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Zhongxiang Zhao

Principal Oceanographer

Affiliate Associate Professor, Oceanography





Department Affiliation

Ocean Physics


B.S. Physics, Shandong University, 1994

Ph.D. Oceanography, University of Delaware, 2004


Air–Sea Momentum Flux in Tropical Cyclones

The intensity of a tropical cyclone is influenced by two competing physical processes at the air–sea interface. It strengthens by drawing thermal energy from the underlying warm ocean but weakens due to the drag of rough ocean surface. These processes change dramatically as the wind speed increases above 30 m/s.

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30 Mar 2018

The project is driven by the following science questions: (1) How important are equilibrium-range waves in controlling the air-sea momentum flux in tropical cyclones? We hypothesize that for wind speeds higher than 30 m/s the stress on the ocean surface is larger than the equilibrium-range wave breaking stress. (2) How does the wave breaking rate vary with wind speed and the complex surface wave field? At moderate wind speeds the wave breaking rate increases with increasing speed. Does this continue at extreme high winds? (3) Can we detect acoustic signatures of sea spray at high winds? Measurements of sea spray in tropical cyclones are very rare. We will seek for the acoustic signatures of spray droplets impacting the ocean surface. (4) What are the processes controlling the air-sea momentum flux?

Monitoring Global Ocean Heat Content Changes by Internal Tide Oceanic Tomography

This study will obtain a 20-year-long record of global ocean heat content changes from 1995–2014 with a method called Internal tide oceanic tomography (ITOT), in which the satellite altimetry data are used to precisely measure travel times for long-range internal tides.

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29 Jul 2016

Ocean Heat Content (OHC) is a key indicator of global climate variability and change. However, it is a great challenge to observe OHC on a global scale. Observations with good coverage in space and time are only available in the last 10 years with the maturing of the Argo profiling float array. This study will obtain a 20-year-long record of global OHC changes from 1995–2014 with a method called Internal tide oceanic tomography (ITOT), in which the satellite altimetry data are used to precisely measure travel times for long-range internal tides. Just like in acoustic tomography, these travel times are analyzed to infer changes in OHC. This analysis will double the 10 years of time series available from Argo floats. More importantly, ITOT will provide an independent long-term, low-cost, environmentally-friendly observing system for global OHC changes. Since ocean warming contributes significantly to sea level rise, which has significant consequences to low-lying coastal regions, these observations have the potential for direct societal benefits. The project will communicate some of its results directly to the public. The team will make an educational animation showing how ocean warming is measured and how the novel ITOT technique works from the vantage point of space. This animation will be played for students visiting the lab, and in science talks and festivals in local K-12 schools. In addition, three summer undergraduate students will be trained in data analysis and interpretation, and poster presentation.

The analysis technique to be applied over the global ocean in this project is based on the preliminary regional analysis already conducted by this team. About 60 satellite-years of altimeter data from 1995-2014 will be analyzed. Specifically, it will (1) quantify annual variability, interannual variability, and bidecadal trend in global M2 and K1 internal tides, (2) construct the conversion function from the internal tide's travel time changes to OHC changes, and (3) construct a record of 20-year-long global OHC changes, and assess uncertainties using Argo measurements. The ultimate goal for this project is to develop the ITOT technique for future global OHC monitoring. This will improve our understanding of the temporal and spatial variability of global OHC, particularly in combination with in situ measurements from Argo floats, XBTs, and WOCE full-depth hydrography. The ITOT observations will provide useful constraints to ECCO2. The internal tide models may be used to correct internal tide noise in the Argo and XBT measurements. In addition, the monthly and yearly internal tide fields produced will provide constraints to global high-resolution, eddy-permitting numerical models of internal tides.


2000-present and while at APL-UW

Development of the yearly mode-1 M2 internal tide model in 2019

Zhao, Z., "Development of the yearly mode-1 M2 internal tide model in 2019," J. Atmos. Ocean. Technol., 39, 463-478, doi:10.1175/JTECH-D-21-0116.1, 2022.

