Periodic Reporting for period 1 - EXTREME (Robust attribution of human-induced thermodynamic and dynamic contributions in historical changes of regional heat and cold waves over Europe)
Periodo di rendicontazione: 2022-06-01 al 2024-05-31
Project Overview: Understanding and Attributing European Climate Extremes
Europe has been grappling with increasingly frequent and severe climate extremes, from scorching heat waves to bone-chilling cold outbreaks. The EXTREME project aims to uncover the drivers behind these events and quantify the human contribution to them.
Context: Challenges of European Climate Extremes
Europe has experienced record-breaking climate events, like the event in 2019 surpassing the notorious 2003 heat wave, alongside a rise in winter cold outbreaks. A classical theory explains that heat and cold waves result from two main factors: regional energy and water vapor budgets (thermodynamic processes), and large-scale atmospheric circulations (dynamic processes). To further understand the role of human activity, a new field called 'event attribution science' emerged, aiming to ‘probabilistically estimate whether and to what degree anthropogenic drivers change the odds of a past extreme event’. A growing body of research is now making more progress in understanding their drivers. However, how human activities influence these events through thermodynamic and dynamic processes remains largely unknown.
Objectives: Quantifying Human Contribution
The main scientific goal of The EXTREME project is to robustly attribute historical changes in regional heat and cold waves over Europe to anthropogenic drivers from thermodynamic and dynamic perspectives, with attribution uncertainty quantifications.
Pathways to Impact: Science, Policy, and Practice
The EXTREME project outputs a suit of high-quality climate datasets and an innovative attribution approach for a wider range of applications, including for attributing rainfall extremes. By shedding light on the human footprint on European climate extremes, the EXTREME project aims to equip policymakers with evidence-based insights for crafting effective mitigation strategies. Additionally, by quantifying attribution uncertainties, it may facilitate adaptive decision-making in the face of climate uncertainty.
Europe has been grappling with increasingly frequent and severe climate extremes, from scorching heat waves to bone-chilling cold outbreaks. The EXTREME project aims to uncover the drivers behind these events and quantify the human contribution to them.
Context: Challenges of European Climate Extremes
Europe has experienced record-breaking climate events, like the event in 2019 surpassing the notorious 2003 heat wave, alongside a rise in winter cold outbreaks. A classical theory explains that heat and cold waves result from two main factors: regional energy and water vapor budgets (thermodynamic processes), and large-scale atmospheric circulations (dynamic processes). To further understand the role of human activity, a new field called 'event attribution science' emerged, aiming to ‘probabilistically estimate whether and to what degree anthropogenic drivers change the odds of a past extreme event’. A growing body of research is now making more progress in understanding their drivers. However, how human activities influence these events through thermodynamic and dynamic processes remains largely unknown.
Objectives: Quantifying Human Contribution
The main scientific goal of The EXTREME project is to robustly attribute historical changes in regional heat and cold waves over Europe to anthropogenic drivers from thermodynamic and dynamic perspectives, with attribution uncertainty quantifications.
Pathways to Impact: Science, Policy, and Practice
The EXTREME project outputs a suit of high-quality climate datasets and an innovative attribution approach for a wider range of applications, including for attributing rainfall extremes. By shedding light on the human footprint on European climate extremes, the EXTREME project aims to equip policymakers with evidence-based insights for crafting effective mitigation strategies. Additionally, by quantifying attribution uncertainties, it may facilitate adaptive decision-making in the face of climate uncertainty.
Three scientific work packages are established to effectively implement the EXTREME project: 1) Collect and homogenize daily datasets; 2) Explore 3-D changes in European heat and cold waves and circulation patterns; and 3) Separate human causes of heat and cold waves and analyse attribution uncertainty. Through completing these three activities, the EXTREME project is to quantitatively attribute human-induced thermodynamic and dynamic contributions in historical changes of European heat and cold waves, serving to understand the role of human activities in a more complete physical way. To make robust the attribution, attribution uncertainties arising from observation, model and attribution method uncertainties are quantified in the project.
Three main mechanisms have been proposed to explain changes in European heat and cold waves: 1) Global warming, 2) Natural variability of midlatitude circulations, and) Regional forcings, including but not limited to aerosol emissions and land use and cover change. To date, however, most studies in this new field have paid little attention to quantifying the role of human-induced changes in atmospheric circulations in such waves. Under global warming, the equator-to-pole temperature gradient has been weakening in the lower troposphere largely due to ‘Arctic amplification’. This could have amplified atmospheric Rossby waves in mid-latitudes and changed the jet stream, blocking and storm track patterns in terms of position, strength, frequency and duration. These changes might have led to regional changes in warm and cold air pathways between the equator and the North Pole, greatly affecting regional heat and cold waves. The EXTREME project attempts to solve this question by enhancing regional signals of climate simulated data, machine learning recognition of regional atmospheric circulations, and multi-model ensemble technique. Furthermore, the uncertainties from observation, model and attribution methods are depicted for making robust the attribution.