Dynamical Simulations of North Atlantic Tropical Cyclone Activity UsingObserved Low-Frequency SST Oscillation Imposed on CMIP5 Model RCP4.5

SST Projections

TIMOTHY E. LAROW, LYDIA STEFANOVA, AND CHANA SEITZ

Florida State University, Tallahassee, Florida

(Manuscript received 7 October 2013, in final form 7 July 2014)

ABSTRACT

The effects on early and late twenty-first-century North Atlantic tropical cyclone statistics resulting from

imposing the patterns of maximum/minimum phases of the observed Atlantic multidecadal oscillation

(AMO) onto projected sea surface temperatures (SSTs) from two climatemodels fromphase 5 of theCoupled

Model Intercomparison Project (CMIP5) are examined using a 100-km resolution global atmospheric model.

By imposing the observed maximum positive and negative phases of the AMO onto two CMIP5 SST pro-

jections from the representative concentration pathway (RCP) 4.5 scenario, this study places bounds on

future North Atlantic tropical cyclone activity during the early (2020–39) and late (2080–99) twenty-first

century. Averaging over both time periods and both AMOphases, the mean named tropical cyclones (NTCs)

count increases by 35% when compared to simulations using observed SSTs from 1982 to 2009. The positive

AMO simulations produce approximately a 68% increase in mean NTC count, while the negative AMO

simulations are statistically indistinguishable from the mean NTC count determined from the 1995–2009

simulations—a period of observed positive AMO phase. Examination of the tropical cyclone track densities

shows a statistically significant increase in the tracks along the East Coast of the United States in the future

simulations compared to the models’ 1982–2009 climate simulations. The increase occurs regardless of AMO

phase, although the negative phase produces higher track densities. The maximum wind speeds increase by

6%, in agreement with other climate change studies. Finally, the NTC-related precipitation is found to in-

crease (approximately by 13%) compared to the 1982–2009 simulations.

1. Introduction

The U.S. Climate Change Science Program Synthesis

and Assessment Product 3.3 report (Karl et al. 2008)

concluded that human-induced greenhouse gas (GHG)

increases have very likely been the cause of the increase in

recent decades in the SSTs in the hurricane genesis re-

gions in the North Atlantic and northwest Pacific (Gillett

et al. 2008). These findings have led to scientific debate as

to whether or not the observed increase in measures of

Atlantic hurricane activity (frequency and intensity) in the

past decades is attributable to the increase in SSTs caused

by anthropogenic forcing (MannandEmanuel 2006;Elsner

2006; Trenberth and Shea 2006) or is part of a natural low-

frequency cycle (e.g., Goldenberg et al. 2001; Bell and

Chelliah 2006; Zhang and Delworth 2006, 2009).

In the past few decades, a number of papers have been

published analyzing the impact of a warming climate on

tropical cyclone activity [see review in Knutson et al.

(2010)]. In the North Atlantic basin the review article

found large uncertainty in the projected level of activity

in a warming climate. Of the 23 studies examined, 10

project an increase in frequency, while 13 project a de-

crease. The percent change ranges from 161% (Sugi

et al. 2002) to262% (Zhao et al. 2009) when compared

against their models’ current climate simulations. The

single statistical–deterministic model (Emanuel et al.

2008) shows a slight increase of 4% in the very late

twenty-first century. Villarini and Vecchi (2012) used

a Poisson regression model to examine the number of

tropical storms from 17 different climate models using

three representative concentration pathways (RCPs)

produced under phase 5 of the Coupled Model In-

tercomparison Project (CMIP5) (Taylor et al. 2012).

They found no statistically discernable trend in future

projections of North Atlantic tropical storm activity

Corresponding author address:TimothyE. LaRow, Florida State

University, P.O. Box 3062741, Tallahassee, FL 32306-2741.

E-mail: tlarow@fsu.edu

1 NOVEMBER 2014 LAROW ET AL . 8055

DOI: 10.1175/JCLI-D-13-00607.1

� 2014 American Meteorological Society

when examined over the entire twenty-first century.

Recently, Emanuel (2013) found an increase in the fre-

quency and intensity of global tropical cyclones in the

twenty-first century, including the North Atlantic, using

downscaled data from the CMIP5 RCP8.5 scenario,

while Knutson et al. (2013) found a 23% reduction in

North Atlantic tropical storm frequency in the late

twenty-first century by dynamically downscaling CMIP5

RCP4.5 model projections.

This study takes a different approach for examining

future projections in North Atlantic tropical cyclone ac-

tivity by specifying the observed twentieth-century maxi-

mum positive and negative Atlantic multidecadal

oscillation (AMO) (Kerr 2000) phases onto two SSTs

from theCMIP5RCP4.5 twenty-first-century simulations.

