Mixed-Layer Depth Intercomparison

CLIVAR GSOP Ocean Synthesis and Air-Sea Flux Evaluation Workshop, 27-30 Nov 2012, WHOI USA11:50-12:10, day 3, Theme VI: Synthesis Evaluation and Intercomparison (chair: M. Balmaseda)

Takahiro Toyoda, Y. Fujii, T. Kuragano, M. Kamachi (JMA/MRI),Y. Ishikawa, S. Masuda, T. Awaji (JAMSTEC)

Many thanks to the providing centers of MLD datasets used in this study

Introduction1. Observational Mixed Layer Depths (MLDs) and

Isothermal Layer Depths (ILDs)• Definition• Comparison of observational MLDs/ILDs• Estimate errors from time-mean and/or gridded TS

2. Intercomparison of MLDs/ILDs by synthesis approach• Discrepancies from Argo-based MLDs/ILDs• Scores

3. Possible application to climate study• North Pacific• North Atlantic• Barrier Layers in the equatorial Indo-Pacific

Summary and discussion

Outline

Importance of MLD analysis• MLD characterizes heat and fresh-water cycle in the ocean surface layer.• Surface water masses are generated in ML, followed by subduction to the ventilated

thermocline layer and thereby forming physical properties in subsurface layer (e.g., T, S, PV).

• Important element of the bio-geochemical cycle (e.g., CO2; lower figure)

• Accurate description of MLD distribution required for better understanding of climate variabilities and prediction.

Annual sea-air CO2 flux from Takahashi (1997; updated)Ocean uptake in deep ML regions is clearly seen.

Syntheses representing interannual variability in the global ocean• Improvement in modeling and assimilation techniques and increase of observations

offer a better description of global ocean processes.• Evaluation of global ocean syntheses is needed in the context of observation-based

estimates.• MLD is one of the most important metrics for the dynamical process of climate variation.• Intercomparison of the seasonal to interannual variabilities in MLDs on global scales will

provide useful information to the assimilation community and the user community (e.g., in CLIVAR) to further activate both the synthesis approach and the study of climate variability and predictability.

• Assimilation community can apply the syntheses to the climate study firstly, since this would be followed by other users and then more robust evaluation would be done from various aspects.

Datasets[16 ocean sysntheses submitted]• GLORYS2V1 (Mercator): 3DVAR, 1/4 deg, 50 levs, 1993-2009• PSY3V3 (Mercator): operational best estimate of GLORYS2V1, 2009-2011• C-GLORS (CMCC): 3DVAR, 1/4 deg, 50 levs, 2000-2010• UR025.4 (Univ. Reading): ensemble OI, 1/4 deg, 46 levs, 1993-2010• ORAS4 (ECMWF): 3DVAR+FGAT, 1x(0.3-1) deg, 42levs, 1958-2011• GECCO2 (Univ. Hamburg): 4DVAR, 1x(1/3-1) deg, 50 levs, 1948-

2011.11• GEOS5 (GMAO): ensemble OI, 1/2x(1/4-1/2) deg, 40 levs, 1993-2011• ECCO (JPL): Kalman filter and RTS smoother, 1x(0.3-1) deg, 46 levs, 1993-2011• ECDA3 (GFDL): coupled EnKF, 1x(0.3-1) deg, 50 levs, 2005-2011• K7-ODA (JAMSTEC): 4DVAR, 1 deg (75S-80N), 45 levs, 1975-2009• MOVE-G2 (MRI): 3DVAR+FGAT, 1x(0.3-0.5) deg, 53 levs, 1979-2011• MOVE-CORE (MRI):3DVAR+FGAT, 1x0.5 deg, 51 levs, 1948-2007• MOVE-C (MRI): 3DVAR, coupled model, 1x(0.3-1) deg (75S-75N), 50 levs, 1950-2011• K7-CDA (JAMSTEC):coupled 4DVAR, 1 deg, 45 levs, 2000-2007.3• EN3v2a (UKMO): no model, OI, 1 deg, 2005-2011• ARMOR-3D (CLS): no model, OI, 1/3 deg (82S-82N), 24 levs (0-1500m), 1993-2010[Additional MLD datasets for references] (with no model)• MILA_GPV (JAMSTEC): based on Argo profiles,2x2 deg, 2001-2011• de Boyer Montegut et al. (2004) based on TS profiles (1941-2008), 2x2 deg,

