ocean syntheses

Ocean Syntheses

David BehringerNOAA/NCEP

NOAA Ocean Climate Observation 8th Annual PI MeetingJune 25-27, 2012

Silver Spring, Maryland

Ocean syntheses are constructed for many different purposes:• Regional analyses, high resolution now-casts, climate monitoring, initialization of

seasonal or decadal forecasts, etc.

They employ a diversity of methods:• OI, 3D and 4D variational, adjoint, Kalman filtering and Kalman smoothing

They have a common requirement for observations:• Temperature and salinity profiles (BTs, CTDs, fixed moorings, Argo), altimetry, SST,

SSS, etc.

Introduction

Here the focus will be on climate scale analyses and their ability to capture climate signals and how that is related to the availability of observations.

Comparison of Upper Ocean (0-300m) Heat Content (HC300) in Operational Analyses

Xue et al., J. Clim., 2012

Comparison of HC300 in Operational Analyses


Anomaly correlations of each analysis with EN3 for 1985-2009


Ensemble MeanLinear Trend1993-2009

Trend Normalizedby Ensemble Spread


Comparison of Upper Ocean (0-300m) Heat Content in Operational Analyses

• Where the climate signal is strong, the number of observations is sufficient, and the models themselves perform well, the model analyses of HC300 correlate well with the observation-only analysis EN3.

• Under the same conditions, the model analyses are consistent among themselves in the sense that the ensemble mean linear trend in HC300 is greater than the ensemble spread.

• Where the above conditions are not met, the HC300 anomaly correlations between the model analyses and EN3 effectively vanish and the ensemble mean linear trend in HC300 is less than the ensemble spread

Armin Koehl, U. Hamburg

CLIVAR/GSOP Ocean Synthesis Comparison



Anomalous Global Heat Content 0-700m 1022 J

Anomalous Global Heat Content 0-700m (HC700) 1022 J

• While the first example revealed regional consistencies among model analyses and between model analyses and observation-only analyses, this not the case when we consider the global ocean as a whole.

• The observation-only analyses are more consistent among themselves, which may produce some confidence in the analyses. However, the consistency may in part be due to a more conservative treatment of observation gaps.

• While some of the problems in the model analyses may trace back to fall rate errors in XBTs, the “spread” among the analyses does not appear to be what might be expected from an ensemble of “good” analyses. A reasonable hypothesis might be that the cause is differences among the models in the treatment (or non-treatment) of model biases in the presence of observation gaps.

Annual Distribution of Observed Profiles

Temperature

Salinity

1985 2011

Vertical Distribution of Global Profile Observations per Month 1978-2011

Temperature

Salinity

CTD

XBT

MRBARG

Number of Global Profile Observations per Month 1978-2011

Temperature

Salinity

Atlantic 30oW Section Jun-Dec 2006

GODASw. Argo

w. bias corr.

GODAS(w/o Argo - w. Argo)

Temperature

Salinity

GODASw. Argo

GODASw/o Argo

Atlantic Overturning Transport (Sv)

Atlantic Overturning Transport (Sv)

Rapid (blue)26.5oN

GODAS (red)30-35oNw. Argo

GODAS (red)30-35oN

w/o Argo

Atlantic Meridional Overturning

• This last example was set up to guarantee a sharp contrast in the results. It nevertheless demonstrates the importance of adequate observations (Argo) and of bias correction. When these conditions are met, the result is an estimate of the Atlantic meridional overturning that agrees well with the independent RAPID data in phase and magnitude and in capturing the “collapse” of 2009-2010.

Concluding Remarks

• Models must continue to improve (resolution, forcing, etc.).• Bias correction must be employed and must be carefully designed (e.g. to

conserve water mass properties).• Bias correction can control erratic behavior in an analysis, but it cannot

enhance a climate signal.

• Data mining should continue.• We need to find a way to sustain the current observing system (Argo, TAO-

TRITON, PIRATA, RAMA, surface drifters, satellite SST, SSH, SSS, and color).• We need more observations at high latitudes and below 2000m.

• If we agree that the observing system must evolve in the face of budgetary constraints, we must be cautious in how we proceed.

• There is considerable risk in using imperfect ocean models to evaluate/design ocean observing systems. Using a multi-model approach may reduce the risk, but not eliminate it.

Thank You

Appendix

ODA Development Plans at NCEP

• Extensive improvements are expected for MOM this fall from GFDL.

• A collaboration with UMD to port their Local Ensemble Transform Kalman Filter (LETKF) is beginning to bear fruit.

• An extension of that collaboration will seek to build a hybrid LETKF/3DVAR system.

Local Ensemble Transform Kalman Filter (4D-LETKF) at NCEP

Observations – Argo, Altimetry, CTDs, etc.Compared against 5 individual forecast days

20-member Ensemble forcing from GEFS provides error estimates of surface fluxes

20-member Ensemble model run at ½-degree resolution using GFDL’s MOM4p1 ocean model provides error estimates of forecasted ocean state

5-day Model Forecasts

LETKF updates ensemble members to better fit observations

Estimated fcst error Estimated analysis error

Steve Penny, (UMD)

Hybrid Assimilation facilitates high-resolution forecasts

20-member ½-degree resolution ensemble

Single ¼-degree resolution forecast

Dynamic Error

estimates3D-Var

LETKF

Re-center ensemble

5-day Model Forecasts

Error estimates are attained with the low-resolution ensemble and incorporated into a single high-resolution member via 3D-Var

Steve Penny, (UMD)

ocean syntheses

Documents

model analyses of hc300

regional analyses

ensemble of good analyses

global ocean

operational analyses

climate scale analyses

comparison of hc300

heat content hc300