NOAA GFDL - Geophysical Fluid Dynamics LaboratoryGFDL - Geophysical Fluid Dynamics Laboratory

Summary of Major Accomplishments and Research Output

  1. Development of GFDL’s HiRAM and Research on Hurricane–Climate Connections: I was the lead developer of GFDL’s High-Resolution Global Atmospheric Model (HiRAM), which led to a major advancement in GFDL’s capability to simulate tropical cyclones (TCs), their historical variability, and future changes in a warming climate (Zhao et al., 2009). HiRAM was one of the GFDL models that participated in CMIP5 and contributed to the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). It also played a key role in motivating the formation of the US CLIVAR Hurricane Working Group, which conducted worldwide, multi-institutional investigations into hurricane-climate connections using high-resolution global climate models (GCMs). HiRAM helped drive the development of GFDL’s subseasonal-to-seasonal prediction system for tropical cyclones, the Madden-Julian Oscillation (MJO), and other extreme weather events. I have published five lead-author papers using HiRAM, along with numerous co-authored papers (e.g., Zhao et al., 2010, 2012, Zhao and Held 2010, 2012, Held and Zhao 2011),  all of which are  extensively cited in the literature. In particular, the HiRAM documentation paper (Zhao et al., 2009) has been cited 546 times according to the Web of Science Core Collection and 778 times according to Google Scholar as of April 2025. HiRAM has been used worldwide and has impacted numerous subsequent studies on TC-climate connections, TC seasonal predictions, global modeling of TC activities, and TC intraseasonal variability (e.g.,Vecchi et al., 2011, Walsh et al., 2015, Knutson et al., 2013, 2015, Camargo et al., 2014, Villarini et al., 2014, Kim et al., 2014). Simulation work with HiRAM contributed significantly to a GFDL Group Gold Medal awarded by the U.S. Department of Commerce in 2011, recognizing “sustained high-quality research, scientific assessment and leadership resulting in an improved understanding of the impact of anthropogenic climate change on past and future hurricane activity”.
  2. Development of GFDL’s Latest-Generation Global Atmospheric Model (AM4), Coupled Physical Climate Model (CM4), Earth System Model (ESM4), and Prediction System (SPEAR): I co-led the GFDL Model Development Team (MDT) Atmospheric Working Group (AWG) from 2013 to 2015 and led it from 2015 to 2018 for the development of AM4. I also co-led the MDT Coupled Working Group (CWG) from 2013 to 2019 for the development of CM4. My responsibilities included developing strategic plans, organizing meetings, analyzing and discussing model results, proposing and creating new configurations and versions of AM4, developing and integrating new moist physics parameterizations, and diagnosing and resolving critical issues that arose during the development of AM4. My work on CM4 focused on reducing biases in sea surface temperatures (SSTs), the El Niño–Southern Oscillation (ENSO), the double Intertropical Convergence Zone (ITCZ) problem, and the global SST response to historical and present-day radiative forcing. A central goal of this effort was to improve climate simulations by reducing biases in AM4 and CM4 through improved atmospheric moist physics. AM4 is documented in Zhao et al., (2018a,2018b), and CM4 is documented in Held et al., (2019). All three papers are recognized as Web of Science Highly Cited Papers, ranking in the top 1% of the academic field of Geosciences based on the citation threshold for the field and publication year. In particular, the two AM4 papers (Part I and II) have received 444 citations in the Web of Science Core Collection and 568 citations on Google Scholar as of April 2025.  Both AM4 and CM4 are widely used around the world. AM4 serves as the foundation for all latest-generation GFDL models, including CM4, the Earth System Model (ESM4), and the latest GFDL subseasonal-to-decadal prediction system (SPEAR), and GFDL’s full chemistry-climate model (AM4.1). I am a co-author of each of the model documentation papers, all of which are extensively cited by the global research community. AM4, CM4, and ESM4 have participated in CMIP6, and contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). ESM4 received the 2022 NOAA Administrator’s Award for advancing the understanding of the Earth System by developing and applying NOAA’s state-of-the-art Coupled Carbon-Chemistry-Climate model. GFDL SPEAR has been running in real-time for short-term climate prediction. It has replaced earlier GFDL prediction systems and contributed to the North American Multi-Model Ensemble, as well as a wide range of research and applications both within and outside of NOAA. My leadership in climate model development, along with other research achievements, was recognized by the  American Geophysical Union’s (AGU) with the 2022 Atmospheric Sciences Ascent Award for growing research accomplishments and leadership in climate model development.
