1. 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 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

2. Lecture 3: Using Physics to Assess Tropical Cyclone Risk in a Changing Climate

3. Tropical Cyclones Do Respond to Climate Change!

4. Atlantic Sea Surface Temperatures and Storm Max Power Dissipation
(Smoothed with a filter)
Years included:
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

5. 10-year Running Average of Aug-Oct NH Surface T and MDR SST

7. 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, Atlantic hurricane trends linked to climate change. EOS, 87,

8. 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, Atlantic hurricane trends linked to climate change. EOS, 87,

9. Effect of Increased Potential Intensity on Hurricane Katrina

10. Paleotempestology 10

11. 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

12. Donnelly and Woodruff (2006)

13. 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

14. Frappier et al., Geology, 2007

15. Assessing Tropical Cyclone Risk: Historical Statistics Are Inadequate

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

17. Top 10 Northeast Storms Since 1851

18. 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

19. 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

20. 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

21. 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

22. 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.

23. 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.

24. Example:

25. Track:
Empirically determined constants: 25

26. 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

27. 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

28. Example: 50 Synthetic Tracks

29. 200 Random Western North Pacific Events

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

31. Return Periods

36. Random Seeding Method: Calibration
Absolute genesis frequency calibrated to North Atlantic during the period 36

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

38. Seasonal Cycles
Western North Pacific

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

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

41. Simulated vs. Observed Power Dissipation Trends, 1980-200641 41

42. Global Percentage of Cat 4 & Cat 5 Storms

43. Now Use Daily Output from IPCC Models to Derive Wind Statistics, Thermodynamic State Needed by Synthetic Track Technique

44. Compare two simulations each from 7 IPCC models:
1. Last 20 years of 20th century simulations 2. Years of IPCC Scenario A1b (CO2 stabilized at 720 ppm)

45. Basin-Wide Percentage Change in Power Dissipation
Different Climate Models 45 45

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

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

51. Feedback of Global Tropical Cyclone Activity on the Climate System51

52. Strong Mixing of Upper Ocean

53. 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

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

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