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
• Economic growth
• Climatology
• Biology
• Geology
• Environmental protection
• Natural resource economics
• Environmental resource management

Selected publications

• Coupled Simultion of Atmospheric CO2 in CAS-ESM

  2025-03-14

  preprintOpen access

  The atmospheric carbon dioxide (CO2) concentration has been increasing rapidly since the Industrial Revolution, which has led to unequivocal global warming and crucial environmental change. It is extremely important to investigate the interactions among atmospheric CO2, the physical climate  system, and the carbon cycle of the underlying surface for a better understanding of the Earth system. Earth system models are widely used to investigate these interactions via coupled carbon–climate simulations. The Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM2.0) has successfully fixed a two-way coupling of atmospheric CO2 with the climate and carbon cycle on land and in the ocean. Using CAS-ESM2.0, we  conducted a coupled carbon–climate simulation by following the CMIP6 proposal of a historical emissions-driven experiment. This paper examines the modeled CO2 by comparison with observed CO2 at the sites of Mauna Loa and Barrow, and the Greenhouse Gases Observing Satellite (GOSAT) CO2 product. The results showed that CAS-ESM2.0 agrees very well with observations in reproducing the increasing trend of annual CO2 during the period 1850–2014, and in capturing the seasonal cycle of CO2 at the two baseline sites, as well as over northern high latitudes. These agreements illustrate a good ability of CAS-ESM2.0 in simulating carbon–climate interactions, even though uncertainties remain in the processes involved. This paper reports an important stage of the development of CAS-ESM with the coupling of carbon and climate, which will provide significant scientific support for climate research and China’s goal of carbon neutrality.

  PublisherDOI

• Global existence in a 2D Keller–Segel–Navier–Stokes system with indirect signal production

  Zeitschrift für angewandte Mathematik und Physik · 2025-05-19 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• Impact of Orographic Drag Schemes on East Asia Rainfall

  Journal of Geophysical Research Atmospheres · 2025-09-15

  articleOpen accessSenior author

  Abstract Current generation of climate models often has significant biases in mountainous regions where the gradient of elevation is steep, and the terrain is complex. Potential reasons for these biases include under‐representation of orographic drag process in climate models. In this study, we assess the impact of orographic drag on East Asia rainfall by comparing the impact of a new orographic drag scheme that considers 3D orographic anisotropy (3D‐oro) with a 2‐D scheme in a general circulation model. Two sets of simulations (medium‐range and seasonal forecast) are carried out for the comparison and validation against observation. It is shown that through local/remote forcing of the drag in the mountainous regions, the 3D‐oro alleviates part of the excessive rainfall in west Tibetan Plateau and parts of insufficient rainfall in Southeast China by about 25%∼50% in the January/winter forecasts; it alleviates about 25%∼50% of the rainfall bias in part of south Tibetan Plateau and of East Asia in the July/summer forecasts. The results suggest the importance of improved orographic drag process and its impact in climate modeling for those regions that are prone to significant impact of hydroclimate events.

  PublisherOA PDFDOI

• Mechanism Analysis and Establishment of a Prediction Model for the Total Pressure Loss in the Multi-Branch Pipeline System of the Pneumatic Seeder

