scholarly journals Mechanisms driving the asymmetric seasonal cycle of Antarctic Sea Ice in the CESM Large Ensemble

2020 ◽  
Vol 61 (82) ◽  
pp. 171-180
Author(s):  
Clare Eayrs ◽  
Daiane Faller ◽  
David M. Holland
Keyword(s):  
Sea Ice ◽  
Annual Cycle ◽  
Low Pressure ◽  
Sea Ice Extent ◽  
The North ◽  
Antarctic Sea Ice ◽  
Ice Edge ◽  
Relationship Of ◽  
The Relationship

AbstractThe yearly paired process of slow growth and rapid melt of some 15 million square kilometers of Antarctic sea ice takes place with a regular asymmetry; the process has been linked to the relationship of the position of the ice edge with the band of low pressure that circles the continent between 60° and 70°S. In autumn, winds to the north of the low-pressure band slow the advancing ice edge. In summer, Ekman divergence created by opposing winds on either side of the low-pressure band opens up warm water regions that rapidly melt sea ice. We use the 40 ensemble members from the CESM-LENS historical run (1920–2005) to examine the relationship between the asymmetry in the annual cycle and the position and intensity of the low-pressure band. CESM-LENS reproduces the magnitude of the annual cycle of Antarctic sea ice extent with a short lag (2 weeks). Melt rate is the characteristic of the annual cycle that varies the most. Our results provide evidence that lower pressure leads to increased melt rates, which supports the importance of the role of divergence in increasing the melt rate of Antarctic sea ice. The role of winds during the growing season remains unquantified.

scholarly journals Changes in the role of Pacific decadal oscillation on sea ice extent variability across the mid-1990s

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hyerim Kim ◽  
Sang-Wook Yeh ◽  
Soon-Il An ◽  
Se-Yong Song
Keyword(s):  
Sea Ice ◽  
Pacific Decadal Oscillation ◽  
Far East ◽  
Low Frequency ◽  
Early Summer ◽  
Sea Ice Extent ◽  
The Pacific ◽  
Before And After ◽  
Relationship Of

Abstract Characteristics of sea ice extent (SIE) have been rapidly changing in the Pacific Arctic sector (PAS) in recent years. The SIE variability in PAS during the late spring and early summer (i.e., April–May–June, AMJ) plays a key role in determining the SIE during the following fall when SIE is at a minimum. We find that the Pacific Decadal Oscillation (PDO), which is the most dominant variability of sea surface temperature (SST) on the low-frequency timescales, differently influences the SIE in PAS during AMJ before and after the mid-1990s. While a positive phase of PDO during the previous winter acts to increases SIE during AMJ before the mid-1990s, it acts to decrease SIE during AMJ after the mid-1990s. Further analysis indicates that atmospheric circulation associated with PDO differently influences the variability of SIE in the PAS during AMJ by modulating poleward moisture transport across the Alaska or the Far East Asia peninsula. This results in the change in the relationship of PDO and SIE in the PAS before and after the mid-1990s.

scholarly journals Global Sea Ice Coverage from Satellite Data: Annual Cycle and 35-Yr Trends

2014 ◽  
Vol 27 (24) ◽  
pp. 9377-9382 ◽  
Author(s):  
Claire L. Parkinson
Keyword(s):  
Sea Ice ◽  
Annual Cycle ◽  
High Amplitude ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Ice Coverage ◽  
High Magnitude ◽  
Lower Amplitude ◽  
Yearly Average ◽  
Lower Magnitude

Abstract Well-established satellite-derived Arctic and Antarctic sea ice extents are combined to create the global picture of sea ice extents and their changes over the 35-yr period 1979–2013. Results yield a global annual sea ice cycle more in line with the high-amplitude Antarctic annual cycle than the lower-amplitude Arctic annual cycle but trends more in line with the high-magnitude negative Arctic trends than the lower-magnitude positive Antarctic trends. Globally, monthly sea ice extent reaches a minimum in February and a maximum generally in October or November. All 12 months show negative trends over the 35-yr period, with the largest magnitude monthly trend being the September trend, at −68 200 ± 10 500 km2 yr−1 (−2.62% ± 0.40% decade−1), and the yearly average trend being −35 000 ± 5900 km2 yr−1 (−1.47% ± 0.25% decade−1).

