earth itself by analyzing ExoCAM and CMIP6 model simulations. We find an analogous relationship between CO2 supplement and amplified warming at non-irradiated regions (night side and polar) - such spatial heterogeneity results in significant global circulation changes. A dynamical systems framework provides additional insight into the vertical dynamics of the atmospheres. Indeed, we demonstrate that adding CO2 increases temporal stability near the surface and decreases stability at low pressures. Although Earth and TRAPPIST-1e take entirely different climate states, they share the relative response between climate dynamics and greenhouse gas supplements., 22 pages, 5 figures, accepted at Nature Scientific Reports

We use Random Matrix Theory (RMT) to describe the eigenvalue spacing of Meghalaya's historical monthly average temperature ($T_{avg}$) in grids. For that, the Nearest Neighbor Spacings ($S_i$) of the eigenvalues of the correlation matrices were found out for 1428 consecutive eigenvalue pair differences. It is found that the distribution of $S_i$ follows Brody distribution at a correlation value of $\beta=0.045$. This value of $\beta(0.045)$ indicates weak repulsion among the eigenvalues as it is closer to Poisson fluctuations, meaning there is a weak correlation among the grids.

We present a study on the pattern underlying the climate dynamics in various locations spread over India, including the Himalayan region, coastal region, central and north eastern parts of India. We try to capture the variations in the complexity of their dynamics derived from temperature and relative humidity data, which can lead to the characterization of the differences in climate dynamics over the last seventy years. Based on the computed measures of their multifractal spectra, we could group the locations into six clusters, each having similar underlying dynamics in their temperature and relative humidity variations. We also report the variations in climate dynamics over time in these locations by estimating the recurrence-based measures using a sliding window analysis on the data sets. They provide evidences of changes in climate dynamics related to global warming and how their variations differ among the different locations of the country. The major changes in dynamics can be related to the consequences of the global climate regime shifts reported in the mid-1970s, late 1980s, and late 1990s. The study can thus contribute to our understanding of the complexity of the dynamics of the climate system over the Indian subcontinent and its variations and shifts over time., 10 pages, 8 figures

'Data' plays a central role in data-driven methods, but is not often the subject of focus in investigations of machine learning algorithms as applied to Earth System Modeling related problems. Here we consider the case of eddy-mean interaction in rotating stratified turbulence in the presence of lateral boundaries, a problem of relevance to ocean modeling, where the eddy fluxes contain dynamically inert rotational components that are expected to contaminate the learning process. An often utilized choice in the literature is to learn from the divergence of the eddy fluxes. Here we provide theoretical arguments and numerical evidence that learning from the eddy fluxes with the rotational component appropriately filtered out results in models with comparable or better skill, but substantially improved robustness. If we simply want a data-driven model to have predictive skill then the choice of data choice and/or quality may not be critical, but we argue it is highly desirable and perhaps even necessary if we want to leverage data-driven methods to aid in discovering unknown or hidden physical processes within the data itself., 23 pages, 8 figures

For modeling extreme rainfall, the widely used Brown-Resnick max-stable model extends the concept of the variogram to suit bloc maxima, allowing the explicit modeling of the extremal dependence shown by the spatial data. This extremal dependence stems from the geometrical characteristics of the observed rainfall, which is associated with different meteorological processes and is usually considered to be constant when designing the model for a study. However, depending on the region, this dependence can change throughout the year, as the prevailing meteorological conditions that drive the rainfall generation process change with the season. Therefore, this study analyzes the impact of the seasonal change in extremal dependence for the modeling of annual block maxima in the Berlin-Brandenburg region. For this study, two seasons were considered as proxies for different dominant meteorological conditions: summer for convective rainfall and winter for frontal/stratiform rainfall. Using maxima from both seasons, we compared the skill of a linear model with spatial covariates (that assumed spatial independence) with the skill of a Brown-Resnick max-stable model. This comparison showed a considerable difference between seasons, with the isotropic Brown-Resnick model showing considerable loss of skill for the winter maxima. We conclude that the assumptions commonly made when using the Brown-Resnick model are appropriate for modeling summer (i.e., convective) events, but further work should be done for modeling other types of precipitation regimes., Comment: 18 pages, 13 figures