  Agriculture · 2025-08-03 · 1 citations

  articleOpen access

  This study aims to clarify the nonlinear pressure loss patterns of the pneumatic system in a pneumatic seeder under varying pipeline structures and airflow parameters, and to develop a rapid prediction equation for the main pipe’s pressure loss. The studied multi-branch pipeline system consists of a main pipe, a header, and ten branch pipes. The main pipe is vertically installed at the center of the header in a straight-line configuration. The ten branch pipes are symmetrically and evenly spaced along the axial direction of the header, distributed on both sides of the main pipe. The outlet directions of the branch pipes are arranged in a 180° orientation opposite to the inlet direction of the main pipe, forming a symmetric multi-branch configuration. Firstly, this study investigated the flow characteristics within the multi-branch pipeline of the pneumatic system and elaborated on the mechanism of flow division in the pipeline. The key geometric factors affecting airflow were identified. Secondly, from a microscopic perspective, CFD simulations were employed to analyze the fundamental causes of pressure loss in the multi-branch pipeline system. Finally, from a macroscopic perspective, a dimensional analysis method was used to establish an empirical equation describing the relationship between the pressure loss (P) and several influencing factors, including the air density (ρ), air’s dynamic viscosity (μ), closed-end length of the header (Δl), branch pipe 1’s flow rate (Q), main pipe’s inner diameter (D), header’s inner diameter (γ), branch pipe’s inner diameter (d), and the spacing of the branch pipe (δ). The results of the bench tests indicate that when 0.0018 m3·s−1 ≤ Q ≤ 0.0045 m3·s−1, 0.0272 m < d ≤ 0.036 m, 0.225 m < δ ≤ 0.26 m, 0.057 m ≤ γ ≤ 0.0814 m, and 0.0426 m ≤ D ≤ 0.0536 m, the prediction accuracy of the empirical equation can be controlled within 10%. Therefore, the equation provides a reference for the structural design and optimization of pneumatic seeders’ multi-branch pipelines.

  PublisherOA PDFDOI

• Rapid Shrinking of the Warming Hole Over the United States in ERA5 and SPEAR

  Journal of Geophysical Research Atmospheres · 2025-11-26

  articleSenior author

  Abstract The “warming hole” over the United States refers to a long‐term summer cooling anomaly in near‐surface temperatures across the central and northern U.S. since the midtwentieth century. It is most pronounced in maximum temperature ( T max ), contrasting with widespread warming elsewhere. Using the European Centre for Medium‐Range Weather Forecasts ERA5 reanalysis and a 30‐member large ensemble of the Seamless System for Prediction and Earth System Research (SPEAR) model, we show that the warming hole has shrunk and weakened in recent decades, with contraction accelerating during 2021–2024. The changes are seasonally asymmetric: June exhibits the strongest weakening, whereas May retains negative anomalies. Composite analyses confirm a well‐known mechanism in which a strengthened southerly low‐level jet enhances precipitation and suppresses T max . We also identify an additional process: enhanced northerly winds from Canada that advect cooler air and increase convergence and rainfall, reinforcing the cooling. In many years, opposite circulation and precipitation anomalies reduced these cooling effects; recently, such anomalies have strengthened in step with the warming trend, contributing to rapid shrinkage. SPEAR reasonably captures the detrended variability and spatial pattern of anomalies but overestimates the warming trend relative to ERA5. Combining SPEAR's detrended variability with the ERA5 trend, we project that the warming hole would vanish around 2050 under a strong external forcing. This result contrasts with recent studies emphasizing seasonal persistence and southerly winds alone, underscoring the importance of both externally forced and internal variability associated with hydrologic cycle changes in shaping the future trajectory of the U.S. warming hole.

  PublisherDOI

• Development of CAS-ESM_MMF: Improving East Asian Summer Precipitation Simulation with a Multiscale Modeling Framework

  2025-09-17

  articleOpen accessSenior author

  Abstract. Traditional global climate models (GCMs) exhibit substantial biases in simulating precipitation over East Asia, largely due to uncertainties in convection parameterizations. To address this issue, we implement a Multiscale Modeling Framework (MMF), which explicitly resolves convection in a cloud resolving model, into the atmospheric component of the Chinese Academy of Sciences Earth System Model (CAS-ESM). Simulations using CAS-ESM with and without MMF reveal that the MMF implementation significantly reduces the wet bias around the Tibetan Plateau and the dry bias over South China and Southeast Asia. The intensity–frequency characteristics of precipitation are more realistically represented in the MMF version. In addition, the CAS-ESM with MMF better captures the monthly evolution of precipitation and simulates a more realistic seasonal migration of the East Asian rainband, albeit with a somewhat step-wise progression. Further enhancement is achieved by incorporating a convective momentum transport (CMT) parameterization, typically neglected in previous MMF implementations. This inclusion leads to a smoother northward migration of the rainband, more consistent with observations. Comparison with ERA5 reanalysis suggests that this improvement is associated with a more accurate simulation of the western Pacific subtropical high. These results demonstrate that MMF, especially when combined with CMT, substantially improves the simulation of East Asian precipitation. This modeling advancement offers a promising approach for evaluating regional precipitation responses to future climate change.

