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On the Role of Atmosphere Physics and Warming Pattern in Climate Feedback
On the Role of Atmosphere Physics and Warming Pattern in Climate Feedback
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153025
- ISBN
- 9798346759324
- DDC
- 551.5
- 저자명
- Wang, Chenggong.
- 서명/저자
- On the Role of Atmosphere Physics and Warming Pattern in Climate Feedback
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 111 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Vecchi, Gabriel A.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약The accurate prediction of future warming depends on understanding key processes like climate feedback, aerosol interactions, and radiative transfer, all of which are explored in this thesis. First, we explore the 6th Phase of the Coupled Model Intercomparison Project (CMIP6), where models exhibit higher estimates of effective climate sensitivity (ECS) due to more positive cloud feedback. Our analysis shows that models with more positive cloud feedback also feature stronger aerosol-cloud interactions, offsetting warming during the historical period. We find that the observed interhemispheric asymmetry in warming is better aligned with models that have weaker aerosol-cloud interactions and lower ECS, helping to reduce uncertainty in future warming projections.Chapter 3 focuses on the variability of the climate feedback parameter, driven by sea surface temperature (SST) patterns and atmospheric model physics. Through a series of targeted atmospheric global climate model (AGCM) experiments, this chapter quantifies the roles of warming pattern of SST and model physics in the intermodel spread of climate feedback and cloud feedback. The findings demonstrate that atmospheric model physics, particularly cloud-related processes, contribute significantly to the variability of climate feedback, emphasizing the importance of model accuracy in future climate predictions.Chapter 4 introduces a novel approach to address the computational challenges in climate modeling. A hybrid radiative transfer framework (HRadNN) that integrates machine learning with traditional physics-based models is developed, achieving a four- to five-fold increase in speed without sacrificing accuracy. The HRadNN model performs well across various climate scenarios, demonstrating its robustness and potential to improve computational efficiency in climate modeling.Together, these chapters provide insights into the complexities of climate sensitivity, feedback mechanisms, and modeling improvements, contributing to a more refined understanding of future climate change projections.
- 일반주제명
- Atmospheric sciences
- 일반주제명
- Climate change
- 일반주제명
- Computational physics
- 키워드
- Warming pattern
- 키워드
- Climate feedback
- 키워드
- Machine learning
- 기타저자
- Princeton University Atmospheric and Oceanic Sciences
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153025
■006m o d
■007cr#unu||||||||
■020 ▼a9798346759324
■035 ▼a(MiAaPQ)AAI31633457
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a551.5
■1001 ▼aWang, Chenggong.
■24510▼aOn the Role of Atmosphere Physics and Warming Pattern in Climate Feedback
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a111 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Vecchi, Gabriel A.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aThe accurate prediction of future warming depends on understanding key processes like climate feedback, aerosol interactions, and radiative transfer, all of which are explored in this thesis. First, we explore the 6th Phase of the Coupled Model Intercomparison Project (CMIP6), where models exhibit higher estimates of effective climate sensitivity (ECS) due to more positive cloud feedback. Our analysis shows that models with more positive cloud feedback also feature stronger aerosol-cloud interactions, offsetting warming during the historical period. We find that the observed interhemispheric asymmetry in warming is better aligned with models that have weaker aerosol-cloud interactions and lower ECS, helping to reduce uncertainty in future warming projections.Chapter 3 focuses on the variability of the climate feedback parameter, driven by sea surface temperature (SST) patterns and atmospheric model physics. Through a series of targeted atmospheric global climate model (AGCM) experiments, this chapter quantifies the roles of warming pattern of SST and model physics in the intermodel spread of climate feedback and cloud feedback. The findings demonstrate that atmospheric model physics, particularly cloud-related processes, contribute significantly to the variability of climate feedback, emphasizing the importance of model accuracy in future climate predictions.Chapter 4 introduces a novel approach to address the computational challenges in climate modeling. A hybrid radiative transfer framework (HRadNN) that integrates machine learning with traditional physics-based models is developed, achieving a four- to five-fold increase in speed without sacrificing accuracy. The HRadNN model performs well across various climate scenarios, demonstrating its robustness and potential to improve computational efficiency in climate modeling.Together, these chapters provide insights into the complexities of climate sensitivity, feedback mechanisms, and modeling improvements, contributing to a more refined understanding of future climate change projections.
■590 ▼aSchool code: 0181.
■650 4▼aAtmospheric sciences
■650 4▼aClimate change
■650 4▼aComputational physics
■653 ▼aWarming pattern
■653 ▼aClimate feedback
■653 ▼aEffective climate sensitivity
■653 ▼aSea surface temperature
■653 ▼aMachine learning
■690 ▼a0725
■690 ▼a0404
■690 ▼a0216
■71020▼aPrinceton University▼bAtmospheric and Oceanic Sciences.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164628▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
