Low-­‐frequency  SST  and  Upper-­‐Ocean  Heat  Content  Variability  in  

the  North  Atlan?c  Martha  W.  Buckley    

George  Mason  University  (GMU/COLA)    

Collaborators:    Rui  M.  Ponte    

Atmospheric  and  Environmental  Research  (AER)    

Patrick  Heimbach  and  Gael  Forget    MassachuseRs  Ins?tute  of  Technology  (MIT)  

               

Observed  Atlan?c  SST  anomalies  

•  Observa(ons  indicate  Atlan(c  SSTs  exhibit  significant  low-­‐frequency  variability  (Bjerknes  1964;  Kushnir  1994;  Ting  et  al.  2009).  

•  The  origin  of  Atlan(c  SST  anomalies  depends  on  (mescale  –  Intraannual  to  interannual:  response  to  local  atmospheric  forcing  (Frankignoul  and  Hasselmann,  1977),  •   e.g.  the  North  Atlan?c  Oscilla?on  (NAO)  tripole  (Cayan,  1992).    

–  Longer  ?mescales  (how  long?)  ocean  circula?on  may  play  a  role.  •  e.g.  basin-­‐scale  SST  anomalies  in  the  North  Atlan?c,  termed  Atlan?c  Mul?decadal  Variability  (Kerr,  2000;  Knight  et  al.,  2005).  

•  thought  to  be  related  to  varia?ons  in  the  Atlan?c  Meridional  Overturning  Circula?on  (AMOC;  e.g.  Kushnir,  1994;  Delworth  and  Mann,  2000)  and/or  gyre  circula?ons  (Hakkinen  and  Rhines,  2004;  Hakkinen  et  al,  2011).  

•  Rela?ve  importance  of  atmospheric  forcing  and  ocean  dynamics  in  SST  variability  has  implica?ons  for  predictability.      – Dominance  of  local  atmospheric  forcing  -­‐>  liRle  predictability.  – Dominance  of  slow  ocean  processes  -­‐>  high  poten?al  for  predictability.    

Approach  

Method:  Es(ma(ng  the  Circula(on  and  Climate  of  the  Ocean  (ECCO)    version  4  state  es(mate  (1992-­‐2010)    •  MITgcm  least  squares  fit  to  observa?ons  using  adjoint  (Forget  et  al.,  2015  a,b)  •   fit  achieved  by  adjus?ng  ini?al  condi?ons,  forcing,  and  model  parameters  •  sa(sfies  equa(ons  of  mo(on  &  preserves  property  budgets  (Wunsch  &  Heimbach,  

2013)  

•  Atmospheric  forcing:  ERA-­‐Interim  •  Ocean  data:    

•  In-­‐situ:  Argo,  CTDs,    XBTs,  mooring  arrays    •  AVHRR  &  AMSR-­‐E  SST  and  satellite  al?metry    

•  Model  details  (Forget  et  al.,  2015b)  •  New  global  grid  (LLC90),  includes  Arc?c,  50  ver?cal  levels,  par?al  cells  •  Nominal  1o  resolu?on  with  telescopic  resolu?on  to  1/3o  near  Equator  •  State  of  the  art  dynamic/thermodynamics  sea  ice  model  

   

What  are  rela?ve  roles  of  local  atmospheric  forcing  and  ocean  dynamics  in  upper-­‐ocean  heat  content  (UOHC)  variability  in  the  North  Atlan?c?  

Low-­‐frequency  Atlan?c  SST  variability  Analysis  of  monthly  data,  seasonal  cycle  removed  by  simply  subtrac?ng  out  the  mean  monthly  climatology.  

•  Atlan?c  SST  variability  in  ECCO  similar  to  Reynolds  (2002)  gridded  SST.  

•  PaRern  resembles  classic  “NAO  tripole”  

•  Spectra  are  red  at  high  frequencies:  slope=-­‐1.6  

•  Spectra  flaRen  out  at  ?mescales  of  2—5  years  

25%   14%  

24%   15%  

Slope=-­‐1.6  

Buckley  et  al,  2014,  J.  Climate  

Upper  ocean  heat  content  variability  Heat  Content  integrated  over  maximum    climatological  mixed  layer  depth,  D:      • Measure  of  heat  contained  in  “ac?ve”  ocean  layers.  •  Relevant  for  explaining  SST  anomalies.  •  Avoids  strong  contribu?ons  from  ver?cal  diffusion  and  eliminates  entrainment    (Deser  et  al,  2003;  Coetlogon  and  Frankignoul,  2003).  