climatology• NODC MLD: from WOA(98) TS, climatology

Definitions of MLDs and ILDs• Based on monthly TS.• MLDs and ILDs are defined as

MLDr003m: σθ(z=MLD)-σθ(z=10m)=Δρ, Δρ=0.03 kg m-3

MLDr0125m: Δρ=0.125 kg m-3

ILDt02m: |T(z=ILD)-T(z=10m)|=ΔT, ΔT=0.2˚CILDt05m: ΔT=0.5˚C

• Interannual-mean (2001-2011 average) values are calculated monthly (or shorter period depending on length of each synthesis).

• Mainly referred to MILA_GPV (JAMSTEC) based on individual TS profiles from Argo floats: one of the most realistic datasets for MLD/ILD in our analysis.

Intercomparison between observational MLDsEN3v2a (UKMO), ARMOR-3D (CLS): based on monthly-mean TS (time-series)MILA_GPV (JAMSTEC), de Boyer Montegut et al. (2004): based on individual TS profilesNODC MLD: from WOA98 (monthly climatological TS)MLD (0.03 kg m-3 criterion) in MarchMILA_GPV (JAMSTEC)

[m][m]

de Boyer Montegut EN3v2a (UKMO) ARMOR-3D (CLS)Difference from MILA_GPV

Zonal-mean RMSD• MLDs derived from monthly-mean gridded TS are

shallower than those from TS profiles (10-20 m shallower in low latitude and much more in mid-high latitudes)

• Consistent with previous studiess (de Boyer Montegut et al.)• MLD Differences can be attributed to the differences in

1) Vertical resolution between observations and datasets2) Instantaneous and mean TS profiles3) Observational data4) The number of observations ↔ horizontal smoothing

[m]

de BoyerMontegut

EN3v2a(UKMO)

ARMOR-3D(CLS)

NODC

MLDr003 MLDr0125

Profiles→MLDs→gridded MLDs

Argo

Effects of vertical resolution

• Assume the above 3 cases: high resolution case (1) and low resolution cases (2).• MLD is estimated realistically in high resolution case (1) and low resolution case (2-1).• But in the low resolution case (2-2), MLD is underestimated.• This skewness of biases in the low resolution cases is due to the fact that density change

in thermocline is often much larger than Δρ of MLD criterion as shown above.• On average, MLD is underestimated when using the low-resolution gridded data

(e.g., syntheses)

MLD

MLD

grid

(real MLD)

case (1)

case (2-1)

case (2-2)

Effects of time-mean and criterion• In addition to MLDs/ILDs based on monthly TS (e.g., MLDr003m), MLDs/ILDs are calculated

from on-line snapshot TS data in the MOVE-G2 experiment (e.g., MLDr003i).• MLDs/ILDs from Interannually-averaged monthly TS (e.g., MLDr003y) are also compared.• Dr. Fabrice Hernandez kindly provided MLDs/ILDs derived from daily TS (e.g., MLDr003d),

which are compared with MLDs/ILDs from monthly TS data of GLORYS2V1 (Mercator).

[from MOVE-G2 (MRI)]MLDr003m － i MLDr0125m－ i

[form GLORYS2V1 (Mercator)]MLDr003m － d MLDr0125m － d

• Underestimation in case of time-mean TS.• Re-stratification in early spring as well as

sudden deepening in early winter likely increases errors.

