scholarly journals Evaluation of the relative contribution of meteorological and oceanic forces to the drift of ice islands offshore Newfoundland

2020 ◽  
Vol 66 (256) ◽  
pp. 203-218
Author(s):  
Reza Zeinali Torbati ◽  
Ian D. Turnbull ◽  
Rocky S. Taylor ◽  
Derek Mueller
Keyword(s):  
Sea Ice ◽  
Coriolis Force ◽  
Sea Surface ◽  
Total Force ◽  
Relative Contribution ◽  
Ice Concentration ◽  
High Concentrations ◽  
Current Drag ◽  
Ice Force ◽  
Ice Forces

AbstractOn 29 April 2015, four beacons were deployed onto an ice island in the Strait of Belle Isle to record positional data. The ice island later broke up into many fragments, four of which were tracked by the beacons. The relative influences of wind drag, current drag, Coriolis force, sea surface height gradient and sea-ice force on the drift of the tracked ice island fragments were analyzed. Using atmospheric and oceanic model outputs, the sea-ice force was calculated as the residual of the fragments' net forces and the sum of all other forces. This was compared against the force obtained through ice concentration-dependent relationships when sea ice was present. The sea-ice forces calculated from the residual approach and concentration-dependent relationships were significant only when sea ice was present at medium-high concentrations in the vicinity of the ice island fragments. The forces from ocean currents and sea surface tilt contributed the most to the drift of the ice island fragments. Wind, however, played a minimal role in the total force governing the drift of the four ice island fragments, and Coriolis force was significant when the fragments were drifting at higher speeds.

scholarly journals Origins and Levels of Seasonal Forecast Skill for Sea Ice in Hudson Bay Using Canonical Correlation Analysis

2011 ◽  
Vol 24 (5) ◽  
pp. 1378-1395 ◽  
Author(s):  
Adrienne Tivy ◽  
Stephen E. L. Howell ◽  
Bea Alt ◽  
John J. Yackel ◽  
Thomas Carrieres
Keyword(s):  
Correlation Analysis ◽  
Sea Ice ◽  
Canonical Correlation Analysis ◽  
North Atlantic ◽  
Canonical Correlation ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Sea Surface ◽  
Hudson Bay ◽  
Ice Concentration

Abstract Canonical correlation analysis (CCA) is used to estimate the levels and sources of seasonal forecast skill for July ice concentration in Hudson Bay over the 1971–2005 period. July is an important transition month in the seasonal cycle of sea ice in Hudson Bay because it is the month when the sea ice clears enough to allow the first passage of ships to the Port of Churchill. Sea surface temperature (quasi global, North Atlantic, and North Pacific), Northern Hemisphere 500-mb geopotential height (z500), sea level pressure (SLP), and regional surface air temperature (SAT) are tested as predictors at 3-, 6-, and 9-month lead times. The model with the highest skill has three predictors—fall North Atlantic SST, fall z500, and fall SAT—and significant tercile forecast skill covering 61% of the Hudson Bay region. The highest skill for a single-predictor model is from fall North Atlantic SST (6-month lead). Fall SST explains 69% of the variance in July ice concentration in Hudson Bay and a possible atmospheric link that accounts for the lagged relationship is presented. CCA diagnostics suggest that changes in the subpolar North Atlantic gyre and the Atlantic multidecadal oscillation (AMO), reflected in sea surface temperature, precedes a deepening/weakening of the winter upper-air ridge northwest of Hudson Bay. Changes in the height of the ridge are reflected in the strength of the winter northwesterly winds over Hudson Bay that have a direct impact on the winter ice thickness distribution; anomalies in winter ice severity are later reflected in the pattern and timing of spring breakup. July ice concentration in Hudson Bay has declined by approximately 20% per decade between 1979 and 2007, and the hypothesized link to the AMO may help explain this significant loss of ice.

scholarly journals Assessing bias corrections of oceanic surface conditions for atmospheric models

2019 ◽  
Vol 12 (1) ◽  
pp. 321-342 ◽  
Author(s):  
Julien Beaumet ◽  
Gerhard Krinner ◽  
Michel Déqué ◽  
Rein Haarsma ◽  
Laurent Li
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
General Circulation Models ◽  
Substantial Improvement ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Analogue Method

Abstract. Future sea surface temperature and sea-ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcings for the downscaling of future climate experiments. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly method and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea-ice concentration (SIC) are presented. For SIC, we also propose a new analogue method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiments and some real-case applications using observations. We find that with respect to other previously existing methods, the analogue method is a substantial improvement for the bias correction of future SIC. Consistency between the constructed SST and SIC fields is an important constraint to consider, as is consistency between the prescribed sea-ice concentration and thickness; we show that the latter can be ensured by using a simple parameterisation of sea-ice thickness as a function of instantaneous and annual minimum SIC.

