GFDL - Geophysical Fluid Dynamics Laboratory

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Ngar-Cheung (Gabriel) Lau

Phone Number: (609)-452-6524
Fax Number: (609)-987-5063

Postal address:

Geophysical Fluid Dynamics Laboratory/NOAA
Princeton University
201 Forrestal Road
Princeton, NJ 08542


Lead Scientist of the Climate Diagnostics Group

at the Geophysical Fluid Dynamics Laboratory which is part of the U.S. Federal government's Office of Atmospheric and Oceanic Research, National Oceanic and Atmospheric Administration, U.S. Department of Commerce.

Lecturer with rank of Professor

of the Program in Atmospheric and Oceanic Sciences, the Department of Geosciences, at Princeton University.

Other Responsibilities


1970: St. Francis Xavier's School, Hong Kong
1974: B.Sc. (Physics), Chinese University of Hong Kong, Hong Kong
1978: Ph.D. (Atmospheric Sciences), University of Washington, Seattle

Awards and Honors

1990: Clarence Leroy Meisinger Award, American Meteorological Society,

    for `Outstanding Studies of Low-Frequency Variability in the Atmosphere by a Synthesis of Modeling and Diagnostics'
1991: Fellow, American Meteorological Society
1991: Unusually Outstanding Performance Award, National Oceanic and Atmospheric Administration, U.S. Department of Commerce
1993: Chen-Ning Yang Visiting Fellow, Chinese University of Hong Kong, Hong Kong
2003: Hong Kong Observatory 120th Anniversary Distinguished Meteorologist
2008: Distinguished Lecturer, Department of Atmospheric Sciences, Peking University
2009: Guest Professor, Peking University

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Courses taught at Princeton University

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Research Interests

  • Observational and modeling studies of the atmospheric general circulation
  • Impact of large-scale air-sea interaction on atmospheric variability
  • Properties of tropical circulation systems
  • Analysis of atmospheric phenomena simulated by high-resolution numerical models

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Summary of research activities

  • I hold a long-standing interest in the origin of atmospheric variability on time scales ranging from several days to a few years, and the dynamical interactions between observed atmospheric phenomena residing in different parts of the frequency spectrum. I have demonstrated that month-to-month changes in the preferred trajectory and intensity of synoptic-scale disturbances are closely related to the pattern of the quasi-stationary flow field.
  • I also take advantage of the extensive datasets resulting from multi-year general circulation model (GCM) at the Geophysical Fluid Dynamics Laboratory. I am intrigued by the role of sea surface temperature (SST) anomalies in altering the atmospheric circulation. The model diagnoses have led to insights on the influences of the El Nino-Southern Oscillation phenomenon on atmospheric variability in both tropical and midlatitude regions. These atmospheric perturbations can in turn lead to changes in the near-surface oceanic conditions in many parts of the globe.

Typical sea surface temperature anomaly patterns in the World Oceans during El Nino events, based on observational data (upper panel) and simulation with an atmospheric GCM coupled to an oceanic mixed-layer model outside the tropical eastern/central Pacific (lower panel).

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  • My investigation on tropical circulation features is mainly concerned with weather systems in the East Asian monsoon region (such as the Plum Rain or Meiyu-Baiu phenomenon in the warm season and cold air outbreaks in the cold season), the structure and propagation characteristics of synoptic-scale and intraseasonal disturbances in the tropical zone, the space-time evolution of monsoon circulation, and the modulation of various tropical phenomena by El Nino events.

Distribution of changes in sea level pressure (left panels) and surface temperature (right panels) in successive 3-hour periods during a cold-air outbreak episode in East Asia, as simulated by a GCM with spatial resolution of ~50 km. Note the large pressure rises and temperature drops behind a sharp cold front advancing equatorward over southern China.

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  • I have analyzed the output from several simulations based on atmospheric GCMs with spatial resolution in the 25-50 km range, and have compared the model results with available observations of comparable resolution, such as datasets based on satellite measurements. Particular attention has been devoted to regional details of the diurnal cycle, and fine-structure of mesoscale meteorological systems.
Climatology of precipitation (shading) and surface wind (arrows) for January (upper panel) and July (lower panel), as simulated by a GCM with spatial resolution of ~50 km.

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