[m]

Annual march of zonal-mean RMSE[MRI] i vs m i vs y[Mercator] d vs m

40˚N 70˚S

Larger criterion → Smaller error

Longer time-mean

↓ Larger error

(MLDr003)

• For ILDs, large errors occur in the subpolar regions associated with the weak mesothermal structure peculier to the subarctic/subpolar regions.

ILDt05m － i (MOVE-G2)

ILDt05m － d (GLORYS2V1)

More errors in ILDs from time-mean temperature

Example from MOVE-G2: TS at (180˚, 70˚S) during Jan-Jun 2001

January June

S T T

Gridded monthlyc.i.=0.05psu c.i.=0.3˚C

[m]

[˚C]• Too deep ILD is estimated during one month

from this time-series when gridded monthly.

Intercomparison with reference to Argo MLDs• Differences from MILA_GPV (JAMSTEC) based on Argo TS profiles are shown.• Note that smaller biases do not necessarily indicate a better synthesis, because

(1) there exist shallow biases due to the use of monthly-mean and gridded (with lower vertical resolution than observations) TS instead of individual profiles and(2) using Argo data in syntheses, difference between products is more reduced by the stronger restoring to observations.

MLDr0125m difference from MILA_GPV (JAMSTEC) in February

[m]

MLDr0125m difference from MILA_GPV (JAMSTEC) in February

[m]

4DVAR

4DVAR

4DVAR

Coupled

(Coupled) Coupled

KF+RTS EnKF

No model No model SEEKSEEK

3DVAR

3DVAR

3DVAR 3DVAR

OI

OI

OI OI

OI

JRA CORE

ERA-IERA-I

1/4

ERA

11/4

1/3 1/41/4

1/2 1 1

ERA-I ERA

1

1/2 1

(NCEP2)

11

1

(NCEP1)

(NCEP)GMAO

Surface flux Surface flux

Surface flux

Surface flux

1/2

MLDr0125m difference from MILA_GPV (JAMSTEC) in August

[m]

Barrier layer thickness comparisonDefined here as: BLT=ILDt05m － MLDr0125m

10 [m]

NPESTMW, NPTW

SPESTMW

ITCZ

• BLs are thin in the smoother approach while thick in the coupled case (K7-CDA).• Correction of ERA-I based on GEWEX in GLORYS2V1 and C-GLORS likely thickens BLs.• Reproductions of BLs related to salty waters from subtropics are similar in each synthesis.

ICWOBSSPCZ

Scores on time-mean values• RMSDs of interannual-mean monthly MLDs/ILDs between MILA_GPV and syntheses.• ILDs are compared within 40˚S-40˚N, because of large biases toward high latitudes.• Shaded dark (light) orange indicates values less than mean by 1-σ or smallest (0.5-σ).• Right 4 columns are for RMSDs of seasonal variation defined here as maximum minus

minimum values in each grid point.RMSD of Seasonal variationRMSDs of monthly values

mldr003mmldr0125m ildt02m ildt05m mldr003mmldr0125m ildt02m ildt05mEN3v2a 29.89 27.40 22.07 17.16 50.45 51.96 35.80 30.25ARMOR- 3D 25.55 26.27 16.03 14.80 45.74 53.40 25.10 26.85GLORYS2V1 24.47 35.69 13.94 15.00 44.24 62.26 21.52 24.64C- GLORS 24.17 30.78 15.10 16.25 44.47 62.11 23.56 26.87UR025.4 25.45 34.25 17.66 46.03 61.08 27.07ORAS4 22.68 27.51 14.42 15.51 42.25 49.19 23.41 27.23GECCO2 29.38 37.52 19.29 20.71 51.57 67.48 33.43 34.22GEOS5 48.06 18.46 95.92 32.35ECCO 44.58 29.51 67.43 48.24ECDA3 25.63 32.07 16.13 17.26 42.85 59.34 27.80 31.51K7- ODA 42.88 54.21 33.25 39.21 66.17 72.96 46.36 51.79MOVE- G2 29.62 37.33 18.59 18.96 62.25 81.29 25.76 26.77MOVE- CORE 22.70 26.80 16.52 17.42 38.64 54.96 24.81 28.44MOVE- C 27.16 30.53 18.68 19.46 47.11 52.21 30.28 31.37K7- CDA 37.84 49.75 25.54 28.55 58.13 74.56 38.49 44.32mean 28.26 36.18 19.13 20.40 49.22 64.41 29.69 32.80standard dev 5.74 8.74 5.33 6.58 7.94 12.29 7.16 8.17de Boyer 17.59 12.86 35.80 23.38 [m]

