argo lpo 201212 arivo - ifremer.fr file1.1$4$pourquoi$les$analyses$arivo$ • objecjfs#inijaux:# –...

Les$analyses$globales$ARIVO$

F.#Gaillard,#N.#Kolodziejczyk,#K.#Von#Schuckmann,#E.#Autret,#T.#Reynaud##

Réunion#ARGOALPO#Décembre#2012# 1#

Plan$

Résultats#•  ObjecJfs#et#méthode##•  Quelques#résultats#publiés#

Discussion/projets:#•  L’analyse#2002A2011#(2012$fin$janvier)##


1.1$4$Pourquoi$les$analyses$ARIVO$

•  ObjecJfs#iniJaux:#–  Assembler#et#synthéJser#les#mesures#de#température#et#salinité#des#profileurs#ARGO#

–  Produire#des#champs#sur#une#grille#régulière#afin#de#suivre#l’évoluJon#des#propriétés#TAS#

–  Fournir#un#produit#de#type#‘RéAanalyse’,#dont#la#qualité#permeZe#des#études#climaJques.#

–  Concevoir#des#indicateurs#significaJfs##

•  Spécificités#–  Prendre#en#compte#toutes#les#données#TAS#disponibles#(CTD,#mouillages,#…)#

–  Respecter#la#résoluJon#ARGO#(temps#et#espace)#–  S’appuyer#sur#et#contribuer#à#Coriolis##


1.2:$Le$processus$de$traitement$

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Principales$étapes:#•  PréparaJon#de#la#configuraJon#(grille,#bathymétrie,#masques,#champs#de#

référence,#variances,#échelles#spaJales)#•  PréparaJon#des#données#(contrôle#préAanalyse,#mise#sur#niveaux#standards,#

gesJon#des#flux#NRT#et#DM)#•  Analyse#(EsJmaJon#opJmale:#ISASAV6)#•  PostAanalyse:#diagnosJcs#de#validité#(isasAdiag),#études#scienJfiques#(arivo)#

2$–$Quelques$résultats$publiés$

•  Gaillard,#F.,##E.#Autret,##V.#Thierry,#P.#Galaup,#C.#Coatanoan,#and#T.#Loubrieu:#Quality#Control#of#Large#Argo#Datasets#,#JAOT#2009#

•  von#Schuckmann,#K.;#Gaillard,#F.;#Le#Traon,#P.#AY.:#Global#hydrographic#variability#paZerns#during#2003A2008.#JGR#2009##

•  Kolodziejczyk,#N.,#and#F.#Gaillard:#ObservaJon#of#spiciness#interannual#variability#in#the#Pacific#pycnocline.#J.G.R.,#in#press.##

•  Kolodziejczyk,#N.,#and#F.#Gaillard:#Variability#of#the#Heat#and#Salt#Budget#in#the#Subtropical#SouthAEastern#Pacific#Mixed#Layer#between#2004#and#2010:#Spice#InjecJon#Mechanism.#JPO,#to#be#submiZed.#


Gaillard,$F.,$$E.$Autret,$$V.$Thierry,$P.$Galaup,$C.$Coatanoan,$and$T.$Loubrieu:$Quality$Control$of$Large$Argo$Datasets$,$JAOT$2009$


moves away from the origin along the salinity axis. Thetime series of salinity residuals averaged over a layerexcluding the highly variable upper 400 m !dS" has aclear trend not seen in temperature (Fig. 13). Given thehigh ocean variability due to mesoscale ocean changesalong the float trajectory, and depending on the form ofthe drift, it is not obvious that a linear fit would neces-sarily express the trend. It was thus preferred to apply anonparametric evaluation of the trend known as a‘‘reverse arrangement test’’ (Bendat and Piersol 2000).The number of arrangements A of the dS series is givenby

A5!N

i51Ai; withAi 5 !

