Project acronym ADAPT
Project Autoxidation of Anthropogenic Volatile Organic Compounds (AVOC) as a Source of Urban Air Pollution
Researcher (PI) Matti Rissanen
Host Institution (HI) TAMPEREEN KORKEAKOULUSAATIO SR
Country Finland
Call Details Consolidator Grant (CoG), PE10, ERC-2020-COG
Summary Previous efforts to raise living standards have been based on relentlessly increasing combustion, causing environmental destruction at all scales. In addition to climate-warming CO2, fossil fuel combustion also produces a large number of organic compounds and particulate matter, which deteriorate air quality.
The atmosphere is cleansed from such pollutants by gas-phase oxidation reactions, which are invariably mediated by peroxy radicals (RO2). Oxidation transforms initially volatile and water-insoluble hydrocarbons into water-soluble forms (ultimately CO2), enabling scavenging by liquid droplets. A minor but crucially important alternative oxidation pathway leads to oxidative molecular growth, and formation of atmospheric aerosols. Aerosols impart a huge influence on the atmosphere, from local air quality issues to global climate forcing, yet their formation mechanisms and structures of organic aerosol precursors remains elusive.
In a paradigm change, RO2 was recently found to undergo autoxidation, enabling rapid aerosol precursor formation even at sub-second time-scales – in stark contrast to the long processing times (days - weeks) previously assumed to be necessary. We have shown how abundant biogenic hydrocarbons (BVOC) autoxidize, but due to key structural differences, the same pathways are not available for anthropogenic hydrocarbons (AVOC), and thus they were not expected to autoxidize. My preliminary experiments reveal that AVOCs do autoxidize, but the mechanism enabling this remain unknown. Crucially, the co-reactants shown to inhibit BVOC seem to enforce AVOC autoxidation – potentially explaining the recent mysterious discovery of new-particle formation in polluted megacities. In ADAPT, I will use a combination of novel mass spectrometric detection methods fortified by theoretical calculations, to solve the mechanism of AVOC autoxidation. This will directly assist both air quality management, and the design of cleaner fuels and engines.
Summary
Previous efforts to raise living standards have been based on relentlessly increasing combustion, causing environmental destruction at all scales. In addition to climate-warming CO2, fossil fuel combustion also produces a large number of organic compounds and particulate matter, which deteriorate air quality.
The atmosphere is cleansed from such pollutants by gas-phase oxidation reactions, which are invariably mediated by peroxy radicals (RO2). Oxidation transforms initially volatile and water-insoluble hydrocarbons into water-soluble forms (ultimately CO2), enabling scavenging by liquid droplets. A minor but crucially important alternative oxidation pathway leads to oxidative molecular growth, and formation of atmospheric aerosols. Aerosols impart a huge influence on the atmosphere, from local air quality issues to global climate forcing, yet their formation mechanisms and structures of organic aerosol precursors remains elusive.
In a paradigm change, RO2 was recently found to undergo autoxidation, enabling rapid aerosol precursor formation even at sub-second time-scales – in stark contrast to the long processing times (days - weeks) previously assumed to be necessary. We have shown how abundant biogenic hydrocarbons (BVOC) autoxidize, but due to key structural differences, the same pathways are not available for anthropogenic hydrocarbons (AVOC), and thus they were not expected to autoxidize. My preliminary experiments reveal that AVOCs do autoxidize, but the mechanism enabling this remain unknown. Crucially, the co-reactants shown to inhibit BVOC seem to enforce AVOC autoxidation – potentially explaining the recent mysterious discovery of new-particle formation in polluted megacities. In ADAPT, I will use a combination of novel mass spectrometric detection methods fortified by theoretical calculations, to solve the mechanism of AVOC autoxidation. This will directly assist both air quality management, and the design of cleaner fuels and engines.
