ocean state estimation for climate research

GODAE Final Symposium, 12 – 15 November 2008, Nice, France

Ocean State Estimation for Climate Research

Tong Lee, NASA JPL

Toshiyuki Awaji, Kyoto University

Magdalena Balmaseda, ECMWF

Eric Greiner, CLS

Detlef Stammer, Universität Hamburg


Three streams of GODAE efforts

• Operational ocean analysis and forecast

(presentations by Hurlburt et al. & Kamachi et al.)

• Initialization of seasonal-interannual prediction

(presentation by Balmaseda et al.)

• State estimation for climate research

(this presentation)


Estimation Systems

• Models: MOMx, POP, MITOGCM, OPA/NEMO, etc.

• Data constraints: SSH, T/S profiles, SST, SSS, velocity data, … elephant-seal data.

• Assimilation methods: OI, 3D-VAR, Kalman filters (and smoother), adjoint (4D-VAR).

• Presentations by Dombrowsky et al. by Cummings et al. for further individual descriptions.


Science Applications

A large number of applications over a wide range of subjects (from physical oceanography & climate to biogeochemistry & geodesy).

Examples (physical oceanography related) • Meridional overturning circulation (MOC) (e.g., Lee & Fukumori ‘03, Schoenefeldt & Schott ‘06, Schott et al. ‘07 & ‘08, Wunsch & Heimbach ‘06, Balmadeda et al. ‘07, Cabanes et al. ‘08, Köhl & Stammer ’08, Rabe et al. ‘08)

• Water-mass pathways (e.g., Fukumori et al. ‘04, Wang et al. ‘04, Masuda et al. ‘06, Toyoda et al. ‘08)

• Heat budget (e.g., Kim et al ‘04, ‘06, ‘07, Halkides & Lee ’08-a & ‘08-b)

• Sea level (SSH) variability (e.g., Carton et al. ‘05, Wunsch et al. ‘07, Köhl & Stammer ‘08)

• Intercomparison

3-D structure of the anomalous subtropical cell (STC) of the Pacific Ocean

Pacific Ocean STC

Conventional bird’s eye view

Poleward Ekman flow

Equatorward thermocline flow

N10S 10N

Tradewind anomaly

Conventional 2-D view via meridional transport stream function

Poleward Ekman flow

Interior thermocline flow

Poleward Ekman flow

LLWBC flow

New view: 3-D structure

Lower branch of the STC has a zonal structure characterized by anti-correlated anomalies of boundary & interior flows, thus opposite roles in regulating tropical heat content (Lee & Fukumori 2003, Schott et a. 2007)

Poleward Ekman flow

Equatorward thermocline flow

Poleward Ekman flow


How do satellite data help constrain estimates of the anti-correlated boundary & interior flows

• Observed SSHA suggest local recirculation in w. Pacific causing anti-correlated boundary & interior flows (Lee & Fukumori 2003, Lee & McPhaden 2008).• Need to monitor LLWBCs, which are not well sampled by existing in-situ systems.

Trend of SSH 1993-2000


Anti-correlated anomalies of boundary & interior flows in G-ECCO product

Schott, Wang, & Stammer (2007) confirms finding of Lee & Fukumori (2003) (shorter period)

10°N

10°S


The North Atlantic Meridional Overturning Circulation (MOC)

Picture from http://www.noc.soton.ac.uk


• Interannual variation of MOC in subtropical N. Atl. is correlated with pycnocline depth difference Δh between west & east coasts

Pycnocline depth difference across basin

MOC anomaly at 1000 m

• Δh variation dominated by W.B. (mainly due to near local wind)

Interannual variation of N. Atl. MOC: mechanism & fingerprinting (Cabanes, Lee, and Fu 2008)

No

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• ΔSSH is not indicative of subtropical N. Atl. MOC variation because of dominant steric contribution in the upper 200 m


Decadal change of N. Atl. MOC at 26N estimated by an ECCO-GODAE product (Wunsch & Heimbach 2006)

• Complex vertical structure:

Weakening northward transport above 1000 m

Strengthening southward transport of NADW

Strengthening northward transport of abyssal water

• No significant decrease of northward heat transport (upper-ocean warming enhances vertical temperature gradient to offset weakening of upper MOC).• Opposite trends of MOC strength at 26N & 50N.