N

j5 i1 1hij; !10"

where hij 5 1 if dSi . dSj and hij 5 0 otherwise.A float will be considered as having a drift when the

number of arrangements A of the series dSi falls out ofthe interval defined by 62.7 std dev of a random dis-tribution. When applied to the 36 selected floats, thistest detected six floats as having a salinity drift(1 SOLO, 3 Apex, 2 Provor), which corresponds to the

detailed screening diagnostic. An additional test wasperformed on salinity to detect a salinity offset: Thesensor is assumed to have an offset if the absolute valueof the time mean of dSi is larger than 0.02. One Provorfloat was found to have an offset (which was confirmedby a later detailed analysis).

b. Error on pressure

As pointed out in section 3a, an error in the pressuresensor leads to errors on temperature and salinity pro-portional to the vertical gradients of these properties.Because the temperature gradient is usually stronger,we focus on this variable to detect errors in the pressuredata. The measurements given by the SOLO float1900360, launched in the South Atlantic in March 2004and still transmitting data at the end of 2006, are shownhere to illustrate the method. The profiles of innovationand residual show anomalous variations over time (seeFig. 14): deeper than 400 m, the level that correspondsto a change in the vertical sampling of the float, the signof the residuals tend to alternate, with shallower profilescorresponding to strong negative anomalies. This be-havior is related to the software error mentioned by theArgo centers. To define a quantitative measure to de-tect this type of error, we computed the pressure errordP equivalent to the temperature residual dT using therelation

dT 5 !›T=›P" dP: !11"

For each profile, this error is averaged over all levelsdeeper than 400 m, and the mean value dP is comparedto the corresponding vertical standard deviation. In thecase of float 1900360, most of the mean pressure errors

FIG. 15. Float 1900360: (top) pressure error deduced from thetemperature residuals and (bottom) density anomaly deducedfrom the temperature and salinity residuals, averaged over levelsdeeper than 400 m. The circles indicate values greater than 1 stddev (the shaded area).

FIG. 14. Float 1900360: temperature (top) innovation and (bot-tom) residual displayed in depth and time coordinates. The strongvariability below 400 m in consecutive profiles results from thepressure labeling error.

FEBRUARY 2009 GA I LLARD ET AL . 349

Analyse$des$résidus:$UPlisaPon$opéraPonnelle$

1. DAC AOML

Profiles detected by the objective analysis: 285 (profiles detected this summer are in blue in the following list)

