Providing efficient and accurate parametrizations for model reduction is a key goal in many areas of science and technology. Here we present a strong link between data-driven and theoretical approaches to achieving this goal. Formal perturbation expansions of the Koopman operator allow us to derive general stochastic parametrizations of weakly coupled dynamical systems. Such parametrizations yield a set of stochastic integro-differential equations with explicit noise and memory kernel formulas to describe the effects of unresolved variables. We show that the perturbation expansions involved need not be truncated when the coupling is additive. The unwieldy integro-differential equations can be recast as a simpler multilevel Markovian model, and we establish an intuitive connection with a generalized Langevin equation. This connection helps setting up a parallelism between the top-down, equations-based methodology herein and the well-established empirical model reduction (EMR) methodology that has been shown to provide efficient dynamical closures to partially observed systems. Hence, our findings support, on the one hand, the physical basis and robustness of the EMR methodology and, on the other hand, illustrate the practical relevance of the perturbative expansion used for deriving the parametrizations.

Climate projections have uncertainties related to components of the climate system and their interactions. A typical approach to quantifying these uncertainties is to use climate models to create ensembles of repeated simulations under different initial conditions. Due to the complexity of these simulations, generating such ensembles of projections is computationally expensive. In this work, we present ArchesClimate, a deep learning-based climate model emulator that aims to reduce this cost. ArchesClimate is trained on decadal hindcasts of the IPSL-CM6A-LR climate model at a spatial resolution of approximately 2.5x1.25 degrees. We train a flow matching model following ArchesWeatherGen, which we adapt to predict near-term climate. Once trained, the model generates states at a one-month lead time and can be used to auto-regressively emulate climate model simulations of any length. We show that for up to 10 years, these generations are stable and physically consistent. We also show that for several important climate variables, ArchesClimate generates simulations that are interchangeable with the IPSL model. This work suggests that climate model emulators could significantly reduce the cost of climate model simulations.

Many modern techniques employed in physics, such a computation of path integrals, rely on random walks on graphs that can be represented as Markov chains. Traditionally, estimates of running times of such sampling algorithms are computed using the number of steps in the chain needed to reach the stationary distribution. This quantity is generally defined as mixing time and is often difficult to compute. In this paper, we suggest an alternative estimate based on the Kolmogorov-Sinai entropy, by establishing a link between the maximization of KSE and the minimization of the mixing time. Since KSE are easier to compute in general than mixing time, this link provides a new faster method to approximate the minimum mixing time that could be interesting in computer sciences and statistical physics. Beyond this, our finding will also be of interest to the out-of-equilibrium community, by providing a new rational to select stationary states in out-of-equilibrium physics: it seems reasonable that in a physical system with two simultaneous equiprobable possible dynamics, the final stationary state will be closer to the stationary state corresponding to the fastest dynamics (smallest mixing time).Through the empirical link found in this letter, this state will correspond to a state of maximal Kolmogorov-Sinai entropy. If this is true, this would provide a more satisfying rule for selecting stationary states in complex systems such as climate than the maximization of the entropy production.

The knowledge of the Venus near-surface atmosphere is sparse. Few spacecrafts landed on the surface and measured winds with amplitudes below 1 m/s. The diurnal cycle of the wind amplitude and orientation is not known. Recent numerical simulations showed that slope winds along topographic structures could strongly impact the direction of winds. This study presents the first mesoscale modelling of such winds on Venus. A change of direction is occurring during the day in the main slopes, with upslope winds at noon due to solar heating and downslope winds at night. This is due to efficient IR cooling of the surface during the night, being colder than its surroundings slope atmospheric environment and leading to displacement of air. The temperature is impacted by the adiabatic cooling/warming induced by those winds. A strong heating effect is occurring for the downslope winds, leading to an anti-correlation between the surface temperature diurnal amplitude and the topography. This diurnal amplitude reaches 4 K in the plains and below 1 K in the mountains. The saltation of sediment by those winds was also quantified, with a higher probability at night along the slopes on the western flanks.

The Solar Diameter Imager and Surface Mapper (SODISM) on board the PICARD space mission provides wide-field images of the photosphere and chromosphere of the Sun in five narrow pass bands (centered at 215.0, 393.37, 535.7, 607.1, and 782.2 nm). PICARD is a space mission, which was successfully launched on 15 June 2010 into a Sun synchronous dawn-dusk orbit. It represents a European asset aiming at collecting solar observations that can serve to estimate some of the inputs to Earth climate models. The scientific payload consists of the SODISM imager and of two radiometers, SOVAP (SOlar VAriability PICARD) and PREMOS (PREcision MOnitor Sensor), which carry out measurements that allow estimating the Total Solar Irradiance (TSI) and the Solar Spectral Irradiance (SSI) from the middle ultraviolet to the red. The SODISM telescope monitors solar activity continuously. It thus produces images that can also feed SSI reconstruction models. Further, the objectives of SODISM encompass the probing of the interior of the Sun via helioseismic analysis of observations in intensity (on the solar disc and at the limb), and via astrometric investigations at the limb. The latter addresses especially the spectral dependence of the radial limb shape, and the temporal evolution of the solar diameter and asphericity. After a brief review of its original science objectives, this paper presents the detailed design of the SODISM instrument, its expected performance, and the scheme of its flight operations. Some observations with SODISM are presented and discussed.

