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The energy surplus resulting from radiative forcing causes warming of the Earth system. This initial warming drives a myriad of changes including in sea surface temperatures (SSTs), leading to different radiative feedbacks. The relationship between the radiative feedbacks and the pattern of SST changes is referred to as the "pattern effect". The current approach to study the pattern effect relies on diagnosing the response of atmosphere-only models to perturbations in the SST boundary condition. Here, we argue that the fluctuation-dissipation relation (FDR), together with coarse-graining procedures, is a computationally cheap and theoretically grounded alternative to model experiments. We introduce a protocol to study the pattern effect and present its application in a state-of-the-art coupled climate model. By focusing on the coupled dynamics, we unveil the role of the slow ocean component in setting the pattern effect. We present a new "sensitivity map", representing a first, qualitative prediction of the response of the average top-of-the-atmosphere (TOA) radiative flux to perturbations in the SST field. We find negative sensitivity throughout the tropics, in contrast to the current understanding of a positive-negative dipole of sensitivity in the tropical Pacific. Considering only the shortest time scales, the response is dominated by the fast atmospheric variability and we recover results in qualitative agreement with the literature. Therefore, the difference between our results and previous studies, largely comes from including the atmosphere-ocean coupling. The framework offers a conceptually novel perspective on the pattern effect: feedbacks in the coupled system are encoded in a temporally and spatially dependent response operator, rather than time-independent maps as for previous studies.

Understanding the climate dynamics at the inner edge of the habitable zone (HZ) is crucial for predicting the habitability of rocky exoplanets. Previous studies using Global Climate Models (GCMs) have indicated that planets receiving high stellar flux can exhibit climate bifurcations, leading to bistability between a cold (temperate) and a hot (runaway) climate. However, the mechanism causing this bistability has not been fully explained, in part due to the difficulty associated with inferring mechanisms from small numbers of expensive numerical simulations in GCMs. In this study, we employ a two-column (dayside and nightside), two-layer climate model to investigate the physical mechanisms driving this bistability. Through mechanism-denial experiments, we demonstrate that the runaway greenhouse effect, coupled with a cloud feedback on either the dayside or nightside, leads to climate bistability. We also map out the parameters that control the location of the bifurcations and size of the bistability. This work identifies which mechanisms and GCM parameters control the stellar flux at which rocky planets are likely to retain a hot, thick atmosphere if they experience a hot start. This is critical for the prioritization of targets and interpretation of observations by the James Webb Space Telescope (JWST). Furthermore, our modeling framework can be extended to planets with different condensable species and cloud types., Comment: 4 figures, submitted to ApJL

Dissolved oxygen (DO) is a non-conservative tracer of interactions at the air-sea interface, respiration and photosynthesis, and advection. In this manuscript, we study extremes in the degree of oxygen saturation (SO), the ratio of DO to the maximum concentration given the water's temperature, salinity, and depth with SO=1 critically saturated. We perform the analysis with the California Underwater Glider Network (CUGN), which operates gliders on four lines that extend from the California coast to several hundred kilometers offshore, profiling to 500m depth every 3km. Since ~2017, the gliders have been equipped with a Sea-Bird 63 optode sensor to measure the DO content. We find that parcels with SO>1.1, hyperoxic extrema, occur primarily near-shore in the upper 50m of the water column and during non-winter months. Along Line 90 which originates in San Diego, these hyperoxic events occur primarily in stratified waters with shallow mixed layers. We hypothesize that photosynthesis elevates DO in sub-surface water that can not rapidly ventilate with the surface. Along the three other lines, hyperoxic extrema occur almost exclusively at the surface and are correlated with elevated Chl-a fluorescence suggesting they are primarily driven by blooms of photosynthesis. We also examine hypoxic extrema, finding that parcels with SO<0.9 and z<50m occur most frequently along the northernmost line where upwelling has greatest impact., Comment: 10 pages, 16 figures; submitted to Journal of Physical Oceanography

Satellite altimetry has been widely utilized to monitor global sea surface dynamics, enabling investigation of upper ocean variability from basin-scale to localized eddy ranges. However, the sparse spatial resolution of observational altimetry limits our understanding of oceanic submesoscale variability, prevalent at horizontal scales below 0.25o resolution. Here, we introduce a state-of-the-art generative diffusion model to train high-resolution sea surface height (SSH) reanalysis data and demonstrate its advantage in observational SSH downscaling over the eddy-rich Kuroshio Extension region. The diffusion-based model effectively downscales raw satellite-interpolated data from 0.25o resolution to 1/16o, corresponding to approximately 12-km wavelength. This model outperforms other high-resolution reanalysis datasets and neural network-based methods. Also, it successfully reproduces the spatial patterns and power spectra of satellite along-track observations. Our diffusion-based results indicate that eddy kinetic energy at horizontal scales less than 250 km has intensified significantly since 2004 in the Kuroshio Extension region. These findings underscore the great potential of deep learning in reconstructing satellite altimetry and enhancing our understanding of ocean dynamics at eddy scales., Comment: 28 pages, 7 figures, and 1 table

With the rapid development of data-driven machine learning (ML) models in meteorology, typhoon track forecasts have become increasingly accurate. However, current ML models still face challenges, such as underestimating typhoon intensity and lacking interpretability. To address these issues, this study establishes an ML-driven hybrid typhoon model, where forecast fields from the Pangu-Weather model are used to constrain the large-scale forecasts of the Weather Research and Forecasting model based on the spectral nudging method (Pangu_SP). The results show that forecasts from the Pangu_SP experiment obviously outperform those by using the Global Forecast System as the initial field (GFS_INIT) and from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF IFS) for the track forecast of Typhoon Doksuri (2023). The predicted typhoon cloud patterns from Pangu_SP are also more consistent with satellite observations. Additionally, the typhoon intensity forecasts from Pangu_SP are notably more accurate than those from the ECMWF IFS, demonstrating that the hybrid model effectively leverages the strengths of both ML and physical models. Furthermore, this study is the first to explore the significance of data assimilation in ML-driven hybrid dynamical systems. The findings reveal that after assimilating water vapor channels from the Advanced Geostationary Radiation Imager onboard Fengyun-4B, the errors in typhoon intensity forecasts are reduced., Comment: 12 pages, 4 figures

