If this picture from the Navy is
reliable, it flies in the face of a lot of evidence coming out over the past
three years. Yet this ice mass is
supposed to be around forty percent of the original ice mass measured back in
1959.
The report makes no mention, or
perhaps I missed it, of a history of hard measurements set to establish ground
truth. Only the Navy has had the
capability to do that and to have done it annually for years. It is a natural task for a submarine mission.
It should be even possible to use
sonar to confirm ice depth. Thus it was
probably done.
Curiously, the total area appears
to be the lowest in 12 years while the actual mass has recovered to the highs
of five years ago. I would hate to do an
error calculation for the data involved.
Arctic Ice Recovering In Three Dimensions
Frank Lansner generated this graph from Navy PIPS data, showing the
significant increase in Arctic ice thickness
over the last few years.
You can see why NSIDC only likes to talk about 5+ year old ice.
(Because it hasn’t yet been five years since 2007.)
Ruth H. Preller, Pamela G. Posey
Naval Research Laboratory • Stennis
Space Center , Mississippi USA
Wieslaw Maslowski, Donald Stark
Naval Postgraduate School • Monterey ,
California
USA
Thomas Thang C. Pham
Fleet Numerical Meteorology and Oceanography
Center • Monterey , California USA
The availability of real-time information on sea ice conditions in ice
covered seas has always been important, not only to strategic military
operations, but to the economies of those countries that border the Arctic and its
marginal seas. Knowledge of the thickness and movement of sea ice as well as
the locations of open water is required for traversing the Arctic
whether in a drill ship, in a cargo vessel or in an ice strengthened ship such
as a Coast Guard ice breaker.
Forecasting these conditions is a difficult task at best. The ice and
snow that cover the cold Arctic Ocean are
highly variable on short time scales, such as days to weeks, and longer time
scales of years to decades. This variability in the sea ice cover is due to a
combination of dynamic and thermodynamic effects.
Surface stresses on the top and bottom of the ice cause the movement of
sea ice, or ice drift, as well as the deformation of the ice.
Heating and cooling from the atmosphere and the ocean are largely
responsible for the growth and decay of sea ice, in combination with the ice motion.
Sea ice has a strong seasonal variability. The thinnest sea ice and
largest amount of open water appear in the summer months from June to
September.
Ice begins to grow back in the fall and builds to a maximum thickness
in the late winter and early spring, March-April. Many of the marginal seas,
such as the Barents and Greenland
Seas
Other marginal seas like the Bering Sea and the Sea of Okhotsk
These seasonal patterns vary inter-annually as well. This variability
is often represented by a seesaw effect when one part of the Arctic experiences
a “mild” ice year while another part of the Arctic
has an increase inice extent and/or ice thickness. A general trend overlying
this inter-annual variability has been seen by several scientists who have
examined long records of satellite data. The trend is for an overall decrease
in ice extent from the late 1970s through the mid-1990s (Parkinson et al.,
1999; Cavalieri et al., 1997;
Johannessen et al., 1995). Whether or not this decrease will continue
and what effect it may have on Arctic economics, operations and climatic change
is a topic of great international interest.
Sea ice forecasts usually focus on short time scales of 5–7 days. In
order to provide an accurate prediction, forecast systems are most often based
on a combination of models and data. Modeling sea ice can be a difficult
problem, as it exists in many different forms
(Figure 1).
It can appear as a field of disjointed ice floes, as a level,
continuous field of ice or as a landscape of ice hills and ridges. Interfacial stresses,
from the atmosphere and the ocean, interacting with natural coastal boundaries
cause the ice to be in a nearly constant state of motion and deformation
resulting in the formation of ice rubble, ridges, leads and ice floes. Most sea
ice models make the assumption that ice exists as a “continuum”. The ice is modeled as having some combination of viscous, plastic, or elastic
material characteristics. The way that the ice behaves as a material when acted
upon by stress is called ice rheology. Ice within a model grid cell is defined
as having a certain thickness and coverage, also defined as a percent of ice
concentration. Thickness and concentration are allowed to vary in time
depending upon the atmospheric and oceanographic conditions.
The Arctic presents a hostile
environment for those trying to gather data on the atmosphere, ice or ocean.
Sub-zero temperatures, strong winds, limited daylight and some
intimidating wildlife (Figure 2) make fieldwork in the Arctic
a difficult task.
