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Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger, larger and more destructive. The accurate detection and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate datasets through the use of deterministic tracking schemes that rely on subjective thresholds. Machine Learning (ML) models can complement deterministic approaches due to their ability to capture the mapping between the input climatic drivers and the geographical position of the TC center from the available data. This study presents a ML ensemble approach for locating TC center coordinates, embedding both TC classification and localization in a single end-to-end learning task. The ensemble combines TC center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis were used for model training and testing jointly with the International Best Track Archive for Climate Stewardship records. Results showed that the ML approach is well-suited for TC detection providing good generalization capabilities on out of sample data. In particular, it was able to accurately detect lower TC categories than those used for training the models. On top of this, the ensemble approach was able to further improve TC localization performance with respect to single model TC center estimates, demonstrating the good capabilities of the proposed approach., 27 pages, 8 figures, 1 table, submitted to Journal of Advances in Modeling Earth Systems

A spatiotemporal oscillator model for El Ni\~no/Southern Oscillation (ENSO) is constructed based on the sea surface temperature (SST) and thermocline depth dynamics. The model is enclosed by introducing a proportional relationship between the gradient in SST and the oceanic zonal current and can be transformed into a standard wave equation that can be decomposed into a series of eigenmodes by cosine series expansion. Each eigenmode shows a spatial mode that oscillates with a natural frequency. The first spatial mode, that highlights SST anomaly (SSTA) contrast in the eastern and western Pacific, the basic characteristics of the eastern Pacific (EP) El Ni\~no, oscillates with a natural period of around 4.3 years, consistent with the quasi-quadrennial (QQ) mode. The second spatial mode, that emphasizes SSTA contrast between the central and the eastern, western Pacific, the basic spatial structure of the central Pacific (CP) El Ni\~no, oscillates with a natural period of 2.3 years that is half of the first natural period, also consistent with the quasi-biennial (QB) modes. The combinations of the first two eigenmodes with different weights can feature complex SSTA patterns with complex temporal variations. In open ocean that is far away from the coastlines, the model can predict waves propagating both eastward and westward. Besides, the net surface heating further complicates the temporal variations by exerting forced frequencies. The model unifies the temporal and spatial variations and may provide a comprehensive viewpoint for understanding the complex spatiotemporal variations of ENSO.

In relaxation-oscillator (RO) climate states, short-lived convective storms with torrential rainfall form and dissipate at regular, periodic intervals. RO states have been demonstrated in two- and three-dimensional simulations of radiative-convective equilibrium (RCE), and it has been argued that the existence of the RO state requires explicitly resolving moist convective processes. However, the exact nature and emergence mechanism of the RO state have yet to be determined. Here, we show that (1) RO states exist in single-column-model simulations of RCE with parameterized convection, and (2) the RO state can be understood as one that has no steady-state solutions of an analytical model of RCE. As with model simulations with resolved convection, these simpler, one-dimensional models of RCE clearly demonstrate RO states emerge at high surface temperatures and/or very moist atmospheres. Emergence occurs when atmospheric instability quantified by the convective available potential energy can no longer support the latent heat release of deep, entraining convective plumes. The proposed mechanism for RO emergence is general to all moist planetary atmospheres, is agnostic of the condensing component, and naturally leads to an understanding of Titan's bursty methane weather., 15 pages, 5 figures, and 1 table

The theory of temperature jets gets extended to account for the influence of the beta effect on their dynamics. Including this effect noticeably changes symmetry properties and laws of conservation inherent to models without the beta effect. Especially nontrivial, the dynamics of jets become near the equator, where the model admits multi-valued solutions that look like jets crossing the equator. For example, the so-called loop solitons moving along the equator with velocities inversely proportional to the cube of their amplitude turn out among them. Estimates based on simple qualitative considerations show that, owing to the beta effect, the temperature jets can form a specific equatorial turbulence, in which they play the role of structural elements. Notably, the spectral slope of the energy density at large wave numbers in such turbulence becomes equal to unity.

Terrestrial gamma-ray flashes (TGFs) are commonly associated with relativistic runaway electron avalanches (RREAs). However, research shows that a single RREA cannot generate observable TGF fluxes. In an attempt to settle this issue the relativistic feedback mechanism was suggested by Joseph Dwyer. The Monte Carlo simulations and analytical descriptions of this type of feedback assume that acceleration region has a large size in a plane perpendicular to the direction of the electric field. Therefore these studies do not take into account transverse diffusion of RREAs starting points and the finite transverse size of the accelerating region. Electrons created by the feedback outside this region can not be accelerated by the electric field and form an avalanche, which may lead to a decrease in the total number of new avalanches and an increase in the requirements for self-sustaining RREA production by the feedback. In this article the transverse propagation of avalanches starting points was described using a modified two-dimensional diffusion equation. A correction to the criterion for self-sustaining production of RREAs was obtained. Monte Carlo simulation was also performed to calculate the correction for the feedback coefficient., 5 pages, 4 figures

This paper utilized the high temporal and spatial resolution temperature profile data observed by the multi-channel microwave radiometer at the Large High Altitude Air Shower Observatory (LHAASO) on the eastern slope of the Tibetan Plateau from February to May and August to November 2021, combined with the ERA5 reanalysis data products for the whole year of 2021, to study the daily, monthly, and seasonal variations of the atmospheric boundary layer height (ABLH). The results are as follows: (1) The ABLH on sunny days showed obvious fluctuations with peaks and valleys. The ABLH continued to rise with the increase of surface temperature after sunrise and usually reached its maximum value in the afternoon around 18:00, then rapidly decreased until sunset. (2) The average ABLH in April was the highest at about 1200 m, while it was only around 600 m in November. The ABLH fluctuated greatly during the day and was stable at around 400 m at night. The ABLH results obtained from ERA5 were slightly smaller overall but had a consistent trend of change with the microwave radiometer. (3) The maximum ABLH appeared in spring, followed by summer and autumn, and winter had the lowest value, with all peaks reached around 14:00-15:00. These results are of great significance for understanding the ABLH on the eastern slope of the Tibetan Plateau, and provide reference for the absolute calibration of photon numbers of the LHAASO telescope and the atmospheric monitoring plan, as well as for evaluating the authenticity and accuracy of existing reanalysis datasets.