scholarly journals Modeling the annual cycle of daily Antarctic sea ice extent

2020 ◽  
Vol 14 (7) ◽  
pp. 2159-2172 ◽  
Author(s):  
Mark S. Handcock ◽  
Marilyn N. Raphael
Keyword(s):  
Sea Ice ◽  
Annual Cycle ◽  
Traditional Method ◽  
Linear Trend ◽  
Phase Variation ◽  
Positive Trend ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice

Abstract. The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and reaching its minimum in February. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each component is interpretable and, when combined, gives a complete picture of the variation in the sea ice. We consider timescales varying from the instantaneous and not previously defined to the multi-decadal curvilinear trend, the longest. Because our representation is daily, these timescales of variability give precise information about the timing and rates of advance and retreat of the ice and may be used to diagnose physical contributors to variability in the sea ice. We define a number of annual cycles each capturing different components of variation, especially the yearly amplitude and phase that are major contributors to SIE variation. Using daily sea ice concentration data, we show that our proposed invariant annual cycle explains 29 % more of the variation in daily SIE than the traditional method. The proposed annual cycle that incorporates amplitude and phase variation explains 77 % more variation than the traditional method. The variation in phase explains more of the variability in SIE than the amplitude. Using our methodology, we show that the anomalous decay of sea ice in 2016 was associated largely with a change of phase rather than amplitude. We show that the long term trend in Antarctic sea ice extent is strongly curvilinear and the reported positive linear trend is small and dependent strongly on a positive trend that began around 2011 and continued until 2016.

Whaling records and changes in Antarctic sea ice: consistency with historical records

Polar Record ◽  
2002 ◽  
Vol 38 (207) ◽  
pp. 355-358 ◽  
Author(s):  
William K. de la Mare
Keyword(s):  
Sea Ice ◽  
Complex Nature ◽  
Limited Data ◽  
Antarctic Sea Ice ◽  
Direct Observations ◽  
Ice Edge ◽  
Sea Ice Edge ◽  
Types Of Information ◽  
The Antarctic ◽  
The Relationship

AbstractA claim that there are substantial discrepancies between direct observations of the Antarctic sea-ice edge and the implicit sea-ice edge derived from whaling records is rebutted. The claimeddiscrepancies are shown to arise largely from comparing the two types of information from different dates. A date-corrected comparison shows generally good agreement between the southernmost limit of whaling and the most comprehensive of the early monthly ice charts of Antarctica. The remaining apparent discrepancies are accounted for either by very limited data or the complex nature of the ice edge in the region of the Weddell ice tongue. Correlation of the southernmost limits of whaling with direct observations of the ice edge provides the most powerful calibration of the relationship between them

scholarly journals Antarctic summer sea ice concentration and extent: comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea ice charts

2008 ◽  
Vol 2 (4) ◽  
pp. 623-647 ◽  
Author(s):  
B. Ozsoy-Cicek ◽  
H. Xie ◽  
S. F. Ackley ◽  
K. Ye
Keyword(s):  
Sea Ice ◽  
Linear Trend ◽  
Passive Microwave ◽  
The Arctic ◽  
Poor Correlation ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Ice Edge

Abstract. Antarctic sea ice cover has shown a slight increase in overall observed ice extent as derived from satellite mapping from 1979 to 2008, contrary to the decline observed in the Arctic regions. Spatial and temporal variations of the Antarctic sea ice however remain a significant problem to monitor and understand, primarily due to the vastness and remoteness of the region. While satellite remote sensing has provided and has great future potential to monitor the variations and changes of sea ice, uncertainties remain unresolved. In this study, the National Ice Center (NIC) ice edge and the AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) ice extent are examined, while the ASPeCt (Antarctic Sea Ice Process and Climate) ship observations from the Oden expedition in December 2006 are used as ground truth to verify the two products during Antarctic summer. While there is a general linear trend between ASPeCt and AMSR-E ice concentration estimates, there is poor correlation (R2=0.41) and AMSR-E tends to underestimate the low ice concentrations. We also found that the NIC sea ice edge agrees well with ship observations, while the AMSR-E shows the ice edge further south, consistent with its poorer detection of low ice concentrations. The northward extent of the ice edge at the time of observation (NIC) had mean values varying from 38 km to 102 km greater on different days for the area as compared with the AMSR-E sea ice extent. For the circumpolar area as a whole in the December period examined, AMSR-E therefore underestimates the area inside the ice edge at this time by up to 14% or, 1.5 million km2 less area, compared to the NIC ice charts. These differences alone can account for more than half of the purported sea ice loss between the pre 1960s and the satellite era suggested earlier from comparative analysis of whale catch data with satellite derived data. Preliminary comparison of satellite scatterometer data suggests better resolution of low concentrations than passive microwave, and therefore better fidelity with ship observations and NIC charts of the area inside the ice edge during Antarctic summer.