A majority of observations of the Arctic ice cover,both in real-time
and over periods of decades, have been provided by satellite imagery and
drifting buoys.
Satellite data derived from the Defense Meteorological Satellite
Program (DMSP) Operational Line-Scan (OLS) Sensor, the National Oceanic and
Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer
(AVHRR), the DMSP Special Sensor Microwave/Imager (SSM/I), and the Canadian RADARSAT
are used by forecasting centers like the U.S. Navy/NOAA National Ice Center
(NIC) to provide a real-time picture of conditions in the Arctic. A network of
automatic drifting data buoys monitoring surface air temperature, pressure and
ice motion, has existed in the Arctic basin since early 1979, first under the
Arctic Ocean Buoy Program and then in 1991, apart of the International Arctic
Buoy Program (IABP).
These data have been used to support real-time operations in the Arctic as well as meteorological and oceanographic
research of the Arctic basin. More information on the IABP is available at http://iabp.apl.washington.edu/.
Forecasts of ice conditions are produced by numerical ice-ocean models
that use these observations to help specify an “initial” state and then run
forward in time. The length of the ice forecasts depends on the atmospheric
forcing that drives the model. Usually, the forcing is derived from an
atmospheric forecast model, and extends about seven days into the future.
Longer forecasts (to 30 days) are sometimes generated using persistent
atmospheric conditions. The main forecast products that describe sea ice
conditions are ice thickness, ice concentration, and ice drift.
Real-time sea ice observations, analyses and forecasts are now
available from ice centers around the world (i.e. Canadian, Swedish, Danish,
Japanese, Icelandic centers). These centers are usually responsible for
providing information on ice conditions closest to their own coastlines. The U.S.
Ice Prediction System (PIPS) version 2.0 to generate analyses and forecasts
of sea ice conditions in the ArcticThe Polar Ice Prediction System (PIPS)
Since the late 1980s, the Naval Research Laboratory (NRL) has been
developing sea ice forecast systems for operational use by the U.S. Oceanography
Center Greenland
Seas
PIPS 1.0 was followed by two
higher resolution regional models for the Barents Sea, theRegional Polar Ice
Prediction System—Barents (RPIPSB) and for the Greenland Sea, the Regional
Polar Ice Prediction System—Greenland Sea (RPIPS-G). The regional models
differed from the PIPS 1.0 model in that they covered a smaller area and used
higher grid resolution (25 km vs. 127 km). The higher resolution was necessary
to provide a better estimate of ice edge and coastlines. These forecast systems
made use of an ocean climatology to provide ocean stresses and heat fluxes to
the ice model.
Atmospheric stresses and fluxes
were provided to the ice model by the Navy Operational Global Atmospheric
Prediction System (NOGAPS; see Rosmond et al. in this issue). Once these forecast
systems were in place, additional requirements were identified. High-resolution
forecasts were needed in all of the ice-covered regions of the Northern Hemisphere
and sea ice models required coupling to an ocean model to provide daily ocean
variability. In response to these requirements, NRL developed the Polar Ice
Prediction System 2.0 (PIPS 2.0). This system became operational in 1996 and
replaced PIPS 1.0, RPIPS-B and RPIPS-G.
The core of the PIPS 2.0 is a coupled ice-ocean model that consists of
the Hibler ice model (Hibler, 1979; 1980) and the Bryan and Cox ocean model
(Cox, 1984). PIPS 2.0 coverage extends from the North Pole south, to
approximately 30°N latitude and includes
marginal seas such as the Sea of Okhotsk, the Sea of Japan and the
Yellow Sea in the Pacific and the Gulf of St. Lawrence and the Labrador Sea in
the Atlantic (Cheng and Preller, 1996). The grid resolution, 0.28°,varies from
17–33 km depending on the location of the grid square within the spherical
coordinate system (Figure 3). PIPS 2.0 uses a rotated coordinate system to avoid
the problem of a numerical singularity at the pole.
For both the ice and ocean models, the lateral boundaries are defined
as solid walls and placed away from any sea ice covered regions in order to
minimize their effect on sea ice forecast areas of interest.