RMSDs from de Boyer Montegut　　　　　　 　 Seasonal variation

RMSDs from NODC MLD　　　　　 　 　 Seasonal variation

RMSDs from other datasets

mldr003m ildt02m mldr003m ildt02m mldr0125m ildt05m mldr0125m ildt05mEN3v2a 32.20 17.20 51.79 21.95 50.25 24.33 54.42 24.93ARMOR- 3D 28.64 11.47 49.09 13.43 54.01 27.65 59.97 25.61GLORYS2V1 27.00 11.32 43.26 17.28 75.54 28.12 74.22 28.62C- GLORS 26.30 13.79 45.06 24.95 60.98 31.18 65.37 32.71UR025.4 26.94 45.27 70.39 34.06 67.20 31.68ORAS4 26.39 13.59 44.43 24.50 62.59 36.48 64.33 31.34GECCO2 31.03 16.17 51.71 26.59 55.86 27.32 72.45 33.86GEOS5 96.02 35.98 105.74 37.88ECCO 63.13 37.88 81.32 54.87ECDA3 28.10 15.51 43.73 30.75 58.30 29.11 72.74 35.79K7- ODA 43.31 31.93 65.55 45.14 70.56 50.31 82.18 56.69MOVE- G2 40.19 16.11 68.97 24.38 79.74 27.92 95.01 36.08MOVE- CORE 24.68 14.72 40.25 25.87 59.84 26.83 68.76 33.58MOVE- C 28.79 19.69 47.31 33.40 65.44 34.54 67.99 37.31K7- CDA 38.83 22.71 58.13 38.91 59.23 36.23 79.76 48.82mean 30.95 17.02 50.35 27.26 65.46 32.53 74.10 36.65sigma 5.77 5.45 8.49 8.41 11.25 6.31 12.82 9.29MILA_GPV 17.59 12.86 35.80 23.38 53.23 64.18

• Comparison of syntheses with no model indicates that ARMOR-3D is closer to MILA_GPV and de Boyer Montegut et al. (2004) whereas EN3v2a is closer to NODC MLD, which might be due to the resolutions.

North Pacific (>20˚N) North Atlantic (>20˚N)

Tropics (20˚S-20˚N) Southern Hemisphere (<20˚S)

Regional RMSDs from MILA_GPVmldr003mmldr0125m ildt02m ildt05m mldr003mmldr0125m ildt02m ildt05m

EN3v2a 21.73 17.14 22.22 16.56 50.06 43.81 32.98 26.55ARMOR- 3D 18.27 16.18 17.21 14.62 37.24 36.29 19.95 18.65GLORYS2V1 13.80 16.00 13.55 14.49 30.69 39.04 17.12 19.39C- GLORS 13.98 16.88 14.60 15.86 35.78 44.07 18.61 21.17UR025.4 17.25 20.98 16.73 31.47 39.43 23.25ORAS4 14.81 17.55 14.11 14.58 36.22 41.12 18.35 20.92GECCO2 22.13 22.60 19.75 19.67 44.81 56.40 22.76 25.79GEOS5 18.23 17.88 53.76 24.98ECCO 37.54 37.61 63.55 41.05ECDA3 16.60 18.49 15.86 16.73 36.13 42.04 21.82 25.91K7- ODA 33.25 39.87 35.33 40.54 61.24 63.32 45.25 51.74MOVE- G2 19.25 21.41 15.77 17.05 31.48 38.50 19.42 21.42MOVE- CORE 18.22 20.33 17.24 18.11 31.58 34.11 21.39 23.13MOVE- C 20.00 22.23 20.87 21.31 36.12 37.08 24.44 26.46K7- CDA 29.36 36.54 28.02 33.36 54.84 59.60 33.04 37.44mean 19.90 22.80 19.54 21.01 39.82 46.14 24.59 27.19standard dev 5.53 7.89 6.18 8.38 9.48 9.91 8.00 8.88

