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Hard Diagrams of Split Links
No full textInternational audienceDeformations of knots and links in ambient space can be studied combinatorially on their diagrams via local modifications called Reidemeister moves. While it is well-known that, in order to move between equivalent diagrams with Reidemeister moves, one sometimes needs to insert excess crossings, there are significant gaps between the best known lower and upper bounds on the required number of these added crossings. In this article, we study the problem of turning a diagram of a split link into a split diagram, and we show that there exist split links with diagrams requiring an arbitrarily large number of such additional crossings. More precisely, we provide a family of diagrams of split links, so that any sequence of Reidemeister moves transforming a diagram with c crossings into a split diagram requires going through a diagram with Ω(√c) extra crossings. Our proof relies on the framework of bubble tangles, as introduced by the first two authors, and a technique of Chambers and Liokumovitch to turn homotopies into isotopies in the context of Riemannian geometry
The subtleties of three-dimensional radiative effects in contrails and cirrus clouds
No full textInternational audienceAbstract. The radiative effect of cirrus, contrails, and contrail cirrus affects the energy budget of the Earth and climate change. Those clouds, and especially contrails, are heterogeneous and their holes and sides exert three-dimensional radiative effects. This study uses the htrdr Monte Carlo radiative transfer code to investigate the sensitivity of the cloud radiative effect (CRE) to the geometrical dimensions and optical depth of optically thin ice clouds (cloud optical depth < 4), with particular emphasis on three-dimensional radiative effects. When the Sun is at zenith, an increase in cloud optical depth causes a linear increase in shortwave (SW) CRE but a saturation of longwave (LW) CRE, causing the net CRE to change sign from positive to negative. The optical depth at which this change in sign occurs depends on the cloud geometry. 3D effects make the one-dimensional SW and LW CREs more positive for a Sun at zenith, reaching the same order of magnitude as the 1D CRE itself for clouds with high aspect ratios. The angular dependence of ice crystal scattering strongly increases shortwave CRE when solar zenith angle increases. 3D effects change sign from positive at zenith to negative at large zenith angles as the Sun’s rays interact more with the cloud sides. Integrating instantaneous CRE and 3D effects over selected days of the year indicates compensation of SW with LW 3D effects for some cloud orientations, but 3D effects remain important in some cases. These results suggest that the 3D structure of cirrus and contrails needs to be considered to finely quantify their CRE and radiative forcing
Using line-by-line Monte Carlo to compute the Earth’s outgoing longwave radiation and CO2’s radiative forcing
No full textInternational audienceThe Earth’s radiation budget is a crucial part of climate and its evolution. Being part of this budget, the outgoing longwave radiation (OLR) has been extensively studied, especially in the context of climate-change and anthropogenic greenhouse gases emissions modifying the Earth’s radiative equilibrium.In this study we present a new line-by-line radiative code RadForcE, we have developed to compute the global OLR and radiative forcing over a 10-year period. Based on a backward longwave Monte Carlo method, RadForcE uses line-by-line spectroscopic data for several molecular gases (CO2, H2O, CH4 and O3) from high-resolution databases GEISA and HITRAN, as well as different continua. The clouds’ vertical distributions are taken into account with a vertical overlap subgrid parameterization that is sampled "on the fly" for each optical path along vertical atmospheric profiles. Those profiles are sampled over a 10-year period all over the globe, either from GCM outputs or from ERA5 reanalysis, to compute the unbiased global OLR at a very small computational cost (~10 minutes on a laptop).We this new method we are also able to directly compute any greenhouse gas radiative forcing, and present estimates of the radiative forcing for a doubling of CO2. The Monte Carlo approach allows us to identify, for each outgoing optical path at the top of the atmosphere, the emitting species as well as the altitude of emission. By doing so, we can visualize the profile of altitude of emission for each species, as well as how some gases can screen other species’ emission or the surface’s emission. We can also visualize, for a doubling of CO2, the increase of stratospheric emission by CO2, and its screening of the surface’s emission and water vapor tropospheric emission
Enhancing Vibration-Based Tension Measurement in External Prestressing Tendons Using a Monte Carlo Markov Chain Algorithm