The Fukushima-Daiichi release of radioactivity is a relevant event to study the atmospheric dispersion modelling of radionuclides. Actually, the atmospheric deposition onto the ground may be studied through the map of measured Cs-137 established consecutively to the accident. The limits of detection were low enough to make the measurements possible as far as 250km from the nuclear power plant. This large scale deposition has been modelled with the Eulerian model ldX. However, several weeks of emissions in multiple weather conditions make it a real challenge. Besides, these measurements are accumulated deposition of Cs-137 over the whole period and do not inform of deposition mechanisms involved: in-cloud, below-cloud, dry deposition. In a previous study (Qu{\'e}rel et al., 2016), a comprehensive sensitivity analysis was performed in order to understand wet deposition mechanisms. It has been shown that the choice of the wet deposition scheme has a strong impact on assessment of deposition patterns. Nevertheless, a ``best'' scheme could not be highlighted as it depends on the selected criteria: the ranking differs according to the statistical indicators considered (correlation, figure of merit in space and factor 2). A possibility to explain the difficulty to discriminate between several schemes was the uncertainties in the modelling, resulting from the meteorological data for instance. Since the move of the plume is not properly modelled, the deposition processes are applied with an inaccurate activity concentration in the air. In the framework of the SAKURA project, an MRI-IRSN collaboration, new meteorological fields at higher resolution (Sekiyama et al., 2013) were provided and allow to reconsider the previous study. An update including these new meteorology data is presented. In addition, the focus is put on the deposition schemes commonly used in nuclear emergency context.

Recent advancements in deep learning (DL) have led to the development of several Large Weather Models (LWMs) that rival state-of-the-art (SOTA) numerical weather prediction (NWP) systems. Up to now, these models still rely on traditional NWP-generated analysis fields as input and are far from being an autonomous system. While researchers are exploring data-driven data assimilation (DA) models to generate accurate initial fields for LWMs, the lack of a standard benchmark impedes the fair evaluation among different data-driven DA algorithms. Here, we introduce DABench, a benchmark dataset utilizing ERA5 data as ground truth to guide the development of end-to-end data-driven weather prediction systems. DABench contributes four standard features: (1) sparse and noisy simulated observations under the guidance of the observing system simulation experiment method; (2) a skillful pre-trained weather prediction model to generate background fields while fairly evaluating the impact of assimilation outcomes on predictions; (3) standardized evaluation metrics for model comparison; (4) a strong baseline called the DA Transformer (DaT). DaT integrates the four-dimensional variational DA prior knowledge into the Transformer model and outperforms the SOTA in physical state reconstruction, named 4DVarNet. Furthermore, we exemplify the development of an end-to-end data-driven weather prediction system by integrating DaT with the prediction model. Researchers can leverage DABench to develop their models and compare performance against established baselines, which will benefit the future advancements of data-driven weather prediction systems. The code is available on this Github repository and the dataset is available at the Baidu Drive., Comment: 37pages, 12 figures, 6 tables

In the present tropical atmosphere, precipitation typically exhibits noisy, small-amplitude fluctuations about an average. However, recent cloud-resolving simulations show that in a hothouse climate, precipitation can shift to a regime characterized by nonlinear oscillations. In this regime, intense precipitation events are separated by several dry days. This raises questions about what triggers the shift from a quasi-equilibrium state of precipitation to nonlinear precipitation oscillations and what factors determine the characteristics of these oscillations. To address these questions, we present a low-order model that includes two nonlinear ordinary differential equations, one for precipitation and the other for convective inhibition (CIN). Three processes govern the development of precipitation: a convective trigger that enhances precipitation, a self-limiting mechanism that reduces intense precipitation, and the effect of CIN in suppressing precipitation. On the other hand, CIN increases due to compensating subsidence from convection and decays exponentially over time due to radiation. A critical parameter in our model is the time-mean CIN (CIN*). As CIN* gradually increases, precipitation transitions from a quasi-equilibrium state to a nonlinear oscillation via a supercritical Hopf bifurcation. In the high CIN* limit, our model reduces to predator-prey dynamics, with CIN as the predator and precipitation as the prey. Here, the nonlinear oscillation's amplitude (maximum precipitation) grows with CIN*, and its period increases with the oscillation amplitude. A suite of cloud-resolving simulations corroborates these predictions from our low-order model, highlighting the effect of convective triggering and inhibition in regulating precipitation variability.

Antarctic ice shelves play a vital role in preserving the physical conditions of the Antarctic cryosphere and the Southern Ocean, and beyond. By serving as a buttressing force, ice shelves prevent sea-level rise by restraining the flow of continental ice and glaciers to the sea. Sea-level rise impacts the global environment in multiple ways, including flooding habitats, eroding coastlines, and contaminating soil and groundwater. It is therefore essential to monitor the stability of Antarctic ice shelves, for which a variety of complementary data sources is required. We have developed RIS-Vis, a novel data visualization platform to monitor Antarctic ice shelves. Although focused on the Ross Ice Shelf (RIS), RIS-Vis could be readily scaled to monitor other ice shelves around Antarctica, and elsewhere. Currently, RIS-Vis is capable of analyzing and visualizing seismic, geodetic, and weather data to provide meaningful information for Antarctic cryosphere research. RIS-Vis was built using Python libraries including Obspy, APScheduler, and the Plotly Dash framework, and uses SQLite as the backing database. Visualizations developed on RIS-Vis include filtered seismic waveforms, spectrograms, and power spectral densities, geodetic-based ice-shelf flow, and meteorological variables such as atmospheric temperature and pressure. The dashboard visualization platform abstracts away the time-intensive analysis process of raw data and allows scientists to better concentrate on RIS science.

Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation of complex climate phenomena. Traditional bias correction methods, which rely on historical observation data and statistical techniques, often neglect unobserved confounders, leading to biased results. This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders. Inspired by recent advances in causality based time series deconfounding, our method first constructs a factor model to learn latent confounders from historical data and then applies them to enhance the bias correction process using advanced time series forecasting models. The experimental results demonstrate significant improvements in the accuracy of precipitation outputs. By addressing unobserved confounders, our approach offers a robust and theoretically grounded solution for climate model bias correction.

One of the main statistical features of near-neutral atmospheric boundary layer (ABL) turbulence is the positive vertical velocity skewness $Sk_w$ above the roughness sublayer or the buffer region in smooth-walls. The $Sk_w$ variations are receiving renewed interest in many climate-related parameterizations of the ABL given their significance to cloud formation and to testing sub-grid schemes for Large Eddy Simulations (LES). The vertical variations of $Sk_w$ are explored here using high Reynolds number wind tunnel and flume experiments collected above smooth, rough, and permeable-walls in the absence of buoyancy and Coriolis effects. These laboratory experiments form a necessary starting point to probe the canonical structure of $Sk_w$ as they deal with a key limiting case (i.e. near-neutral conditions) that has received much less attention compared to its convective counterpart in atmospheric turbulence studies. Diagnostic models based on cumulant expansions, realizability constraints, and the now-popular constant mass flux approach routinely employed in the convective boundary layer as well as prognostic models based on third-order budgets are used to explain variations in $Sk_w$ for the idealized laboratory conditions. The failure of flux-gradient relations to model $Sk_w$ from the gradients of the vertical velocity variance $\sigma_w^2$ are explained and corrections based on models of energy transport offered. Novel links between the diagnostic and prognostic models are also featured, especially for the inertial term in the third order budget of the vertical velocity fluctuation. The co-spectral properties of $w'/\sigma_w$ versus $w'^2/\sigma_w^2$ are also presented for the first time to assess the dominant scales governing $Sk_w$ in the inner and outer layers, where $w'$ is the fluctuating vertical velocity and $\sigma_w$ is the vertical velocity standard deviation.