A regression-based model was previously developed to forecast the total electron content (TEC) at middle latitudes. We present a more sophisticated model using neural networks (NN) instead of linear regression. This regional model prototype simulates and forecasts TEC variations in relation to space weather conditions. The development of a prototype consisted of the selection of the best set of predictors, NN architecture and the length of the input series. Tests made using the data from December 2014 to June 2018 show that the PCA-NN model based on a simple feed-forward NN with a very limited number (up to 6) of space weather predictors performs better than the PCA-MRM model that uses up to 27 space weather predictors. The prototype is developed on a TEC series obtained from a GNSS receiver at Lisbon airport and tested on TEC series from three other locations at middle altitudes of the Eastern North Atlantic. Conclusions on the dependence of the forecast quality on longitude and latitude are made., arXiv admin note: text overlap with arXiv:2201.03477

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

In a recent methodological paper, we showed how to learn chaotic dynamics along with the state trajectory from sequentially acquired observations, using local ensemble Kalman filters. Here, we more systematically investigate the possibility to use a local ensemble Kalman filter with either covariance localisation or local domains, in order to retrieve the state and a mix of key global and local parameters. Global parameters are meant to represent the surrogate dynamical core, for instance through a neural network, which is reminiscent of data-driven machine learning of dynamics, while the local parameters typically stand for the forcings of the model. Aiming at joint state and parameter estimation, a family of algorithms for covariance and local domain localisation is proposed. In particular, we show how to rigorously update global parameters using a local domain ensemble Kalman filter (EnKF) such as the local ensemble transform Kalman filter (LETKF), an inherently local method. The approach is tested with success on the 40-variable Lorenz model using several of the local EnKF flavors. A two-dimensional illustration based on a multi-layer Lorenz model is finally provided. It uses radiance-like non-local observations. It features both local domains and covariance localisation in order to learn the chaotic dynamics and the local forcings. This paper more generally addresses the key question of online estimation of both global and local model parameters.

Sub-seasonal climate forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. Additionally, we explore mechanisms to enhance the ML models by using forecasts from dynamical models. Empirical results illustrate that, on average, ML models outperform dynamical models while the ML models tend to be conservatives in their forecasts compared to the SubX models. Further, we illustrate that ML models make forecasting errors under extreme weather conditions, e.g., cold waves due to the polar vortex, highlighting the need for separate models for extreme events. Finally, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve the forecasting performance of the ML models. The SSF dataset constructed for the work, dynamical model predictions, and code for the ML models are released along with the paper for the benefit of the broader machine learning community.

The understanding of nonlinear, high dimensional flows, e.g, atmospheric and ocean flows, is critical to address the impacts of global climate change. Data Assimilation techniques combine physical models and observational data, often in a Bayesian framework, to predict the future state of the model and the uncertainty in this prediction. Inherent in these systems are noise (Gaussian and non-Gaussian), nonlinearity, and high dimensionality that pose challenges to making accurate predictions. To address these issues we investigate the use of both model and data dimension reduction based on techniques including Assimilation in Unstable Subspaces, Proper Orthogonal Decomposition, and Dynamic Mode Decomposition. Algorithms that take advantage of projected physical and data models may be combined with Data Analysis techniques such as Ensemble Kalman Filter and Particle Filter variants. The projected Data Assimilation techniques are developed for the optimal proposal particle filter and applied to the Lorenz'96 and Shallow Water Equations to test the efficacy of our techniques in high dimensional, nonlinear systems., 30 pages, 13 figures, 3 tables To appear in Computers & Mathematics with Applications, 2021,ISSN 0898-1221

Large-eddy simulations (LES) are employed to investigate the role of time-varying currents on the form drag and vortex dynamics of submerged 3D topography in a stratified rotating environment. The current is of the form Uc + Utsin(2πftt), where Uc is the mean, Ut is the tidal component, and ft is its frequency. A conical obstacle is considered in the regime of low Froude number. When tides are absent, eddies are shed at the natural shedding frequency fs,c. The relative frequency is varied in a parametric study, which reveals states of high time-averaged form drag coefficient. There is a twofold amplification of the form drag coefficient relative to the no-tide (Ut = 0) case when lies between 0.5 and 1. The spatial organization of the near-wake vortices in the high drag states is different from a Kármán vortex street. For instance, the vortex shedding from the obstacle is symmetric when and strongly asymmetric when . The increase in form drag with increasing stems from bottom intensification of the pressure in the obstacle lee which we link to changes in flow separation and near-wake vortices.

In this work, we analytically study the existence of periodic vortex cap solutions for the homogeneous and incompressible Euler equations on the rotating unit 2-sphere, which was numerically conjectured by Dritschel-Polvani and Kim-Sakajo-Sohn. Such solutions are piecewise constant vorticity distributions, subject to the Gauss constraint and rotating uniformly around the vertical axis. The proof is based on the bifurcation from zonal solutions given by spherical caps. For the one--interface case, the bifurcation eigenvalues correspond to Burbea's frequencies obtained in the planar case but shifted by the rotation speed of the sphere. The two--interfaces case (also called band type or strip type solutions) is more delicate. Though, for any fixed large enough symmetry, and under some non-degeneracy conditions to avoid spectral collisions, we achieve the existence of at most two branches of bifurcation., 40 pages, 4 figures

