Constraining uncertainties in multi-model projections of the future climate with observations

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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Schlund, Manuel [verfasserIn] Eyring, Veronika [akademischer betreuerIn] Gentine, Pierre [akademischer betreuerIn]
Körperschaften:	Universität Bremen [Grad-verleihende Institution]
Hochschulschrift:	Dissertation ; Universität Bremen ; 2021

Format:	E-Book
Sprache:	Englisch

Erschienen:	Bremen: 2021

Rechteinformationen:	Namensnennung 3.0 Deutschland ; CC BY 3.0 DE

Schlagwörter:	Climate Change Climate Modeling Climate Projections Climate Sensitivity Climate Model Weighting Emergent Constraints Machine Learning
Formangabe:	Hochschulschrift

Umfang:	1 Online-Ressource (xi, 162 Seiten) ; Illustrationen

Weitere Ausgabe:	Erscheint auch als Druck-Ausgabe Schlund, Manuel: Constraining uncertainties in multi-model projections of the future climate with observations - Bremen, 2021

Links:	Link aufrufen Link aufrufen Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	urn:nbn:de:gbv:46-elib51448 10.26092/elib/941

Katalog-ID:	176811997X

Internformat


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author_variant	m s ms
matchkey_str	schlundmanueleyringveronikagentinepierre:2021----:osriignetiteimlioepoetosfhftrc
oclc_num	1266223933
hierarchy_sort_str	2021
publishDate	2021
allfields	urn:nbn:de:gbv:46-elib51448 urn 10.26092/elib/941 doi (DE-627)176811997X (DE-599)KXP176811997X (OCoLC)1266223933 DE-627 ger DE-627 rda eng XA-DE-HB 551.601 DE-101 550 DE-101 Schlund, Manuel verfasserin (orcid)0000-0001-5251-0158 aut Constraining uncertainties in multi-model projections of the future climate with observations Manuel Schlund Bremen 2021 1 Online-Ressource (xi, 162 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2021 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. DE-46 Namensnennung 3.0 Deutschland CC BY 3.0 DE cc http://creativecommons.org/licenses/by/3.0/de/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Climate Change Climate Modeling Climate Projections Climate Sensitivity Climate Model Weighting Emergent Constraints Machine Learning Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eyring, Veronika akademischer betreuerin dgs Gentine, Pierre akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Schlund, Manuel Constraining uncertainties in multi-model projections of the future climate with observations Bremen, 2021 xi, 162 Seiten (DE-627)1768121400 http://dx.doi.org/10.26092/elib/941 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 Resolving-System kostenfrei https://d-nb.info/1338946250/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/5144 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459307553X x 12-10-24 21 01 0046 3971981526 ebook_2021_dissbremen Kostenloser Zugriff zza 31-08-21 22 01 0018 4593178932 SUBolrd xu 12-10-24 23 01 0830 4593228220 olr-d x 12-10-24 30 01 0104 4593275016 z 12-10-24 40 01 0007 4593308720 xsn 12-10-24 60 01 0705 4593366097 OLRD z 12-10-24 63 01 3401 4593422221 ORD x 12-10-24 70 01 0089 4593472121 z 12-10-24 105 01 0841 4593864178 z 12-10-24 110 01 3110 459357661X x 12-10-24 132 01 0959 4593620465 OLR-DISS x 12-10-24 151 01 0546 4593664365 OLR-ODISS z 12-10-24 161 01 0960 459368711X ORD z 12-10-24 293 01 3293 4593814472 ORD z 12-10-24 370 01 4370 4593853478 x 12-10-24 2403 01 DE-LFER 3984652801 00 --%%-- --%%-- n --%%-- l01 06-10-21 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 21 01 0046 https://doi.org/10.26092/elib/941 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 2403 01 DE-LFER http://dx.doi.org/10.26092/elib/941 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2021_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
spelling	urn:nbn:de:gbv:46-elib51448 urn 10.26092/elib/941 doi (DE-627)176811997X (DE-599)KXP176811997X (OCoLC)1266223933 DE-627 ger DE-627 rda eng XA-DE-HB 551.601 DE-101 550 DE-101 Schlund, Manuel verfasserin (orcid)0000-0001-5251-0158 aut Constraining uncertainties in multi-model projections of the future climate with observations Manuel Schlund Bremen 2021 1 Online-Ressource (xi, 162 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2021 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. DE-46 Namensnennung 3.0 Deutschland CC BY 3.0 DE cc http://creativecommons.org/licenses/by/3.0/de/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Climate Change Climate Modeling Climate Projections Climate Sensitivity Climate Model Weighting Emergent Constraints Machine Learning Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eyring, Veronika akademischer betreuerin dgs Gentine, Pierre akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Schlund, Manuel Constraining uncertainties in multi-model projections of the future climate with observations Bremen, 2021 xi, 162 Seiten (DE-627)1768121400 http://dx.doi.org/10.26092/elib/941 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 Resolving-System kostenfrei https://d-nb.info/1338946250/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/5144 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459307553X x 12-10-24 21 01 0046 