Brief Article

The Author

June 19, 2013

H = ⇢oCp

Z ⌘

�D

T dz

⇢oCp

Z ⌘

�D

@T

@tdz = �⇢oCp

Z ⌘

�D

r · (uT + u⇤T ) dz

| {z }Cadv

� ⇢oCp

Z ⌘

�D

r ·K dz

| {z }Cdiff

+Qnet

ug =

1

f⇢oz⇥rp,

uek =

⌧ ⇥ z

⇢ofDek

Dek = min(D, 100 m)

w(�D) ⇡Z ⌘

�D

rH · u dz ⇡Z ⌘

�Dek

rH · uek dz

| {z }⌘wek(�D)

+

Z ⌘

�D

rH · ug dz

| {z }⌘wg(�D)

1

Buckley  et  al.,  2014,  J.  Climate  

10  m  

100  m  

1000  m  

10  m  

100  m  

1000  m  

Mixed  layer  depths  in  ECCO  v4  compare  favorable  to  those  in  Argo.    

Forget  et  al.,  2015,  Ocean  Sci.  Discuss.    

ECCO  v4  

Argo  

Upper  ocean  heat  content  variability  Heat  Content  integrated  over  maximum    climatological  mixed  layer  depth,  D:        •  EOFs  of  H  resemble  those  of  SST,  but  

more  variability  where  deep  MLD  •  PC  ?me  series    

•  have  more  low  frequency  variability  

•  Have  less  high  frequency  variability  

•  Spectra  of  PC  ?me  series  are  steeper  

•  Associated  with  significant  SST  variability  that  resembles  first  EOFs  of  SST.  

   

Brief Article

The Author

June 19, 2013

H = ⇢oCp

Z ⌘

�D

T dz

⇢oCp

Z ⌘

�D

@T

@tdz = �⇢oCp

Z ⌘

�D

r · (uT + u⇤T ) dz

| {z }Cadv

� ⇢oCp

Z ⌘

�D

r ·K dz

| {z }Cdiff

+Qnet

ug =

1

f⇢oz⇥rp,

uek =

⌧ ⇥ z

⇢ofDek

Dek = min(D, 100 m)

w(�D) ⇡Z ⌘

�D

rH · u dz ⇡Z ⌘

�Dek

rH · uek dz

| {z }⌘wek(�D)

+

Z ⌘

�D

rH · ug dz

| {z }⌘wg(�D)

1

Slope  PC1:  -­‐2.6  PC2:  -­‐2.2  

Buckley  et  al.,  2014,  J.  Climate  

50%   11%  

Upper  ocean  heat  content  budget  

•  Advec?on  is  important  in  crea?ng  Ht  variability  along  the  Gulf  Stream  Path  and  in    regions  in  the  subpolar  gyre  

•  Diffusive  transports  only  important  in  boundary  regions  of  subpolar  gyre  

Brief Article

The Author

April 3, 2013

[H] = ⇢oCp

Z ⌘

�D

T dz

⇢oCp

Z ⌘

�D

@T

@tdz = �⇢oCp

Z ⌘

�D

r · (uT + u⇤T ) dz

| {z }C

adv

� ⇢oCp

Z ⌘

�D

r ·K dz

| {z }C

diff

+Qnet

ug =

1

f⇢oz⇥rp,

uek =

⌧ ⇥ z

⇢ofDek

Dek = min(D, 100 m)

w(�D) =

Z ⌘

�D

rH · u dz ⇡Z ⌘

�Dek

rH · uek dz

| {z }⌘w

ek

(�D)

+

Z ⌘

�D

rH · ug dz

| {z }⌘w

g

(�D)

1

Origin  of  advec?ve  heat  transport  convergence  

Mann Eddy region centered at 45�N, 40�W) and along the boundaries in the subpolar gyre.258

Cadv and Qnet are correlated over broad regions of the subtropical and subpolar gyres and259

anti-correlated over the tropics (see Figure 7a), patterns which likely reflect the role of the260

winds in creating both air-sea heat flux and Ekman transport anomalies. Cadv and Cdiff are261

anti-correlated over the regions where Cdiff plays a sizable role in the H budget (see Figure262