This paper proposes an anticipative transformer-based model for short-term solar irradiance forecasting. Given a sequence of sky images, our proposed vision transformer encodes features of consecutive images, feeding into a transformer decoder to predict irradiance values associated with future unseen sky images. We show that our model effectively learns to attend only to relevant features in images in order to forecast irradiance. Moreover, the proposed anticipative transformer captures long-range dependencies between sky images to achieve a forecasting skill of 21.45 % on a 15 minute ahead prediction for a newly introduced dataset of all-sky images when compared to a smart persistence model., 10 pages, 6 figures, Best Paper submission for CVPR 2023 workshop EARTHVISION 2023

This thesis presents a new algorithm to mitigate cloud masking in the analysis of sea surface temperature (SST) data generated by remote sensing technologies, e.g., Clouds interfere with the analysis of all remote sensing data using wavelengths shorter than 12 microns, significantly limiting the quantity of usable data and creating a biased geographical distribution (towards equatorial and coastal regions). To address this issue, we propose an unsupervised machine learning algorithm called Enki which uses a Vision Transformer with Masked Autoencoding to reconstruct masked pixels. We train four different models of Enki with varying mask ratios (t) of 10%, 35%, 50%, and 75% on the generated Ocean General Circulation Model (OGCM) dataset referred to as LLC4320. To evaluate performance, we reconstruct a validation set of LLC4320 SST images with random ``clouds'' corrupting p=10%, 20%, 30%, 40%, 50% of the images with individual patches of 4x4 pixel^2. We consistently find that at all levels of p there is one or multiple models that reconstruct the images with a mean RMSE of less than 0.03K, i.e. lower than the estimated sensor error of VIIRS data. Similarly, at the individual patch level, the reconstructions have RMSE 8x smaller than the fluctuations in the patch. And, as anticipated, reconstruction errors are larger for images with a higher degree of complexity. Our analysis also reveals that patches along the image border have systematically higher reconstruction error; we recommend ignoring these in production. We conclude that Enki shows great promise to surpass in-painting as a means of reconstructing cloud masking. Future research will develop Enki to reconstruct real-world data., 33 pages, 18 figures; Masters Thesis

Utilizing observations from the ELFIN satellites, we present a statistical study of $\sim$2000 events in 2019-2020 characterizing the occurrence in magnetic local time (MLT) and latitude of $\geq$50 keV electron isotropy boundaries (IBs) at Earth, and the dependence of associated precipitation on geomagnetic activity. The isotropy boundary for an electron of a given energy is the magnetic latitude poleward of which persistent isotropized pitch-angle distributions ($J_{prec}/J_{perp}\sim 1$) are first observed to occur, interpreted as resulting from magnetic field-line curvature scattering (FLCS) in the equatorial magnetosphere. We find that energetic electron IBs can be well-recognized on the nightside from dusk until dawn, under all geomagnetic activity conditions, with a peak occurrence rate of almost 90% near $\sim$22 hours in MLT, remaining above 80% from 21 to 01 MLT. The IBs span a wide range of IGRF magnetic latitudes from $60^\circ$-$74^\circ$, with a maximum occurrence between $66^\circ$-$71^\circ$ (L of 6-8), shifting to lower latitudes and pre-midnight local times with activity. The precipitating energy flux of $\geq$50 keV electrons averaged over the IB-associated latitudes varies over four orders of magnitude, up to $\sim$1 erg/cm$^2$-s, and often includes electron energies exceeding 1 MeV. The local time distribution of IB-associated energies and precipitating fluxes also exhibit peak values near midnight for low activity, shifting toward pre-midnight for elevated activity. The percentage of the total energy deposited over the high-latitude regions ($55^\circ$ to $80^\circ$; or IGRF $L\gtrsim 3$) attributed to IBs is 10-20%, on average, or about 10 MW of total atmospheric power input, but at times can be up to $\sim$100% of the total $\geq$50 keV electron energy deposition over the entire sub-auroral and auroral zone region, exceeding 1 GW in atmospheric power input.

Evaporation (ET) is one of the crucial components of the water cycle, which serves as the nexus between global water, energy, and carbon cycles. Accurate quantification of ET is, therefore, pivotal in understanding various earth system processes and subsequent societal applications. The prevailing approaches for ET retrievals are either limited in spatiotemporal coverage or largely influenced by choice of input data or simplified model physics, or a combination thereof. Here, using an independent mass conservation approach, we develop water balance-based ET datasets (ET-WB) for the global land and the selected 168 major river basins. We generate 4669 probabilistic unique combinations of the ET-WB leveraging multi-source datasets (23 precipitation, 29 runoff, and 7 storage change datasets) from satellite products, in-situ measurements, reanalysis, and hydrological simulations. We compare our results with the four auxiliary global ET datasets and previous regional studies, followed by a rigorous discussion of the uncertainties, their possible sources, and potential ways to constrain them. The seasonal cycle of global ET-WB possesses a unimodal distribution with the highest (median value: 65.61 mm/month) and lowest (median value: 36.11 mm/month) values in July and January, respectively, with the spread range of roughly ±10 mm/month from different subsets of the ensemble. Auxiliary ET products illustrate similar intra-annual characteristics with some over/under-estimation, which are completely within the range of the ET-WB ensemble. We found a gradual increase in global ET-WB from 2003 to 2010 and a subsequent decrease during 2010–2015, followed by a sharper reduction in the remaining years primarily attributed to the varying precipitation. Multiple statistical metrics show reasonably good accuracy of monthly ET-WB (e.g., a relative bias of ±20 %) in most river basins, which ameliorates at annual scales. The long-term mean annual ET-WB varies within 500–600 mm/yr and is consistent with the for auxiliary ET products (543–569 mm/yr). Observed trend estimates, though regionally divergent, are evidence of the increasing ET in a warming climate. The current dataset will likely be useful for several scientific assessments centering around water resources management to benefit society at large. The dataset is publicly available at https://doi.org/10.5281/zenodo.7314920 (Xiong et al., 2023).

Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other., Presented at the ICLR 2023 workshop on "Tackling Climate Change with Machine Learning"

In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is crucial to study the magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow storage, can drastically impact the water supplies in basins where snow predominates, such as in the western United States. Hence, it is important to detect the time and severity of snow droughts efficiently. We propose Snow Drought Response Index or SnoDRI, a novel indicator that could be used to identify and quantify snow drought occurrences. Our index is calculated using cutting-edge ML algorithms from various snow-related variables. The self-supervised learning of an autoencoder is combined with mutual information in the model. In this study, we use random forests for feature extraction for SnoDRI and assess the importance of each variable. We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index. We evaluate the index by confirming the coincidence of its interpretation and the actual snow drought incidents.

We study the spectral energy transfer due to wave-triad interactions in the Garrett-Munk spectrum of internal gravity waves (IGWs) based on a numerical evaluation of the collision integral in the wave kinetic equation. Our numerical evaluation builds on the reduction of the collision integral on the resonant manifold for an horizontally isotropic spectrum. We directly evaluate the downscale energy flux available for ocean mixing, whose value is in close agreement with the empirical finescale parameterization. We further decompose the energy transfer into contributions from different mechanisms, including local interactions and three types of nonlocal interactions, namely parametric subharmonic instability (PSI), elastic scattering (ES) and induced diffusion (ID). Through analysis on the role of each type of interaction, we resolve two long-standing paradoxes regarding the mechanism for forward cascade in frequency and zero ID flux for GM76 spectrum. In addition, our analysis estimates the contribution of each mechanism to the energy transfer in each spectral direction, and reveals new understanding of the importance of local interactions and ES in the energy transfer.

The tipping of the Atlantic Meridional Overturning Circulation (AMOC) to a 'shutdown' state due to changes in the freshwater forcing of the ocean is of particular interest and concern due to its widespread ramifications, including a dramatic climatic shift for much of Europe. A clear understanding of how such a shutdown would unfold requires analyses of models from across the complexity spectrum. For example, detailed simulations of sophisticated Earth System Models have identified scenarios in which deep-water formation first ceases in the Labrador Sea before ceasing in the Nordic Seas, en route to a complete circulation shutdown. Here, we study a simple ocean box model with two polar boxes designed to represent deep-water formation at these two distinct sites. A bifurcation analysis reveals how, depending on the differences of freshwater and thermal forcing between the two polar boxes, transitions to 'partial shutdown' states are possible. Our results shed light on the nature of the tipping of AMOC and clarify dynamical features observed in more sophisticated models., 13 pages, 13 figures

The climate is a complex non-equilibrium dynamical system that relaxes toward a steady state under the continuous input of solar radiation and dissipative mechanisms. The steady state is not necessarily unique. A useful tool to describe the possible steady states under different forcing is the bifurcation diagram, that reveals the regions of multi-stability, the position of tipping points, and the range of stability of each steady state. However, its construction is highly time consuming in climate models with a dynamical deep ocean, interactive ice sheets or carbon cycle, where the relaxation time becomes larger than thousand years. Using a coupled setup of MITgcm, we test two techniques with complementary advantages. The first is based on the introduction of random fluctuations in the forcing and permits to explore a wide part of phase space. The second reconstructs the stable branches and is more precise in finding the position of tipping points., Comment: 9 pages, 5 figures, Supplemental Material, accepted for publication in Phys. Rev. E

Accurate short-term prediction of phase-resolved water wave conditions is crucial for decision-making in ocean engineering. However, the initialization of remote-sensing-based wave prediction models first requires a reconstruction of wave surfaces from sparse measurements like radar. Existing reconstruction methods either rely on computationally intensive optimization procedures or simplistic modeling assumptions that compromise real-time capability or accuracy of the entire prediction process. We therefore address these issues by proposing a novel approach for phase-resolved wave surface reconstruction using neural networks based on the U-Net and Fourier neural operator (FNO) architectures. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids, that is generated by the high-order spectral method for wave simulation and a geometric radar modeling approach. The investigation reveals that both models deliver accurate wave reconstruction results and show good generalization for different sea states when trained with spatio-temporal radar data containing multiple historic radar snapshots in each input. Notably, the FNO-based network performs better in handling the data structure imposed by wave physics due to its global approach to learn the mapping between input and desired output in Fourier space., 19 pages, 13 figures (without appendix), preprint

Atmospheric turbulence is one of the lead disturbance factors for free-space quantum communication. The quantum states of light in such channels are affected by fluctuating losses characterized by the probability distributions of transmittance (PDT). We obtain the PDT for different scenarios via numerical simulations of light transmission through the atmosphere. The results are compared with analytical models: truncated log-normal distribution, beam-wandering model, elliptic-beam approximation, and the model based on the law of total probability. Their applicability is shown to be strongly dependent on the receiver aperture radius. We introduce an empirical model based on the beta distribution, which is in good agreement with numerical simulations for a wide range of channel parameters. However, there are still scenarios where none of the above analytical models fits the numerically-simulated data. The numerical simulation is then used to analyze the transmission of quadrature-squeezed light through free-space channels., 19 pages, 15 figures, the ancillary files contain nummerically-simulated data, an interactive tool for their visualization, and the corresponding Python 3 code

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order to fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted for the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods are discussed. Specifically this paper covers perceptrons, artificial neural networks, convolutional neural networks and U-networks. Like the part 1 paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets.

The Paris Agreement aims to reach net zero greenhouse gas (GHG) emissions in the second half of the 21st century, and the Oil & Gas sector plays a key role in achieving this transition. Understanding progress in emission reductions in the private sector relies on the disclosure of corporate climate-related data, and the Carbon Disclosure Project (CDP) is considered a leader in this area. Companies report voluntarily to CDP, providing total emissions and breakdowns into categories. How reliable are these accounts? Here, we show that their reliability is likely very poor. A significant proportion of Oil & Gas companies' emission reports between 2010 and 2019 fail a 'simple summation' mathematical test that identifies if the breakdowns add up to the totals. Companies' reports reflect unbalanced internal bookkeeping in 38.9% of cases, which suggests worryingly low quality standards for data guiding the private sector's contribution to halting climate change.

