増田周平 , 淡路敏之 , 杉浦望実 , 石川洋一 , 五十嵐弘道 , 日吉善久 , 佐々木祐二 , 土居知将 JAMSTEC Kyoto University

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JAMSTECKyoto University

Data Synthesis Efforts in Oceanography

Data AssimilaionAn optimal synthesis of observational data and model results.Data assimilation can provide analysis fields in superb quality through 4-dimensional dynamical interpolation of in-situ observations. Observations Numerical modelMerit Truthreal ocean Equal quality in 4-d continuum DemeritSpatially and temporally sporadic sometimes Unrealistic; Model bias, parameterization

No Modelz-Level ModelEN3DePreSysMITMOMPOPOPA/NEMOERA40NCEPMercatorURDGSODAGFDLGODASK-7HOPEECMWFINGV3D-Var/OI4D-Var.25ox.25o1ox1o2ox2oCOREGECCOBias corr.E-P.Relax.Relax.Relax.Relax.QSCATGPCPDATA Worlds Ocean Data Synthesis efforts (CLIVAR/GSOP)

4D-VAR approachA 4-dimensional variational (4D-VAR) adjoint data assimilation system can provide a dynamically self-consistent dataset.The obtained products are applicable to dynamical analysis, adjoint sensitivity experiment, Observation System Experiment, ecosystem modeling, forecast study.High computational cost is required .

Adjoint sensitivity analysis by using a 4D-VAR ocean DA systemsurfaceThe adjoint sensitivity analysis gives the temporal rate of change of a physical variable in a fixed time and space when model variables (e.g., water temperature, salinity, velocity, or surface air-sea fluxes) are arbitrarily changed in the 4-dimensional continuum of one temporal and three spatial coordinates. This is equivalent to specifying the sensitivity of a variable to small perturbations in the parameters governing the oceanic state. An adjoint sensitivity analysis moves the ocean representation backward in time!TTmodel Tobs

Search of best time trajectory4D-VAR data assimilation approach seeks for optimized 4-dimensional model states by minimizing a cost function (differences between observed and model analysis fields).In that process, Forward & Backward model are executed iteratively within assimilation window. Assimilation window should be well chosen taking the memory of oceanic phenomena of interest into consideration. In general, the longer the memory of the phenomenon is, the longer the assimilation window must be.

Best time trajectory

K7 Ocean Data Assimilation System

Recent high quality observational surveys conducted during the WOCE and the WOCE revisit have revealed the sobering fact that the deepest waters of the major oceans have warmed significantly during recent decades Bottom-water warming

Blue Earth (2004)Such temporal changes are important to understand the variability of abyssal circulation which have implications for large-scale thermohaline transport and thus for the global 3-dimensional heat budget that is presently of vital concern.MIRAI RV participate in WOCE revisit in the subarctic North Pacific in 1999

OGCMGFDL MOM3,quasi-global75oS-80oNhorizontal res1ox1o, vertical res:45 levelsSpinup1. 3000-year with a climatological forcing (accelerated method)2. 120-year as climatological seasonal march.3. 10-year with interannual forcings from NCEP/DOE.Use of optimal parametersGreens function method is applied to some physical parameters (Toyoda et al.,20XX).Data sysnthesismethodstrong constrain 4D-VAR adjoint.adjoint coding: by TAMC with some modifications.assimilation window50 years (1957-2006)control variablesinitial conditions, 10-daily surface fluxesfirst guessresults from Spinup 3assimilated elementsOISST,T,S Ensembles ver.3 + Mirai RV independent dataset ,AVISO SSH anomaly.System

Greens functionOptimization of physical parametersModeling: 1. Short-wave radiation scheme is modified (collaboration with Ocean Circulation Team) 2. BBL scheme, GM scheme 3. Anomalous mending for polar region dynamics, Bland-new QCed Data Download EN3_v2a_NoCWT_WijffelsTable1XBTCorr with independent RV MIRAI dataDeep ocean data synthesisState-of-the-art adjoint coding.Low resolution compiling for deep ocean obseravations. Assimilation with long-term window Control of model trend (collaboration with Univs.).