Beginning in March 2014, the OSIRIS (Optical, Spectroscopic, and Infrared Remote Imaging System) cameras began capturing images of the nucleus and coma (gas and dust) of comet 67P/Churyumov-Gerasimenko using both the wide angle camera (WAC) and the narrow angle camera (NAC). The many observations taken since July of 2014 have been used to study the morphology, location, and temporal variation of the comet's dust jets. We analyzed the dust monitoring observations shortly after the southern vernal equinox on May 30 and 31, 2015 with the WAC at the heliocentric distance Rh = 1.53 AU, where it is possible to observe that the jet rotates with the nucleus. We found that the decline of brightness as a function of the distance of the jet is much steeper than the background coma, which is a first indication of sublimation. We adapted a model of sublimation of icy aggregates and studied the effect as a function of the physical properties of the aggregates (composition and size). The major finding of this article was that through the sublimation of the aggregates of dirty grains (radius a between 5 microm and 50 microm) we were able to completely reproduce the radial brightness profile of a jet beyond 4 km from the nucleus. To reproduce the data we needed to inject a number of aggregates between 8.5 x $10^{13}$ and 8.5 x $10^{10}$ for a = 5 microm and 50 microm respectively, or an initial mass of $H_2O$ ice around 22kg.

What kind of environment may exist on terrestrial planets around other stars? In spite of the lack of direct observations, it may not be premature to speculate on exoplanetary climates, for instance to optimize future telescopic observations, or to assess the probability of habitable worlds. To first order, climate primarily depends on 1) The atmospheric composition and the volatile inventory; 2) The incident stellar flux; 3) The tidal evolution of the planetary spin, which can notably lock a planet with a permanent night side. The atmospheric composition and mass depends on complex processes which are difficult to model: origins of volatile, atmospheric escape, geochemistry, photochemistry. We discuss physical constraints which can help us to speculate on the possible type of atmosphere, depending on the planet size, its final distance for its star and the star type. Assuming that the atmosphere is known, the possible climates can be explored using Global Climate Models analogous to the ones developed to simulate the Earth as well as the other telluric atmospheres in the solar system. Our experience with Mars, Titan and Venus suggests that realistic climate simulators can be developed by combining components like a "dynamical core", a radiative transfer solver, a parametrisation of subgrid-scale turbulence and convection, a thermal ground model, and a volatile phase change code. On this basis, we can aspire to build reliable climate predictors for exoplanets. However, whatever the accuracy of the models, predicting the actual climate regime on a specific planet will remain challenging because climate systems are affected by strong positive destabilizing feedbacks (such as runaway glaciations and runaway greenhouse effect). They can drive planets with very similar forcing and volatile inventory to completely different states.

Several large scale circulation patterns have been identified in relation to extreme Northern Hemisphere summer heatwaves. Three main ones are a double jet over Eurasia, a positive phase of the summer northern annular mode, and a quasi-wave-3 geopotential height anomaly. While there is some evidence suggesting these patterns are related to each other, the explicit nature of their relation, as well as the explicit mechanisms by which they are related to extreme heatwaves is still not known. The double jet structure has gained attention recently due to evidence that its persistence has been increasing, possibly explaining the rise in the number of extreme heatwaves over Europe. In this paper we study the occurrence and persistence of double jet states in ERA5 and in stationary simulations with the CESM1.2 model, using an index which measures the degree of jet separation. Additionally, we perform simulations with CESM1.2 coupled to a rare event algorithm in order to improve the statistics of rare summer-long double jet states. We find that extreme double jet states are characterised by three centers of extreme high surface temperature and 500hPa geopotential height anomalies, alongside a strong low pressure over the Arctic. The geopotential height anomaly pattern is consistent with both a positive Northern Annular Mode (NAM) and quasi-wave-3 patterns found in the literature. Moreover, we find a large percentage of co-occurrence of heatwaves at these centers, and a double jet state, with the percentage increasing with the duration of the double jet state.

Hot Jupiters are among the best-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5-12 $\mu$m with JWST's Mid-Infrared Instrument (MIRI). The spectra reveal a large day-night temperature contrast (with average brightness temperatures of 1524$\pm$35 and 863$\pm$23 Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ~100 mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Contrary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2$\sigma$ upper limit of 1-6 parts per million, depending on model assumptions).

Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric extension for heavy-tailed distributions. Our goal is to improve ensemble quality for all types of precipitation events, heavy-tailed included, subject to a good overall performance. Our hybrid proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the M{\'e}t{\'e}o-France ensemble prediction system called PEARP. They provide calibrated pre-dictive distributions and compete favourably with state-of-the-art methods like Analogs method or Ensemble Model Output Statistics. In particular, hybrid forest-based procedures appear to bring an added value to the forecast of heavy rainfall.

This article extends the multivariate extreme value theory (MEVT) to discrete settings, focusing on the generalized Pareto distribution (GPD) as a foundational tool. The purpose of the study is to enhance the understanding of extreme discrete count data representation, particularly for discrete exceedances over thresholds, defining and using multivariate discrete Pareto distributions (MDGPD). Through theoretical results and illustrative examples, we outline the construction and properties of MDGPDs, providing practical insights into simulation techniques and data fitting approaches using recent likelihood-free inference methods. This framework broadens the toolkit for modeling extreme events, offering robust methodologies for analyzing multivariate discrete data with extreme values. To illustrate its practical relevance, we present an application of this method to drought analysis, addressing a growing concern in Europe.

The Spectrograph of the RISTRETTO instrument is now currently being manufactured. RISTETTO is an instrument designed to detect and characterize the reflected light of nearby exoplanets. It combines high contrast imaging and high resolution spectroscopy to detect the light of exoplanets. The high resolution spectrograph subject of this paper uses the doppler effect to disentangle the planetary signal from the stellar light leaks. In this paper we describe the final design of the spectrograph and report the status of its construction. The RISTRETTO spectrograph has seven diffraction limited spaxels. The spectrograph's resolution is 130000 in the 620-840 nm band. It is designed in a similar way as HARPS and ESPRESSO, being a warm, thermally controlled spectrograph under vacuum. It is designed to be compact and self contained so that it could be installed on different telescopes. It is however tailored to be installed on a nasmyth platform of a VLT telescope. We present updates to the design and the manufacturing of the instrument. In particular we present the performance of the thermal enclosure.

Spectroscopic phase curves of hot Jupiters measure their emission spectra at multiple orbital phases, thus enabling detailed characterisation of their atmospheres. Precise constraints on the atmospheric composition of these exoplanets offer insights into their formation and evolution. We analyse four phase-resolved emission spectra of the hot Jupiter WASP-43b, generated from a phase curve observed with the MIRI/LRS onboard the JWST, to retrieve its atmospheric properties. Using a parametric 2D temperature model and assuming a chemically homogeneous atmosphere within the observed pressure region, we simultaneously fit the four spectra to constrain the abundances of atmospheric constituents, thereby yielding more precise constraints than previous work that analysed each spectrum independently. Our analysis reveals statistically significant evidence of NH3 (4$\sigma$) in a hot Jupiter's emission spectra for the first time, along with evidence of H2O (6.5$\sigma$), CO (3.1$\sigma$), and a non-detection of CH4. With our abundance constraints, we tentatively estimate the metallicity of WASP-43b at 0.6-6.5$\times$solar and its C/O ratio at 0.6-0.9. Our findings offer vital insights into the atmospheric conditions and formation history of WASP-43b by simultaneously constraining the abundances of carbon, oxygen, and nitrogen-bearing species.

Among the lines of evidence for a buried ocean on Titan is the possible detection, in 2005, by the Permittivity, Wave and Altimetry (PWA) analyzer on board the ESA Huygens probe of Schumann-like Resonances (SR). SR are Extremely Low Frequency electromagnetic waves resonating between two electrically conductive layers. On Titan, it has been proposed that they propagate between the moon's ionosphere and a salty subsurface water ocean. Their characterization by electric field sensors can provide constraints on Titan's cavity characteristics and in particular on the depth of Titan's ocean which is key to better assess Titan's habitability. For this work we have developed a numerical model of Titan's electromagnetic cavity as well as a surrogate model to conduct simulations and sensitivity analyses at a low computational cost. This surrogate model is used both to re-assess PWA/Huygens measurements and to predict the future performance of the EFIELD experiment on board the NASA Dragonfly mission. We demonstrate that the PWA/Huygens measurements, in particular due to their low spectral resolution, do not bring any meaningful constraint on Titan's ocean depth. On the other hand, the finer resolution of the EFIELD experiment and its ability to capture several harmonics of SR should provide more robust constraints on Titan's internal structure, especially if the electrical properties of the ice crust and the atmosphere can be better constrained.