3971981526 ebook_2021_dissbremen Kostenloser Zugriff zza 31-08-21 22 01 0018 4593178932 SUBolrd xu 12-10-24 23 01 0830 4593228220 olr-d x 12-10-24 30 01 0104 4593275016 z 12-10-24 40 01 0007 4593308720 xsn 12-10-24 60 01 0705 4593366097 OLRD z 12-10-24 63 01 3401 4593422221 ORD x 12-10-24 70 01 0089 4593472121 z 12-10-24 105 01 0841 4593864178 z 12-10-24 110 01 3110 459357661X x 12-10-24 132 01 0959 4593620465 OLR-DISS x 12-10-24 151 01 0546 4593664365 OLR-ODISS z 12-10-24 161 01 0960 459368711X ORD z 12-10-24 293 01 3293 4593814472 ORD z 12-10-24 370 01 4370 4593853478 x 12-10-24 2403 01 DE-LFER 3984652801 00 --%%-- --%%-- n --%%-- l01 06-10-21 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 21 01 0046 https://doi.org/10.26092/elib/941 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 2403 01 DE-LFER http://dx.doi.org/10.26092/elib/941 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2021_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
allfields_unstemmed	urn:nbn:de:gbv:46-elib51448 urn 10.26092/elib/941 doi (DE-627)176811997X (DE-599)KXP176811997X (OCoLC)1266223933 DE-627 ger DE-627 rda eng XA-DE-HB 551.601 DE-101 550 DE-101 Schlund, Manuel verfasserin (orcid)0000-0001-5251-0158 aut Constraining uncertainties in multi-model projections of the future climate with observations Manuel Schlund Bremen 2021 1 Online-Ressource (xi, 162 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2021 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. DE-46 Namensnennung 3.0 Deutschland CC BY 3.0 DE cc http://creativecommons.org/licenses/by/3.0/de/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Climate Change Climate Modeling Climate Projections Climate Sensitivity Climate Model Weighting Emergent Constraints Machine Learning Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eyring, Veronika akademischer betreuerin dgs Gentine, Pierre akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Schlund, Manuel Constraining uncertainties in multi-model projections of the future climate with observations Bremen, 2021 xi, 162 Seiten (DE-627)1768121400 http://dx.doi.org/10.26092/elib/941 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 Resolving-System kostenfrei https://d-nb.info/1338946250/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/5144 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459307553X x 12-10-24 21 01 0046 3971981526 ebook_2021_dissbremen Kostenloser Zugriff zza 31-08-21 22 01 0018 4593178932 SUBolrd xu 12-10-24 23 01 0830 4593228220 olr-d x 12-10-24 30 01 0104 4593275016 z 12-10-24 40 01 0007 4593308720 xsn 12-10-24 60 01 0705 4593366097 OLRD z 12-10-24 63 01 3401 4593422221 ORD x 12-10-24 70 01 0089 4593472121 z 12-10-24 105 01 0841 4593864178 z 12-10-24 110 01 3110 459357661X x 12-10-24 132 01 0959 4593620465 OLR-DISS x 12-10-24 151 01 0546 4593664365 OLR-ODISS z 12-10-24 161 01 0960 459368711X ORD z 12-10-24 293 01 3293 4593814472 ORD z 12-10-24 370 01 4370 4593853478 x 12-10-24 2403 01 DE-LFER 3984652801 00 --%%-- --%%-- n --%%-- l01 06-10-21 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 21 01 0046 https://doi.org/10.26092/elib/941 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 2403 01 DE-LFER http://dx.doi.org/10.26092/elib/941 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2021_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
allfieldsGer	urn:nbn:de:gbv:46-elib51448 urn 10.26092/elib/941 doi (DE-627)176811997X (DE-599)KXP176811997X (OCoLC)1266223933 DE-627 ger DE-627 rda eng XA-DE-HB 551.601 DE-101 550 DE-101 Schlund, Manuel verfasserin (orcid)0000-0001-5251-0158 aut Constraining uncertainties in multi-model projections of the future climate with observations Manuel Schlund Bremen 2021 1 Online-Ressource (xi, 162 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2021 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. DE-46 Namensnennung 3.0 Deutschland CC BY 3.0 DE cc http://creativecommons.org/licenses/by/3.0/de/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Climate Change Climate Modeling Climate Projections Climate Sensitivity Climate Model Weighting Emergent Constraints Machine Learning Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eyring, Veronika akademischer betreuerin dgs Gentine, Pierre akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Schlund, Manuel Constraining uncertainties in multi-model projections of the future climate with observations Bremen, 2021 xi, 162 Seiten (DE-627)1768121400 http://dx.doi.org/10.26092/elib/941 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 Resolving-System kostenfrei https://d-nb.info/1338946250/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/5144 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459307553X x 12-10-24 21 01 0046 3971981526 ebook_2021_dissbremen Kostenloser Zugriff zza 31-08-21 22 01 0018 4593178932 SUBolrd xu 12-10-24 23 01 0830 4593228220 olr-d x 12-10-24 30 01 0104 4593275016 z 12-10-24 40 01 0007 4593308720 xsn 12-10-24 60 01 0705 4593366097 OLRD z 12-10-24 63 01 3401 4593422221 ORD x 12-10-24 70 01 0089 4593472121 z 12-10-24 105 01 0841 4593864178 z 12-10-24 110 01 3110 459357661X x 12-10-24 132 01 0959 4593620465 OLR-DISS x 12-10-24 151 01 0546 4593664365 OLR-ODISS z 12-10-24 161 01 0960 459368711X ORD z 12-10-24 293 01 3293 4593814472 ORD z 12-10-24 370 01 4370 4593853478 x 12-10-24 2403 01 DE-LFER 3984652801 00 --%%-- --%%-- n --%%-- l01 06-10-21 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 21 01 0046 https://doi.org/10.26092/elib/941 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 2403 01 DE-LFER http://dx.doi.org/10.26092/elib/941 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2021_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