Inertial particles in stably stratified flows play a fundamental role in geophysics, from the dynamics of nutrients in the ocean to the dispersion of pollutants in the atmosphere. We consider the Maxey-Riley equation for small neutrally buoyant inertial particles in the Boussinesq approximation, and discuss its limits of validity. We show that particles behave as forced damped oscillators, with different regimes depending on the particles Stokes number and the fluid Brunt-V\"ais\"al\"a frequency. Using direct numerical simulations we study the particles dynamics and we show that small neutrally buoyant particles in these flows tend to cluster in regions of low local vorticity. The particles, albeit small, behave fundamentally differently than tracers.

Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants. To measure the atmosphere's ability to dilute air pollutants, Air Stagnation Index (ASI) has been introduced as an important meteorological index. Therefore, making long-lead ASI forecasts is vital to make plans in advance for air quality management. In this study, we found that autumn Ni\~no indices derived from sea surface temperature (SST) anomalies show a negative correlation with wintertime ASI in southern China, offering prospects for a prewinter forecast. We developed an LSTM-based model to predict the future wintertime ASI. Results demonstrated that multivariate inputs (past ASI and Ni\~no indices) achieve better forecast performance than univariate input (only past ASI). The model achieves a correlation coefficient of 0.778 between the actual and predicted ASI, exhibiting a high degree of consistency., Comment: ICLR 2023 Tackling Climate Change with Machine Learning Workshop

We developed an emulator for Integrated Assessment Models (emIAM) based on a marginal abatement cost (MAC) curve approach (Xiong et al., 2022a,b). Using the output of IAMs in the ENGAGE Scenario Explorer and the GET model, we derived a large set of MAC curves: ten IAMs; global and eleven regions; three gases CO2, CH4, and N2O; eight portfolios of available mitigation technologies; and two emission sources. We tested the performance of emIAM by coupling it with a simple climate model ACC2 (Tanaka et al., 2021). We found that the optimizing climate-economy model emIAM-ACC2 adequately reproduced a majority of original IAM emission outcomes under similar conditions, allowing systematic explorations of IAMs with small computational resources. emIAM can expand the capability of simple climate models as a tool to calculate cost-effective pathways linked directly to a temperature target.References1. Tanaka, K., O. Boucher, P. Ciais, D. J. A. Johansson, J. Morfeldt (2021) Cost-effective implementation of the Paris Agreement using flexible greenhouse gas metrics. Science Advances, 7, eabf9020. doi:10.1126/sciadv.abf9020. 2. Xiong, W., K. Tanaka, P. Ciais, D. J. A. Johansson, M. Lehtveer (2022a) emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves. arXiv: 2212.12060 on 23 December 2022 (version 1). https://arxiv.org/abs/2212.120603. Xiong, W., K. Tanaka, P. Ciais, D. Johansson, M. Lehtveer (2022b) Data for "emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves" [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7478234

The El Niño Southern Oscillation (ENSO) is the most important driver of interannual global climate variability and can trigger extreme weather events and disasters in various parts of the globe. Depending on the region of maximal warming, El Niño events can be partitioned into 2 types, Eastern Pacific (EP) and Central Pacific (CP) events. The type of an El Niño has a major influence on its impact and can even lead to either dry or wet conditions in the same areas on the globe. Here we show that the zonal difference $ΔT_{WP-CP}$ between the sea surface temperature anomalies (SSTA) in the equatorial western Pacific and central Pacific gives an early indication of the type of an upcoming El Niño: When at the end of a year, $ΔT_{WP-CP}$ is positive, an event in the following year will be probably an EP event, otherwise a CP event. Between 1950 and present, 3/4 of the EP forecasts and all CP forecasts are correct. When combining this approach with a previously introduced climate-network approach, we obtain reliable forecasts for both the onset and the type of an event: at a lead time of about one year, 2/3 of the EP forecasts and all CP forecasts in the regarded period are correct. The combined model has considerably more predictive power than the current operational type forecasts with a mean lead time of about 1 month and should allow early mitigation measures., 16 pages, 10 figures

Solar energy supply is usually highly volatile which limits its integration in the power grid. Accurate probabilistic intraday forecasts of solar resources are essential to increase the share of photovoltaic (PV) energy in the grid and enable cost-efficient balancing of power demand and supply. Solar PV production mainly depends on downwelling surface solar radiation (SSR). By estimating SSR from geostationary satellites, we can cover large areas with high spatial and temporal resolutions, allowing us to track cloud motion. State-of-the-art probabilistic forecasts of solar resources from satellite imagery account only for the advective motion of clouds. They do not consider the evolution of clouds over time, their growth, and dissipation, even though these are major sources of forecast uncertainty. To address the uncertainty of cloudiness evolution, we present SolarSTEPS, the first optical-flow probabilistic model able to simulate the temporal variability of cloudiness. We demonstrate that forecasting the autocorrelated scale-dependent evolution of cloudiness outperforms state-of-the-art probabilistic advection-based forecasts by 9% in continuous ranked probability score (CRPS). This corresponds to an extension of the forecast lead time by about 45 minutes at constant CRPS. Our work is motivated by the scale-dependent predictability of cloud growth and decay. We demonstrate that cloudiness is more variable in time at smaller spatial scales than at larger ones. Specifically, the temporal autocorrelation of cloudiness is related to its spatial scale by a rational function. We also demonstrate that decomposing cloudiness into multiple spatial scales in the forecasts further improves the forecast skill, reducing the CRPS by 10% and the RMSE by 9%., 17 pages plus appendix, 10 figures. To be submitted

Quantifying forecast uncertainty is a key aspect of state-of-the-art numerical weather prediction and data assimilation systems. Ensemble-based data assimilation systems incorporate state-dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this work a machine learning method is presented based on convolutional neural networks that estimates the state-dependent forecast uncertainty represented by the forecast error covariance matrix using a single dynamical model integration. This is achieved by the use of a loss function that takes into account the fact that the forecast errors are heterodastic. The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman-like analysis update and the machine learning based estimation of a state-dependent forecast error covariance matrix. Observing system simulation experiments are conducted using the Lorenz'96 model as a proof-of-concept. The promising results show that the machine learning method is able to predict precise values of the forecast covariance matrix in relatively high-dimensional states. Moreover, the hybrid data assimilation method shows similar performance to the ensemble Kalman filter outperforming it when the ensembles are relatively small.

