# Presentation: 143340

## Hurricanes and Climate Change

**Hurricanes and Climate Change**

**Kerry Emanuel**
**Massachusetts Institute of Technology**

**Notes:**
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. 

## Program

**Program**

**Effect of climate change on hurricane activity**
**Hurricanes in the climate system**

## Effect of Climate Change on Hurricanes

**Effect of Climate Change on Hurricanes**

## No Obvious Trend in Global TC Frequency, 1970-2006

**Data Sources: NOAA/TPC and NAVY/JTWC**

## Better Intensity Metric:The Power Dissipation Index

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

- Power Dissipation Based on 3 Data Sets for the Western North Pacific(smoothed with a 1-3-4-3-1 filter)

**aircraft recon**

**Data Sources: NAVY/JTWC, Japan Meteorological Agency, UKMO/HADSST1, Jim Kossin, U. Wisconsin**

**Years included: 1949-2004**

## Atlantic Storm Maximum Power Dissipation

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

**Power Dissipation Index (PDI)**

**Years included: 1870-2006**

**Data Source: NOAA/TPC **

**Notes:**
This graph shows the PDI of hurricanes in the 20th century to represent the change of hurricane intensity.  This curve has been smoothed as in the previous slides.  The hurricane wind speed data used to make this graph are considered unreliable prior to 1948, the year that aircraft surveillance of hurricanes began. 

## Atlantic Sea Surface Temperatures and Storm Max Power Dissiaption

**Atlantic Sea Surface Temperatures and Storm Max Power Dissiaption**
- (Smoothed with a 1-3-4-3-1 filter)

**Scaled Temperature**

**Power Dissipation Index (PDI)**

**Years included: 1870-2006**

**Data Sources: NOAA/TPC, UKMO/HADSST1**

**Notes:**
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. 

## Energy Production

## Distribution of Entropy in Hurricane Inez, 1966

**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

**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?

**What is Causing Changes in Tropical Atlantic Sea Surface Temperature?**

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

**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 SST(blue), Global Mean Surface Temperature (red), ****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 aersol radiative forcing**

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

**Best Fit Linear Combination of Global Warming and 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**

## Pushing Back the Record of Tropical Cyclone Activity:Paleotempestology

**Pushing Back the Record of Tropical Cyclone Activity:****Paleotempestology**

## Paleotempestology

**barrier beach**

**backbarrier marsh**

**lagoon**

**barrier beach**

**backbarrier marsh**

**lagoon**

**a)**

**b)**

**Source:  Jeff Donnelly, WHOI**

**upland**

**upland**

**flood tidal delta**

**terminal lobes**

**overwash fan**

**overwash fan**

**Paleotempestology**

## Source:  Jeff Donnelly, Jon Woodruff, Phil Lane; WHOI

**Source:  Jeff Donnelly, Jon Woodruff, Phil Lane; WHOI**

## Source:  Jeff Donnelly, Jon Woodruff, Phil Lane; WHOI

**Source:  Jeff Donnelly, Jon Woodruff, Phil Lane; WHOI**

## Source:  Jeff Donnelly, Jon Woodruff, Phil Lane; WHOI

**Source:  Jeff Donnelly, Jon Woodruff, Phil Lane; WHOI**

## Projecting into the Future: Downscaling from Global Climate Models

**Projecting into the Future: Downscaling from Global Climate Models**

## Today’s global climate models are far too coarse to simulate tropical cyclones

**Today’s global climate models are far too coarse to simulate tropical cyclones**

## Our Approach

**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

**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 < 

- 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 10o and 30o N latitude, and 80o and 30o W longitude, using only post-1970 hurricane data