  3. Studies of Clouds, Cloud Feedbacks, and Climate Sensitivity, and Co-Leadership of the GFDL Cloud Climate Initiative (CCI): Since 2013, I have co-led the GFDL Cloud Climate Initiative (CCI), authoring five first-author papers and contributing to numerous co-authored publications. Zhao (2014) identified key physical processes, such as cumulus mixing and precipitation microphysics, and introduced critical diagnostic quantities, such as precipitation efficiency or cloud detrainment efficiency, into GCMs to better understand the effects of convection on clouds and cloud feedbacks. This paper has inspired numerous subsequent studies, including a chapter titled “Precipitation Efficiency and Climate Sensitivity” in the AGU Monograph Series on Clouds and Their Climatic Impacts: Radiation, Circulation, and Precipitation (2023). Zhao et al. (2016) used a version of AM4 with modifications limited to the treatment of convective microphysics to demonstrate that convective precipitation microphysics, one of the most uncertain processes in GCM parameterizations, can profoundly affect cloud feedbacks and climate sensitivity on its own. Moreover, its impact can be better understood through the concept of precipitation efficiency. This paper received the 2018 NOAA OAR Outstanding Scientific Paper Award and helped motivate the NOAA CPO MAPP Climate Sensitivity Task Force, which I co-led. Both papers are well-cited in the literature, with a total of 211 citations in the Web of Science Core Collection and 294 citations on Google Scholar as of April 2025. More recently, Zhao (2022a) led an investigation into the equilibrium climate sensitivity (ECS) in GFDL’s latest climate models, CM4 and SPEAR. Using a series of coupled and uncoupled simulations, Zhao (2022a) identified and quantified three major processes that contributed to an increase in CM4’s ECS compared to earlier-generation GFDL models. These processes include changes in vegetation, Southern Hemisphere sea-ice concentrations, and sea surface temperature (SST) warming patterns. This paper also demonstrated the limitations of the traditional Cess approach (i.e., uniform SST warming) in studies of cloud feedbacks and climate sensitivity and proposed a new, modified framework for understanding cloud feedbacks and climate sensitivity using atmosphere-only models. In 2024, I published two first-author papers: one (Zhao and Knutson, 2025) on the crucial role of SST warming patterns in near-term climate projections, published in Nature‘s npj Climate and Atmospheric Science; and the other (Zhao, 2025) on the cloud radiative effect associated with daily weather regimes, published in AGU’s Geophysical Research Letters. Both papers have already garnered significant attention from the global research community, attracting numerous inquiries from colleagues worldwide and receiving substantial download counts. For example, Zhao and Knutson (2025) demonstrated that climate model biases in SST trend patterns have profound implications for near-term projections of high-impact storm statistics, including the frequency of atmospheric rivers, tropical storms and mesoscale convection systems, as well as for hydrological and climate sensitivity. In particular, if the future SST warming pattern continues to resemble the observed pattern from the past few decades rather than the model-predicted patterns, these results suggest: 1) a drastically different projection of high-impact storms and associated hydroclimate changes, especially over the Western Hemisphere; 2) stronger global hydrological sensitivity; and 3) substantially less global warming due to enhanced negative feedbacks and lower climate sensitivity. The paper has already been cited 7 times in the Web of Science Core Collection and 10 times on Google Scholar since its publication last year. It has also attracted media attention from sources such as Climate.gov and the American Enterprise Institute.
  4. Studies on Other High-Impact Weather Events (e.g., Atmospheric Rivers, Mesoscale Convective Systems, Extreme Cold Weather, and Storm-Related Extreme Sea Levels) and Their Response to Global Warming: In recent years, I have expanded my studies on tropical cyclones (TCs) and climate to include other high-impact weather events. For example, Zhao (2020) investigated atmospheric rivers (ARs), their variability, and their changes under warmer climates. The study demonstrated the superior performance of the GFDL high-resolution AM4 simulations in capturing present-day AR statistics and variability. Previous studies on AR responses to global warming typically used an integrated vapor transport (IVT) threshold based on present-day conditions to detect ARs, which led to a large increase in the frequency of AR conditions in warmer climates. However, Zhao (2020) argued that it is essential to use an IVT threshold that accounts for the increased moisture due to global warming, particularly when the magnitude of warming is substantial. As a result, Zhao (2020) found a much smaller increase in AR frequency but a substantially larger increase in AR intensity with warming. This paper was highlighted in the January 2021 issue of the Bulletin of the American Meteorological Society in the ‘Papers of Note’ section and has been widely cited since its publication. It has received 47 citations in the Web of Science Core Collection, and 65 citations on  Google Scholar. Zhao (2022b) used satellite observations, reanalysis data, and high-resolution AM4 to quantify the collective role of AR, tropical storms (TS), and mesoscale convective system (MCS) in producing both global and regional mean and extreme precipitation. This is the first-ever study to quantify their collective contribution to both mean and extreme precipitation on a global scale. The study not only demonstrates the model’s capability in simulating  storm-associated mean and extreme precipitation, but also reveals the changing nature of storm-associated precipitation in a warmer climate. This work has important implications for future flash flood-driven disasters and water resource management. It has also been highly cited since its publication in 2022, with 35 citations in the Web of Science Core Collection and 46 in Google Scholar. My recent work was recognized with the 2022 NOAA OAR Employee Of the Year Award for exemplary scientific leadership in the development and utilization of high-resolution climate models for studying extreme weather and extreme precipitation under climate change. In addition to the two single-author papers mentioned above, both highlighted in GFDL’s quarterly bulletin, I have co-authored numerous studies on weather–climate connections. These include studies on ARs (Dong et al., 2024a, 2025), published in Science Advances and Nature’s npj Climate and Atmospheric Science, respectively; investigations into MCSs (Dong et al., 2021, 2023, 2024b) using high-resolution AM4 simulations; a study on the effects of ocean circulation on extreme cold weather events in the U.S. (Yin and Zhao, 2021); and an analysis of storm-related extreme sea levels along the U.S. Atlantic Coast (Yin et al., 2020), among many others.
