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Climatic analysis of Sea-ice variability in the Canadian Arctic from operational charts, 1980–2004

Published online by Cambridge University Press:  14 September 2017

Christophe Kinnard
Affiliation:
Department of Geography, University of Ottawa, 60 University Street, Ottawa, Ontario K1N 6N5, Canada, E-mail: ckinn045@uottawa.caNational Glaciology Program, Geological Survey of Canada, 601 Booth Street, Ottawa, Ontario K1A 0E8, Canada
Christian M. Zdanowicz
Affiliation:
National Glaciology Program, Geological Survey of Canada, 601 Booth Street, Ottawa, Ontario K1A 0E8, Canada
David A. Fisher
Affiliation:
National Glaciology Program, Geological Survey of Canada, 601 Booth Street, Ottawa, Ontario K1A 0E8, Canada
Bea Alt
Affiliation:
Balanced Environment Associates, 5034 Leitrim Road, Carlsbad Springs, Ontario K0A 1K0, Canada
Steve Mccourt
Affiliation:
Canadian Ice Service, Environment Canada, 373 Sussex Drive, Block E, Ottawa, Onatio K1A 0H3, Canada
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Abstract

We present a complete climatic analysis of Sea-ice conditions in the Canadian Arctic based on digitized operational charts from the Canadian Ice Service for the period 1980–2004. The Seasonal climatology, Spatial variance and linear trends in total ice concentrations (CT) were calculated. The maximum reduction rate in CT was found in the Beaufort Sea (>24% decade–1) and in the Davis Strait/Labrador Sea region (>18% decade–1) during Spring and Summer. Empirical orthogonal function (EOF) analysis performed on monthly CT deviations yielded four Significant EOF modes explaining 32% of the total variance. The Spatial pattern, temporal behaviour and Seasonality of these four EOF modes are discussed and correlated with fields of Sea-level pressure, Surface winds, Surface air temperature and Sea-surface temperature monthly anomalies. These results point to the dominant influence of the North Atlantic Oscillation on CT decadal anomalies during the cold Season, while climate variability in the Pacific influences CT variations in the Beaufort Sea region during Spring–summer.

Information

Type
Research Article
Copyright
Copyright © The Author(s) [year] 2006 
Figure 0

Table 1.Definition of acronyms used in the text

Figure 1

Fig. 1.Map of the Canadian Arctic Showing localities discussed in the text. HB: Hudson Bay; DS: Davis Strait; LS: Labrador Sea; NOW: North Open Water polynya; FB: Foxe Basin; HS: Hudson Strait. 1. Sverdrup and Peary Channels; 2. Norwegian Bay; 3. Parry Channel; 4. Cape Bathurst Polynya; 5. Coronation Gulf; 6. M’Clin-tock Channel; 7. Queen Maud Gulf; 8. Gulf of Boothia; 9. Committee Bay.

Figure 2

Fig. 2.Maps of Seasonal CT averages, 1980–2004: (a) winter; (b) Spring; (c) Summer; and (d) fall.

Figure 3

Fig. 3.Maps of Seasonal CT Standard deviation, 1980–2004: (a) winter; (b) Spring; (c) Summer; and (d) fall. ct in the DS/LS region (Gloersen and others, 1999), whereas our analysis Shows a Substantial reduction in this area.

Figure 4

Fig. 4.(a) Map of linear trends in CT monthly anomalies, 1980–2004. Inset Shows areas with Statistically Significant trends (p < 0.05). (b) Normalized total ice area (NTA) and corresponding least-squares linear trend for the whole Canadian Arctic, 1980–2004. NTA is calculated by dividing the total ice area by the area covered by all available gridpoints. This minimizes the influence of any missing gridpoint on the calculated ice area.

Figure 5

Fig. 5.Maps of Seasonal linear trends in CT: (a) winter; (b) Spring; (c) Summer; and (d) fall. Insets Show areas of Statistical Significance (p < 0.05).

Figure 6

Fig. 6.Homogeneous correlation maps for the first four EOF modes. Only Statistically Significant correlations (p < 0.05) are Shown.

Figure 7

Fig. 7.Standardized expansion coefficient Series for the first four EOF modes. The thick lines represent a 13-point (0.5,1. . .1,0.5) running mean to highlight interannual variability.

Figure 8

Fig. 8.Monthly Standard deviation for the first four EOF modes.

Figure 9

Fig. 9.Heterogeneous correlation maps (lag 0) between EOF1 and monthly anomalies of (a) SLP; (b) u- and v-wind components; (c) SAT; and (d) SST. Color bars and arrow length represent the Strength of the correlation coefficient. Black contours delineate areas of Statistical Significance (p < 0.05). indices (fig. 13a) revealed a Statistically Significant relationship with pna at lag 0 (r = 0.16), with ENSO at lag +4 months (r = 0.22) and with the nino3 index at lag +5 months (r = 0.34). the better correlation with the nino3 and ENSO indices indicates that the link between EOF3 and the pna is mostly due to ENSO fluctuations. the variability of EOF3 over time is dominated by a few high-amplitude events, which are often preceded by an ENSO event (fig. 13b). the cross-correlation function with ENSO and nino3 Suggests an oscillation with a period of ∽2–4 years, consistent with the timescale of ENSO variations (2–6 years) and with the low-frequency part of EOF3.

Figure 10

Fig. 10.Same as Figure 9 but for EOF2.

Figure 11

Fig. 11.Same as Figure 9 but for EOF3.

Figure 12

Fig. 12.Same as Figure 9 but for EOF4.

Figure 13

Fig. 13.(a) Cross-correlation plot between EOF3 expansion coefficients and monthly indices values of the PNA, ENSO and Nino3 indices. Note: El Niño events are defined by a negative ENSO index and positive Nino3 index, while the PNA tends to be inversely correlated with ENSO. (b) Plot of EOF3 expansion coefficients (black curve) and Nino3 index (grey curve). Black dots represent NAO events below one Standard deviation of the monthly NAO index (only the maximum monthly value within the extreme year is Shown).