No full textInternational audienceMeasuring the tension in external prestressing tendons is crucial for assessing the remaining durability of prestressed bridges. A common method for this measurement is to use the eigenfrequencies of the tendon, modelled as a vibrating cable. However, this method can be quite time-consuming, primarily due to the need to position multiple sensors along the tendon to ensure that no eigenfrequencies are missed due to the impossibility to detect a mode with a sensor positioned on a modal node. In this paper, we propose a more efficient approach by employing a metaheuristic algorithm to identify the missing modes without the need for multiple sensors. Our approach utilizes a two-stage Monte Carlo Markov Chain Algorithm. In the first stage, the algorithm explores the parameter space related to tension and flexural rigidity. The second stage employs simulated annealing to determine the most likely mode order using the aforementioned parameters as a seed. The algorithm output a couple of parameters -tension and flexural rigidity-that best fit the observed data. These results are then compared with the traditional method, which relies on multiple sensors, to demonstrate the effectiveness of our approach in both numerical and experimental situation
The optical properties of the stratospheric aerosol layer perturbation of the Hunga Tonga–Hunga Ha'apai volcano eruption of 15 January 2022
No full textInternational audienceThe Hunga Tonga-Hunga Ha'apai volcano violently erupted on 15 January 2022 and produced the largest stratospheric aerosol layer perturbation of the last 30 years. In comparison to background conditions and other recent moderate stratospheric eruptions, one notable effect of the Hunga Tonga-Hunga Ha'apai eruption was the significant modification of the size distribution (SD) of the stratospheric aerosol layer, resulting in a larger mean particle size and a smaller SD spread for Hunga Tonga-Hunga Ha'apai. Starting from satellitebased SD retrievals and the assumption of pure sulfate aerosol layers, in this work, we calculate the optical properties of both background and Hunga Tonga-Hunga Ha'apai-perturbed stratospheric aerosol scenarios using a Mie code. We found that the intensive optical properties of the stratospheric aerosol layer (i.e. the singlescattering albedo (SSA), the asymmetry parameter, the aerosol extinction per unit mass, and the broad-band average ultraviolet-visible (UV-Vis) to mid-infrared (MIR) Ångström exponent (AE)) were not significantly perturbed by the Hunga Tonga-Hunga Ha'apai eruption with respect to background conditions. The calculated AE was found to be consistent with multi-instrument satellite observations of the same parameter. Thus, the basic impact of the Hunga Tonga-Hunga Ha'apai eruption on the optical properties of the stratospheric aerosol layer was an increase in the stratospheric aerosol extinction (or optical depth), without any modification of the shortwave (SW) and longwave (LW) relative absorption, angular scattering, and broad-band spectral trend of the extinction, with respect to background. This highlights a marked difference between the Hunga Tonga-Hunga Ha'apai perturbation of the stratospheric aerosol layer and perturbations from other larger stratospheric eruptions, such as Pinatubo 1991 and El Chichón 1982. With simplified radiative forcing estimations, we show that the Hunga Tonga-Hunga Ha'apai eruption produced an aerosol layer likely 1.5-10 times more effective in producing a net cooling of the climate system with respect to the Pinatubo and El Chichón eruptions due to more effective SW scattering. As intensive optical properties are seldom directly measured, e.g. from satellite, our calculations can support the estimation of radiative effects for the Hunga Tonga-Hunga Ha'apai eruption with climate or offline radiative models
Observed Circulation Trends in Boreal Summer Linked to Two Spatially Distinct Teleconnection Patterns
No full textInternational audienceVarious regions in the Northern Hemisphere midlatitudes have seen pronounced trends in upperatmosphere summer circulation and surface temperature extremes over recent decades (since 1979). Several of these regional trends lie outside the range of historic CMIP6 model simulations, and they might constitute a joined dynamic response that is missed by climate models. Here, we examine if the regional trends in circulation are indeed part of a coherent circumglobal wave pattern. Using ERA5 reanalysis data and CMIP6 historical simulations, we find that the observed upper-atmospheric circulation trends consist of at least two separate regional signatures: a U.S.-Atlantic pattern and a Eurasian trend pattern. The circulation trend can explain on average 15% and 26% of the observed regional temperature trends in the U.S.-Atlantic and Eurasian regions, respectively. The circulation trend in the CMIP6 multimodel mean does not resemble the observed trend pattern and is much weaker overall. Some individual CMIP6 models do show a resemblance to the observed pattern in ERA5, although still weak with a maximum pattern correlation of 0.47. The pattern correlation is higher for the two individual regions (U.S.-Atlantic and Eurasia), reaching a maximum of 0.69 and 0.78. We show that both regional wave patterns in ERA5 are associated with distinct sea surface temperature and outgoing longwave radiation anomalies in the 3 weeks leading up to the atmospheric configuration, resembling known teleconnection patterns. CMIP6 models appear to lack these tropical-extratropical teleconnections. Our findings highlight the limitations of CMIP6 models in reproducing teleconnections and their associated regional imprint, creating deep uncertainty for regional climate projections on decadal-to-multidecadal time scales
