Hurricanes and Hurricane Risk in a Changing Climate Kerry Emanuel Massachusetts Institute of Technology

Program How should we assess hurricane risk? What have hurricanes been like in the past, and how will they be affected by global warming?

Hurricane Risks: Wind Storm Surge Rain

Windstorms Account for Bulk of Insured Losses Worldwide

Limitations of a strictly statistical approach >50% of all normalized U.S. hurricane damage caused by top 8 events, all category 3, 4 and 5 >90% of all damage caused by storms of category 3 and greater Category 3,4 and 5 events are only 13% of total landfalling events; only 30 since 1870 Landfalling storm statistics are inadequate for assessing hurricane risk

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

Jeffrey P. Donnelly and Jonathan D. Woodruff, Nature, 2007

Risk Assessment by Direct Numerical Simulation of Hurricanes: The Problem The hurricane eyewall is a front, attaining scales of ~ 1 km or less At the same time, the storm’s circulation extends to ~1000 km and is embedded in much larger scale flows The computational nodes of global models are typically spaced 100 km apart

Histograms of Tropical Cyclone Intensity as Simulated by a Global Model with 50 km grid point spacing. (Courtesy Isaac Held, GFDL) Category 3 Global models do not simulate the storms that cause destruction Observed Modeled

Numerical convergence in an axisymmetric, nonhydrostatic model (Rotunno and Emanuel, 1987)

How to deal with this? Option 1: Brute force and obstinacy Option 2: Applied math and modest resources

Time-dependent, axisymmetric model phrased in R space Hydrostatic and gradient balance above PBL Moist adiabatic lapse rates on M surfaces above PBL Boundary layer quasi-equilibrium convection Deformation-based radial diffusion Coupled to simple 1-D ocean model Environmental wind shear effects parameterized

Application to Real-Time Hurricane Intensity Forecasting Must add ocean model Must account for atmospheric wind shear

Ocean columns integrated only along predicted storm track. Predicted storm center SST anomaly used for input to ALL atmospheric points.

Comparing Fixed to Interactive SST: Model with Fixed Ocean Temperature Model including Ocean Interaction

How Can We Use This Model to Help Assess Hurricane Risk in Current and Future Climates?

Risk Assessment Approach: Step 1: Seed each ocean basin with a very large number of weak, randomly located cyclones Step 2: Cyclones are assumed to move with the large scale atmospheric flow in which they are embedded, plus a correction for the earth’s rotation and sphericity Step 3: Run the CHIPS model for each cyclone, and note how many achieve at least tropical storm strength Step 4: Using the small fraction of surviving events, determine storm statistics Details: Emanuel et al., Bull. Amer. Meteor. Soc, 2008

Synthetic Track Generation: Generation of Synthetic Wind Time Series Extract winds from climatological or global climate model output Postulate that TCs move with vertically averaged environmental flow plus a “beta drift” that accounts for the earth’s rotation and sphericity Approximate “vertically averaged” by weighted mean flow in the upper and lower atmosphere

Comparison of Random Seeding Genesis Locations with Observations

Cumulative Distribution of Storm Lifetime Peak Wind Speed, with Sample of 1755Synthetic Tracks Cumulative Distribution of Storm Lifetime Peak Wind Speed, with Sample of 1755 Synthetic Tracks 90% confidence bounds

Sample Storm Wind Swath

Return Periods

Accumulated Rainfall (mm)

Storm Surge Simulation SLOSH mesh ~ 10 3 m ADCIRC mesh ~ 10 2 m Battery ADCIRC model (Luettich et al. 1992) SLOSH model (Jelesnianski et al. 1992) ADCIRC mesh ~ 10 m (Colle et al. 2008)

Woodruff et al., Geochemistry, Geophysics, and Geosystems, 2008

Taking Climate Change Into Account

Heat Engine Theory Predicts Maximum Hurricane Winds MPH

Downscaling of 5 AR5 GCMs GFDL-CM3 HadGEM2-ES MPI-ESM-MR MIROC-5 MRI-CGCM3 Historical: , RCP

Global annual frequency of tropical cyclones averaged in 10-year blocks for the period , using historical simulations for the period and the RCP 8.5 scenario for the period In each box, the red line represents the median among the 5 models, and the bottom and tops of the boxes represent the 25 th and 75 th percentiles, respectively. The whiskers extent to the most extreme points not considered outliers, which are represented by the red + signs. Points are considered outliers if they lie more than 1.5 times the box height above or below the box.

Change in track density, measured in number of events per 4 o X 4 o square per year, averaged over the five models. The change is simply the average over the period minus the average over The white regions are where fewer than 4 of the 5 models agree on the sign of the change.

Global Tropical Cyclone Power Dissipation

Change in Power Dissipation

Return Periods based on GFDL Model

Return Periods of Storm Total Rainfall at New Haven GFDL Model

GCM flood height return level (assuming SLR of 1 m for the future climate ) Black: Current climate ( ) Blue: A1B future climate ( ) Red: A1B future climate ( ) with R 0 increased by 10% and R m increased by 21% Lin et al. (2012)

From: American Climate Prospectus Economic Risks in the United States Sea level rise alone Sea level rise + changing storms

Summary History is too short and imperfect to estimate hurricane risk Better estimates can be made from paleotempestology and downscaling hurricane activity from climatological or global model output Hurricanes clearly vary with climate and there is a risk that hurricane threats will increase over this century