7b).263

b. Separation of advective convergence into Ekman and geostrophic parts264

A primary goal of this study is to determine the dynamical mechanisms that are respon-265

sible for Cadv, specifically whether advective heat transports are due to the local response to266

surface windstress variability (Ekman transports) or ocean dynamics (likely primarily heat267

transport by geostrophic currents). We first note that monthly-averaged Cadv can be written268

as269

Cadv = �⇢oCp

Z ⌘

�D

r·(uT+u⇤T ) dz = �⇢oCp

Z ⌘

�D

r · (uT ) dz| {z }

linear: Clin

�⇢oCp

Z ⌘

�D

r · (u0T 0 + u⇤T ) dz

| {z }bolus: C

bol

,

(2)

where overbars denote monthly mean variables and primes denote deviations from monthly270

means. The variance of Clin and Cbol are plotted in Figure 8a and 8b. The variance of Clin is271

qualitatively quite similar to that of Cadv (see Figure 6b). The correlation coe�cient between272

monthly anomalies of Cadv and Clin (see Figure 9a) is greater than 0.8 almost everywhere.273

Figure 9c shows the fraction of the variance4 of Cadv that is captured by Clin. With the274

exception of shallow regions (particularly in the subpolar gyre) and the region of the Mann275

4The fraction of the variance of a quantity X explained by an estimate Y is given by f = 1� �2(X�Y )

�2X

.

13

Cadv  is  well  reproduced  by  Clin  in  many  regions  

What  por?on  Cadvis  explained  by  Clin?  

             

F = 1� var(Cadv � Clin)

var(Cadv)

Cek = ⇢oCp

Z ⌘

�Dek

r · (uekT ) dz + ⇢oCp wek(�D) T (�D)

Cg = ⇢oCp

Z ⌘

�D

r · (ugT ) dz + ⇢oCp wg(�D) T (�D)

C local = Cadv +Qnet

Hg = ⇢oCp

Z ⌘

�D

r · (ugT ) dz + ⇢oCp wg(�D) T (�D)

H = ⇢oCp

Z xe

xw

Z z

�H

vT dz dx

Hg = ⇢oCp

Z xe

xw

Z z

�H

vgT dz dx

Hek = ⇢oCp

Z xe

xw

Z z

�H

vekT dz dx

corr(T, Tek + Tg + Tair�sea) = 0.98

corr(T, Tair�sea) = 0.90

corr(T, Tek + Tair�sea) = 0.96

2

Overbars  denote  monthly  means,  primes  are  devia?ons  from  monthly  means  

Ekman  +  geostrophic  convergences  Separate  (linear)  advec?ve  heat  transport  into  Ekman  and  geostrophic  parts.  Henceforth  drop  overbars  to  indicate  monthly  means        •  uek,  ug  are  horizontal  Ekman  and  geostrophic  veloci?es    •  wek,  wg  calculated  from  uek,  ug    using  the  con?nuity  equa?on  

b. Separation into Ekman and geostrophic convergences132

BPFH demonstrate that Clin

can be decomposed into Ekman and geostrophic parts, a133

decomposition which is successful away from shallow boundary regions (see Figures 9b and134

9d in BPFH). The Ekman and geostrophic components of Clin

are approximated as:135

Cek

(uek, wek

, T ) = �⇢o

Cp

R⌘

�Dekr · (uekT ) dz + ⇢

o

Cp

wek

(�D) T (�D),

Cg

(ug, wg

, T ) = �⇢o

Cp

R⌘

�D

r · (ugT ) dz + ⇢o

Cp

wg

(�D) T (�D),

(3)

where uek and ug are given by Equations 4 and 5 in BPFH, respectively, and wek

(�D) and136

wg

(�D) are given by Equation 6 in BPFH. Dek

is the depth of the Ekman layer, taken to137

be the shallower of D and a depth of 100 m (a choice motivated by the assumption that138

Dek

= 100 m in the RAPID AMOC estimates at 26�N). Variance maps of Cek

and Cg

(see139

Figures 1e–f) exhibit their largest values in regions of strong currents/fronts. Unlike Cg

, Cek

140

also exhibits significant variability over the interior of the gyres.141

Figure 2b shows the fraction of the variance of Ht

explained by Cek

+ Cg

+ Qnet

. The142

similarity of this quantity to the fraction of the variance of Ht

explained by Clin

(see Fig-143

ure 2a) indicates that approximating the linear convergence as the sum of the Ekman and144

geostrophic parts does not lead to any substantial additional error in the budget for Ht

