V2-263
Status and Prospects of Ocean State
Estimation (Reanalysis)
Detlef Stammer
KlimaCampus
University of Hamburg
Why (Ocean) Synthesis?
• A complete picture of the ocean for the purpose of
climate research and applications will only come from
a synergy between observations, modeling and data
assimilation.
• Goal of ocean reanalysis is therefore to obtain best
possible description of the ocean by combining all
available data with the dynamics of an ocean
circulation model.
Use of Ocean Syntheses
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Description of a complex local/global flow field and its
interaction with biology.
Description of the interaction of the ocean with ocean
properties and biology and with the atmosphere, sea
ice, etc.
Use of estimated flow field for studies of biology, CO2
sequestering, regional impacts, regional and global
sea level, ...
Initialization of coupled forecast models (SI, IPCC).
As a global framework to embed in regional/basin
scale research efforts.
Evaluation of the observing system.
Evaluate hypotheses drawn from incomplete data sets.
Typical Science Questions:
1)
THE PLANETARY HEAT BALANCE:
– heat storage,
– MOC and heat transports and
– ocean/atmosphere feedbacks.
2) THE GLOBAL HYDROLOGICAL CYCLE:
– water balance,
– rainfall variability
– salinity and convection.
3) SEA LEVEL:
– sea level rise
– sea level variability.
4) Biology and CO2 Sequestering
Ocean State Estimation
• State estimation or “data assimilation” is just leastsquares fitting of models to data. (Nudging, 4DVAR,
3DVAR, adjoint, OI, OM, Kalman filter, RTS smoother,
ensemble KF, AD, Pontryagin principle, relaxation, linesearches, breeding vectors, SVD, optimals, Hessians,
quelling, dual,....)
• The apparently different methods are variant algorithms
used to find the minimum of an objective (or cost)
function, the extent to which an approximation to that
minimum is acceptable, and whether one seriously seeks
an estimate of the error of the result.
In Generic Form of Assimilation:
initial conditions
observations
control vector, mainly
meteorology
GCM
Finding a minimum, subject to the model, is a numerical, not a
conceptual or mainly scientific problem.
But the nature of the minimum, in addition to the model structure,
depends directly on the weight matrices in J’. If P,Q,R are incorrect,
so is the solution, no matter how wondrous the numerics: An
oceanographic and meteorological problem, rather than one of
numerics.
State Estimation vs Reanalysis
A “reanalysis” has come to mean an estimate over
a finite interval of time.
In the atmosphere it is a repetition of the analysis,
focusing on optimizing forecasts.
In the ocean the emphasis is also on the past.
Forecasting and nowcasting are special cases that
tend to make oceanographic and climate syntheses
differ in their goals and emphasis.
Consistency of Assimilation
The temporal evolution of data-assimilated estimates is physically
inconsistent (e.g., budgets do not close) unless the assimilation’s data
increments are explicitly ascribed to physical processes (i.e., inverted).
Filtered Estimate: x(t+1)=Ax(t)+Gu(t)+Δ(t)
x: model state, u: forcing etc, Δ: data increment
Smoothed Estimate:
x(t+1)=Ax(t)+Gu(t)
Data
Data increment: Δ
Model Physics: A, G
time
Climate Syntheses need to preserve first principles
(Bengtsson et al., 2007)
Ongoing Ocean Synthesis
• Several global ocean data assimilation products are
available today that in principle can be used for climate
applications.
• Underlying assimilation schemes range from simple and
computationally efficient (e.g., optimal interpolation) to
sophisticated and computationally intensive (e.g., adjoint
and Kalman filter-smoother).
• Intrinsically those efforts can be summarized as having
three different goals, namely
– climate-quality hintcasts,
– high-resolution nowcasts, and
– the best initialization of forecast models.
Some Examples of Existing Syntheses
RTS smoother
3D-VAR
Example: The GECCO State Estimate
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
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Ocean synthesis, performed over the period 1952 through 2001 on a
1º global grid with 23 layers in the vertical, using the ECCO/MIT adjoint
technology.
•
Model started from Levitus and NCEP forcing and uses state of the art
physics modules (GM, KPP).
