Inter-annual Variability of Gulf Stream Warm-Core Rings In Response to the North
Atlantic Oscillation
By
Ayan H. Chaudhuri, Avijit Gangopadhyay and James J. Bisagni
University of Massachusetts School of Marine Sciences and School for Marine Science
and Technology, University of Massachusetts, Dartmouth, New Bedford, Massachusetts,
02744.
Keywords: Gulf Stream, Warm Core Rings, Eddy Kinetic Energy, North Atlantic
Oscillation, Shelf-Slope front
1
ABSTRACT
2
3
A quality-controlled database of Gulf Stream Warm-Core Rings (WCRs) between 75° and
4
50°W during 1978 -1999 is analyzed, and demonstrates a significant response between
5
WCR occurrences and variations in large-scale atmospheric forcing related to the state of
6
the North Atlantic Oscillation (NAO). The influences of the NAO on Gulf Stream (GS)
7
position and Gulf Stream region (GSR) eddy kinetic energy (EKE) are presented as two
8
hypotheses linking the NAO with the rate of WCR occurrences. Variability in GS
9
movement is studied by analyzing annual mean positions of the Gulf Stream North Wall
10
obtained from satellite-derived sea surface temperature (SST) frontal charts. Response of
11
GSR EKE to fluctuations in the state of the NAO is examined with a numerical simulation
12
of the North Atlantic basin from 1980-1999. The North Atlantic basin is simulated using a
13
1/6o-resolution eddy-resolving ROMS model that spins-up with Southampton Oceanography
14
Center (SOC) Ocean-Atmosphere atlas-derived atmospheric forcing fields. Model-derived
15
EKE estimates are observed to be in good agreement with TOPEX/Poseidon altimeter-based
16
EKE estimates as well as results from other modeling studies for the North Atlantic basin.
17
We suggest that lateral movement of the GS may not be the primary mechanism causing
18
variation in the rate of WCR occurrences, because GS position is observed to respond at a
19
lag of 1-year; whereas annual rates of WCR occurrences respond at 0-year lag to similar
20
NAO conditions. Based on results from numerical simulations of the North Atlantic basin,
21
adjustment to NAO-induced wind forcing is seen to impact the GSR EKE distribution and
22
possibly the related baroclinic instability structure of the GS at 0-year lag. These results
23
suggest that NAO-induced inter-annual variability in GSR EKE is the most likely
24
mechanism affecting WCR occurrences. This study shows that high (low) phases in the state
25
of the NAO exhibit higher (lower) EKE in the GSR, providing a greater (lesser) source of
26
baroclinic instability to the GS front, further resulting in higher (lower) occurrences of
27
WCRs.
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29
1. INTRODUCTION
30
31
The Gulf Stream (GS) is the western boundary current limb of the North Atlantic
32
sub-tropical gyre that transports heat and mass northwards up to Cape Hatteras. Downstream
33
of Cape Hatteras the GS separates from the coast and turns northeastward into deeper
34
waters, forming large amplitude meanders due to baroclinic and barotropic instability
35
processes. Individual meanders, if large enough (surface radii of 2-4 times the internal
36
Rossby radius) can separate from the main GS current, loop back onto themselves and form
37
independent warm-core and cold-core rings [Saunders, 1971; Csanady, 1979]. Warm Core
38
Rings (WCRs) form from meander crests on the northern side of the GS, engulfing parcels
39
of warm, salty Sargasso Sea water in their core [Parker, 1971]. The ring formation process
40
is dominated by the growth of baroclinic instability modes [Robinson et al., 1988, Spall and
41
Robinson, 1990] that converts mean potential energy to eddy kinetic energy.
42
43
The common occurrence of WCRs in the Slope Sea (SS) and their role in affecting
44
the physical, chemical and biological oceanography of the SS region have been well
45
documented through satellite imagery [Bisagni, 1976, Halliwell and Mooers, 1979; Brown
46
et al., 1986; Auer, 1987], theoretical models [Flierl, 1977; Csanady, 1979; Olson et al.,
47
1985] and field observations [Saunders, 1971; Lai and Richardson, 1977; Joyce, 1985].
48
However, most reported results concerning WCRs are deduced from single surveys or time-
49
series surveys from individual WCRs. Long term impacts of these energetic features and
50
their inter-annual variability (IAV) have not been studied.
51
52
The Slope Sea, a region where WCRs reside during their life-times, is bounded by
53
the Gulf Stream North Wall (GSNW) and Shelf Slope Front (SSF) and exhibit considerable
54
IAV in their mean positions. Halliwell and Mooers [1979] perform empirical orthogonal
55
function and spectral analysis on satellite-derived weekly frontal charts of sea surface
56
temperature (SST) to study the GS, the SSF and WCR positions from September, 1975 to
57
August, 1977 and suggest that both fronts vary interannually over distances of 50-100 km.
58
Similar results are reported by Drinkwater et al. [1994] with IAV of 80 km for the SSF and
59
the GS. Climatological studies of WCRs by Auer [1987] and Cerone [1984] have shown that
60
the highest frequency of WCRs are formed in the vicinity of the New England Seamounts
61
Chain (NESC), and studies by Richardson [1980], Hurlburt and Thompson, [1984] and
62
Spall and Robinson [1990] have discussed the possible influence of GS interaction with the
63
NESC on WCR formation. Therefore, variability in the position of the GS may affect its
64
interaction with the NESC.
65
66
Halliwell and Mooers [1979] have suggested large scale atmospheric circulation as a
67
possible forcing mechanism affecting the occurrences of WCRs by the GS on inter-annual
68
scales. On these time scales, a prominent atmospheric factor affecting the circulation of the
69
North Atlantic basin is the North Atlantic Oscillation (NAO) [Hurrell, 1995]. NAO is the
70
north south alternation of atmospheric masses centered near the quasi-permanent Azores
71
(subtropical) high and Icelandic (subpolar) low [Hurrell et al., 2003]. The NAO is most
72
dominant during winter, modifying climate parameters like wind, solar irradiation, and
73
precipitation along with air and water temperatures. The NAO also has profound basin scale
74
biological and physical linkages like IAV of buoyancy and wind induced momentum fluxes,
75
affecting the water temperature and nutrient concentrations in the euphotic zone that
76
significantly impact marine ecosystems in the North Atlantic region [Drinkwater et al.