In Bangladesh, a nation vulnerable to climate change, accurately quantifying the risk of extreme weather events is crucial for planning effective adaptation and mitigation strategies. Downscaling coarse climate model projections to finer resolutions is key in improving risk and uncertainty assessments. This work develops a new approach to rainfall downscaling by integrating statistics, physics, and machine learning and applies it to assess Bangladesh's extreme rainfall risk. Our method successfully captures the observed spatial pattern and risks associated with extreme rainfall in the present climate. It also produces uncertainty estimates by rapidly downscaling multiple models in a future climate scenario(s). Our analysis reveals that the risk of extreme rainfall is projected to increase throughout Bangladesh mid-century, with the highest risk in the northeast. The daily maximum rainfall at a 100-year return period is expected to rise by approximately 50 mm per day. However, using multiple climate models also indicates considerable uncertainty in the projected risk.

A quasi-consensus has steadily formed in the scientific literature on the fact that the prevention measures implemented by most countries to curb the 2020 COVID-19 pandemic have led to significant reductions in pollution levels around the world, especially in urban environments. Fewer studies have looked at the how these reductions at ground level translate into variations in the whole atmosphere. In this study, we examine the columnar values of aerosols at 51 mainland European stations of the Aerosol Robotic Network (AERONET). We show that when considered in the context of the long-term trend over the last decade, the columnar aerosol levels for 2020, at the regional level, do not appear exceptional. Both the yearly means and the number of episodes with extreme values for this period are within the one standard deviation of the long-term trends. We conclude that the spatially and temporally very localized reductions do not add up to statistically significant reductions in the global levels of aerosols. Furthermore, considering that pandemic lockdowns can be thought of as a simulation of a climate change mitigation scenario, we conclude that such lifestyle-based changes present a very low potential as a global climate change mitigation strategy.

Quantitative forecasting of average rainfall into the next season remains highly challenging, but in some favourable isolated cases may be possible with a series of relatively simple steps. We chose to explore predictions of austral springtime rainfall in SE Australia regions based on the surrounding ocean surface temperatures during the winter. In the first stage, we search for correlations between the target rainfall and both the standard ocean climate indicators as well as the time series of surface temperature data expanded in terms of Empirical Orthogonal Functions (EOFs). In the case of the Indian Ocean, during the winter the dominant EOF shows stronger correlation with the future rainfall than the commonly used Indian Ocean Dipole. Information sources with the strongest correlation to the historical rainfall data are then used as inputs into deep learning artificial neural networks. The resulting hindcasts appear accurate for September and October and less reliable for November. We also attempt to forecast the rainfall in several regions for the coming austral spring.

Accurately describing the distribution of CO$_2$ in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems to aid implementation of international climate agreements. Large deep neural networks are poised to revolutionize weather prediction, which requires 3D modeling of the atmosphere. While similar in this regard, atmospheric transport modeling is subject to new challenges. Both, stable predictions for longer time horizons and mass conservation throughout need to be achieved, while IO plays a larger role compared to computational costs. In this study we explore four different deep neural networks (UNet, GraphCast, Spherical Fourier Neural Operator and SwinTransformer) which have proven as state-of-the-art in weather prediction to assess their usefulness for atmospheric tracer transport modeling. For this, we assemble the CarbonBench dataset, a systematic benchmark tailored for machine learning emulators of Eulerian atmospheric transport. Through architectural adjustments, we decouple the performance of our emulators from the distribution shift caused by a steady rise in atmospheric CO$_2$. More specifically, we center CO$_2$ input fields to zero mean and then use an explicit flux scheme and a mass fixer to assure mass balance. This design enables stable and mass conserving transport for over 6 months with all four neural network architectures. In our study, the SwinTransformer displays particularly strong emulation skill (90-day $R^2 > 0.99$), with physically plausible emulation even for forward runs of multiple years. This work paves the way forward towards high resolution forward and inverse modeling of inert trace gases with neural networks., Comment: Code: https://github.com/vitusbenson/carbonbench

Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather. By explicitly resolving convective dynamics within the atmosphere they afford meteorologists the nuance needed to provide outlook on hazard. Deep learning models have thus far not proven skilful at km-scale atmospheric simulation, despite being competitive at coarser resolution with state-of-the-art global, medium-range weather forecasting. We present a generative diffusion model called StormCast, which emulates the high-resolution rapid refresh (HRRR) model-NOAA's state-of-the-art 3km operational CAM. StormCast autoregressively predicts 99 state variables at km scale using a 1-hour time step, with dense vertical resolution in the atmospheric boundary layer, conditioned on 26 synoptic variables. We present evidence of successfully learnt km-scale dynamics including competitive 1-6 hour forecast skill for composite radar reflectivity alongside physically realistic convective cluster evolution, moist updrafts, and cold pool morphology. StormCast predictions maintain realistic power spectra for multiple predicted variables across multi-hour forecasts. Together, these results establish the potential for autoregressive ML to emulate CAMs -- opening up new km-scale frontiers for regional ML weather prediction and future climate hazard dynamical downscaling.

We use satellite observations of atmospheric methane from the TROPOMI instrument to estimate total annual methane emissions for 2019-2023 from four large Southeast US landfills with gas collection and control systems. The emissions are on average 6$\times$ higher than the values reported by the landfills to the US Greenhouse Gas Reporting Program (GHGRP) which are used by the US Environmental Protection Agency (EPA) for its national Greenhouse Gas Inventory (GHGI). We find increasing emissions over the 2019-2023 period whereas the GHGRP reports a decrease. The GHGRP requires gas-collecting landfills to estimate their annual emissions either with a recovery-first model (estimating emissions as a function of methane recovered) or a generation-first model (estimating emissions from a first-order-decay applied to waste-in-place). All four landfills choose to use the recovery-first model, which yields emissions that are one-quarter of those from the generation-first model and decreasing over 2019-2023, in contrast with the TROPOMI observations. Our TROPOMI estimates for two of the landfills agree with the generation-first model, with increasing emissions over 2019-2023 due to increasing waste-in-place or decreasing methane recovery, and are still higher than the generation-first model for the other two landfills. Further examination of the GHGRP emissions from all reporting landfills in the US shows that the 19% decrease in landfill emissions reported by the GHGI over 2005-2022 reflects an increasing preference for the recovery-first model by the reporting landfills, rather than an actual emission decrease. The generation-first model would imply an increase in landfill emissions over 2013-2022, and this is more consistent with atmospheric observations.

