scholarly journals Estimation and Prediction of Record Values Using Pivotal Quantities and Copulas

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1678
Author(s):  
Jeongwook Lee ◽  
Joon Jin Song ◽  
Yongku Kim ◽  
Jung In Seo
Keyword(s):  
Computational Complexity ◽  
Sea Ice ◽  
Record Values ◽  
Arctic Sea Ice ◽  
Dependence Structure ◽  
Pivotal Quantity ◽  
Unknown Parameters ◽  
Arctic Sea ◽  
Exact Confidence Intervals ◽  
The Relationship

Recently, the area of sea ice is rapidly decreasing due to global warming, and since the Arctic sea ice has a great impact on climate change, interest in this is increasing very much all over the world. In fact, the area of sea ice reached a record low in September 2012 after satellite observations began in late 1979. In addition, in early 2018, the glacier on the northern coast of Greenland began to collapse. If we are interested in record values of sea ice area, modeling relationships of these values and predicting future record values can be a very important issue because the record values that consist of larger or smaller values than the preceding observations are very closely related to each other. The relationship between the record values can be modeled based on the pivotal quantity and canonical and drawable vine copulas, and the relationship is called a dependence structure. In addition, predictions for future record values can be solved in a very concise way based on the pivotal quantity. To accomplish that, this article proposes an approach to model the dependence structure between record values based on the canonical and drawable vine. To do this, unknown parameters of a probability distribution need to be estimated first, and the pivotal-based method is provided. In the pivotal-based estimation, a new algorithm to deal with a nuisance parameter is proposed. This method allows one to reduce computational complexity when constructing exact confidence intervals of functions with unknown parameters. This method not only reduces computational complexity when constructing exact confidence intervals of functions with unknown parameters, but is also very useful for obtaining the replicated data needed to model the dependence structure based on canonical and drawable vine. In addition, prediction methods for future record values are proposed with the pivotal quantity, and we compared them with a time series forecasting method in real data analysis. The validity of the proposed methods was examined through Monte Carlo simulations and analysis for Arctic sea ice data.

scholarly journals Statistical indicators of Arctic sea-ice stability – prospects and limitations

2016 ◽  
Vol 10 (4) ◽  
pp. 1631-1645 ◽  
Author(s):  
Sebastian Bathiany ◽  
Bregje van der Bolt ◽  
Mark S. Williamson ◽  
Timothy M. Lenton ◽  
Marten Scheffer ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
Ice Coverage ◽  
Arctic Sea ◽  
Complex Models ◽  
Large Heat ◽  
Annual Time Series ◽  
Arctic Sea Ice Loss ◽  
The Relationship

Abstract. We examine the relationship between the mean and the variability of Arctic sea-ice coverage and volume in a large range of climates from globally ice-covered to globally ice-free conditions. Using a hierarchy of two column models and several comprehensive Earth system models, we consolidate the results of earlier studies and show that mechanisms found in simple models also dominate the interannual variability of Arctic sea ice in complex models. In contrast to predictions based on very idealised dynamical systems, we find a consistent and robust decrease of variance and autocorrelation of sea-ice volume before summer sea ice is lost. We attribute this to the fact that thinner ice can adjust more quickly to perturbations. Thereafter, the autocorrelation increases, mainly because it becomes dominated by the ocean water's large heat capacity when the ice-free season becomes longer. We show that these changes are robust to the nature and origin of climate variability in the models and do not depend on whether Arctic sea-ice loss occurs abruptly or irreversibly. We also show that our climate is changing too rapidly to detect reliable changes in autocorrelation of annual time series. Based on these results, the prospects of detecting statistical early warning signals before an abrupt sea-ice loss at a "tipping point" seem very limited. However, the robust relation between state and variability can be useful to build simple stochastic climate models and to make inferences about past and future sea-ice variability from only short observations or reconstructions.

scholarly journals The calculation of surface temperature and albedo of Arctic sea ice from AVHRR