Assimilation of Satellite-retrieved Sea Ice Concentration and Prospects for September Predictions of Arctic Sea Ice

2020 ◽  
pp. 1-49
Author(s):  
Yong-Fei Zhang ◽  
Mitchell Bushuk ◽  
Michael Winton ◽  
Bill Hurlin ◽  
Xiaosong Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
Prediction System ◽  
Observational Error ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Concentration ◽  
Analysis Errors

AbstractThe current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea-ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice-ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SST) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (∼10%) from up to 3 times larger than this (∼30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from 5-daily to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.

scholarly journals Reliance of deep-sea benthic macrofauna on ice-derived organic matter highlighted by multiple trophic markers during spring in Baffin Bay, Canadian Arctic

Elem Sci Anth ◽  
2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Gustavo Yunda-Guarin ◽  
Thomas A. Brown ◽  
Loïc N. Michel ◽  
Blanche Saint-Béat ◽  
Rémi Amiraux ◽  
...  
Keyword(s):  
Sea Ice ◽  
Food Web ◽  
Carbon Sources ◽  
Food Web Structure ◽  
Sea Ice Concentration ◽  
Baffin Bay ◽  
Benthic Food Web ◽  
Relative Contribution ◽  
Ice Concentration ◽  
Benthic Food

Benthic organisms depend primarily on seasonal pulses of organic matter from primary producers. In the Arctic, declines in sea ice due to warming climate could lead to changes in this food supply with as yet unknown effects on benthic trophic dynamics. Benthic consumer diets and food web structure were studied in a seasonally ice-covered region of Baffin Bay during spring 2016 at stations ranging in depth from 199 to 2,111 m. We used a novel combination of highly branched isoprenoid (HBI) lipid biomarkers and stable isotope ratios (δ13C, δ15N) to better understand the relationship between the availability of carbon sources in spring on the seafloor and their assimilation and transfer within the benthic food web. Organic carbon from sea ice (sympagic carbon [SC]) was an important food source for benthic consumers. The lipid biomarker analyses revealed a high relative contribution of SC in sediments (mean SC% ± standard deviation [SD] = 86% ± 16.0, n = 17) and in benthic consumer tissues (mean SC% ± SD = 78% ± 19.7, n = 159). We also detected an effect of sea-ice concentration on the relative contribution of SC in sediment and in benthic consumers. Cluster analysis separated the study region into three different zones according to the relative proportions of SC assimilated by benthic macrofauna. We observed variation of the benthic food web between zones, with increases in the width of the ecological niche in zones with less sea-ice concentration, indicating greater diversity of carbon sources assimilated by consumers. In zones with greater sea-ice concentration, the higher availability of SC increased the ecological role that primary consumers play in driving a stronger transfer of nutrients to higher trophic levels. Based on our results, SC is an important energy source for Arctic deep-sea benthos in Baffin Bay, such that changes in spring sea-ice phenology could alter benthic food-web structure.

Climatic trends of sea surface temperature and sea ice concentration in the Barents Sea

2021 ◽  
Author(s):  
Bayoumy Mohamed ◽  
Frank Nilsen ◽  
Ragnheid Skogseth
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Seasonal Cycle ◽  
Barents Sea ◽  
Warming Trend ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration

<p>Sea ice loss in the Arctic region is an important indicator for climate change. Especially in the Barents Sea, which is expected to be free of ice by the mid of this century (Onarheim et al., 2018). Here, we analyze 38 years (1982-2019) of daily gridded sea surface temperature (SST) and sea ice concentration (SIC) from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) project. These data sets have been used to investigate the seasonal cycle and linear trends of SST and SIC, and their spatial distribution in the Barents Sea. From the SST seasonal cycle analysis, we have found that most of the years that have temperatures above the climatic mean (1982-2019) were recorded after 2000. This confirms the warm transition that has taken place in the Barents Sea over the last two decades. The year 2016 was the warmest year in both winter and summer during the study period.   </p><p>Results from the linear trend analysis reveal an overall statistically significant warming trend for the whole Barents Sea of about 0.33±0.03 °C/decade, associated with a sea ice reduction rate of about -4.9±0.6 %/decade. However, the SST trend show a high spatial variability over the Barents Sea. The highest SST trend was found over the eastern part of the Barents Sea and south of Svalbard (Storfjordrenna Trough), while the Northern Barents Sea shows less distinct and non-significant trends. The largest negative trend of sea ice was observed between Novaya Zemlya and Franz Josef Land. Over the last two decades (2000-2019), the data show an amplified warming trend in the Barents Sea where the SST warming trend has increased dramatically (0.46±0.09 °C/decade) and the SIC is here decreasing with rate of about -6.4±1.5 %/decade.  Considering the current development of SST, if this trend persists, the Barents Sea annual mean SST will rise by around 1.4 °C by the end of 2050, which will have a drastic impact on the loss of sea ice in the Barents Sea.   </p><p> </p><p>Keywords: Sea surface temperature; Sea ice concentration; Trend analysis; Barents Sea</p>

Digital Image Processing for Sea Ice Observations in Support to Arctic DP Operations

2012 ◽  
Author(s):  
Qin Zhang ◽  
Roger Skjetne ◽  
Sveinung Løset ◽  
Aleksey Marchenko
Keyword(s):  
Image Processing ◽  
Sea Ice ◽  
Unmanned Aerial Vehicles ◽  
Remotely Sensed Data ◽  
Sensor Platforms ◽  
Ice Concentration ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Support Estimation ◽  
Ice Forces

Various types of remotely sensed data and imaging technology will aid the development of sea ice observation to support estimation of ice forces that are critical to Dynamic Positioning (DP) operations in Arctic waters. The use of cameras as sensors on mobile sensor platforms such as unmanned aerial vehicles in Arctic DP operations will be explored for measurements of ice statistics and ice properties. Several image processing algorithms are adopted to analyze ice concentration, ice floe boundaries, and ice types. The resulting image processing methods for ice observation, including a discussion of possibilities, limitations, and further improvements, are presented in this paper.

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