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.

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

2019 ◽  
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 troughing in March. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each component is interpretable and, when combined, give a complete picture of the variation of the sea ice. We consider time scales varying from the instantaneous, and not previously defined, to the multidecadal 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 of 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.

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 Analysis of 24 years of mesopause region OH rotational temperature observations at Davis, Antarctica. Part 2: Evidence of a quasi-quadrennial oscillation (QQO) in the polar mesosphere

2020 ◽  
Author(s):  
W. John R. French ◽  
Andrew R. Klekociuk ◽  
Frank J. Mulligan
Keyword(s):  
Sea Ice ◽  
Rotational Temperature ◽  
Meridional Wind ◽  
Meridional Flow ◽  
Substantial Part ◽  
Research Station ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice

Abstract. Observational evidence of a quasi-quadrennial oscillation (QQO) in the polar mesosphere is presented based on the analysis of 24 years of hydroxyl (OH) nightglow rotational temperatures derived from scanning spectrometer observations above Davis Research Station, Antarctica (68° S, 78° E). After removal of long term trend and solar cycle responses, the residual winter mean temperature variability contains an oscillation over an approximately 3.5–4.5 year cycle with an amplitude of 3–4 K. Here we investigate this QQO feature in the context of the global temperature, pressure, wind and surface fields using the Aura/MLS and TIMED/SABER satellite data, ERA5 reanalysis and the Extended-Reconstructed Sea Surface Temperature and Optimally-Interpolated sea ice concentration data sets. We find a significant anti-correlation between the QQO and the meridional wind at 86 km altitude measured by a medium frequency spaced antenna radar at Davis. The QQO signal is also correlated with vertical transport as determined from evaluation of carbon monoxide (CO) concentrations in the mesosphere. Together this relationship suggesting that a substantial part of the QQO is the result of adiabatic heating and cooling driven by the meridional flow. The presence of quasi-stationary or persistent patterns in the ERA5 data geopotential anomaly and the meridional wind anomaly data during warm and cold phases of the QQO suggests a tidal or planetary wave influence in its formation, which may act on the filtering of gravity waves to drive an adiabatic response in the mesosphere. The QQO signal potentially arises from an ocean-atmosphere response, and appears to have a signature in Antarctic sea ice extent.

scholarly journals Brief communication: lack of agreement in remote sensing detection of cyclonic drift caused by Atlantic weather in Antarctic sea ice

2021 ◽  
Author(s):  
Wayne de Jager ◽  
Marcello Vichi
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Alternative Methods ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
The Impact

Abstract. Sea-ice extent variability, a measure based on satellite-derived sea ice concentration measurements, has traditionally been used as an essential climate variable to evaluate the impact of climate change on polar regions. However, concentration- based measurements of ice variability do not allow to discriminate the relative contributions made by thermodynamic and dynamic processes, prompting the need to use sea-ice drift products and develop alternative methods to quantify changes in sea ice dynamics that would indicate trends in Antarctic ice characteristics. Here, we present a new method to automate the detection of rotational drift features in Antarctic sea ice at daily timescales using currently available remote sensing ice motion products from EUMETSAT OSI SAF. Results show that there is a large discrepancy in the detection of cyclonic drift features between products, both in terms of intensity and year-to-year distributions, thus diminishing the confidence at which ice drift variability can be further analysed. Product comparisons showed that there was good agreement in detecting anticyclonic drift, and cyclonic drift features were measured to be 1.5–2.2 times more intense than anticyclonic features. The most intense features were detected by the merged product, suggesting that the processing chain used for this product could be injecting additional rotational momentum into the resultant drift vectors. We conclude that it is therefore necessary to better understand why the products lack agreement before further trend analysis of these drift features and their climatic significance can be assessed.

scholarly journals Better constraints on the sea-ice state using global sea-ice data assimilation

2012 ◽  
Vol 5 (2) ◽  
pp. 1627-1667 ◽  
Author(s):  
P. Mathiot ◽  
C. König Beatty ◽  
T. Fichefet ◽  
H. Goosse ◽  
F. Massonnet ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ice Thickness ◽  
Initial State ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

scholarly journals Decadal trends in the Antarctic sea ice extent ultimately controlled by ice–ocean feedback

2014 ◽  
Vol 8 (2) ◽  
pp. 453-470 ◽  
Author(s):  
H. Goosse ◽  
V. Zunz
Keyword(s):  
Sea Ice ◽  
Mixed Layer Depth ◽  
Initial Perturbation ◽  
Salt Transport ◽  
Freshwater Flux ◽  
Positive Trend ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic

Abstract. The large natural variability of the Antarctic sea ice is a key characteristic of the system that might be responsible for the small positive trend in sea ice extent observed since 1979. In order to gain insight of the processes responsible for this variability, we have analysed in a control simulation performed with a coupled climate model a positive ice–ocean feedback that amplifies sea ice variations. When sea ice concentration increases in a region, in particular close to the ice edge, the mixed layer depth tends to decrease. This can be caused by a net inflow of ice, and thus of freshwater, that stabilizes the water column. A second stabilizing mechanism at interannual timescales is associated with the downward salt transport due to the seasonal cycle of ice formation: brine is released in winter and mixed over a deep layer while the freshwater flux caused by ice melting is included in a shallow layer, resulting in a net vertical transport of salt. Because of this stronger stratification due to the presence of sea ice, more heat is stored at depth in the ocean and the vertical oceanic heat flux is reduced, which contributes to maintaining a higher ice extent. This positive feedback is not associated with a particular spatial pattern. Consequently, the spatial distribution of the trend in ice concentration is largely imposed by the wind changes that can provide the initial perturbation. A positive freshwater flux could alternatively be the initial trigger but the amplitude of the final response of the sea ice extent is finally set up by the amplification related to the ice–ocean feedback. Initial conditions also have an influence as the chance to have a large increase in ice extent is higher if starting from a state characterized by a low value.