Max ERC Funding
2 689 147 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym AeroSurf
Project Comprehensive Investigations of Aerosol Droplet Surfaces and Their Climate Impacts
Researcher (PI) Bryan BZDEK
Host Institution (HI) UNIVERSITY OF BRISTOL
Country United Kingdom
Call Details Starting Grant (StG), PE10, ERC-2020-STG
Summary By serving as cloud droplet seeds, aerosols represent the largest negative (cooling) and most uncertain climate forcing. Particulate matter is also a major contributor to air pollution, attributed to ~7 million annual deaths. Aerosol surfaces hold the greatest source of uncertainty for atmospheric chemistry and climate impacts. Surfactants are now routinely identified within atmospheric aerosol samples, and surface tension governs the fraction of particles that activate into cloud droplets, significantly impacting aerosol-cloud climate effects. Sunlight-driven interfacial reactions have recently emerged as important modifiers of atmospheric composition and proceed via unique pathways relative to bulk solutions. A complete understanding of aerosol climate and health impacts requires detailed knowledge of aerosol surface composition and reactivity. However, few approaches directly interrogate droplet surfaces, hindering incorporation of surface-mediated processes into climate and air quality models. This project will study directly the droplet-air interface of picolitre droplets in size ranges relevant to growing cloud droplets to develop a comprehensive, molecular level understanding of interfacial composition, reactivity, and climate and health impacts. Aerosol droplet surfaces will be studied with novel, sensitive approaches. The dynamic and equilibrium partitioning of surfactants to aerosol droplet surfaces will be investigated directly for the first time, providing information required for accurate cloud droplet activation predictions. Entirely new approaches to selectively analyse the surface and bulk molecular composition of a levitated micron-sized droplet by mass spectrometry will allow direct investigation of chemistry on aerosol surfaces. Together, these approaches will address outstanding questions in interfacial photochemistry, link directly droplet surface tension to climate impacts, and resolve a poorly understood aspect of aerosol chemistry.
Summary
By serving as cloud droplet seeds, aerosols represent the largest negative (cooling) and most uncertain climate forcing. Particulate matter is also a major contributor to air pollution, attributed to ~7 million annual deaths. Aerosol surfaces hold the greatest source of uncertainty for atmospheric chemistry and climate impacts. Surfactants are now routinely identified within atmospheric aerosol samples, and surface tension governs the fraction of particles that activate into cloud droplets, significantly impacting aerosol-cloud climate effects. Sunlight-driven interfacial reactions have recently emerged as important modifiers of atmospheric composition and proceed via unique pathways relative to bulk solutions. A complete understanding of aerosol climate and health impacts requires detailed knowledge of aerosol surface composition and reactivity. However, few approaches directly interrogate droplet surfaces, hindering incorporation of surface-mediated processes into climate and air quality models. This project will study directly the droplet-air interface of picolitre droplets in size ranges relevant to growing cloud droplets to develop a comprehensive, molecular level understanding of interfacial composition, reactivity, and climate and health impacts. Aerosol droplet surfaces will be studied with novel, sensitive approaches. The dynamic and equilibrium partitioning of surfactants to aerosol droplet surfaces will be investigated directly for the first time, providing information required for accurate cloud droplet activation predictions. Entirely new approaches to selectively analyse the surface and bulk molecular composition of a levitated micron-sized droplet by mass spectrometry will allow direct investigation of chemistry on aerosol surfaces. Together, these approaches will address outstanding questions in interfacial photochemistry, link directly droplet surface tension to climate impacts, and resolve a poorly understood aspect of aerosol chemistry.
Max ERC Funding
2 315 245 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym BOOGIE
Project Breathing Oceans: understanding the organic skin that modulates the exchange of greenhouse gases between the atmosphere and the ocean
Researcher (PI) Ryan Pereira
Host Institution (HI) HERIOT-WATT UNIVERSITY
Country United Kingdom
Call Details Starting Grant (StG), PE10, ERC-2020-STG
Summary Oceans are a global reservoir of greenhouse gases, estimated to account for 20–40% of the post-industrial sink for anthropogenic carbon dioxide (CO2). However, quantifying the exchange of gases such as CO2, methane (CH4), and nitrous oxide (N2O) between the ocean and atmosphere is a major challenge. Understanding how the ocean’s organic skin layer modulates this exchange is critical to estimate the intrinsic oceanic sinks and sources of these key greenhouse gases both now and in the future. Organic substances in the skin layer, known as surfactants, span across traditional operational definitions and are derived from multiple sources undergoing biotic and abiotic transformations along the land-ocean continuum. This proposal will investigate a land-ocean transect from South America toward the African Continent to investigate organic matter control of air-water gas exchange. Central to this work is the application of new technologies, using novel in-situ sensor platforms and advanced geochemical characterisation techniques. This new and unique data will be incorporated into hydrological and gas flux models to examine spatial and temporal effects of surfactant suppression of gas exchange – both now and in the future.
