Tropical Cyclones and Climate - A Model Intercomparison Project

Lead PI: Dr. Suzana J. Camargo , Prof. Adam Sobel , , Daehyun Kim

Unit Affiliation: Ocean and Climate Physics, Lamont-Doherty Earth Observatory (LDEO)

January 2012 - December 2016
Project Type: Research

DESCRIPTION: The main objectives of this project are as follows (i) to improve the understanding of natural variability and trends in tropical cyclone (TC) activity since the beginning of the 20th century; (ii) to quantify projected changes in the characteristics of tropical cyclones under a warming climate. The multi-model dataset generated by the recently established U.S. Climate Predictability and Variability program working group on hurricanes and climate, will be analyzed. The common set of diagnostics will include cyclone tracks and intensities, analysis of key tropical TC statistics, analysis of environmental conditions associated with TC genesis and intensification in the models.

The work aims at better understanding of the relationship between TC in the present, as well as more robust and quantitative projects of TC activity in the future, based on the analysis of a unique high-resolution multi-model dataset. This data set will advance the understanding of the relationship between climate and TC; in addition international and national collaboration will be enhances as a result of interactions between several modeling groups. It will promote training through mentoring of post-doctoral scientists as well through outreach activities in the New York area.

OUTCOMES: Examined a subset of HWG models and found that storm frequency changes are not robust between models which may be due to the tracking algorithm used by each group.


National Science Foundation (NSF)




Geophysical Fluids Dynamics Laboratory, National Oceanic and Atmospheric Administration (NOAA), University of Melbourne



ocean and climate physics climate change el nino southern oscillation (enso) variability sea surface temperature models climate tropical cyclones u.s. clivar


Modeling and Adapting to Future Climate