scholarly journals New estimates of Arctic and Antarctic sea ice extent during September 1964 from recovered Nimbus I satellite imagery

2013 ◽  
Vol 7 (1) ◽  
pp. 35-53 ◽  
Author(s):  
W. N. Meier ◽  
D. Gallaher ◽  
G. G. Campbell
Keyword(s):  
Sea Ice ◽  
Satellite Imagery ◽  
Passive Microwave ◽  
The Arctic ◽  
Large Decrease ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Ice Edge ◽  
The 1960S ◽  
The Antarctic

Abstract. Satellite imagery from the 1964 Nimbus I satellite has been recovered, digitized, and processed to estimate Arctic and Antarctic sea ice extent for September 1964. September is the month when the Arctic reaches its minimum annual extent and the Antarctic reaches its maximum. Images were manually analyzed over a three-week period to estimate the location of the ice edge and then composited to obtain a hemispheric average. Uncertainties were based on limitations in the image analysis and the variation of the ice cover over the three week period. The 1964 Antarctic extent is higher than estimates from the 1979–present passive microwave record, but is in accord with previous indications of higher extents during the 1960s. The Arctic 1964 extent was near the 1979–2000 average from the passive microwave record, suggesting relatively stable summer extents until the recent large decrease. This early satellite record puts the recently observed into a longer-term context.

scholarly journals Assessment of Arctic and Antarctic Sea Ice Predictability in CMIP5 Decadal Hindcasts

2016 ◽  
Author(s):  
Chao-Yuan Yang ◽  
Jiping Liu ◽  
Yongyun Hu ◽  
Radley M. Horton ◽  
Liqi Chen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
North Atlantic ◽  
Lead Time ◽  
The Arctic ◽  
Sea Ice Extent ◽  
The North ◽  
Antarctic Sea Ice ◽  
Predictive Skill ◽  
The North Atlantic

Abstract. This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic sea ice. We analyze decadal hindcasts/predictions of 11 CMIP5 models. Decadal hindcasts exhibit a large multi-model spread in the simulated sea ice extent, with some models deviating significantly from the observations. For the models having large biases and using full-field initialization, the predicted sea ice extent quickly drifts away from the initial constraint, deteriorating the decadal predictive skill. The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Sea ice extent in the north Pacific has better predictive skill than that in the north Atlantic (particularly at a lead-time of 3–7 years), but there is a re-emerging predictive skill in the north Atlantic at a lead-time of 6–8 years. In contrast to the Arctic, Antarctic sea ice decadal hindcasts do not show broad predictive skill at any time scales, and there is no obvious improvement linking the areal extent of significant predictive skill to lead-time increase. This might be because nearly all the models predict a retreating Antarctic sea ice cover, opposite to the observations. For the Arctic, the predictive skill of the MMEE outperforms most models and the persistence prediction at longer time scales, which is not the case for the Antarctic.

Can Antarctic sea-ice extent be determined from whaling records?

Polar Record ◽  
2000 ◽  
Vol 36 (199) ◽  
pp. 345-347 ◽  
Author(s):  
Stephen Vaughan
Keyword(s):  
Sea Ice ◽  
Accurate Method ◽  
Reliable Information ◽  
New Method ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
The Subject ◽  
Ice Edge ◽  
Sea Ice Edge ◽  
Sighting Data

SummeryThe subject of retreating global sea-ice extent is a matter of grave concern, and any new method that promises reliable information about past ice-extent parameters must be welcomed. However, the method proposed by De la Mare should be viewed with caution for four reasons. First, his predictions of sea-ice extent do not correspond with known observations of sea-ice extent from research published in 1936 and 1972. Second, his predictions correlate much more closely with the whale-sighting data recorded by Hansen (1936). Third, since Hansen's sea-ice extent data do not correspond closely with his whalesighting data, it must be questioned whether whale-based data should be used for retrospective predictions relating to sea-ice extent. And finally, information from the IWC indicates that De la Mare's datasets are not considered accurate. Predicting sea-ice edge extent is complex, and, it would seem, a purely biological approach is not necessarily the most accurate method to adopt.

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