This approach is followed for two reasons. First, in the

present-day climate, the AMO is thought to affect North

Atlantic tropical cyclone activity by altering the local SSTs

(Goldenberg et al. 2001; Zhang and Delworth 2006;

Knight et al. 2006; Camargo et al. 2013). During the

positive phase of the AMO, the observed frequency of

tropical cyclone activity in the North Atlantic increases in

conjunction with the number of intense hurricanes (winds

speeds greater than 50 ms21). During the negative phase

of the AMO, the converse is generally the norm. Second,

the AMO, also termed the Atlantic multidecadal vari-

ability (Ting et al. 2011) might not have a true periodic

oscillation in the climate system, but rather a complex time-

dependent phase (Ting et al. 2009; Zhang and Delworth

2009;Wu et al. 2011; Terray 2012).Model-inherent internal

variability and imperfections contribute to limitations in

forecasting changes in the phase (Kravtsov and Spannagle

2008; Knight 2009). As a result, prediction of the quasi

periodicity of the AMO is only possible to some degree for

the nearest decade (Keenlyside et al. 2008; Hurrell et al.

2010) or in a probabilistic sense (Enfield and Cid-Serrano

2006). This paper therefore examines the range of North

Atlantic tropical cyclone activities during two 20-yr time

periods in the twenty-first century with the assumption

that the AMO continues into the twenty-first century

with similar amplitude, phase, and pattern as observed

in the twentieth century. The AMO replacement

methodology is discussed in section 3.

2. Model and data

The Florida State University (FSU) Center for

Ocean–Atmospheric Prediction Studies (COAPS) at-

mospheric model is used in this study (Cocke and LaRow

2000). The model’s horizontal resolution is T126 spectral

truncation (with Gaussian grid spacing of approximately

0.958 in longitude and latitude). This model has shown

skill in simulating the interannual variability of tropical

cyclone activity in the North Atlantic with prescribed

observed and predicted sea surface temperatures

(LaRow et al. 2008; LaRow 2013) and forecasting North

Atlantic tropical cyclone activity (LaRow et al. 2010).

The SSTs used in this study come from the Commu-

nity Climate System Model, version 4 (CCSM4), (Gent

et al. 2011) and from the Second Generation Canadian

Earth System Model (CanESM2) (Chylek et al. 2011)

from the CMIP5 simulations. We examined tropical

cyclone activity using SSTs from the CMIP5 RCP4.5

scenario. The RCP4.5 scenario assumes the radiative

forcing stabilizes at 4.5 Wm22 after 2100 [experiment

number 4.1 in Taylor et al. (2012)]. Two 20-yr time pe-

riods are examined, 2020–39 and 2080–99, with the CO2

concentration in each time period held fixed at 435 and

534 ppm, respectively. These concentrations represent

the average RCP4.5 values during the two selected time

periods. No other trace gasses were modified. The ex-

perimental design is shown in Table 1.

The objective tropical cyclone detection/tracking al-

gorithm used is the same as in LaRow et al. (2008, 2010)

and similar to that used by Knutson et al. (2007) and

Zhao et al. (2009). Briefly, for a storm to be detected and

tracked, it must satisfy three criteria. First, the 850-hPa

relative vorticity must exceed 1.0 3 1024 s21. Next, the

relative vorticity maximum must be accompanied by

a minimum in the sea level pressure field, and finally,

a warm core must be found between 500 and 200 hPa.

Once a tropical storm is detected, it must last at least

2 days and have surface winds greater than or equal to

15m s21. The choice of 15 m s21 for the minimum wind

speed is based on themodel’s T126 horizontal resolution

(Walsh et al. 2007). LaRow (2013) demonstrated using

forecasted SSTs with bias correction from the Climate

TABLE 1. Experiment setup.

RCP4.5 SSTs

AMO1 AMO2

CanESM2 20 yr (2020–39) 20 yr (2020–39)

20 yr (2080–99) 20 yr (2080–99)

CCSM4 20 yr (2020–39) 20 yr (2020–39)

20 yr (2080–99) 20 yr (2080–99)

Observed SSTs (OISST.v2; Smith et al. 2008) 15 yr (1995–2009) 13 yr (1982–94)

8056 JOURNAL OF CL IMATE VOLUME 27

Forecast System version 1 (Saha et al. 2010) that the FSU/

COAPSmodel is able to reproduce the observed increase

in North Atlantic tropical cyclone activity after 1994.

3. CMIP5 model selection

The two CMIP5 models were selected by comparing

their El Niño characteristics (average return period andexplained variance) from the historical SST simulationswith the observation. The comparison forms a simpleindex of ENSO. The average return period is calculatedby counting all complete cycles of the phase of the firstprincipal component of the complex empirical orthogo-nal function (CEOF1) and dividing by the number ofcomplete cycles. Figure 1 shows a scatterplot of the

ENSO frequency bias versus the ENSO-explained var-

iance bias for selectedmodels from the CMIP5 historical

simulation based on the first CEOF of the global SST.