77
2003]. The NAO has been observed to be driving the IAV observed in the mean position of
78
the GS [Taylor and Stephens, 1998, Rossby and Benway, 2000, Taylor and Gangopadhyay,
79
2001]. Taylor and Stephens [1998] suggest that fluctuations in zonal westerly wind stress
80
generate westward traveling baroclinic Rossby waves that deflect off the western Atlantic
81
coast and impact the GS structure and separation as the probable mechanism for observed
82
IAV in GSNW position. Rossby [1999] and Rossby and Benway [2000] speculate that the
83
southward extent and transport of Labrador Seawater outflow into the SS to be the likely
84
mechanism in determining the GSNW position. Given that the GSNW position is known to
85
respond to the state of the NAO, this study investigates the possible influence of the NAO
86
on WCR occurrence.
87
88
Recent studies [Stammer and Wunsch, 1999, Penduff et al., 2004, Volkov 2005] have
89
shown that eddy kinetic energy (EKE) varies interannually in phase with the NAO index
90
over the North Atlantic. Instability processes from the mean flow generate excess energy
91
and is the source of EKE and lead to the formation of eddies and rings [Gill et al. 1974,
92
Stammer 1998]. Stammer [1998] has verified that baroclinic instability is a major eddy
93
source term throughout the ocean, especially for the western boundary currents. Thus, EKE
94
estimates can be useful as proxy data to study baroclinic instability processes for the GS.
95
Brachet et al. [2004] assimilate 8 years of altimeter data from 1993-2000 into a Parallel
96
Ocean Program (POP) model to analyze the mesoscale variability of EKE in the North
97
Atlantic basin. They hypothesize that contraction and dilation of the subtropical and
98
subpolar gyres by NAO-induced wind forcing results in the variations of EKE and hence
99
imply that the lateral movement of the GS due to contraction and dilation of the subtropical
100
gyre would account for EKE variability and consequent eddy generation. Penduff et al.
101
[2004] use model simulations and altimeter data to study EKE IAV and present a strong
102
positive covariance between NAO and EKE during 1994-2001 at a lag of 4-12 months. They
103
hypothesize that the observed redistribution of EKE at lags of 4-12 months include time-
104
scales for adjustment of the Gulf Stream-North Atlantic Current system to wind-induced
105
forcing (several months) as well as growth rate of mesoscale eddies like WCRs (few weeks).
106
Their results indicate that intensification (weakening) of wind stress in the North Atlantic
107
during high (low) NAO years enhance (diminish) Gulf Stream EKE, and thus, may be
108
indicative of the level of formation of baroclinic instabilities.
109
110
This study presents a comprehensive analysis of 22-years of WCR data (1978-1999)
111
collected between 75o and 50oW in the western North Atlantic. Atmospheric forcing related
112
to variation in the state of the NAO is seen to significantly co-vary with the annual
113
occurrence of WCRs in the SS. We present the following two hypotheses to study the
114
possible dynamical pathways involved in the observed covariance between the NAO and
115
WCR frequency over the study domain:
116
(1) NAO-GSNW-WCRs: Given that the state of the NAO is known to correlate with the
117
GSNW position, does the IAV in GSNW mean position have any influence on WCR activity?
118
(2) NAO-GSR EKE-WCRs: Given that EKE estimates can be useful to study baroclinic
119
instability processes that generate WCRs, does the NAO have any influence on the GSR
120
EKE distribution?
121
122
In Section 2, the influence of GS mean position on WCR occurrence is examined by
123
comparing the time lagged correlations between NAO-induced annual mean GSNW
124
satellite-derived frontal positions and the number of WCRs observed annually from satellite-
125
derived frontal charts. In Section 3, we introduce the North Atlantic Basin model used to
126
simulate realistic ocean conditions in response to the NAO fluctuations from 1980-1999 for
127
the North Atlantic. Model-derived EKE estimates are compared with results from Penduff et
128
al. [2004]. As part of model validation we test North Atlantic basin response to fluctuations
129
in the state of the NAO. Subsequent to model validation we proceed to analyze the GSR
130
EKE response to variations in phase of the NAO. Time-lagged cross-correlations between
131
model-derived GSR EKE, the NAO annual index, and WCR occurrences are examined to
132
deduce possible linkages.
133
important conclusions in Section 4.
We complete our analysis by summarizing our results and
134
135
2. INFLUENCE OF NAO-INDUCED GSNW MOVEMENT ON WCR OCCURRENCES
136
137
This section of the study discusses the possible implications of NAO-induced
138
GSNW movement on WCR occurrences. The data sources and methodologies for analysis
139
of the NAO-GSNW-WCR hypothesis are introduced, and subsequently a detailed discussion
140
of the results is presented.
141
2.1 WCR and GSNW Database
142
The positions of all WCR edges located in the SS (Fig. 1) between 75° and 50°W
143
during 1978-1999 were obtained from work conducted at Bedford Institute of Oceanography
144
(BIO), through hand digitization of satellite images derived from both NOAA and U. S.
145
Navy frontal charts. Each WCR observation from the satellite derived charts are uniquely
146
time-stamped based on year of formation, chronological WCR number for a given year and
147
date of observation. The WCRs are binned into individual years based on the year of
148
formation. The WCR features present in the dataset are quality controlled by visual
149
inspection, such that features like shingles and extraneous filaments are discarded. A total of
150
459 WCRs show significant IAV from 1978-1999 with approximately 21 (Sdev=6.5) WCRs
151
occurring each year, ranging from a maximum of 31 WCRs observed in 1990 to a minimum
152
of 7 WCRs seen in 1978 (Fig. 2a). The average of 21 WCRs occurring each year is similar
153
to results presented by Auer [1987]. The WCR dataset also has positions of the SSF and the
154
Gulf Stream North Wall (GSNW) for the same spatial domain and temporal period.
155
156
A multitude of NAO indices representing different frequencies such as daily,
157
monthly, seasonal including the important winter-time months and annual periods are
158
available
159
(http://www.cgd.ucar.edu/cas/jhurrell/indices.html). The NAO annual index (NAOAI) (Fig.
160
2b) is chosen for this study as a suitable proxy for representing fluctuations in the state of
161
the NAO on inter-annual time-scales. NAOAI is calculated by subtracting the normalized
162
annual mean sea-level pressure (SLP) at Stykkisholmur, Iceland from that of Ponta Delgada,
163
Azores [Hurrell, 1995]. The normalization of the SLP anomalies is done by dividing each
164
annual mean SLP by the long-term mean (1865-1984) standard deviation. A positive
for
studying
the
state
of
the
NAO
165
NAOAI for a specific year implies that the mean annual pressure is above average, resulting
166
in stronger westerlies and colder and drier conditions over the eastern Canada, Greenland
167
and the Mediterranean region, whereas conditions are warmer and wetter than average in
168
northern Europe, the eastern United States, and parts of Scandinavia. Conversely, negative
169
NAOAI implies lower than average annual mean pressure, resulting in colder winters in
170
northern Europe, the eastern United States, and warmer conditions in the Mediterranean
171
region and Greenland. [Hurrell, 1995].
