Three Lectures on Tropical Cyclones Spring School on Fluid Mechanics of Environmental Hazards Three Lectures on Tropical Cyclones Are hurricanes becoming more powerful and destructive? Are these changes due to a natural cycle of hurricane activity or are they caused by human-induced climate change? Although this is currently a hot debate among scientists, new research suggests that the destructive potential of hurricanes is increasing due to the heating of the oceans. Image: Satellite image of Hurricane Floyd approaching the east coast of Florida in 1999. The image has been digitally enhanced to lend a three-dimensional perspective. Credit: NASA/Goddard Space Flight Center. Kerry Emanuel Massachusetts Institute of Technology 1

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 (Smoothed with a 1-3-4-3-1 filter) Years included: 1870-2006 Power Dissipation Index (PDI) Scaled Temperature This graph is similar to the previous graph,  but the ocean surface temperature has been added for comparison. The low value of storm power in the early 1940s is thought to be due to the lack of reports from ships at sea because of the radio silence imposed during WWII. Data Sources: NOAA/TPC, UKMO/HADSST1 4

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) 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 10

Paleotempestology upland overwash fan backbarrier marsh a) lagoon barrier beach 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, WHOI 11

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: 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 19

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 21

250 hPa zonal wind modeled as Fourier series in time with random phase: 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.

The time series of other flow components: or where each Fi has a different random phase, and A satisfies where COV is the symmetric matrix containing the variances and covariances of the flow components.

Example:

Track: Empirically determined constants: 25

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 Tracks terminated when v < 26

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 27

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 36

Genesis rates Western North Pacific Southern Hemisphere Eastern North Pacific North Indian Ocean Atlantic Calibrated to Atlantic 37

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 41 41

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 45 45

Basin-Wide Percentage Change in Storm Frequency Different Climate Models 46 46

7 Model Consensus Change in Storm Frequency Reds: Increases Blues: Decreases 47 47

Feedback of Global Tropical Cyclone Activity on the Climate System 51

Strong Mixing of Upper Ocean

Emanuel (2001) estimated global rate of heat input as Direct mixing by tropical cyclones Emanuel (2001) estimated global rate of heat input as 1.4 X 1015 Watts Source: Rob Korty, CalTech 53

TC Mixing May Induce Much or Most of the Observed Poleward Heat Flux by the Oceans 90 S EQ 90 N Trenberth and Caron, 2001 54

TC-Mixing may be Crucial for High-Latitude Warmth and Low-Latitude Moderation During Warm Climates, such as that of the Eocene 55