Three Lectures on Tropical Cyclones

Three Lectures on Tropical Cyclones

Three Lectures on Tropical Cyclones

Kerry EmanuelMassachusetts Institute of Technology

Spring School on Fluid Mechanics of Environmental HazardsSpring School on Fluid Mechanics of Environmental Hazards

Lecture 3:Lecture 3:Using Physics to Assess Tropical Using Physics to Assess Tropical

Cyclone Risk in a Changing Cyclone Risk in a Changing ClimateClimate

Tropical Cyclones Do Respond to Tropical Cyclones Do Respond to Climate Change!Climate Change!

Atlantic Sea Surface Temperatures and Atlantic Sea Surface Temperatures and Storm Max Power DissipationStorm Max Power Dissipation

(Smoothed with a 1-3-4-3-1 filter)

Sca

led

Tem

per

atu

re

Po

wer

Dis

sip

atio

n In

dex

(P

DI)

Years included: 1870-2006

Data Sources: NOAA/TPC, UKMO/HADSST1

10-year Running Average of Aug-Oct NH Surface T and MDR SST10-year Running Average of Aug-Oct NH Surface T and MDR SST

Tropical Atlantic SST(blue), Global Mean Surface Tropical Atlantic SST(blue), Global Mean Surface Temperature (red), Temperature (red),

Aerosol Forcing (aqua)Aerosol Forcing (aqua)

Mann, M. E., and K. A. Emanuel, 2006. Atlantic hurricane trends linked to climate change. EOS, 87, 233-244.

Global mean surface temperature

Tropical Atlantic sea surface temperature

Sulfate aerosol radiative forcing

Best Fit Linear Combination of Global Warming Best Fit Linear Combination of Global Warming and Aerosol Forcing (red) versus Tropical Atlantic and Aerosol Forcing (red) versus Tropical Atlantic

SST (blue)SST (blue)

Mann, M. E., and K. A. Emanuel, 2006. Atlantic hurricane trends linked to climate change. EOS, 87, 233-244.

Tropical Atlantic sea surface temperature

Global Surface T + Aerosol Forcing

Effect of Increased Potential Effect of Increased Potential Intensity on Hurricane KatrinaIntensity on Hurricane Katrina

PaleotempestologyPaleotempestology

barrier beach

a)

b)

Source: Jeff Donnelly, WHOI

Paleotempestologybarrier beach

backbarrier marshlagoon

barrier beach

backbarrier marshlagoon

Source: Jeff Donnelly, WHOI

upland

upland

flood tidal delta

terminal lobes

overwash fan

overwash fan

Donnelly and Woodruff (2006)

Photograph of stalagmite ATM7 showing depth of radiometric dating samples, micromilling track across approximately annually laminated couplets, and age-depth curve.

Frappier et al., Geology, 2007

Frappier et al., Geology, 2007

Assessing Tropical Cyclone Risk: Assessing Tropical Cyclone Risk: Historical Statistics Are InadequateHistorical Statistics Are Inadequate

U.S. Hurricane Damage, 1900-2004,Adjusted for U.S. Hurricane Damage, 1900-2004,Adjusted for Inflation, Wealth, and PopulationInflation, Wealth, and Population

Top 10 Northeast Storms Since 1851Top 10 Northeast Storms Since 1851

Issues with Direct Use of Issues with Direct Use of Global Climate Models: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: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,Synthetic Track Generation,Using Synthetic Wind Time SeriesUsing 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 seriesSynthetic 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 250 hPa zonal wind modeled as Fourier series in time with random phase:series in time with random phase:

2250 250 250 1( , , , ) ( , , ) ' ( , , ) ( )u x y t u x y u x y F t

32

1 13 1

1

2sin 2

N

nNn

n

ntF n XTn

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 is, for each n, a random number between 0 and 1.

1nX

The time series of other flow components:

250 250 21 1 22 2

850 850 31 1 32 2 33 3

850 850 41 1 42 2 43 3 44 4

( , , , ) ( , , ) ( ) ( ),

( , , , ) ( , , ) ( ) ( ) ( ),

( , , , ) ( , , ) ( ) ( ) ( ) ( ),

v x y t v x y A F t A F t

u x y t u x y A F t A F t A F t

v x y t v x y A F t A F t A F t A F t

or V = V AFwhere each Fi has a different random phase, and A satisfies

TA A = COV

where COV is the symmetric matrix containing the variances and covariances of the flow components.