. The145

areas where the fraction of the variance of Ht

explained by Cek

+ Cg

+ Qnet

is ⇠ 1 are the146

regions where (1) di↵usion and bolus transports and negligible and (2) the decomposition147

of Clin

into Ekman and geostrophic parts is successful. These are the key regions of interest148

for this study.149

We now further decompose Cek

and Cg

into convergences due to variability in the tem-150

7

èCek+Cg≈Clin    

           

ug =

1

f⇢o

z⇥rp,

uek =

⌧⇥z⇢o

fDek

, Dek = min(D, 100 m)

w(�D) ⇡R ⌘

�DrH · u dz

⇡Z ⌘

�Dek

rH · uek dz

| {z }⌘w

ek

(�D)

+

Z ⌘

�D

rH · ug dz

| {z }⌘w

g

(�D)

F = 1� var(Cadv � Clin)

var(Cadv)

F = 1� var(Clin � Cek � Cg)

var(Clin)

F (A,B) = 1� var(A� B)

var(A)

Cek = ⇢oCp

Z ⌘

�Dek

r · (uekT ) dz + ⇢oCp wek(�D) T (�D)

Cg = ⇢oCp

Z ⌘

�D

r · (ugT ) dz + ⇢oCp wg(�D) T (�D)

C local = Cadv +Qnet

Tek ⌘R t

0

Cek

⇢o

Cp

Vdt

Tg ⌘R t

0

Cg

⇢o

Cp

Vdt

Tloc ⌘ Tair�sea + Tek

Tbol ⌘R t

0

Cbol

⇢o

Cp

Vdt

3

Contribu?ons  of  Ekman  and  geostrophic  parts  

• Both  Cek  and  Cg  largest  in  regions  of  strong  currents/fronts    In  these  regions  Cg>Cek  

• Cek>Cg  over  por?ons  of  the  ocean’s  interior,  including  the  region  south  of  the  Gulf  Stream  and  the  eastern  basin.    

Ekman  heat  transport  convergences  

perature field and the velocity field:151

Cek

(uek, wek

, T ) = Cek

(uek, wek

, T ) + Cek

(uek0, w0

ek

, T )| {z }C

vek

+Cek

(uek, wek

, T 0)| {z }C

Tek

+Cek

(u0ek, w

0ek

, T 0)| {z }C

vTek

Cg

(ug, wg

, T ) = Cg

(ug, wg

, T ) + v Cg

(u0g, w

0g

, T )| {z }

C

vg

+Cg

(ug, wg

, T 0)| {z }C

Tg

+Cg

(u0g, w

0g

, T 0)| {z }

C

vTg

(4)

Figures 3d–f show variances of monthly anomalies of Cv

ek

, CT

ek

, and CvT

ek

normalized by152

the variance of Cek

. In most regions the variance of Cek

is almost solely due to Cv

ek

, which153

confirms that variability in Cek

is indeed primarily driven by local wind variability rather154

than changes in the temperature field, as hypothesized in BPFH. Exceptions include the155

tropics where CT

ek

plays a significant role and the Eighteen Degree Water formation region,156

the Mann Eddy region, and shallow boundary regions in the subpolar gyre where CvT

ek

plays157

a minor role. Cv

ek

and CT

ek

are essentially uncorrelated except a small region in the tropics158

(see Figure 4d).159

Figures 3g–i show variances of monthly anomalies of Cv

g

, CT

g

, and CvT

g

normalized by160

the variance of Cg

. Both Cv

g

and CT

g

play a dominant role in setting the variance of Cg

161

and are strongly anticorrelated (see Figure 4c). The fact that variability in Cv

g

and CT

g

are162

related is not surprising since geostrophic velocity anomalies are in thermal wind balance163

with temperature anomalies. The anticorrelation between Cv

g

and CT

g

can be explained by164

considering the the propagation of Rossby waves living in the presence of a eastward mean165

zonal flow u (see schematic in Figure 6). A wave ridge is associated with high sea surface166

height, a depressed thermocline, and a warmH anomaly. The anticyclonic circulation around167

the high brings cold water from the north to the east of the ridge (Cv

g

< 0) and warm water168

from the south to the west of the ridge (Cv

g

> 0), leading to the westward propagation of169

8

Separate  Ekman  heat  transport  convergences  into  parts  due  to  variability  in  the  velocity  field,  temperature  field,  and  their  covariability.      

