Geoscience Reference
In-Depth Information
not included). Dynamic/thermodynamic sea ice models represent the next level
of complexity and are typically driven by wind, temperature, and ocean heat flux
data through application of the momentum balance and thermodynamic equations
described in
Chapter 7
. Model outputs include fields of ice motion, thickness, and
concentration. Dynamic/thermodynamic sea ice models of varying complexity can
in turn be coupled to ocean models. Modern models typically include ice thickness
distributions - that is, the fraction of the ice cover in each of (typically) 5-10 ice
thickness categories. An area of continuing development is data assimilation, such
as of satellite-derived ice velocity and concentration.
Global Climate Models (GCMs)
: Global climate modeling is an especially
active research area, with applications to understanding the earth's past, present,
and potential future climate. GCMs (also known as General Circulation Models)
are applied to a three-dimensional grid over the globe. There is a wide variety of
model types. Atmosphere-only models (often referred to as AGCMs) include LSMs
of varying complexity, and either prescribed SSTs or an upper-ocean mixed layer
typically 50-100 m in depth. More robust GCMs are coupled atmosphere-ocean
general circulation models (AOGCMs). They involve coupling AGCMs with ocean
general circulation models, sea ice models, and LSMs. Some GCMs now simu-
late biogeochemical cycles (e.g., carbon and nitrogen cycles), land use, vegetation
dynamics, and atmospheric chemistry. Such highly complex models are known as
Earth System Models. Most GCMs utilize a spectral coordinate system. In such a
framework, prognostic field variables (e.g., temperature and winds) are represented
as sums of a finite set of spectral modes rather than at grid points. The advantage is
that horizontal derivatives can be calculated exactly for the spectral modes repre-
sented in the model.
Whereas the growing complexity of GCMs from AGCMs to AOGCMs Earth
System Models reflects the growth in computing power since the first-generation
models were developed in the 1970s, major modeling centers, such as NCAR, have
developed well-structured modeling systems enabling different levels of model com-
plexity and coupling to be selected based on the sensitivity experiment of interest.
For example, to better understand the role of changing ocean conditions on the evo-
lution of the Arctic sea ice cover through the twenty-first century, one might compare
a simulation from the modeling system in a fully coupled mode with a simulation in
which there is no ocean coupling. Large modeling centers routinely conduct ensem-
ble simulations in which a series of simulations are conducted using the same set
of climate forcings (e.g., observed changes in atmospheric composition and other
factors over the twentieth century along with projected changes over the twenty-first
century) but starting from slightly different initial conditions. In starting with differ-
ent initial conditions, at any given time, each simulation (ensemble member) may be
in a different phase of the model's own internal climate variability. By then looking
at the ensemble members as a group, one can assess both the forced component of
change (e.g., the temperature change over the twentieth century averaging results
from all ensemble members together) in comparison to natural variability (e.g., from
the spread in simulated temperatures from the different ensemble members).
