Improving interpretation of sea-level projections through a machine-learning-based local explanation approach

Process-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. To this end, we adopt the local attribution approach developed in the machine learning community by combining the game-theoretic approach known as &lsquo;SHAP&rsquo; (SHapley Additive exPlanation) with machine-learning regression models. We apply our methodology to a subset of the multi-model ensemble study of the future contribution of the Greenland ice sheet to sea-level, taking into account different modelling choices related to (1) the numerical implementation, (2) the initial conditions, and (3) the modelling of ice-sheet processes. This allows us to quantify the influence of particular modelling decisions, which is directly expressed in terms of sea level change contribution. This type of diagnosis can be performed on any member of the ensemble, and we show in the Greenland case how the aggregation of the local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to spatial model resolution and to retreat parametrisation.</p

Adaptation to multi-meter sea-level rise should start now

Sea-level rise will fundamentally change coastal zones worldwide (Cooley et al 2022). A global two meters rise of sea level will be exceeded sooner or later within a time window ranging from one century to as long as two millennia, depending on future greenhouse gas emissions and polar ice-sheet melting (Fox-Kemper et al 2021). Here, we show that in addition to climate mitigation to slow this rise, adaptation to two meters of sea-level rise should start now. This involves changing our mindset to define a strategic vision for these threatened coastal areas and identify realistic pathways to achieve this vision. This can reduce damages, losses, and lock-ins in the future, identify problems before they become critical and exploit opportunities if they emerge. To meet this challenge, it is essential that coastal adaptation becomes core to coastal development, especially for long-lived critical infrastructure. Coastal adaptation will be an ongoing process for many decades and centuries, requiring the support of climate services, which make the links between science, policy and adaptation practice

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Progress in Numerical Modeling of Antarctic Ice-Sheet Dynamics

Numerical modeling of the Antarctic ice sheet has gone through a paradigm shift over the last decade. While initially models focussed on long-time diffusive response to surface mass balance changes, processes occurring at the marine boundary of the ice sheet are progressively incorporated in newly developed state-of-the-art ice-sheet models. These models now exhibit fast, short-term volume changes, in line with current observations of mass loss. Coupling with ocean models is currently on its way and applied to key areas of the Antarctic ice sheet. New model intercomparisons have been launched, focusing on ice/ocean interaction (MISMIP+, MISOMIP) or ice-sheet model initialization and multi-ensemble projections (ISMIP6). Nevertheless, the inclusion of new processes pertaining to ice-shelf calving, evolution of basal friction, and other processes, also increase uncertainties in the contribution of the Antarctic ice sheet to future sea-level rise.SCOPUS: re.jinfo:eu-repo/semantics/publishe

SMOS reveals the signature of Indian Ocean Dipole events

The tropical Indian Ocean experiences an interannual mode of climatic variability, known as the Indian Ocean Dipole (IOD). The signature of this variability in ocean salinity is hypothesized based on modeling and assimilation studies, on account of scanty observations. Soil Moisture and Ocean Salinity (SMOS) satellite has been designed to take up the challenge of sea surface salinity remote sensing. We show that SMOS data can be used to infer the pattern of salinity variability linked with the IOD events. The core of maximum variability is located in the central tropical basin, south of the equator. This region is anomalously salty during the 2010 negative IOD event, and anomalously fresh during the 2011 positive IOD event. The peak-to-peak anomaly exceeds one salinity unit, between late 2010 and late 2011. In conjunction with other observational datasets, SMOS data allow us to draw the salt budget of the area. It turns out that the horizontal advection is the main driver of salinity anomalies. This finding is confirmed by the analysis of the outputs of a numerical model. This study shows that the advent of SMOS makes it feasible the quantitative assessment of the mechanisms of ocean surface salinity variability in the tropical basins, at interannual timescales

Relation between neighbouring grains in the upper part of the NorthGRIP ice core - Implications for rotation recrystallization

We propose a new method to investigate the relationships between neighbouring crystals and apply it to the textures measured along the upper 900 m of the NorthGRIP ice core. This method shows unexpected correlations between neighbours in the so-called normal grain growth regime, questioning the classical view on the onset of rotation recrystallization in ice-sheets. Moreover, the fractionation rate associated to the rotation recrystallization appears constant through time. Finally, grains with low-angle boundaries do not present a special distribution pattern of their c-axes. This suggests that rotation recrystallization is an isotropic process not affected by the direction of the macroscopic strain. © 2007 Elsevier B.V. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Downbeat tracking with multiple features and deep neural networks

International audience<p>In this paper, we introduce a novel method for the automatic estimation of downbeat positions from music signals. Our system relies on the computation of musically inspired features capturing important aspects of music such as timbre, harmony, rhythmic patterns, or local similarities in both timbre and harmony. It then uses several independent deep neural networks to learn higher-level representations. The downbeat sequences are finally obtained thanks to a temporal decoding step based on the Viterbi algorithm. The comparative evaluation conducted on varied datasets demonstrates the efficiency and robustness across different music styles of our approach.</p

Feature Adapted Convolutional Neural Networks for Downbeat Tracking

International audience<p>We define a novel system for the automatic estimation of downbeat positions from audio music signals. New rhythm and melodic features are introduced and feature adapted convolutional neural networks are used to take advantage of their specificity. Indeed, invariance to melody transposition, chroma data augmentation and length-specific rhythmic patterns prove to be useful to learn downbeat likelihood. After the data is segmented in tatums, complementary features related to melody, rhythm and harmony are extracted and the likelihood of a tatum being at a downbeat position is computed with the aforementioned neural networks. The downbeat sequence is then extracted with a flexible temporal hidden Markov model. We then show the efficiency and robustness of our approach with a comparative evaluation conducted on 9 datasets.</p

Improving interpretation of sea-level projections through a machine-learning-based local explanation approach

International audienceProcess-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. To this end, we adopt the local attribution approach developed in the machine learning community known as "SHAP" (SHapley Additive exPlanations). We apply our methodology to a subset of the multi-model ensemble study of the future contribution of the Greenland ice sheet to sea level, taking into account different modelling choices related to (1) numerical implementation, (2) initial conditions, (3) modelling of ice-sheet processes, and (4) environmental forcing. This allows us to quantify the influence of particular modelling decisions, which is directly expressed in terms of sea-level change contribution. This type of diagnosis can be performed on any member of the ensemble, and we show in the Greenland case how the aggregation of the local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to spatial model resolution and to retreat parametrisation