1993 ◽  
Vol 17 ◽  
pp. 391-397 ◽  
Author(s):  
R. Lindsay ◽  
D. Rothrock
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Calculations ◽  
Arctic Sea ◽  
Arctic Atmosphere ◽  
Using Data ◽  
Cover Thickness ◽  
The Relationship

The temperature and albedo distributions of Arctic sea ice are calculated from images obtained from the AVHRR satellite sensor. The temperature estimate uses a split window correction incorporating regression coefficients appropriate for the arctic atmosphere. The albedo estimate is found assuming a clear and dry atmosphere. Both estimates are made with published correction techniques. Inherent errors due to the uncertainty of the atmospheric interference produced by humidity, aerosols, and diamond dust are judged to be 2–5°C in surface temperature and 0.10–0.20 in surface albedo. Cloudy regions are masked out manually using data from all five channels. The relationship between temperature and albedo is shown for a sample scene. A simple model of a surface composed of only cold, bright ice and warm, dark water is inadequate. Model calculations based on the surface energy balance allow us to relate albedo and temperature to ice thickness and snow-cover thickness and to further assess the accuracy of the surface estimates.

scholarly journals North Atlantic Oscillation in winter is largely insensitive to autumn Barents-Kara sea ice variability

2021 ◽  
Vol 7 (31) ◽  
pp. eabg4893
Author(s):  
Peter Yu Feng Siew ◽  
Camille Li ◽  
Mingfang Ting ◽  
Stefan P. Sobolowski ◽  
Yutian Wu ◽  
...  
Keyword(s):  
Sea Ice ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Climate Models ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Sea Ice Extent ◽  
Large Spread ◽  
Arctic Sea ◽  
The Relationship

Arctic sea ice extent in autumn is significantly correlated with the winter North Atlantic Oscillation (NAO) in the satellite era. However, questions about the robustness and reproducibility of the relationship persist. Here, we show that climate models are able to simulate periods of strong ice-NAO correlation, albeit rarely. Furthermore, we show that the winter circulation signals during these periods are consistent with observations and not driven by sea ice. We do so by interrogating a multimodel ensemble for the specific time scale of interest, thereby illuminating the dynamics that produce large spread in the ice-NAO relationship. Our results support the importance of internal variability over sea ice but go further in showing that the mechanism behind strong ice-NAO correlations, when they occur, is similar in longer observational records and models. Rather than sea ice, circulation anomalies over the Urals emerge as a decisive precursor to the winter NAO signal.

scholarly journals The calculation of surface temperature and albedo of Arctic sea ice from AVHRR

1993 ◽  
Vol 17 ◽  
pp. 391-397 ◽  
Author(s):  
R. Lindsay ◽  
D. Rothrock
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Calculations ◽  
Arctic Sea ◽  
Arctic Atmosphere ◽  
Using Data ◽  
Cover Thickness ◽  
The Relationship

The temperature and albedo distributions of Arctic sea ice are calculated from images obtained from the AVHRR satellite sensor. The temperature estimate uses a split window correction incorporating regression coefficients appropriate for the arctic atmosphere. The albedo estimate is found assuming a clear and dry atmosphere. Both estimates are made with published correction techniques. Inherent errors due to the uncertainty of the atmospheric interference produced by humidity, aerosols, and diamond dust are judged to be 2–5°C in surface temperature and 0.10–0.20 in surface albedo. Cloudy regions are masked out manually using data from all five channels. The relationship between temperature and albedo is shown for a sample scene. A simple model of a surface composed of only cold, bright ice and warm, dark water is inadequate. Model calculations based on the surface energy balance allow us to relate albedo and temperature to ice thickness and snow-cover thickness and to further assess the accuracy of the surface estimates.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

Eastern-Western Arctic Sea Ice Analysis: 1988

1988 ◽  
Author(s):  
NAVAL POLAR OCEANOGRAPHY CENTER WASHINGTON DC
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Western Arctic
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