Hurricanes and Climate Change Kerry Emanuel Massachusetts Institute of Technology

Program Effect of climate change on hurricane activityEffect of climate change on hurricane activity Hurricanes in the climate systemHurricanes in the climate system

Effect of Climate Change on Hurricanes

No Obvious Trend in Global TC Frequency, Data Sources: NOAA/TPC and NAVY/JTWC

Better Intensity Metric: The Power Dissipation Index 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 Power Dissipation Based on 3 Data Sets for the Western North Pacific (smoothed with a filter) aircraft recon Data Sources: NAVY/JTWC, Japan Meteorological Agency, UKMO/HADSST1, Jim Kossin, U. Wisconsin Years included:

Atlantic Storm Maximum Power Dissipation (Smoothed with a filter) Power Dissipation Index (PDI) Years included: Data Source: NOAA/TPC

Atlantic Sea Surface Temperatures and Storm Max Power Dissiaption (Smoothed with a filter) Scaled Temperature Power Dissipation Index (PDI) Years included: 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: Air-sea enthalpy disequilibrium Surface temperature Outflow temperature Ratio of exchange coefficients of enthalpy and momentum

Heat Engine Theory Predicts Maximum Hurricane Winds MPH

Combine with Ocean Surface Energy Balance Net outgoing radiation Surface Trade Wind speed Ocean mixed layer entrainment Sea Surface Temperature Temperature at top of storm Incoming solar radiation 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) Mann, M. E., and K. A. Emanuel, Atlantic hurricane trends linked to climate change. EOS, 87, Global mean surface temperature Tropical Atlantic sea surface temperature Sulfate aersol radiative forcing

Best Fit Linear Combination of Global Warming and Aerosol Forcing (red) versus Tropical Atlantic SST (blue) Mann, M. E., and K. A. Emanuel, Atlantic hurricane trends linked to climate change. EOS, 87, Tropical Atlantic sea surface temperature Global Surface T + Aerosol Forcing

Pushing Back the Record of Tropical Cyclone Activity: Paleotempestology

barrier beach backbarrier marsh lagoon barrier beach backbarrier marsh lagoon a) b) Source: Jeff Donnelly, WHOI upland flood tidal delta terminal lobes overwash fan Paleotempestology

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

Track: Empirically determined constants:

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 Intensity

Example: 200 Synthetic Tracks

6-hour zonal displacements in region bounded by 10 o and 30 o N latitude, and 80 o and 30 o 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 Absolute genesis frequency calibrated to North Atlantic during the period

Genesis rates

Seasonal Cycles Atlantic

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,

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

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

ModelInstitution Atmospheric Resolution Designation in this paper Potential Intensity Multiplicative Factor Community Climate System Model, 3.0 National Center for Atmospheric Research T85, 26 levelsCCSM31.2 CNRM-CM3 Centre National de Recherches Météorologiques, Météo- France T63, 45 levelsCNRM1.15 CSIRO-Mk3.0 Scientific and Research Organization T63, 18 levelsCSIRO1.2 ECHAM5Max Planck InstitutionT63, 31 levelsECHAM0.92 GFDL-CM2.0 NOAA Geophysical Fluid Dynamics Laboratory 2.5 o X 2.5 o, 24 levels GFDL1.04 MIROC3.2CCSR/NIES/FRCGC, JapanT42, 20 levelsMIRO1.07 mri_cgcm2.3.2aMeteorological Research Institute, T42, 30 levelsMRI0.97

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? Critical control parameter in CHIPS: Entropy difference between boundary layer and middle troposphere increases with temperature at constant relative humidity

Change in Frequency when T held constant in

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 Source: Rob Korty, CalTech Emanuel (2001) estimated global rate of heat input as 1.4 X Watts

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 “…motions below the thermocline were very weak, but they 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 m. Mixing in the thermocline…appears to respond mostly to wind stress. “…the strongest atmospheric disturbances are likely to cause an inordinately large fraction of the total mixing. Profound errors could occur in climate models, which fail to take this into account.” September 2001, 10 o N, 95 o W Raymond et al. (2004) report that background mixing is essentially zero in the tropical eastern Pacific. Slide courtesy of Rob Korty, CalTech

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 Zonally averaged temperature trend due to global warming in a coupled climate model. Source: Manabe et al, 1991 TC-Mixing may explain difference between observed and modeled ocean warming

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

 SST: elevated mixing to 360 meters – uniform 10 x CO 2 in both experiments Source: Rob Korty, CalTech Interactive TC-Mixing Moderates Tropical Warming and Amplifies High-Latitude Warming in Coupled Climate Models

Climate Forcing SS T Multiple Equilibria and Hysteresis in a Two-Column Coupled Model (Emanuel, JGR, 2002)

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 J.

Variations in Solar Output (IPCC, 2007)

Variation with Time of Natural Climate Forcings:

Comparing (quiet) to (active) HURDAT tracks1000 Synthetic tracks Cumulative distributions of storm lifetime maximum wind

Sensitivity to Shear and Potential Intensity

Examples of Annual Cycles of Storm Counts by Month NCAR CCSM3GFDL CM2.0 ATLANTIC

Examples of Annual Cycles of Storm Counts by Month NCAR CCSM3GFDL CM2.0 Western North Pacific

Examples of Shifts in Hurricane Track Density (GFDL CM2.0)

Examples of Shifts in Hurricane Track Density (GFDL CM2.0)