Following the technique of Sarmiento and Bryan (1982), the ocean model
temperature and salinity fields are loosely constrained to the Levitus (1982)
climatological data set using a 250 day restoring time at all levels of the
ocean. This weak constraint on the ocean temperature and salinity does not
adversely affect the PIPS 2.0 ability to forecast ocean variability on the
daily to weekly time scales. The bathymetry used by the ocean model is derived
from a U.S. Navy 5 minute data base called the Navy Digital Bathymetry Data
Base 5' x 5' (DBDBV; NAVOCEANO, 1997).
In this coupled system, the ocean model provides input to the ice model
in terms of ocean currents, salinity and heat fluxes (temperatures). The ice
model provides salinity and temperature changes due to the growth and decay of
sea ice and surface stresses to the ocean model. The ocean model contains 15
vertical levels, each level increasing in thickness with depth. Direct interaction
between the ice and ocean model occurs in the first level that is 30 m deep and
represents the ocean mixed layer.
Figure 4 is a schematic of the design of PIPS 2.0. At the center of the
system, is the coupled Hibler ice model/Cox ocean model. Atmospheric forcing is
provided by NOGAPS. The atmospheric forcing fields used by the forecast system
are surface wind stress, surface air temperature, surface pressure, surface
vapor pressure, shortwave radiation, sensible plus latent heat flux, and the
total heat flux. The system produces a 120- hour forecast each day. In addition
to the output of the model (ice drift, thickness, concentration, growth/decay
of ice, ocean currents, temperature and salinity), restart fields consisting of
the model’s 24-hour forecast are written to a file to be used to initialize the
next day’s forecast. If the model restart fields are not available, a
model-derived climatology is used to initialize the forecast.
The quality of each forecast depends strongly on the accuracy of the
initial conditions and the atmospheric forecasts, as well as on the inherent
accuracy of the forecast model. If the unaltered 24-hour forecast is used to
initialize the forecast, these errors will inevitably compound eventually
leading to unacceptably large forecast errors. To reduce this effect, the 24- hour
forecast fields are used in combination with observed ice concentration data to
initialize the model.
The PIPS 2.0 assimilates ice concentration data from the DMSP Special
Sensor Microwave/Imager (SSM/I).
These data are chosen for three reasons: they are available daily, in
real time, at FNMOC; the resolution of the data is similar to the resolution of
the model; and the data cover the entire model domain. The SSM/I brightness
temperatures are converted into ice concentration data using the Navy CAL/VAL
algorithm (Hollinger et al., 1991) at FNMOC. This algorithm is sensitive to the
ice/water boundary and to thin ice. As such it provides a good estimate of the
location of the ice edge and a better estimate of thin ice than most other
algorithms.
However, due to its sensitivity to thin ice, it often saturates too
quickly to 100% ice concentration, producing an overestimate of high ice
concentrations. For more information on algorithm descriptions and comparisons
see
After the PIPS 2.0 restart fields are read into the model, the daily
SSM/I ice concentration data, interpolated to the model grid, are read in.
Based on the characteristics of the CAL/VAL algorithm, the SSM/I data replaces
the model-derived ice concentration only at locations where observed
concentration is greater than 80% or less than 50% and, the difference between the
two fields is greater than 10% or 5% respectively.
The model-derived ice thickness field and the ocean surface temperature
field are then adjusted to be consistent with the concentration data. That is,
if theSSM/I data indicate there is no ice where the model had produced ice, ice
is removed from the model field and the ocean temperature is raised one degree
above freezing to restrict immediate ice growth in this location. If the model
did not have ice in a location where the SSM/I sees ice, a small amount of ice
(0.3–0.6 m) is added to the model thickness field and the ocean temperature is
set to the freezing temperature of sea ice.
These adjustment values were determined from a series of model
experiments. As the adjustments are usually confined to a few grid cells near
the ice edge, the experiments showed no serious dynamical/numerical problems
associated with the technique.
Forecast Products
Since the end of July 1996, PIPS 2.0 has been producing operational
120-hour forecasts daily. Each day a set of 11 products is generated from the
PIPS 2.0 forecast (Table 1). These products, and the interval at which they are
generated, are determined by the NIC. They are transmitted to the NIC in GRIB
format and may be viewed as a graphic product on NIC workstations. The NIC uses
PIPS 2.0 products as guidance in generating their ice analysis and forecast
products.