A persistent spatial organization of eddies is identified in the lowest portion of the stably-stratified planetary boundary layer. The analysis uses flow realizations from published large-eddy simulations (Sullivan et al., J Atmos Sci 73(4):1815-1840, 2016) ranging in stability from neutral to nearly z-less stratification. The coherent turbulent structure is well approximated as a series of uniform momentum zones (UMZs) and uniform temperature zones (UTZs) separated by thin layers of intense gradients that are significantly greater than the mean. This pattern yields stairstep-like instantaneous flow profiles whose shape is distinct from the mean profiles that emerge from long-term averaging. However, the scaling of the stairstep organization is closely related to the resulting mean profiles. The differences in velocity and temperature across the thin gradient layers remain proportional to the surface momentum and heat flux conditions regardless of stratification. The vertical thickness of UMZs and UTZs is proportional to height above the surface for neutral and weak stratification, but becomes thinner and less dependent on height as the stability increases. Deviations from the logarithmic mean profiles for velocity and temperature observed under neutral conditions are therefore predominately due to the reduction in zone size with increasing stratification, which is empirically captured by existing Monin-Obukhov similarity relations for momentum and heat. The zone properties are additionally used to explain trends in the turbulent Prandtl number, thus providing a connection between the eddy organization, mean profiles, and turbulent diffusivity in stably stratified conditions., Comment: 35 pages, 12 figures

Intuitively, one would expect a more skillful forecast if predicting weather averaged over one week instead of the weather averaged over one day, and similarly for different spatial averaging areas. However, there are few systematic studies of averaging and forecast skill with modern forecasts, and it is therefore not clear how much improvement in forecast performance is produced via averaging. Here we present a direct investigation of averaging effects, based on data from operational numerical weather forecasts. Data is analyzed for precipitation and surface temperature, for lead times of roughly 1 to 7 days, and for time- and space-averaging diameters of 1 to 7 days and 100 to 4500 km, respectively. For different geographic locations, the effects of time- or space-averaging can be different, and while no clear geographical pattern is seen for precipitation, a clear spatial pattern is seen for temperature. For temperature, in general, time averaging is most effective near coastlines, also effective over land, and least effective over oceans. Based on all locations globally, time averaging was less effective than one might expect. To help understand why time averaging may sometimes be minimally effective, a stochastic model is analyzed as a synthetic weather time series, and analytical formulas are presented for the decorrelation time. In effect, while time averaging creates a time series that is visually smoother, it does not necessarily cause a substantial increase in the predictability of the time series.

Complex physical models are the most advanced tools available for producing realistic simulations of the climate system. However, such levels of realism imply high computational cost and restrictions on their use for policymaking and risk assessment. Two central characteristics of climate change are uncertainty and that it is a dynamic problem in which international actions can significantly alter climate projections and information needs, including partial and full compliance of global climate goals. Here we present AIRCC-Clim, a simple climate model emulator that produces regional probabilistic climate change projections of monthly and annual temperature and precipitation, as well as risk measures, based both on standard and user-defined emissions scenarios for six greenhouse gases. AIRCC-Clim emulates 37 atm-ocean coupled general circulation models with low computational and technical requirements for the user. This standalone, user-friendly software is designed for a variety of applications including impact assessments, climate policy evaluation and integrated assessment modelling.

While it is known that trapped lee waves propagating at low levels in a stratified atmosphere exert a drag on the mountains that generate them, the distribution of the corresponding reaction force exerted on the atmospheric mean circulation, defined by the wave momentum flux profiles, has not been established, because for inviscid trapped lee waves these profiles oscillate indefinitely downstream. A framework is developed here for the unambiguous calculation of momentum flux profiles produced by trapped lee waves, which circumvents the difficulties plaguing the inviscid trapped lee wave theory. Using linear theory, and taking Scorer's two-layer atmosphere as an example, the waves are assumed to be subject to a small dissipation, expressed as a Rayleigh damping. The resulting wave pattern decays downstream, so the momentum flux profile integrated over the area occupied by the waves converges to a well-defined form. Remarkably, for weak dissipation, this form is independent of the value of Rayleigh damping coefficient, and the inviscid drag, determined in previous studies, is recovered as the momentum flux at the surface. The divergence of this momentum flux profile accounts for the areally-integrated drag exerted by the waves on the atmosphere. The application of this framework to this and other types of trapped lee waves potentially enables the development of physically-based parametrizations of the effects of trapped lee waves on the atmosphere., 24 pages, 5 figures; submitted to Quarterly Journal of the Royal Meteorological Society