Decadal change of N. Atl. MOC at 26N estimated by ECMWF ocean reanalysis product (Balmaseda, Smith, and Haines, et al. 2007)

Assimilation improves comparison with observational analysis

Bryden et al. (2005) Cunningham et al. (2007)

• Large month-to-month variability and related uncertainty in model & data.• Complex structure of trends (both in vertically & meridionally).• Upper-ocean warming enhances vertical T gradient to offset weakening MOC.


Using passive tracer adjoint to study water-mass pathways

Origin of “NINO-3” waters: Fukumori, Lee, Cheng, & Menemenlis 2004)

Other

Other examples: • Wang et al. (2004): cause for interannual co-variability of T & S in the cold tongue.• Toyoda et al. (2008): interannual variation of subtropical Pacific mode water.


Global sea level rise in SODA ocean reanalysis (Carton, Giese, and Grodsky 2005)

The apparent acceleration of sea level rise in1990s was explainable to by fluctuations in warming and thermal expansion of the oceans (upper 1000 m)

Levitus et al. 2005

Miller & Douglas 2004


Studying patterns of decadal trend (1993-2004) of SSH in an ECCO-GODAE product (Wunsch, Ponte, &

Heimbach 2007)Zonal sums of trends in ρ (a), T (b) and S (c) over various depth ranges showing

non-negligible contributions from below 850 m

(a)

(b)

(c)

Observational errors & potential impact on state estimation


Physically consistent state estimation allows budget closure – important to climate diagnostic analysis

Mixed-layer temperature (MLT) budget: Kim, Lee, & Fukumori (2004):

N. Pac. SST warming during 1997-2000

Budget for MLT interannual anomaly

Ocean dynamics contributed to the mid-latitude warming significantly.


CLIVAR/GODAE global ocean reanalysis product evaluation: the ability to detect climate signals.

Many thanks to

• various groups for providing a large number of indices & participate the discussion;

• several analysts for performing the intercomparison;

• APDRC/IPRC (Peter Hacker’s group) for assistance.

Examples …

Intercomparison - climate time scales

(presentation by Hernandez et al. for shorter time scales)

Comparison for anomaly of ITF volume transport among 13 ocean reanalysis products (1993-2001)

r.m.s. diff. 1 Sv r.m.s. diff. 2 Sv

r.m.s. diff. 1 Sv

Ensemble mean: 13.8

r.m.s. difference of time mean: 3.7

Averaged temporal variability (>monthly) of different products: 3.6

r.m.s. difference of anomalies: 1 - seasonal, 2 - non-seasonal, 1 - interannual

• r.m.s. diff. for time mean larger than that for anomaly.

• “Signal-to-noise” ratio > 1

• Requirements for observational accuracy (e.g., from INSTANT observations) to distinguish different products (also have statistics for T & S fluxes).

Statistics of ITF volume transport comparison for the 13 products (all in Sv)


EQPAC EQATL EQIND TRPAC TRATL NPAC NATL GLOBAL

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T300:1993-2002T300: 1960-2002 S300: 1993-2002S300: 1960-2002

Signal/Noise ratio of upper 300m averaged T & S in various regions for 20 products (Balmaseda & Weaver 2008)

How well do ocean reanalysis detect climate variability? --- Signal/Noise Ratio


Time evolution & ensemble spread of upper 300m mean T & S for 20 products (Balmaseda & Weaver’08)

The colored envelopes (or their differences) indicate the spread due to different forcing, model, or assimilation methods


Challenges ahead

• Model physics• Model & data errors• Physical consistency for sequential/filter methods• Control space for adjoint & Green’s function methods• Coupled assimilation• Resources issues


Summary

• Significant advancement in ocean state estimation for climate research has been achieved in the past decades.

• Global ocean synthesis evaluation help identify pros & cons.

• Ongoing challenges require sustained resources and effort & collaboration with a broader community.

ocean state estimation for climate research

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