Using applied mathematics to assess hurricane risk
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Using Applied Mathematics to Assess Hurricane Risk. Kerry Emanuel Program in Atmospheres, Oceans, and Climate Massachusetts Institute of Technology. Limitations of a strictly actuarial approach. >50% of all normalized damage caused by top 8 events , all category 3, 4 and 5

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Using Applied Mathematics to Assess Hurricane Risk

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Using applied mathematics to assess hurricane risk

Using Applied Mathematics to Assess Hurricane Risk

Kerry Emanuel

Program in Atmospheres, Oceans, and Climate

Massachusetts Institute of Technology


Limitations of a strictly actuarial approach

Limitations of a strictly actuarial approach

  • >50% of all normalized 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


Additional problem nonstationarity of climate

Additional Problem:Nonstationarity of climate


Using applied mathematics to assess hurricane risk

Atlantic Sea Surface Temperatures and Storm Max Power Dissipation

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

Years included: 1870-2006

Power Dissipation Index (PDI)

Scaled Temperature

Data Sources: NOAA/TPC, UKMO/HADSST1


Risk assessment by direct numerical simulation of hurricanes the problem

Risk Assessment by Direct Numerical Simulation of Hurricanes:The Problem

  • The hurricane eyewall is a region of strong frontogenesis, 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


Using applied mathematics to assess hurricane risk

Global Models do not simulate the storms that cause destruction

Modeled

Histograms of Tropical Cyclone Intensity as Simulated by a Global Model with 50 km grid point spacing. (Courtesy Isaac Held, GFDL)

Observed

Category 3


Numerical convergence in an axisymmetric nonhydrostatic model rotunno and emanuel 1987

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


How to deal with this

How to deal with this?

  • Option 1: Brute force and obstinacy


Multiply nested grids

Multiply nested grids


How to deal with this1

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

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


Angular momentum distribution

Angular Momentum Distribution


How can we use this model to help assess hurricane risk in current and future climates

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


Risk assessment approach

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 beta drift

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

Synthetic Track Generation:Generation of Synthetic Wind Time Series

  • Postulate that TCs move with vertically averaged environmental flow plus a “beta drift” correction

  • Approximate “vertically averaged” by weighted mean of 850 and 250 hPa flow


Synthetic wind time series

Synthetic wind time series

  • Monthly mean, variances and co-variances from re-analysis or global climate model data

  • Synthetic time series constrained to have the correct monthly mean, variance, co-variances and an power series


Risk assessment approach1

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 beta drift

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


Using applied mathematics to assess hurricane risk

Comparison of Random Seeding Genesis Locations with Observations


Using applied mathematics to assess hurricane risk

Example: 50 Synthetic Tracks


Cumulative distribution of storm lifetime peak wind speed with sample of 1755 synthetic tracks

Cumulative Distribution of Storm Lifetime Peak Wind Speed, with Sample of 1755Synthetic Tracks

95% confidence bounds


Return periods

Return Periods


Coupling large hurricane event sets to surge models with ning lin

Coupling large hurricane event sets to surge models (with Ning Lin)

  • Couple synthetic tropical cyclone events (Emanuel et al., BAMS, 2008) to surge models

    • SLOSH

    • ADCIRC (fine mesh)

    • ADCIRC (coarse mesh)

  • Generate probability distributions of surge at desired locations


Example intense event in future climate downscaled from cnrm model

Example: Intense event in future climate downscaled from CNRM model


Adcirc mesh

ADCIRC Mesh


Using applied mathematics to assess hurricane risk

Peak Surge at each point along Florida west coast


Using applied mathematics to assess hurricane risk

Application to Other Climates


Summary

Summary:

  • History too short and inaccurate to deduce real risk from tropical cyclones

  • Global (and most regional) models are far too coarse to simulate reasonably intense tropical cyclones

  • Globally and regionally simulated tropical cyclones are not coupled to the ocean


Using applied mathematics to assess hurricane risk

  • We have developed a technique for downscaling global models or reanalysis data sets, using a very high resolution, coupled TC model phrased in angular momentum coordinates

  • Model shows skill in capturing spatial and seasonal variability of TCs, has an excellent intensity spectrum, and captures well known climate phenomena such as ENSO and the effects of warming over the past few decades


Spare slides

Spare Slides


Using applied mathematics to assess hurricane risk

  • Ocean Component:

    ((Schade, L.R., 1997: A physical interpreatation of SST-feedback.

    Preprints of the 22nd Conf. on Hurr. Trop. Meteor., Amer. Meteor. Soc., Boston, pgs. 439-440.)