  PublisherOA PDFDOI

• 3D Continuous Forcing Dataset from 3D Constrained Variational Analysis at SGP

  OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2025-01-01

  articleOpen accessSenior author

  The continuous 3D large-scale forcing (VARANAL3D) data set derived from 3D constrained variational analysis (3DCVA) extends the conventional constrained variational analysis method by incorporating multiple sub-columns within the analysis domain. This advancement introduces spatial variability into the large-scale forcing fields, thereby enriching the data set’s applicability. The VARANAL3D data set spans from 2004 to 2018 and covers a region of 5˚×4.5˚ domain around the ARM SGP site. The analysis domain is divided into 10×9 sub-columns with 0.5˚ resolution. The 3D large-scale forcing data provides necessary variables to drive and evaluate single-column models (SCM), cloud-resolving models (CRM) ,and large-eddy simulations (LES), as well as information for testing model sensitivity to spatial variability of the large-scale forcing data, facilitating more rigorous testing and refinement of physical processes in SCM/CRM/LES.

  PublisherDOI

• Anti-gamma-aminobutyric acid B receptor antibody-associated limbic encephalitis in relapsing polychondritis: a rare case report and literature review

  Frontiers in Immunology · 2025-10-29 · 3 citations

  articleOpen access

  Relapsing polychondritis (RP) is an immune-mediated disorder that primarily involves the targeting of cartilaginous tissues for inflammation and destruction. Limbic encephalitis (LE) is a rare central nervous system (CNS) manifestation of RP. We report the case of a 39-year-old man who was diagnosed with RP complicated by anti-gamma-aminobutyric acid B receptor (anti-GABABR) antibody-associated LE and presented with recurrent headaches, fever, bilateral auricular swelling, scleral injection, and cognitive impairment. Laboratory tests revealed positive anti-GABABR IgG antibodies in both the serum (titer 1:100) and the cerebrospinal fluid (CSF) (titer 1:1), along with CSF lymphocytic pleocytosis. A brain MRI revealed bilateral frontal and parietal subcortical and periventricular T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) hyperintensities. Immunosuppressive therapy with high-dose methylprednisolone and cyclophosphamide induced rapid symptom resolution, and no relapse occurred during a follow-up period of 1 year. This case expands the spectrum of RP-associated LE, emphasizes the necessity of neuronal autoantibody screening in RP patients with neurological symptoms, and suggests potential pathogenic links involving antigenic cross-reactivity between cartilage and neural tissues and GABAergic metabolism dysregulation.

  PublisherOA PDFDOI

• Genome-Wide Identification of the SiNHX Gene Family in Foxtail Millet (Setaria Italica) and Functional Characterization of SiNHX7 in Arabidopsis

  International Journal of Molecular Sciences · 2025-07-24 · 3 citations

  articleOpen access

  Plant growth is susceptible to abiotic stresses like salt and drought, and Na+/H+ antiporters (NHXs) play a pivotal role in stress responses. NHX proteins belong to the CPAs (cation/proton antiporters) family with a conserved Na+ (K+)/H+ exchange domain, which is widely involved in plant growth, development, and defense. While NHX genes have been extensively studied in model plants (e.g., Arabidopsis thaliana and Oryza sativa), research in other species remains limited. In this study, we identified nine NHX genes in foxtail millet (Setaria italica) and analyzed their systematic phylogeny, gene structure, protein characteristics, distribution of the chromosome, collinearity relationship, and cis-elements prediction at the promoter region. Phylogenetic analysis revealed that the members of the SiNHX gene family were divided into four subgroups. RT-qPCR analysis of the SiNHX family members showed that most genes were highly expressed in roots of foxtail millet, and their transcriptional levels responded to salt stress treatment. To determine SiNHX7’s function, we constructed overexpression Arabidopsis lines for each of the two transcripts of SiNHX7, and found that the overexpressed plants exhibited salt tolerance. These findings provide valuable insights for further study of the function of SiNHX genes and are of great significance for breeding new varieties of salt-resistant foxtail millet.

  PublisherOA PDFDOI

• 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 training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.

  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

• Interfacing Major Field Experiments with Physical Parameterizations in Atmospheric General Circulation Models

  NSF · $499k · 2009–2012

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)