Our efforts to reproduce bottom-water warming in reanalysis dataset (legacy of 4th)

Tsujino and Suginohara (2000)Hasumi and Suginohara (1999)Gargett (1986)(~N-)OGCM(Cummins et al., 1990)

3Gargett (1986) KGGT=10-3N-1 (in cgs)(e.g., Marzeion et al., 2007)2000m (Schmitt, 1988; Marmorino and Caldwell, 1976)(Redi, 1982; Gent and McWilliams, 1990) 0=(fTJN,fGGT,fHSM,HHSM,fsf,fdc,Aisp,Athk,KBBL) =(1/3,1/3,1/3, 700, 1, 1,103,103,2*10-4)

(CTL)(Bryan, 1969)40003204000m1000.00023209(Menemenlis et al., 2005) x20yRBR-1/(EN3 by Met Office)CTD/XCTD5% =(fTJN, fGGT, fHSM, HHSM, fsf, fdc, Aisp, Athk, KBBL) =(0.43,0.08,0.72, 580,0.91,0.91,1.0*103,1.3*103,2.1*10-4) 320(ADJ)Tsujino, Gargett, HasumiTJN,GGT,HSM

Mean profile of vertical diffusive coefficient.Distributions of VDC at 2000m-depth CTLADJTJNGGTHSMTJNGGTHSMADJKADJ=0.43KTJN+0.08KGGT+0.72KHSM+

Water temperature at 4000-5500m-depthADJ(Greens functions)WOAObservation

leftTsujino midGargett rightHasumi(Toyoda et al.09JOS annual mtg.)

Improved Ocean State Estimate

Cost functionAssimilated elements: Temperature, Salinity (ENSEBLES v.3+JAMSTEC observations), SST (reconstructed Reynolds+OISST ver.2), SSH anomaly data (AVISO).First guess is generated from momentum, net heat, shortwave, latent heat flux of NCEP/DOE .

Time change of the each component of the cost function, i.e. the difference between simulation and observation. Reduction by iteration processes means progress in synthesis. Optimal Synthesis (dynamical interporation) by 4D-VAR adjoint method

Our results provides a consistent view

Estimated net heat flux (a control variable)

Our reanalysis data x ERA40Estimated wind stress field (a control variable)Stress JanStress JulCurl JanCurl Jul

Climate indices during 1957-2006****Nino3 SST

DMI

ITF mass transport(8-14Sv)

ACC mass transport(130-140Sv)

Atlantic MOC(14-20Sv)*Bryden et al. (2005) Our result provides realistic time series of important climate indices.

Temporal change of global heat contentComparison with observed heat content trends.

Comparison of year to year changes in heat content from 1960 implies => Our trend would be robust

Comparing with TOGA-TAO ADCPAlso, obtained 4-D velocity field is by and large consistent with independent observations by TAO array. Validation for un-easily-observable variables

Atlantic 48NIndian Ocean 10S Equatorial PacificAtlantic 25NCLIVAR/GSOPIntercomparison: Heat Transport Anomaly (PW)

Heat Transport Correlation (5 ys low pass)GlobalECMWF GECCO INGV SODA GODAS K7

The observation number of salinity had been small before ARGO era (00). As a result, the interannual variance of an OI dataset is relatively small for these periods. 4D-VAR dataset can resolve this issue thanks to both numerical model & adjoint method. interannual variance (psu2)Number of obs. for subsurface salinity0-100m100-400m400-700m700-2000m2000m- [ ] 1957-1966 [ ] 1967-1976 [ ] 1977-1986 [ ] 1987-1996 [ ] 1997-2006 [blue] an OI dataset [yellow] a model free run [red] our 4D-VAR reanalysisSalinity variancesToyoda et al. (GSOP08)--Advantage of our dataset

T,Ssteric height

Observed heat content Estimation form 50yr DA exp.Bottom water warming is of particular interest as it can be closely related to changes in the global thermohaline circulation and the warming trend of the global ocean (e.g., Fukasawa et al., 2004). Water temperature difference between WOCE/WOCE-revisit periods at 4000-5500m-depth is O(0.001-0.003K).Bottom water warming seems to be successfully reproduced in our reanalysis field. Bottom water warming in our reanalysis field

Dynamical Analysis for Climate Change

NPZDKouketsu et al. (2010)Toyoda et al. (2010)Reanalysis data allow us to diagnose the real ocean

(Masuda et al. 2008)Reanalysis data allow us to reveal the physical mechanism of climate changes.