172
173
The SSF and GSNW frontal positions are available as part of the quality controlled
174
database for WCRs mentioned earlier. The data are binned at each longitude between 75-
175
50oW to obtain monthly mean GSNW and SSF positions [Drinkwater et al. 1994] for 22
176
years, 1978 – 1999. Long-term SSF and GSNW mean positions are calculated by averaging
177
data from all months at each of the 26 longitudes, along with a line mid-way between the
178
long-term mean positions of both fronts (Fig. 1). The mid-line is examined together with the
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monthly mean positions of the GSNW and the SSF from 1978-99 to verify that neither the
180
SSF (in its extreme seaward position) nor the GSNW (in its extreme shoreward position)
181
cross the mid-line at any point in time. No instances of either front crossing the mid-line are
182
found within the period of study. The SS region being bounded by the SSF and GSNW
183
fronts is divided into two sub-regions by the mid-line.
184
185
The area bounded by the mid-line and long term mean position of the GSNW
186
becomes the long term mean area for the GSNW and similarly, the area bounded by the
187
mid-line and the long-term mean position of the SSF is defined as the SSF long-term mean
188
area. Monthly mean GSNW and SSF positions are further averaged annually, to obtain a
189
time-series of yearly mean GSNW and SSF positions at 26 longitude points from 1978-
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1999. The annual mean positions of GSNW and the SSF during 1995 is shown in Fig. 1.
191
Areas bounded by the SS mid-line and the annual mean position for each individual year are
192
calculated for both the GSNW and SSF. Subsequently, a 22 year time-series of GSNW and
193
SSF area anomalies is computed by subtracting the respective long-term mean areas from
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their annual mean area for each year. Annual area anomalies signify the movement of the
195
GSNW and the SSF for each year and show their individual anomaly contributions in
196
affecting IAV of the total SS area for any single year (Fig. 3).
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198
2.2 Relation between NAOAI, GSNW movement and WCR occurrences
199
A comparison between the NAOAI and the WCR time-series in Fig. 2 shows
200
discernible phase coherence for the period from 1978-1999. During years when the NAO
201
was largely in its positive phase (1989-1994), data show higher occurrences of WCRs and
202
conversely, negative phases of the NAO during early 80’s coincide with lower number of
203
WCRs. Lagged correlations between the NAOAI and WCR occurrences (Fig. 4a) confirm
204
the relationship between both time-series. The NAOAI is observed to be positively
205
correlated (r = 0.49, 95% significance, two-tailed test) to annual WCR activity at 0-year lag.
206
Cross-correlation analysis suggests that positive (negative) phases of the NAO correspond to
207
more (less) WCRs with predominantly high-NAO (low-NAO) years characterized by more
208
(less) WCR activity.
209
210
The zero-lag response of the WCRs to variations in the state of the NAO is
211
somewhat surprising as it is widely understood that the GS requires 1-3 years (discussed
212
below) to adjust to fluctuations in the state of the NAO, as reflected in the lateral variations
213
of the mean position of the GS. Taylor and Stephens [1998] and Taylor and Gangopadhyay
214
[2001] suggest that NAO-induced fluctuations in zonal westerly wind stress generate
215
westward traveling baroclinic Rossby waves that deflect off the western Atlantic coast and
216
impact the GS. This probable mechanism for observed IAV in GSNW mean position is
217
shown to occur at lags of 2-3 years in phase with the NAO. Rossby [1999] and Rossby and
218
Benway [2000] speculate that the observed displacement in GS mean position is due to
219
variations in NAO-induced buoyancy fluxes. They suggest that the southward extent of
220
“spilling” and transport of Labrador Seawater outflow into the SS occurs at lags of 1.5
221
years. Frankignoul et al. [2001] studied GS mean positions obtained from altimeter data and
222
concluded that the GS position lags the NAO by 11-18 months. They attributed variations in
223
both buoyancy and wind forcing to explain the observed lag.
224
225
Our analysis of GSNW mean position movement is derived from estimating the
226
GSNW and SSF area anomalies depicted in Fig. 3. The area anomaly estimates suggest
227
significant year-to-year variability in the mean position of the GSNW and SSF fronts. Both
228
the fronts are seen to be south of their global mean positions in the early to mid 1980’s and
229
early 1990’s; the fronts subsequently undergo phase reversals in the late 1980’s and mid
230
1990’s respectively, where they move north of their global mean positions. The movement
231
of the GSNW and SSF front appears to be in the same direction as each other with the
232
exception of 1988-1991 and 1999. Thus apart from 4 years in the 22-year analysis duration,
233
both the fronts move north or south of their mean positions in phase with each other. The
234
GSNW and SSF area anomalies exhibit significant positive correlation (not shown) at 0-year
235
lag (r = 0.67, 95% significance, two-tailed test). The net SS area is seen to increase during
236
the early 1980’s followed by a sharp decrease by the late 1980’s through early 1990’s. The
237
variations in the GSNW and SSF positions are estimated to affect the long term mean SS
238
area by approximately 9%.
239
240
Lagged correlations between the NAOAI and GSNW area anomalies display
241
maximum significant correlation observed (Fig. 4b) when GS area anomalies lag the NAO
242
by 1 year (r = 0.65, 95% significance, two-tailed test). The correlation between NAO and
243
GNSW movement supported by the work done by Rossby [1999], Rossby and Benway
244
[2000] and Frankignoul et al. [2001] signifies that an increase (decrease) in the GS area
245
anomaly i.e., GSNW moving southward (northward) of its mean position, responds to NAO
246
variations at a lag of over 1 year. Cross correlation between WCR occurrences and GSNW
247
area anomalies (Fig. 4c) shows a maximum significant correlation (r = 0.63, 95%
248
significance, two-tailed test) with the annual rate of WCR occurrence leading the GS area
249
anomalies by 1 year. Hence, higher occurrences of WCRs are observed during high (low)
250
NAO years, whereas the GSNW is observed northward (southward) of its mean position
251
during high (low) NAO years after a lag of 1 year. Therefore, results from recent work and
252
the present study suggests that the lateral movement of the GS is presumably not affecting
253
the generation of WCRs, as they respond at different phases to variations in the state of the
254
NAO.
