Constraining uncertainties in multi-model projections of the future climate with observations
|Authors:||Schlund, Manuel||Supervisor:||Eyring, Veronika||1. Expert:||Eyring, Veronika||2. Expert:||Gentine, Pierre||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.
|Keywords:||Climate Change; Climate Modeling; Climate Projections; Climate Sensitivity; Climate Model Weighting; Emergent Constraints; Machine Learning||Issue Date:||2-Aug-2021||Type:||Dissertation||DOI:||10.26092/elib/941||URN:||urn:nbn:de:gbv:46-elib51448||Institution:||Universität Bremen||Faculty:||Fachbereich 01: Physik/Elektrotechnik (FB 01)|
|Appears in Collections:||Dissertationen|
checked on Sep 27, 2021
checked on Sep 27, 2021
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