Sea Ice Distribution in the Barents Sea

2016 ◽  
Author(s):  
Sindre M. Fritzner ◽  
Trond Sagerup
Keyword(s):  
Sea Ice ◽  
Normal Distribution ◽  
Barents Sea ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
The Barents Sea ◽  
Ice Edge ◽  
Sea Ice Edge

This paper provides a statistical description of the sea ice occurrence in the Barents Sea, using yearly maximum sea ice data for the last 36 years from the European Centre for Medium-Range Weather Forecasts (ECMWF). A set of four distribution functions have been estimated with the maximum likelihood method. The distribution functions used were Extreme Value distribution, Gumbel distribution, Normal distribution and kernel density estimation. The normal distribution was found to fit the data best and provide the most likely result. Our results verify dependency of the North Atlantic current on the sea ice edge. Warm water northwards prevents the ice from extending south; this makes the extreme value distribution unlikely since this will prevent long tailed distributions. The results for sea ice occurrence are compared to the boundaries given in the proposed revision to NORSOK N-003. These boundaries were found to be too simplistic and not necessarily conservative. Here we have proposed new and more accurate boundaries for the sea ice occurrence. We have found trends indicating northwards movement of the sea ice edge in the Norwegian Sea and eastern parts of the Barents Sea. These trends are mostly due to less ice in the last ten years and not trends for the whole period. In the south-western parts of the Barents Sea where oil and gas operations are imminent no trends have been discovered. The lack of trend is related to the islands in the western Barents Sea.

DIscrepancy of Gear Tooth Allowable Bending Stress Using the Extreme Value Distribution From Different Test Methods

1991 ◽  
Author(s):  
Chienann A. Hou ◽  
Shijun Ma
Keyword(s):  
Gear Tooth ◽  
Extreme Value Distribution ◽  
Test Methods ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
Test Method ◽  
Bending Stress ◽  
Gear Design ◽  
Experimental Approaches

Abstract The allowable bending stress Se of a gear tooth is one of the basic factors in gear design. It can be determined by either the pulsating test or the gear-running test. However, some differences exist between the allowable bending stress Se obtained from these different test methods. In this paper, the probability distribution functions corresponding to each test method are analyzed and the expressions for the minimum extreme value distribution are presented. By using numerical integration, Se values from the population of the same tested gear tooth are obtained. Based on this investigation it is shown that the differences in Se obtained from the different test methods are significant. A proposed correction factor associated with the different experimental approaches is also presented.

scholarly journals Tail Properties and Asymptotic Expansions for the Maximum of the Logarithmic Skew-Normal Distribution

2013 ◽  
Vol 50 (3) ◽  
pp. 900-907 ◽  
Author(s):  
Xin Liao ◽  
Zuoxiang Peng ◽  
Saralees Nadarajah
Keyword(s):  
Asymptotic Expansion ◽  
Convergence Rate ◽  
Normal Distribution ◽  
Asymptotic Expansions ◽  
Random Variables ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Skew Normal Distribution ◽  
Skew Normal

We discuss tail behaviors, subexponentiality, and the extreme value distribution of logarithmic skew-normal random variables. With optimal normalized constants, the asymptotic expansion of the distribution of the normalized maximum of logarithmic skew-normal random variables is derived. We show that the convergence rate of the distribution of the normalized maximum to the Gumbel extreme value distribution is proportional to 1/(log n)1/2.

scholarly journals Dynamics of the Spatial Chlorophyll-A Distribution at the Polar Front in the Marginal Ice Zone of the Barents Sea during Spring

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 101
Author(s):  
Pavel R. Makarevich ◽  
Veronika V. Vodopianova ◽  
Aleksandra S. Bulavina
Keyword(s):  
Species Composition ◽  
Sea Ice ◽  
Chlorophyll A ◽  
Phytoplankton Community ◽  
Barents Sea ◽  
Sea Water ◽  
Polar Front ◽  
Ice Melting ◽  
The Barents Sea ◽  
Ice Edge

Effects of the sea-ice edge and the Polar Frontal Zone on the distribution of chlorophyll-a levels in the pelagic were investigated during multi-year observations in insufficiently studied and rarely navigable regions of the Barents Sea. Samples were collected at 52 sampling stations combined into 11 oceanographic transects over a Barents Sea water area north of the latitude 75° N during spring 2016, 2018, and 2019. The species composition, abundance and biomass of the phytoplankton community, chlorophyll-a concentrations, hydrological and hydrochemical parameters were analyzed. The annual phytoplankton evolution phase, defined as an early-spring one, was determined throughout the transects. The species composition of the phytoplankton community and low chlorophyll-a levels suggested no phytoplankton blooming in April 2016 and 2019. Not yet started sea-ice melting prevented sympagic (sea-ice-associated) algae from being released into the seawater. In May 2018, ice melting began in the eastern Barents Sea and elevated chlorophyll-a levels were recorded near the ice edge. Chlorophyll-a concentrations substantially differed in waters of different genesis, especially in areas influenced by the Polar Front. The Polar Front separated the more productive Arctic waters with a chlorophyll-a concentration of 1–5 mg/m3 on average from the Atlantic waters where the chlorophyll-a content was an order of magnitude lower.

Biomarker distributions in surface sediments of the northern Barents Sea: a basis for accurate palaeo sea-ice reconstructions

2020 ◽  
Author(s):  
Anna J. Pienkowski ◽  
Katrine Husum ◽  
Simon Belt ◽  
Lukas Smik
Keyword(s):  
Sea Ice ◽  
Surface Sediments ◽  
Barents Sea ◽  
General Pattern ◽  
Environmental Parameters ◽  
Sea Ice Concentration ◽  
Marine Surface ◽  
Ice Edge ◽  
Spring Sea Ice ◽  
Sea Ice Edge

<p>An understanding of modern sea-ice proxy distributions relative to measured environmental parameters underpins accurate palaeo reconstructions necessary for correct future projections. We here present new data on highly-branched isoprenoid (HBI) lipid biomarkers produced by sea-ice diatoms (IP<sub>25</sub>, IPSO<sub>25</sub>) and phytoplankton (HBI III, HBI IV) in marine surface sediments taken in a south-north transect east of Svalbard as part of the Nansen Legacy project. Collectively, these biomarkers can be used to reconstruct seasonal spring sea-ice (SpSIC) and the seasonal sea-ice edge. Eight sites at ~78-83°N were sampled by multicorer. All cores contain abundant biomarkers, except the northernmost station. Biomarker-based SpSIC shows a general south-north increase, mimicking observational sea-ice concentration satellite-based means (1988-2017). The HBI T<sub>25</sub> index suggests ice edge phytoplankton blooms at southern stations, agreeing with the general pattern of increased phytoplankton HBIs previously reported from the eastern Barents Sea. As a next step, these new biomarker findings will be used to reconstruct longer-term (Holocene) variability in sea-ice in this region. </p>

scholarly journals Optimization of Parameters in the Generalized Extreme-Value Distribution Type 1 for Three Populations Using Harmonic Search

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 257 ◽  
Author(s):  
Juan Pablo Molina-Aguilar ◽  
Alfonso Gutierrez-Lopez ◽  
Jose Angel Raynal-Villaseñor ◽  
Luis Gabriel Garcia-Valenzuela
Keyword(s):  
Extreme Values ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Type I ◽  
Generalized Extreme Value ◽  
Heuristic Technique ◽  
Distribution Type

Due to its geographical position, Mexico is exposed annually to cold fronts and tropical cyclones, registering extremely high values that are atypical in the series of maximum annual flows. Univariate mixed probability distribution functions have been developed based on the theory of extreme values, which require techniques to determine their parameters. Therefore, this paper explores a function that considers three populations to analyze maximum annual flows. According to the structure of the Generalized Extreme-Value Distribution (GEV), the simultaneous definition of nine parameters is required: three of location, three of scale, and three of probability of occurrence. Thus, the use of a meta-heuristic technique was proposed (harmonic search). The precision of the adjustment was increased through the optimization of the parameters, and with it came a reduction in the uncertainty of the forecast, particularly for cyclonic events. It is concluded that the use of an extreme value distribution (Type I) structured with three populations and accompanied by the technique of harmonic search improves the performance in respect to classic techniques for the determination of its parameters.

Tail equivalence and its applications

1971 ◽  
Vol 8 (01) ◽  
pp. 136-156 ◽  
Author(s):  
Sidney I. Resnick
Keyword(s):  
Markov Chain ◽  
Random Variables ◽  
Domain Of Attraction ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
Normalizing Constants ◽  
Independent Identically Distributed

If for two c.d.f.'s F(·) and G(·), 1 – F(x)/1 – G(x) → A, 0 <A <∞ , as x → ∞, then for normalizing constants an > 0, bn, n > 1, Fn (anx + bn ) → φ(x), φ(x) non-degenerate, iff Gn (anx + bn )→ φ A−1(x). Conversely, if Fn (anx+bn )→ φ(x), Gn (anx + bn ) → φ'(x), φ(x) and φ'(x) non-degenerate, then there exist constants C >0 and D such that φ'(x) =φ(Cx + D) and limx→∞ 1 — F(x)/1 — G(x) exists and is expressed in terms of C and D, depending on which type of extreme value distribution φ(x) is. These results are used to study domain of attraction questions for products of distribution functions and to reduce the limit law problem for maxima of a sequence of random variables defined on a Markov chain (M.C.) to the independent, identically distributed (i.i.d.) case.

scholarly journals Changes in Barents Sea ice Edge Positions in the Last 440 years: A Review of Possible Driving Forces

2020 ◽  
Vol 10 (02) ◽  
pp. 97-164 ◽  
Author(s):  
Nils-Axel Mörner ◽  
Jan-Erik Solheim ◽  
Ole Humlum ◽  
Stig Falk-Petersen
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Driving Forces ◽  
Ice Edge ◽  
Sea Ice Edge

Modeling the wind speed in North Morocco

2021 ◽  
pp. 0309524X2110639
Author(s):  
Zuhair Bahraoui
Keyword(s):  
Distribution Function ◽  
Wind Speed ◽  
Ground Cover ◽  
Extreme Value Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Generalized Extreme Value

The change of the wind speed is strictly related to several natural factors such as local topographical and the ground cover variations, then any adjustment has to take into account the statistical variation for each specific region under study. Unlike the Weibull distribution, which is most used in wind speed modeling, we investigate two alternative distribution functions for wind speed by using the extreme value theory. The generalized Champernowne distribution function and the mixture Log-normal-Pareto distribution function are considered. We demonstrate that the proper generalized extreme value distribution gives a good fit for wind speed in the North Moroccan. In order to validate the models, a comparison of the produced aggregate wind energy in the aeolian wind turbine was being established. The empirical study shows that the generalized extreme value distribution reflects better the intensity of the wind power energy.

scholarly journals Changes in Barents Sea Ice Edge Positions in the Last 442 Years. Part 2: Sun, Moon and Planets

2021 ◽  
Vol 11 (02) ◽  
pp. 279-341
Author(s):  
Jan-Erik Solheim ◽  
Stig Falk-Petersen ◽  
Ole Humlum ◽  
Nils-Axel Mörner
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Ice Edge ◽  
Sea Ice Edge
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