The biases are calculated with respect to the observa-

tional values of 3.8 yr and 41.3%. Solid filled triangles

represent the CCSM4 model simulations, and the solid

squares represent the CanESM2 simulations. The other

symbols represent the remaining models listed with

expaned names in Table 2. The CCSM4 model shows

the least amount of bias, while the CanESM2 model

exhibits slightly larger negative biases. The othermodels

with the least amount of bias are the MPI-ESM-LR

(represented by the rhombus) and the INM-CM4.0

(represented by the star). However, these models were

not selected because of the larger Niño-3 and Niño-4SST anomaly standard deviations compared to theCanESM2 (Guilyardi et al. 2012). Finally, the CCSM4

and CanESM2 ensemble member closest to the origin in

Fig. 1 was selected for this study.

AMO replacement

Anthropogenically induced changes in North Atlantic

tropical cyclone (TC) activity are obscured by natural

multidecadal variability, particularly that of the AMO.

Whether or not there is an anthropogenically forced

component to the AMO variability is an open question.

To date, there is no consensus on the degree to which the

AMO is a forced versus an internal mode of variability

(e.g., Enfield and Cid-Serrano 2009; Knight 2009; Ting

et al. 2009). Table 2 lists the CMIP5 models’ historical

simulation AMO frequency. In addition, there is no

agreement across models regarding projected changes

of AMO for the remainder of the twenty-first century.

For the purposes of this study, we assume that the an-

thropogenically forced SST trend and the AMO are

independent; we further assume that the observed

minimum/maximum AMO phases of the twentieth

century are a reasonable approximation for the future

(twenty-first century) minimum/maximumAMO phase.

FIG. 1. Scatterplot of the ENSO frequency bias vs the ENSO-explained variance bias for

selected models from the CMIP5 historical simulation, based on the CEOF1 of the global SST.

The biases are calculated with respect to the observation. Solid filled triangles represent the

CCSM4 simulations, and the solid squares represent the CanESM2 simulations. The observed

frequency and explained variance are 3.8 yr and 41.3%. Selected models are from Table 2.

1 NOVEMBER 2014 LAROW ET AL . 8057

To avoid confounding issues with the models’ projected

changes to AMO (which are unlikely to be reliable); we

remove the models’ AMO-like signal and replace it with

the minimum/maximum of the observed twentieth-

century AMO.

The spatial pattern of North Atlantic low-frequency

variability for these models, along with the corre-

sponding observations, is shown in Fig. 2. Here, fol-

lowing Enfield and Mestas-Nuñez (1999), we have

subtracted the CEOF1 from the original data with the

trend and high-frequencymodes (,1.5 yr) removed, and

then performed an EOF analysis in the North Atlantic

domain (08–908N, 908W–08). The characteristic horse-

shoe pattern of the observed AMO signal (Fig. 2, top) is

seen in the second EOF (EOF2) of CanESM2 (Fig. 2,

middle) and spread among the three leading EOFs of

the CCSM4 (Fig. 2, bottom). To prepare the SSTs for

use in the FSU/COAPS climate model, the low-

frequency North Atlantic variability contained in the

first 3 EOFs is removed and replacedwith themaximum/

minimumAMOphase of the observed record. Figure 3

shows the observed positive AMO SST anomaly pat-

tern and magnitude used in the study. The classic

horseshoe-shaped dipole pattern, with maximums lo-

cated in the eastern tropical North Atlantic between

108 and 208N and south of Greenland, characterizes

the positive phase. The negative AMO SST anomaly

pattern used in this study is identical to the positive

phase but with anomalies of opposite sign. This results

in SST fields that contain the projected anthropo-

genic trend, but whose AMO signal is assumed un-

modulated by anthropogenic forcing and is therefore

set to observed values.

4. Results

a. RCP4.5 SSTs

The CanESM2 and CCSM4 monthly SST bias is lin-

early corrected, and the mean August–October (ASO)

SST time series for three selected domains are shown in

Fig. 4. The three domains are the Niño-3.4 (N34;58S–58N, 1208–1708W), main development region (MDR;58–158N, 208–808W)and the global tropics (308S–308N). The

thick black lines show the SSTs from the CMIP5 his-

torical and the bias-corrected RCP4.5 simulations. The

red (blue) lines show the SSTs with the observed posi-

tive (negative) phases of the AMO (denoted as AMO1and AMO2) added to the RCP4.5 bias-corrected SST.

For reference, the light green lines show the National

Oceanic and Atmospheric Administration (NOAA)

extended reconstructed SST, version 3b (ERSST.v3b),

ASO SST from 1940 to 2005, while the dark green lines

show the average bias-corrected time series from the

TABLE 2. Period of the AMO from the CMIP5 models’ historical simulations (1860–2005). For models with multiple realizations, the

period of the AMO is determined by calculating the ensemble average of the SSTs prior to calculating the AMO.

