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Constraining Uncertainties in Multi-Model Projections of the Future Climate with Observations

Schlund, Manuel (2021) Constraining Uncertainties in Multi-Model Projections of the Future Climate with Observations. Dissertation, Universität Bremen. doi: 10.26092/elib/941.

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Official URL: https://media.suub.uni-bremen.de/handle/elib/5144

Abstract

Earth system models (ESMs) are common tools to project climate change. The main focus of this thesis is the analysis of climate projections from ESMs participating in the Coupled Model Intercomparison Project (CMIP) with the aim to reduce uncertainties in climate projections with observations. In a first step, climate sensitivity is evaluated in CMIP6 models. For the effective climate sensitivity (ECS), a multi-model range of 1.8-5.6 K is found. This range is higher than in any previous CMIP ensemble before. Possible reasons for this are changes in cloud parameterizations. To reduce uncertainties in the ECS of the CMIP6 models, 11 published emergent constraints on ECS are analyzed. Emergent constraints are approaches to reduce uncertainties in climate projections by combining observations and ESM output. The application of the emergent constraints to CMIP6 data shows a decrease in the skill of the emergent relationships. This is likely related to the increased multi-model spread of ECS in CMIP6, but may in some cases also be due to spurious statistical relationships. The results support previous studies concluding that emergent constraints should be based on independently verifiable physical mechanisms. To overcome these issues of emergent constraints, an alternative approach based on machine learning (ML) is introduced. As target variable, gross primary production (GPP) is studied. In a first step, an existing emergent constraint is used to constrain the global mean GPP at the end of the 21st century in Representative Concentration Pathway (RCP) 8.5 simulations with CMIP5 ESMs to (171 ± 12) GtC yr-1. In a second step, an ML model is used to constrain gridded future absolute GPP. For this, observational data is fed into the ML algorithm that has been trained on CMIP5 data to learn relationships between present-day physically relevant diagnostics and the target variable. In a perfect model setup, the ML model shows superior performance.

Item URL in elib:https://elib.dlr.de/145235/
Document Type:Thesis (Dissertation)
Title:Constraining Uncertainties in Multi-Model Projections of the Future Climate with Observations
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Schlund, ManuelDLR, IPAhttps://orcid.org/0000-0001-5251-0158UNSPECIFIED
Date:2 August 2021
Refereed publication:No
Open Access:Yes
DOI:10.26092/elib/941
Number of Pages:174
Status:Published
Keywords:Climate Change; Climate Modeling; Climate Projections; Climate Sensitivity; Climate Model Weighting; Emergent Constraints; Machine Learning
Institution:Universität Bremen
Department:Institut für Umweltphysik
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: Schlund, Manuel
Deposited On:05 Nov 2021 15:02
Last Modified:05 Nov 2021 15:02

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