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Machine learning for improved understanding and projections of climate change

Schwabe, Mierk and Eyring, Veronika (2023) Machine learning for improved understanding and projections of climate change. TRR 165/181 Conference, 2023-10-27 - 2023-10-30, Ingolstadt.

Full text not available from this repository.

Abstract

Earth system models are fundamental to understanding and projecting climate change. The models have continued to improve over the years, but considerable biases and uncertainties in their projections remain. A large contribution to this uncertainty stems from differences in the representation of phenomena such as clouds and convection that occur at scales smaller than the resolved model grid. The long-standing deficiencies in cloud parameterizations have motivated developments of global high-resolution cloud-resolving models that can explicitly resolve clouds and convection. Short simulations from the computationally costly high-resolution models together with observations can serve as information to develop machine learning (ML)-based parameterizations that are then incorporated into Earth system models. The ICOsahedral Non-hydrostatic (ICON) model is an open-access modelling framework, which is used on a variety of timescales and resolutions, ranging from numerical weather predictions to climate projections. Here we utilize existing regional and global cloud-resolving ICON simulations with data-driven techniques to train ML-based parametrizations. The newly developed parameterizations are coupled to the ICON Earth system model (ICON-ESM) via the Fortran-Keras Bridge, resulting in the ICON-ESM-ML hybrid model.

Item URL in elib:https://elib.dlr.de/198340/
Document Type:Conference or Workshop Item (Speech)
Title:Machine learning for improved understanding and projections of climate change
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Schwabe, MierkDLR, IPAhttps://orcid.org/0000-0001-6565-5890UNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Date:28 March 2023
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:machine learning, climate modelling
Event Title:TRR 165/181 Conference
Event Location:Ingolstadt
Event Type:international Conference
Event Start Date:27 October 2023
Event End Date:30 October 2023
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Deposited By: Schwabe, Dr. Mierk
Deposited On:20 Oct 2023 10:20
Last Modified:24 Apr 2024 20:58

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