The ability of pollen to enable the glaciation of supercooled liquid water has been demonstrated in laboratory studies; however, the potential large-scale effect of trees and pollen on clouds, precipitation and climate is pressing knowledge to better understand and project clouds in the current and future climate. Combining ground-based measurements of pollen concentrations and satellite observations of cloud properties within the United States, we show that enhanced pollen concentrations during springtime lead to a higher cloud ice fraction. We further establish the link from the pollen-induced increase in cloud ice to a higher precipitation frequency. In light of anthropogenic climate change, the extended and strengthened pollen season and future alterations in biodiversity can introduce a localized climate forcing and a modification of the precipitation frequency and intensity.

The effect of changing greenhouse gas concentrations on climate was examined. Calculations of the climate sensitivity, the warming of the Earth due to a doubling of atmospheric CO2, are discussed. Ontario was responsible for 0.35% of the world's CO2 emissions in 2019 and this amount was 20% lower than in 2005. The predictions of Global Climate Models (GCM) were compared to observations. Records since 1880 show an overall warming of about 1 C. The GCMs do not account for observed decadal temperature fluctuations and consistently overestimate the warming. Ontario's contribution to global warming is only 9.2 x 10^{-5} C/year using the Intergovernmental Panel on Climate Change recommended climate sensitivity value. Measurements of the polar ice caps reveal a decrease in the minimum September Arctic sea ice extent during 1979-2022 but the trend levelled off after 2007; while the average Antarctic sea ice extent slightly increased. Sea level increased slightly throughout the 20th century. Ontario's contribution to anthropogenic sea level rise is about 0.005 mm/year. Sea level along Ontario's Hudson Bay coast is decreasing due to isostatic rebound of the land following the last Ice Age. The change to ocean acidity due to CO2 absorption from the atmosphere is negligible compared to that due to tides, ocean depth and seasonal effects. Ontario's contribution to ocean acidification is estimated to be 6 x 10^{-6} pH/year. No changes in precipitation in North America over the 19th and 20th centuries, nor at Toronto since 1843, were found. The Great Lake levels are remarkably constant over the past century showing no evidence of a change in the incidence of flooding. No evidence was found that the frequency of extreme events such as hurricanes or tornadoes increased during recent decades. The number of forest fires in Canada and Ontario decreased during 1990 to 2020.

Proceeding from a stormy day of 22 September 2022, when 7 thunderstorm ground enhancements occurred (TGEs, 3 of them very large), we perform an analysis of the most important conditions, on which depend the origination of the large particle fluxes in the thunderous atmosphere. Among these conditions are the near-surface electric field (NSEF), graupel fall, and lightning activity. We estimate the intensity of the largest particle flux of 1,25 mln gamma rays hitting each square meter of surface on Aragats with energy spectra extended up to 70 MeV. Only one TGE from 7 meets the conditions to recover the electron energy spectrum; the fraction of electrons with energies above 10 MeV relative to the gamma ray flux reaches 45%. By carefully examining the graupel fall, we demonstrate that the lower dipole, which accelerates electrons, is formed by the main negative and lower positively charged regions. The lower dipole decays with a graupel fall that coincides with TGE terminations (usually by a lightning flash).

The shallow water equations describe the horizontal flow of a thin layer of fluid with varying height. We show that the equations can be rewritten as a d=2+1 dimensional gauge theory with a Chern-Simons term. The theory contains two Abelian gauge fields, corresponding to the conserved height and conserved vorticity of the fluid. In a certain linearised approximation, the shallow water equations reduce to relativistic Maxwell-Chern-Simons theory. This describes Poincar\'e waves. The chiral edge modes of the theory are identified as coastal Kelvin waves., Comment: 13 pages + appendix. v2: Improved derivation of the linearised Chern-Simons action. References added and typos fixed. v3: Improved explanations. References added

The supersaturation equation for a vertically moving adiabatic cloud parcel is analysed. The effects of turbulent updrafts are incorporated in the shape of a stochastic Lagrangian model, with spatial and time correlations expressed in terms of turbulent kinetic energy. Using the Fokker-Planck equation, the steady-state probability distributions of supersaturation are analytically computed for a number of approximations involving the timescale separation between updraft fluctuations and phase-relaxation, multiplicative noise simplification and droplet or ice particle size fluctuations. While the analytical results are presented in general for single-phase clouds, the calculated distributions are used to compute mixed-phase cloud properties -- mixed fraction and mean liquid water content in an initially icy cloud -- and are argued to be useful for generalising and constructing new parametrisation schemes.

