Geoscience Reference
In-Depth Information
aim of developing better models. Of special relevance to regional climate models,
GCMs, and LSMs (with or without coupling to an atmospheric model) is suboptimal
parameterization. For simulations of the Arctic region, the parameterization of snow
and sea ice albedo stands out. From
Chapter 5
, we know that the factors control-
ling snow and sea ice albedo are quite complex. The level of detail at which albedo
is treated can impact strongly on simulated snow and ice mass, the surface energy
balance, and hence the strength of ice-albedo feedbacks. For atmospheric models,
another problem with first-order impacts on the surface energy budget is the sim-
ulation of Arctic cloud cover. Especially for global climate models, the relatively
coarse representations of topography, sea ice margins, and coasts can have strong
expressions on the simulated atmospheric circulation, such as in the placement of
storm tracks. Global and regional models, including coupled ice-ocean models, also
vary widely in their treatment of the ocean, which, among other things, will impact
sea ice growth and melt. In turn, different assumptions regarding ice interaction
may lead to different realizations of ice circulation and ice thickness distributions.
Turning back to the land surface, projected future changes in the Arctic's terrestrial
carbon budget are dependent on assumptions regarding the function and structure
of Arctic ecosystems.
Many of the model types reviewed in this chapter require specified driving fields
(e.g., “stand alone” LSMs and sea ice models, active layer thickness models, and
regional climate models). The quality of the model output will depend in part on
the quality of these driving fields. Some simulations rely on sparse station data or
remotely sensed fields (e.g., snow water equivalent) of uncertain quality. However,
for many simulations, the driving fields represent output from another model, in
particular, from NWP. Outputs from NWP models have their own sources of error,
variously from difficulties in specifying the initial atmospheric state to biases in
surface fields related to suboptimal parameterization of albedo, evaporation, and
convective precipitation. In conclusion, while modeling is one of the most impor-
tant tools for understanding the Arctic climate system, model output must always be
viewed with appropriate caveats.
Focus Questions and Exercises
1) A common shortcoming of global climate models is that they cannot properly
simulate the observed distribution and radiative properties of Arctic cloud
cover. Why should this problem concern us?
2) It has sometimes been argued that it is more productive to examine spatio-
temporal variability in Arctic precipitation using output from an atmospheric
reanalysis like MERRA than using data from the available gauge network. Do
you agree or disagree with this argument? Why?
3) Even the best land surface model in the world can produce large biases in
simulated snow depth, evaporation, latent and sensible heat fluxes, and other
variables. Why is this so?