In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities. Downscaling (DS), a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions from global-scale forecast results. Previous downscaling methods, inspired by CNN and Transformer-based super-resolution models, lacked tailored designs for meteorology and encountered structural limitations. Notably, they failed to efficiently integrate topography, a crucial prior in the downscaling process. In this paper, we address these limitations by pioneering the selective state space model into the meteorological field downscaling and propose a novel model called MambaDS. This model enhances the utilization of multivariable correlations and topography information, unique challenges in the downscaling process while retaining the advantages of Mamba in long-range dependency modeling and linear computational complexity. Through extensive experiments in both China mainland and the continental United States (CONUS), we validated that our proposed MambaDS achieves state-of-the-art results in three different types of meteorological field downscaling settings. We will release the code subsequently.

Recent observational constraints on the internal structure of Jupiter and Saturn suggest that these planets have ``fuzzy" cores, i.e., radial gradients of the concentration of heavy elements that might span $50\%$ to $70\%$ of each planet's radius. These cores could be composed of a semi-convective staircase, i.e., multiple convective layers separated by diffusive interfaces arising from double-diffusive instabilities. However, to date, no study has demonstrated how such staircases can avoid layer mergers and persist over evolutionary time scales. In fact, previous work has found that these mergers occur rapidly, quickly leading to only a single convective layer. Using 3D simulations of convective staircases in non-rotating and rotating flows, we demonstrate that rotation prolongs the lifetime of a convective staircase by increasing the timescale for both layer merger and erosion of the interface between the final two layers. We present an analytic model for the erosion phase, predicting that rotation increases the erosion time by a factor of approximately $\mathrm{Ro}^{-1/2}$, where $\mathrm{Ro}$ is the Rossby number of the convective flows (the ratio of the rotation period to the convective turnover time). For Jovian conditions at early times after formation (when convection is vigorous enough to mix a large fraction of the planet), we find the erosion time to be roughly $10^{9}~\mathrm{yrs}$ in the non-rotating case and $10^{11}~\mathrm{yrs}$ in the rotating case. Thus, the current existence of convective staircases within the deep interiors of giant planets is a strong possibility, and rotation could be an important factor in the preservation of their fuzzy cores., Comment: Submitted to AAS Journals, Comments welcome

Spatiotemporal partial differential equations (PDEs) find extensive application across various scientific and engineering fields. While numerous models have emerged from both physics and machine learning (ML) communities, there is a growing trend towards integrating these approaches to develop hybrid architectures known as physics-aware machine learning models. Among these, the finite volume neural network (FINN) has emerged as a recent addition. FINN has proven to be particularly efficient in uncovering latent structures in data. In this study, we explore the capabilities of FINN in tackling the shallow-water equations, which simulates wave dynamics in coastal regions. Specifically, we investigate FINN's efficacy to reconstruct underwater topography based on these particular wave equations. Our findings reveal that FINN exhibits a remarkable capacity to infer topography solely from wave dynamics, distinguishing itself from both conventional ML and physics-aware ML models. Our results underscore the potential of FINN in advancing our understanding of spatiotemporal phenomena and enhancing parametrization capabilities in related domains.

At least two global "Snowball Earth" glaciations occurred during the Neoproterozoic Era (1000-538.8 million years ago). Post-glacial surface environments during this time are recorded in cap carbonates: layers of limestone or dolostone that directly overlie glacial deposits. Postulated environmental conditions that created the cap carbonates lack consensus largely because single hypotheses fail to explain the cap carbonates' global mass, depositional timescales, and geochemistry of parent waters. Here, we present a global geologic carbon cycle model before, during, and after the second glaciation (i.e. the Marinoan) that explains cap carbonate characteristics. We find a three-stage process for cap carbonate formation: (1) low-temperature seafloor weathering during glaciation generates deep-sea alkalinity; (2) vigorous post-glacial continental weathering supplies alkalinity to a carbonate-saturated freshwater layer, rapidly precipitating cap carbonates; (3) mixing of post-glacial meltwater with deep-sea alkalinity prolongs cap carbonate deposition. We suggest how future geochemical data and modeling refinements could further assess our hypothesis., Comment: Main text is 30 pages double spaced. 8 figures, 1 table. Supplementary Material included at end of file. Published in Nature Communications

In this work, eddy diffusivity is derived from the energy spectra for the stable and convective regimes in the planetary boundary layer. The energy spectra are obtained from a spectral model for the inertial subrange that considers the anomalous behavior of turbulence. This spectrum is expressed as a function of the Hausdorff fractal dimension. The diffusivity eddies are employed in a classical Eulerian dispersion model, where the derivatives are of integer order and in fractional dispersion model, where the derivatives are of fractional order. The eddy diffusivity proposed considers the anomalous nature of geophysical turbulent flow. The results obtained with the fractional and classic dispersion models using the eddy diffusivity proposed is satisfactory when compared with the experimental data of the Prairie Grass and Hanford experiments in a stable regime, and the Copenhagen experiment in a convective regime.

Data-driven surrogate models have shown immense potential as quick, inexpensive approximations to complex numerical and experimental modelling tasks. However, most surrogate models characterising physical systems do not quantify their uncertainty, rendering their predictions unreliable, and needing further validation. Though Bayesian approximations offer some solace in estimating the error associated with these models, they cannot provide they cannot provide guarantees, and the quality of their inferences depends on the availability of prior information and good approximations to posteriors for complex problems. This is particularly pertinent to multi-variable or spatio-temporal problems. Our work constructs and formalises a conformal prediction framework that satisfies marginal coverage for spatio-temporal predictions in a model-agnostic manner, requiring near-zero computational costs. The paper provides an extensive empirical study of the application of the framework to ascertain valid error bars that provide guaranteed coverage across the surrogate model's domain of operation. The application scope of our work extends across a large range of spatio-temporal models, ranging from solving partial differential equations to weather forecasting. Through the applications, the paper looks at providing statistically valid error bars for deterministic models, as well as crafting guarantees to the error bars of probabilistic models. The paper concludes with a viable conformal prediction formalisation that provides guaranteed coverage of the surrogate model, regardless of model architecture, and its training regime and is unbothered by the curse of dimensionality.

