Tropical Cyclones and Climate
Suzana J. Camargo
Lamont-Doherty Earth Observatory
Columbia University
Collaborators:
Chia-Ying Lee, Adam H. Sobel,
Michael K. Tippett, Allison A. Wing
HURRICANE BASIC SCIENCE
Self-aggregation: spontaneous organization of
convection
Applying ideas of self-aggregation to tropical cyclone
formation
Spontaneous tropical cyclogenesis in idealized numerical simulations of rotating
Radiative-Convective Equilibrium
What is the role of radiative-convective feedbacks in tropical cyclogenesis?
Allison Wing, NSF Postdoctoral Research
Fellow, Lamont-Doherty Earth Observatory
TROPICAL CYCLONE GENESIS
INDICES IN FUTURE CLIMATES
Camargo, S.J, M.K. Tippett, A.H. Sobel, G.A. Vecchi, and M.
Zhao, 2014: Testing the performance of tropical cyclone
genesis in future climates using the HiRAM model. J. Climate,
27, 9171-9196.
Genesis Indices
• Relate key large-scale environmental variables
to tropical cyclone (TC) frequency globally
• Lack of quantitative genesis theory – empirical
methods are useful
• Reproduce seasonal and spatial variability of
TC frequency using few variables
• Combination of dynamical reasoning and
statistical modeling to understand the
probability of TC formation
Tropical Cyclone Genesis Index
• Robust, objective and easily reproducible procedure
• Poisson Regression: objective and provides a framework
for the selection of variables
– Tippett, Camargo & Sobel, J. Climate (2011)
• Same methodology successfully applied for
– Tornadoes (Tippett, Sobel, Camargo, GRL, 2012)
– Hail (Allen, Tippett, Sobel, JAMES, 2015; Nature Geoscience, 2015)
– Monsoon depressions (Ditcheck, Boos, Camargo & Tippett, in prep.)
TCGI – Present climate
Climate Change & Genesis indices
• Most model predictions: reduction in TC
frequency with climate change
• Genesis indices predict: increase in TC
frequency with climate change
“Best Index”: Saturation Deficit & PI
DEVELOPMENT OF A HURRICANE
RISK MODEL
Chia-Ying Lee, Michael K. Tippett,
Adam H. Sobel and Suzana J. Camargo
Hurricane Risk Model
• What’s the probability that a category 5
hurricane hit New York City or Boston or
Washington DC?
• How does it depend on climate?
Development of a tropical cyclone “risk model” – generator of
synthetic TC tracks as function of environment.
Applications to climate change, variability, risk assessment, insurance.
The critical component is a model for TC intensity as function of
environment. We will use a statistical-dynamical model with a
stochastic component.
Genesis densities from observations (left), downscaling model (right) from
Emanuel et al. (2008, BAMS)
Approach: probabilistic
multiple-linear regression
model – TC intensity &
environmental variables
Tracks and intensity forecasts for Hurricanes Rita (2005), Earl (2010),
Irene (2011) and Isaac (2012).
Lee, C-Y, M.K. Tippett, S.J. Camargo, and A.H. Sobel,
Monthly Weather Review, 2015
Development of a probabilistic stochastic model
V0=60 kt
V0=40 kt
PDF [%]
Forecast time
Predicted intensity
forecast time[hr]
The performance is comparable to that of current operational models
Mean absolute error (MAE) of intensity predictions from the persistence (gray-solid line),
the SHIFOR (gray-dashed line), the SHIPS forecast (black-solid line), the SHIPS dependent
(black-dashed line), the MLR forecast (red-solid line), and the MLR dependent (red-dashed
line) models.
Simulation of global intensities using all observed tracks for 31 years
Upper: Storm intensity from 1981 to 2012 from observations (left) and stochastic
model(right). Lower: Similar to the upper two figures, but for probabilities (%) of major
storms per year per degree.
• Goal: develop a global TC synthetic track
generator to be used in risk assessment.
• We have developed a hurricane intensity MLR
model whose forecast skill is comparable to
operational models (but can handle climate
applications)
• We have developed a stochastic model which
gives a decent simulation of the global
distribution of intensities.
Summary
• Overview of TC research at Columbia
University.
• A variety of methods are being used to study
tropical cyclones and climate at Columbia
University.
• The chosen approaches are problem
dependent, varying from idealized hurricane
models to sophisticated statistical models,
including global climate models.