  • Mixing by bulk-Richardson number closure

  • Mixed-layer current driven by hurricane model surface wind


Couple to storm surge model slosh

Couple to Storm Surge Model (SLOSH)

Courtesy of Ning Lin


Using applied mathematics to assess hurricane risk

Surge map for single event


Using applied mathematics to assess hurricane risk

Histogram of the SLOSH-model simulated storm surge at the Battery for 7555 synthetic tracks that pass within 200 km of the Battery site.


Using applied mathematics to assess hurricane risk

Surge Return Periods, New York City


Using applied mathematics to assess hurricane risk

Genesis Distributions


Why does frequency decrease

Why does frequency decrease?

Critical control parameter in CHIPS:

Entropy difference between boundary layer and middle troposphere increases with temperature at constant relative humidity


Using applied mathematics to assess hurricane risk

Change in Frequency when T held constant in


Probability density by storm lifetime peak wind speed explicit and downscaled events

Probability Density by Storm Lifetime Peak Wind Speed, Explicit and Downscaled Events


Change in power dissipation with global warming

Change in Power Dissipation with Global Warming


Using applied mathematics to assess hurricane risk

Analysis of satellite-derived tropical cyclone

lifetime-maximum wind speeds

Trends in global satellite-derived tropical cyclone maximum wind speeds by quantile, from 0.1 to 0.9 in increments of 0.1.

Box plots by year. Trend lines are shown

for the median, 0.75 quantile, and 1.5 times the interquartile range

Elsner, Kossin, and Jagger, Nature, 2008


Bringing physics and math to bear

Bringing Physics and Math to Bear


Physics of mature hurricanes

Physics of Mature Hurricanes


Theoretical upper bound on hurricane maximum wind speed potential intensity

Theoretical Upper Bound on Hurricane Maximum Wind Speed:POTENTIAL INTENSITY

Surface temperature

Ratio of exchange coefficients of enthalpy and momentum

Outflow temperature

Air-sea enthalpy disequilibrium


Using applied mathematics to assess hurricane risk

Annual Maximum Potential Intensity (m/s)


Using applied mathematics to assess hurricane risk

Compare two simulations each from 7 IPCC models:

1.Last 20 years of 20th century simulations2. Years 2180-2200 of IPCC Scenario A1b (CO2 stabilized at 720 ppm)


Ipcc emissions scenarios

IPCC Emissions Scenarios

This study


Basin wide percentage change in power dissipation

Basin-Wide Percentage Change in Power Dissipation


Basin wide percentage change in storm frequency

Basin-Wide Percentage Change in Storm Frequency


7 model consensus change in storm frequency

7 Model Consensus Change in Storm Frequency


Using applied mathematics to assess hurricane risk

Tracks of 18 “most dangerous” storms for the Battery, under the current (left) and A1B (right) climate, respectively


Using applied mathematics to assess hurricane risk

Explicit (blue dots) and downscaled (red dots) genesis points for June-October for Control (top) and Global Warming (bottom) experiments using the 14-km resolution NICAM model. Collaborative work with K. Oouchi.


Using applied mathematics to assess hurricane risk

Return levels for Tampa, including three cases : control (black), A1B (blue), A1B with 1.1ro and 1.21 rm (red)


Using applied mathematics to assess hurricane risk

Simulated vs. Observed Power Dissipation Trends, 1980-2006


Using applied mathematics to assess hurricane risk

Total Number of United States Landfall Events, by Category, 1870-2004


Using applied mathematics to assess hurricane risk

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


250 hpa zonal wind modeled as fourier series in time with random phase

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.


Example

Example:


The time series of other flow components

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.Solved by Cholesky decomposition.


Track

Track:

Empirically determined constants:


Using applied mathematics to assess hurricane risk

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


Calibration

Calibration

Absolute genesis frequency calibrated to globe during the period 1980-2005


Genesis rates

Genesis rates

Western North Pacific

Southern Hemisphere

Eastern North Pacific

Atlantic

North Indian Ocean


Using applied mathematics to assess hurricane risk

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


Seasonal cycles

Seasonal Cycles

Atlantic


3000 tracks within 100 km of miami

3000 Tracks within 100 km of Miami

95% confidence bounds


Sample storm wind swath

Sample Storm Wind Swath


Using applied mathematics to assess hurricane risk

5000 synthetic storm tracks under current climate. (Red portion of each

track is used in surge analysis.)


Using applied mathematics to assess hurricane risk

SLOSH mesh for the New York area (Jelesnianski et al., 1992)


Using applied mathematics to assess hurricane risk

Surge Return Periods for The Battery, New York


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