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The models adjoint (obtained using TAF) is used to bring the model
into consistency with most of the available ocean observations over the
full period by adjusting control parameters.
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At this stage control parameters are the models initial temperature and
salinity fields as well as the time varying surface forcing, leading to a
dynamically self-consistent solution (next step is to include mixing).
Input Data Sets and Controls
Köhl and Stammer (J.Clim. In press.)
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Data Coverage:
MBT and XBT Data Sets
Flow of information
XBT Data
RMS
T residual;
opt.
Estimates of Surface Flux Estimates
100 W/m^2
Heat
Freshwater
Windstress
Runoff
-100
100
-100
The
Time-mean
SSH;
1992-2003
Mean Ocean Circulation, global
Niiler, Maximenko et al.
SSH Trends
1992 - 2001
Altimetry
GECCO: most of the changes
are due to changes in heat
content. Those changes are
primarily redistribution in the
ocean due caused by
changing winds, but partly
also due to heat fluxes over
the northern hemisphere.
Atlantic Meridional Overturning Circulation
Picture from http://www.noc.soton.ac.uk
Estimates of un-observables:
MOC, Ocean Heat and Freshwater Transports
Comparison of maximum MOC at 25N
Bryden et al.
(2005)
Duality of Adjoint Models
• Data assimilation
• Sensitivity Experiments
Adjoint Sensitivity Study: Mechanisms of MOC
variability at 25 N (Köhl, 2005)
1) Local Ekman transport
2) Coastal down-welling at east coast
3) Kelvin wave propagation along the west coast
4) Baroclinically unstable long Rossby waves
Decadal MOC Changes at 25 N
The dominant mechanism for changing the strengths of
the MOC at 25N appears to be the impact of Rossby and
boundary waves which together explain ~60% of
variability.
Changes of density in subpolar region could be a
consequence of variability of Denmark Strait overflow that
can be monitored using altimetry.
Influence of temperature and salinity
at 310 m depth (level of largest sensitivity)
Wave-like signal in the Canary basin
Neg. T/pos. S anomalies in the
Labrador/Irminger Sea
Gradient south east of Iceland
enhances NAC inflow into GIN sea ->
more outflow though Denmark Strait
Southward Propagation of anomalies of maximum MOC:
Southward propagation of anomalies of maximum MOC in the North
Atlantic from GECCO (left) and from MOC sensitivity study (right)
Synthesis Evaluation Effort
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Is needed to determine the quality of existing global ocean
analysis/synthesis products and to assess their usefulness for climate
research.
Is needed to make recommendations for resource allocations in the
future.
Begin a quantitative evaluation of synthesis results with respect to their
skill and usefulness for climate research.
Identify strength and weakness of systems and explain differences
among them.
Define pilot set of climate-indices and diagnostic quantities to be
produced on a regular basis as prototype synthesis support of global
and regional CLIVAR research.
Define data set required as input and identify present gabs.
Discus modeling standards for ocean synthesis.
Discus forcing standards for ocean synthesis.
1994-2001 time-mean Atlantic MOC for contributing groups this time
New contributions this time: K-7, MERCATOR, & ECMWF (26N time series only)
MOC strength at 900 m (near the depth of MAX MOC strength)
25N
Vecchi 2006 (pers. Communication)
Climate Forecasts
• An ultimate goal of ocean data
assimilation is to improve SI and
decadal climate forecasts.
IMPACT of ASSIMILATION and SURFACE FORCING on NINO-3 SST FORECASTS
ECMWF: Alves, Balmaseda, Anderson & Stockdale: QJRMS 2004
Legend:
No-assimilation cases
Mean drift of Forecast SST (1991-1996)
• Drift is larger for
non-assimilation
cases
Assimilation cases
• Assimilation
compensates for
forcing
uncertainties
Forecast SST anomalies for 1997/98 El Nino
Sensitivity to initial
conditions Assimilation
initialization
performs better
Under-prediction of
anomaly due to
model error
Further Advances in (ENSO) Prediction
• Model Improvements - reducing systematic errors
• Better Constraining Initial Conditions
– Particularly important in ocean because the memory of
ENSO resides there.
– The importance of ocean subsurface data in making ENSO
predictions has been demonstrated in a number of studies.
