Abstract:
High-resolution 4-D ocean models allowing for the existence of eddies are used with sea
surface height (SSH) measurements from the TOPEX/POSEIDON altimeters to understand the
variability seen at the ocean's surface and how this signal is related to the changes
in the temperature and salinity in the ocean's interior. These analyses will continue
with data from the Jason-1 satellite. Altimeters only "see" the surface, and a realistic
ocean simulation helps to understand the physical processes contributing to the SSH
changes. We also hope to understand how initial conditions affect the predictions in
an ocean-atmosphere coupled model.
Objectives
Through the combined use of ocean simulations from a twenty-year time series, we are
trying to understand the causes of the variability in global ocean circulation patterns,
and determine if this variability can be predicted.
Approach
Several ocean simulations have been created or will be created by our research group.
These simulations currently include a 20-year (1979-1998) simulation as well as a
41-year simulation (1958-1998). The ocean models are 4D simulations, with 20 to 40
vertical levels representing the varying ocean depths. Horizontal spacing has a
resolution of about 1/6 to 2/3 degree (for example, each model grid represents an
approximate 18 km to 60 km box). The simulations require that the ocean models be
forced at the surface by winds, by heat (from the sun and from heat losses by the
ocean), and by precipation and evaporation. To produce a simulation that corresponds
to the referenced time periods, we use data produced by the European Centre for
Medium Weather Forecasts and the U.S. National Centers for Environmental Prediction.
These data are a combination of observed measurements and a model, and when applied
to the ocean model, result in a realistic simulation.
Evaluation and analysis of the simulations
The simulations are evaluated using both satellite data and in situ data such as
measurements from tide gauges and buoys. The in situ data, especially the measurements
of sea level made by the tide gauges, add to the confidence in the realism of the
simulation during periods when no satellite data are available. The altimeter data
sets are Geosat (approximately 1987-1989), TOPEX/POSEIDON (1992-present), ERS-1 and 2
(1990s), and will include Jason-1 data. The model output, its forcing, and the
observations are compared using a variety of methods, for example, time series
analysis at a given location and spatial statistical methods. From these analyses,
we can determine how much of the variability is a direct result of the forcing
applied, and how much of the variability comes from other processes. We can also
identify regions where the model represents the variability of the ocean particularly
well. Figure 1 shows a time series of the average height anomaly along 35°N in
the Pacific from a model (black line) and as measured from TOPEX/POSEIDON and Geosat
(blue line). The heat content of the top 160 m of the model is in red and the North
Pacific Climate Index is the black dashed line. The figure indicates that the
simulation is similar to the observations and that the upper ocean, indicated
by the heat content curve, is contributing to most of the variability at the
low frequency.
Predictability of the ocean's variability
To understand if and where the ocean's circulation can be predicted through the
use of satellite-measured SSH, we will examine how the sea level at one location
influences the variability downstream. A good example of this type of predictability
is detecting the occurrence of an El Niño in the tropical ocean and then
knowing that the signal travels northward along the western edge of the Americas
at a later time. We will examine various locations around the globe to further
explore this type of cause-and-effect variability.
Coupled ocean-atmosphere models are also useful to understand the predictability
of the ocean's circulation. Depending on the initial conditions from which a
simulation starts, the coupled models will produce different results. We will
run a prototype coupled ocean-atmosphere model using, as part of our initial
conditions, the state of the current ocean (temperature, salinity, and sea level)
from the output of an assimilated model, run along with simulations using just the
observed SSH and temperatures. In this type of simulation, the coupled ocean model
can be initialized with observed data (including altimeter data) and then allowed
to run into the future using the winds and other forcings from the atmospheric model.
The resulting predicted field of sea level can then be compared with future altimeter
observations.
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Figure 2 shows plots of SSH anomalies across 15°N in the North Pacific. On the
right is the simulated data plotted with longitude across the bottom and time on
the vertical axis. The right panel is the SSH data from two satellites, TOPEX/POSEIDON
for the 1990s and Geosat during the 1980s. The figure shows how some of the signal
moves westward in time, and if correlated with the underlying temperature field, the
changes occuring in the east can indicate a change in the water temperature at the
western boundary at a later date. This figure also shows the wide variation in the
SSH with time which is important to know in the initialization of coupled
ocean-atmosphere models.
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Anticipated results
The anticipated results are to show:
- that altimeter data are useful in
identifying areas of an ocean simulation which are realistic, and how and where the
simulation can be improved. Also, what contributes to signal variability at a given
location (for example, forcing and advection).
- that sea level measurements can
be used to identify changes in sea surface height, and perhaps temperature, at a
later time.
- the influence of the initial
conditions on the predictability of a coupled ocean-atmosphere model.
Significance of results
Ocean models are becoming more common as tools in trying to understand the ocean
and its variability. More realistic simulations, result in higher confidence in
the models, which in turn, helps in understanding scientific problems such as the
role of the ocean in our global climate system. In addition, knowing how satellite
altimeter data can be used to infer subsurface changes can aid in monitoring of our
global oceans.