Summary
Oceans are a global reservoir of greenhouse gases, estimated to account for 20–40% of the post-industrial sink for anthropogenic carbon dioxide (CO2). However, quantifying the exchange of gases such as CO2, methane (CH4), and nitrous oxide (N2O) between the ocean and atmosphere is a major challenge. Understanding how the ocean’s organic skin layer modulates this exchange is critical to estimate the intrinsic oceanic sinks and sources of these key greenhouse gases both now and in the future. Organic substances in the skin layer, known as surfactants, span across traditional operational definitions and are derived from multiple sources undergoing biotic and abiotic transformations along the land-ocean continuum. This proposal will investigate a land-ocean transect from South America toward the African Continent to investigate organic matter control of air-water gas exchange. Central to this work is the application of new technologies, using novel in-situ sensor platforms and advanced geochemical characterisation techniques. This new and unique data will be incorporated into hydrological and gas flux models to examine spatial and temporal effects of surfactant suppression of gas exchange – both now and in the future.
Max ERC Funding
1 960 748 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym CausalEarth
Project Advanced spatio-temporal causal inference for climate research
Researcher (PI) Jakob Runge
Host Institution (HI) TECHNISCHE UNIVERSITAT BERLIN
Country Germany
Call Details Starting Grant (StG), PE10, ERC-2020-STG
Summary CausalEarth is an interdisciplinary project, aiming to improve our understanding of the interdependencies between major drivers (modes) of climate variability by developing novel statistical causal inference methods for both observations and model data.
Disentangling the interdependencies of the major modes, such as El Nino Southern Oscillation and the North Atlantic Oscillation, is key to understand regional climate, and essential for process-based climate model evaluation. The modes' interdependencies are characterized by common drivers, indirect effects, nonlinearities, regime-dependence, and heterogeneous spatio-temporal causal relations. Currently, observational analyses are mostly based on the correlation of scalar (one-dimensional) time series derived from regional averaging or principal component analysis, restricted to supposed causal regimes, e.g., the winter season or phases of multi-decadal climate indices, where dependencies are expected to be stationary. Such scalar correlation approaches fall short in capturing the modes' complex regime-dependent spatio-temporal causal interdependencies.
CausalEarth will develop innovative methods to move (1) from representing complex phenomena as scalar indices to learning spatio-temporal features, (2) from supposing causal regimes to learning them from data, and (3) from correlation to causal dependencies. To this end, CausalEarth will combine recent developments in machine learning with causal inference algorithms.
These methods will be used to infer the causal interdependencies and drivers of major climate modes from observations and to construct the next generation of causal metrics for climate model evaluation.
CausalEarth will push the limits of what can be learned from observational data about causal relations and drive model development towards breakthroughs in projecting our future climate.
Summary
CausalEarth is an interdisciplinary project, aiming to improve our understanding of the interdependencies between major drivers (modes) of climate variability by developing novel statistical causal inference methods for both observations and model data.
Disentangling the interdependencies of the major modes, such as El Nino Southern Oscillation and the North Atlantic Oscillation, is key to understand regional climate, and essential for process-based climate model evaluation. The modes' interdependencies are characterized by common drivers, indirect effects, nonlinearities, regime-dependence, and heterogeneous spatio-temporal causal relations. Currently, observational analyses are mostly based on the correlation of scalar (one-dimensional) time series derived from regional averaging or principal component analysis, restricted to supposed causal regimes, e.g., the winter season or phases of multi-decadal climate indices, where dependencies are expected to be stationary. Such scalar correlation approaches fall short in capturing the modes' complex regime-dependent spatio-temporal causal interdependencies.
CausalEarth will develop innovative methods to move (1) from representing complex phenomena as scalar indices to learning spatio-temporal features, (2) from supposing causal regimes to learning them from data, and (3) from correlation to causal dependencies. To this end, CausalEarth will combine recent developments in machine learning with causal inference algorithms.