Improving satellite remote sensing estimates of the global terrestrial water cycle via neural network modeling
No full textInternational audienceSatellite remote sensing provides important observations of Earth’s water cycle, but combining different satellite datasets often fails to produce a balanced water budget, highlighting the errors and uncertainties in these observations. This study introduces a novel approach combining optimal interpolation with neural network modeling to improve global water cycle estimates. We first balance water budget components (precipitation, evapotranspiration, runoff, and water storage change) across 1,358 river basins using optimal interpolation. We then train neural networks to reproduce these results and extend them to ungaged basins. After validating the approach on 340 independent basins, we apply it globally to create calibrated water cycle estimates at 0.5°resolution. Our method significantly reduces water budget imbalances in validation basins, decreasing the mean imbalance from 11 to 0.03 mm/month and reducing its variance from 44 to 24 mm/month. The calibrated datasets perform particularly well when applied to estimating evapotranspiration via the water budget method, achieving accuracy comparable to state-of-the-art methods. This is particularly useful in regions without ground-based measurements, and has broad applications in water resources planning and management. This study helps identify where satellite datasets need correction and demonstrates the benefits of machine learning for studying the water cycle at the global scale
A Framework to Attribute Tropical Multiscale Precipitation Extremes to Rain Event Morphology in Deep Convective Systems
No full textInternational audienceAbstract The different spatiotemporal scales used to calculate extreme precipitation intensities can lead to diverging interpretation when investigating their physical origin, impacts, and sensitivity to climate. Besides, the contribution of mesoscale convective systems (MCSs) to tropical precipitation extremes remains loosely quantified on various scales, in particular on kilometer scales. Here, we construct a framework to analyze the cooccurrence of extreme precipitation at km‐scale and 1° × 1 day scale to compare their properties in terms of precipitation morphology and regional predominance. Using a storm‐tracking algorithm, we contrast the occurrence and precipitation statistics for two types of convective systems across 10 global storm‐resolving models and one geostationary satellite product. We do not find a large statistical dependence between rain extremes on these two scales, and they occur in distinct regions. Heavy km‐scale events occur mostly over continents and 40% of them are produced by MCSs in observations. Their intensity is independent from the area of rain features. Conversely, heavy 1° × 1 day rain intensities are dependent on the area of rain features, and occur more frequently over oceans, and a third of these events are produced by MCSs. Overall, the transition from deep to MCSs connect extremes across both scales. Compared to observations, models consistently underestimate the precipitating surface and show large discrepancies in the contribution of convective systems to precipitation extremes at each scale. This diagnostic is a key criterion for evaluating the ability of global storm‐resolving models to represent how individual convective systems produce realistic heavy rain distributions
The Complexity of Multidimensional Learning in Agriculture
No full textInternational audienceStudies on agricultural technology adoption often focus on one input, practice or package, which is analytically useful, but may overlook the complexities involved with multidimensional learning needed for a lot of agricultural decisions. In Kenya, we study farmers' dynamic learning (from oneself and others) and adoption decisions over six seasons after randomly inviting them to participate in agronomic research trials, comparing different combinations of inputs during three consecutive seasons. As a response to the trials, adoption increases steadily despite the absence of positive profits multiple seasons after exposure to the trials. Know-how increases rapidly and faster for high skill farmers who experiment the most, at the cost of making new mistakes. The findings are consistent with a theoretical model with multidimensionality of input and practice decisions and differential learning from one's own experience by skills, where complementarities imply that adoption of an input requires finding how to re-optimize other dimensions, which adds to the cost of adoption
Low-thrust Interplanetary Trajectories with Missed Thrust Events: a Numerical Approach
No full textThe problem under consideration is to drive a spatial vehicle to a target at a given final time while minimizing fuel consumption. This is a classical optimal control problem in a deterministic setting. However temporary stochastic failures of the engine may prevent reaching the target after the engine usage is recovered. Therefore, a stochastic optimal control problem is formulated under the constraint of ensuring a minimal probability of hitting the target. This problem is modeled, improved and finally solved by dualizing the probability constraint and using an Arrow-Hurwicz stochastic algorithm. Numerical results concerning an interplanetary mission are presented