Figures 5b–d are examples of standard products from the system: ice
displacement (based on the ice drift), ice concentration and ice thickness.
Figure 5a represents the observed ice motion derived from the available IABP
drifting buoys. Figures 5a and 5b show the qualitative similarities between the
PIPS 2.0 forecast and the observed ice motion. PIPS 2.0 also has the capability
to forecast ocean currents, ocean temperature and salinity. Recently these
fields were requested by the NIC as input to a marginal ice zone specific ice model
that is being developed and tested for the center’s use.
In the pre-operational evaluation of PIPS 2.0 products (Preller and
Posey, 1995) the original PIPS and PIPS 2.0 ice drift were evaluated against Arctic buoy data. These results showed that PIPS 2.0
produced an improved ice drift forecast over PIPS with a decrease in the Root
Mean Squared (RMS) Error of about 10–15%.
Due to the combination of the increased resolution and the
incorporation of an ocean model versus an ocean climatology, the PIPS 2.0 ice
edge was a substantial improvement over the existing PIPS models, including the
higher resolution regional models.
A recent study by a group of scientists from the NIC and NOAA (Van
Woert et al., 2001), showed that although the PIPS 2.0 forecasts (48-hour) were
better than persistence on average, there were still substantial biases in its
prediction of the growth and decay of sea ice in the marginal ice zone. PIPS
2.0 often over-predicts the amount of ice in the Barents
Sea and therefore often places the ice edge too far south. In
contrast, PIPS 2.0 often under-predicts the ice extent in the Labrador Sea and Hudson Bay . As forecasts are a combination ofmany
different components (Figure 4)—atmospheric forcing, oceanographic forcing,
model parameterizations and initialization—the combination of even small errors
from each of these pieces could add up to a substantial error in the forecast.
In recent years, there has been a request for the capability to predict
the location of regions of open water within the ice pack. Approximately 1% of
the central Arctic ice pack is estimated to be open water in winter. In summer,
the amount increases to 10–20% (Gow and Tucker, 1990). Openings in the ice pack
usually appear in two forms: leads and polynyas. Leads are crack-like openings
in the ice pack, usually tens to hundreds of meters wide, while polynyas are
larger, more permanent openings. Leads form in regions where large-scale
divergent wind patterns produce divergent stresses in the ice causing the ice
to break apart. Leads appear as linear features in compact ice that often have a
preferential orientation (direction). Polynyas form near coastlines where
divergence or shearing of the pack ice separates the ice from the coast or from
a fast ice boundary. Leads and polynyas are significant because they provide
traversable areas in the ice pack for ships. In addition they are important
because they are regions of very large heat-loss to the atmosphere. Winter
leads rapidly refreeze, due to the heat loss to the atmosphere, forming young
thin ice that is easily deformed. Leads and polynyas are a major source of new
ice growth in winter. Although PIPS 2.0 can predict large-scale polynyas, it
does not have the capability to produce smaller polynyas or leads or to provide
guidance on lead orientation.
PIPS 2.0 is based on the scientific research efforts of late 1970s and
early 1980s. During the past 10 years (1990s), great strides have been made in
understanding sea ice dynamics and thermodynamics as well as observing ice
conditions. An additional and very important factor in the improvement of ice
modeling and forecast capabilities is the advance in computer technology over
the past 10 years. Computer codes now make use of multiple processors and can
perform more extensive computations in operationally acceptable time periods.
In 1998, the Office of Naval Research (ONR) and the Oceanographer of
the Navy via the Space andNaval Warfare Systems Command (SPAWAR) joined forces
to fund an effort to combine this new technology and data into an improved sea
ice forecasting system.
This system, aptly named PIPS 3.0 (http://www.oc.-nps.navy.mil/~pips3
) is presently being developed through a joint effort among the U.S. Naval
Postgraduate School
These improvements to the PIPS 2.0 are being tested in an incremental
fashion. The first tests were designed to examine the effects of higher
resolution on an ice model similar to that of PIPS 2.0 coupled to an improved
ocean model with improved bathymetry. At the same time, techniques for the
assimilation of satellite derived ice drift into the ice model were developed and
tested. Results from these tests are described below.