These methods will be used to infer the causal interdependencies and drivers of major climate modes from observations and to construct the next generation of causal metrics for climate model evaluation.
CausalEarth will push the limits of what can be learned from observational data about causal relations and drive model development towards breakthroughs in projecting our future climate.
Max ERC Funding
1 499 631 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym CENAE
Project compound Climate Extremes in North America and Europe: from dynamics to predictability
Researcher (PI) Gabriele Messori
Host Institution (HI) UPPSALA UNIVERSITET
Country Sweden
Call Details Starting Grant (StG), PE10, ERC-2020-STG
Summary Different climate extremes, such as heavy rains and strong winds, can interact and result in compound extremes with a larger socio-economic impact than the sum of their individual components. Elucidating the nature of these compound extremes is both a key step in furthering our scientific understanding of the climate system and a societally relevant goal. However, it is not easily realised, as the multivariate nature and inherent rarity of the compound extremes poses a formidable challenge to current analysis techniques.
In CENÆ I aim to provide a step-change in our understanding of the drivers and predictability of compound climate extremes, and illuminate how climate change may affect these two aspects. I will specifically focus on two high-impact compound extremes which have occurred with an ostensibly high frequency in recent years: (i) wintertime wet and windy extremes in Europe; and (ii) same as (i) but with the additional occurrence of (near-)simultaneous cold spells in North America.
CENÆ builds upon my ongoing contribution to developing dynamical systems analysis tools for climate extremes. It further leverages the work of my research group on the atmospheric circulation and machine learning for the study of atmospheric predictability. I will use this interdisciplinary knowledge base to elucidate the atmospheric precursors to compound extremes, provide a nuanced understanding of their predictability and point to new predictability pathways. The analysis framework I will develop in CENÆ will be highly flexible and applicable to multivariate extremes beyond climate science.
This effort is timely: the World Climate Research Programme has highlighted understanding current and future climate extremes as a grand challenge of climate science. Moreover, my unconventional research in dynamical systems and machine learning has opened up previously unforeseen opportunities for the study of compound climate extremes which should be rapidly and systematically exploited.
Summary
Different climate extremes, such as heavy rains and strong winds, can interact and result in compound extremes with a larger socio-economic impact than the sum of their individual components. Elucidating the nature of these compound extremes is both a key step in furthering our scientific understanding of the climate system and a societally relevant goal. However, it is not easily realised, as the multivariate nature and inherent rarity of the compound extremes poses a formidable challenge to current analysis techniques.
In CENÆ I aim to provide a step-change in our understanding of the drivers and predictability of compound climate extremes, and illuminate how climate change may affect these two aspects. I will specifically focus on two high-impact compound extremes which have occurred with an ostensibly high frequency in recent years: (i) wintertime wet and windy extremes in Europe; and (ii) same as (i) but with the additional occurrence of (near-)simultaneous cold spells in North America.
CENÆ builds upon my ongoing contribution to developing dynamical systems analysis tools for climate extremes. It further leverages the work of my research group on the atmospheric circulation and machine learning for the study of atmospheric predictability. I will use this interdisciplinary knowledge base to elucidate the atmospheric precursors to compound extremes, provide a nuanced understanding of their predictability and point to new predictability pathways. The analysis framework I will develop in CENÆ will be highly flexible and applicable to multivariate extremes beyond climate science.
This effort is timely: the World Climate Research Programme has highlighted understanding current and future climate extremes as a grand challenge of climate science. Moreover, my unconventional research in dynamical systems and machine learning has opened up previously unforeseen opportunities for the study of compound climate extremes which should be rapidly and systematically exploited.