255
256
3. NAO INFLUENCE ON GSR EKE DISTRIBUTION AND WCR OCCURRENCES
257
258
Fluctuations in the state of the NAO are hypothesized to affect GSR EKE
259
distributions and related WCR occurrences in this section. The numerical model set-up used
260
for testing the hypothesis is discussed, together with steps for computation of EKE from
261
model output fields. Model results are validated by comparing model-derived EKE
262
estimates for the entire North Atlantic domain with a comparative study by Penduff et al.
263
[2004]. Subsequent to suitable validation of model results, GSR EKE estimates are
264
computed and analyzed for possible responses to the state of the NAO. Interpretation of
265
results and physical mechanisms involved in affecting the NAO-GSR EKE link are
266
presented.
267
268
3.1 North Atlantic Basin Model (NABM)
269
The response of GSR EKE to variability in NAO-induced ocean conditions is
270
studied by numerical simulation of the North Atlantic basin (NAB). The modeling study is
271
part of a North Atlantic basin climatology project designed to understand ocean response to
272
NAO-related momentum and heat flux patterns. The EKE computations for the model are
273
designed in accordance to equations used in the modeling study by Penduff et al. [2004] as
274
part of the CLIPPER experiment (henceforth the Penduff et al. [2004] model is referred as
275
CLIPPER). CLIPPER uses a geopotential-coordinate primitive equation model to simulate
276
the entire Atlantic Ocean from 1979-1999. The model is forced using (European Centre for
277
Medium-Range Weather Forecasts) ECMWF derived re-analysis data and compared with
278
Topex/Poseidon (T/P) altimeter data for model validation. The results obtained from our
279
modeling study for the NAB are compared with CLIPPER and T/P-derived results.
280
281
The NAB is simulated using the Regional Ocean Modeling System (ROMS version
282
2.2) model. ROMS is a free-surface, terrain-following, primitive equation ocean model that
283
uses a split-explicit time-stepping scheme that solves primitive momentum equations by
284
separating them into finite baroclinic and barotropic time steps (Shchepetkin and McWilliams
285
2004). Enhancements to the model include sigma-coordinate pressure-gradient error reduction
286
(Shchepetkin and McWilliams 2003), subgrid-scale parameterizations (Gent and McWilliams
287
1990, Griffies et al. 1998) and improved time-stepping algorithm (Shchepetkin and McWilliams
288
2004). The spatial domain for the North Atlantic Basin Model (NABM) is from 75o N, 100o
289
W to 15o N, 20o E (Fig 5a). The domain is implemented using a 1/6o horizontal and 50 level
290
vertical resolution Mercator grid, with the bottom depth set to 5500m. The grid bathymetry
291
is derived from the ETOPO2 topography database for the GSR and ETOPO5 for the rest of
292
the NAB. NABM is implemented with a rigid lid, closed boundary schema. Lateral tracer
293
viscosity and diffusion are both set to laplacian and biharmonic constants. The model is also
294
configured for linear and quadratic bottom drag effects.
295
296
The NAO annual simulations from 1980-1999 are initialized with NABM-derived
297
model fields obtained from NAO climatological simulations. The NAO climatological phase
298
of the NABM simulations is part of the larger basin-scale climatology study of the North
299
Atlantic response to NAO fluctuations. The climatology study involves NABM start-up,
300
spin-up and adjustment to high and low NAO climatological forcing fields. The details of
301
the climatological simulations are not discussed in this paper as we focus on the NAO
302
annual simulations from 1980-1999. Since the period in the mid to late 70’s before the
303
beginning of this study were years when the NAO was predominantly in its high phase, we
304
initialize the 20-year NAO annual simulation with tracer and climatology fields obtained
305
from the high NAO climatological simulation.
306
307
The atmospheric forcing fields consisting of monthly mean net heat flux, shortwave
308
radiation, meridional and zonal wind stress components for each month of each individual
309
year starting from 1980 are derived from the Southampton Ocean Center (SOC) Ocean-
310
Atmosphere atlas [Josey, 2001]. The availability of the SOC dataset beginning from 1980
311
restricts our modeling analysis to the 1980-1999 period as opposed to our data analysis from
312
1978-1999. Josey [2001] has shown large discrepancies between net heat flux values for the
313
NCEP reanalysis and ECMWF reanalysis data in comparison to SOC net heat flux values
314
primarily due to underestimation of shortwave gain and overestimation of latent heat loss
315
components by these models. In addition, Josey [2001] demonstrate that the SOC data
316
shows good agreement with carefully measured values of net, latent, sensible, and longwave
317
heat radiation components for the Northeast Atlantic region. The model results are saved at
318
3-day intervals for the 20-year simulation of the NAB. Velocity vectors generated by the
319
NABM are used to compute and study NAB and GSR EKE estimates.
320
321
3.2 EKE Analysis
322
A time series of zonal (u) and meridional velocity (v) vectors obtained from the 20
323
year NABM simulation are used to compute EKE for the NAB. The velocity vectors for the
324
EKE computation are chosen at 55m depth, thus eliminating possible Ekman or mixed layer
325
effects as suggested by Penduff et al. [2004] . Since model results are saved at 3 day
326
intervals, a time series of 120 velocity vector observations for each year from 1980-1999 are
327
used to compute EKE based upon Equation 1.
  
2
y
328
329
2
y
y
EKE

[
u

u
v

v
/
2
it
it]
it
it
it
,
Equation 1 after Penduff et al. [2004]
330
where
331
332
y
it

6
months

 1i
u

u
i
it






y



 120
v
i
it

6
months
v
i

it


333
334
The superscript y in Eq. 1 denotes the current year, ranging from 1980-1999, whereas
335
subscript it is the current time step of the yth year ranging from 1-120. The instantaneous
336
zonal and meridional velocities are represented as uit and vit respectively. The zonal ( u ) and
337
meridional ( v ) running mean velocities are computed by averaging over 60 data points (or 6
338
months) before and after the current time stepping index, irrespective of the year. The
339
resulting estimates are a continuous 1 year running window time-series of EKE. The
340
advantage of this method for computing EKE is that it preserves mesoscale as well as inter-
341
annual scale velocity fluctuations at the loss of possible seasonal trends [Penduff et al.,
342
2004].