Status of corrections: DONE

Float : 1900438 - Cycle : 312 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_3808 - Date : 2010 8 1 Float : 1900769 - Cycle : 192 - PI : Breck Owens - Data mode : R - INST REF : SOLOIR_SBE_SL713 - Date : 2012 8 20 Float : 1900830 - Cycle : 112 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_4200 - Date : 2010 7 29 Float : 1900830 - Cycle : 121 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_4200 - Date : 2010 9 2 Float : 1900832 - Cycle : 34 - PI : CARL SZCZECHOWSKI - Data mode : R - INST REF : APEX_SBE_5885 - Date : 2012 8 24 Float : 1901373 - Cycle : 122 - PI : STEPHEN RISER - Data mode : A - INST REF : APEX_SBE_4281 - Date : 2012 8 23 Float : 1901415 - Cycle : 19 - PI : STEPHEN RISER - Data mode : A - INST REF : APEX_SBE_4375 - Date : 2010 8 1 Float : 1901429 - Cycle : 96 - PI : DEAN ROEMMICH - Data mode : R - INST REF : SOLO_SBE_2896 - Date : 2012 8 27 Float : 1901450 - Cycle : 86 - PI : BRECK OWENS - Data mode : R - INST REF : SOLO_SBE_SL862 - Date : 2012 8 15 Float : 1901453 - Cycle : 88 - PI : BRECK OWENS - Data mode : R - INST REF : SOLO_SBE_0967 - Date : 2012 8 23 Float : 1901495 - Cycle : 15 - PI : ARIEL TROISI - Data mode : R - INST REF : SOLO_SBE_0980 - Date : 2012 8 21 Float : 1901501 - Cycle : 47 - PI : Breck Owens - Data mode : R - INST REF : SOLOIR_SBE_0941 - Date : 2012 8 11 Float : 1901514 - Cycle : 64 - PI : GREGORY C. JOHNSON - Data mode : A - INST REF : APEX_SBE_4466 - Date : 2012 8 29 Float : 1901530 - Cycle : 39 - PI : Breck Owens - Data mode : R - INST REF : SOLOIR_SBE_1058 - Date : 2012 7 30 Float : 2900828 - Cycle : 365 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_3285 - Date : 2010 8 1 Float : 2900834 - Cycle : 331 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_3803 - Date : 2010 8 1 Float : 2901107 - Cycle : 157 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_4116 - Date : 2010 8 1 Float : 2901108 - Cycle : 157 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_4117 - Date : 2010 8 1 Float : 2901144 - Cycle : 105 - PI : DR. CHARLIE HORTON - Data mode : A - INST REF : APEX_SBE_4612 - Date : 2010 8 1 Float : 2901356 - Cycle : 146 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_4195 - Date : 2010 8 1 Float : 2901357 - Cycle : 146 - PI : DR. CHARLIE HORTON - Data mode : R - INST REF : APEX_SBE_4196 - Date : 2010 8 1 Float : 2901362 - Cycle : 74 - PI : DR. CHARLIE HORTON - Data mode : A - INST REF : APEX_SBE_4491 - Date : 2010 8 1 Float : 2901372 - Cycle : 194 - PI : CARL SZCZECHOWSKI - Data mode : A - INST REF : APEX_SBE_4746 - Date : 2012 8 1 Float : 2901431 - Cycle : 36 - PI : CARL SZCZECHOWSKI - Data mode : A - INST REF : APEX_SBE_5511 - Date : 2012 8 18 Float : 2901431 - Cycle : 37 - PI : CARL SZCZECHOWSKI - Data mode : A - INST REF : APEX_SBE_5511 - Date : 2012 8 22 Float : 2901431 - Cycle : 38 - PI : CARL SZCZECHOWSKI - Data mode : A - INST REF : APEX_SBE_5511 - Date : 2012 8 26 Float : 3900408 - Cycle : 180 - PI : GREGORY C. JOHNSON - Data mode : A - INST REF : APEX_SBE_1960 - Date : 2010 8 1 Float : 3900626 - Cycle : 176 - PI : DEAN ROEMMICH - Data mode : R - INST REF : SOLO_SBE_2711 - Date : 2012 8 25 Float : 3900650 - Cycle : 174 - PI : MAURICIO M. MATA - Data mode : R - INST REF : SOLO_SBE_SL676 - Date : 2012 8 5 Float : 3900650 - Cycle : 175 - PI : MAURICIO M. MATA - Data mode : R - INST REF : SOLO_SBE_SL676 - Date : 2012 8 15 Float : 3900650 - Cycle : 176 - PI : MAURICIO M. MATA - Data mode : R - INST REF : SOLO_SBE_SL676 - Date : 2012 8 25 Float : 3900652 - Cycle : 171 - PI : MAURICIO M. MATA - Data mode : R - INST REF : SOLO_SBE_SL686 - Date : 2012 8 20 Float : 3900671 - Cycle : 139 - PI : GREGORY C. JOHNSON - Data mode : A - INST REF : APEX_SBE_3586 - Date : 2012 8 5 Float : 3900699 - Cycle : 143 - PI : DEAN ROEMMICH - Data mode : R - INST REF : SOLO_SBE_2805 - Date : 2012 8 28 Float : 3900701 - Cycle : 143 - PI : DEAN ROEMMICH - Data mode : R - INST REF : SOLO_SBE_2807 - Date : 2012 8 28 Float : 3900702 - Cycle : 143 - PI : DEAN ROEMMICH - Data mode : R - INST REF : SOLO_SBE_2808 - Date : 2012 8 28 Float : 3900715 - Cycle : 212 - PI : Robert A. Weller - Data mode : R - INST REF : SOLOIR_SBE_0856 - Date : 2012 7 27 Float : 3900715 - Cycle : 213 - PI : Robert A. Weller - Data mode : R - INST REF : SOLOIR_SBE_0856 - Date : 2012 8 6 Float : 3900715 - Cycle : 214 - PI : Robert A. Weller - Data mode : R - INST REF : SOLOIR_SBE_0856 - Date : 2012 8 16 Float : 3900715 - Cycle : 215 - PI : Robert A. Weller - Data mode : R - INST REF : SOLOIR_SBE_0856 - Date : 2012 8 26 Float : 3900779 - Cycle : 97 - PI : Gregory C. Johnson - Data mode : R - INST REF : APEXIR_SBE_4674 - Date : 2012 8 13 Float : 3900780 - Cycle : 85 - PI : Gregory C. Johnson - Data mode : R - INST REF : APEXIR_SBE_4678 - Date : 2012 8 2 Float : 3900837 - Cycle : 9 - PI : Breck Owens - Data mode : R - INST REF : SOLO2IR_SBE_7026 - Date : 2012 7 25