Max ERC Funding
1 492 660 €
Duration
Start date: 2021-03-01, End date: 2026-02-28
Project acronym COEVOLVE
Project Coevolution of Life and Planet: role of trace metal availability in the evolution of biogeochemically relevant redox metalloenzymes
Researcher (PI) Donato Giovannelli
Host Institution (HI) UNIVERSITA DEGLI STUDI DI NAPOLI FEDERICO II
Country Italy
Call Details Starting Grant (StG), PE10, ERC-2020-STG
Summary Earth’s geosphere and biosphere have coevolved over time, influencing each other’s stability and keeping our planet habitable over the last 4 billion years. Biogeochemical cycles are crucial in this mechanism, connecting long-term geological cycles and the much faster evolution of the Earth’s outer envelopes. A small set of microbial-encoded proteins containing a redox-sensitive transition metal as their core catalytic center carry out the majority of the key biogeochemical reactions. Metals such as Fe, Co, Ni, Zn, Mo, Mn, W, V, and Cu are used in these proteins to access diverse redox couples as a function of what the planet has provided to biology over time. Despite the importance of this process, the relationship between metal availability and metabolism evolution and diversity has not been investigated in detail. COEVOLVE will elucidate the impact of transition metal availability on microbial functional diversity in deep time, combining fieldwork, laboratory experiments, and computational approaches. COEVOLVE will: 1) investigate the relationship between the availability of trace metals and microbial functional diversity in extant ecosystems and organisms; 2) link metabolic diversity and metal availability to different geological, geochemical, and mineralogical conditions; 3) link metabolic diversity and dependence on metal availability to the emergence and evolution of different metabolisms; and 4) determine the timing of major steps in metabolic evolution and link them to geochemical proxies of planetary surface redox change. Understanding the role of trace metal environmental distribution and availability in influencing microbial functional diversity might hold the key to understanding the co-evolution of life and our planet, unlocking a number of important discoveries at the core of diverse fields such as earth sciences, astrobiology, microbial ecology, and biotechnology.
Summary
Earth’s geosphere and biosphere have coevolved over time, influencing each other’s stability and keeping our planet habitable over the last 4 billion years. Biogeochemical cycles are crucial in this mechanism, connecting long-term geological cycles and the much faster evolution of the Earth’s outer envelopes. A small set of microbial-encoded proteins containing a redox-sensitive transition metal as their core catalytic center carry out the majority of the key biogeochemical reactions. Metals such as Fe, Co, Ni, Zn, Mo, Mn, W, V, and Cu are used in these proteins to access diverse redox couples as a function of what the planet has provided to biology over time. Despite the importance of this process, the relationship between metal availability and metabolism evolution and diversity has not been investigated in detail. COEVOLVE will elucidate the impact of transition metal availability on microbial functional diversity in deep time, combining fieldwork, laboratory experiments, and computational approaches. COEVOLVE will: 1) investigate the relationship between the availability of trace metals and microbial functional diversity in extant ecosystems and organisms; 2) link metabolic diversity and metal availability to different geological, geochemical, and mineralogical conditions; 3) link metabolic diversity and dependence on metal availability to the emergence and evolution of different metabolisms; and 4) determine the timing of major steps in metabolic evolution and link them to geochemical proxies of planetary surface redox change. Understanding the role of trace metal environmental distribution and availability in influencing microbial functional diversity might hold the key to understanding the co-evolution of life and our planet, unlocking a number of important discoveries at the core of diverse fields such as earth sciences, astrobiology, microbial ecology, and biotechnology.
Max ERC Funding
2 080 438 €
Duration
Start date: 2021-05-01, End date: 2026-04-30
Project acronym CRUSLID
Project Formation, magmatic evolution and present-day structure of the CRUsts of Stagnant-LID planets
Researcher (PI) Chloe MICHAUT
Host Institution (HI) ECOLE NORMALE SUPERIEURE DE LYON
Country France
Call Details Consolidator Grant (CoG), PE10, ERC-2020-COG
Summary The low density of the Earth’s continental crust has been proposed to be at the origin of plate tectonics. Physical studies on the continental crust have shown that its low density was acquired by differentiation of the crust and loss of dense mafic residues. What is the composition and vertical structure of the crusts of the other terrestrial planets, which do not show plate tectonics? How did they form and what are the modifications they have undergone following their formations? Are they far from being of continental-type? To answer these questions, I propose to study from a physical perspective the crust structure and the processes of crust formation and evolution on terrestrial planets other than Earth using innovative thermal, mechanical and dynamical models combined with new planetary observations. Temperature is a crucial control variable as it dictates phase changes, buoyancy, mechanical properties and stress state. In the crust and stagnant lithosphere of terrestrial planets, temperature is controlled by the distribution of heat producing elements. Lithosphere cooling being the most likely cause of quakes on stagnant-lid planets, we propose to constrain the concentration and distribution of heat producing elements on Mars and the Moon by comparing recorded and predicted seismicity from thermal evolution models. From these thermal evolution models, we will also evaluate the potential for planetary crust differentiation and evolution. From magma ascent models, sensitive to crust density and mechanical state, combined with systematic in quantitative observations of volcanic structures and deposits on terrestrial planets, we will constrain the crust structure and thermal state. Finally, we will develop new models of primitive crust formation in a stagnant lid regime of convection to evaluate the characteristics of primitive crusts on terrestrial bodies.
