scholarly journals Complexity of Fracturing in Terms of Non-Extensive Statistical Physics: From Earthquake Faults to Arctic Sea Ice Fracturing

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1194
Author(s):  
Filippos Vallianatos ◽  
Georgios Michas
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Statistical Physics ◽  
Probability Distributions ◽  
Fracture Initiation ◽  
Arctic Sea Ice ◽  
Long Range Correlations ◽  
Arctic Sea ◽  
Ice Fracturing ◽  
Earthquake Faults

Fracturing processes within solid Earth materials are inherently a complex phenomenon so that the underlying physics that control fracture initiation and evolution still remain elusive. However, universal scaling relations seem to apply to the collective properties of fracturing phenomena. In this article we present a statistical physics approach to fracturing based on the framework of non-extensive statistical physics (NESP). Fracturing phenomena typically present intermittency, multifractality, long-range correlations and extreme fluctuations, properties that motivate the NESP approach. Initially we provide a brief review of the NESP approach to fracturing and earthquakes and then we analyze stress and stress direction time series within Arctic sea ice. We show that such time series present large fluctuations and probability distributions with “fat” tails, which can exactly be described with the q-Gaussian distribution derived in the framework of NESP. Overall, NESP provide a consistent theoretical framework, based on the principle of entropy, for deriving the collective properties of fracturing phenomena and earthquakes.

Arctic Ocean Sea Ice Area Extent Cyclicity and Non-Stationarity

2020 ◽  
Author(s):  
Reginald Muskett ◽  
Syun-Ichi Akasofu
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Wave Pattern ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Weather System ◽  
Sea Ice Extent ◽  
Temperature Variations ◽  
Arctic Sea ◽  
Arctic Temperature

<p>Arctic sea ice is a key component of the Arctic hydrologic cycle. This cycle is connected to land and ocean temperature variations and Arctic snow cover variations, spatially and temporally. Arctic temperature variations from historical observations shows an early 20th century increase (i.e. warming), followed by a period of Arctic temperature decrease (i.e. cooling) since the 1940s, which was followed by another period of Arctic temperature increase since the 1970s that continues into the two decades of the 21st century. Evidence has been accumulating that Arctic sea ice extent can experience multi-decadal to centennial time scale variations as it is a component of the Arctic Geohydrological System. </p><p><br>We investigate the multi-satellite and sensor daily values of area extent of Arctic sea ice since SMMR on Nimbus 7 (1978) to AMSR2 on GCOM-W1 (2019). From the daily time series we use the first year-cycle as a wave-pattern to compare to all subsequent years-cycles through April 2020 (in progress), and constitute a derivative time series. In this time series we find the emergence of a multi-decadal cycle, showing a relative minimum during the period of 2007 to 2014, and subsequently rising. This may be related to an 80-year cycle (hypothesis). The Earth’s weather system is principally driven the solar radiation and its variations. If the multi-decadal cycle in Arctic sea ice area extent that we interpret continues, it may be linked physically to the Wolf-Gleissberg cycle, a factor in the variations of terrestrial cosmogenic isotopes, ocean sediment layering and glacial varves, ENSO and Aurora.</p><p>Our hypothesis and results give more evidence that the multi-decadal variation of Arctic sea ice area extent is controlled by natural physical processes of the Sun-Earth system. </p>

scholarly journals A simple approach to providing a more consistent Arctic sea ice extent time series from the 1950s to present

2012 ◽  
Vol 6 (6) ◽  
pp. 1359-1368 ◽  
Author(s):  
W. N. Meier ◽  
J. Stroeve ◽  
A. Barrett ◽  
F. Fetterer
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Linear Trend ◽  
Statistical Significance ◽  
Recent Decade ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Combined Time Series

Abstract. Observations from passive microwave satellite sensors have provided a continuous and consistent record of sea ice extent since late 1978. Earlier records, compiled from ice charts and other sources exist, but are not consistent with the satellite record. Here, a method is presented to adjust a compilation of pre-satellite sources to remove discontinuities between the two periods and create a more consistent combined 59-yr time series spanning 1953–2011. This adjusted combined time series shows more realistic behavior across the transition between the two individual time series and thus provides higher confidence in trend estimates from 1953 through 2011. The long-term time series is used to calculate linear trend estimates and compare them with trend estimates from the satellite period. The results indicate that trends through the 1960s were largely positive (though not statistically significant) and then turned negative by the mid-1970s and have been consistently negative since, reaching statistical significance (at the 95% confidence level) by the late 1980s. The trend for September (when Arctic extent reaches its seasonal minimum) for the satellite period, 1979–2011 is −12.9% decade−1, nearly double the 1953–2011 trend of −6.8% decade−1 (percent relative to the 1981–2010 mean). The recent decade (2002–2011) stands out as a period of persistent decline in ice extent. The combined 59-yr time series puts the strong observed decline in the Arctic sea ice cover during 1979–2011 in a longer-term context and provides a useful resource for comparisons with historical model estimates.

scholarly journals Interannual variability in Transpolar Drift ice thickness and potential impact of Atlantification

2020 ◽  
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Tatiana A. Alekseeva ◽  
Sergei V. Frolov ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Ice Growth ◽  
Arctic Sea ◽  
Transpolar Drift ◽  
The Impact

Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exits the Arctic Ocean through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age and number of freezing degree days. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea, but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate the impact of Atlantification on sea ice growth in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea ice covered Arctic.