The Coupled Model Intercomparison Project (phase 6) (CMIP6) global circulation models (GCMs) predict equilibrium climate sensitivity (ECS) values ranging between 1.8 and 5.7 $^\circ$C. To narrow this range, we group 38 GCMs into low, medium and high ECS subgroups and test their accuracy and precision in hindcasting the mean global surface warming observed from 1980-1990 to 2011-2021 in the ERA5 T2m, HadCRUT5, GISTEMP v4, and NOAAGlobTemp v5 global surface temperature records. We also compare the GCM hindcasts to the satellite-based UAH MSU v6 lower troposphere global temperature record. We use 143 GCM ensemble averaged simulations under four slightly different forcing conditions, 688 GCM member simulations, and Monte Carlo modeling of the internal variability of the GCMs under three different model accuracy requirements. We found that the medium and high ECS GCMs run too hot up to over 95% and 97% of cases, respectively. The low ECS GCM group agrees best with the warming values obtained from the surface temperature records, ranging between 0.52 and 0.58 $^\circ$C. However, when comparing the observed and GCM hindcasted warming on land and ocean regions, the surface-based temperature records appear to exhibit a significant warming bias. If the satellite based UAH MSU lt record is accurate, actual surface warming from 1980 to 2021 may have been around 0.40 $^\circ$C (or less), i.e. up to about 30% less than what is reported by the surface based temperature records. The latter situation implies that even the low ECS models would have produced excessive warming from 1980 to 2021. These results suggest that the actual ECS may be relatively low, i.e. lower than 3 $^\circ$C or even less than 2 $^\circ$C. Therefore, the projected global climate warming over the next few decades could be moderate and probably not particularly alarming., 26 pages, 13 figures. Climate Dynamics (2022)

Ultraviolet (UV) photolysis of fatty acid surfactants which cover the surfaces of atmospheric liquid aerosols and are found in the oceans such as nonanoic acid (NA) has recently been suggested as a source of hydroxyl (OH) radicals in the troposphere. We used laser-induced fluorescence to directly observe OH radicals desorbed from the surface of neat liquid NA as a primary photoproduct following 213 nm irradiation. The upper limit of photoreaction cross section for the OH radical desorption was estimated to be 9.0(4.1) $\times$ 10$^{-22}$ cm$^2$, which is only 1.2$\pm$0.8% of the photoreaction cross section established for the photolysis of gas-phase acetic acid monomers. Vibrational sum-frequency generation spectroscopy for liquid NA revealed the hydrogen-bonded, cyclic, dimer structure of the NA molecules at the liquid surface. This dimerization can inhibit the formation of OH radicals and lead the present low photochemical reactivity of liquid NA.

Along-track wavenumber spectral densities of sea surface height (SSH) are estimated from Jason-2 altimetry data as a function of spatial location and calendar month, to understand the seasonality of meso- and submesoscale balanced dynamics across the global ocean. Regions with significant mode-1 and mode-2 baroclinic tides are rejected, restricting the analysis to the extratropics. Where balanced motion dominates, the SSH spectral density is averaged over all pass segments in a region for each calendar month, and is fit to a 4-parameter model consisting of a flat plateau at low wavenumbers, a transition at wavenumber $k_0$ to a red power law spectrum $k^{-s}$, and a white spectrum at high wavenumbers that models the altimeter noise. The monthly time series of the model parameters are compared to the evolution of the mixed layer. The annual mode of the spectral slope $s$ reaches a minimum after the mixed layer deepens, and the annual mode of the bandpassed kinetic energy in the ranges $[2k_0,4 k_0]$ and $[k_0,2 k_0]$ peak $\sim$2 and $\sim$4 months, respectively, after the maximum of the annual mode of the mixed layer depth. This analysis is consistent with an energization of the submesoscale by a winter mixed layer instability followed by an inverse cascade to the mesoscale, in agreement with prior modeling studies and in situ measurements. These results are compared to prior modeling, in situ, and satellite investigations of specific regions, and are broadly consistent with them within measurement uncertainties., Comment: Submitted to the Journal of Physical Oceanography. The code used for analyzing and displaying data can be found at https://github.com/albionlawrence/sm-seasonality

