Ecoinformatics Problems of Global Climate Change - New Ecoinformatics Tools in Environmental Science: Applications and Decision-making - page 386

In recent years, satellite data have indicated an even more evident reduction in
regional ice cover. Table 5.4 , Figs. 5.6 and 5.7 contain some characteristics of sea
ice extent and its variations. All estimated values in Table 5.4 , were based on the
NSIDC passive microwave satellite data, which represent the best method to
monitor sea ice, because of the ability to snow data through most clouds and during
darkness. Observations of polar oceans derived from microwave instruments are
essential for tracking the ice edge, estimating sea ice concentration, and classifying
sea ice types.
Figure 5.8 characterizes yearly distribution of the arctic sea ice area. This and
analogous data regularly delivered by different satellite instruments mainly of the U.
S. National Snow and Ice Data Center (NSIDC). Kelley et al. (2003) developed an
algorithm to estimate global snow cover volume from spaceborne passive micro-
wave remote sensing observations. This algorithm is a simple dynamic approach to
retrieve global snow depth estimation. This approach, in contrast to statistic
approach, assumes that snowpack properties are spatially and temporally dynamic
and requires two simple empirical models of density and snowpack grain radius
evolution, plus a dense media radiative transfer model based on the quasicrystalline
approximation and sticky particle theory.
Ananicheva (2005) notes that the main parameter, which determines the extent
and evolution of much of the terrestrial cryosphere, is the position of the bottom
boundary of equal snow line altitudes, where potential snow accumulation and
melting are of the same value. There exist and other indicators that characterize
climate zones:

climatic snow line (CSL) and

￿

glacier equilibrium line altitude (ELA).

￿

Table 5.4 Characteristics of the yearly dynamics of the ice extent
Year of
september
average
extent

Extent
(million
km 2 )

Anomaly rel-
ative to
1981

Anomaly
relative to
1981

Anomaly
relative to
previous
record
(million
km 2 )

Anomaly
relative to
previous
record
(%)

Linear
trend
since
1979
(km 2 per
year)

Linear trend
since 1979
relative to
1981

2010
average
(million km 2 )

2010
average (%)

- -

2010
average (% per
decade)

-

2002 5.96 − 0.56 − 8.6 − 0.17 − 2.8 − 51,000 − 7.8

2003 6.15 − 0.37 − 5.7 0.19 3.2 − 52,800 − 8.1

2004 6.05 − 0.47 − 7.2 0.09 1.5 − 54,600 − 8.4

2005 5.57 − 0.95 − 14.6 − 0.39 − 6.5 − 59,400 − 9.1

2006 5.92 − 0.6 − 9.2 0.35 6.3 − 60,200 − 9.2

2007 4.3 − 2.22 − 34 − 1.27 − 22.8 − 71,600 − 11

2008 4.73 − 1.79 − 27.5 0.43 10 − 77,700 − 11.9

2009 5.39 − 1.13 − 17.3 1.09 25.3 − 78,200 − 12

2010 4.93 − 1.59 − 24.4 0.63 14.7 − 80,700 − 12.4

2011 4.63 − 1.89 − 29 0.33 7.7 − 84,000 − 12.9

2012 3.63 − 2.89 − 44.3 − 0.67 − 15.6 − 91,400 − 14.0

2013 5.35 − 1.16 − 17.9 1.74 48.2 − 89,530 − 13.7