allfieldsSound	urn:nbn:de:gbv:46-elib51448 urn 10.26092/elib/941 doi (DE-627)176811997X (DE-599)KXP176811997X (OCoLC)1266223933 DE-627 ger DE-627 rda eng XA-DE-HB 551.601 DE-101 550 DE-101 Schlund, Manuel verfasserin (orcid)0000-0001-5251-0158 aut Constraining uncertainties in multi-model projections of the future climate with observations Manuel Schlund Bremen 2021 1 Online-Ressource (xi, 162 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2021 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. DE-46 Namensnennung 3.0 Deutschland CC BY 3.0 DE cc http://creativecommons.org/licenses/by/3.0/de/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Climate Change Climate Modeling Climate Projections Climate Sensitivity Climate Model Weighting Emergent Constraints Machine Learning Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eyring, Veronika akademischer betreuerin dgs Gentine, Pierre akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Schlund, Manuel Constraining uncertainties in multi-model projections of the future climate with observations Bremen, 2021 xi, 162 Seiten (DE-627)1768121400 http://dx.doi.org/10.26092/elib/941 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 Resolving-System kostenfrei https://d-nb.info/1338946250/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/5144 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459307553X x 12-10-24 21 01 0046 3971981526 ebook_2021_dissbremen Kostenloser Zugriff zza 31-08-21 22 01 0018 4593178932 SUBolrd xu 12-10-24 23 01 0830 4593228220 olr-d x 12-10-24 30 01 0104 4593275016 z 12-10-24 40 01 0007 4593308720 xsn 12-10-24 60 01 0705 4593366097 OLRD z 12-10-24 63 01 3401 4593422221 ORD x 12-10-24 70 01 0089 4593472121 z 12-10-24 105 01 0841 4593864178 z 12-10-24 110 01 3110 459357661X x 12-10-24 132 01 0959 4593620465 OLR-DISS x 12-10-24 151 01 0546 4593664365 OLR-ODISS z 12-10-24 161 01 0960 459368711X ORD z 12-10-24 293 01 3293 4593814472 ORD z 12-10-24 370 01 4370 4593853478 x 12-10-24 2403 01 DE-LFER 3984652801 00 --%%-- --%%-- n --%%-- l01 06-10-21 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 21 01 0046 https://doi.org/10.26092/elib/941 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 2403 01 DE-LFER http://dx.doi.org/10.26092/elib/941 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2021_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
language	English
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building	20 21:z 22:u 23 30 40:s 60 63 70 105 110 132 151 161 293 370 2403:0
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topic_facet	Climate Change Climate Modeling Climate Projections Climate Sensitivity Climate Model Weighting Emergent Constraints Machine Learning
hochschulschrift_txt_mv	Dissertation Universität Bremen 2021
sw_local_iln_str_mv	21:Universität Bremen DE-46:Universität Bremen 21:Fachbereich 01: Physik/Elektrotechnik (FB 01) DE-46:Fachbereich 01: Physik/Elektrotechnik (FB 01)
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authorswithroles_txt_mv	Schlund, Manuel @@aut@@ Eyring, Veronika @@dgs@@ Gentine, Pierre @@dgs@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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topic_title	551.601 DE-101 550 DE-101 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) Constraining uncertainties in multi-model projections of the future climate with observations Manuel Schlund Climate Change Climate Modeling Climate Projections Climate Sensitivity Climate Model Weighting Emergent Constraints Machine Learning
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title_full	Constraining uncertainties in multi-model projections of the future climate with observations Manuel Schlund
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title_sort	constraining uncertainties in multi-model projections of the future climate with observations
title_auth	Constraining uncertainties in multi-model projections of the future climate with observations
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Constraining uncertainties in multi-model projections of the future climate with observations
url	http://dx.doi.org/10.26092/elib/941 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib51448 https://d-nb.info/1338946250/34 https://media.suub.uni-bremen.de/handle/elib/5144
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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. 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Constraining uncertainties in multi-model projections of the future climate with observations

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