GFDL - Geophysical Fluid Dynamics Laboratory

Approaching storm


GFDL is engaged in comprehensive research fundamental to NOAA’s mission. Scientists at GFDL develop and use mathematical models and computer simulations to improve our understanding and prediction of the behavior of the atmosphere, the oceans, and climate. GFDL scientists focus on model-building relevant for society, such as hurricane research, weather and ocean prediction, seasonal forecasting, and understanding global and regional climate change.

GFDL’s research encompasses the predictability and sensitivity of global and regional climate; the structure, variability, dynamics and interaction of the atmosphere and the ocean; and he ways that the atmosphere and oceans influence, and are influenced by various trace constituents. This science incorporates a variety of disciplines including meteorology, oceanography, hydrology, classical physics, fluid dynamics, chemistry, applied mathematics, and numerical analysis.  GFDL has set the agenda for much of the world’s research on the modeling of global climate change since 1955.

Research is also facilitated by the Atmospheric and Oceanic Sciences Program, which is a collaborative program at GFDL with Princeton University. Under this program, Princeton faculty, research scientists, and graduate students participate in theoretical studies, both analytical and numerical, and in observational experiments in the laboratory and in the field. The program is supported in part by NOAA funding.


Research Highlights

Read more GFDL Research Highlights

Events & Seminars

  • October 25, 2017: Understanding How Tropical Cyclone Intensification Rates Could Increase with Climate Change (abstract)
    Kieran Bhatia (GFDL)
    Time: 12:00 pm - 1:00 pm
    Location: Smagorinsky Seminar Room
  • October 26, 2017: How much has human-caused climate change influenced wildfire extent across western US forests? (abstract)
    Formal Seminar - John Abatzoglou (University of Idaho)
    Time: 2:00 pm - 3:00 pm
    Location: Smagorinsky Seminar Room
  • October 31, 2017: Wave breaking in ocean-atmosphere interactions (abstract)
    Luc Deike (Princeton University)
    Time: 10:30 am - 11:30 am
    Location: Smagorinsky Seminar Room
  • November 1, 2017: Convection, climate, and their sensitivities in cloud-resolving FV3 (abstract)
    Nadir Jeevanjee (GFDL)
    Time: 12:00 pm - 1:00 pm
    Location: Smagorinsky Seminar Room
  • November 8, 2017: TBD (abstract)
    Pu Lin (GFDL)
    Time: 12:00 pm - 1:00 pm
    Location: Smagorinsky Seminar Room
  • November 9, 2017: TBD (abstract)
    Formal Seminar
    Time: 2:00 pm - 3:00 pm
    Location: Smagorinsky Seminar Room
  • November 15, 2017: TBD (abstract)
    Laure Zanna (GFDL )
    Time: 12:00 pm - 1:00 pm
    Location: Smagorinsky Seminar Room

More events & seminars...

GFDL Research as the Foundation for Applications

The National Weather Service announced in July 2016 that it has adopted the FV3 core, developed at GFDL, as the backbone for the next generation US weather prediction model. This next generation model, with the ability to represent weather processes at very small spatial scales, should provide a major leap forward in US weather prediction capabilities, leading to improved prediction of extreme storms
Since 2013, GFDL has been a major contributor to the US component of the North American Multi Model Ensemble (NMME) for seasonal forecasting. GFDL's leading edge prediction models have formed a crucial backbone of this prediction activity in support of NOAA's mission, and provide significant advances in seasonal prediction of El Niño and hurricane activity.
Scientists at GFDL have developed and released a new model code at the forefront of ocean simulation and prediction. The code, MOM6, uses state-of-the-art techniques to substantially improve our ability to model and predict the ocean, including ocean biogeochemistry and ecosystems. The National Center for Atmospheric Research (NCAR) has announced that they will use MOM6 for their next generation of the Community Earth System Model.