Many atmosphere and climate phenomena lie in the gray zone between weather and climate: they are not amenable to deterministic forecast, but they still depend on the initial condition. A natural example is medium-range forecasting, which is inherently probabilistic because it lies beyond the deterministic predictability time of the atmosphere, but for which statistically significant prediction can be made, which depends on the current state of the system. Similarly, one may ask the probability of occurrence of an El Niño event several months ahead of time. We introduce a quantity that corresponds precisely to this type of prediction problem: the committor function is the probability that an event takes place within a given time window, as a function of the initial condition. We compute it in the case of a low-dimensional stochastic model for El Niño, the Jin and Timmermann model. In this context, we show that the ability to predict the probability of occurrence of the event of interest may differ strongly depending on the initial state. The main result is the new distinction between probabilistic predictability (when the committor function is smooth and probability can be computed, which does not depend sensitively on the initial condition) and probabilistic unpredictability (when the committor function depends sensitively on the initial condition). We also demonstrate that the Jin and Timmermann model might be the first example of a stochastic differential equation with weak noise for which transition between attractors does not follow the Arrhenius law, which is expected based on large deviation theory and generic hypothesis. Significance Statement A key problem for atmospheric and climate phenomena is to predict events beyond the time scale over which deterministic weather forecast is possible. In a simple model of El Niño, we demonstrate the existence of two regimes, depending on initial conditions. For initial conditions in the “probabilistic predictability” regime, the system is unpredictable deterministically because of chaos, but the probability of occurrence of the event can still be predicted because it depends only weakly on the initial condition. In the “probabilistic unpredictability” regime, even predicting probabilities is difficult, because the probability depends strongly on initial conditions. These new concepts of probabilistic predictability and unpredictability should be key in understanding the predictability potential for rare events in climate problems, as well as in other complex dynamics.

Small grains play an essential role in astrophysical processes such as chemistry, radiative transfer, gas/dust dynamics. The population of small grains is mainly maintained by the fragmentation process due to colliding grains. An accurate treatment of dust fragmentation is required in numerical modelling. However, current algorithms for solving fragmentation equation suffer from an over-diffusion in the conditions of 3D simulations. To tackle this challenge, we developed a Discontinuous Galerkin scheme to solve efficiently the non-linear fragmentation equation with a limited number of dust bins., Comment: 16 pages, Accepted for publication in MNRAS

We derive a thermodynamically consistent model for phase change in sea ice by adding salt to the framework introduced by Penrose and Fife. Taking the salt entropy relative to the liquid water molar fraction provides a transparent mechanism for salt rejection under ice formation. We identify slow varying coordinates, including salt density relative to liquid water molarity weighted by latent heat, and use multiscale analysis to derive a quasi-equilibrium Stefan-type problem via a sharp interface scaling. The singular limit is under-determined and the leading order system is closed by imposing local conservation of salt under interface perturbation. The quasi-steady system determines interface motion as balance of curvature, temperature gradient, and salt density. We resolve this numerically for axisymmetric surfaces and show that the thermal gradients typical of arctic sea ice can have a decisive impact on the mode of pinch-off of cylindrical brine inclusions and on the size distribution of the resultant spherical shapes. The density and distribution of inclusion sizes is a key component of sea ice albedo which factors into global climate models.

Methane (CH4) emissions estimates from top-down studies over oil and gas basins have revealed systematic under-estimation of CH4 emissions in current national inventories. Sparse but extremely large amounts of CH4 from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall O&G contribution. However, attribution to specific facilities remains a major challenge unless high-resolution images provide the sufficient granularity within O&G basin. In this paper, we monitor known oil-and-gas infrastructures across the globe using recurrent Sentinel-2 imagery to detect and quantify more than 800 CH4 emissions. In combination with emissions estimates from airborne and Sentinel-5P measurements, we demonstrate the robustness of the fit to a power law from 0.1 tCH4/hr to 600 tCH4/hr. We conclude here that the prevalence of ultra-emitters (> 25tCH4/hr) detected globally by Sentinel-5P directly relates to emission occurrences below its detection threshold. Similar power law coefficients arise from several major oil and gas producers but noticeable differences in emissions magnitudes suggest large differences in maintenance practices and infrastructures across countries., Comment: Preprint version of https://pubs.acs.org/doi/abs/10.1021/acs.est.1c08575