Recently, Transformers have gained traction in weather forecasting for their capability to capture long-term spatial-temporal correlations. However, their complex architectures result in large parameter counts and extended training times, limiting their practical application and scalability to global-scale forecasting. This paper aims to explore the key factor for accurate weather forecasting and design more efficient solutions. Interestingly, our empirical findings reveal that absolute positional encoding is what really works in Transformer-based weather forecasting models, which can explicitly model the spatial-temporal correlations even without attention mechanisms. We theoretically prove that its effectiveness stems from the integration of geographical coordinates and real-world time features, which are intrinsically related to the dynamics of weather. Based on this, we propose LightWeather, a lightweight and effective model for station-based global weather forecasting. We employ absolute positional encoding and a simple MLP in place of other components of Transformer. With under 30k parameters and less than one hour of training time, LightWeather achieves state-of-the-art performance on global weather datasets compared to other advanced DL methods. The results underscore the superiority of integrating spatial-temporal knowledge over complex architectures, providing novel insights for DL in weather forecasting.

An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit, are revolutionizing climate science. Big data and associated algorithms, coalesced under the field of Machine Learning (ML), offer the opportunity to study the physics of the climate system in ways, and with an amount of detail, infeasible few years ago. The inference provided by ML has allowed to ask causal questions and improve prediction skills beyond classical barriers. Furthermore, when paired with modeling experiments or robust research in model parameterizations, ML is accelerating computations, increasing accuracy and allowing for generating very large ensembles at a fraction of the cost. In light of the urgency imposed by climate change and the rapidly growing role of ML, we review its broader accomplishments in climate physics. Decades long standing problems in observational data reconstruction, representation of sub-grid scale phenomena and climate (and weather) prediction are being tackled with new and justified optimism. Ultimately, this review aims at providing a perspective on the benefits and major challenges of exploiting ML in studying complex systems., Comment: 25 pages, 4 figures

The dispersive motion of surface waves is now routinely used to remotely measure the currents close beneath the surface of oceans and other natural flows. The current manifests as wavelength-dependent Doppler shifts in the spatiotemporal wave spectrum, which is obtained by performing a Fourier transform of an observed wave field, a procedure which assumes that the current is horizontally uniform within the field of view. This assumption is frequently not satisfied. We here analyze the effects of the presence of horizontal shear in the velocity field, with special emphasis on biases: ``measured'' velocities which differ systematically in magnitude and/or direction compared to the average current within the field of view. We generate random synthetic wave data according to a spectrum prescribed at the domain center while varying the current and its gradient, and the mean direction $\theta_0$ and angular spread of the waves' propagation direction, from which we extract Doppler shift velocities (DSVs) using one of the most common numerical schemes. Assuming currents vary by up to 10\% of a characteristic wave phase velocity across the field of view, we find that strong biases can occur for highly directional wave spectra, strongly dependent on $\theta_0$ and the current direction relative to that of its magnitude gradient. We discuss how biases arise in the DSV extraction procedure and suggest practical steps to discover the presence of biases., Comment: 9 pages, 5 figures, submitted to IEEE Transactions on Geoscience and Remote Sensing

Growing evidence is found in observations and numerical modelling of the importance of steep seafloor topography for turbulent diapycnal mixing leading to redistribution of suspended matter and nutrients, especially in waters with abundant internal tides. One of the remaining questions is the extent of turbulent mixing away from and above nearly flat topography, which is addressed in this paper. Evaluated are observations from an opportunistic, week-long mooring of high-resolution temperature sensors above a small seafloor slope in about 1200 m water depth of the Eastern Mediterranean. The environment has weak tides, so that near-inertial motions and -shear dominate internal waves. Vertical displacement shapes suggest instabilities to represent locally generated turbulent overturns, rather than partial salinity-compensated intrusions dispersed isopycnally from turbulence near the slope. This conclusion is supported by the duration of instabilities, as all individual overturns last shorter than the mean buoyancy period and sequences of overturns last shorter than the local inertial period. The displacement shapes are more erratic than observed in stronger stratified waters in which shear drives turbulence, and better correspond with predominantly buoyancy-driven convection-turbulence. This convection-turbulence is confirmed from spectral information, generally occurring dominant close to the seafloor and only in weakly stratified layers well above it. Mean turbulence values are 10-100 times smaller than found above steep ocean topography, but 10 times larger than found in the open-ocean interior., Comment: 33 pages, 13 figures

The striking recent putative detection of "dark oxygen" (dark O$_2$) sources on the abyssal ocean floor in the Pacific at $\sim 4$ km depth raises the intriguing scenario that complex (i.e., animal-like) life could exist in underwater environments sans oxygenic photosynthesis. In this work, we thus explore the possible (astro)biological implications of this discovery. From the available data, we roughly estimate the concentration of dissolved O$_2$ and the corresponding O$_2$ partial pressure, as well as the flux of O$_2$ production, associated with dark oxygen sources. Based on these values, we infer that organisms limited by internal diffusion may reach maximal sizes of $\sim 0.1-1$ mm in habitats with dark O$_2$, while those with circulatory systems might achieve sizes of $\sim 0.1-10$ cm. Optimistically, the estimated dark oxygen flux can potentially support biomass densities up to $\sim 3-30$ g m$^{-2}$, perhaps surpassing typical reported densities at similar depths in global deep-sea surveys. Finally, we outline how oceanic settings with dark O$_2$ may facilitate the origin(s) of life via the emergence of electrotrophy. Our findings indicate that complex life fueled by dark oxygen is plausibly capable of inhabiting submarine environments devoid of photosynthesis on Earth, conceivably extending likewise to extraterrestrial locations such as icy worlds with subsurface oceans (e.g., Enceladus and Europa), which are likely common throughout the Universe., Comment: 32 pages; 0 figures

Turbulent water motions are important for the exchange of momentum, heat, nutrients, and suspended matter including sediments in the deep-sea that is generally stably stratified in density. To maintain ocean-density stratification, an irreversible diapycnal turbulent transport is needed. The geological shape and texture of marine topography is important for water mixing as most of deep-sea turbulence is generated via breaking internal waves at sloping seafloors. For example, slopes of semidiurnal internal tidal characteristics can critically match the mean seafloor slope. In this paper, the concept of critical slopes are revisited from a global internal wave-turbulence viewpoint seafloor topography -- and using moored high-resolution temperature sensor data. Observations suggest that turbulence generation via internal wave breaking at 5+/-1.5% of all seafloors is sufficient to maintain ocean-density stratification. However most, >90%, turbulence contribution is found at supercritical, rather than the more limited critical, slopes measured at 1-minute-scales that cover about 50% of seafloors at water depths < 2000 m. Internal tides (about 60%) dominate over near-inertial waves (about 40%), which is confirmed from comparison of NE-Atlantic data with East-Mediterranean data (no tides). Seafloor-elevation spectra show a wavenumber (k) fall-off rate of k^-3, which is steeper than previously found. The fall-off rate is even steeper, resulting in less elevation-variance, in a one-order-of-magnitude bandwidth around k_T=0.5 cycle-per-km. The corresponding length is equivalent to the internal tidal excursion. The reduction in seafloor-elevation variance seems associated with erosion by internal wave breaking. Potential robustness of the seafloor-internal wave interaction is discussed., Comment: 38 pages, 6 figures

