Spring School on Fluid Mechanics of Environmental Hazards
Three Lectures on Tropical
Cyclones
Kerry Emanuel
Massachusetts Institute of Technology
Lecture 3:
Using Physics to Assess Tropical
Cyclone Risk in a Changing
Climate
Tropical Cyclones Do Respond to
Climate Change!
Atlantic Sea Surface Temperatures and
Storm Max Power Dissipation
Data Sources: NOAA/TPC, UKMO/HADSST1
Years
included:
1870-2006
Scaled Temperature
Power Dissipation Index (PDI)
(Smoothed with a 1-3-4-3-1 filter)
10-year Running Average of Aug-Oct NH Surface T and MDR SST
Tropical Atlantic SST(blue), Global Mean Surface
Temperature (red),
Aerosol Forcing (aqua)
Global mean surface temperature
Tropical Atlantic sea surface temperature
Sulfate aerosol radiative forcing
Mann, M. E., and K. A. Emanuel, 2006. Atlantic hurricane trends linked to climate change. EOS, 87, 233-244.
Best Fit Linear Combination of Global Warming
and Aerosol Forcing (red) versus Tropical Atlantic
SST (blue)
Tropical Atlantic sea surface temperature
Global Surface T + Aerosol Forcing
Mann, M. E., and K. A. Emanuel, 2006. Atlantic hurricane trends linked to climate change. EOS, 87, 233-244.
Effect of Increased Potential
Intensity on Hurricane Katrina
Paleotempestology
Paleotempestology
barrier beach
upland
overwash fan
backbarrier marsh
a)
lagoon
barrier beach
upland
overwash fan
b)
backbarrier marsh
lagoon
terminal lobes
flood tidal delta
Source: Jeff Donnelly, WHOI
Donnelly and Woodruff (2006)
Photograph of stalagmite
ATM7 showing depth of
radiometric dating samples,
micromilling track across
approximately annually
laminated couplets, and agedepth curve.
Frappier et al., Geology, 2007
Frappier et al., Geology, 2007
Assessing Tropical Cyclone Risk:
Historical Statistics Are Inadequate
U.S. Hurricane Damage, 1900-2004,Adjusted for
Inflation, Wealth, and Population
Top 10 Northeast Storms Since 1851
Issues with Direct Use of
Global Climate Models:
• Today’s global models are too coarse to
simulate high intensity events
• Not practical to run models for long
enough to generate high quality regional
statistics
• Embedding regional models is feasible but
expensive
Our Approach:
• Step 1: Randomly seed ocean basins with weak (12 m/s)
warm-core vortices
• Step 2: Determine tracks of candidate storms using a
simple model that moves storms with mean background
wind
• Step 3: Run a deterministic coupled tropical cyclone
intensity model along each synthetic track, discarding all
storms that fail to achieve winds of at least 17 m/s
(random seeding method)
• Step 4: Assess risk using statistics of surviving events
Synthetic Track Generation,
Using Synthetic Wind Time Series
• Postulate that TCs move with vertically averaged
environmental flow plus a “beta drift” correction
(Beta and Advection Model, or “BAMS”)
• Approximate “vertically averaged” by weighted
mean of 850 and 250 hPa flow
Synthetic wind time series
• Monthly mean, variances and co-variances
from NCEP re-analysis data
• Synthetic time series constrained to have the
correct mean, variance, co-variances and an
power series  3
250 hPa zonal wind modeled as Fourier
series in time with random phase:
u250 ( x, y, , t )  u250 ( x, y, )  u ' ( x, y, ) F1 (t )
2
250
F1 
2
N
n
N
n
3 n 1
3
2
 
sin 2 nt  X 1n
T

n 1
where T is a time scale corresponding to the period of the
lowest frequency wave in the series, N is the total number
of waves retained, and X 1n is, for each n, a random number
between 0 and 1.
The time series of other flow components:
v250 ( x, y, , t )  v250 ( x, y, )  A21F1 (t )  A22 F2 (t ),
u850 ( x, y, , t )  u850 ( x, y, )  A31F1 (t )  A32 F2 (t )  A33 F3 (t ),
v850 ( x, y, , t )  v850 ( x, y, )  A41F1 (t )  A42 F2 (t )  A43 F3 (t )  A44 F4 (t ),
or
V = V  AF
where each Fi has a different random phase, and A satisfies
T
A A = COV
where COV is the symmetric matrix containing the variances and covariances of
the flow components.
Example:
u250  30 ms
1
N  15
u '2250 ( x, y, )  10 ms 1
T  15 days
Track:
Vtrack   V850  1   V250  V ,
Empirically determined constants:
  0.8,
1
u  0 ms ,
v  2.5 ms
1
Tropical Cyclone Intensity
• Run coupled deterministic model (CHIPS,
Emanuel et al., 2004) along each track
• Use monthly mean potential intensity,
ocean mixed layer depth, and sub-mixed
layer thermal stratification
• Use shear from synthetic wind time series
• Initial intensity specified as 12 ms1
1
• Tracks terminated when v < 17 ms
6-hour zonal displacements in region bounded by 10o and 30o
N latitude, and 80o and 30o W longitude, using only post-1970
hurricane data
Example: 50 Synthetic Tracks
200 Random Western North Pacific Events
Cumulative Distribution of Storm Lifetime Peak
Wind Speed, with Sample of 2946 Synthetic Tracks
Return Periods
Random Seeding Method: Calibration
• Absolute genesis frequency calibrated to
North Atlantic during the period 1980-2005
Genesis rates
Western North
Pacific
Southern
Hemisphere
Eastern North
Pacific
Atlantic
Calibrated to Atlantic
North
Indian
Ocean
Seasonal Cycles
Western North Pacific
Captures effects of regional climate
phenomena (e.g. ENSO, AMM)
Year by Year Comparison with Best Track and
with Knutson et al., 2007
Simulated vs. Observed Power Dissipation Trends, 1980-2006
Global Percentage of Cat 4 & Cat 5 Storms
Now Use Daily Output from IPCC
Models to Derive Wind
Statistics, Thermodynamic State
Needed by Synthetic Track
Technique
Compare two simulations each
from 7 IPCC models:
1. Last 20 years of 20th century
simulations
2. Years 2180-2200 of IPCC Scenario
A1b (CO2 stabilized at 720 ppm)
Basin-Wide Percentage Change in
Power Dissipation
Different
Climate
Models
Basin-Wide Percentage Change in
Storm Frequency
Different
Climate
Models
7 Model Consensus Change in
Storm Frequency
Reds: Increases
Blues: Decreases
Feedback of Global Tropical
Cyclone Activity on the Climate
System
Strong Mixing of Upper Ocean
Direct mixing by tropical cyclones
Emanuel (2001) estimated global rate of heat input as
1.4 X 1015 Watts
Source: Rob Korty, CalTech
TC Mixing May Induce Much or Most of the Observed
Poleward Heat Flux by the Oceans
90 S
EQ
Trenberth and Caron, 2001
90 N
TC-Mixing may be Crucial for High-Latitude
Warmth and Low-Latitude Moderation During
Warm Climates, such as that of the Eocene