Multi-aspect Assessment of CMIP6 Models for Arctic Sea Ice Simulation

2020 ◽  
pp. 1-37
Author(s):  
Mengyuan Long ◽  
Lujun Zhang ◽  
Siyu Hu ◽  
Shimeng Qian
Keyword(s):  
Sea Ice ◽  
Temporal Variation ◽  
Spatial Patterns ◽  
Barents Sea ◽  
Linear Trend ◽  
Arctic Sea Ice ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
East Greenland ◽  
Arctic Sea

AbstractThis paper evaluates the ability of 35 models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6) to simulate Arctic sea ice by comparing simulated results with observation from the aspects of spatial patterns and temporal variation. The simulation ability of each model is also quantified by Taylor score and e score from these two aspects. Results show that biases between observed and simulated Arctic sea ice concentration (SIC) are mainly located in the East Greenland, Barents, Bering Sea and Sea of Okhotsk. The largest difference between the observed and simulated SIC spatial patterns occurs in September. Since the beginning of the 21st century, the ability of most models to simulate summer SIC spatial patterns has decreased. We also find that models with Sea Ice Simulator (SIS) sea-ice component in CMIP6 show a consistent larger positive simulation biases of SIC in the East Greenland and Barents Sea. In addition, for most models, the higher the model resolution is, the better the match between the simulated and observed spatial patterns of winter Arctic SIC is. Furthermore, this paper makes a detailed assessment for temporal variation of Arctic sea ice extent (SIE) with regard to climatological average, seasonal SIE, multi-year linear trend and detrended standard deviation of SIE. The sensitivity of September Arctic SIE to a given change of Arctic surface air temperature (SAT) over 1979-2014 in each model has also been investigated. Most models simulate a smaller loss of September Arctic SIE per degree of warming than observed (1.37×106 km2 K-1).

scholarly journals Positive Trend in the Antarctic Sea Ice Cover and Associated Changes in Surface Temperature

2017 ◽  
Vol 30 (6) ◽  
pp. 2251-2267 ◽  
Author(s):  
Josefino C. Comiso ◽  
Robert A. Gersten ◽  
Larry V. Stock ◽  
John Turner ◽  
Gay J. Perez ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Climate Models ◽  
Ice Cover ◽  
Southern Annular Mode ◽  
Positive Trend ◽  
Growth Season ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
The Antarctic

Abstract The Antarctic sea ice extent has been slowly increasing contrary to expected trends due to global warming and results from coupled climate models. After a record high extent in 2012 the extent was even higher in 2014 when the magnitude exceeded 20 × 106 km2 for the first time during the satellite era. The positive trend is confirmed with newly reprocessed sea ice data that addressed inconsistency issues in the time series. The variability in sea ice extent and ice area was studied alongside surface ice temperature for the 34-yr period starting in 1981, and the results of the analysis show a strong correlation of −0.94 during the growth season and −0.86 during the melt season. The correlation coefficients are even stronger with a one-month lag in surface temperature at −0.96 during the growth season and −0.98 during the melt season, suggesting that the trend in sea ice cover is strongly influenced by the trend in surface temperature. The correlation with atmospheric circulation as represented by the southern annular mode (SAM) index appears to be relatively weak. A case study comparing the record high in 2014 with a relatively low ice extent in 2015 also shows strong sensitivity to changes in surface temperature. The results suggest that the positive trend is a consequence of the spatial variability of global trends in surface temperature and that the ability of current climate models to forecast sea ice trend can be improved through better performance in reproducing observed surface temperatures in the Antarctic region.

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 Variability in Antarctic sea ice from 1998-2017

2018 ◽  
Author(s):  
Zhankai Wu ◽  
Xingdong Wang
Keyword(s):  
Sea Ice ◽  
First Year ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Melt ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic ◽  
Melt Duration ◽  
Multiyear Ice

This study was based on the daily sea ice concentration data from the National Snow and Ice Data Center (Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA) from 1998 to 2017. The Antarctic sea ice was analysed from the total sea ice area (SIA), first year ice area, first year ice melt duration, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20 years and for two 10-year periods. The results showed that the total SIA increased by 0.0083×106 km2 yr-1 (+2.07% dec-1) between 1998 and 2017. However, the total SIA in the two 10-year periods showed opposite trends, in which the total SIA increased by 0.026×106 km2 yr-1 between 1998 and 2007 and decreased by 0.0707×106 km2 yr-1 from 2008 to 2017. The first year ice area increased by 0.0059×106 km2 yr-1 and the melt duration decreased by 0.0908 days yr-1 between 1998 and 2017. The multiyear ice area increased by 0.0154×106 km2 yr-1 from 1998 to 2017, and the increase in the last 10 years was about 12.1% more than that in the first 10 years. On a spatial scale, the Entire Antarctica was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The results showed that WA had clear warming in recent years; the total sea ice and multiyear ice areas showed a decreasing trend; multiyear ice area sharply decreased and reached the lowest value in 2017, and accounted for only about 10.1% of the 20-year average. However, the total SIA and multiyear ice area all showed an increased trend in EA, in which the multiyear ice area increased by 0.0478×106 km2 yr-1. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA. Different sea ice parameters in WA and EA showed an opposite trend from 1998 to 2007. However, the total SIA, first year ice area, and multiyear ice area all showed a decreasing trend from 2008-2017, especially the total sea ice and first year ice, which changed almost the same in 2014-2017. In summary, although the Antarctic sea ice has increased slightly over time, it has shown a decreasing trend in recent years.

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