With the recent increase in deforestation, forest fires, and regional temperatures, the concerns around the rapid and complete collapse of the Amazon rainforest ecosystem have heightened. The thresholds of deforestation and the temperature increase required for such a catastrophic event are still uncertain. However, our analysis presented here shows that signatures of changing Amazon are already apparent in historical climate data sets. Here, we extend the methods of climate network analysis and apply them to study the temporal evolution of the connectivity between the Amazon rainforest and the global climate system. We observe that the Amazon rainforest is losing short-range connectivity and gaining more long-range connections, indicating shifts in regional-scale processes. Using embeddings inspired by manifold learning, we show that Amazon connectivity patterns have become more variable in the twenty-first century. By investigating edge-based network metrics on similar regions to the Amazon we see the changing properties of the Amazon are significant in comparison. Furthermore, we simulate diffusion and random walks on these networks and observe a faster spread of perturbations from the Amazon in recent decades. Our methodology innovations can act as a template for examining the spatiotemporal patterns of regional climate change and its impact on global climate using the toolbox of climate network analysis.

Stochastic generators are useful for estimating climate impacts on various sectors. Projecting climate risk in various sectors, e.g. energy systems, requires generators that are accurate (statistical resemblance to ground-truth), reliable (do not produce erroneous examples), and efficient. Leveraging data from the North American Land Data Assimilation System, we introduce TemperatureGAN, a Generative Adversarial Network conditioned on months, locations, and time periods, to generate 2m above ground atmospheric temperatures at an hourly resolution. We propose evaluation methods and metrics to measure the quality of generated samples. We show that TemperatureGAN produces high-fidelity examples with good spatial representation and temporal dynamics consistent with known diurnal cycles.

A number of studies have investigated the large-scale drivers and upstream-precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote in both time and space from the impacted region. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building story-lines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high-impact weather, and providing early warning of their possible development. However, operational applications of this developing knowledge-base is limited so far, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large-scale precursors of categorical weather events in an automated fashion, and for reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional- to national-scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large-scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how for more continental-scale applications the regionally-specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales and regions., 3 figure SI, 22 manuscript pages, 10 figures, submitted to QJRMS

In recent years, several climate subsystems have been identified that may undergo a relatively rapid transition compared to the changes in their forcing. Such transitions are rare events in general, and simulating long-enough trajectories in order to gather sufficient data to determine transition statistics would be too expensive. Conversely, rare events algorithms like TAMS (trajectory-adaptive multilevel sampling) encourage the transition while keeping track of the model statistics. However, this algorithm relies on a score function whose choice is crucial to ensure its efficiency. The optimal score function, called the committor function, is in practice very difficult to compute. In this paper, we compare different data-based methods (analog Markov chains, neural networks, reservoir computing, dynamical Galerkin approximation) to estimate the committor from trajectory data. We apply these methods on two models of the Atlantic Ocean circulation featuring very different dynamical behavior. We compare these methods in terms of two measures, evaluating how close the estimate is from the true committor and in terms of the computational time. We find that all methods are able to extract information from the data in order to provide a good estimate of the committor. Analog Markov Chains provide a very reliable estimate of the true committor in simple models but prove not so robust when applied to systems with a more complex phase space. Neural network methods clearly stand out by their relatively low testing time, and their training time scales more favorably with the complexity of the model than the other methods. In particular, feedforward neural networks consistently achieve the best performance when trained with enough data, making this method promising for committor estimation in sophisticated climate models., Comment: 22 pages, 5 figures

We investigated the dynamics of highly turbulent thermally driven anabatic (upslope) flow in a large water tank using particle image velocimetry (PIV) and a thermocouple grid. The results showed that the flow exhibited pronounced variations in velocity and temperature and, importantly, could not be modeled as a two-dimensional quasi-steady flow. Five compelling findings were presented to underscore the three-dimensional and helical nature of the flow. Namely, the 'B-shaped' mean velocity profiles, 'B-shaped' turbulent flux profiles, synthetic streaks that revealed particles flowing perpendicular to the laser sheet, average vorticity maps revealing helical structure splitting, and identified vortices shooting from the boundary towards the plume. Future work should address the complete three-dimensional nature of the flow, improve the spatial and temporal resolution, and introduce roughness and additional geometries. Collectively, these findings can offer novel insights into the flow behavior patterns of thermally driven complex terrain flows, which influence local weather and microclimates and are responsible for scalar transport, e.g., pollution.

