The glacial drillings bring us important information on the past climates, in particular through the analyses of enclosed air bubbles and which are authentic samples of the past atmospheres. The deep drilling which goes the deepest in the past is the one from the european EPICA project with its 800,000 years old ice. But scientists seek to go further back in time, to document the Mid-Pleistocene Transition (MPT) which occurred between 1.2 and 0.8 million of years into the past and during which the large glacial cycles with a period of ~100,000 years were born (Fischer et al., 2013). It is why the european H2020 project Beyond EPICA (2019-2026) was born and aims at drilling a 1.5 million years ice core at the Little Dome C (LSC) site. With its relatively low ice thickness, it is improbable that the LDC site will allow to go more than 1.5 million years back in time with a sufficient temporal resolution (Chung et al., 2023a, b). The current project aims at studying the basal ice layer in Antarctica, to determine its role in the large scale dynamics of the ice sheet and to determine and characterize drilling sites allowing to go further back in time (2 million years or more). This old period has encountered greenhouse gases concentrations as high or potentially higher than the present and we want to study the climate dynamics as well as the East Antarctic ice sheet dynamics during this period. A potentially interesting site, called North Patch, has already been determined and will have to be confirmed (Parrenin et al., 2017; Chung et al., 2023a, b). After a geophysical campaign and some numerical modeling, a deep drilling and on-site analyses will be carried out with innovative techniques. The technical developments for the deep drilling and for the on-site analyses of the ice will be inspired by developments currently happening in the laboratories but they will require adaptation to be deployed on-site. Our project relies on 4 pillars: - Radar sounding of the ice sheet, - modeling of the age of the ice and determination of a drilling site, - Construction and deployment of a rapid access drilling, - Ice analysis, dating of the ice and reconstruction of climatic parameters to study this old and warm period. Our project is NOT a concurrent to the Beyond EPICA project, but is rather situated upstream of a future European deep drilling project which will aim to go further back in time than 1.5 million years. Indeed, the aim of our project is not to perform all possible analyses on a large diameter ice core, but just to qualify a potential drilling site and to deduce the first information on the dynamics of this old past period.

The ice shelves of Antarctica act as giant dams limiting ice flow from the interior into the ocean. Recently, several of these dams have shown dramatic signs of damage. Once the ice is fracturing, the dam is weakened, allowing the ice to flow faster and increasing mass loss from the interior of the ice sheet, which results in an accelerated contribution to sea-level. As Antarctica is the largest reservoir of fresh water, correctly predicting its evolution is crucial to anticipate when and how much sea-level will rise. Damage is currently absent from projection of Antarctica evolution, because of the difficulty of representing this process in numerical models as well as the lack of observation to constrain them. The aim of the IceDaM project is to quantify and understand the evolution of damage on ice shelves around Antarctica. My team and I will first use a novel approach based on deep learning to automatically identify the evolution of fractures on satellite imagery. By combining this record with inversions from a high-order ice flow model, we will quantify for the first time the links between fractures and changes in ice rigidity, which controls the strength of ice shelves. To better understand the damage variability, we will measure the evolution of key variables that positively impact the rheological weakening of ice shelves. These time series will be analyzed in a unique fashion, to determine the major processes that led to the evolution of damage in the satellite observation era. Based on these results, we will set up the first “sentinel” of ice shelves by systematically mapping the evolution of fractures in near real time. This will be used to establish new vulnerability indices, based on changes in ice rigidity and their impact on glacier mass balance. This project will open a new window on the processes affecting ice shelves at an unprecedented level of resolution, ultimately allowing us to improve our ability to predict the fate of future sea level rise.

Over a large part of Antarctica, the surface mass balance (SMB) is controlled by a few extreme events, resulting in a high natural variability of this parameter. In particular, extreme moisture intrusions linked to Southern Ocean Atmospheric Rivers (ARs) have been recently demonstrated to be major sources of both snow accumulation, heating and surface melt. Despite their key role, there is a general omission of AR variability, and more broadly of extreme events, in studies of past and future Antarctic climate and SMB. ARCA will assess the impact of ARs on the surface mass balance of Antarctica and will explore to what extent past AR activity can be recorded in ice cores. To reach this goal, ARCA is organized in 4 working packages. 1) ARCA will use recent novel numerical methodologies for identifying ARs applied to global and regional circulation models (GCMs and RCMs respectively). New algorithms will be applied to historical, present and future climate simulations. 2) ARCA will provide new field measurements of water stable isotopes and chemistry composition of snow precipitation and air masses from Adelie and Wilkes Lands, and 3) apply a regional scale modeling of water stable isotopes to interpret the signal observed in the field. 4) ARCA will finally revisit data from existing ice cores (aerosol content, e.g. sea salt, insoluble particles, water isotopes). Following this methodology, ARCA proposes to: 1) understand how natural variability and external forcings control the AR activity. 2) quantify AR moisture and heat transport towards Antarctica and their impacts on the SMB of Antarctica. 3) describe AR impact on the isotopic and aerosol contents of air masses transported through East Antarctica, 4) analyze the processes (e.g., moisture origin, sublimation of hydrometeors) producing characteristic signals in air masses during ARs, 5) estimate the induced bias in ice core records in regards to past temperature reconstructions. 6) Evaluate (qualitatively) past AR variability and the resulting bias in current estimates of past millenium climate in Antarctica. The ARCA project will deliver products that describe AR climatology and variability (occurrence maps, statistics), their atmospheric moisture signature (time-series of isotopes and aerosol content), and their impacts on Antarctic climate and SMB (through maps of induced melting and accumulation). Results will be presented for the 20th and 21st centuries, aiming in particular at projecting observationally constrained impacts of ARs on the SMB. ARCA will define a multi proxy approach to define how past AR could be retrieved in ice cores and provide a metric using water isotopic composition in ice cores to qualitatively define periods of higher and lower AR activity over the past millennium. Finally, ARCA will define the regions of Adelie and Wilkes Lands where ice cores should be drilled to best capture the AR and their influence in past climate variability. The ARCA consortium presents recognized experts from the IGE, LSCE and LOCEAN in particular in atmospheric modeling with polar-Regional Circulation Models and General Circulation Models, AR detection and estimation surface mass balance for Antarctica. The project will also rely on the broad expertise of the group in the interpretation of water isotopes and aerosol contents in air samples and ice cores.