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22 Jan 2022

The yearly mode-1 M2 internal tide model in 2019 is constructed using sea-surface height measurements made by six concurrent satellite altimetry missions: Jason-3, Sentinel-3A, Sentinel-3B, CryoSat-2, Haiyang-2A and SARAL/AltiKa. The model is developed following a three-step procedure consisting of two rounds of plane wave analysis with a spatial bandpass filter in between. Prior mesoscale correction is made on the altimeter data using AVISO gridded mesoscale fields. The model is labeled Y2019, because it represents the one-year-coherent internal tide field in 2019. In contrast, the model developed using altimeter data from 1992–2017 is labeled MY25, because it represents the multi-year-coherent internal tide field in 25 years. Thanks to the new mapping technique, model errors in Y2019 are as low as those in MY25. Evaluation using independent altimeter data confirms that Y2019 reduces slightly less variance (∼6%) than MY25. Further analysis reveals that the altimeter data from five missions (without Jason-3) can yield an internal tide model of almost same quality. Comparing Y2019 and MY25 shows that mode-1 M2 internal tides are subject to significant interannual variability in both amplitude and phase, and their interannual variations are a function of location. Along southward internal tides from Amukta Pass, the energy flux in Y2019 is two times large and the phase speed is about 1.1% faster. This mapping technique has been applied successfully to 2017 and 2018. This work demonstrates that yearly internal tides can be observed by concurrent altimetry missions and their interannual variations can be determined.

Enhanced diapycnal mixing with polarity-reversing internal solitary waves revealed by seismic reflection data

Gong, Y., H. Song, Z. Zhao, Y. Guan, K. Zhang, Y. Kuang, and W. Fan, "Enhanced diapycnal mixing with polarity-reversing internal solitary waves revealed by seismic reflection data," Nonlin. Processes Geophys., 28, 445-465, doi:10.5194/npg-28-445-2021, 2021.

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14 Sep 2021

Shoaling internal solitary waves near the Dongsha Atoll in the South China Sea dissipate their energy and enhance diapycnal mixing, which have an important impact on the oceanic environment and primary productivity. The enhanced diapycnal mixing is patchy and instantaneous. Evaluating its spatiotemporal distribution requires comprehensive observation data. Fortunately, seismic oceanography meets the requirements, thanks to its high spatial resolution and large spatial coverage. In this paper, we studied three internal solitary waves in reversing polarity near the Dongsha Atoll and calculated their spatial distribution of diapycnal diffusivity. Our results show that the average diffusivities along three survey lines are 2 orders of magnitude larger than the open-ocean value. The average diffusivity in internal solitary waves with reversing polarity is 3 times that of the non-polarity reversal region. The diapycnal diffusivity is higher at the front of one internal solitary wave and gradually decreases from shallow to deep water in the vertical direction. Our results also indicate that (1) the enhanced diapycnal diffusivity is related to reflection seismic events, (2) convective instability and shear instability may both contribute to the enhanced diapycnal mixing in the polarity-reversing process, and (3) the difference between our results and Richardson-number-dependent turbulence parameterizations is about 2–3 orders of magnitude, but its vertical distribution is almost the same.

Seasonal mode-1 M2 internal tides from satellite altimetry

Zhao, Z., "Seasonal mode-1 M2 internal tides from satellite altimetry," J. Phys. Oceanogr., 51, 3015-3055, doi:10.1175/JPO-D-21-0001.1, 2021.

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1 Sep 2021

The seasonal variability of mode-1 M2 internal tides is investigated using 25 years of multi-satellite altimeter data from 1992–2017. Four seasonal internal tide models are constructed using seasonally-subsetted altimeter data and World Ocean Atlas seasonal climatologies. This work is made possible by a newly-developed mapping procedure that can significantly suppress model errors. Seasonal-mean and seasonally-variable internal tide models are derived from the four seasonal models. All the models are inter-compared and evaluated using independent CryoSat-2 data. The seasonal-mean model is overall the best model because averaging the four seasonal models further reduces model errors. The seasonally-variable models are better in the tropical zone, where large seasonal signals may overcome model errors. Each seasonal model works best in its own season and worst in its opposite season. These internal tide models reveal that mode-1 M2 internal tides are subject to significant seasonal variability and their seasonal variations are a function of location. Large seasonal variations dominantly occur in the tropical zone, where the World Ocean Atlas climatology shows strong seasonal variations in ocean stratification. Seasonal phase variations are obtained from the directionally-decomposed internal tide components. They are dominantly ±60° at the equator and up to ±120° in the central Arabian Sea. Incoherence caused by seasonal phase variations is usually <10%, but may be up to 40–50% in the tropical zone.

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Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center