The Earth climate is a complex and high-dimensional dynamical system. At large scale its variability is dominated by recurrent patterns, interacting with each others on a vast range of spatial and temporal scales. Identifying such patterns and their linkages offers a powerful strategy to simplify, study and understand climate dynamics. We propose a data-driven framework to first reduce the dimensionality of a spatiotemporal climate field into a set of regional modes and then infer their time-dependent causal links. Causality is inferred through the fluctuation-response formalism, as shown in Baldovin et al. (2020) [1]. The framework allows us to estimate how a spatiotemporal system would respond to local external perturbation, therefore inferring causal links in the interventional sense. We showcase the methodology on the sea surface temperature field in two cases with different dynamics: weekly variability in the tropical Pacific and monthly variability over the entire globe. In both cases, we demonstrate the usefulness of the methodology by studying few individual links as well as "link maps", visualizing the cumulative degree of causation between a given region and the whole system. Finally, each climate mode is ranked in terms of its "causal strength", quantifying its relative ability to influence the system dynamics. We argue that the methodology allows to explore and characterize causal relationships in high-dimensional spatiotemporal fields in a rigorous and physical way.

Probabilistic forecasting is crucial to decision-making under uncertainty about future weather. The dominant approach is to use an ensemble of forecasts to represent and quantify uncertainty in operational numerical weather prediction. However, generating ensembles is computationally costly. In this paper, we propose to generate ensemble forecasts at scale by leveraging recent advances in generative artificial intelligence. Our approach learns a data-driven probabilistic diffusion model from the 5-member ensemble GEFS reforecast dataset. The model can then be sampled efficiently to produce realistic weather forecasts, conditioned on a few members of the operational GEFS forecasting system. The generated ensembles have similar predictive skill as the full GEFS 31-member ensemble, evaluated against ERA5 reanalysis, and emulate well the statistics of large physics-based ensembles. We also apply the same methodology to developing a diffusion model for generative post-processing: the model directly learns to correct biases present in the emulated forecasting system by leveraging reanalysis data as labels during training. Ensembles from this generative post-processing model show greater reliability and accuracy, particularly in extreme event classification. In general, they are more reliable and forecast the probability of extreme weather more accurately than the GEFS operational ensemble. Our models achieve these results at less than 1/10th of the computational cost incurred by the operational GEFS system.

The clear sky instantaneous radiative forcings of the 14 halogenated gases previously shown to have the largest contribution to global warming, were found. The calculation used the absorption cross sections for the halogenated gases which are assumed to be independent of temperature as well as over 1/3 million line strengths for the 5 naturally occurring greenhouse gases: H$_2$O, CO$_2$, O$_3$, CH$_4$ and N$_2$O, from the Hitran database. The total radiative forcing of the halogenated gases at their 2020 concentrations is 0.52 (0.67) W/m$^2$ at the tropopause (mesopause). Over half of this forcing is due to CFC11 and CFC12 whose concentrations are declining as a result of the Montreal Protocol. The rate of total forcing change for all 14 halogenated gases is 1.5 (2.2) mW/m$^2$/year at the tropopause (mesopause). The calculations assumed a constant altitude concentration for all halogenated gases except CFC11, CFC12 and SF$_6$. Using the observed altitude dependence for those 3 molecules reduced their radiative forcings by about 10%. The global warming potential values were comparable to those given by the Intergovernmental Panel on Climate Change. The contribution of a gas to global warming was estimated using the forcing power per molecule defined as the derivative of its radiative forcing with respect to its column density. For the present atmosphere, the per-molecule forcing powers of halogenated gases are orders of magnitude larger than those for the 5 naturally occuring greenhouse gases because the latter have much higher concentrations and are strongly saturated. But, the rates of concentration increase of the 5 main greenhouse gases are orders of magnitude greater than that of any halogenated gas. Assuming the temperature increase caused by each gas is proportional to its radiative forcing increase, the 14 halogenated gases are responsible for only 2% of the total global warming., arXiv admin note: text overlap with arXiv:2103.16465, arXiv:2006.03098

Deep learning models have gained popularity in climate science, following their success in computer vision and other domains. For instance, researchers are increasingly employing deep learning techniques for downscaling climate data, drawing inspiration from image super-resolution models. However, there are notable differences between image data and climate data. While image data typically falls within a specific range (e.g., [0, 255]) and exhibits a relatively uniform or normal distribution, climate data can possess arbitrary value ranges and highly uneven distributions, such as precipitation data. This non-uniform distribution presents challenges when attempting to directly apply existing computer vision models to climate science tasks. Few studies have addressed this issue thus far. In this study, we explore the effects of loss functions and non-linear data pre-processing methods for deep learning models in the context of climate downscaling. We employ a climate downscaling experiment as an example to evaluate these factors. Our findings reveal that L1 loss and L2 loss perform similarly on some more balanced data like temperature data while for some imbalanced data like precipitation data, L2 loss performs significantly better than L1 loss. Additionally, we propose an approach to automatically learn the non-linear pre-processing function, which further enhances model accuracy and achieves the best results., Presented at CVPR 2023 Workshop on Multimodal Learning for Earth and Environment

