a comparison of observed and model-generated …...us clivar hurricane workshop, 5 june 2013 sarah...

US CLIVARHurricane

Workshop, 5 June2013

Sarah E. Strazzo,James B. Elsner,

Timothy E.LaRow, Ming

Zhao, and DanielJ. Halperin

Introduction

Data and SpatialFramework

SpatialDistributions

Sensitivity

Future Work

A comparison of observed andmodel-generated tropical cycloneclimatologies using a spatial lattice

Sarah E. Strazzo, James B. Elsner, Timothy E. LaRow,Ming Zhao, and Daniel J. Halperin

Florida State University

5 June 2013

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Introduction



Sensitivity

Future Work

Objectives

I Quantitatively compare the spatial distributions ofobserved and GCM-generated track data

I Examine statistical relationships between the track dataand various environmental and climate variables (herewe focus on SST)

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Introduction



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Data: observations

Global TC Track Data:

I International Best Track Archive for ClimateStewardship (IBTrACS)

I Data from 1981-2008 are used

I Interpolated from 6-hourly to hourly intervals followingthe method described in Elsner et al. (2013)

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Introduction



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Data: the models

Model 1: GFDL-HiRAM (Zhao et al. 2009)

I 50 km horizontal grid spacing

I 32 vertical levels

I Forced with HadISST from 1981-2008

I Global

Model 2: FSU-COAPS (LaRow et al. 2008)

I Spectral model with T126 horizontal resolution

I 27 vertical levels

I Forced with HadISST from 1981-2008

I Use data from North Atlantic basin

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Observations: 2005 Model: 2005

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The spatial framework

Observed TCs in the Gulf of Mexico in 2005

Number of Cyclones

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(see Elsner et al. (2012) for more details)

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Part I: Spatial Distributions

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Comparing spatial distributions (Global)

IBTrACS

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Introduction



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GFDL-HiRAM

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(IBTrACS) – (GFDL-HiRAM)

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Model Performance Metrics

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Bias:

Bias =TP + FP

TP + FN(1)

Critical Success Index:

CSI =TP

TP + FP + FN. (2)

Probability of Detection:

POD =TP

TP + FN. (3)

Success Ratio:

SR = 1 − FP

TP + FP. (4)

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Success Ratio

Pro

babi

lity

of D

etec

tion

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NA

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SP

●NI

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SI

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ALL

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Comparing Spatial Distributions: North Atlantic

Observations

FSU-COAPS

GFDL-HiRAM

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Spatial Distribution Summary:

Globally

I The overall spatial distribution of TCs generated by theGFDL-HiRAM matches reasonalby well withobservations, although there are clear areas ofover/under prediction by the model.

I Quantitative performance metrics verify that theGFDL-HiRAM is generally generating TCs in the correctareas.

North Atlantic

I There are significant areas of overprediction by theFSU-COAPS model in the central portion of the basin.

I Interestingly, neither model correctly reproduces thespatial distribution of storms in the Caribbean and Gulfof Mexico.

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Part II: Sensitivity ofLimiting Intensity to SST

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Per Hexagon TC Counts

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Per Hexagon Wind Speed Distributions

Wind speed distribution for hexagon #37

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Limiting Intensity

Statistical model for hexagon #37

LI = u − σ/ξ (5)

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Per Hexagon Average SST

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Sensitivity of Limiting Intensity to SST

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Sensitivity of Limiting Intensity to SST

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Data (Best−track) GCM (FSU−COAPS run 1)

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Sensitivity Summary:

I Sensitivity of limiting intensity to SST

I Observations: 7.9 ± 1.19 m s−1 K−1

I GFDL-HiRAM (unadjusted): 1.8 ± 0.42 m s−1 K−1

I GFDL-HiRAM (adjusted): 6.9 ± 3.33 m s−1 K−1

I FSU-COAPS: 2.9 ± 2.64 m s−1 K−1

I Sensitivities from model-generated data are significantlylower than those from observations

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Introduction



Sensitivity

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Future Work

I Add more models for a comprehensive intercomparison

I Examine relationships between sensitivity and climate(e.g., ENSO, relative SST)

I Explore the use of Bayesian models to examine regionaland temporal relationships between LI and SST

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Introduction



Sensitivity

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Thank you

Questions

a comparison of observed and model-generated …...us clivar hurricane workshop, 5 june 2013 sarah...

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