343
344
Sea level anomaly (SLA) data are obtained from T/P-based observations
345
(http://podaac.jpl.nasa.gov) for the North Atlantic at 10-day intervals from October 1992 to
346
June 2000. T/P-based instantaneous estimates of zonal (uit) and meridional (vit) velocities
347
are obtained using Equation .2.
348
349
350
 u it  g ˆ
   k  SLAit 
 vit  f
Equation 2 after Penduff et al. [2004]
351
where, g is acceleration due to gravity and f is the Coriolis parameter. T/P-derived EKE
352
estimates are computed using the same methodology described in Equation 1.
353
354
To study the response of NAB EKE to the state of the NAO for model validation and
355
GSR EKE to the state of the NAO for hypothesis testing, the instantaneous EKE time-series
356
is averaged for suitable comparison with the NAOAI. The 20-year instantaneous EKE time-
357
series obtained from NABM-derived velocity vectors are temporally averaged by year and
358
spatially averaged into three distinct domains signifying the GSR, the sub-tropical gyre
359
region (STG) and the sub-polar gyre (SPG) region as shown in Fig 5a. Similarly, T/P-
360
derived instantaneous EKE estimates are averaged yearly and spatially within SPG and STG
361
regions. The STG and SPG regions are chosen based on the basin-scale dipole spatial
362
variability patterns reported by Esselborn and Eden [2001] from analysis of T/P-derived sea
363
surface height (SSH) data. Global mean values are subtracted from each of the respective
364
SPG and STG annual mean EKE time-series to obtain SPG and STG EKE anomaly time-
365
series. The resulting STG EKE anomaly time-series is differenced from the SPG EKE
366
anomaly time-series to obtain an EKE gyre contrast anomaly time-series. The EKE gyre
367
contrast anomaly is defined as an index to compare the STG and SPG relative to each other
368
and used as an indicator of variability in the dipole structure.
369
anomaly time-series is compared with the NAOAI to investigate possible correlations. A
370
similar EKE contrast time-series constructed by [Penduff et al., 2004] and analogously
371
computed T/P-derived EKE gyre contrast anomalies are used to evaluate and validate
372
NABM results.
373
The EKE gyre contrast
374
The GSR EKE time-series is computed to understand possible influence of the NAO
375
within the region. Instantaneous EKE estimates during 1980-1999 are averaged into annual
376
means for the 20-year period within the GSR region depicted in Fig. 5a. The global mean
377
GSR EKE is subtracted from the annual mean GSR EKE time-series to obtain annual GSR
378
EKE anomalies. The annual GSR EKE anomalies signify the variation in the change of EKE
379
for the GSR, and are further examined for possible response to the state of the NAO, and
380
thus potentially explaining the variability observed in WCR occurrences.
381
382
3.3 NABM-derived EKE estimates
383
Spatial and temporal distributions of NAB-derived EKE observations are presented and
384
validated with T/P-derived and CLIPPER-derived EKE estimates.
385
386
3.3.1 Basin-wide EKE distribution
387
NABM-derived zonal and meridional velocity vectors at 55m depth are used to
388
estimate the long-term mean EKE for the NAB simulation by averaging over the 20-year
389
period from 1980-99. The long-term mean EKE estimates display considerable spatial
390
variability (Fig. 5a) including energetic regions such as the south Equatorial current system
391
together with the North Brazil Current retroflection region, the Gulf Stream-North Atlantic
392
Current system and the Caribbean Current system. These high energy regions with mean
393
EKE greater than 500 cm2 sec-2 shown in Fig. 5b are known to evidence high occurrences
394
of cyclonic and anti-cyclonic mesoscale eddies such as WCRs [Johns et al., 1990, Auer
395
1987, Carton and Chao, 1999]. Similar spatial variability is also reported by Penduff et al.
396
[2004]. Another distinctive feature observed in Fig 5a is the Labrador Current in the
397
western North Atlantic that transports water over the continental shelf and slope east of
398
Newfoundland and Labrador, between Hudson Strait and the southern tip of the Grand
399
Banks [Lazier and Wright, 1993]. The Labrador Current exhibits little deviation from its
400
mean flow (Fig. 5a) although the eddy variability comparatively increases around the edges
401
of the current. The Labrador Current is conjectured to be a buoyancy driven current [Lazier
402
and Wright, 1999, Han and Tang, 1999] and may thus explain the observed lack of eddy
403
variability. Most of the other regions such as the Northeast Atlantic do not present any large
404
EKE variability.
405
406
The GSR displays maximum EKE near the GS separation point in the vicinity of
407
Cape Hatteras (35oN, Fig 5b). The EKE maxima continues northward past the separation
408
region, due to the presence of a large standing eddy (represented by the 1000 cm2 sec
409
contour in Fig. 5b). Penduff et al. [2004] also report a similar overshoot of the GS due to the
410
presence of a standing eddy in the vicinity of the GS separation point based on their
411
CLIPPER-derived mean EKE field. The standing-eddy problem in the Gulf Stream
412
simulations by eddy-resolving models is well documented [Thompson and Schmitz, 1989,
413
Dengg, 1993] and hence the unrealistic northward continuation of maximum EKE energy
414
past the known GS separation point in comparison to known GS path can be attributed to
415
shortcomings in the models. Downstream of the GS separation region, the GSR continues to
416
be energetic as the GS traverses northeastward to around 55oW as observed by the 500 cm2
417
sec -2 contour in Fig. 5b. Eastward of 55oW the EKE tapers off as the GS reaches south of
418
the Tail of Grand Banks. Most of the remnant GS EKE continues to move northeastward
419
into the SPG region, however a small portion of the EKE signature turns around the Flemish
420
Cap and moves northwestward between 40-50oW as seen by the 50 cm2 sec
-2
-2
contour in
421
Fig. 5b. A similar spatial pattern is reported for the SPG region by White and Heywood
422
[1995].
423
424
3.3.2 Temporal variability of EKE in SPG and STG
425
Annual mean EKE estimates spatially averaged over the STG and SPG areas provide
426
a 20-year time series from 1980-1999, to study the IAV of EKE within the two regions (Fig.