##

Report#from#ObjecJve#Analysis#Warning#–C.#Coatanoan,#Coriolis##Summary##DAC#AOML#DAC#BODC###DAC#CSIO##DAC#CSIRO#DAC#INCOIS#DAC#JMA/JAMSTEC#DAC#KMA##DAC#KORDI##DAC#MEDS##DAC#NMDIS#

von$Schuckmann,$K.;$Gaillard,$F.;$Le$Traon,$P.$4Y.:$Global$hydrographic$variability$paWerns$during$200342008.$JGR$2009$$

et al., 2002; Boning et al., 2008] which is linked to changesin the atmospheric circulation [Morrow et al., 2008].

3.2. Annual Cycle

[31] While the seasonal temperature and salinity changesin the surface layer are well described in the literature [e.g.,Antonov et al., 2004; Boyer and Levitus, 2002] the globalocean annual cycle at depth is poorly analyzed. Therefore,we focus on the description of the vertical penetration of theannual cycle of the hydrographic field during 2003–2008.The vertical distribution of the seasonal cycle of tempera-ture is illustrated in Figure 6a for the upper 400 m depth.Amplitudes of the annual harmonic of temperature aremaximum in the surface layer at midlatitudes and arecharacterized by a hemispheric asymmetry with largestamplitudes in the Northern Hemisphere, reflecting thecontrast between the two hemispheres in the distributionof landmass [Fu and Cazenave, 2001; Antonov et al., 2004;Stammer, 1997]. At midlatitudes, amplitudes decrease withincreasing depth but still exceed 1!C at depth greater than100 m and the explained variance of the annual harmonicaccounts for 80% of total variance. In the subpolar regions,surface intensified maxima of the seasonal amplitude oftemperature are also observed. One is centered at about60!S and is known to occur also in some coupled ocean-atmosphere simulations [Gleckler et al., 2006]. Althoughamplitudes remain small in this region, the annual cycleexplains 80% of total variance in our results (Figure 6a).Another peak appears north of 60!N, i.e., in the area ofseasonal sea ice coverage, and the seasonal amplitude as