Adjoint Sensitivity Analysis

How about the Physical Mechanism? Our aim is to identify the possible causal dynamics, timescales and pathways involved in the observed bottom-water warming.Difference of the heat storage between WOCE-WOCE revisit observational periods.The physical mechanisms governing bottom-water warming are poorly understood since in-situ observations are spatially and temporally sporadic. The changes in heat storage between WOCE-WOCE revisit imply northward running of the warming signal, but

Adjoint sensitivity analysis by using a 4D-VAR ocean DA systemsurfaceT(Bottom-water warming)The adjoint sensitivity analysis gives the temporal rate of change of a physical variable in a fixed time and space when model variables (e.g., water temperature, salinity, velocity, or surface air-sea fluxes) are arbitrarily changed in the 4-dimensional continuum of one temporal and three spatial coordinates. This is equivalent to specifying the sensitivity of a variable to small perturbations in the parameters governing the oceanic state. An adjoint sensitivity analysis moves the ocean representation backward in time!

t0x0Qt(

---A contour surface shows bottom-water warming rate when a constant change in water temperature is given--- Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North PacificAfter 0-year

---Shade shows bottom-water warming rate when a constant change in water temperature is given--- Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North Pacific 170oE cross-section 160oE cross-sectionAfter 48-year

---Contour shows bottom-water warming rate when a constant change in surface heat flux is given--- Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North PacificAfter 45-yearSource region: Antarctic Sea off Adelie CoastTime scale: 40 year in contrast to the previous estimation of O(multi-centennium)!!

WOCE WOCE_revPossible mechanism for bottom water warming Masuda et al. (2010)

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Our scenario should be tested by direct observationsAlso, using 4D-VAR data synthesis, sustainable observations is needed to better representation for a global ocean. Argo, revisit cruise is really enhancing the quality of reanalysis products.

Toward an Optimal Ocean Observing System

NINO3adTENSO0-month

-2-month

-4-month

0-month

(0-100m0-200mAdjoint source region

Summary 4D-VAR data assimilation method is applied to deep ocean reanalysis experiment to obtain a dynamically-self consistent ocean state estimation from surface to bottom (1967-2006).The reanalysis dataset is capable of representation of the recent climate change also in the abyssal ocean. An adjoint sensitivity analysis implies that an increase in the heat input into the Southern Ocean off the Adlie Coast of Antarctica leads to bottom water warming in the North Pacific on a relatively short time scale (within four decades). An adjoint sensitivity analysis was applied to detect an optimal ocean observation system in the equatorial Pacific. Tentative results show further applications in line with this study are promising.

Future Work

Down-scaling attempt with MOM4We are trying to implement a 1/16 x 1/16 x 45level regional model with CDA products as boundary conditions (

Application to Ecosystem modelNEMURO is incorporated in our system; collaboration with Environmental Biogeochemical Cycle Research Program

END

*****Adjoint sensitivity analysis is very useful method for a climate research.More intuitively speaking,

*S-I

***Recent observational surveys have shown significant oceanic bottom-water warming. (a) In-situ mean water temperature at 4500-m depth compiled in 5o x 5o bin for deep ocean assimilation. The units are in degrees Kelvin. (b) Reproduced bottom-water warming during the recent decade; potential temperature difference between the mean value for 2001-2005 and that for 1991-1995. (Positive values show a warming trend.) The units are in degrees Kelvin.

***********************In spite of its crucial importance, the physical mechanisms governing bottom-water warming are poorly understood since in-situ observations are spatially and temporally sporadic. Adjoint sensitivity analysis is very useful method for a climate research.More intuitively speaking,

**Two experiments are conductedIn twin exp..*source at 47.5N,168.5E,5260m.* Positive values (indicative of warming in the lowest layer) gradually fill the North Pacific Ocean floor over a 15-year period * Positive values (indicative of warming in the lowest layer) gradually fill the North Pacific Ocean floor over a 15-year period *and then penetrate into the South Pacific Ocean through the narrow passage located to the northwest of New Zealand*This warming trend can be traced back to the deep Southern Ocean across the Antarctic Circumpolar Circulation region*At the end of a 40-year period, the warm signature finally appears at the sea surface in a confined source region that lies off the Adlie Coast of Antarctica*Depth-latitude cross-section*consistently suggest that the cause of the observed bottom-water warming in the North Pacific is the increase in the net surface air-sea heat flux into this localized source region off the Adlie Coast (Fig. 2f). (The integrated effect in this region covers 73% of that over the whole Pacific basin, when the variance of the heat flux is taken into consideration.) **Two experiments are conductedIn twin exp..******

Documents

増田周平 , 淡路敏之 , 杉浦望実 , 石川洋一 , 五十嵐弘道 , 日吉善久 , 佐々木祐二 , 土居知将 JAMSTEC Kyoto University