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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.

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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
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 ISI Web of Science:No
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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