well as its explained variance are high until depth of about300 m. The annual cycle of temperature in the tropical basinis subsurface intensified and exceeds 2!C at about 80 mdepth. Values of explained variance account for 40% of totalvariance. Two areas of peaking seasonal amplitudes occur inthe tropical subsurface layer, i.e., in the northern equatorialband and centered at about 10!N. These subsurface maximaare associated with dynamical processes mainly triggered byseasonal mixed layer depth variations, seasonal changes ofthe mean zonal currents and equatorial wave dynamicsunder the effect of wind stress forcing, rather than surfaceheating [e.g., Schott and McCreary, 2001; Keenlyside andKleeman, 2002; Arhan et al., 2006; Kessler and Gourdeau,2006; Forget and Wunsch, 2007].[32] The use of the ARIVO product allows us to analyze

the seasonal cycle of global salinity at depth (Figure 6b). Ascan be already seen in previous estimations, the annualcycle of SSS is dominant in regions different from thoseobserved for SST [Antonov et al., 2004; Boyer and Levitus,2002]. Indeed, seasonal amplitudes of salinity at depthincrease in areas where seasonal changes of temperatureare high, but the largest salinity changes occur in areaswhere seasonal changes of evaporation, precipitation andice formation are important (Figures 6a and 6b). Moreover,seasonal salinity changes are surface intensified at alllatitudes indicating that mostly coupled ocean-atmospherefluxes trigger the largest seasonal signatures of global oceansalinity, rather than dynamical processes. The global aver-age in Figure 6b also shows that the space scales of theannual cycle of salinity are smaller and more regional

Figure 6. Global zonal average of the amplitude of the dominant harmonic of ARIVO (a) temperatureand (b) salinity in the upper 400 m during 2003–2008. Contours show the variance in percent explainedby the seasonal signal. White dashed lines indicate the equator.

C09007 VON SCHUCKMANN ET AL.: GLOBAL HYDROGRAPHIC VARIABILITY

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C09007

Réunion#ARGOALPO#Décembre#2012# 8#compared to those of temperature (see also Reverdin et al.[2007] for the Atlantic).[33] Regions of high amplitudes include the tropical basin

and subpolar areas, still exceeding 0.1 below 100 m depthespecially in the latter domain. In the tropical basin,seasonal salinity amplitudes increase just north of theequator, i.e., in the ITCZ realm, and 40% of variabilityare explained by the annual cycle in the upper 100 m depth.A second peak of seasonal salinity persists just south of theequator and its signatures are weaker and shallower com-pared to its northern counterpart. Poleward of 60!N, theamplitude of seasonal salinity change is large, and the totalvariability in this area is explained with 40% by the annualcycle from the surface down to about 200 m depth.Enhanced amplitudes south of 60!S, which are mostlylocated in the Weddell and Ross seas (not shown), areconfined to the upper 50 m depth and the explained varianceaccounts for more than 60% of total variance. In this area, asecond maximum of annual salinity amplitude occurs at100–200 m depth with values between 0.1 and 0.2. Itexplains 20% of the total variance. However, the largestamplitudes of the annual cycle of salinity can be observed inthe Northern Hemisphere induced by deep winter convec-tion processes and seasonal ice formation.

4. Interannual Fluctuations

[34] The mean seasonal cycle is estimated at each gridpoint from the temperature and salinity fields over the6 years of measurements. The mean seasonal cycle is then

removed in order to emphasize interannual variability. Thecharacteristic magnitude of the ARIVO anomalies rangebetween ±0.2!C for temperature and ±0.02 for salinity(Figures 8–10). Extreme values exceed ±0.5!C/±0.1 in someparts of the global ocean, mostly occurring in the near-surfacelayer (not shown). Those ranges of magnitude are of com-parable size to previous estimates [e.g., Levitus and Antonov,1997; Antonov et al., 2002]. The estimation on the error ofthese fields has shown that the values of the large-scaleanomaly fields are significant (Figures 3c and 3d).[35] The first general view indicates that various types of

