Robust attribution of human-induced thermodynamic and dynamic contributions in h...
Robust attribution of human-induced thermodynamic and dynamic contributions in historical changes of regional heat and cold waves over Europe
The exceptionally increasing European heat and cold waves in recent decades have been widely attributed to global warming from a thermodynamic perspective. However, contribution of warming-induced circulation changes to such regio...
ver más
¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video
Información proyecto EXTREME
Duración del proyecto: 24 meses
Fecha Inicio: 2022-05-17
Fecha Fin: 2024-05-31
Líder del proyecto
GOETEBORGS UNIVERSITET
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
207K€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
The exceptionally increasing European heat and cold waves in recent decades have been widely attributed to global warming from a thermodynamic perspective. However, contribution of warming-induced circulation changes to such regional waves (termed the 'human-induced dynamic contribution') remains largely unknown, while midlatitude atmospheric circulations have been altering under global warming. Human fingerprint will not be fully detected if the human-induced dynamic contribution is not included. Dr. Chunlüe Zhou who has an outstanding academic profile in daily data homogenization and regional climate change attribution, will interact with Prof. Deliang Chen (University of Gothenburg, Sweden) to make a robust attribution to quantify human-induced thermodynamic and dynamic contributions in historical changes of regional heat and cold waves over Europe. Human activities include greenhouse gas emissions, aerosol emissions and land use/cover changes. To make robust the attribution, attribution uncertainties arising from observation, model and attribution method uncertainties will be improved through: i) reducing observation errors by removing spurious shifts in the mean and variance of daily series; ii) improving regional details of heat and cold waves and circulation patterns by downscaling large ensembles of global-simulated data using the Weather Research and Forecasting (WRF) model and exploiting feature-based machine learning recognition; and iii) developing a Bayesian statistical approach to separate the human-induced thermodynamic and dynamic contributions. This robust attribution will address these current challenges, but also push event attribution research to a regional scale, greatly increasing confidence in attribution to human causes. Through this EXTREME project, I will increase my research skills, but also gain adequate soft skills in project management, supervision and dissemination with the ultimate intent to build a vibrant research group in Europe.