•  Overbars  are  averages  over  ENTIRE  19-­‐year  ECCO  es?mate  •  Primes  are  devia?ons  from  these  averages  

Changes  in  Ekman  mass  transports  due  to  local  wind  variability—reflects  local  atmospheric  forcing  

Changes  in  temperature  field  

Co-­‐variability  of  Ekman  transports  and  temperature  

Buckley  et  al,  2015,  J.  Climate  

Variance  of  Ht  explained  by  various  terms  

•  Over  much  of  the  subtropical  and  subpolar  gyres,  Qnet+Cek+  Cg  explains  most  of  the  variance  of  Ht.  

•  Cekv+Qnet  explains  more  of  the  variance  than  Qnet  alone,  par?cularly  in  gyre  interiors.  •  Cg  plays  a  role  along  the  Gulf  Stream  path.  

0.8   0.8  

0.5   0.7  

Role  of  local  atmospheric  forcing  

Cloc*  explains  >70%  of  the  variance  of  Ht  in  the  interior  of  the  subtropical  and  subpolar  gyres.    

•  Response  of  the  atmosphere  to  mid-­‐la?tude  SST  anomalies    is    modest  compared  to  internal  atmospheric  variability  (Kushnir  et  al.,  2002;  Schneider  and  Fan,  2012)  

•  Hypothesis:  Cekv+Qnet=  Cloc*  is  a  measure  of  impact  of  local  atmospheric  forcing  on  H  

Analysis  of  budgets  in  various  regions  and  ?mescales  will  aid  in  confirming/rejec?ng  this  hypothesis  (see  Buckley  et  a.,  2014,  2015).  

             

0.7  

Conclusions  •  We  u?lize  a  dynamically  consistent  ocean  state  es?mate  (ECCO)  to  

quan?fy  the  upper-­‐ocean  heat  budget  in  the  North  Atlan?c  on  monthly  to  interannual  ?mescales.    

•  We  introduce  3  novel  techniques:    

•  Heat  content  is  integrated  over  the  maximum  climatological  mixed  layer  depth  (integral  denoted  as  H).        

•  Advec?ve  heat  transports  are  separated  into  Ekman  and  geostrophic  parts,  a  technique  which  is  successful  away  from  boundary  regions  

•  Air-­‐sea  heat  fluxes  and  Ekman  heat  transport  convergences  due  to  velocity  variability  are  combined  into  one  “local  forcing”  term.    

•  We  find:  

•  Over  broad  swaths  of  the  North  Atlan?c,  including  the  interiors  of  the  subtropical  and  subpolar  gyres,    >70%  of  the  variance  of  Ht  can  be  explained  by  local  air-­‐sea  heat  flux  +  Ekman  transport  variability.  

•  Geostrophic  convergences  play  a  role  along  Gulf  Stream  Path.  

Conclusions  (cont.)  North  Atlan?c  separated  into  regions  based  on  underlying  dynamics  and  budgets  of  H  analyzed  in  detail  (see  Buckley  et  al.,  2014,  2015)  • Subtropical  gyre  

•  local  forcing  dominates  on  all  ?mescales  resolved  by  the  19-­‐year  ECCO  state  es?mate.  

• Gulf  Stream  •  local  forcing  dominates  for  periods  less  than  6  months;  geostrophic  convergences  increasingly  important  on  longer  ?mescales.    • Geostrophic  transports  are  an?correlated  with  air-­‐sea  heat  fluxes,  sugges?ng  H  variability  is  forced  by  geostrophic  convergences  and  damped  by  air-­‐sea  fluxes.  

• Subpolar  gyre:    •  local  forcing  dominates  for  periods  less  than    1  year  •   geostrophic  transports,  bolus  transports,  and  diffusion  play  a  role  on  longer  ?mescales.  

The  (mescale  at  which  ocean  dynamics  becomes  important  in  seLng  H  depends  strongly  on  region.    

Future  work  •  Can  origin  of  upper-­‐ocean  heat  content  (UOHC)  anomalies  aid  in  understanding  regional  varia?ons  (and  model  spread)  in  predictability  of  UOHC?    •  Dominance  of  atmospheric  forcing  -­‐>  low  predictability?  •  Role  of  geostrophic  ocean  dynamics  -­‐>  high  predictability?  •  Applying  these  ideas  to  understand  predictability  in  CMIP5  models.    

