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Combining Analytical, Numerical, and AI Models to Improve Cloud and Convection Representation in Climate Simulations
Combining Analytical, Numerical, and AI Models to Improve Cloud and Convection Representation in Climate Simulations
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103503
- ISBN
- 9798280719965
- DDC
- 551.5
- 저자명
- Hu, Zeyuan.
- 서명/저자
- Combining Analytical, Numerical, and AI Models to Improve Cloud and Convection Representation in Climate Simulations
- 발행사항
- [Sl] : Harvard University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 188 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Kuang, Zhiming.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2025.
- 초록/해제
- 요약Accurate representation of small-scale processes, such as convection and cloud formation, remains one of the greatest challenges in climate modeling, even in kilometer-scale storm-resolving simulations. These processes are essential for determining large-scale atmospheric behavior, but computational constraints prevent their full representation in global climate models. For instance, deep convection may not exhibit convergent behavior with increasing resolution, and significant uncertainties persist in ice microphysics parameterization. This thesis explores these challenges in two parts: the first (Chapters 2 and 3) examines how small-scale convection and cloud processes influence large-scale atmospheric states in idealized radiative-convective equilibrium simulations, while the second (Chapter 4) investigates machine learning (ML) as a tool for efficiently emulating these processes in climate models. Chapter 2 addresses a fundamental question: what controls the vertical thermal structure of an equilibrium atmosphere? To answer this, I developed a refined zero-buoyancy plume model that analytically solves equilibrium atmospheric profiles given boundary conditions. The model highlights how plume-environment mixing influences vertical temperature profiles, upper-tropospheric convective mass flux, and cloud fraction. These findings align with convection-permitting simulations, which reveal that higher horizontal resolution---acting as a proxy for enhanced plume-environment mixing---leads to increased cloud fraction and mass flux in the upper troposphere.Chapter 3 explores the impact of microphysics scheme uncertainties on equilibrium atmospheric states, particularly focusing on deep convective overshoots into the tropical tropopause layer (TTL). We find that different microphysics schemes produce distinct heat balance regimes in the TTL. Two schemes lead to a "hard-landing" scenario, where frequent, strong convective overshoots induce significant cooling (~0.2 K day−1), while a third scheme results in a "soft-landing" scenario, with weaker overshoots and minimal cooling (~0.03 K day−1). This difference arises from variations in upper-tropospheric stratification driven by atmospheric cloud radiative effects (ACRE). The scheme producing the soft-landing scenario generates stronger ACRE, leading to a ~3K warmer, more stable upper-tropospheric layer that buffers convective updrafts.Chapter 4 demonstrates how ML can emulate these small-scale processes efficiently by learning directly from high-resolution simulations. Using data from superparameterized climate simulations, we train ML models to replace the embedded cloud-resolving models. While previous studies show that such hybrid ML-physics simulations can reproduce key climate statistics, they often suffer from online instability, particularly in setups with real geography and explicit cloud condensate coupling. By integrating an expressive U-Net architecture with cloud microphysics constraints, we achieve stable and skillful multi-year hybrid climate simulations with realistic cloud climatology and explicit cloud condensate coupling.
- 일반주제명
- Atmospheric sciences
- 일반주제명
- Climate change
- 키워드
- Cloud formation
- 키워드
- Machine learning
- 기타저자
- Harvard University Earth and Planetary Sciences
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798280719965
■035 ▼a(MiAaPQ)AAI32001761
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a551.5
■1001 ▼aHu, Zeyuan.▼0(orcid)0000-0003-2041-879X
■24510▼aCombining Analytical, Numerical, and AI Models to Improve Cloud and Convection Representation in Climate Simulations
■260 ▼a[Sl]▼bHarvard University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a188 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Kuang, Zhiming.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2025.
■520 ▼aAccurate representation of small-scale processes, such as convection and cloud formation, remains one of the greatest challenges in climate modeling, even in kilometer-scale storm-resolving simulations. These processes are essential for determining large-scale atmospheric behavior, but computational constraints prevent their full representation in global climate models. For instance, deep convection may not exhibit convergent behavior with increasing resolution, and significant uncertainties persist in ice microphysics parameterization. This thesis explores these challenges in two parts: the first (Chapters 2 and 3) examines how small-scale convection and cloud processes influence large-scale atmospheric states in idealized radiative-convective equilibrium simulations, while the second (Chapter 4) investigates machine learning (ML) as a tool for efficiently emulating these processes in climate models. Chapter 2 addresses a fundamental question: what controls the vertical thermal structure of an equilibrium atmosphere? To answer this, I developed a refined zero-buoyancy plume model that analytically solves equilibrium atmospheric profiles given boundary conditions. The model highlights how plume-environment mixing influences vertical temperature profiles, upper-tropospheric convective mass flux, and cloud fraction. These findings align with convection-permitting simulations, which reveal that higher horizontal resolution---acting as a proxy for enhanced plume-environment mixing---leads to increased cloud fraction and mass flux in the upper troposphere.Chapter 3 explores the impact of microphysics scheme uncertainties on equilibrium atmospheric states, particularly focusing on deep convective overshoots into the tropical tropopause layer (TTL). We find that different microphysics schemes produce distinct heat balance regimes in the TTL. Two schemes lead to a "hard-landing" scenario, where frequent, strong convective overshoots induce significant cooling (~0.2 K day−1), while a third scheme results in a "soft-landing" scenario, with weaker overshoots and minimal cooling (~0.03 K day−1). This difference arises from variations in upper-tropospheric stratification driven by atmospheric cloud radiative effects (ACRE). The scheme producing the soft-landing scenario generates stronger ACRE, leading to a ~3K warmer, more stable upper-tropospheric layer that buffers convective updrafts.Chapter 4 demonstrates how ML can emulate these small-scale processes efficiently by learning directly from high-resolution simulations. Using data from superparameterized climate simulations, we train ML models to replace the embedded cloud-resolving models. While previous studies show that such hybrid ML-physics simulations can reproduce key climate statistics, they often suffer from online instability, particularly in setups with real geography and explicit cloud condensate coupling. By integrating an expressive U-Net architecture with cloud microphysics constraints, we achieve stable and skillful multi-year hybrid climate simulations with realistic cloud climatology and explicit cloud condensate coupling.
■590 ▼aSchool code: 0084.
■650 4▼aAtmospheric sciences
■650 4▼aClimate change
■653 ▼aCloud formation
■653 ▼aTropical tropopause layer
■653 ▼aMachine learning
■690 ▼a0725
■690 ▼a0800
■690 ▼a0404
■71020▼aHarvard University▼bEarth and Planetary Sciences.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357376▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
