S ll S ll l d h t l d h t t i bilit f t i bilit f Small

Small
S
Smallll-scale
l and
d shortshort
h t-term
t
variability
i bilit off
ocean surface winds: Applications to
objective
bj ti analyses
l
and
d ocean modeling
d li
Alexey Kaplan
Lamont-Doherty
Lamont
Doherty Earth Observatory (LDEO) of Columbia University
In collaboration with:
D Gombos (MIT),
D.Gombos
(MIT)
J.C.H. Chiang (U of CA, Berkeley), D.Witter (KSU),
M.A. Cane, D. Chen, Y.Kushnir, R.Seager (LDEO),
H.-P.Huang (ASU)
Importance of highhigh-quality
continuous
ti
wind
i d products:
d t
1. Major influence of wind
f
forcing
i
on the
h patterns off
decadal--bidecadal sea level
decadal
trends
Global vs Regional Sea Level Rise
Aviso
Importance of highhigh-quality
continuous wind products:
2. Ocean model responses to
errors in winds
Zonal wind
Variability
Inside
4degx4deg
x1mon bins
(NSCAT)
RMS SSH [Npac-DUACS],
[Npac DUACS],
Curchitser et al. 2005
SSH response to
perturbations from AMIP
fluxes (GMAO Poseydon
ocean model, Borovikov
et al
al. 2005)
RMS SSH [Npac-DUACS],
C rchitser et al
Curchitser
al. 2005
Importance of highhigh-quality
continuous
ti
wind
i d products:
d t
3. Learning relationships to
constrain
t i reconstructions
t
ti
off
wind fields in pre
pre--satellite era
Number of observations in ICOADS
http://icoads.noaa.gov/
htt //i
d
/
Reduced
R
d
dS
Space
Optimal Analysis
K
Kriging,
i i
Traditional OI
EOFs of zonal wind anomaly
Independent
ENSO
Indices
H
How
persistent
i t t are
climate variables
near Equator?
Persistence: Anomaly autocorrelations
with 1 month lag
Persistence:
Anomaly
autocorrelations
with
1 month lag
Verification from the satellite data
Persistence in AMIP experiments:
zonal wind anomaly
J h Chiang
John
Chi
ett all [2001] approach
h
to surface wind modeling:
g
Linearized dynamical core of a GCM
[Seager and Zebiak, 1995] is set up
to take both sea surface temperature
and elevated atmospheric heating
as forcing for the atmospheric
dynamics. Elevated heating is
parameterized via precipitation.
precipitation
Persistence of the actual forcing
SST and
Precip
correlations
GFDL model
(CM2 1)
(CM2.1)
Observations
Ob
ti
(NCEP OI and
CMAP))
Simulation skill
Consistency of persistence pattern in
ERS (colors) and simulation (contours)
What is a good wind product
f
from
a ttropical
i l ocean
modeler’s
modeler s point of view?
Why equatorial persistence is so important?
RMS of sea level response to the
wind noise in a single location
Conclusions and Outlook
• Univariate statistical analyses of historical
•
•
•
•
climate data sets are useful but additional
constraints are needed.
needed
Within ~10 degree of Equator there is a
persistence of surface wind and pressure
p
p
anomalies.
It is driven by the persistence in SST and
precipitation (via elevated heating)
heating).
It can be used in historical analyses of
instrumental data by either fitting AR model to
th wind
the
i d or pressure data
d t or by
b including
i l di
temperature and precipitation in the analysis.
Wind analyses suitable for driving ocean models
must be persistent near Equator.