Minghua Zhang

About

Minghua Zhang is a Distinguished Professor at Stony Brook University, working within the Office of the Dean SOMAS Atmospheric Sciences. His research concerns numerical modeling of climate and global climate change, including the development and analysis of parameterization components in general circulation models, diagnostic studies of physical processes and feedback mechanisms in the climate system, and modeling and analysis of past and future climate changes using models, satellite measurements, and other observations. His main focus on parameterization development involves moist processes related to clouds, radiation, convections, boundary layer physics, and their interactions, with the goal of improving global models to more accurately predict climate change across various time scales. Zhang is involved in several field experiments that collect comprehensive upper air and surface data within atmospheric columns, analyzing these data to interface with physical parameterizations in atmospheric models. Additionally, he studies the dynamics of large-scale atmospheric waves, including their excitation, propagation, dissipation, and influence on atmospheric circulation variability, which enhances understanding of weather and short-term climate variations. His academic background includes a PhD from the CAS - Institute of Atmospheric Physics obtained in 1987.

Research topics

• Geography
• Computer Science
• Environmental science
• Environmental planning
• Oceanography
• Meteorology
• Atmospheric sciences
• Economics
• Ecology
• Medicine

Selected publications

• Description and Climate Simulation Performance of CAS‐ESM Version 2

  Journal of Advances in Modeling Earth Systems · 2020 · 138 citations

  Abstract The second version of Chinese Academy of Sciences Earth System Model (CAS‐ESM 2) is described with emphasis on the development process, strength and weakness, and climate sensitivities in simulations of the Coupled Model Intercomparison Project (CMIP6) DECK experiments. CAS‐ESM 2 was built as a numerical model to simulate both the physical climate system as well as atmospheric chemistry and carbon cycle. It is a newcomer in the international modeling community to provide sufficiently in…

  DOI

• Climate Change, Drought, and Potential Environmental Migration Flows Under Different Policy Scenarios

  International Migration Review · 2022 · 60 citations

  Unmitigated climate change will likely produce major problems for human populations worldwide. Although many researchers and policy-makers believe that drought may be an important “push” factor underlying migration in the future, the precise relationship between drought and migration remains unclear. This article models the potential scope of such movements for the emissions policy choices facing all nation-states today. Applying insights from climate science and computational modeling to migrat…

  PublisherDOI

• Cloud radiative effect dominates variabilities of surface energy budget in the dark Arctic

  Scientific Reports · 2025-01-23 · 5 citations

  articleOpen accessCorresponding

  Climate models simulate a wide range of temperatures in the Arctic. Here we investigate one of the main drivers of changes in surface temperature: the net surface heat flux in the models. We show that in the winter months of the dark Arctic, there is a more than two-fold difference in the net surface heat fluxes among the models, and this difference is dominated by the downward infrared radiation from clouds. Owing to the small amount of water vapor in the winter Arctic, infrared radiation from…

  PublisherOA PDFDOI

• Multifunctional computational fluorescence self-interference holographic microscopy

  Photonics Research · 2024-09-16 · 3 citations

  article

  Fluorescence microscopy is crucial in various fields such as biology, medicine, and life sciences. Fluorescence self-interference holographic microscopy has great potential in bio-imaging owing to its unique wavefront coding characteristics; thus, it can be employed as three-dimensional (3D) scanning-free super-resolution microscopy. However, the available approaches are limited to low optical efficiency, complex optical setups, and single imaging functions. The geometric phase lens can efficien…

  PublisherDOI

• Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

  Journal of Advances in Modeling Earth Systems · 2025-06-01 · 2 citations

  articleOpen accessCorresponding

  Abstract In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep train…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research EaSM2: Mechanisms, Predictability, Prediction, and Regional and Societal Impacts of Decadal Climate Variability

  NSF · $666k · 2013–2019

• Collaborative Research: Climate Process Team on Low-Latitude Cloud Feedbacks on Climate Sensitivity

  NSF · $304k · 2003–2007

• Developing Integrated Datasets from Field Experiments to Interface with Models

  NSF · $419k · 2003–2008

Frequent coauthors

• Randy A. Dahlgren

  University of California, Davis

  120 shared

• Hailong Liu

  79 shared

• He Zhang

  Shaanxi University of Science and Technology

  71 shared

• Juanxiong He

  59 shared

• Yuzhou Luo

  California Department of Pesticide Regulation

  58 shared

• Rucong Yu

  57 shared

• Jian Li

  57 shared

• Qingcun Zeng

  Institute of Atmospheric Physics

  57 shared

Education

• Ph.D.

  Stony Brook University

Awards & honors

• Nobel Peace Prize (2007)

Similar researchers at Stony Brook University

• Paul Shepsonh-86
• Pavlos Kolliash-66
• Brian Colleh-58