427
6). The SPG is observed to have a mean EKE of 94 cm2 sec-2 (Sdev=4.7) with lower EKE
428
values for most of the 80’s, followed by a considerable increase in EKE from the early to
429
mid 90’s, reaching a maximum of 109.1 cm2 sec-2 in 1995 (Fig. 6a). The following year,
430
1996, witnesses the single largest decrease of EKE to 88.4 cm2 sec-2 during the study period
431
and consistently remains lower than average through to 1999. The NABM-derived EKE
432
time-series estimates for SPG are observed to be in reasonable agreement with CLIPPER-
433
derived EKE estimates (compare Fig. 6a with Fig. 4a in Penduff et al. [2004]). Both time-
434
series estimate a rise in EKE in 1984, and a drop in 1987, followed by a steady rise in EKE
435
until 1995. Subsequently, both time-series record large drops in 1996. However, the
436
CLIPPER-derived EKE display a rise in EKE from 1997-1999, which is not observed in the
437
NABM-derived EKE estimates. In comparison to the T/P-derived EKE time-series data
438
observations (Fig. 6a) for a shorter period from 1993-1999, NABM-derived estimates and
439
T/P-derived EKE estimates display similar temporal patterns, with the exception of a rise in
440
T/P-derived EKE after 1998.
441
442
The STG has a mean EKE of 88 cm2 sec-2 (Sdev=5.6) with lower EKE in the early
443
80’s, followed by a considerable increase in 1985 and subsequent decrease in 1986 (Fig.
444
6b). Thereafter, the EKE consistently increases from the early 90’s reaching a maximum of
445
96.2 cm2 sec-2 in 1997. A decreasing trend is observed in the next couple of years in 1998
446
and 1999. The NABM-derived EKE and the CLIPPER-derived EKE (compare Fig. 6b with
447
Fig. 4b in Penduff et al. [2004]) for the STG region display similar IAV, exhibiting
448
increases in EKE in 1982 and 1985 and decreases in 1983-84 and 1986, followed by a
449
consistent trend of EKE increase through the mid 90’s. The NABM-derived EKE also
450
display good agreement in IAV with the T/P data derived EKE estimates (Fig. 6b) for the
451
1993-1999 period. Both time-series exhibit EKE peaks in 1995 and 1997 followed by EKE
452
decrease in 1996 and 1998-99 respectively. T/P-derived EKE display higher mean EKE in
453
STG as opposed to SPG (Fig 6a and Fig 6b). In contrast, the NABM-derived EKE estimates
454
display higher mean EKE in SPG in comparison to STG. However, Penduff et al. [2004]
455
too report that CLIPPER-derived EKE values for the SPG are higher in comparison to STG
456
values and suggest that CLIPPER underestimates the EKE in the STG region by 30% in
457
comparison to T/P data.
458
459
The estimates of the NABM-derived EKE are observed to display similar IAV with
460
EKE estimates derived from the CLIPPER for both the STG and SPG regions. In
461
comparison to T/P-derived EKE, NABM-derived EKE time-series exhibits good agreement
462
with the IAV pattern observed in the T/P data for the STG region, even though NABM EKE
463
observations are considerably underestimated, as also evidenced in the CLIPPER-derived
464
EKE estimates. The NABM EKE estimates for the SPG region are in better agreement with
465
IAV observed with the CLIPPER-derived EKE estimates rather than T/P data derived EKE
466
estimates.
467
468
469
3.4 NAO-induced EKE response in NAB
470
The difference between the SPG and STG EKE anomaly time-series obtained by
471
removing the respective long-term means, presents an EKE gyre contrast anomaly time-
472
series (Fig. 7a). The EKE gyre contrast anomaly time-series signifies the IAV in EKE
473
meridional distribution between the SPG and STG, such that positive anomalies suggest
474
greater than average differences between the two gyres, and negative anomalies represent
475
diminished differences between the two gyres. The EKE gyre contrast anomaly displays
476
positive anomalies in all years except 1985, 1996 and 1997 during the study period. The
477
higher positive anomalies are seen in 1983-84 and 1994-95 whereas higher negative
478
anomalies are seen in 1996-97. NABM-derived EKE gyre contrast anomaly is in good
479
agreement with CLIPPER-derived EKE contrast anomaly (compare Fig. 7a with Fig. 4c in
480
Penduff et al. [2004]) which displays positive anomalies from early 80’s through mid 90’s
481
with a peak in positive anomaly in 1984. A negative EKE anomaly is evidenced from 1995
482
to 1996-97 in the CLIPPER-derived EKE contrast anomaly, followed by a reversal in trend,
483
such that positive EKE anomalies are observed in 1998 and 1999. The T/P data derived
484
EKE contrast anomalies (Fig. 7a) and NABM-derived EKE contrast anomalies are seen to
485
be in phase with each other with the exception of 1995, when T/P begins to decrease a year
486
earlier to NABM, as also observed in the CLIPPER-derived EKE gyre contrast anomaly
487
time-series.
488
489
In order to study the wind-induced influence of the atmosphere on the energy in the
490
ocean we present lagged correlations between the NAOAI and NABM-derived EKE gyre
491
contrast anomaly time-series. Lagged correlations between the NAOAI and EKE gyre
492
contrast anomalies display maximum significant correlation observed (Fig. 7b) at zero year
493
lag (r = 0.56, 95% significance, two-tailed test). The cross-correlation suggests that EKE
494
meridional contrast between the STG and SPG gyres is higher during high NAO phases and
495
the EKE gyre contrast is lower during low NAO phases. Penduff et al. [2004] present a
496
similar correlation between EKE meridional gyre contrast and the monthly NAO index and
497
evidence the correlation to be significant only during the 1994-1999 period with the EKE
498
lagging the NAO index by 4-11 months. Since, no significant correlation is observed prior
499
to 1994, Penduff et al. [2004] hypothesize from their results that the magnitude of the NAO
500
index, rather than only the phase may be important in affecting the wind-induced energy
501
distribution in the ocean. Thus, periods when the NAO fluctuates by large magnitudes may
502
result in a significant ocean response in the form of EKE redistribution. Hence, a significant
503
correlation between NAO index and EKE contrast anomaly is obtained during 1994-1999, a
504
period when the NAO index exhibits large fluctuations in magnitude.
505
506
However, the present study displays a significant correlation over the entire 20-year
507
time period thus suggesting that the NAO-EKE correlation may always be present but may
508
be more discernible in years when the NAO index exhibits large fluctuations in phase. The
509
different conclusions may be due to the use of NAOAI in the present study instead of the
510
monthly NAO used by Penduff et al. [2004]. Another significant difference between the
511
CLIPPER and NABM is the different atmospheric forcing used in the two models. NABM
512
utilizes the SOC atlas for its atmospheric forcing fields; whereas CLIPPER applies ECMWF
513
derived re-analysis data which is observed to have large discrepancies with observed data
514
[Josey, 2001] and may have resulted in the different conclusions. However, since the
515
NABM-derived EKE estimates are in agreement with T/P data and CLIPPER derived EKE
516
estimates, a strong case can not be made with regard to the differences in atmospheric
517
forcing used in the models.