Aerosol interactions with clouds represent a significant uncertainty in our understanding of the Earth system. Deep convective clouds may respond to aerosol perturbations in several ways that have proven difficult to elucidate with observations. Here, we leverage the two busiest maritime shipping lanes in the world, which emit aerosol particles and their precursors into an otherwise relatively clean tropical marine boundary layer, to make headway on the influence of aerosol on deep convective clouds. The recent seven-fold change in allowable fuel sulfur by the International Maritime Organization allows us to test the sensitivity of the lightning to changes in ship plume aerosol size distributions. We find that, across a range of atmospheric thermodynamic conditions, the previously documented enhancement of lightning over the shipping lanes has fallen by over 40\%. The enhancement is therefore at least partially aerosol-mediated, a conclusion that is supported by observations of droplet number at cloud base, which show a similar decline over the shipping lane. These results have fundamental implications for our understanding of aerosol-cloud interactions, suggesting that deep convective clouds are impacted by the aerosol number distribution in the remote marine environment.

Deep Learning based Weather Prediction (DLWP) models have been improving rapidly over the last few years, surpassing state of the art numerical weather forecasts by significant margins. While much of the optimization effort is focused on training curriculum to extend forecast range in the global context, two aspects remains less explored: limited area modeling and better backbones for weather forecasting. We show in this paper that MetMamba, a DLWP model built on a state-of-the-art state-space model, Mamba, offers notable performance gains and unique advantages over other popular backbones using traditional attention mechanisms and neural operators. We also demonstrate the feasibility of deep learning based limited area modeling via coupled training with a global host model., Comment: Typo and grammar; Minor elaboration and clarifications; Use full organization name in the author section

Capitalizing on the recent availability of ERA5 monthly averaged long-term data records of mean atmospheric and climate fields based on high-resolution reanalysis, deep-learning architectures offer an alternative to physics-based daily numerical weather predictions for subseasonal to seasonal (S2S) and annual means. A novel Deep UNet++-based Ensemble (DUNE) neural architecture is introduced, employing multi-encoder-decoder structures with residual blocks. When initialized from a prior month or year, this architecture produced the first AI-based global monthly, seasonal, or annual mean forecast of 2-meter temperatures (T2m) and sea surface temperatures (SST). ERA5 monthly mean data is used as input for T2m over land, SST over oceans, and solar radiation at the top of the atmosphere for each month of 40 years to train the model. Validation forecasts are performed for an additional two years, followed by five years of forecast evaluations to account for natural annual variability. AI-trained inference forecast weights generate forecasts in seconds, enabling ensemble seasonal forecasts. Root Mean Squared Error (RMSE), Anomaly Correlation Coefficient (ACC), and Heidke Skill Score (HSS) statistics are presented globally and over specific regions. These forecasts outperform persistence, climatology, and multiple linear regression for all domains. DUNE forecasts demonstrate comparable statistical accuracy to NOAA's operational monthly and seasonal probabilistic outlook forecasts over the US but at significantly higher resolutions. RMSE and ACC error statistics for other recent AI-based daily forecasts also show superior performance for DUNE-based forecasts. The DUNE model's application to an ensemble data assimilation cycle shows comparable forecast accuracy with a single high-resolution model, potentially eliminating the need for retraining on extrapolated datasets., Comment: Excluding Appendix: 18 pages, 14 figures

Since 1978, sensors on remote-sensing satellites have provided global, multi-band images at optical wavelengths to assess ocean color. In parallel, sophisticated radiative transfer models account for attenuation and emission by the Earth's atmosphere and ocean, thereby estimating the water-leaving radiance or and remote-sensing reflectance Rrs. From these Rrs measurements, estimates of the absorption and scattering by seawater are inferred. We emphasize an inherent, physical degeneracy in the radiative transfer equation that relates Rrs to the absorption and backscattering coefficients a and b_b, aka inherent optical properties (IOPs). Because Rrs depends solely on the ratio of b_b to a, meaning one cannot retrieve independent functions for the non-water IOPs, a_nw and b_bnw, without a priori knowledge. Moreover, water generally dominates scattering at blue wavelengths and absorption at red wavelengths, further limiting retrievals of IOPs in the presence of noise. We demonstrate that all previous and current multi-spectral satellite observations lack the statistical power to measure more than 3 parameters total to describe a_nw and b_bnw. Due to the ubiquitous exponential-like absorption by color dissolved organic matter at short wavelengths (l<500nm), multi-spectral Rrs do not permit the detection of phytoplankton absorption a_ph without very strict priors. Furthermore, such priors lead to biased and uncertain retrievals of a_ph. Hyperspectral observations may recover a 4th and possibly 5th parameter describing only one or two aspects of the complexity of a_ph. These results cast doubt on decades of literature on IOP retrievals, including estimates of phytoplankton growth and biomass. We further conclude that NASA/PACE will greatly enhance our ability to measure the phytoplankton biomass of Earth, but challenges remain in resolving the IOPs., Comment: submitted to Nature Communications; 30 pages, 20 figures (3 main)

In this paper, we compare the performance of three common deep learning architectures, CNN-LSTM, LSTM, and 3D-CNN, in the context of surrogate storm surge modeling. The study site for this paper is the Tampa Bay area in Florida. Using high-resolution atmospheric data from the reanalysis models and historical water level data from NOAA tide stations, we trained and tested these models to evaluate their performance. Our findings indicate that the CNN-LSTM model outperforms the other architectures, achieving a test loss of 0.010 and an R-squared (R2) score of 0.84. The LSTM model, although it achieved the lowest training loss of 0.007 and the highest training R2 of 0.88, exhibited poorer generalization with a test loss of 0.014 and an R2 of 0.77. The 3D-CNN model showed reasonable performance with a test loss of 0.011 and an R2 of 0.82 but displayed instability under extreme conditions. A case study on Hurricane Ian, which caused a significant negative surge of -1.5 meters in Tampa Bay indicates the CNN-LSTM model's robustness and accuracy in extreme scenarios.