Summary
The low density of the Earth’s continental crust has been proposed to be at the origin of plate tectonics. Physical studies on the continental crust have shown that its low density was acquired by differentiation of the crust and loss of dense mafic residues. What is the composition and vertical structure of the crusts of the other terrestrial planets, which do not show plate tectonics? How did they form and what are the modifications they have undergone following their formations? Are they far from being of continental-type? To answer these questions, I propose to study from a physical perspective the crust structure and the processes of crust formation and evolution on terrestrial planets other than Earth using innovative thermal, mechanical and dynamical models combined with new planetary observations. Temperature is a crucial control variable as it dictates phase changes, buoyancy, mechanical properties and stress state. In the crust and stagnant lithosphere of terrestrial planets, temperature is controlled by the distribution of heat producing elements. Lithosphere cooling being the most likely cause of quakes on stagnant-lid planets, we propose to constrain the concentration and distribution of heat producing elements on Mars and the Moon by comparing recorded and predicted seismicity from thermal evolution models. From these thermal evolution models, we will also evaluate the potential for planetary crust differentiation and evolution. From magma ascent models, sensitive to crust density and mechanical state, combined with systematic in quantitative observations of volcanic structures and deposits on terrestrial planets, we will constrain the crust structure and thermal state. Finally, we will develop new models of primitive crust formation in a stagnant lid regime of convection to evaluate the characteristics of primitive crusts on terrestrial bodies.
Max ERC Funding
1 606 031 €
Duration
Start date: 2021-10-01, End date: 2026-09-30
Project acronym EARLI
Project Detection of Early seismic signal using ARtificiaL Intelligence
Researcher (PI) Quentin Bletery
Host Institution (HI) INSTITUT DE RECHERCHE POUR LE DEVELOPPEMENT
Country France
Call Details Starting Grant (StG), PE10, ERC-2020-STG
Summary Earthquakes caused nearly one million fatalities in the last two decades. The hazardous nature of earthquakes is largely due to their unpredictability. The question of whether this unpredictability is ontological (i.e. earthquakes are a chaotic phenomenon that physics cannot predict) or a consequence of our incapacity to model them is still open. In the first case, one may never hope to predict earthquakes and efforts should be focused towards developing early-warning approaches so that the population can prepare for imminent shaking and tsunami. In the second case, earthquake prediction becomes theoretically achievable. In both cases, Artificial Intelligence (AI) may lead to giant steps in anticipating destructive events. I propose here to use AI to identify weak early seismic signals to both speed up early-warning and explore the possibility of earthquake prediction. The first part of the project will be devoted to implementing an early-warning approach not based on P-waves as all current systems but on an earlier signal recently identified. This signal is due to the perturbation of the gravity field generated by an earthquake – which propagates at the speed of light – but is ~6 orders of magnitude smaller than seismic waves, strongly limiting its detection with standard techniques. AI has proven very efficient at detecting low-amplitude signals. I will implement an AI algorithm to systematically detect gravity perturbations generated by magnitude > 7 earthquakes and rapidly estimate from them the location and magnitude of the earthquake. Though the existence of earthquake precursors (i.e. signals preceding the origin of earthquakes themselves) is hypothetical, AI represents a new prowerful mean to discover them. In the second part of the project, I will adapt the AI algorithm developed in the first part to search for earthquake precursors.
