Hurricanes and Climate
Change
Kerry Emanuel
Massachusetts Institute of Technology
Program
• Effect of climate change on hurricane activity
• Hurricanes in the climate system
Effect of Climate Change on
Hurricanes
No Obvious Trend in Global TC Frequency, 1970-2006
Data Sources: NOAA/TPC and NAVY/JTWC
Better Intensity Metric:
The Power Dissipation Index
PDI V dt 3
max
0
A measure of the total frictional dissipation of kinetic
energy in the hurricane boundary layer over the
lifetime of the storm
Power Dissipation Based on 3 Data Sets for
the Western North Pacific
(smoothed with a 1-3-4-3-1 filter)
Years
included:
1949-2004
aircraft recon
Data Sources: NAVY/JTWC, Japan Meteorological Agency, UKMO/HADSST1, Jim Kossin, U. Wisconsin
Atlantic Storm Maximum Power Dissipation
(Smoothed with a 1-3-4-3-1 filter)
Years
Power Dissipation Index (PDI)
included:
1870-2006
Data Source: NOAA/TPC
Atlantic Sea Surface Temperatures and
Storm Max Power Dissiaption
(Smoothed with a 1-3-4-3-1 filter)
Years
included:
Power Dissipation Index (PDI)
1870-2006
Scaled Temperature
Data Sources: NOAA/TPC, UKMO/HADSST1
Energy Production
Distribution of Entropy in Hurricane Inez, 1966
Source: Hawkins and Imbembo, 1976
Theoretical Upper Bound on
Hurricane Maximum Wind Speed:
Surface
temperature
C T T
|V pot |2 k s o k *k
C T 0
D o
Ratio of Outflow Air-sea enthalpy
exchange temperature disequilibrium
coefficients of
enthalpy and
momentum
Heat Engine Theory Predicts
Maximum Hurricane Winds
MPH
Combine with Ocean Surface Energy
Balance
Net outgoing radiation
Sea Surface
Temperature Incoming solar Ocean mixed layer
radiation entrainment
Ts To F F Fentrain
V 2
CD | Vs |
pot
To
Temperature at top of storm Surface Trade Wind speed
Derived by combining potential intensity expression with ocean
surface energy balance
Observed Tropical Atlantic Potential Intensity
Data Sources: NCAR/NCEP re-analysis with pre-1979 bias correction, UKMO/HADSST1
What is Causing Changes in
Tropical Atlantic Sea Surface
Temperature?
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 aersol 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.
Pushing Back the Record of
Tropical Cyclone Activity:
Paleotempestology
Paleotempestology
barrier beach
upland
overwash fan
backbarrier marsh
a) lagoon
barrier beach
upland
overwash fan
backbarrier marsh
b) lagoon
terminal lobes
flood tidal delta
Source: Jeff Donnelly, WHOI
Source: Jeff Donnelly, Jon Woodruff,
Phil Lane; WHOI
Source: Jeff Donnelly, Jon Woodruff, Phil Lane; WHOI
Source: Jeff Donnelly, Jon Woodruff, Phil Lane; WHOI
Projecting into the Future:
Downscaling from Global
Climate Models
Today’s global climate
models are far too coarse to
simulate tropical cyclones
Our Approach
• Step 1: Randomly seed ocean basins with weak
(25 kt) warm-core vortices
• Step 2: Determine tracks of candidate storms
using a beta-and-advection model
• 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 35 kts
• 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
Track:
Vtrack V850 1 V250 V ,
Empirically determined constants:
0.8,
1
u 0 ms ,
1
v 2.5 ms
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
• Tracks terminated when v < 17 ms 1
Example: 200 Synthetic Tracks
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
Present Climate: Spatial
Distribution of Genesis Points
Observed
Synthetic
Calibration
• Absolute genesis frequency calibrated
to North Atlantic during the period
1980-2005
Genesis rates
Seasonal Cycles
Atlantic
Seasonal Cycles
Western North Pacific
Cumulative Distribution of Storm Lifetime
Peak Wind Speed, with Sample of 2946
Synthetic Tracks
Atlantic ENSO Influence
Year by Year Comparison with Best Track
and with Knutson et al., 2007
Simulated vs. Observed Power Dissipation Trends, 1980-2006
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)
Model Institution Atmospheric Designation in this Potential
Resolution paper Intensity
Multiplicative
Factor
Community National Center for T85, 26 levels CCSM3 1.2
Climate System Atmospheric Research
Model, 3.0
CNRM-CM3 Centre National de T63, 45 levels CNRM 1.15
Recherches
Météorologiques, Météo-
France
CSIRO-Mk3.0 Scientific and Research T63, 18 levels CSIRO 1.2
Organization
ECHAM5 Max Planck Institution T63, 31 levels ECHAM 0.92
GFDL-CM2.0 NOAA Geophysical Fluid 2.5o X 2.5 o , 24 GFDL 1.04
Dynamics Laboratory levels
MIROC3.2 CCSR/NIES/FRCGC, Japan T42, 20 levels MIRO 1.07
mri_cgcm2.3.2a Meteorological Research T42, 30 levels MRI 0.97
Institute,
Genesis Distributions
Basin-Wide Percentage Change
in Power Dissipation
Basin-Wide Percentage Change
in Storm Frequency
7 Model Consensus Change in
Storm Frequency
Why does frequency decrease?