High Resolution Coupled Model Tests Similar to the PIPS 2.0, the
coupled ice-ocean model used in these experiments extends from the North
Pacific (at ~30°N) through the Arctic Ocean and the Nordic Seas into the North
Atlantic (to ~45°N; Figure 6) and uses a rotated coordinate system. The ice and
ocean model horizontal grid is configured at 1
⁄12° (~9 km) with 45 vertical levels in the ocean. In the vertical
direction, the upper 100 meters of the ocean are resolved by eleven layers, and
the upper 500 meters of the ocean by nineteen layers. This resolution allows detailed
representation of bathymetry over the vast Arctic shelves and in marginal seas
as well as many local bathymetric features important to large-scale ocean
circulation (e.g. St. Anna Trough, Canadian Archipelago, or Bering Strait). The
bathymetry data used to define the grid shown in Figure 6 consists of the ETOPO5
database (National Geophysical Data Center, 1988), NRL charts and Canadian Hydrographic Center
2.5 km resolution International Bathymetric Chart of the Arctic Ocean (IBCAO) database (Jakobsson et al., 2000) is
used. These data represent a considerable improvement over previous data
available for this area used in PIPS 2.0. This high resolution, coupled
iceocean model is an extension of previous modeling research using a horizontal
resolution of 1⁄6° and 30 vertical levels (Maslowski et al., 2000, 2002; Zhang
et al., 1999, http://www.oc.nps.navy.mil/sbi
). The coupled model adapts the Los Alamos National Laboratory (LANL) global Parallel Ocean
model is its ability to utilize modern parallel computer architectures.
A 27-year spin-up integration of the coupled iceocean model has been
run, forced with a daily-averaged annual cycle of climatological atmospheric
fields derived from the European Centre for Medium-range Weather Forecasts
(ECMWF) 1979–1993 reanalysis.
Forcing fields include: 10-meter winds, surface pressure, surface air
temperature and dew point, and longwave and short-wave radiation. During the
spinup, the ocean surface temperature and salinity are restored with a 30-day
time scale to monthly mean climatology derived from the University
of Washington ’s Polar Science
Center
Following the 27-year integration, an additional 12-year run has been
completed using the repeated 1979 ECMWF annual cycle for the first 6 years and 1979–1981
inter-annual fields for the last 6 years. This approach has been used to force
the sea ice and ocean states towards conditions of the late 1970s and early 1980s.
These conditions may then be used to initializethe new version of the PIPS 2.0
ice model and spin it up to the present day for forecast use.
Understanding the ice thickness distribution and its variability is
important in both short-term operational forecasts and longer-term climate
studies.
Model-derived ice thickness fields are useful in obtaining a
large-scale, but detailed picture of ice distribution and, more importantly, an
understanding of its temporal variability.
In Figure 7, the 3-year mean ice thickness distribution is presented
from the first three years of the 1979–1981 output. The modeled distribution of
ice thickness compares reasonably well to that known from observations (Bourke
and Garrett, 1987; Bourke and McClaren, 1992). In agreement with data, the
thickest ice (> 6.0 m) in the Arctic is found along the Canadian Archipelago
and northern coast of Greenland . Farther north
near the pole, the ice thickness decreases to values of 3.0–3.5 m. This
simulation also shows relatively thick ice (> 3.5 m) on the East Siberian
shelf. Since few observations are available for that region and that time, it
is difficult to quantitatively verify these results. In the 1990s, measurements
of ice thickness were made in the central Arctic Ocean from U.S. Navy submarines and at a number of key
pointlocations (e.g. the Fram
Strait
These models are required to go through rigorous validation studies to
prove their capability to produce accurate short term variability. Data
assimilation plays a major role in the accuracy of these forecasts. Once operational,
continuous quality control and evaluation of the products may be used to upgrade
the system and improve forecast accuracy. In addition, forecast systems are limited by the amount of computer resources
available for each forecast as they compete with other forecast models each
day. Therefore the combined model/assimilation system must be designed to “fit”
within these limitations. This places restrictions on the “size” or grid
resolution of the models as well as the complexity of the model
parameterizations and the data assimilation techniques. Each of these issues
must be taken into account when developing a new forecast systemPIPS 3.0 Ice
Motion Data Assimilation As stated earlier, a popular strategy to reduce the initial
error in numerical forecast models is to use information from real-time
observations to correct the previous forecast field through data assimilation.
