hurricanes: their physics and relationship to...
TRANSCRIPT
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Hurricanes: Their physics and relationship to climate
Hurricanes: Their physics and relationship to climate
Kerry EmanuelMassachusetts Institute of Technology
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TopicsTopics
Overview of Tropical CyclonesOverview of Tropical Cyclones
Tropical Cyclone PhysicsTropical Cyclone Physics
What have TCs been like in the past, What have TCs been like in the past, and how will they be affected by global and how will they be affected by global warming?warming?
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Brief Overview of Tropical Brief Overview of Tropical CyclonesCyclones
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The View from SpaceThe View from Space
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Igor, 2010Igor, 2010
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Hurricane Structure: Wind Speed
Azimuthal component of wind< 5 m/s < 5 m/s -- > 70 m/s> 70 m/s
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Updraft Speed
Vertical Air Motion
Strong upward motion in the eyewall
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Hurricane Temperature Perturbations
No temperature difference - > 16°C (29°F) warmerNo temperature difference - > 16°C (29°F) warmer
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Annual Cycle of Tropical Cyclones
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec NHJul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun SH
a
a
Northern Hemisphere
(NH)
Southern Hemisphere
(SH)
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Physics of Mature HurricanesPhysics of Mature Hurricanes
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Energy ProductionEnergy Production
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Distribution of Entropy in Hurricane Inez, 1966
Source: Hawkins and Imbembo, 1976
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Carnot Theorem: Maximum efficiency Carnot Theorem: Maximum efficiency results from a particular energy cycle:results from a particular energy cycle:
Isothermal expansionAdiabatic expansionIsothermal compressionAdiabatic compressionNote: Last leg is not adiabatic in hurricane: Air cools radiatively. But since environmental temperature profile is moist adiabatic, the amount of radiative cooling is the same as if air were saturated and descending moist adiabatically.
Maximum rate of energy production:s o
s
T TP QT−=
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Theoretical Upper Bound on Hurricane Theoretical Upper Bound on Hurricane Maximum Wind Speed:Maximum Wind Speed:
*2| |0
C T Tk s oV k kpot TC oD
⎛ ⎞⎜ ⎟⎜ ⎟⎝ ⎠
−≅ −
Air-sea enthalpy disequilibrium
Surface temperature
Outflow temperature
Ratio of exchange coefficients of enthalpy and momentum
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0o 60oE 120oE 180oW 120oW 60oW
60oS
30oS
0o
30oN
60oN
0 10 20 30 40 50 60 70 80
Annual Maximum Potential Intensity (m/s)Annual Maximum Potential Intensity (m/s)
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Hurricanes and Climate:Hurricanes and Climate:Some Empirical ResultsSome Empirical Results
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Intensity Metric:Intensity Metric:
Hurricane PowerHurricane Power(Power Dissipation Index)(Power Dissipation Index)
0
3maxPDI V dt
τ≡ ∫
A measure of the total frictional dissipation of A measure of the total frictional dissipation of kinetic energy in the hurricane boundary layer kinetic energy in the hurricane boundary layer
over the lifetime of the stormover the lifetime of the storm
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Atlantic Tropical Cyclone Power Dissipation during an Atlantic Tropical Cyclone Power Dissipation during an era of high quality measurements, 1970era of high quality measurements, 1970--2011 (smoothed 2011 (smoothed
with 1with 1--33--44--33--1 filter)1 filter)
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Atlantic Tropical Cyclone Power Dissipation and Sea Surface Atlantic Tropical Cyclone Power Dissipation and Sea Surface Temperature during an era of high quality measurements, 1970Temperature during an era of high quality measurements, 1970--
2011 (smoothed with 12011 (smoothed with 1--33--44--33--1 filter)1 filter)
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Use Linear Regression to Predict Power Dissipation Use Linear Regression to Predict Power Dissipation back to 1870 based on sea surface temperature:back to 1870 based on sea surface temperature:
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Now Compare to Observed Power DissipationNow Compare to Observed Power Dissipation
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Tropical cyclone power Tropical cyclone power dissipation has more than doubled dissipation has more than doubled since the 1980s, though there has since the 1980s, though there has been an increase of only 0.5been an increase of only 0.5oo C in C in
sea surface temperaturesea surface temperature
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What is Causing Changes in What is Causing Changes in Tropical Atlantic Sea Surface Tropical Atlantic Sea Surface
Temperature?Temperature?
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1010--year Running Average of Augyear Running Average of Aug--Oct Northern Oct Northern Hemisphere Surface Temp and Hurricane Region Ocean Hemisphere Surface Temp and Hurricane Region Ocean
TempTemp
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Tropical Atlantic SST(blue), Global Mean Surface Temperature (reTropical Atlantic SST(blue), Global Mean Surface Temperature (red), d), 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
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Best Fit Linear Combination of Global Warming and Best Fit Linear Combination of Global Warming and Aerosol Forcing (red) versus Tropical Atlantic SST (blue)Aerosol Forcing (red) versus Tropical Atlantic 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
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The Genesis PuzzleThe Genesis Puzzle
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Global Tropical Cyclone Frequency, 1980Global Tropical Cyclone Frequency, 1980--20112011
Data Sources: NOAA/TPC and NAVY/JTWC
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Tropical Cyclones Often Develop Tropical Cyclones Often Develop from Cloud Clusters:from Cloud Clusters:
When/Why Does Convection When/Why Does Convection Form Clusters?Form Clusters?