Model Expansion AMO period (yr)

CanESM2 Second Generation Canadian Earth System Model 57

CCSM4 Community Climate System Model, version 4 64

CNRM-CM5 Centre National de Recherches Météorologiques Coupled GlobalClimate Model, version 5

48

CSIRO Mk3.6.0 Commonwealth Scientific and Industrial Research Organisation,

Mark version 3.6.0

62

GFDL-ESM2G Geophysical Fluid Dynamics Laboratory Earth System

Model coupled with the Generalized Ocean Layer Dynamics

(GOLD) component (ESM2G)

72

GISS-E2-H Goddard Institute for Space Studies Model E2, coupled with the

Hybrid Coordinate Ocean Model (HYCOM)

36

GISS-E2-R Goddard Institute for Space Studies Model E2, coupled with the

Russell ocean model

36

HadCM3 Hadley Centre Coupled Model, version 3 77

HadGEM2-CC Hadley Centre Global Environment Model, version 2–Carbon Cycle 44

HadGEM2-ES Hadley Centre Global Environment Model, version 2–Earth System 48

INM-CM4.0 Institute of Numerical Mathematics Coupled Model, version 4.0 72

IPSL-CM5A-LR L’Institut Pierre-Simon Laplace Coupled Model, version 5A,

low resolution

59

MIROC5 Model for Interdisciplinary Research on Climate, version 5 47

MPI-ESM-LR Max Planck Institute Earth System Model, low resolution 51

MRI-CGCM3 Meteorological Research Institute Coupled Atmosphere–Ocean

General Circulation Model, version 3

55

NorESM1-M Norwegian Earth System Model, version 1 (intermediate resolution) 39

Observation — 70

8058 JOURNAL OF CL IMATE VOLUME 27

RCP4.5models shown in Table 2. The SSTwarming in the

twenty-first century is clearly evident in all three domains,

when compared to the observations from 1940 to 2005.

Both CanESM2 and CCSM4 models exhibit larger-than-

observed interannual SST variability in the Niño-3.4 do-main, with standard deviations nearly twice the observedvalue (0.6 K), while the average bias-corrected SSTs fromthe RCP4.5 models show very damped variability (darkgreen lines). In the MDR, the positive (negative) AMOadds (subtracts) 0.49K to bias-corrected MDR SSTs. By

the late twenty-first century, theASOSST in theMDRare

an average of 0.8–2.6Kwarmer than the ERSST.v3bASO

SST averaged over 1940–2005. During the first 20 years of

simulation (2020–39), the linear trends in the SSTs in the

three domains are positive, whereas in the late twenty-first

century, the SSTs trends have flattened as a result of the

specifications of the RCP4.5 scenario. The slopes of the

ASO SST trends are statistically significantly different

than zero (at the 90% level) during the first 20 years of

simulation in the MDR and global tropic domains.

b. Tropical cyclones

Before discussing future projections of North Atlantic

named tropical cyclone (NTC) activity, it is important to

first establish the creditability of the FSU/COAPS

model in simulating the correct response to changes in

the observed AMO. In previous studies (LaRow et al.

2008; LaRow 2013) the FSU/COAPS atmospheric

model demonstrated skill in predicting North Atlantic

interannual variability of NTC counts from 1982 to 2009

(LaRow 2013; correlation of r 5 0.76) using the ob-

served Reynolds optimum interpolation SSTs, version 2

(OISST.v2; Smith et al. 2008). Figure 5 shows a box-and-

whisker plot of the observed NTC counts from the In-

ternational Best TrackArchive for Climate Stewardship

(IBTrACS) (Knapp et al. 2010) and three simulations

per year using the FSU/COAPS model forced by the

OISST.v2. The figure is divided into two AMO time

periods. The first time period is 1982–94 and corre-

sponds to a negative AMO phase, while the second time

FIG. 2. First 3 EOFmodes of the ENSO-removed Atlantic SSTs for (top) observations, (middle) CanESM2, and (bottom) CCSM4. For

the EOF–time series, the abscissa ranges from 1860 to 2005, and the ordinate ranges from 21 to 1. The color bar in kelvin on the right

refers to the spatial pattern in all panels.

1 NOVEMBER 2014 LAROW ET AL . 8059

period (1995–2009) corresponds to a positive AMO phase

(Goldenberg et al. 2001). The negative AMO phase

IBTrACS data are labeled OBS1 and the model data

are labeled BC1. The positive AMO phase IBTrACS

data are labeled OBS2, while the model data are la-

beled BC2. The 10th and 90th percentiles define the

boxes, and the whiskers denote the maximum and

minimum of the data. For the IBTrACS and model

simulations, only storms lasting two days or longer are

counted. The FSU/COAPS model captures the in-

crease in NTC activity after 1994, simulating an aver-

age value of 12.8 NTC counts in the positive phase and

9.6 NTC counts in the negativeAMOphase. During the

same time periods, the IBTrACS data have 14.4 (8.4)

NTC counts in the positive (negative) phase. The mean

NTC counts between the two time periods are statis-

tically significant in both the observations and model

(p value , 0.001).