Data assimilation analysis makes use of a predictor-corrector strategy, where
the model predicted ice state (known as the background field) is corrected by
whatever observations are available.
The specific process of correction fundamentally determines the nature
of the assimilation. Current ice state observations tend to be either scattered
through space or infrequent in time.
They may also contain noise that can introduce non-physical structure
into the model. The direct replacement of model values with the observed values
(known as insertion) can have undesirable consequences. It is therefore,
preferable to employ some sort of self-consistent procedure that reduces the
error and distributes the observational knowledge widely over the domain.
Statistical assimilation methods meet these criteria.
One of the first investigations of the assimilation of ice motion data
into a stand-alone ice model (similar to the original PIPS 1.0) employs the
optimal interpolation methodology (Meier et al., 2000). Optimal interpolation is
the simplest type of statistical assimilation. Statistical assimilation means
that the background field is corrected to “minimize the error statistics” of
the assimilated field. This can be done, only if the error statistics of the observations
and the model are known. The technique is “optimal” since the error variance is
minimized.
Individual ice velocity vectors of the background field are corrected
by applying a linear combination of the nearby observed ice motions. The
correction coefficient for each observed value results from solving a linear
system dependent, among other things, on the ratio of the model to data error
covariances. When these weights are applied to the corresponding observations, the
resulting correction minimizes the error variance of the corrected solution.
This formula illustrates that it is not sufficient to be concerned with the
observed ice motions alone. The error statistics for the model and the data are
just as important. They instruct the algorithm as to which observations to use,
and what relative
weight they must have, in order for the correction to minimize the
error. Two types of ice motion observations are used. The first, which provides
the data to be assimilated, is not
directly observable. It is derived from the SSM/I passive microwave
brightness temperature imagery through a technique known as feature tracking.
Individual swaths of the SSM/I satellite are combined or “composited”
into a daily gridded field. The displacement of features common to the fields
on consecutive days can provide an estimate of the ice velocity.
The derived ice motion observations used here were obtained from the
Polar Remote Sensing Group at the NASA Jet Propulsion Laboratory (Kwok et al.,
1998).
They were chosen for the assimilation analysis because they are similar
to the real-time ice motion products that will be available from FNMOC. Ice
motion observations are available daily from the beginning of October through
the end of May. The data are not available for the summer months due to the
susceptibility of the passive microwave signal to excessive error from moisture
in the form of either heavy summer cloud cover or the formation of melt ponds
in the ice.
The second set of observations comes from IABP drifting ice buoys
(Rigor and Heiberg, 1997). In the operational configuration of the model, the
buoy motions will be included as observations to be assimilated. This will be
especially important in the summer months when SSM/I derived motions are not
available.
The buoys were withheld in this study to be used for validation
purposes, and to compute the error covariances. For the period of the study,
there are roughly 30 buoys, irregularly distributed throughout the Arctic , atany time. Each buoy transmits its location
twice per day. The ice buoy velocity can then be calculated from the spatial
displacement of the buoy location.
As a test of concept, this proposed PIPS 3.0 ice motion assimilation
method was instituted in a previously tested high resolution fully coupled
seaice/ocean model (Zhang et al., 1999). The coupled seaice/ocean model employs
a reduced domain consisting of the Arctic basin, with a closed Bering Strait,
and extending south into the Atlantic to about
50°N. The spatial resolution of the model is roughly 18 km: half the resolution
proposed for the PIPS 3.0 model. The model can be run in either control (no assimilation)
or assimilation mode. Two model runs
were generated for the period from January 1992 through December 1994. The ECMWF
reanalysis product provided the atmosphericforcing in both configurations. In
the control run, no assimilation takes place. In the assimilative run, the model
digests the derived SSM/I ice motion product for the months of October through
May of each year.
During the summer months, no assimilation takes place. The drifting ice
buoy data are withheld as validation. Figure 8 shows selected trajectories of
the IABP drifting buoys, the control run and the assimilative run.
Trajectories from the control run and the assimilative run are produced
by integrating forward in time from the initial buoy location using a standard
fourth-order Runge-Kutta method and the respective daily instantaneous velocity
field. The trajectories displayed represent the range of ice motion behavior
seen during the spring of 1992, and the period covering the fall of 1992 through
the spring of 1993. The subset of trajectories displayed in the figure was
chosen for its coverage ofthe figure domain. The trajectories for the control
run and the assimilative run have the same starting locations, and the same
starting and ending dates as the buoys.