• Determine  origin  of  geostrophic  convergence  anomalies  over  the  Gulf  Stream  path  •  Shiv  of  Gulf  Stream  path  due  to  remote  wind  forcing?  •  Change  in  strength  of  deep  western  boundary  current?  

 • H  variability  is  associated  with  AMOC  variability.    Is  AMOC  variability  passive  thermal  wind  response  to  H  variability  or  does  AMOC  play  role  in  H  budget  in  some  regions  (e.g.  Gulf  Stream)?  

• Analysis  has  determined  regions  where  more  complex  dynamics  are  important  in  H  budget.  •  e.g.  diffusion  and  bolus  transports  are  important  in  Mann  Eddy  region.      •  Region  may  be  important  in  decadal  AMOC  variability  (Buckley  and  Marshall,  subm.)  

Acknowledgements    Thanks  to  the  CVP  program  for  organizing  this  webinar  series.      Funding  for  this  work  was  provided  by  the  NOAA  Climate  Variability  and  Predictability  Program  (CPV)    Grants:    •   NA100AR4310199  (AER,  PI  R.  M.  Ponte)  •   NA130AR4310134  (AER,  PI  M.  W.  Buckley)  •   NA10OAR4310135  (MIT,  PI  P.  Heimbach)  

References  related  to  this  work    1. Buckley,  M.  W.,  R.  M.  Ponte,  G.  Forget,  and  P.  Heimbach  (2014).  Low-­‐frequency  SST  and  upper-­‐ocean  heat  content  variability  in  the  North  Atlan?c.  J.  Climate,  27,  4996—5018.    

2. Buckley,  M.  W.,  R.  M.    Ponte,  G.  Forget,  and  P.  Heimbach  (2015).    Determining  the  origins  of  advec?ve  heat  transport  variability  in  the  North  Atlan?c,  J.  Climate,  28,  3943—3956.  

3. Buckley,  M.  W.  and  J.  Marshall,  Observa?ons,  Inferences,  and  Mechanisms  of  Atlan?c  MOC  Variability:  a  Review.    SubmiRed  to  Reviews  of  Geophysics.    

4. Forget,  G.  and  R.  M.  Ponte  (2015).  The  par??on  of  regional  sea  level  variability,  Progress  in  Oceanography,  137,  173—195.  

5. Forget,  G.,  J.-­‐M.  Campin,  P.  Heimbach,  C.  N.  Hill  R.  M.  Ponte  and  C.  Wunsch  (2015).  ECCO  version  4:  an  integrated  framework  for  non-­‐linear  inverse  modeling  and  global  ocean  state  es?ma?on.  Geosci.  Model  Dev.  Discuss.,  8,  3653—3743.  

6. Forget,  G.,  D.  Ferreira  and  X.  Liang  (2015).  On  the  observability  of  turbulent  transport  rates  by  Argo:  suppor?ng  evidence  from  an  inversion  experiment.  Ocean  Sci.  Discuss.,  12,  1107—1143.  

7. Heimbach,  P.  and  C.  Wunsch  (2013).  Dynamically  and  kinema?cally  consistent  global  ocean  circula?on  and  ice  state  es?mates.    Ocean  circula=on  and  climate:  observing  and  modelling  the  global  ocean.      

 

Extra  slides  

Mean  temperature:  comparison  to  in-­‐situ  data  

Non-­‐op?mized  simula?on                  Op?mized  solu?on:  ECCO  v4  (r3/iter3)      

Misfits  to  in-­‐situ  observa(ons:  Te-­‐To  

Forget  et  al.,  2015b  GMD  

Regional  Analysis  Divide  into  regions  based  on  dynamics:                    •  Gyre  interiors:  frac?on  of  variance  of  Ht  

explained  by  Cloc*>0.7  •  Subtropical  and  subpolar  gyres  divided  

by  zero  in  mean  barotropic  streamfunc?on.  

•  Gulf  Stream  path:  mean  speed  >  7  cm  s-­‐1    and  frac?on  variance  of  Ht  explained  by  Cloc*<0.7  

SST  and  upper-­‐ocean  temperature      T=H/(ρoCpV)  

SST  T  

Regional  budgets  

Present  budgets  in  two  ways:  •  Fluxes  contribu?ng  to  Ht  •  Temporally  integrated  budgets  (contribu?ng  to  T),  V=volume  of  region  

•             

•  Similarly  dividing  Clin,  Cbol,  Cek,  Cg,  Cloc  by  ρoCpV  and  integra?ng  in  ?me  yields  Tlin,  Tbol,Tek,  Tg,  and  Tloc.  