During the last few years discontinuous Galerkin (DG) methods have received increased interest from the geophysical community. In these methods the solution in each grid cell is approximated as a linear combination of basis functions. Ensemble data assimilation (EnDA) aims to approximate the true state by combining model outputs with observations using error statistics estimated from an ensemble of model runs. Ensemble data assimilation in geophysical models faces several well-documented issues. In this work we exploit the expansion of the solution in DG basis functions to address some of these issues. Specifically, it is investigated whether a DA-DG combination 1) mitigates the need for observation thinning, 2) reduces errors in the field's gradients, 3) can be used to set up scale-dependent localisation. Numerical experiments are carried out using stochastically generated ensembles of model states, with different noise properties, and with Legendre polynomials as basis functions. It is found that strong reduction in the analysis error is achieved by using DA-DG and that the benefit increases with increasing DG order. This is especially the case when small scales dominate the background error. The DA improvement in the 1st derivative is on the other hand marginal. We think this to be a counter-effect of the power of DG to fit the observations closely, which can deteriorate the estimates of the derivatives. Applying optimal localisation to the different polynomial orders, thus exploiting their different spatial length, is beneficial: it results in a covariance matrix closer to the true covariance than the matrix obtained using traditional optimal localisation in state-space.

The impact of heatwaves (HWs) on urban heat island (UHI) is a contentious topic with contradictory research findings. A comprehensive understanding of the response of urban and rural areas to HWs, considering the underlying cause of surface energy budget changes, remains elusive. This study attempts to address this gap by investigating a 2020 HW event in the Greater Sydney Area using the Advanced Weather Research and Forecasting (WRF) model. Findings indicate that the HW intensifies the nighttime surface UHI by approximately 4{\deg}C. An analysis of surface energy budgets reveals that urban areas store more heat during the HW due to receiving more solar radiation and less evapotranspiration compared to rural areas. The maximum heat storage flux in urban during the HW can be around 200 W/m2 higher than that during post-HW. The stored heat is released at nightime, raising the air temperature in the urban areas. Forests and savannas have relatively lower storage heat fluxes due to high transpiration and albedo, and the maximum heat storage flux is only around 50 W/m2 higher than that during post-HW. In contrast, a negative synergistic effect is detected between the 2-m UHI and HW. This may be because other meteorological conditions including wind have substantial impacts on the air temperature pattern. The strong hot and dry winds coming from the west and the proximity of tall buildings to the coast diminish the sea breeze coming from the east, resulting in a higher air temperature in the western urban district. Meanwhile, the western forest area also experiences higher temperatures due to the westward winds. In addition, changes in wind direction alter the temperature distribution in the northern rural region. Based on the present study, urban climate simulation data and associated findings can be used to develop urban heat mitigation strategies for UHI during HW.

The objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi-scale dynamics. By considering a toy climate model, namely, the two-scale Lorenz96 model and producing experiments in perfect-model setting, we explore in detail how several built-in choices need to be carefully tested. We also demonstrate the importance of introducing physical expertise in the range of parameters, a priori to running HM. Finally we revisit a classical procedure in climate model tuning, that consists of tuning the slow and fast components separately. By doing so in the Lorenz96 model, we illustrate the non-uniqueness of plausible parameters and highlight the specificity of metrics emerging from the coupling. This paper contributes also to bridging the communities of uncertainty quantification, machine learning and climate modeling, by making connections between the terms used by each community for the same concept and presenting promising collaboration avenues that would benefit climate modeling research., Submission to JAMES journal (AGU), added link to code

The Thorndike et al., (\emph{J. Geophys. Res.} {\bf 80} 4501, 1975) theory of the ice thickness distribution, $g(h)$, treats the dynamic and thermodynamic aggregate properties of the ice pack in a novel and physically self-consistent manner. Therefore, it has provided the conceptual basis of the treatment of sea-ice thickness categories in climate models. The approach, however, is not mathematically closed due to the treatment of mechanical deformation using the redistribution function $\psi$, the authors noting ``The present theory suffers from a burdensome and arbitrary redistribution function $\psi .$'' Toppaladoddi and Wettlaufer (\emph{Phys. Rev. Lett.} {\bf 115} 148501, 2015) showed how $\psi$ can be written in terms of $g(h)$, thereby solving the mathematical closure problem and writing the theory in terms of a Fokker-Planck equation, which they solved analytically to quantitatively reproduce the observed winter $g(h)$. Here, we extend this approach to include open water by formulating a new boundary condition for their Fokker-Planck equation, which is then coupled to the observationally consistent sea-ice growth model of Semtner (\emph{J. Phys. Oceanogr.} {\bf 6}(3), 379, 1976) to study the seasonal evolution of $g(h)$. We find that as the ice thins, $g(h)$ transitions from a single- to a double-peaked distribution, which is in agreement with observations. To understand the cause of this transition, we construct a simpler description of the system using the equivalent Langevin equation formulation and solve the resulting stochastic ordinary differential equation numerically. Finally, we solve the Fokker-Planck equation for $g(h)$ under different climatological conditions to study the evolution of the open-water fraction., Comment: 8 pages, 10 figures

The response of the stratosphere to a steady geopotential forcing is considered in two separate theoretical models. Solutions to the linearized quasi-geostrophic potential vorticity equations are first used to show that the vertical length scale of a tropopause geopotential anomaly is initially shallow, but significantly increased by diabatic heating from radiative relaxation. This process is deemed as geostrophic adjustment of the stratosphere to tropospheric forcing. Idealized, time-dependent calculations show that tropopause geopotential anomalies can appreciably rise in the stratosphere on time scales of a couple months. A previously developed, coupled troposphere-stratosphere model is introduced and modified to further understand how tropospheric geopotential forcing can induce upwelling in the stratosphere. Solutions to steady, zonally-symmetric sea-surface-temperature forcings in the linear $\beta$-plane model show that the upwards stratospheric penetration of the thermally induced tropopause geopotential anomaly is controlled by a non-dimensional parameter that depends on the ratio between the time scale of wave-drag to that of radiation. It is also shown that the horizontal scale of the tropopause geopotential anomaly modulates the vertical scale of the anomaly. When Earth-like non-dimensional parameters are used, the theoretical model predicts stratospheric temperature anomalies around two times larger in magnitude than those in the boundary layer, approximately in line with observational data. The results are argued to show that wave-drag alone may not suffice to explain certain observed features of the lower stratosphere, foremost of which is the anti-correlation between sea-surface temperature and lower stratospheric temperature.

