www.ecco-group.org/abstracts/woce_katja2.txt

Ocean mixed layer depth: A subsurface proxy of ocean-atmosphere
variability

K. Lorbacher, D. Dommenget, P.P. Niiler and R.E. Davis, SIO

The spatial and temporal variability of dominant atmospheric forcing
mechanisms setting up the structure of the ocean turbulent surface layer
are well documented. To quantify, understand and ultimately predict
ocean-atmosphere interactions on climate relevant time scales
identifying the depth of this layer, in addition to the SST, is cruical.
However,
observed ocean mixed layer depths (MLDs) -- in the quality of SST
observations --
do not exist yet.

We investigate hydrographic data with high and low vertical resolution
obtained during the WOCE period to determine the quality of the observed
ocean MLD applying two criteria for this depth provided by the recent
literature. Considerable discrepancies in the results motivates us to
optimize a criterion estimating ocean MLDs. We include a solution of the
global ECCO state estimation in our analysis to test the sensitivity of
both observed and diagnosed MLDs and their climate variability to
different criteria.
1	Ocean mixed layer depth: A subsurface proxy of ocean-atmosphere
2	variability
3
4	K. Lorbacher, D. Dommenget, P.P. Niiler and R.E. Davis, SIO
5
6	The spatial and temporal variability of dominant atmospheric forcing
7	mechanisms setting up the structure of the ocean turbulent surface layer
8	are well documented. To quantify, understand and ultimately predict
9	ocean-atmosphere interactions on climate relevant time scales
10	identifying the depth of this layer, in addition to the SST, is cruical.
11	However,
12	observed ocean mixed layer depths (MLDs) -- in the quality of SST
13	observations --
14	do not exist yet.
15
16	We investigate hydrographic data with high and low vertical resolution
17	obtained during the WOCE period to determine the quality of the observed
18	ocean MLD applying two criteria for this depth provided by the recent
19	literature. Considerable discrepancies in the results motivates us to
20	optimize a criterion estimating ocean MLDs. We include a solution of the
21	global ECCO state estimation in our analysis to test the sensitivity of
22	both observed and diagnosed MLDs and their climate variability to
23	different criteria.