Summary
Earthquakes caused nearly one million fatalities in the last two decades. The hazardous nature of earthquakes is largely due to their unpredictability. The question of whether this unpredictability is ontological (i.e. earthquakes are a chaotic phenomenon that physics cannot predict) or a consequence of our incapacity to model them is still open. In the first case, one may never hope to predict earthquakes and efforts should be focused towards developing early-warning approaches so that the population can prepare for imminent shaking and tsunami. In the second case, earthquake prediction becomes theoretically achievable. In both cases, Artificial Intelligence (AI) may lead to giant steps in anticipating destructive events. I propose here to use AI to identify weak early seismic signals to both speed up early-warning and explore the possibility of earthquake prediction. The first part of the project will be devoted to implementing an early-warning approach not based on P-waves as all current systems but on an earlier signal recently identified. This signal is due to the perturbation of the gravity field generated by an earthquake – which propagates at the speed of light – but is ~6 orders of magnitude smaller than seismic waves, strongly limiting its detection with standard techniques. AI has proven very efficient at detecting low-amplitude signals. I will implement an AI algorithm to systematically detect gravity perturbations generated by magnitude > 7 earthquakes and rapidly estimate from them the location and magnitude of the earthquake. Though the existence of earthquake precursors (i.e. signals preceding the origin of earthquakes themselves) is hypothetical, AI represents a new prowerful mean to discover them. In the second part of the project, I will adapt the AI algorithm developed in the first part to search for earthquake precursors.
Max ERC Funding
1 499 518 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym FireIce
Project Fire in the land of ice: Climatic drivers and feedbacks
Researcher (PI) Sander VERAVERBEKE
Host Institution (HI) STICHTING VU
Country Netherlands
Call Details Consolidator Grant (CoG), PE10, ERC-2020-COG
Summary 2019 was the largest fire year since at least 1997 within the Arctic Circle, largely driven by Siberian fires. The arctic-boreal region stores about two atmospheres worth of soil carbon with 90 % currently locked in permafrost soils, or perennially frozen ground. Fire releases parts of this carbon stock, which may induce a vigorous climate warming feedback.
FireIce will investigate feedbacks between climate warming and arctic-boreal fires by studying direct and longer-term carbon emissions from fires. FireIce will acquire highly needed observations of carbon emissions from Siberian forest and tundra fires. On top of the direct fire emissions, fires accelerate permafrost degradation, which leads to greenhouse gas emissions for several decades. Their sum may be substantially larger than the direct emissions, yet is largely unknown. In addition, FireIce will investigate the relative contribution of CH4 from smoldering fires to fire emissions. CH4 emissions represent a small, yet not well known, fraction of carbon emissions from fires, but CH4 is a more potent greenhouse gas than CO2.
FireIce will investigate feedbacks between climate warming and arctic-boreal fires by studying controls on fire size and ignition. Fire growth can be limited because of fuel or fire weather limitations. The fire weather control is sensitive to warming, which may lead to larger future fires. Lightning ignition is the main source of burned area in arctic-boreal regions, and more lightning is expected in the future. By combining contemporary controls on fire size and ignition, and future predictions of climate and lightning, FireIce will assess the vulnerability of arctic-boreal permafrost and soil carbon to increases in fire.
FireIce’s results will be relevant to evidence-based policy. FireIce’s innovations are conceptual, i.e. unstudied aspects of an emerging warming feedback loop, methodological, e.g. inclusion of novel spaceborne data, and geographical, i.e. a focus on Siberia.
Summary
2019 was the largest fire year since at least 1997 within the Arctic Circle, largely driven by Siberian fires. The arctic-boreal region stores about two atmospheres worth of soil carbon with 90 % currently locked in permafrost soils, or perennially frozen ground. Fire releases parts of this carbon stock, which may induce a vigorous climate warming feedback.
FireIce will investigate feedbacks between climate warming and arctic-boreal fires by studying direct and longer-term carbon emissions from fires. FireIce will acquire highly needed observations of carbon emissions from Siberian forest and tundra fires. On top of the direct fire emissions, fires accelerate permafrost degradation, which leads to greenhouse gas emissions for several decades. Their sum may be substantially larger than the direct emissions, yet is largely unknown. In addition, FireIce will investigate the relative contribution of CH4 from smoldering fires to fire emissions. CH4 emissions represent a small, yet not well known, fraction of carbon emissions from fires, but CH4 is a more potent greenhouse gas than CO2.