The nocturnal mesopause region of the Earth's atmosphere radiates chemiluminescent emission from various roto-vibrational bands of hydroxyl (OH), which is therefore a good tracer of the chemistry and dynamics at the emission altitudes. Intensity variations can, e.g., be caused by the general circulation, gravity waves, tides, planetary waves, and the solar activity. While the basic OH response to the different dynamical influences has been studied quite frequently, detailed comparisons of the various individual lines are still rare. Such studies can improve our understanding of the OH-related variations as each line shows a different emission profile. We have therefore used about 90,000 spectra of the X-shooter spectrograph of the Very Large Telescope at Cerro Paranal in Chile in order to study 10 years of variations of 298 OH lines. The analysis focuses on climatologies of intensity, solar cycle effect, and residual variability (especially with respect to time scales of hours and about 2 days) for day of year and local time. For a better understanding of the resulting variability patterns and the line-specific differences, we applied decomposition techniques, studied the variability depending on time scale, and calculated correlations. As a result, the mixing of thermalized and nonthermalized OH level populations clearly influences the amplitude of the variations. Moreover, the local times of the variability features shift depending on the effective line emission height, which can mainly be explained by the propagation of the migrating diurnal tide. This behavior also contributes to remarkable differences in the effective solar cycle effect., 35 single-column pages and 12 figures; accepted for publication in J. Geophys. Res. Atmos

Data-driven weather prediction models (DDWPs) have made rapid strides in recent years, demonstrating an ability to approximate Numerical Weather Prediction (NWP) models to a high degree of accuracy. The fast, accurate, and low-cost DDWP forecasts make their use in operational forecasting an attractive proposition, however, there remains work to be done in rigorously evaluating DDWPs in a true operational setting. Typically trained and evaluated using ERA5 reanalysis data, DDWPs have been tested only in a simulation, which cannot represent the real world with complete accuracy even if it is of a very high quality. The safe use of DDWPs in operational forecasting requires more thorough "real-world" verification, as well as a careful examination of how DDWPs are currently trained and evaluated. It is worth asking, for instance, how well do the reanalysis datasets, used for training, simulate the real world? With an eye towards climate justice and the uneven availability of weather data: is the simulation equally good for all regions of the world, and would DDWPs exacerbate biases present in the training data? Does a good performance in simulation correspond to good performance in operational settings? In addition to approximating the physics of NWP models, how can ML be uniquely deployed to provide more accurate weather forecasts? As a first step towards answering such questions, we present a robust dataset of in-situ observations derived from the NOAA MADIS program to serve as a benchmark to validate DDWPs in an operational setting. By providing a large corpus of quality-controlled, in-situ observations, this dataset provides a meaningful real-world task that all NWPs and DDWPs can be tested against. We hope that this data can be used not only to rigorously and fairly compare operational weather models but also to spur future research in new directions., 10 pages, 6 figures, in prep to be submitted to NeurIPS main conference

Seismically generated sound waves that propagate through the ocean are used to infer temperature anomalies and their vertical structure in the deep East Indian Ocean. These T waves are generated by earthquakes off Sumatra and received by hydrophone stations off Diego Garcia and Cape Leeuwin. Between repeating earthquakes, a T wave's travel time changes in response to temperature anomalies along the wave's path. What part of the water column the travel time is sensitive to depends on the frequency of the wave, so measuring travel time changes at a few low frequencies constrains the vertical structure of the inferred temperature anomalies. These measurements reveal anomalies due to equatorial waves, mesoscale eddies, and decadal warming trends. By providing direct constraints on basin-scale averages with dense sampling in time, these data complement previous point measurements that alias local and transient temperature anomalies.

Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate change conditions remains questionable, mainly due to the stationarity assumption. We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. By better capturing uncertainty, statistical downscaling models allow for superior planning against extreme weather events, a source of various negative social and economic impacts. Since no observational future data exists, we rely on a pseudo reality experiment to assess the suitability of deep ensembles for quantifying the uncertainty of climate change projections. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change., Accepted at the ICLR 2023 Tackling Climate Change with Machine Learning Workshop

This paper presents the first study of the long-term impact of climate change on photovoltaic potential in South America. This region has great potential for implementing renewable energy, mainly solar energy solutions, due to its high solar irradiance levels. Based on the Coordinated Regional Downscaling Experiment (CORDEX) for the South American region, we estimate how climate change could affect photovoltaic power potential by the end of the century. The evidence suggests that photovoltaic potential could have a maximum decrease of $15\%$, and a maximum increase of $7\%$, primarily due to changes in solar irradiance of different zones. Furthermore, it is observed that regions with increased temperature also show increased solar irradiance levels, which could, to some extent, compensate for the losses caused by the rise in temperature. Therefore, photovoltaic production in most of the territory will not be negatively affected.

Diffusion models have been widely adopted in image generation, producing higher-quality and more diverse samples than generative adversarial networks (GANs). We introduce a latent diffusion model (LDM) for precipitation nowcasting - short-term forecasting based on the latest observational data. The LDM is more stable and requires less computation to train than GANs, albeit with more computationally expensive generation. We benchmark it against the GAN-based Deep Generative Models of Rainfall (DGMR) and a statistical model, PySTEPS. The LDM produces more accurate precipitation predictions, while the comparisons are more mixed when predicting whether the precipitation exceeds predefined thresholds. The clearest advantage of the LDM is that it generates more diverse predictions than DGMR or PySTEPS. Rank distribution tests indicate that the distribution of samples from the LDM accurately reflects the uncertainty of the predictions. Thus, LDMs are promising for any applications where uncertainty quantification is important, such as weather and climate., 18 pages, 6 figures. Submitted for publication