The increasing generation of data in different areas of life, such as the environment, highlights the need to explore new techniques for processing and exploiting data for useful purposes. In this context, artificial intelligence techniques, especially through deep learning models, are key tools to be used on the large amount of data that can be obtained, for example, from weather radars. In many cases, the information collected by these radars is not open, or belongs to different institutions, thus needing to deal with the distributed nature of this data. In this work, the applicability of a personalized federated learning architecture, which has been called adapFL, on distributed weather radar images is addressed. To this end, given a single available radar covering 400 km in diameter, the captured images are divided in such a way that they are disjointly distributed into four different federated clients. The results obtained with adapFL are analyzed in each zone, as well as in a central area covering part of the surface of each of the previously distributed areas. The ultimate goal of this work is to study the generalization capability of this type of learning technique for its extrapolation to use cases in which a representative number of radars is available, whose data can not be centralized due to technical, legal or administrative concerns. The results of this preliminary study indicate that the performance obtained in each zone with the adapFL approach allows improving the results of the federated learning approach, the individual deep learning models and the classical Continuity Tracking Radar Echoes by Correlation approach., Comment: Accepted for publication in Earth Science Informatics

In recent years, several studies have been made in which atmospheric and oceanic data were used to decompose horizontal velocity statistics into a rotational component, associated with vertical vorticity, and a divergent component, associated with horizontal divergence. Making the assumption of statistical homogeneity in a horizontal plane, this can be accomplished by relating the rotational and divergent components of the difference between the velocities at two points to the corresponding longitudinal and transverse components, where the longitudinal and transverse directions are parallel respectively perpendicular to the line between the points. In previous studies, the decomposition has most often been made under the assumption of statistical isotropy. Some attempts have also been made to analyse the anisotropic problem. We derive the full anisotropic equations relating the rotational, divergent and the rotational-divergent components of the second order structure functions to the longitudinal, transverse and longitudinal-transverse components and solve the equations analytically. We also derive some results for third order structure functions, with special focus on the components associated with cyclone-anticyclone asymmetry. Based on the analysis of these components and results from previous analyses of aircraft data, it is concluded that there is an exclusively rotational flow component that is giving rise to strong dominance of cyclonic motions in the upper troposphere and a strong dominance of anticyclonic motions in the lower stratosphere in the range of scales from ten to one thousand km

Operational numerical weather prediction systems consist of three fundamental components: the global observing system for data collection, data assimilation for generating initial conditions, and the forecasting model to predict future weather conditions. While NWP have undergone a quiet revolution, with forecast skills progressively improving over the past few decades, their advancement has slowed due to challenges such as high computational costs and the complexities associated with assimilating an increasing volume of observational data and managing finer spatial grids. Advances in machine learning offer an alternative path towards more efficient and accurate weather forecasts. The rise of machine learning based weather forecasting models has also spurred the development of machine learning based DA models or even purely machine learning based weather forecasting systems. This paper introduces FuXi Weather, an end-to-end machine learning based weather forecasting system. FuXi Weather employs specialized data preprocessing and multi-modal data fusion techniques to integrate information from diverse sources under all-sky conditions, including microwave sounders from 3 polar-orbiting satellites and radio occultation data from Global Navigation Satellite System. Operating on a 6-hourly DA and forecasting cycle, FuXi Weather independently generates robust and accurate 10-day global weather forecasts at a spatial resolution of 0.25\textdegree. It surpasses the European Centre for Medium-range Weather Forecasts high-resolution forecasts in terms of predictability, extending the skillful forecast lead times for several key weather variables such as the geopotential height at 500 hPa from 9.25 days to 9.5 days. The system's high computational efficiency and robust performance, even with limited observations, demonstrates its potential as a promising alternative to traditional NWP systems., Comment: 34 pages, 4 figures

Firstly, by establishing a prediction model for global sea-level rise and calculating with Maple, it is shown that the global sea-level rise rate in 2009 is 2.68 mm/a. The height and rate of global sea-level rise will be about 9.11 cm and 3.22 mm/a in 2020. Based on the study and the actual land subsidence in Shanghai Lingang New City, the rate of relative sea-level rise near Lingang New City is calculated to be 12.68 mm/a in 2009. Then, through setting up the extrapolation prediction model with a linear trend term and a significant tidal cycle, the rise rate of average sea-level near Lingang New City was predicted. The result showed it will be 0.33 mm/a in 2020.

The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds may be exceeded by future events, which undermines attribution and planning for heatwave impacts. Here, we show how the occurrence and relative probability of observed events that exceed a priori upper bound estimates, so-called "impossible" temperatures, has changed over time. We find that many unprecedented events are actually within data-driven upper bounds, but only when using modern spatial statistical methods. Furthermore, there are clear connections between anthropogenic forcing and the "impossibility" of the most extreme temperatures. Robust understanding of heatwave thresholds provides critical information about future record-breaking events and how their extremity relates to historical measurements.

To understand mechanisms of salt intrusion in estuaries, we develop a semi-analytical model, that explicitly accounts for salt transport by both exchange flow and tidal flow. This model, after calibration, successfully hindcasts hydrodynamics and salinity dynamics in three estuaries that have strongly different characteristics. We find, from analyzing the model results for these three estuaries, that salt transport processes by exchange flow and tidal flow interact through the subtidal stratification. Transport by exchange flow creates stratification, thereby generating a phase shift of tidal salinity with respect to the tidal flow, which is important for the magnitude of the tidal salt transport. Conversely, the strength of tidal currents determines the vertical mixing that breaks down stratification. A new analytical formulation is presented for the component of the salt transport driven by the depth-averaged tidal flow. This salt transport is larger than the component associated with the vertical shear of the tidal current. Finally, a method that yields analytical equations that quantify the importance of different contributions to the salt transport using only primary information is developed using approximate solutions for the subtidal stratification. This method performs well for the estuaries considered.

Deploying hydrogen technologies is one option to reduce energy carbon dioxide emissions, but recent studies have called attention to the indirect climate implications of fugitive hydrogen emissions. We find that biases in hydroxyl (OH) radical concentrations and reactivity in current atmospheric chemistry models may cause a 20% overestimate of the hydrogen Global Warming Potential (GWP). A better understanding of OH chemistry is critical for reliable estimates of the hydrogen GWP.