Brief Article

The Author

January 26, 2014

Z t

0

Ht

⇢oCpVdt

| {z }⌘(T�T

o

)

=

Z t

0

Cadv

⇢oCpVdt

| {z }⌘T

adv

+

Z t

0

Cdiff

⇢oCpVdt

| {z }⌘T

diff

+

Z t

0

Qnet

⇢oCpVdt

| {z }⌘T

Q

. (1)

[H] = ⇢oCp

Z ⌘

�D

T dz

⇢oCp

Z ⌘

�D

@T

@tdz = �⇢oCp

Z ⌘

�D

r · (uT ) dz| {z }

Cadv

� ⇢oCp

Z ⌘

�D

r ·K dz

| {z }C

diff

+Qnet

ug =

1

f⇢oz⇥rp,

uek =

⌧ ⇥ z

⇢ofDek

Dek = min(D, 100 m)

1

Subtropical  gyre  interior  FLUXES  •  Dominant  terms:  Qnet,  Cekv,    •  Cloc*  dominates  on  intrannual  ?mescales  

•  CekT+CekvT  role  for  τ>2  yrs.  •  Cg,  Cdiff,  Cbol  negligible  =>  Cloc=Cek+Qnet  

•  Cloc  dominates  on  all  ?mescales  

 TIME  INTEGRATED  •  Tloc  explains  92%  of  the  

variance  of  T    •  T  anomalies  are  locally  

forced  on  all  ?mescales  resolved  by  ECCO  

10 5 2 1

0.1 1 1010−2

100

102

(W m

−2)2 c

py−1

cpy

yrs

Ht

Qnet

Cekv C

ekT +C

ekvT C

gC

diff+C

bol

10 5 2 1

0.1 1 100.4

0.6

0.8

1

cpy

yrs

Qnet

Cloc* C

locC

loc+C

g

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010−0.5

0

0.5

Time

° C

T−To

TQ

Tekv T

ekT +T

ekvT T

gT

diff+T

bolT

loc

Power  spectra                                                                      Coherence          

Gulf  Stream  Region  

FLUXES  •  Cloc  dominates  for  τ<  6  mo.  •   Cg  plays  an  increasing  role  on  longer  ?mescales  

•  CekT+CekvT  negligible  •   Cloc*  and  Cloc  are  indis?nguishable  

 TIME  INTEGRATED  •  Tg  important  in  T-­‐To  budget  •  Tg  and  TQ  highly  

an?correlated  (-­‐0.90)    •  TQ  likely  reflects  damping  

of  T  anomalies  created  by  ocean  dynamics  

Power  spectra                                                                      Coherence          10 5 2 1

0.1 1 10100

102

(W m

−2)2 c

py−1

cpy

yrs

Ht

Qnet

Cekv C

ekT +C

ekvT C

gC

diff+C

bol

10 5 2 1

0.1 1 100.4

0.6

0.8

1

cpy

yrs

Qnet

Cloc* C

locC

loc+C

g

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010−2

−1

0

1

2

Time

° C

T−To

TQ

Tekv T

ekT +T

ekvT T

gT

diff+T

bolT

loc

Subpolar  gyre  interior  

FLUXES  •  Cloc  dominates  for  τ<  1  yr.  •   Cg  ,  Cdiff,  Cbol  play  a  role  for  τ>  1  year  

 TIME  INTEGRATED  •  Tdiff  +Tbol  has  significant  low  frequency  variability  

•  Tdiff  +Tbol  and  TQ  are  an?correlated  (-­‐0.77)  

•  TQ  likely  reflects  damping  of  T  anomalies  created  by  ocean  dynamics  

Power  spectra                                                                      Coherence          

10 5 2 1

0.1 1 10

100

102

(W m

−2)2 c

py−1cpy

yrs

Ht

Qnet

Cekv C

ekT +C

ekvT C

gC

diff+C

bol

10 5 2 1

0.1 1 100.4

0.6

0.8

1

cpy

yrs

Qnet

Cloc* C

locC

loc+C

g

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

−1

−0.5

0

0.5

1

Time

° C

T−To

TQ

Tekv T

ekT +T

ekvT T

gT

diff+T

bolT

loc