Chapter 7: APPLICATIONS
the near-surface ocean affects the overlying atmosphere on a variety of time
and space scales. The exchanges of gases with the near-surface ocean, and
fluxes of aerosols and salt into the atmosphere are crucial elements of the
climate system as well.
The ocean plays a vital role in the Earth’s climate and its variations.
Thus, numerical models of the ocean are essential tools for understanding,
assessing and predicting climate variability and change, along with the
associated impacts. For example, understanding the relative roles of different
ocean processes during El Niño-Southern Oscillation (ENSO) events has
depended strongly on experiments with forced ocean models and coupled
ocean-atmosphere models. Ocean models are now used routinely to
assimilate observations to provide initial conditions for coupled oceanatmosphere models that are used to predict seasonal-to-interannual climate
variability. Global climate models that are used in greenhouse gas scenarios
of future climates depend significantly not only on accurate representation of
ocean physics, but also on representation of ocean biogeochemistry because
the absorption and sequestration of CO 2 in the ocean is a very important
element of global change projections.
Ocean models are important in their own right as tools for managing
marine transportation, for search and rescue efforts, to support coastal
engineering, for marine hazards mitigation, and for management of marine
ecosystems, among other applications. The international Global Ocean Data
Assimilation Experiment is underway as this is written, aimed at
significantly advancing our ability to assess the state of the oceans, with
these and other applications as objectives.
Successfully modeling the ocean requires adequate boundary conditions
(usually expressed in the form of air-sea fluxes) and accurate
parameterization of the grid-scale impacts of processes that are not resolved
by the model grid. Forcing and parameterization errors may accumulate
unevenly in the model domain, and in coupled models they may be amplified
considerably by positive feedback mechanisms. For oceanic general
circulation models (GCMs) on global and regional scales, the near-surface
processes discussed throughout this book cannot be resolved. Global
parameterizations of such processes are sought, but in many cases this is an
elusive goal.
Another approach to parameterization is to incorporate submodels that
can be used to provide a statistical representation of the grid-scale impacts of
physics that cannot be resolved by the main model. An example of such an
enhanced-resolution submodel is the non-hydrostatic cloud-resolving model
embedded in an atmospheric GCM (Randall et al., 2003).
Submodels may be needed to represent variables that cannot easily be
represented in the main model. For example, growing recognition of the
497
the near-surface ocean affects the overlying atmosphere on a variety of time
and space scales. The exchanges of gases with the near-surface ocean, and
fluxes of aerosols and salt into the atmosphere are crucial elements of the
climate system as well.
The ocean plays a vital role in the Earth’s climate and its variations.
Thus, numerical models of the ocean are essential tools for understanding,
assessing and predicting climate variability and change, along with the
associated impacts. For example, understanding the relative roles of different
ocean processes during El Niño-Southern Oscillation (ENSO) events has
depended strongly on experiments with forced ocean models and coupled
ocean-atmosphere models. Ocean models are now used routinely to
assimilate observations to provide initial conditions for coupled oceanatmosphere models that are used to predict seasonal-to-interannual climate
variability. Global climate models that are used in greenhouse gas scenarios
of future climates depend significantly not only on accurate representation of
ocean physics, but also on representation of ocean biogeochemistry because
the absorption and sequestration of CO 2 in the ocean is a very important
element of global change projections.
Ocean models are important in their own right as tools for managing
marine transportation, for search and rescue efforts, to support coastal
engineering, for marine hazards mitigation, and for management of marine
ecosystems, among other applications. The international Global Ocean Data
Assimilation Experiment is underway as this is written, aimed at
significantly advancing our ability to assess the state of the oceans, with
these and other applications as objectives.
Successfully modeling the ocean requires adequate boundary conditions
(usually expressed in the form of air-sea fluxes) and accurate
parameterization of the grid-scale impacts of processes that are not resolved
by the model grid. Forcing and parameterization errors may accumulate
unevenly in the model domain, and in coupled models they may be amplified
considerably by positive feedback mechanisms. For oceanic general
circulation models (GCMs) on global and regional scales, the near-surface
processes discussed throughout this book cannot be resolved. Global
parameterizations of such processes are sought, but in many cases this is an
elusive goal.
Another approach to parameterization is to incorporate submodels that
can be used to provide a statistical representation of the grid-scale impacts of
physics that cannot be resolved by the main model. An example of such an
enhanced-resolution submodel is the non-hydrostatic cloud-resolving model
embedded in an atmospheric GCM (Randall et al., 2003).
Submodels may be needed to represent variables that cannot easily be
represented in the main model. For example, growing recognition of the
497