The general circulation of the atmosphere determines the long-term variability of weather processes. This circulation is driven by the temperature differences between the poles and the equator, causing air to move along the Earth's surface. However, this requires enhanced pressure at the poles, which is not observed. To sustain the circulation, an additional non-hydrostatic pressure gradient is required. In my research, I propose the emergence of an additional non-hydrostatic pressure gradient resulting from the centrifugal force generated by the Earth's rotation. This centrifugal force creates a non-hydrostatic vertical pressure gradient, which is essential for the closed circulation of unequally heated air in the meridional direction. The circulation is composed of three distinct streams flowing in opposite directions, with the polar and tropical tropopause acting as boundaries. The temperature in the atmosphere decreases from the surface to the polar tropopause and remains constant above it., 17 pages, 12 figures

In this paper we present the development of a dataset consisting of 91 Multi-band Cloud and Moisture Product Full-Disk (MCMIPF) from the Advanced Baseline Imager (ABI) on board GOES-16 geostationary satellite with 91 temporally and spatially corresponding CLDCLASS products from the CloudSat polar satellite. The products are diurnal, corresponding to the months of January and February 2019 and were chosen such that the products from both satellites can be co-located over South America. The CLDCLASS product provides the cloud type observed for each of the orbit's steps and the GOES-16 multiband images contain pixels that can be co-located with these data. We develop an algorithm that returns a product in the form of a table that provides pixels from multiband images labelled with the type of cloud observed in them. These labelled data conformed in this particular structure are very useful to perform supervised learning. This was corroborated by training a simple linear artificial neural network based on the work of Gorooh et al. (2020), which gave good results, especially for the classification of deep convective clouds., 6 pages, 7 figures. Submitted to 2022 IEEE Biennial Congress of Argentina (ARGENCON). Presented in 2022 IEEE Biennial Congress of Argentina (ARGENCON), San Juan, Argentina

The microseismic motion, which is the ambient ground vibration caused by ocean waves, affects ground-based gravitational detectors. In this study, we characterized the properties of the microseismic motion at the KAGRA site and the ocean waves at 13 coasts of Japan, such as the seasonal variation and the correlation between them. As a result, it almost succeeded to explain the microseismic motion at the KAGRA site by the principal components of the ocean wave data. One possible application of this study is the microseismic forecast and its example is also shown.

Along the east coast of India (ECI), local winds are oriented alongshore during the summer monsoon, favouring coastal upwelling. The East India Coastal Current (EICC) during this period converges at 17$^{\circ}$N with northward flow from the south and southward flow from the north. A high-resolution Ocean general circulation model (OGCM) which includes runoff from major rivers (RIVER) simulates the upwelling along the east coast of India during the summer monsoon reasonably well. The model simulation shows that the upwelling increases towards south, where salinity influence is lesser compared to the northern parts. The presence of rivers suppresses upwelling, the depth of the source of subsurface water becomes shallow from 100 to 60 m, and reduces the SST cooling by 0.5$^{\circ}$C owing to the increase in stratification of the water column., 36 pages, 12 Figures

Deep-learning-based surrogate models provide an efficient complement to numerical simulations for subsurface flow problems such as CO$_2$ geological storage. Accurately capturing the impact of faults on CO$_2$ plume migration remains a challenge for many existing deep learning surrogate models based on Convolutional Neural Networks (CNNs) or Neural Operators. We address this challenge with a graph-based neural model leveraging recent developments in the field of Graph Neural Networks (GNNs). Our model combines graph-based convolution Long-Short-Term-Memory (GConvLSTM) with a one-step GNN model, MeshGraphNet (MGN), to operate on complex unstructured meshes and limit temporal error accumulation. We demonstrate that our approach can accurately predict the temporal evolution of gas saturation and pore pressure in a synthetic reservoir with impermeable faults. Our results exhibit a better accuracy and a reduced temporal error accumulation compared to the standard MGN model. We also show the excellent generalizability of our algorithm to mesh configurations, boundary conditions, and heterogeneous permeability fields not included in the training set. This work highlights the potential of GNN-based methods to accurately and rapidly model subsurface flow with complex faults and fractures.