  5. Development of a Convection Parameterization Scheme and Improvements in Tropical Cyclone (TC) and Madden–Julian Oscillation (MJO) Predictions: As a core developer of GFDL AM3, I implemented, further developed, and optimized the University of Washington Shallow Cumulus Scheme (UWShCu), and unified the plume model used by both UWShCu and Donner’s deep convection scheme to enhance the model’s consistency and efficiency. My efforts led to major improvements in AM3’s climate simulations and contributed to the model receiving a Group Gold Medal from the U.S. Department of Commerce in 2012. During my development of HiRAM, I further adapted the UWShCu scheme to represent both shallow and deep convection. [See Zhao et al. (2009), Appendix, for details on my modifications to the UWShCu scheme, as well as a simple statistical cloud scheme].  During my development of AM4/CM4, I  further advanced the convection scheme by introducing an additional deep plume to better represent deep convection (Zhao et al. 2016, Zhao et al. 2018b). The new Double Plume Convection (DPC) scheme emphasizes the importance of a non-intrusive convection parameterization, allowing for a smoother transition between parameterized convection and explicit (large-scale) clouds. This scheme has been instrumental in many recent improvements in GFDL models, particularly in simulating tropical transients such as tropical cyclones, mesoscale convective systems, and the Madden-Julian Oscillation (MJO). Additionally, it improves model simulations of large-scale atmospheric circulation, mean precipitation, cloud properties, and cloud radiative effects. The DPC scheme has been used not only in the latest GFDL climate and Earth System Models (CM4, ESM4) but also in GFDL’s latest prediction systems (SPEAR). When run in forecast mode, the DPC scheme has substantially improved the models’ retrospective forecasts of the MJO, surface air temperature, and TC genesis  (e.g., Xiang et al.  2015a, 2015b, 2019, 2022, 2023). Recently, the DPC scheme was also adopted in a version of NCAR’s CAM5 model (Chu et al. 2021).
  6. Studies of Tropical Convection, Clouds, and Climate through the Development and Use of Model Hierarchies with Varying Complexities: Throughout my research career, I have contributed to the development and application of a variety of models with varying complexities to investigate convection, clouds, climate, and their interactions, as well as the impact of physics parameterizations on these processes. The models include large-eddy-simulation (LES) models (e.g., Zhao and Austin 2005a,b), cloud resolving models (e.g., Wing et al., 2020 ), single-column models (SCMs, e.g., Zhao and Austin 2003, Wyant et al., 2007, Zhang et al., 2013), doubly periodic dynamical radiative-convective equilibrium models using GCM physics with (Held and Zhao 2008) and without ambient rotation (Held et al. 2007), aquaplanet models (APM, e.g. Kang et al., 2008, Medeiros et al., 2008, Merlis et al., 2013, 2016, Ballinger et al., 2015), uncoupled global atmosphere and land models (AGCMs, e.g., Zhao 2014, Zhao et al., 2016), coupled ocean-atmosphere-land-sea ice physical climate models (CGCMs, e.g., Zhao 2022a, Held et al., 2019), and full Earth System Models (ESM, e.g., Dunne et al., 2020). My work, along with the idealized models I developed, has not only motivated but also provided valuable guidance and support to numerous graduate students and postdocs at GFDL and Princeton University. These frameworks have fostered collaborative research, facilitated the exploration of complex atmospheric processes, and driven innovation and progress in the field.