A Physics-informed Deep Learning Based Clustering to Investigate the Impact of Regional European Radiative Forcing on Arctic Climate and Upper Atmospheric Dynamics
Heterogeneous radiative forcing in mid-latitudes, such as those caused by aerosols, has been observed to influence the Arctic climate, although the underlying mechanisms continue to be a subject of scientific discussion. In this research, we employed Deep Learning (DL) methodologies to explore the complex response of the Arctic climate system to localized radiative forcing over Europe. We performed sensitivity experiments using the Max Planck Institute Earth System Model (MPI-ESM1. 2). By applying a DL-driven clustering approach, we classified atmospheric circulation patterns within a reduced-dimensional framework, with a particular focus on Poleward Moist Static Energy Transport (PMSET) as our primary parameter of interest. Additionally, we developed a new methodology to assess the contributions of these circulation patterns to anomalies in various climatic parameters.Our results demonstrate that …
%0 Journal Article
%1 mehrdad2024physicsinformed
%A Mehrdad, Sina
%A Handorf, Dörthe
%A Höschel, Ines
%A Karami, Khalil
%A Quaas, Johannes
%A Dipu, Sudhakar
%A Jacobi, Christoph
%D 2024
%I Copernicus Meetings
%K imported topic_earthenvironment
%N EGU24-16391
%T A Physics-informed Deep Learning Based Clustering to Investigate the Impact of Regional European Radiative Forcing on Arctic Climate and Upper Atmospheric Dynamics
%X Heterogeneous radiative forcing in mid-latitudes, such as those caused by aerosols, has been observed to influence the Arctic climate, although the underlying mechanisms continue to be a subject of scientific discussion. In this research, we employed Deep Learning (DL) methodologies to explore the complex response of the Arctic climate system to localized radiative forcing over Europe. We performed sensitivity experiments using the Max Planck Institute Earth System Model (MPI-ESM1. 2). By applying a DL-driven clustering approach, we classified atmospheric circulation patterns within a reduced-dimensional framework, with a particular focus on Poleward Moist Static Energy Transport (PMSET) as our primary parameter of interest. Additionally, we developed a new methodology to assess the contributions of these circulation patterns to anomalies in various climatic parameters.Our results demonstrate that …
@article{mehrdad2024physicsinformed,
abstract = {Heterogeneous radiative forcing in mid-latitudes, such as those caused by aerosols, has been observed to influence the Arctic climate, although the underlying mechanisms continue to be a subject of scientific discussion. In this research, we employed Deep Learning (DL) methodologies to explore the complex response of the Arctic climate system to localized radiative forcing over Europe. We performed sensitivity experiments using the Max Planck Institute Earth System Model (MPI-ESM1. 2). By applying a DL-driven clustering approach, we classified atmospheric circulation patterns within a reduced-dimensional framework, with a particular focus on Poleward Moist Static Energy Transport (PMSET) as our primary parameter of interest. Additionally, we developed a new methodology to assess the contributions of these circulation patterns to anomalies in various climatic parameters.Our results demonstrate that …},
added-at = {2024-11-29T12:02:08.000+0100},
author = {Mehrdad, Sina and Handorf, Dörthe and Höschel, Ines and Karami, Khalil and Quaas, Johannes and Dipu, Sudhakar and Jacobi, Christoph},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2372e27d24455db075db6f9fe8277dbab/joum576e},
citation = {EGU24, 2024},
interhash = {36df797173468345326db4853e5d7d17},
intrahash = {372e27d24455db075db6f9fe8277dbab},
keywords = {imported topic_earthenvironment},
number = {EGU24-16391},
publisher = {Copernicus Meetings},
timestamp = {2024-11-29T12:02:08.000+0100},
title = {A Physics-informed Deep Learning Based Clustering to Investigate the Impact of Regional European Radiative Forcing on Arctic Climate and Upper Atmospheric Dynamics},
year = 2024
}