Figure 6 shows box-and-whisker plots of the NTC

counts (Fig. 6, top) and hurricane counts (Fig. 6, bottom)

for the North Atlantic. The boxes and whiskers are de-

fined as in Fig. 5. The early twenty-first-century negative

(positive) AMO simulations are denoted as EN (EP),

while the late twenty-first-century negative (positive)

AMO simulations are labeled LN (LP). Also shown in

the figure are the present-day observed OBS1/OBS2

and model BC1/BC2 simulations. The number of NTCs

averaged over the early and late twenty-first-century

simulations for both the positive and negative AMO

phase is 17.3 (15.2) in the CanESM2 (CCSM4) simula-

tions (Fig. 6, top). This represents an increase of ap-

proximately 35% compared to the mean (12.1) in the

current climate simulation (1982–2009). The mean

number of NTCs in the AMO1 simulations (21.7 in

CanESM2 and 18.6 in CCSM4) is higher (statistically

significant at the 99% level) than the mean of either the

BC2 simulation (13.8) or OBS2 (15.2).

The bottom panels of Fig. 6 show the impact of the

AMO phases on the North Atlantic hurricane counts.

Storms are considered hurricanes if the 10-mwind speed

exceeds 32m s21 at any point during the storm’s lifetime.

The future simulations show that a larger percentage of

the tropical storms intensify into hurricanes compared

to the simulations using the OISST.v2. Averaging over

both time periods and both AMO phases, the CanESM2

hurricane count (12.3) is approximately 43% higher,

when compared to the mean of the model’s BC2 simu-

lation, and the average increase from the CCSM4 sim-

ulations is approximately 24%. Averaging the AMO1phase from both the CCSM4 and CanESM2 yields an

average increase of 72% over the mean from the BC2

simulation. The impact of the AMO2 phase on the

average number of hurricanes is 8.8 (7.7) in the Can-

ESM2 (CCSM4) simulations. These numbers are not

statistically different from the BC2 simulation (8.6) and

indicate that the mean minimum level of activity in the

future will be analogous to the present-day level of in-

creased activity (since 1995).

Despite the increase in the local (MDR) SSTs over the

twenty-first century (Fig. 4), the AMO1 mean NTC

counts are not statistically different between the early

and late twenty-first century. The same is true for the

AMO2 simulations. A possible reason for this is the fact

that the differential warming between the North At-

lantic Ocean and the global tropics remained relatively

constant over the twenty-first century (i.e., the mean

relative SST index did not change). The relative SST

index is defined as the MDR SSTs anomalies relative to

the global tropic anomalies (Vecchi and Soden 2007;

Swanson 2008; Vecchi et al. 2008). The SST anomalies

are calculated with respect to the individual models

2006–19 bias-corrected ASOmeans. The initial 14 years

of the RCP4.5 simulation is chosen for the climatology

because of the AMO alterations made to the SSTs.

Figure 7 shows the relationship between theNTC counts

FIG. 3. Positive AMO SST anomaly pattern added to the CMIP5

models for both the early and late twenty-first-century simulations.

The AMO2 SST anomaly pattern is identical to AMO1 pattern,

but multiplied by 21. The contour interval is 0.1K.

8060 JOURNAL OF CL IMATE VOLUME 27

and the relative SST index for the eight simulations.

In the figure, the open squares and circles are from the

AMO1 and AMO2 simulations, respectively, and

the solid squares and circles denote the mean values.

The relative SST index ranges between 20.4 and

10.3K, with the CanESM2 simulations producing a

larger range in values. In all simulations, the slope of the

linear regression lines are positive, with the CanESM2

simulations producing larger slopes in the linear trends

and higher coefficients of determination (ranging from

0.30 to 0.56) compared to values of only 0.01–0.26 in the

CCSM4 simulations. Villarini and Vecchi (2012) noted

a similar lack in the tropical storm trends when averaged

over the entire twenty-first century using a statistical

downscaling approach applied to all the CMIP5 RCP

scenarios for SSTs. They reason that the increase in the

tropical activity during the first half of the century

is related, in part, to the warming of the Atlantic SSTs

relative to the global tropics arising, in part, from

a reduction in the anthropogenic aerosols over the

North Atlantic.