Mitigating methane emissions is the fastest way to stop global warming in the short-term and buy humanity time to decarbonise. Despite the demonstrated ability of remote sensing instruments to detect methane plumes, no system has been available to routinely monitor and act on these events. We present MARS-S2L, an automated AI-driven methane emitter monitoring system for Sentinel-2 and Landsat satellite imagery deployed operationally at the United Nations Environment Programme's International Methane Emissions Observatory. We compile a global dataset of thousands of super-emission events for training and evaluation, demonstrating that MARS-S2L can skillfully monitor emissions in a diverse range of regions globally, providing a 216% improvement in mean average precision over a current state-of-the-art detection method. Running this system operationally for six months has yielded 457 near-real-time detections in 22 different countries of which 62 have already been used to provide formal notifications to governments and stakeholders.

We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning (DL). The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known. Experiments are performed with the Lorenz 96 dynamics, which display spatiotemporal chaos and for which solid benchmarks for DA performance exist. The accuracy of the states obtained from the learned analysis approaches that of the best possibly tuned ensemble Kalman filter (EnKF), and is far better than that of variational DA alternatives. Critically, this can be achieved while propagating even just a single state in the forecast step. We investigate the reason for achieving ensemble filtering accuracy without an ensemble. We diagnose that the analysis scheme actually identifies key dynamical perturbations, mildly aligned with the unstable subspace, from the forecast state alone, without any ensemble-based covariances representation. This reveals that the analysis scheme has learned some multiplicative ergodic theorem associated to the DA process seen as a non-autonomous random dynamical system.

Weather regimes describe the large-scale atmospheric circulation in the mid-latitudes in terms of a few circulation states that modulate regional surface weather. Subseasonal forecasts of prevailing weather regimes have proven skillful and valuable to energy applications. Previous studies have mainly focused on the mean surface weather associated with a regime. However, we show in this paper that variability of surface weather within a regime cannot be ignored. These intra-regime variations, caused by different `subflavors' of the same regime, can be captured by continuous regime indices and allow a refined application of weather regimes. Here we discuss wintertime temperature and wind speed regime anomalies for four selected countries, and provide guidance on the operational use and interpretation of regime forecasts. In an accompanying supplementary dataset we provide similar analysis for all European countries, seasons and key energy variables, useful as an applied reference., Comment: 13 pages, 4 figures, supplement in SI.pdf. Submitted to Geophysical Research Letters

We propose an approach for characterizing source-impact pathways, the interactions of a set of variables in space-time due to an external forcing, in climate models using in-situ analyses that circumvent computationally expensive read/write operations. This approach makes use of a lightweight open-source software library we developed known as CLDERA-Tools. We describe how CLDERA-Tools is linked with the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) in a minimally invasive way for in-situ extraction of quantities of interested and associated statistics. Subsequently, these quantities are used to represent source-impact pathways with time-dependent directed acyclic graphs (DAGs). The utility of CLDERA-Tools is demonstrated by using the data it extracts in-situ to compute a spatially resolved DAG from an idealized configuration of the atmosphere with a parameterized representation of a volcanic eruption known as HSW-V.

The total energy of a fireball is commonly obtained from optical measurements with an assumed value for luminous efficiency. Acoustic energy measurements offer an independent means of energy estimation. Here we combine optical and acoustic methods to validate the luminous efficiency model of Borovi\v{c}ka et al. (2020). Our goal is to compare these models with acoustic measurements of meteoroid energy deposition. Employing theoretical blast scaling laws following the approach of McFadden et al. (2021), we determine explosive yields for both fireball fragmentation events and cylindrical shocks for four different bright fireballs. We model fireballs using the MetSim software (Vida et al., 2023) and find that the Borovi\v{c}ka et al. (2020) model produces agreement better than a factor of two for our three chondritic fireball case studies. The major exception is an iron meteorite-producing fireball where the luminous efficiency is an order of magnitude higher than model predictions calibrated with stony fireballs. We suggest that large disparities between optical and acoustic energies could be a signature of iron fireballs and hence useful as a discriminant of that population., Comment: Accepted for publication in Icaurs

Using nonlinear simulations in two settings, we demonstrate that QG$^\mathrm{+1}$, a potential-vorticity (PV) based next-order-in-Rossby balanced model, captures several aspects of ocean submesoscale physics. In forced-dissipative 3D simulations under baroclinically unstable Eady-type background states, the statistical equilibrium turbulence exhibits long cyclonic tails and a plethora of rapidly-intensifying ageostrophic fronts. Despite that the model requires setting an explicit, small value for the fixed scaling Rossby number, the emergent flows are nevertheless characterized by $O(f)$ vorticity and convergence, as observed in upper-ocean submesoscale flows. Simulations of QG$^\mathrm{+1}$ under the classic strain-induced frontogenesis set-up show realistic frontal asymmetry and a finite time blow-up, quantitatively comparable to simulations of the semigeostrophic equations. The inversions in the QG$^\mathrm{+1}$ model are straightforward elliptic problems, allowing for the reconstruction of all flow fields from the PV and surface buoyancy, while avoiding the semigeostrophic coordinate transformation. Together, these results suggest QG$^\mathrm{+1}$ as a useful tool for studying upper-ocean submesoscale dynamics.

Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1,000-10,000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part I, we construct an ensemble weather forecasting system based on Spherical Fourier Neural Operators (SFNO), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. Using large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states, and the ML ensemble thus passes a crucial spectral test in the literature. The IFS and ML ensembles have similar Extreme Forecast Indices, and we show that the ML extreme weather forecasts are reliable and discriminating.

Paris is experimenting and implementing strategies to increase the capital's resilience and promote climate change adaptation. Major heatwaves have been hitting the French Capital lately (Bador et al., 2017) and have thus focused attention on UHI countermeasures, such as pavement-watering solutions and urban greening. The Oasis Project is one such strategy aiming to transform schoolyards into urban cool islands that would benefit surrounding neighborhoods and their inhabitants during heatwaves.The work presented here focuses on identifying high-priority schoolyards among the 670 city-owned schools. This is conducted using a GIS tool used to identify areas with high cooling potential, which would benefit most from UHI countermeasures. After extracting bare schoolyard from the facilities, we built a cooling indicator based on the ratio of high solar irradiance surface to the whole schoolyard area. We were thus able to identify 38 schoolyards with high cooling potential, 157 with medium cooling potential and 286 facilities with moderate cooling potential, out of the 670 facilities.The methodology can be applied to other cities, and therefore helps set up GIS tools that can provide municipalities with meaningful insight into their urban cooling strategy.

The performance of a number of urban cooling techniques has been thoroughly studied by the scientific community. However, decision-makers lack the tools to spatialize their deployment as part of their urban cooling and climate change adaptation strategies. Among other indicators, a spatial assessment of the cooling potential for a technique in a given area is lacking. To this end, we analyze the physical mechanisms on which these techniques are based and identify corresponding geographical indicators that influence their cooling performance. Solar irradiance, existing material properties and underground infrastructure stand out as essential indicators for this purpose.