Consider the trajectories numbered 1–10. Both the control run (green)
and the assimilative run (black) generated motions are consistently slower than
the buoy motion (red). The pattern of buoy twists and turns is strongly
reflected in the assimilative run trajectories for about a half of the cases.
The error, for the assimilative run trajectories, is largely due to the difference
in speed. The error in the control run is manifested in terms of an even
greater speed differential as well as large errors in the direction of the
motion. In nine out of the ten cases, the assimilative run produces a
trajectory that more closely follows the buoy trajectory then does the control
run’s trajectory. The assimilation process generally increases the ice speed,
and preserves the observed direction of the ice motion. On average, the
trajectories with assimilation contain half the error of the trajectories
without assimilation.
The remaining two trajectories (11 and 12) illustrate a different
situation. The marginal ice zone (MIZ)in the Greenland Sea
is extremely dynamic, commonly exhibiting the largest ice speeds of anywhere in
the domain. In addition, south of 80°N there are very few SSM/I derived
observations that can be assimilated, and those that exist tend to have large
error bounds. As a result, trajectories south of 80°N can have quite large errors.
For trajectory 12, which starts east of Spitsbergen ,
the buoy moves clockwise, encircling the island. In contrast, the trajectories
representing the control run and the assimilative run speed south into the Norwegian Sea . The similarity between these two trajectories
is due to the lack of SSM/I derived ice motions to assimilate. The difference
in the path of the two trajectories reflects the difference between the ice states
for that location in the two model runs. Since the current ice state for a
given location is a function of its history, each model can have ice with
different concentration and thickness characteristics. The different ice characteristics
can result in different reactions to the same forcing.
Trajectory 11 shows the buoy flowing south into the Fram Strait
and along eastern Greenland . Initially, the
three trajectories are indistinguishable. As they approach Fram Strait
From the starting location to approximately 79°N, the assimilative run
produces a trajectory that coincides well with the buoy. Past this point, where
there are no longer any SSM/I derived motions for the model to assimilate, the
three trajectories rapidly diverge.
Eventually, both the control run and the assimilative run produce
trajectories that travel up to twice as far as the buoy, with the control run’s
trajectory traveling the farthest. Neither of the model runs, with or without data
assimilation, realistically represents the buoy motion south of the Fram Strait
Summary
The U.S.
The models have provided forecasts of ice motion, ice thickness and ice
coverage over regions that are physically difficult to observe due to hostile
environmental conditions. Advancements in satellite technology provide these
forecast systems with continuously improving data for assimilation, thus
enabling improvements to the daily forecasts.
Advancements in computer technology provide these forecast
systems larger and faster computers to generate improved forecasts.
The most recent upgrade to the Navy’s ice prediction capability is the
development of the next generation forecast system, PIPS 3.0. Improvements to
this new forecast system include, higher horizontal resolution, a more
sophisticated ocean model, improved data assimilation and perhaps most
important, an improved sea ice model. The ongoing upgrade of the sea ice model
will include a Lagrangian formulation for calculating a multi-category ice
thickness distribution, a snow layer, a brine pocket parameterization,
non-linear profiles of temperature and salinity (Bitz and Lipscomb, 1999), and
a Coulombic yield curve for the viscous-plastic rheology (Hibler, 2000). These
improvements are geared towards providing better forecasts of ice edge, ice
motion, ice thickness and regions of lead formation and lead orientation. The
PIPS 3.0 is presently going through its final development and will begin its
adaptation for operational use next year with a scheduled transition into
operational use in late 2002 or early 2003.
Acknowledgements
The authors appreciate the helpful comments from Dr. Mary Alice
Rennick, Mr. R. Michael Clancy and Dr.Michael Steele. The authors also thank
Mr. Ignatius Rigor for providing Figure 5a. This work has been funded through
the Office of Naval Research’s Navy Ocean Modeling and Prediction Program
(program element 602435), the Office of Naval Research’s High Latitude Dynamics
Program (program element 61153) and the Naval Space and Warfare Systems Command
(program element 603207N). This paper, NRL contribution NRL/JA/7320/01/0019, is
approved for public release, distribution unlimited.
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