There is an increasing interest in mesoscale eddy parameterizations that are scale-aware, normally interpreted to mean that a parameterization does not require re-tuning of parameters as the model resolution changes. Here we examine whether Gent--McWilliams (GM) based version of GEOMETRIC, a mesoscale eddy parameterization that is constrained by a parameterized eddy energy budget, is scale-aware in its energetics. It is generally known that GM-based schemes severely damp out explicit eddies, so the parameterized component would be expected to dominate across resolutions, and we might expect a negative answer to the question of energetic scale-awareness. A consideration of why GM-based schemes damp out explicit eddies leads a suggestion for what we term a splitting procedure: a definition of a `large-scale' field is sought, and the eddy-induced velocity from the GM-scheme is computed from and acts only on the large-scale field, allowing explicit and parameterized components to co-exist. Within the context of an idealized re-entrant channel model of the Southern Ocean, evidence is provided that the GM-based version of GEOMETRIC is scale-aware in the energetics as long as we employ a splitting procedure. The splitting procedure also leads to an improved representation of mean states without detrimental effects on the variability., 24 pages, 8 figures, 1 table

Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data. One such component is the eddy diffusivity model, where vertical turbulent fluxes of a quantity are parameterized from a variable eddy diffusion coefficient and the mean vertical gradient of the quantity. In this work, we improve a parameterization of vertical mixing in the ocean surface boundary layer by enhancing its eddy diffusivity model using data-driven methods, specifically neural networks. The neural networks are designed to take extrinsic and intrinsic forcing parameters as input to predict the eddy diffusivity profile and are trained using output data from a second moment closure turbulent mixing scheme. The modified vertical mixing scheme predicts the eddy diffusivity profile through online inference of neural networks and maintains the conservation principles of the standard ocean model equations, which is particularly important for its targeted use in climate simulations. We describe the development and stable implementation of neural networks in an ocean general circulation model and demonstrate that the enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and upper ocean stratification. Our results demonstrate the potential for data-driven physics-aware parameterizations to improve global climate models.

Low-rank matrix completion is the task of recovering unknown entries of a matrix by assuming that the true matrix admits a good low-rank approximation. Sometimes additional information about the variables is known, and incorporating this information into a matrix completion model can lead to a better completion quality. We consider the situation where information between the column/row entities of the matrix is available as a weighted graph. In this framework, we address the problem of completing missing entries in air temperature data recorded by weather stations. We construct test sets by holding back data at locations that mimic real-life gaps in weather data. On such test sets, we show that adequate spatial and temporal graphs can significantly improve the accuracy of the completion obtained by graph-regularized low-rank matrix completion methods., Accepted by EUSIPCO2023

The Gimbal video is a well-know footage of an unidentified anomalous phenomena (UAP). Recorded in January 2015 off the coast of Florida by a U.S. Navy F/A-18F Super Hornet's targeting pod, the video shows an infrared-significant object skimming over clouds. Towards the end of the 34-second clip, the object appears to stop and rotate in mid-air. Naval aviators who participated in the event indicate that: (1) The UAP was within 10 nautical miles of the F/A-18F, (2) that, from the perspective of the aircrew's top-down radar display, it was seen to stop and reverse direction with no radius of turn, and (3) that the UAP was accompanied by a formation of 4-6 other unknown objects. Using data from the ATFLIR video, it is possible to reconstruct potential flight paths for the object as a function of distance. We show that, at the range provided by the aviators, potential flight paths align with eyewitness accounts: The object decelerates from a few hundred knots before rapidly reversing direction in a vertical U-turn. Such a maneuver would have been observed on the overhead radar display as an abrupt reversal of direction with no radius of turn. The highly anomalous flight path found at the range provided by the aircrew, along with the remarkable match between the reconstructed flight path, eyewitness recollections, and the object's rotation, raises intriguing questions about the nature of the object. This is especially the case because, at this distance, no wings or infrared signatures consistent with conventional means of propulsion are visible. An alternative hypothesis, which proposes that Gimbal shows infrared glare from the exhaust of a conventional jet aircraft viewed approximately tail-on 30 nautical miles from the F/A-18F, is also discussed. Our goal is to provide an overview of analyses of the Gimbal encounter, and encourage aeronautics professionals to share their expertise.