sm sb
Critical control parameter in m * ,
CHIPS: s0 sb
Lv q *
sm sb sm s* (H 1) Rv Hq *ln H,
T
Entropy difference between boundary layer
and middle troposphere increases with
temperature at constant relative humidity
Change in Frequency when T held constant in m
Feedback of Global Tropical
Cyclone Activity on the
Climate System
The wake of Hurricane Emily (July 2005).
Hurricane Dennis
(one week earlier)
Source: Rob Korty, CalTech
Direct mixing by tropical cyclones
Emanuel (2001) estimated global rate of heat input as
1.4 X 1015 Watts
Source: Rob Korty, CalTech
Response of Ocean to Point Mixing:
Scott, J. R. and J.
Marotzke, 2002: The
location of diapycnal
mixing and the
meridional
overturning
circulation. J. Phys.
Ocean., 32, 3578–
3595
TC Mixing May Induce Much or Most of the
Observed Poleward Heat Flux by the Oceans
Trenberth and Caron, 2001
Results from EPIC 2001
50
Raymond et al. (2004) report that
background mixing is essentially zero
in the tropical eastern Pacific.
100
“…motions below the thermocline
were very weak, but they
200 intensified…as energy from a strong
storm worked its way downward. The
accompanying mixing accounted for
most of what little mixing there was
between depths of 100-200 m. Mixing
in the thermocline…appears to
respond mostly to wind stress.
September 2001, 10oN, 95oW Slide courtesy of Rob Korty, CalTech
“…the strongest atmospheric
Diffusivity Estimated from Analysis of ERA-40
Wake Recoveries
Figure courtesy of Ron Sriver and Matt Huber, Purdue University
Linear trend (1955–2003)
of the zonally integrated
heat content of the world
ocean by one-degree
latitude belts for 100-m
thick layers. Source:
Levitus et al., 2005
TC-Mixing may explain
difference between
observed and modeled
ocean warming
Zonally averaged
temperature trend due
to global warming in a
coupled climate model.
TC-Mixing may be Crucial for High-Latitude Warmth
and Low-Latitude Moderation During Warm Climates,
such as that of the Eocene
Interactive TC-Mixing Moderates Tropical Warming and
Amplifies High-Latitude Warming in Coupled Climate Models
DSST: elevated mixing to 360 meters – uniform
10 x CO2 in both experiments
Source: Rob Korty, CalTech
Multiple Equilibria and Hysteresis in a Two-Column
Coupled Model (Emanuel, JGR, 2002)
SS
T
Climate Forcing
Summary:
• Tropical cyclones are sensitive to the
climate state
• Observations together with detailed
modeling suggest that TC power
dissipation increases by ~65% for a
10% increase in potential intensity
• Storm-induced mixing of the upper tropical
ocean may be the principal driver of the
ocean’s thermohaline circulation
• Increased TC power dissipation in a warming
climate will drive a larger poleward heat flux
by the oceans, tempering tropical warming
but amplifying the warming of middle and
high latitudes
• This feedback between TCs and ocean heat
flux is not included in any current climate
model; its inclusion may change our
understanding of climate dynamics and our
predictions of the earth’s response to
increased greenhouse gases
Transects of SSH
anomalies from passage
of Hurricane Edouard,
which passed through
transect on Day 239.
Scale of anomlies is 10
cm. (Analysis and figure
courtesy of Peter
Huybers.) Height rise
implies net heat input of
2 X 1021 J.
Variations in Solar Output (IPCC, 2007)
Variation with Time of Natural Climate Forcings:
Comparing 1980-1990 (quiet) to
1995-2005 (active)
104-156 HURDAT tracks 1000 Synthetic tracks
Cumulative distributions of storm lifetime maximum wind
Sensitivity to Shear and Potential
Intensity
Examples of Annual Cycles of Storm
Counts by Month
ATLANTIC
NCAR CCSM3 GFDL CM2.0
Examples of Annual Cycles of Storm
Counts by Month
Western North Pacific
NCAR CCSM3 GFDL CM2.0
Examples of Shifts in Hurricane Track
Density (GFDL CM2.0)
1980-1999 2180-2199
Examples of Shifts in Hurricane Track
Density (GFDL CM2.0)
1980-1999 2180-2199