interannual fluctuations occur in several latitudinal bands ofthe global ocean, i.e., at midlatitudes between 30 and 60!, inthe subtropics between 20 and 35! latitudes and in thetropics between 20!S and 20!N (Figures 7a and 7b). Atmidlatitudes, temperature as well as salinity fluctuations aresurface intensified and reach down to depth of more than1000 m. At northern midlatitudes, standard deviations arestrong compared to their southern counterpart reflecting aclear hemispheric asymmetry as could be figured out for thelong-term and seasonal variability. Deep reaching interan-nual fluctuations characterize also the subtropical basinswith maxima in the surface layer as well as about 500 mdepth. In the tropical basins dominant anomalies are mostlyconfined to the upper 500 m depth layer (Figure 7a).Differences between temperature and salinity fluctuationpatterns emerge mostly in the upper layer, predominantlyin the tropical band. In the subsurface layers temperatureand salinity variations are generally correlated [Forget andWunsch, 2007].

Figure 7. Global zonal averages of standard deviations of annual mean (a) temperature and (b) salinityderived from the ARIVO anomaly field 2003–2008 from the near-surface layer down to 2000 m depth.Mean and maximum pycnocline depths are added (black lines).

C09007 VON SCHUCKMANN ET AL.: GLOBAL HYDROGRAPHIC VARIABILITY

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C09007

•  DescripJon#des#cycles#saisonniers#de#température#et#de#salinité:#pénétraJon#verJcale#plus#importante#que#dans#les#climatologies#antérieures#(WOA)#

•  PénétraJon#verJcale#de#la#variabilité#interannuelle#en#T#et#S##

Kolodziejczyk,$N.,$and$F.$Gaillard:$ObservaPon$of$spiciness$interannual$variability$in$the$Pacific$pycnocline.$J.G.R.,$in$press$


Anomalies#de#sel#compensées#en#densité#(spiciness)#•  Forte#variabilité#de#S#sur#l’isopycne#25.5#

(située#sous#la#couche#de#mélange)#•  AdvecJon#d’anomalies#d’Est#en#ouest##le#

long#des#lignes#de#courant#dans#les#2#hémisphères#

•  Au#Nord#ces#anomalies#ont#été#formées#aux#moyennes#laJtude.#

•  Au#sud#elles#sont#formées#localement#(dans#la#SEP).##

#

•  Dans#la#couche#de#mélange,#on#note#la#producJon#d’anomalies#de#densité#inférieure#à#25.5.##

•  Fortes#anomalies#posiJves#en#2007#et#2010#(après#El#Nino)#


•  La#composante#principale#de#la#MLD#est#corrélée#à#ENSO#

Kolodziejczyk,$N.,$and$F.$Gaillard:$Variability$of$the$Heat$and$Salt$Budget$in$the$Subtropical$South4Eastern$Pacific$Mixed$Layer$between$2004$and$2010:$Spice$InjecPon$Mechanism.$JPO$

−0.25 PSS−0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 0.2 24.524.7525

25

25.25

25.25

25.5

25.75Dept

h (m

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d) S ano.

Jan Apr Jul Oct

250200150100

500

−2.5 oC−2 −1.5 −1 −0.5 0 0.5 1 1.5 2 24.524.7525

25

25.25

25.25

25.5

25.75Dept

h (m

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c) T ano.250200150100

500

Réunion#ARGOALPO#Décembre#2012# 11#J F M A M J J A S O N D J

10−4

10−3

10−2

10−1

(m2 .s

−1)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec−200

−100

0

100

200

Heat

Flux F

orcing

(W/m

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Total Atm. Fl. Adv. Ent.

a)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec−200

−100

0

100

200ρ cphdT/dt εT

Heat

Tend

ency

(W/m

2 )

b)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec−10−8−6−4−2

02468

10

Salt F

lux Fo

rcing

(kg.m

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o−1 ) E−P Adv. Ent.

c)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec−10−8−6−4−2

02468

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Salt T

ende

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kg.m−2

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d)

argo lpo 201212 arivo - ifremer.fr file1.1$4$pourquoi$les$analyses$arivo$ • objecjfs#inijaux:# –...

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