518
519
3.5 NAO-induced EKE response in GSR
520
Comparable results between the T/P, CLIPPER and NABM for the STG and SPG
521
provides reasonable confidence to study the NABM-derived EKE estimates for the GSR.
522
The GSR has a mean EKE of 425 cm2 sec-2 (Sdev=26) and this value is subtracted from the
523
GSR EKE annual time-series to derive a GSR EKE anomaly time-series (Fig. 8a). Instances
524
when the EKE anomaly is positive signifies above average EKE availability; whereas
525
negative EKE anomalies suggest below mean availability of EKE in the GSR. The GSR
526
EKE time-series is positive from 1980-1985, and then negative for a couple of years in 1986
527
and 1987. Following 1987, the EKE anomaly remains positive for nearly 8 years through
528
1994, exhibiting the largest positive anomaly in 1990 for the study period. A reversal in
529
trend occurs after 1995, as 1996 and 1997 display negative anomalies, followed by positive
530
anomalies in 1998 and 1999. Lagged correlations between the NAOAI and GSR EKE
531
anomalies exhibit a maximum significant positive correlation observed (Fig. 8b) at 0-year
532
lag (r = 0.52, 95% significance, two-tailed test). The correlation suggests that EKE in GSR
533
significantly responds to wind-induced atmospheric forcing related to the state of the NAO
534
within a lag of a year. Therefore, higher (lower) EKE distribution is observed in the GSR
535
during high (low) NAO years. The significant response of the GSR EKE and WCRs to the
536
state of the NAO at similar timescales provides a clear indication that the variability in the
537
GS EKE is the most probable mechanism affecting the annual occurrences of WCRs.
538
539
540
3.6 Mechanism of NAO influence on ocean EKE
541
Although a significant correlation between the state of the NAO and EKE for the
542
NAB and GSR region has been has been observed with the present study and other related
543
studies, the physical mechanism linking the ocean EKE response to the NAO is not clearly
544
understood. White and Heywood [1995] use Geosat and T/P altimeter data to reveal that the
545
maximum EKE observed for the SPG in their study coincided with maximum wind stress
546
for the same period with a lag of a couple of months and in phase with the line of zero wind
547
stress curl. Esselborn and Eden [2001] observe variability in circulation in the SPG region
548
due to NAO fluctuations from T/P data and GFDL MOM model, and establish that changes
549
in the wind stress curl is the primary factor in creating Svedrup-type circulation anomalies
550
on inter-annual timescales. Therefore, circulation changes affect EKE redistributions and
551
related sea-surface height anomalies. Furthermore, Eden and Willebrand [2001] suggest
552
variations in wind-induced Ekman transport in response to the state of the NAO as the main
553
driving mechanism for circulation changes in the STG based on numerical simulations.
554
Volkov and Aken [2003] report from T/P and ERS derived sea-level anomaly data [2003]
555
that the ocean responds to the NAO index within one year.
556
557
Alternatively, Bersch [2002] reveals a contraction in the SPG in 1996-97 coinciding
558
with the low NAO years in the 90’s from analysis of hydrographic data. A decrease in SPG
559
circulation is also observed together with gyre contraction. Similarly, Brachet et al. [2004]
560
hypothesize that contraction and dilation of the STG and SPG in response to NAO
561
fluctuations could affect the mesoscale eddy variability for the NAB. However, a
562
contraction or dilation in the STG would imply a southward lateral movement of the GSNW
563
during STG gyre contraction and northward lateral movement of the GSNW during STG
564
gyre dilation. In order to ascertain a probable link between STG gyre variability and
565
mesoscale eddy activity, we present a lagged correlation between GSNW area anomalies
566
and NABM-derived GS EKE anomalies (Fig. 8c). Lagged correlations display a weak
567
correlation (r = 0.46, 95% significance, two-tailed test) with the GSNW area anomalies
568
lagging the EKE gyre contrast anomalies by two years. The correlation suggests that the GS-
569
EKE redistribution and GS lateral movements occur over different phases. We can conclude
570
that although the gyre contraction/dilation hypothesis may be true for the SPG as shown by
571
Bersch [2002], IAV in the STG EKE redistribution is clearly not due to the
572
contraction/dilation of the STG.
573
574
Penduff et al. [2004] suggest that EKE response to the state of the NAO within a
575
year ranges between fast barotropic (months) and slow baroclinic (years) adjustment modes.
576
The barotropic ocean response to wind fluctuations would take months, and the growth rate
577
of instabilities to match scales of mesoscale eddies would take an order of a few weeks.
578
Penduff et al. [2004] present evidence suggesting circulation fluctuations may impart
579
changes in baroclinic shear and instability that affect local EKE redistributions, however,
580
NAO-induced circulation anomalies do not completely explain the NAO-EKE link. An
581
integration of a multitude of nonlinear adjustment mechanisms such as Rossby wave
582
adjustment, local and remote atmospheric forcing, baroclinic and barotropic modes as well
583
as nonlinear advective dynamics could most likely explain the observed EKE response to
584
the state of the NAO.
585
586
587
588
4. SUMMARY AND CONCLUSIONS
589
590
We present evidence suggesting an annual response of WCR occurrences to
591
annual variability in the state of the NAO based on correlations between the NAOAI and
592
satellite-derived WCR observations from 1978-1999. Higher (Lower) occurrences of
593
WCRs are seen during phases when the NAO is high (low) at 0-year lag. NAO-induced
594
IAV in the GSNW position and redistribution of GS EKE are hypothesized as possible
595
dynamical mechanisms involved in explaining the observed covariance between the NAO
596
and WCR frequency over the study domain. Investigation into possible impacts of the
597
GSNW position and the state of the NAO on WCR occurrence reveal that the GSNW
598
responds to fluctuations in the state of the NAO at a lag of 1-year. Since the annual rate
599
of WCR occurrence has been shown to co-vary with the NAO at 0-year lag; whereas the
600
GSNW mean position responds to NAO forcing after a lag of 1-year, we suggest that GS
601
frontal movement may not be the primary mechanism causing variation in the rate of
602
WCR occurrence as both parameters are seen to respond at different phases.