The correlations between the NTC and hurricane

(HR) counts with the average ASO SSTs in three do-

mains is shown in Table 3. Also shown in the table are

the count correlations with the ASO vertical wind shear

over the MDR. The vertical wind shear is defined as the

difference between the 200- and 850-hPa wind magni-

tudes. The values in the table are for both the early and

late twenty-first-century simulations. The three SST

domains are Niño-3.4, the MDR, and the relative SSTindex. Boldface values denote significance at the 90%level. Overall, the CanESM2 simulations producea larger number of statistically significant correlations(25/32) between the NTC/HR counts and the SSTs thanthe CCSM4 simulations (10/32). In the CanESM2 simula-tions, the relative SST index correlates (at the 90% level)with both theNTC and hurricane counts for both phases ofthe AMO and during both the early and late twenty-first

FIG. 4. Mean ASO SST (K) time series in the (left) N34, (center) MDR, and (right) the global tropics (see text for definition of regions)

for (top) CanESM2 and (bottom) CCSM4. Thick black lines are the historical and selected bias-corrected RCP4.5 simulation; red lines

show the SSTs for the AMO1 simulations; and blue lines show the SSTs for the AMO2 simulations. For reference, the light green lines

are the ERSST.v3b SSTs from 1940 to 2005. The dark green lines are the average bias-corrected time series from all RCP4.5 models. The

vertical dashed lines bracket the time periods of study.

1 NOVEMBER 2014 LAROW ET AL . 8061

century. This is in contrast to the CCSM4 relative SSTindex, which correlates at the 90% level with the NTCcounts only in the late twenty-first century. The Can-ESM2 NTC counts and the ASO MDR SSTs are statis-tically significant for both phases of the AMO and alsofor both the early and late twenty-first-century simula-tions. Interestingly, the CanESM2 hurricane counts arestatistically significant only for the negative AMO phasein the late twenty-first century, indicating that the in-tensity of the storms in the model is not strongly corre-lated with the local SSTs. In the CanESM2 experiment,the correlation of the mean hurricane count is stati-stically significant with the relative SST index and alsowith the Niño-3.4 region in the AMO1 phase. In the

AMO2 phase, the CanESM2 hurricane counts correlate

strongly with the relative SST index and MDR wind

shear.

Averaging the 10-m maximum wind speed from each

North Atlantic NTC in the CanESM2 simulations yields

a 7% increase, when compared to the average of the

BC1 and BC2 simulations. The CCSM4 simulations

produce a slightly smaller increase (5%). These values

are similar to other recent climate change studies (e.g.,

Knutson and Tuleya 2004; Bengtsson et al. 2007; Zhao

et al. 2009; Villarini and Vecchi 2013; Emanuel 2013).

Box-and-whisker plots of the maximum 10-m wind

speeds from each North Atlantic tropical cyclone from

the model’s current climate and the AMO simulations

is shown in Fig. 8. The black dots in the figure represent

outliers. Both the positive and negative AMO simula-

tions show an increase in the tropical cyclone intensity,

when compared to the current climate simulations.

Regardless of the AMO phase, the mean and median

wind speeds increase in the twenty-first-century simu-

lations compared to the BC1 and BC2 simulations. The

mean wind speed in the CanESM2 simulations increase

with the increasing sea surface temperatures during the

twenty-first century; increasing from approximately

38m s21 in the EN simulation to 39 m s21 in the LP

simulation. The difference in the means is statistically

significant (p value , 0.1). The CCSM4 simulations

also show an increase of only 1 m s21 (from 37 to

38m s21) in themean wind speed between the early and

late period simulations. These values compare to

a mean maximum wind speed of 36 m s21 in the BC2

simulation. The increase in the maximum wind speeds

during the positive AMO phase is similar to the ob-

servations that show, on average, the positive phase of

the AMO produces more intense (category 3 and

above) North Atlantic tropical cyclones (Goldenberg

et al. 2001). We note that global models with much

higher horizontal resolution are needed [O(20) km] to

achieve a more realistic wind speed distribution

(Murakami et al. 2012).

FIG. 5. Box-and-whisker plot of the observed IBTrACS (OBS1/OBS2) and model ensemble

(BC1/BC2) average NTC counts for AMO2 period (1982–94) and AMO1 period (1995–

2009). OBS1 and BC1 are the counts for the AMO2 period, while OBS2 and BC2 are for the

AMO1 period. The 10th and 90th percentiles define the boxes, and the whiskers denote the

max/min of the data.

8062 JOURNAL OF CL IMATE VOLUME 27

The contoured difference in average track densities

between AMO phase and current climate simulation

(1982–2009) for every 38 3 38 grid box per year is shown

in Fig. 9. The tracks are binned in 38 3 38 grid boxes

before calculating the densities. Figures 9a and 9c show

the differences in the average track densities of the

AMO1 CanESM2 and CCSM4 simulations with the

current climate simulations. Figures 9b and 9d show

the average differences for the AMO2 simulations with

the current climate simulations. To increase the track

sample sizes, the tracks from the AMO1 simulations

from the early and late twenty-first-century simulations

are combined. A similar procedure was done for the

AMO2 simulations. The shaded regions in the figures

denote differences that are statistically significant at the

90% level using a one-sided t test. Regardless of the

AMO phase, all four plots show statistically significant

increases in the landfall potential in the southeastern

United States. Interestingly, theAMO2 phase (Figs. 9b,d)

shows statistically significant increases along almost the

entire East Coast of the United States, while in the east-

ern North Atlantic, the differences in the track densities

are negative in contrast to the AMO1 phase. Addition-

ally, the difference between the AMO2 phase–track

densities and the current climate is larger along the East

Coast, when compared to the AMO1 phase, indicating

increased tropical systems impacting the East Coast in

the future AMO2 phase. We find that during the

AMO2 phase, the main genesis region is slightly west of

the main genesis region in the AMO1 phase (not

shown). The change in the genesis location is accom-

panied by a stronger North Atlantic subtropical high,

allowing the storms to track farther westward before

turning poleward (Colbert and Soden 2012). The track

density differences were also analyzed based only on the

time period of the simulations (e.g., early twenty-first-

century positive and negative AMO grouped together).

Similar to the results shown in Fig. 9, the track density

differences show an increase along the East Coast of the

United States (not shown). While the differences in the

track densities should be viewed with caution, it is worth

noting that Knutson et al. (2013) found a similar track

response in their downscaled CMIP5 simulations for

storms of category 4–5 strength.

Finally, the storm-related precipitation is examined.

To ensure that the majority of each storm precipitation

field is included, the precipitation is area averaged over

a 48 3 48 box centered on each storm. When averaged

FIG. 6. Box-and-whisker plots of the (top) NTC counts and (bottom) HR counts in the North Atlantic for (left)

CanESM2 and (right) CCSM4. In each box, the thick horizontal line represents themedian value, boxes represent the

10th and 90th percentiles, and the whiskers denote the max/min of the data.

1 NOVEMBER 2014 LAROW ET AL . 8063

over both AMO phases and for both early and late

twenty-first-century simulations, the storm-related pre-

cipitation is found to increase by an average of 13%,

when compared to the storms in the model’s current

climate simulations. The AMO positive phase produces

larger increases (;18%) in the storm-related pre-

cipitation. These values are similar to the 21% increase

in accumulated precipitation along the storm track

reported in Bengtsson et al. (2007) and an 18% increase in

the average precipitation rate within 100 km of the storm

center noted in Knutson and Tuleya (2004). Figure 10

shows the exceedance counts of the NTC area average

storm-centered precipitation for three thresholds: 100,

150, and 200mmday21 for the AMO simulations and

the current climate (all NTCs from BC1 and BC2). The

figure shows the following increases: (i) in the late

FIG. 7. Scatterplots of the NTC counts vs relative SST index: (top) Early twenty-first century and (bottom) late

twenty-first century for (left) CanESM2 and (right) CCSM4. Open squares denote AMO1 phase, and open circles

denote AMO2 phase. Dashed lines are the corresponding linear trend lines. The solid squares and circles denote the

mean values. Anomalies are calculated with respect to the 2006–19 bias-corrected means.

TABLE 3. Correlations of HR and NTC counts with selected ASO SST indices and MDR vertical wind shear for the eight AMO

simulations. The vertical wind shear is defined as the difference between the 200- and 850-hPa wind magnitudes. Values shown in the

tables are for the early (late) twenty-first-century simulations. Boldface numbers are significant at 90% using the Spearman rank cor-

relation test. See text for definition of SST domains.

Niño-3.4 SST MDR SST Relative SST index MDR shear

AMO1CanESM2 HR 20.37 (20.45) 0.17 (0.30) 0.46 (0.58) 20.30 (20.46)

CanESM2 NTC 20.47 (20.60) 0.42 (0.37) 0.57 (0.74) 20.34 (20.46)

AMO2CanESM2 HR 20.62 (20.23) 0.27 (0.63) 0.66 (0.67) 20.54 (20.41)

CanESM2 NTC 20.43 (20.33) 0.38 (0.50) 0.54 (0.69) 20.45 (20.49)

AMO1CCSM4 HR 20.16 (0.20) 0.35 (20.03) 0.36 (0.31) 20.35 (20.25)

CCSM4 NTC 20.37 (20.70) 0.19 (20.11) 0.24 (0.28) 20.37 (20.38)

AMO2CCSM4 HR 20.32 (20.44) 0.06 (0.34) 0.04 (0.35) 20.30 (20.52)

CCSM4 NTC 20.41 (20.74) 0.02 (0.26) 0.09 (0.51) 20.30 (20.50)

8064 JOURNAL OF CL IMATE VOLUME 27

twenty-first century relative to the earlier period, (ii) in

the positive AMO phase relative to the negative phase,

and (iii) in all cases relative to current climate. In both

models, the AMO1 produces higher TC precipitation

rates than the AMO2 phase within a given period. Al-

though not shown, we note that the mean/median pre-

cipitation difference between the late period AMO2and the early periodAMO1 is smaller than between the

early period AMO2 and the early period AMO1,

suggesting that in terms of storm precipitationmeans the

warming SST trend is more impactful than the AMO

phase for this time span.

5. Conclusions

This paper examines the simulated response in the

early and late twenty-first century on North Atlantic

tropical cyclone activity in an atmospheric GCM result-

ing from specifying the observed maximum/minimum

phases of the Atlantic multidecadal oscillation onto two

CMIP5 models’ simulated SSTs from the RCP4.5 sce-

nario. The two CMIP5 models are the CanESM2 and

CCSM4. The selection was based on their ability to

simulate the pattern and magnitudes of both the warm

and cold ENSO phases in the CMIP5 historical simu-

lation.

The AMO positive phase simulations are found to

significantly increase the frequency of North Atlantic

tropical cyclone activity in both the early and late

twenty-first century. The increase occurs regardless of

the choice of model SST and is found to be approxi-

mately 68% above the model’s current climate simula-

tions. The projections of the NTC counts using the

negative AMO phase in the CanESM2 and CCSM4 SST

was found to be statistically similar to the models’

present-day NTC counts (since 1995), where the ob-

served AMO phase is positive.

In terms of the projected change in hurricane activity,

we find no statistically significant differences between

the model’s mean hurricane count and the observed

mean count when both are averaged over the 1995–2009

period. The projected changes in the hurricane counts

using the CanESM2 and CCSM4 negative AMO phase

in the early (2020–39) and late (2080–99) twenty-first

century are found not to be statistically different than

the simulated 1995–2009 hurricane counts. However,

the positive AMO phase simulations did produce large

increases (statistically significant) in the hurricane

counts in both the early and late twenty-first-century

simulations compared to the 1995–2009 simulations.

Averaging theAMO1 phase for CCSM4 and CanESM2

simulations yields an average increase of 72% over the

mean from the BC2 simulation. Therefore, averaging

over both time periods and both AMO phases, the

CanESM2 hurricane count is approximately 43%

higher, when compared to the mean of the model’s BC2

simulation, and the average increase from the CCSM4

simulations is approximately 24%. Using these two

phases of the AMO to bracket the level of projected

hurricane activity in the twenty-first century, the simu-

lations show that a below-average season in the early

and late twenty-first century can be expected to be

similar to the level of activity experienced since 1995.

Additionally, no significant trend was found in the mean

NTC counts between the early and late twenty-first-

century simulations.

The intensity of the North Atlantic tropical cyclones

(measured by the maximum 10-m wind speed) is found

to increase in the twenty-first century, when compared

to the present-day simulations. The average increase is

between 5% and 7%. This result is similar to other recent

CMIP5 studies (Villarini and Vecchi 2013; Emanuel

2013). The projected mean maximum wind speeds in the

positive phase of the AMO are statistically significantly

FIG. 8. Box-and-whiskers plot of the10-m wind speed from all NTCs in the North Atlantic; dots denote outliers.

Maximumwind speed (m s21) is obtained for each storm.Outliers are considered those that lie outside 1.5 times the

interquartile range.

1 NOVEMBER 2014 LAROW ET AL . 8065

different than the maximum wind speeds in the negative

phase.

The differences between theAMOpositive andAMO

negative NTC track density show an increase in the

number of landfalling systems in the negative AMO

phase. This is in contrast to the observations. In general,

genesis in the negative AMO phase tended to occur

slightly west of the main genesis region found in the

FIG. 9. Difference in average track density between AMO phase and current climate simulation (1982–2009) for every 38 3 38 grid box

per year: (a) CanESM2AMO1 simulations, (b) CanESM2AMO2 simulations, (c) CCSM4AMO1 simulations, and (d) CCSM4AMO2simulations. Contour interval 0.2 storms yr21. Shaded regions are statistically significant at the 90% level using a one-sided t test.

8066 JOURNAL OF CL IMATE VOLUME 27

AMO positive phase. This permitted the storms in the

negative AMO phase to track farther westward before

turning poleward and thereby increasing the landfall

potential.

The storm-related precipitation was examined in the

different AMO phases and compared to the models’

current storm precipitation. When averaged over both

AMO phases for both the early and late twenty-first

century, the average storm-related precipitation was

found to increase by 13%, when compared to the aver-

age of BC1 and BC2. The AMO positive phase was

found to produce larger increases (;18%) in the storm-

related precipitation compared to the negative AMO

phase.

Finally, the large range in the models’ response to the

AMO phases in simulating North Atlantic NTC activity

help highlight the importance of understanding low-

frequency oscillations like the AMO in climate model

projections and the role these oscillations might have on

altering the probability of future extreme events.

Acknowledgments. This research was supported by

grants from the U.S. Department of Energy Office of

Science (BER) and NOAA’s Climate Program Office.

We acknowledge the World Climate Research Pro-

gramme’s Working Group on Coupled Modelling,

which is responsible for CMIP, and we thank the climate

modeling groups for producing and making available

their model output. For CMIP the U.S. Department of

Energy’s Program for Climate Model Diagnosis and

Intercomparison provides coordinating support and led

development of software infrastructure in partnership

with the Global Organization for Earth System Science

Portals.

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