Modeling the biological response to the eddy-resolved circulation in the California Current
Emanuele Di Lorenzo
Arthur J. Miller
Douglas J. Neilson
Bruce D. Cornuelle
John R. Moisan
GaTech, Atlanta, GA 30332
SIO, La Jolla, 92093 CA
SIO, La Jolla, 92093 CA
SIO, La Jolla, 92093 CA
NASA GSFC/Wallops
Over fifty years of hydrographic and other physical and biological data have been collected by the California Cooperative Oceanic Fisheries Investigations (CalCOFI) in the California Current System. The coarse sampling (70 km),
however, has precluded definitive study of the dynamics controlling eddies in the system. In recent years, additional data from ADCP upper ocean currents, satellite altimetry of sea level, ocean color by SeaWiFS and other variables has
been contemporaneously sampled. This study is aimed at using these data to test model dynamics, understand ecosystem processes and eventually to assess predictive timescales. Model skill is quantified by the model-data mismatch (rms
error) during the fitting interval. The physical fields are used to drive a 3D NPZD-type model constrained by sub-surface chlorophyll-a (Chla), nitrate and bulk zooplankton from CalCOFI and surface Chl-a from SeaWiFS.
Scientific Issues
Data Sources
Can a dynamical eddy-resolving ocean model fit a
non-synoptic 3-week survey of all the available
physical data?
Our effort has relied heavily on the fifty year CalCOFI data
record. In addition, datasets collected within the last ten years:
ADCP, Topex and Drifters, are also been utilized. For our July
1997 test case, we plotted these datasets over our model
domain as shown here with the model’s ETOPO5-derived
bathymetry.
If so, then the resulting time-dependent 3D
reconstruction of the evolving flow fields can be
used in three ways.
February 1998 Fit
The physical field reconstruction of the Feb 98 CalCOFI hydrographic data are here used to drive the 3D
ecosystem model. Day 1 corresponds to the equilibrated (steady state) biology after 45 days of frozen
physical field forcing. Day 15 corresponds to the evolving physical fields driving the ecosystem model
from Day 1 initial conditions.
Temperature
DAY 1 (23 Jan 1998)
Phytoplankton
* Diagnose eddy dynamics
- baroclinic/barotropic instabilities
- coastline/topographic influences
- atmospheric forcing effects
* Determine predictive timescales
- deep ocean eddies
- shelf/slope eddies
- atmospheric-forced surface fields
* Drive ecosystem response
- fit part of biology controlled by physics
- diagnose ecosystem balances
- determine ecosystem predictive timescales
Test Case: Fit of
February 1998 El
Nino Cruise
The circulation pattern
observed during the
CalCOFI cruise was
characterized by a
strong
coastal
northward
current.
The low-salinity core
of
the
California
Current was located
unusually far offshore.
Two month later in the
April 98 cruise the jet
of
the
California
Current was found
inshore and the coastal
countercurrent
was
absent and replaced by
a southward flow.
Here we present a
model simulation in
this same period. After
inverting for the initial
condition for the
month of February 98
we integrated the
February 98 : Model Velocity [cm/sec]
36
0.41
0.4
Poleward Coastal
Current
35
0.39
0.37
0.36
0.34
0.33
0.32
0.3
34
0.29
0.28
0.26
0.25
33
0.24
0.22
Latitude
Abstract
0.21
0.2
0.18
32
0.17
0.16
0.14
0.13
31
0.12
0.1
0.09
0.07
30
0.06
0.05
0.03
0.02
29
-125
-124
-123
-122
-121
-120
Longitude
-119
-118
-117
0
-116
April 98 : Model Velocity [cm/sec]
36
0.23
0.23
0.22
Southward Coastal
Current
35
0.21
0.2
0.2
0.19
0.18
0.17
34
0.16
0.16
0.15
0.14
33
0.13
0.13
0.12
0.11
0.1
32
0.1
0.09
0.08
0.07
31
0.07
0.06
0.05
0.04
30
Zooplankton
0.04
SeaWiFS Chla
0.03
0.02
0.01
29
-125
1. Adjust only initial state, boundary conditions and physical forcing
2. Minimize weighted misfits and correction to initial state
3. Reduce size of problem by limiting corrections to largest/eddy space scales
using basis functions
4. Determine the sensitivity matrix with set of perturbation runs of the non-linear
model, invert it assuming linearity, test degree of non-linearity and repeat process
iteratively
Average Fields (23 Jan 1998 – 14 Feb 1998)
CalCOFI Chl-a (in situ obs.)
-123
-122
-121
-120
-119
-118
-117
-116
0
NPZD Ecosystem Model
How do we fit the physical data?
(inverse method strategy)
CalCOFI Dynamic Height (obs.)
-124
Free-Surface (model average)
Chl-a (model average)
model forward and forced it using weekly wind stresses from the Leetmaa
Ocean Analysis (from CDC/NOAA). Qualitatively the model captured the
changes in the current as shown in the figure
In April there was a well developed southward current.
* Phytoplankton
* Zooplankton
* Ammonia
* Nitrate
* Small Detritus
* Large Detritus
* Chlorophyll
Source/sink
coupling
terms
computed following Fasham et al.
strategy. Test model with
SeaWiFS chlorophyll, and subsurface
CalCOFI
data
(chlorophyll,
nitrate,
bulk/acoustic zooplankton).
Choose/fit parameters to allow a
steady-state ecosystem response
to frozen physical forcing.
Disallow periodic or aperiodic
ecosystem
behavior.
Allow
ecosystem and physical model to
evolve from that initial to
qualitative test strategy.
SeaWiFS Chl-a (satellite obs.)
DAY 15 (Feb 6 1998)
Temperature
Zooplankton
Phytoplankton
SeaWiFS Chla
Conclusions
The quantitative dynamical fitting procedure has been tested successfully
for CalCOFI hydrographic data by adjusting temperature and salinity
initial conditions for July 1997 (68% error variance reduction) and
February 1998 (36% error variance reduction).
The alongshore current reversal observed in the Southern California Bight
has been qualitatively simulated using observed initial conditions from
February 1998, evolving boundary conditions from NCEP's ocean
analysis, and atmospheric forcing from NCEP's atmospheric analysis.
The 3D NPZD ecosystem model has been qualitatively tested for February
1998 using frozen physical fields to obtain a balanced initial state and
subsequent evolving physical fields as 3D time-dependent forcing.
Ongoing Work
·
Further develop a qualitative and quantitative procedure for fitting
observed 3D biological fields (SeaWiFS chl-a and CalCOFI nitrate, chl-a
and zooplankton) for February 1998.
·
Examine the dynamical and ecosystem balances that hold in the
model’'s evolving fields (reconstructed by the inverse) and assess their
consistency with other model studies.
·
Forecast independent data in subsequent CalCOFI hydrographic and
ADCP surveys, TOPEX datasets and SeaWiFS observations to determine
predictive timescales in the various regions of the Southern California
Bight.
Publications
Di Lorenzo, E., A.J. Miller, D.J. Neilson, B.D. Cornuelle, and J.R. Moisan, 2004:
Modeling observed California Current mesoscale eddies and the ecosystem
response, International Journal of Remote Sensing, 25 (7-8), 1307-1312
Miller, A.J., E. Di Lorenzo, D.J. Neilson, B. Cornuelle, and J.R. Moisan, 2000: Modeling
CalCOFI observations during El Nino: Fitting physics and biology.
CalCOFI Reports, 41, 87-97