mldr003mmldr0125m ildt02m ildt05m mldr003mmldr0125m ildt02m ildt05mEN3v2a 16.40 13.71 18.05 13.93 35.13 33.73 25.87 19.65ARMOR- 3D 14.24 13.17 13.52 12.11 31.45 33.67 18.82 17.37GLORYS2V1 11.21 12.93 11.63 11.74 31.88 50.62 17.07 18.84C- GLORS 10.69 12.19 12.10 12.11 28.41 39.04 19.00 20.74UR025.4 12.62 13.20 12.15 33.71 47.51 23.44ORAS4 10.40 11.20 11.21 11.22 28.49 35.73 18.53 19.85GECCO2 12.67 14.33 15.49 16.50 37.09 49.90 23.81 25.57GEOS5 12.81 12.40 70.34 23.81ECCO 14.60 16.65 58.84 39.65ECDA3 11.03 11.95 12.00 11.69 33.62 43.48 20.12 21.38K7- ODA 18.05 19.53 18.19 19.52 55.31 74.75 48.67 59.23MOVE- G2 15.15 17.76 17.72 16.61 40.25 52.11 20.78 22.37MOVE- CORE 12.17 13.06 13.77 13.53 28.58 34.93 19.51 20.50MOVE- C 12.19 12.80 12.81 12.55 36.04 41.45 24.67 25.70K7- CDA 15.68 18.76 19.14 18.78 49.09 67.48 32.56 37.85mean 13.27 14.13 14.64 14.10 36.08 48.91 24.12 26.40standard dev 2.32 2.44 2.80 2.67 7.76 13.17 8.47 10.76

Interannual anomalies• MILA_GPV provides monthly MLDs/ILDs from Jan 2001.• Coverage (with 2x2 deg grid) is small and distribution is fluctuated in early years.

mldr003mmldr0125m ildt02m ildt05mEN3v2a 28.57 31.55 16.69 16.71ARMOR- 3D 23.46 28.12 15.36 15.18GLORYS2V1 26.70 37.13 15.64 16.42C- GLORS 26.25 38.14 16.02 16.63UR025.4 26.37 39.13 19.01ORAS4 27.27 36.07 16.53 17.17GECCO2 26.90 33.37 18.42 19.66GEOS5 48.88 18.79ECCO 46.49 21.87ECDA3 24.58 31.57 17.03 17.86K7- ODA 29.02 37.24 20.63 20.55MOVE- G2 30.31 40.97 16.96 17.68MOVE- CORE 22.13 30.37 16.04 16.74MOVE- C 25.07 32.16 16.96 17.57K7- CDA 29.08 35.68 20.36 21.02mean 26.59 36.46 17.22 18.19standard dev 2.26 5.60 1.65 1.84

[m]Jan2001 Dec2004

Dec2004

Dec2011

RMSDs of interannual anomaliy from MILA_GPV

• For MLDs, results from CORE forcing (MOVE-CORE) and coupled case (ECDA3 and MOVE-C) look fine.

• For ILDs, correction based on GEWEX works better on ERA-I (GLORYS2V1 and C-GLORS).

Use for climate studies• Datasets taking temporal and spatial coverage has the important implication to climate

studies.1. Ensemble-mean monthly time-series during 1948-2011 are calculated from all submitted

syntheses.2. Time-series of annual maxima of MLDs/ILDs are constructed for the analysis in mid-

latitudes, since annual maxima are dynamically important and contain integrated thermodynamical processes from surface cooling in whole winter although the timing when they occur differs among syntheses.

