Advancement of benthic indicators and biomarker-based tools for biomonitoring and risk assessment in the Barents Sea region

<p>Marine risk embraces an assessment of likelihoods and consequences of impacts from climate fluctuations in order to identify time and regions vulnerable to climate hazards. This information can support sustainable and safe marine activities. The marine risk assessment is a part of the marine service provided by the DNV GL (short for Det Norske Veritas and Germanischer Lloyd). In their current risk application, likelihoods of extreme conditions on the sea are based on historical observations and atmospheric reanalyses. We assess predicted likelihoods of extreme conditions over 1990-2017 in the boreal summer (prediction months 2-4) from the seasonal forecast system provided by the German Meteorological Service (DWD). We chose summer as it represents the time of the open-water season, when the highest marine activity in the Barents Sea takes place. We selected three indicators from the marine risk assessment. Two of them represent meteorological properties such as wind speed and 2-meter temperature (T2m). The third indicator &#8211; the wind chill index (WCI) is a combination of the previous two and represents heat loss from the human body to its surroundings during cold and windy weather. As expected, the prediction skill assessment suggests different levels of predictability for the three indicators, with T2m having the highest skill followed by WCI and wind speed. The prediction skill represents the "trust layer" superimposed on the predicted likelihoods and used as input fields for marine risk assessment. From the likelihood maps for the test period of summer 2020 follows that large areas of the Barents Sea represent favorable conditions for marine operations considering high prediction skill and low likelihood for extreme WCI (>1000 W/m<sup>2</sup>) and T2m (<0 &#176;C) conditions in July and August. The wind speed (>13.9 m/s) is poorly predictable beyond the first lead month. Thus, if risk assessment is based on a suite of climate indicators with the heterogeneous prediction skill, the total risk assessment might be limited by the skill of the indicator with the lowest prediction skill. However, not all climate indicators are equally contributing to the risk assessment. The study describes a workflow for application of seasonal climate predictions and points to a few lessons learned, which can be useful to future climate services.</p>

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Life history and ecology of long rough dab Hippoglossoides platessoides (F) in the Barents Sea

Netherlands Journal of Sea Research ◽

10.1016/s0077-7579(96)90057-5 ◽

1996 ◽

Vol 36 (3-4) ◽

pp. 285-310 ◽

Cited By ~ 1

Author(s):

S Walsh

Keyword(s):

Life History ◽

Barents Sea ◽

The Barents Sea ◽

Hippoglossoides Platessoides

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Method application of optimal multi-parameter analysis to assess the distribution of water masses as an example of CTD data of the Barents Sea in summer 2017

Hydrosphere Еcology (Экология гидросферы) ◽

10.33624/2587-9367-2019-2(4)-38-51 ◽

2019 ◽

pp. 38-51

Author(s):

Valeriy G. Yakubenko ◽

Anna L. Chultsova

Keyword(s):

Barents Sea ◽

Field Measurements ◽

Structural Similarity ◽

Water Masses ◽

Parameter Analysis ◽

Water Dynamics ◽

Phosphorus Concentration ◽

Nitrogen And Phosphorus ◽

The Barents Sea ◽

Expert Assessments

Identification of water masses in areas with complex water dynamics is a complex task, which is usually solved by the method of expert assessments. In this paper, it is proposed to use a formal procedure based on the application of the method of optimal multiparametric analysis (OMP analysis). The data of field measurements obtained in the 68th cruise of the R/V “Academician Mstislav Keldysh” in the summer of 2017 in the Barents Sea on the distribution of temperature, salinity, oxygen, silicates, nitrogen, and phosphorus concentration are used as a data for research. A comparison of the results with data on the distribution of water masses in literature based on expert assessments (Oziel et al., 2017), allows us to conclude about their close structural similarity. Some differences are related to spatial and temporal shifts of measurements. This indicates the feasibility of using the OMP analysis technique in oceanological studies to obtain quantitative data on the spatial distribution of different water masses.

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