603
604
The response of GSR EKE to variability in NAO-induced ocean conditions is
605
studied by numerical simulation of the NAB from 1980-1999. A 1/6o resolution eddy-
606
resolving NABM is used for the simulation with forcing parameters derived from the SOC
607
Ocean-Atmosphere atlas. EKE estimates are computed from the NABM-derived upper-
608
ocean zonal and meridional velocity vectors. NABM-derived EKE estimates are observed to
609
be spatially and temporally congruent with T/P-derived observations and CLIPPER EKE
610
values presented by Penduff et al. [2004] for the NAB. Model validations are done by
611
comparing results from CLIPPER-derived and T/P-derived EKE estimates and further
612
analyzing the NAB response to NAO fluctuations by computing gyre contrast anomalies.
613
Lagged correlations between the NAOAI and EKE gyre contrast anomalies display
614
maximum significant correlation observed at 0-year lag. The result implies that the basin-
615
wide NAO-EKE correlation may always be present but may be more discernible in years
616
when the NAO index exhibits large fluctuations in phase.
617
618
Comparison between GSR EKE anomalies which is a subset of the NAB data, and
619
NAOAI reveal a discernable response of GSR EKE to variations in the state of the NAO at a
620
lag of 0 year. The significant responses of the GSR EKE and WCRs to the state of the NAO
621
at similar timescales provide a clear indication that the variability in the GS EKE is the most
622
probable mechanism affecting the annual occurrences of WCRs.
623
624
A multitude of mechanisms have been suggested in explaining the link observed
625
between NAO variations and EKE response. White and Heywood [1995], Esselborn and
626
Eden [2001] and Eden and Willebrand [2001] have presented evidence suggesting that
627
the NAO-induced wind stress curl variations cause circulation anomalies in the STG and
628
SPG in accordance with Svedrup dynamics. In addition, Brachet et al. [2004]
629
hypothesize that contraction and dilation of the STG and SPG in response to NAO
630
fluctuations could affect the mesoscale eddy variability for the NAB supported by results
631
from Bersch [2002] for the SPG. However, we conclude that although the gyre
632
contraction/dilation hypothesis may be true for the SPG as shown by Bersch [2002], IAV
633
in the STG EKE redistribution is clearly not due to the contraction/dilation of the STG as
634
the movement of the GSNW is observed to be out of phase with GSR EKE distribution.
635
In agreement with Penduff et al. [2004], we support that the EKE response is a result of
636
an overlap of different complex adjustment modes ranging between fast barotropic and
637
slow baroclinic adjustment modes.
638
639
In conclusion, as baroclinic instability is a major eddy source term throughout the
640
ocean, especially for western boundary currents, and lead to the occurrence of eddies and
641
rings, we suggest that high (low) phases in the state of the NAO exhibit higher (lower)
642
EKE in the GSR, thus providing a greater (lesser) source of baroclinic instability to the
643
GS front, resulting in higher (lower) occurrences of WCRs.
ACKNOWLEDGEMENTS
The authors would like to thank Dr. K. Drinkwater, Institute for Marine Research, Bergen,
Norway and R. Pettipas, Bedford Institute of Oceanography, Dartmouth, Nova Scotia,
Canada, for providing digitized Gulf Stream north wall, shelf-slope front and warm-core
ring positional data. This work is being supported by NASA’s Interdisciplinary Science
(IDS) Program, under grant number NNG04GH50G.
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FIGURES
Figure 1. Study domain including positions of SS mid line (dashed black) and mean GSNW
(bold gray) and SSF (bold black) from 22 years (1978-1999) of frontal position data. Also
shown are the annual mean positions of the GSNW (gray points) and SSF (black points) for
1995. Some important hydrographical regions in the WNA include the Tail of Grand Banks
(TGB), Georges Bank (GB) and the Mid Atlantic Bight (MAB) and the Gulf of Maine
(GoM). Some important geographical regions include Newfoundland (NFLD), Nova Scotia
(NS), Flemish Cap (FC) and Cape Hatteras (CH). Thin solid and dotted lines indicate the
200m and 100m bathymetric contours.
Figure 2. (a): The total number of warm core rings per year from 1978-1999. (b): The
NAOAI for the same time period (http://www.cgd.ucar.edu/cas/jhurrell/indices.html).
Figure 3. GSNW and SSF area anomalies in km2, showing the movement of both fronts
with respect to each other. Positive anomalies signify northward or shoreward movement
whereas negative anomalies signify southward or seaward migration. The sign of the GS
area anomalies have been reversed to understand the frontal movement. The net change in
SS area anomalies (bold black) displays significant IAV due to the movement of both the
GSNW and SSF fronts.
Figure 4. A series of correlations are presented along with bounds of 95% significance with
43 degrees of freedom (a): lagged correlation between NAOAI and annual rate of WCR
formation showing maximum significant positive correlation at zero lag. (b): lagged
correlation between NAOAI and GSNW area anomaly showing maximum significant
positive correlation at 1 year. The GS movement responds to NAO forcing after a lag of 1
year. Therefore, the GSNW is observed north (south) of its mean position during high (low)
NAO years. (c): lagged correlation between number of WCRs and GSNW area anomalies
showing maximum significant correlation at 1-year lag.
Figure 5. (a): Mean EKE (cm2 sec-2) for the NAB computed at 55m depth and averaged 20year period from 1980-1999. The solid rectangular boxes represent the sub-tropical gyre
(STG) and the sub-polar gyre (SPG) regions, whereas the dotted rectangular box represents
the Gulf Stream Region (GSR). EKE estimates are spatially averaged over each of the
respective boxes. (b) Contours of high EKE regions (greater than 500 cm2 sec-2) in the NAB
(from Fig. 6a) showing that the GSR, North Brazil Current retroflection (NBC) and
Caribbean Current System (CCS) regions are most energetic. The Labrador Current (LS) is
also shown.
Figure 6. (a): Annual mean EKE estimates for the SPG from 1980-1999. (b): Annual mean
EKE estimates for the STG from 1980-1999. The SPG is observed to have higher mean
EKE in comparison to the STG.
Figure 7. (a): EKE Gyre contrast anomalies calculated by subtracting the annual EKE
means between SPG and STG and removing the long term mean difference from 1980-1999
(b): Lagged correlation between NAOAI and EKE contrast anomalies showing maximum
significant positive correlation at zero year lag (39 degrees of freedom).
Figure 8. (a): GSR EKE anomalies computed by removing the long term GSR EKE mean
from the annual GSR EKE means during 1980-1999 (b): Lagged correlation between
NAOAI and GSR EKE anomalies showing maximum significant positive correlation at 1
year. Correlation suggests higher (lower) GSR EKE availability during high (low) NAO
years. (c): Lagged correlation between number of GSR EKE and GSNW area anomalies
showing weak correlation at 2 year lag.