FireIce will investigate feedbacks between climate warming and arctic-boreal fires by studying controls on fire size and ignition. Fire growth can be limited because of fuel or fire weather limitations. The fire weather control is sensitive to warming, which may lead to larger future fires. Lightning ignition is the main source of burned area in arctic-boreal regions, and more lightning is expected in the future. By combining contemporary controls on fire size and ignition, and future predictions of climate and lightning, FireIce will assess the vulnerability of arctic-boreal permafrost and soil carbon to increases in fire.
FireIce’s results will be relevant to evidence-based policy. FireIce’s innovations are conceptual, i.e. unstudied aspects of an emerging warming feedback loop, methodological, e.g. inclusion of novel spaceborne data, and geographical, i.e. a focus on Siberia.
Max ERC Funding
2 371 691 €
Duration
Start date: 2021-10-01, End date: 2026-09-30
Project acronym FluidMICS
Project Fluid Inclusion Microthermometry in Speleothems
Researcher (PI) Anna Nele MECKLER
Host Institution (HI) UNIVERSITETET I BERGEN
Country Norway
Call Details Consolidator Grant (CoG), PE10, ERC-2020-COG
Summary Ongoing global warming is rapidly moving us away from the climate states we are used to and understand from observational time series. This poses the urgent need to extend our “climate memory” by deciphering climate information stored in geologic archives. However, obtaining quantitative estimates for past climate changes is challenging, particularly in terrestrial archives due to common limitations in time coverage, resolution, and chronology. FluidMICS will employ a novel technique to reconstruct past temperatures based on physical properties of relict drip water preserved in cave formations (speleothems). The behavior of such micrometer-scale fluid inclusions during cooling and heating is directly related to the temperature at which the inclusions were once closed off. This physical basis makes the method uniquely robust and distinguishes it from other paleo-thermometers that depend on empirical calibrations. Combined with the distinct advantages of speleothems, which cover long time periods and can be absolutely dated, this approach will lead to unprecedented insights into magnitude, timing, and distribution of past temperature changes, lifting paleoclimate research to a new level. Our pilot data show that the microthermometry method faithfully discloses past temperatures several hundred thousand years ago. In FluidMICS, we will generate a solid understanding of potential non-thermal effects, further increasing precision and accuracy of the reconstructed temperatures, streamlining the analysis, and extending the applicability of the method. These advances will enable us to generate uniquely accurate and precise terrestrial temperature records far back in time that are distributed over large areas of the globe. These new datasets will serve as invaluable resources to better understand the complexities of our climate system under different atmospheric CO2 concentrations and in times of rapid change, and to test climate models used for future projections.
Summary
Ongoing global warming is rapidly moving us away from the climate states we are used to and understand from observational time series. This poses the urgent need to extend our “climate memory” by deciphering climate information stored in geologic archives. However, obtaining quantitative estimates for past climate changes is challenging, particularly in terrestrial archives due to common limitations in time coverage, resolution, and chronology. FluidMICS will employ a novel technique to reconstruct past temperatures based on physical properties of relict drip water preserved in cave formations (speleothems). The behavior of such micrometer-scale fluid inclusions during cooling and heating is directly related to the temperature at which the inclusions were once closed off. This physical basis makes the method uniquely robust and distinguishes it from other paleo-thermometers that depend on empirical calibrations. Combined with the distinct advantages of speleothems, which cover long time periods and can be absolutely dated, this approach will lead to unprecedented insights into magnitude, timing, and distribution of past temperature changes, lifting paleoclimate research to a new level. Our pilot data show that the microthermometry method faithfully discloses past temperatures several hundred thousand years ago. In FluidMICS, we will generate a solid understanding of potential non-thermal effects, further increasing precision and accuracy of the reconstructed temperatures, streamlining the analysis, and extending the applicability of the method. These advances will enable us to generate uniquely accurate and precise terrestrial temperature records far back in time that are distributed over large areas of the globe. These new datasets will serve as invaluable resources to better understand the complexities of our climate system under different atmospheric CO2 concentrations and in times of rapid change, and to test climate models used for future projections.
Max ERC Funding
1 999 634 €
Duration
Start date: 2021-09-01, End date: 2026-08-31