scholarly journals Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter

2019 ◽  
Vol 229 ◽  
pp. 48-59 ◽  
Author(s):  
Stephen E.L. Howell ◽  
David Small ◽  
Christoph Rohner ◽  
Mallik S. Mahmud ◽  
John J. Yackel ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea

scholarly journals A means-corrected estimate for the Arctic sea-ice volume in 1990–2019

2021 ◽  
Author(s):  
Petteri Uotila ◽  
Joula Siponen ◽  
Eero Rinne ◽  
Steffen Tietsche
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Satellite Altimetry ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Basin Scale ◽  
Arctic Sea

<p>Decadal changes in sea-ice thickness are one of the most visible signs of climate variability and change. To gain a comprehensive understanding of mechanisms involved, long time series, preferably with good uncertainty estimates, are needed. Importantly, the development of accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is compared to a set of five ocean reanalysis (ECCO-V4r4, GLORYS12V1, ORAS5 and PIOMAS).</p><p>The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002–2017. The CCI product performs well in the validation of the reanalyses: overall root-mean-square difference (RMSD) between monthly sea-ice thickness from CCI and the reanalyses ranges from 0.4–1.2 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval.</p><p>The CCI and reanalysis basin-scale sea-ice volumes have a good match in terms of year-to-year variability and long-term trends but rather different monthly mean climatologies. These findings provide a rationale to construct a multi-decadal sea-ice volume time series for the Arctic Ocean and its sub-basins from 1990–2019 by adjusting the ocean reanalyses ensemble toward CCI observations. Such a time series, including its uncertainty estimate, provides new insights to the evolution of the Arctic sea-ice volume during the past 30 years.</p>

scholarly journals Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification

2021 ◽  
Vol 15 (6) ◽  
pp. 2575-2591
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Tatiana A. Alekseeva ◽  
Gerit Birnbaum ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Arctic Sea ◽  
Transpolar Drift

Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exiting the Arctic Ocean does so through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated summer (July–August) time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison of this time series with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years at the end of the Transpolar Drift. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate for the impact processes, such as Atlantification, have on sea ice thickness in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea-ice-covered Arctic.

scholarly journals Snow depth on Arctic sea ice derived from radar: In situ comparisons and time series analysis

2015 ◽  
Vol 120 (6) ◽  
pp. 4260-4287 ◽  
Author(s):  
Benjamin Holt ◽  
Michael P. Johnson ◽  
Dragana Perkovic‐Martin ◽  
Ben Panzer
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Series Analysis ◽  
Arctic Sea

scholarly journals The Variability of Arctic Sea Ice Extent from Spring to Summer and Its Linkage to the Decline of SIE in September

2015 ◽  
Vol 2015 ◽  
pp. 1-21
Author(s):  
Zhenhao Bao ◽  
Gordon Huang ◽  
Jinliang Liu ◽  
Hengchun Ye
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Correlation Coefficient ◽  
Arctic Sea Ice ◽  
Polar Regions ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Original Time ◽  
Arctic Sea Ice Extent ◽  
Sea Ice Reduction

The satellite record analysis for monthly differences of Arctic sea ice extent (SIE) shows that the most significant accelerated monthly sea ice reduction occurred between June and July although, on average, the largest sea ice reduction occurred between July and August. The monthly difference of June minus July (JJ) SIE has the strongest correlation with September SIE, with a correlation coefficient of −0.786 (original time series) and −0.625 (detrended time series) at confidence level of 99%. Furthermore, it is found that the correlation coefficient between JJ SIE and July minus August (JA) SIE is so low (0.068) that they can be thought to be independent from each other, considering that the JA SIE is also significantly negatively correlated to September SIE. A simple regression forecasting model for September SIE was established using monthly SIE differences for the JJ and JA. This study also shows that the JJ SIE is significantly correlated not only with sea level pressure (SLP) in polar regions and midlatitudes over eastern Atlantic in July, a pattern which resembles the negative phase of North Atlantic Oscillation (NAO), but also with sea surface temperature (SST) in midlatitudes over central North Pacific in the preceding spring.

scholarly journals A Methodology for Fitting the Time Series of Snow Depth on the Arctic Sea Ice

2019 ◽  
Vol 36 (8) ◽  
pp. 1449-1462
Author(s):  
Yuzhe Wang ◽  
Haidong Pan ◽  
Daosheng Wang ◽  
Xianqing Lv
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Sea Ice ◽  
Snow Depth ◽  
Spline Interpolation ◽  
Optimal Number ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Earth Observing System ◽  
Arctic Sea

AbstractSnow depth is an important geophysical variable for investigating sea ice and climate change, which can be obtained from satellite data. However, there is a large number of missing data in satellite observations of snow depth. In this study, a methodology, the periodic functions fitting with varying parameter (PFF-VP), is presented to fit the time series of snow depth on Arctic sea ice obtained from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The time-varying parameters are obtained by the independent point (IP) scheme and cubic spline interpolation. The PPF-VP is validated by experiments in which part of the observations are artificially removed and used to compare with the fitting results. Results indicate that the PPF-VP performs better than three traditional fitting methods, with its fitting results closer to observations and with smaller errors. In the practical experiments, the optimal number of IPs can be determined by only considering the fraction of missing data, particularly the length of the longest gaps in the snow-depth time series. All the experimental results indicate that the PPF-VP is a feasible and effective method to fit the time series of snow depth and can provide continuous data of snow depth for further study.

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