In most mountain ranges around the Earth, river floods caused by intense precipitation, or the drainage of natural dammed lakes are expected to progressively increase in frequency and magnitude with global climate change. Those more frequent and stronger extreme floods will likely more predominantly set the pace at which alpine rivers evolve and cause hazards for populations over daily to multi-year timescales. However, our limited knowledge of the physics of extreme floods and their impact on river landscapes impedes anticipation of future river landscape changes. Extreme floods can be very damaging, and thus often cannot be observed by traditional measurement methods. This instrumental limit is the main cause of our inability to answer fundamental scientific questions, such as (1) how much, how far and how fast is material (water and sediments) conveyed by rivers during extreme floods? (2) how do the physics of extreme floods impact the long term dynamics of rivers? (3) to what extent do extreme floods determine the long-term evolution of landscapes under a changing climate? The goal of this project is to bring new insights into answering these fundamental questions by overcoming traditional instrumentation difficulties through the innovative use of seismic observations. We present a novel strategy using seismic observations to provide unprecedented, quantitative observational constraints on extreme flood physics, and to quantify the short to long-term impacts of extreme floods on river landscapes. Our approach builds on recent advances in the emerging field of fluvial seismology, from which an innovative theoretical seismo-mechanical framework was proposed to crucially link river physics (turbulent flow, sediment transport) to seismic signals. The first aim of this project is to demonstrate the validity of the seismo-mechanical framework in natural settings. For this we will monitor designated field sites with state-of-the-art instrumentation of river physical parameters. This will enable us to thoroughly test and validate the newly established seismo-mechanical framework. The second aim of this project is to apply the seismo-mechanical framework to specific river landscapes where extreme floods likely dominate the pace at which landscapes evolve, i.e. they control most of the river geomorphic changes and are the main source of hazard risk for populations. In doing so, we will provide unique quantitative constraints on the physics of moderate to large floods occurring around the world, and on their legacy in the landscape. The large dataset gathered at the multiple targeted sites will allow extracting generic relationships to be used to answer the fundamental question asked above. The third and last aim of the project is to build a communicative platform around the SEISMORIV accomplishments as well as a database gathering all findings acquired in SEISMORIV. This step will set very good grounds to initiate an international, scientific collaborative effort to facilitate the growth and establishment of the novel and rapidly growing field of environmental seismology.

Sea level rise has become a challenge facing society due particularly to the ice sheets that are contributing more significantly than previously anticipated. The mass loss of the ice sheets is due to increased surface melt runoff and outflow of ice associated with current climate warming. Here, we propose to improve our understanding of the dynamic component. Indeed, the ice discharge modulated by changes in ice velocity and thickness changes remains the largest uncertainty in the current and future contribution of the ice sheets to sea level rise. Quantifying and understanding the past/present/future contribution of the ice sheets to sea level rise under the current warming climate requires answering fundamental questions as: How has the ice velocity, thickness and so discharge of outlet glaciers changed on sub-annual to decadal time scales? What are the main and most important external forcing that are controlling changes in glacier ice discharge into the ocean? How can we use ice dynamic observations of the recent past to teach numerical ice flow model and get more precise projection of sea level rise? Until recently the answers to those questions were limited, mainly because the ice sheet observations were spatially incomplete and temporarily sparse, resulting in averaged products to maximize spatial coverage at the expense of temporal information. However, in the last few years, we entered a new era of spaceborne ice sheet observations with the launch of the ESA’s CryoSat-2 in 2010, USGS’ Landsat-8 in 2013 and ESA’s four Sentinel-1 & 2 between 2014 & 2016. Used in the synergistic manner, these satellites offer the first chance for sustained, continuous data acquisition over the ice sheets to map ice motion and elevation. Taking the opportunity offered by these new satellites, the SOSIce project will reconstruct at high temporal and spatial resolution the ice flow for the largest glaciers of Greenland and Antarctica to refine mass balance estimates and improve the forecasting skills of the numerical ice flow models. We have envisioned this work in 3 successive steps: derive time series of the (1) dynamical and geometrical structure of the glaciers from these new sensors, (2) assimilate them into the state-of-the-art ice flow model Elmer/Ice, and (3) disseminate our results using public data archive for the scientific community. By taking advantage of the continuous observations and by assimilating them in an model, we will follow the ice sheet evolution in a fundamentally new way compared to current approaches. Significant technical and scientific issues would be solved from the results of this project, including securing the capacity to process large quantities of data for ice sheet studies, better understanding of the underlying physical processes causing increased in glacier ice discharge, improving ice-sheet model initialization before computing projections, and precisely reassessing the sea-level budget. This project will set very good grounds to initiate an international, scientific collaborative effort to facilitate the growth and establishment of the novel and rapidly growing field of remote sensing of the cryosphere over large datasets.