Example:Example:1

250 30u ms 2 1250' ( , , ) 10u x y ms

15N 15T days

Track:Track:

850 2501 ,track V V V V

Empirically determined constants:

0.8, 10 ,u ms

12.5v ms

• 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

• Tracks terminated when v <

Tropical Cyclone IntensityTropical Cyclone Intensity

112 ms

117 ms

6-hour zonal displacements in region bounded by 106-hour zonal displacements in region bounded by 10oo and 30 and 30oo N latitude, and 80N latitude, and 80oo and 30 and 30oo W longitude, using only post-1970 W longitude, using only post-1970

hurricane datahurricane data

Example: 50 Synthetic TracksExample: 50 Synthetic Tracks

200 Random Western North Pacific Events200 Random Western North Pacific Events

Cumulative Distribution of Storm Lifetime Peak Cumulative Distribution of Storm Lifetime Peak Wind Speed, with Sample of 2946 Synthetic TracksWind Speed, with Sample of 2946 Synthetic Tracks

Return PeriodsReturn Periods

Random Seeding Method: CalibrationRandom Seeding Method: Calibration

• Absolute genesis frequency calibrated to Absolute genesis frequency calibrated to North Atlantic during the period 1980-2005North Atlantic during the period 1980-2005

Genesis ratesGenesis rates

AtlanticAtlantic

Eastern North Pacific

Western North Pacific

North Indian Ocean

Southern Hemisphere

Calibrated to AtlanticCalibrated to Atlantic

Seasonal CyclesSeasonal Cycles

Western North PacificWestern North Pacific

Captures effects of regional climate Captures effects of regional climate phenomena (e.g. ENSO, AMM)phenomena (e.g. ENSO, AMM)

Year by Year Comparison with Best Track and Year by Year Comparison with Best Track and with Knutson et al., 2007with Knutson et al., 2007

Simulated vs. Observed Power Dissipation Trends, 1980-2006Simulated vs. Observed Power Dissipation Trends, 1980-2006

Global Percentage of Cat 4 & Cat 5 StormsGlobal Percentage of Cat 4 & Cat 5 Storms

Now Use Daily Output from IPCC Now Use Daily Output from IPCC Models to Derive Wind Models to Derive Wind

Statistics, Thermodynamic State Statistics, Thermodynamic State Needed by Synthetic Track Needed by Synthetic Track

TechniqueTechnique

1. Last 20 years of 20Last 20 years of 20thth century century simulationssimulations

2.2. Years 2180-2200 of IPCC Scenario Years 2180-2200 of IPCC Scenario A1b (COA1b (CO22 stabilized at 720 ppm) stabilized at 720 ppm)

Compare two simulations each Compare two simulations each from 7 IPCC models:from 7 IPCC models:

Basin-Wide Percentage Change in Basin-Wide Percentage Change in Power DissipationPower Dissipation

Different Different Climate Climate ModelsModels

Basin-Wide Percentage Change in Basin-Wide Percentage Change in Storm FrequencyStorm Frequency

Different Different Climate Climate ModelsModels

7 Model Consensus Change in 7 Model Consensus Change in Storm FrequencyStorm Frequency

Reds: Increases Blues: DecreasesReds: Increases Blues: Decreases

Feedback of Global Tropical Feedback of Global Tropical Cyclone Activity on the Climate Cyclone Activity on the Climate

SystemSystem

Strong Mixing of Upper Ocean

Direct mixing by tropical cyclones

Source: Rob Korty, CalTech

Emanuel (2001) estimated global rate of heat input as

1.4 X 1015 Watts

TC Mixing May Induce Much or Most of the Observed Poleward Heat Flux by the Oceans

Trenberth and Caron, 2001Trenberth and Caron, 200190 S EQ 90 N

TC-Mixing may be Crucial for High-Latitude Warmth and Low-Latitude Moderation During Warm Climates, such as that of the Eocene