How
well can Arctic climate be simulated by computer-based models?
Arctic
climate: a perspective for modeling
Dr. Amanda Lynch
University of Colorado
Computer climate models have long predicted that global
warming will affect Arctic and subarctic regions earlier
and more dramatically than other parts of the world. A string
of scientific studies released in the past two years show
marked increases in temperature and changes in the sea ice
distribution, precipitation, permafrost distribution and
many other climate variables across much of the Far North.
The studies have
made news headlines worldwide and have been put forward by
environmentalists as observable signs of anthropogenic (human-induced)
global warming. However, it remains to be determined whether
these variations are consistent with, or indicators of, the
type of polar amplification predicted by global climate model
experiments. In order to understand the variations both observed
and predicted by models, we need to understand and be able
to model the processes behind natural variations in Arctic
climate.
One way to understand
the natural variations in Arctic climate is from the point
of view of atmospheric regimes. A regime is a particular
mode of circulation in which the transition time from one
mode to another is much shorter than the residence time
in a particular mode. The existence of quasi-stationary
(that is, fairly constant in time) regimes have been found
explicitly both in intermediate models of the atmospheric
global circulation and in global climate models. Can such
quasi-stationary regimes be diagnosed from observed data?
From a synoptic point of view, the existence of persistent
anomalies has been well established. Corresponding to this,
there seems little doubt that atmospheric regimes can be
defined on a sectorial basis. Examples of such sectoral
regimes are the North Atlantic Oscillation and the El Niño/Southern
Oscillation.
Not only the
natural variability of the atmosphere can be viewed in terms
of regimes. Recent suggestions have been made that anthropogenic
forcing would manifest by a projection onto the patterns
of natural internal variability that is, certain
regimes would occur more or less frequently than in the
natural state. The winter index of the North Atlantic Oscillation
(NAO) is associated with a near-hemispheric pattern of sea
level pressure variability, with atmospheric mass shifting
between the Icelandic Low and the Azores High. The recent
increase in the winter NAO index is thus related to an intensification
of both those features and an increase in the Atlantic westerly
winds. The NAO can be seen through various manifestations
in terms of sea-level pressure (SLP), storm-tracks, sea
surface temperature (SST), temperature and precipitation.
Physical understanding
of this approach is still problematic the pattern
of forced climate change may resemble one or more states
of the systems dominant modes of variation if both are controlled
and amplified by the same mechanisms and feedbacks. Thus,
this approach does imply that we need models that can simulate
circulation regimes and their associated variability if
we are to characterize fully the current and future evolution
of the Arctic circulation.
It is possible
that the zonalization and loss of planetary-wave amplitude,
characteristic of many global model integrations, would
lead to less sensitivity to change compared with the real
atmosphere. In such a situation it is possible that the
global model response to anthropogenic forcing would have
a weaker projection onto the model's patterns of internal
variability than in the real world.
However, this
reduced sensitivity must be juxtaposed with the strong polar
amplification seen in many global models of climate change.
As models become more complex, and include a better and
more inclusive representation of the myriad of positive
and negative feedbacks in the Arctic and global climate
system, this polar amplification response has been mitigated
somewhat. The results so far are strongly model dependent,
which points to the need for better understanding of the
important physical mechanisms and dominant feedbacks in
the climate system, and the way these processes interact
on a range of timescales. Such understanding must include
cryospheric, oceanic and terrestrial processes, as well
as the atmosphere. Our understanding of many of these mechanisms
or their interactions is sufficiently limited that there
remains a strong role for limited area models and individual
process models, which are not limited by the important of
model bias and errors from other regions of the globe, to
continue the investigation.
Although today's
climate models are better than ever, further refinement
and improvement is necessary. However, accurate model predictions
are neither necessary nor sufficient for effective policy
responses, and hence with care, model products can be of
great use already.
Reference
Arctic
modeling
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