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Monsoonal Thunderstorms, Bangladesh and India July 1985
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Simplest Statistical Equilibrium Simplest Statistical Equilibrium State:State:
RadiativeRadiative--Convective EquilibriumConvective Equilibrium
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Vertically integrated water vapor at 4 days (Nolan et al., Vertically integrated water vapor at 4 days (Nolan et al., QJRMS, 2007)QJRMS, 2007)
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Vertically integrated water vapor at 4 (a), 6 (b), 8 (c), Vertically integrated water vapor at 4 (a), 6 (b), 8 (c), and 10 (d) days (Nolan et al., QJRMS, 2007)and 10 (d) days (Nolan et al., QJRMS, 2007)
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Nolan et al., QJRMS, 2007Nolan et al., QJRMS, 2007
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Variation of tropical relative humidity profiles with a Simple Convective Aggregation Index (SCAI).
Courtesy Isabelle Tobin, Sandrine Bony, and Remy Roca
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Empirical Necessary Conditions for SelfEmpirical Necessary Conditions for Self--Aggregation Aggregation (after Held et al., 1993; Bretherton et al., 2005; Nolan et al.; 2007)
Small vertical shear of horizontal windInteraction of radiation with clouds and/or water vaporFeedback of convective downdraft surface winds on surface fluxesSufficiently high surface temperature
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SelfSelf--Aggregation is TemperatureAggregation is Temperature--DependentDependent(Nolan et al., 2007; Emanuel and Khairoutdinov, in preparation, 2012)
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Extension to Rotating PlanetExtension to Rotating Planet
Distance between vortex centers
scales as Vpot/f
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TCTC--World Scaling World Scaling
Frequency ~
Intensity ~
Power Dissipation ~
2
2pot
fV
potV
2potV f
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HypothesisHypothesis
At high temperature, convection self-aggregates→Horizontally averaged humidity drops dramatically→Reduced greenhouse effect cools system→Convection disaggregates→Humidity increases, system warms→System wants to be near phase transition to aggregated state
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Recipe for SelfRecipe for Self--Organized CriticalityOrganized Criticality(First proposed by David Neelin, but by different (First proposed by David Neelin, but by different
mechanism)mechanism)
System should reside near critical threshold for self-aggregation
Convective cluster size should follow power law distribution
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Hypothetical Subcritical Bifurcation
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Future Tropical Cyclone RiskFuture Tropical Cyclone Risk
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MIT Approach to Downscaling Tropical MIT Approach to Downscaling Tropical Cyclones from Climate ModelsCyclones from Climate Models
Step 1Step 1: Seed each ocean basin with a very large number : Seed each ocean basin with a very large number of weak, randomly located vorticesof weak, randomly located vortices
Step 2Step 2: Vortices are assumed to move with the large : Vortices are assumed to move with the large scale atmospheric flow in which they are embeddedscale atmospheric flow in which they are embedded
Step 3Step 3: Run a coupled, ocean: Run a coupled, ocean‐‐atmosphere computer atmosphere computer model for each vortex, and note how many achieve at model for each vortex, and note how many achieve at least tropical storm strength; discard othersleast tropical storm strength; discard others
Step 4:Step 4: Using the small fraction of surviving events, Using the small fraction of surviving events, determine storm statistics. determine storm statistics.
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60 Synthetic U.S. Landfalling tracks (color coded 60 Synthetic U.S. Landfalling tracks (color coded by Saffirby Saffir‐‐Simpson Scale)Simpson Scale)
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Wind SwathWind Swath
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Accumulated Rainfall (mm)Accumulated Rainfall (mm)
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Cumulative Distribution of Storm Lifetime Peak Wind Cumulative Distribution of Storm Lifetime Peak Wind Speed, with Sample of 1755Speed, with Sample of 1755 Synthetic TracksSynthetic Tracks
90% confidence bounds
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Application to Future ClimatesApplication to Future Climates
Apply to global climate model simulations run in support of the upcoming IPCC report
Run for historical simulations, 1950-2005
Run for CMIP5 emissions scenario RCP 8.5
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Application to Other ClimatesApplication to Other Climates
Federov et al., 2010
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SummarySummary
Hurricanes are giant Carnot heat engines that convert thermal energy into wind
Tropical cyclones may be a subclass of aggregated convection, which may tend toward self-organized critical states, thus stabilizing tropical climate
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Simple but high resolution coupled TC model can be used to ‘downscale” TC activity from global climate data sets
Hurricane power is increasing in the Atlantic and is projected to increase globally as temperatures rise