- 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

## Present Climate: Spatial Distribution of Genesis Points

- Observed

- Synthetic

## Calibration

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

## Genesis rates

## Seasonal Cycles

- Atlantic

## Seasonal Cycles

- Western North Pacific

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

- 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,  1980-2006

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

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

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

## 1. Last 20 years of 20th century simulations2. Years 2180-2200 of IPCC Scenario A1b (CO2 stabilized at 720 ppm)

**1.** Last 20 years of 20th century simulations**2.** Years 2180-2200 of IPCC Scenario A1b (CO2 stabilized at 720 ppm)

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

## Slide 45

| Model | Institution | Atmospheric Resolution | Designation in this paper | Potential Intensity Multiplicative Factor |
| --- | --- | --- | --- | --- |
| Community Climate System Model, 3.0 | National Center for Atmospheric Research | T85, 26 levels | CCSM3 | 1.2 |
| CNRM-CM3 | Centre National de Recherches Météorologiques, Météo-France | T63, 45 levels | CNRM | 1.15 |
| CSIRO-Mk3.0 | Scientific  and Research Organization | T63, 18 levels | CSIRO | 1.2 |
| ECHAM5 | Max Planck Institution | T63, 31 levels | ECHAM | 0.92 |
| GFDL-CM2.0 | NOAA Geophysical Fluid Dynamics Laboratory | 2.5o X 2.5 o , 24 levels | GFDL | 1.04 |
| MIROC3.2 | CCSR/NIES/FRCGC, Japan | T42, 20 levels | MIRO | 1.07 |
| mri_cgcm2.3.2a | Meteorological Research Institute,   | T42, 30 levels | MRI | 0.97 |

## 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 

- Change in Frequency when T held constant in

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

**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**

**Notes:**
We know that tropical cyclones are responsible for strong mixing of the upper oceans and this can be seen in satellite images...

## Direct mixing by tropical cyclones

**Source:  Rob Korty, CalTech**

**Emanuel (2001) estimated global rate of heat input as **
**1.4 X 10****15**** 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 100-200 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.”

- 50

- 100

- 200

- September 2001, 10oN, 95oW

- Raymond et al. (2004) report that background mixing is essentially zero in the tropical eastern Pacific.

**Slide courtesy of Rob Korty, CalTech**

**Notes:**
The problem in the present climate is that although the heat flux is quite sensitive to mixing in the tropical thermocline, numerous experiments have repeatedly found that there is only very weak mixing here.  But I want to draw attention to a finding from measurements taken in the eastern tropical Pacific.  A single wind burst excited motions in the thermocline as the energy worked down the water column.  They conclude that mixing is a highly nonlinear event that responds most vigorously to the strongest forcing, meaning that the extreme events are likely responsible for the bulk of what mixing occurs here. 

This might not matter for models of the present climate: ocean models don’t seem to care if the mixing is slow and uniform or concentrated in isolated bursts; you can get the same circulations either way.  But when you move to different climates, this will matter, as the mixing should evolve with the climatology of the strong events.

## Diffusivity Estimated from Analysis of ERA-40 Wake Recoveries

**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

**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

**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 

- 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

**Climate Forcing**

**SST**

**Multiple Equilibria and Hysteresis in a Two-Column Coupled Model (Emanuel, JGR, 2002)**

## Summary:

**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

**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

**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 1021 J. 

**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 10****21**** J. **

## Variations in Solar Output (IPCC, 2007)

**Variations in Solar Output (IPCC, 2007)**

## Variation with Time of Natural Climate Forcings:

## Comparing 1980-1990 (quiet) to 1995-2005 (active)

- 104-156 HURDAT tracks

- 1000 Synthetic tracks

- Cumulative distributions of storm lifetime maximum wind

## Sensitivity to Shear and Potential Intensity

## Examples of Annual Cycles of Storm Counts by Month

- Examples of Annual Cycles of Storm Counts by Month

- NCAR CCSM3

- GFDL CM2.0

**ATLANTIC**

## Examples of Annual Cycles of Storm Counts by Month

- Examples of Annual Cycles of Storm Counts by Month

- NCAR CCSM3

- GFDL CM2.0

**Western North Pacific**

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

- 1980-1999

- 2180-2199

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

- 1980-1999

- 2180-2199