During accretion, the young rocky planets are so hot that they become endowed with a magma ocean. From that moment, the mantle convective thermal flux control the cooling of the planet and an atmosphere is created by outgassing. This atmosphere will then play a key role during this cooling phase. Studying this cooling phase in details is a necessary step to explain the great diversity of the observed telluric planets and especially to assess the presence of surface liquid water. We used here a radiative-convective 1D atmospheric model (H2O, CO2) to study the impact of the Bond albedo on the evolution of magma ocean planets. We derived from this model the thermal emission spectrum and the spectral reflectance of these planets, from which we calculated their Bond albedos. Compared to Marcq et al. (2017), the model now includes a new module to compute the Rayleigh scattering, and state of the art CO2 and H2O gaseous opacities data in the visible and infrared spectral ranges. We show that the Bond albedo of these planets depends on their surface temperature and results from a competition between Rayleigh scattering from the gases and Mie scattering from the clouds. The colder the surface temperature is, the thicker the clouds are, and the higher the Bond albedo is. We also evidence that the relative abundances of CO2 and H2O in the atmosphere strongly impact the Bond albedo. The Bond albedo is higher for atmospheres dominated by the CO2, better Rayleigh scatterer than H2O. Finally, we provide the community with an empirical formula for the Bond albedo that could be useful for future studies of magma ocean planets.

This Letter reports on the detection of two super-Earth planets in the Gl581 system, already known to harbour a hot Neptune. One of the planets has a mass of 5 M_Earth and resides at the ``warm'' edge of the habitable zone of the star. It is thus the known exoplanet which most resembles our own Earth. The other planet has a 7.7 M_Earth mass and orbits at 0.25 AU from the star, close to the ``cold'' edge of the habitable zone. These two new light planets around an M3 dwarf further confirm the formerly tentative statistical trend for i) many more very low-mass planets being found around M dwarfs than around solar-type stars and ii) low-mass planets outnumbering Jovian planets around M dwarfs.

The major sources of the Soft X-ray Background (SXRB), besides distinct structures as supernovae and superbubbles (e.g. Loop I), are: (i) an absorbed extragalactic emission following a power law, (ii) an absorbed thermal component ~2x10^6 K) from the galactic disk and halo, (iii) an unabsorbed thermal component, supposedly at 10^6 K, attributed to the Local Bubble and (iv) the very recently identified unabsorbed Solar Wind Charge-eXchange (SWCX) emission from the heliosphere and the geocorona. We study the SWCX heliospheric component and its contribution to observed data. In a first part, we apply a SWCX heliospheric simulation to model the oxygen lines (3/4 keV) local intensities during shadowing observations of the MBM12 molecular cloud and a dense filament in the south galactic hemisphere with Chandra, XMM-Newton, and Suzaku telescopes. In a second part, we present a preliminary comparison of SWCX model results with ROSAT and Wisconsin surveys data in the 1/4 keV band. We conclude that, in the 3/4 keV band, the total local intensity is entirely heliospheric, while in the 1/4 keV band, the heliospheric component seems to contribute significantly to the local SXRB intensity and has potentially a strong influence on the interpretation of the ROSAT and Wisconsin surveys data in terms of Local Bubble hot gas temperature.

Accurate precipitation forecasts have a high socio-economic value due to their role in decision-making in various fields such as transport networks and farming. We propose a global statistical postprocessing method for grid-based precipitation ensemble forecasts. This U-Net-based distributional regression method predicts marginal distributions in the form of parametric distributions inferred by scoring rule minimization. Distributional regression U-Nets are compared to state-of-the-art postprocessing methods for daily 21-h forecasts of 3-h accumulated precipitation over the South of France. Training data comes from the Météo-France weather model AROME-EPS and spans 3 years. A practical challenge appears when consistent data or reforecasts are not available. Distributional regression U-Nets compete favorably with the raw ensemble. In terms of continuous ranked probability score, they reach a performance comparable to quantile regression forests (QRF). However, they are unable to provide calibrated forecasts in areas associated with high climatological precipitation. In terms of predictive power for heavy precipitation events, they outperform both QRF and semi-parametric QRF with tail extensions.

In this article, we present a new approach to averaging in non-Hamiltonian systems with periodic forcing. The results here do not depend on the existence of a small parameter. In fact, we show that our averaging method fits into an appropriate nonlinear equivalence problem, and that this problem can be solved formally by using the Lie transform framework to linearize it. According to this approach, we derive formal coordinate transformations associated with both first-order and higher-order averaging, which result in more manageable formulae than the classical ones. Using these transformations, it is possible to correct the solution of an averaged system by recovering the oscillatory components of the original non-averaged system. In this framework, the inverse transformations are also defined explicitly by formal series; they allow the estimation of appropriate initial data for each higher-order averaged system, respecting the equivalence relation. Finally, we show how these methods can be used for identifying and computing periodic solutions for a very large class of nonlinear systems with time-periodic forcing. We test the validity of our approach by analyzing both the first-order and the second-order averaged system for a problem in atmospheric chemistry.