– Is the best ocean analysis/reanalysis the best initialization?
The Initial Condition Practices
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(Best) State Estimate
– Data Assimilation in the Separate Component Models
– Leads to initialization shocks.
Coupled Model Climate ≠ Observed Climate
– Anomaly Initialization
– Improve model
Coupled “Modes” of Coupled Model ≠ Observed Coupled
“Modes”
– Initializing the Coupled Modes
– Identify ensemble perturbations
Way out: Do the Coupled Assimilation Problem
Coupled Model Initialization of K7
(T. Awaij)
Improvement of predictability
Good initialization by 4D-VAR:
4D-VAR approach allows us to make
initial conditions as close as possible
to the observed climate attractors for
a target phenomenon
Four-Dimensional Variational Coupled
Data Assimilation by K7
1. Oceanic initial condition
2.
Bulk parameters controlling Air-sea fluxes of
Momentum
Sensible heat
Latent heat
Fv = − ρα M C M v v
Fθ = ρ c pα H C H v (θ g − θ )
Fq = ρα E C E v (q g − q )
For Smaller-scale Parameterization:
we made pre-optimization using the Green function approach.
Experimental Settings
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Coupled Model on ES (CFES):
– T42L24 AFES for AGCM (originally CCSR/NIES AGCM and then improved:
e.g.: new radiation code MstrnX [Nakajima et al. 2000] and diagnostic code of
marine stratocumulus [Mochizuki et al. 2006] )
– 1x1deg L36 MOM3 for OGCM
– Canopy type Model for Land (MATSIRO)
– IARC Sea-Ice Model
– No flux correction between different spheres
Assimilation Method: 4DVAR
– Adjoint OGCM and adjoint AGCM are coupled
Assimilation Window
– Climatological seasonal exp.: 1-year window (using 1-month OBS data)
– 1996,97,98 (3-year-long specific) reanalysis exp.: a sequence of 9-month
window (using series of 10-daily means of OBS data) and 11ensemble
experiments were conducted to cope with weather modes
Major Assimilated Elements
– Atmosphere: NCEP’s PREFBUFR data and SSM/I sea wind
– Ocean: WOA data, T/P altimeter data, FNMOC dataset, OISST values, and
ARGO float data from the Coriolis Data Center.
For IOD case,
DMI
index
Observed
time series
Full assimilation
Assimilation only for
parameter fitting
4DVAR CDA
improved DMI
index
Assimilation only
for initial value
Correction by both
oceanic initial
conditions and
parameters plays an
important role
T. Awaji, 2007
1997
1998
Comparison between Adjusted bulk parameter
values
and experimental results
同化(ens4_test4s2)による中立時抵抗係数と
Charnockの比較
2.3
Comparison with Charnock’s experiment
2.1
1.7
1.5
Adjusted
values lie in
reasonable
range
±１σ
adjusted
平均
mean
1.3
Charnock
Charnock’s
experimental law
1.1
0.9
U海上10mの中立風速(m/s)
10 m/s
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0.7
0
CD*0.001(10m)
1.9
Standard
deviation
Challenge: Data Issue
Data Coverage
Data Quality:
XBT bias
ARGO
biasses
Flow of
information
SST offsets
Altimetry drifts
•Requires Data Reanalysis and expansion of proxys
•Requires exploitation of new measurements.
We need to do all required efforts to maintain the existing
observing capabilities and use existing data as much as
possible!
2005 June Argo network
Temperature
Salinity
But: Can we really assess climate change in the 20th
Century? (Probably not!)
Required: Reference Data Sets
Examples include:
•SST Fields: e.g., GHRSST-PP SST Reanalysis
•SSH Fields: TOPEX/Poseidon and JASON-1 sea level anomaly
•Time-mean sea surface topography and geoid.
•Ocean colour data sets.
•De-tided tide-gauge data at selected stations with IB correction applied.
•Hydrographic lines (one-time and repeated).
•TOGA-TAO, BATS, HOT, and Station P time series.
•Levitus climatological of temperature and salinity.
•Velocity Fields at surface und subsurface as well as transport timeseries.
•Surface Flux fields.
•High-latitude observations.