• Maxima during January-April in the Northern Hemisphere and during July-October in the Southern Hemisphere are used in the following analysis.

Histogram of annual maxima of MLDr0125m from all syntheses

Explained variance by the ensemble mean

dataset time

dataset timeensens

mldmld

mldmldmldmld

r 2

mld: MLD/ILD from each synthesisoverbar: monthly average over 2001-2011mldens: ensemble mean MLD/ILD

“r” for annual maxima of MLDr0125mLarge values of “r” indicates consistent interannual variability among syntheses.

• An interannual variance derived from all datasets is defined here at each grid point as

inconsistent consistent

EOF analysis in the North Pacific• An EOF analysis is performed for the ensemble-mean

annual-maximum MLDr0125m field during 2001-2011 in the region with r>0.3 in the North Pacific.

• Time-series and pattern of each principal component are projected in the remnant regions and to the period before 2000, respectively.

“r” in the North Pacific

EOF #1(40%)

EOF #2(21%)

EOF #3(7%)

• The 1st & 2nd principal components seem to be related to PDO and Mode Waters.

• These are discussed in the following slides.• The 3rd and higher modes are confined on

smaller scales.

[m]

Pacific Decadal Oscillation (PDO) defined as the leading EOF of winter SST anomalies in the Pacific Ocean (>20˚N). Patterns of SST (color), SLP (contour), and wind (arrow) anomalies from Mantua et al. (1997).

(Contours denote the mean MLD.)

[˚C]

Comparison of time-series

EOF1PDO( － )

Projection of EOF map during 1948-2000. EOF analysis

－ EOF1×factor－ each member－ ensemble mean

(180˚-160˚W, 30˚N-40˚N)Central Mode Water

(140˚W-130˚W, 25˚N-30˚N)Eastern Subtropical Mode Water

EOF1×(-40)

EOF1×20

0m

0m

(135˚E-160˚E, 30˚N-35˚N)Subtropical Mode Water

15

[˚C] 20

EOF2×20 ensemble mean

－ STMW temp from JMA obs along 137˚E

EOF #2

EOF #1

EOF analysis in the North AtlanticLeading principal component (56%) of the MLD variability in the North Atlantic (>20N; r>0.3) during 2001-2011

“r” in the North Atlantic

EOF analysisProjection

EOF1

NAO( － )

[m]

Positive NAO from NOAA/Lamont-Doherty Earth Observatory NAO pamphlet

• MLD variability in the STMW & SPMW formation regions related to North Atlantic Oscillation (NAO).

• Consistent with previous study (Joice et al., 2000).• “r” is not so large in the Norwegian Sea but EOF1

map is similar to the tri-polar distribution of SSTA in the North Atlantic (Tanimono and Xie, 2002).

Barrier layer in the equatorial Indo-Pacific

Nino3.4

－　＋

－　＋

[m]

X axis: 40˚E-80˚WY axis: 1993-2011

Summary and discussionWe have performed the intercomparison of MLDs/ILDs from syntheses with various models, resolutions, forcings, and assimilation methods.Syntheses have an advantage in their spacio-temporal coverage. The ensemble of synthesis datasets would enhance our understanding of interannual MLD variability, at least in open oceans where a similar variability is seen among syntheses.

Our result could be more discussed in connection with other intercomparisons presented in this WS.More observations or maintenance of current observations are of great value for analysis/synthesis of the global MLD variability.Comparison with regional assimilation results with higher resolution might be more beneficial.A variety of syntheses (e.g., with a σ-model) would strengthen the robustness of the ensemble approach.

Acknowledgements• To Dr. Fabrice Hernandez in the Mercator Ocean and many providers of MLD/ILD data.• The MLD and ILD data based on Argo profiling float data (Hosoda et al., 2010) which we

used in this study are provided by the Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology.