Sensitivity of Arctic Sea Ice Thickness to Intermodel Variations in the Surface Energy Budget - Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications - page 86

as shown by Bitz and Roe [2004]. figure 1 of Bitz [this vol-
ume] shows that the thinning of sea ice due to global warm-
ing is indeed faster for models which produce thicker sea ice
in their 20th century climate simulations. A faster thinning
rate for initially thicker ice should be good news for climate
change simulations, since the differential thinning reduces
the future consequences of present-day thickness errors.
nevertheless, Bitz's analysis shows that much of the spread
in future thickness projections can be understood as a conse-
quence of thickness spread in present-day simulations.
the large spread of Arctic sea ice thickness in the 20c3M
ensemble is clearly undesirable, particularly when model
projections are used for policy decisions. However, uncer-
tain projections can still provide useful guidance, so long
as their uncertainties are properly understood and acknowl-
edged. But uncertainties in climate modeling can never be
fully understood, and there is a danger that the uncertainties
will be underestimated or overlooked. As Oreskes [2000,
p. 81] puts it, “Modeling may lead to greater rigor in the
evaluation of earth processes, but it may also propagate the
illusion that things are better known than they really are.”
for the 20c3M model ensemble, uncertainty is commonly
assessed through the models' ability to simulate present-day
observations (e.g., sea ice thickness and concentration), with
the spread of the ensemble serving as an error bar. there is,
of course, no absolute guarantee that fidelity to observations
means fidelity to the underlying physics, particularly given
the large number of parameterizations and parameter set-
tings which must be chosen by the modeler. likewise, there
is no guarantee that the ensemble spread is a good measure
of the uncertainty. thus, diagnostics which relate simulation
quality for gross features like sea ice thickness to under-
lying physical processes can play a valuable role in making
sure that the simulations really are as good (albeit marginally
good) as they look.

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Acknowledgment. This research was supported by the Office
of Science (BEr), u.S. department of Energy, grant dE-fG02-
03Er63604 to E. deWeaver. We thank cecilia Bitz, Steve Vavrus,
Bruce Briegleb, dave Bailey, and two anonymous reviewers for
sharing their insights. We also thank the Program for climate
Model diagnosis and Intercomparison (PcMdI) for collecting and
archiving the model data. the 20c3M data Archive at lawrence
Livermore National Laboratory is supported by the Office of Sci-
ence, u.S. department of Energy.

rEfErEncES

Amstrup, S. c., B. G. Marcot, and d. c. douglas (2008), A Baye-
sian network modeling approach to forecasting the 21st century
worldwide status of polar bears, this volume.