Sea Ice Thickness Forecast Performance in the Barents Sea

2021 ◽  
Author(s):  
Laura Hume-Wright ◽  
Emma Fiedler ◽  
Joana Mendes ◽  
Nicolas Fournier ◽  
Ed Blockley ◽  
...  
Author(s):  
Laura Hume-Wright ◽  
Emma Fiedler ◽  
Nicolas Fournier ◽  
Joana Mendes ◽  
Ed Blockley ◽  
...  

Abstract The presence of sea ice has a major impact on the safety, operability and efficiency of Arctic operations and navigation. While satellite-based sea ice charting is routinely used for tactical ice management, the marine sector does not yet make use of existing operational sea ice thickness forecasting. However, data products are now freely available from the Copernicus Marine Environment Monitoring Service (CMEMS). Arctic asset managers and vessels’ crews are generally not aware of such products, or these have so far suffered from insufficient accuracy, verification, resolution and adequate format, in order to be well integrated within their existing decision-making processes and systems. The objective of the EU H2020 project “Safe maritime operations under extreme conditions: The Arctic case” (SEDNA) is to improve the safety and efficiency of Arctic navigation. This paper presents a component focusing on the validation of an adaption of the 7-day sea ice thickness forecast from the UK Met Office Forecast Ocean Assimilation Model (FOAM). The experimental forecast model assimilates the CryoSat-2 satellite’s ice freeboard daily data. Forecast skill is evaluated against unique in-situ data from five moorings deployed between 2015 and 2018 by the Barents Sea Metocean and Ice Network (BASMIN) Joint Industry Project. The study shows that the existing FOAM forecasts produce adequate results in the Barents Sea. However, while studies have shown the assimilation of CryoSat-2 data is effective for thick sea ice conditions, this did not improve forecasts for the thinner sea ice conditions of the Barents Sea.


Author(s):  
Jianfei Liu ◽  
Guoqing Feng ◽  
Huilong Ren ◽  
Wenjia Hu ◽  
Yuwei Sun

Abstract Ships performing their missions in the polar regions will inevitably suffer from sea ice collision, which will lead to structural safety problems. Therefore, ships should be designed according to the characteristics of polar sea ice to enable them to navigate safely in the polar regions. Based on the probability density curve of sea ice thickness and the occurrence frequency of sea ices of different sizes of the Kara Sea and the Barents Sea, this paper preliminarily designs ship’s bow sailing in the Kara Sea and the Barents Sea, establishes the ship’s bow-ice collision model and carries out numerical simulation to obtain the stress distribution. Then it optimizes the structure of the parts of the ship’s bow. After the optimization, the bow structure meets the strength requirements and the weight of the ship’s bow is relatively light.


2011 ◽  
Vol 52 (57) ◽  
pp. 261-270 ◽  
Author(s):  
Sanja Forsström ◽  
Sebastian Gerland ◽  
Christina A. Pedersen

AbstractModern satellite measurements of sea-ice thickness are based on altimeter measurements of the difference in elevation between the snow or ice surface and the local sea surface. For retrieval of sea-ice thickness, it is assumed that the ice is in hydrostatic equilibrium, and that the snow load on the ice and the density of the sea ice and sea water are known. This study presents data from in situ sea-ice thickness drillings and snow and ice density measurements from Fram Strait, the Barents Sea and the Svalbard coast, in the European Arctic. the error in the altimetry ice thickness products is assessed based on the spatial variability of snow and ice density and snow thickness data. Ice thickness uncertainty related to snow depth was found to be ∼40 cm (radar altimeter) and ∼90 cm (laser altimeter), while uncertainty related to ice density is 25 cm for both techniques. the assumption of hydrostatic equilibrium at the scales of the measurements (10–100 m) was found to hold better in the case of level landfast ice near Svalbard than for Fram Strait drift ice, which consists of mixed ice types, where the deviation between the calculated and measured ice thicknesses was on average ∼0.5 m.


2020 ◽  
Author(s):  
Amey Vasulkar ◽  
Lars Kaleschke ◽  
Martin Verlaan ◽  
Cornelis Slobbe

<p>In an experiment to validate an ice forecast and route optimization system, an array of 15 ice drift beacons/buoys were deployed between Edgeøya and Kong Karls Land in the east of Svalbard to measure the sea ice movement. These beacons recorded data at a sampling frequency of 15 minutes in the duration from March 2014 to May 2014 with different start and end dates based on their life. The particularly short time step captures the small scale effect of tides on the drifting ice. In this region of the Barents Sea, the frequency of the inertial motion is very close to the M2 tidal frequency. Hence, it is not possible to extract the tidal motion from the time series data of the buoys by using a Fourier analysis. It is also likely that these effects will interact. Instead, we develop a physics-based <em>free drift</em> ice model that can simulate the drift at all tidal and other frequencies.</p><p>The model is forced by winds obtained from the ERA5 Reanalysis dataset of ECMWF and ocean currents obtained from the Global Ocean Analysis product of CMEMS. Due to the effect of tides, the model is also forced by the tides obtained from the Global Tide and Surge Model (GTSM v3.0) which is built upon Delft3D-FM unstructured mesh code. This free drift model is validated against 8 of the 15 beacon trajectories. The model along with the observed data can be then be used to obtain insights on the relationship between the sea ice velocities and the tides. This will be particularly useful to obtain the effect of ice drift on tides in tidal models.</p><p>The model uncertainty is mainly due to oceanic and atmospheric drag coefficients, C<sub>dw</sub> and C<sub>da</sub>, respectively, and the sea ice thickness, h<sub>i</sub>. This study also focuses on optimizing the ratio of drag coefficients (C<sub>dw</sub>/C<sub>da</sub>) for the different beacon trajectories while varying the ice thickness between 0.1 m - 1.5 m and the ice-air drag coefficient between (0.5-2.5)x10<sup>-3</sup>. This ratio facilitates the evaluation of the frictional drag between the ice-water interface and thus, helps in determining the effect of ice on tides in tidal models.</p>


2017 ◽  
Author(s):  
Thomas Kaminski ◽  
Frank Kauker ◽  
Leif Toudal Pedersen ◽  
Michael Voßbeck ◽  
Helmuth Haak ◽  
...  

Abstract. Assimilation of remote sensing products of sea ice thickness (SIT) into sea ice-ocean models has been shown to improve the quality of sea ice forecasts. Open questions are whether the assimilation of rawer products such as radar freeboard (RFB) can achieve yet a better performance and what performance gain can be achieved by the joint assimilation with a snow depth product. The Arctic Mission Benefit Analysis (ArcMBA) system was developed to address this type of question. Using the quantitative network design (QND) approach, the system can evaluate, in a mathematically rigorous fashion, the observational constraints imposed by individual and groups of data products. We present assessments of the observation impact (added value) in terms of the uncertainty reduction in a four-week forecast of sea ice volume (SIV) and snow volume (SNV) for three regions along the Northern Sea Route by a coupled model of the sea ice-ocean system. The assessments cover seven satellite products, three real products and four hypothetical products. The real products are monthly SIT, sea ice freeboard (SIFB), and RFB, all derived from CryoSat-2 by the Alfred Wegener Institute. These are complemented by two hypothetical monthly laser freeboard (LFB) products (one with low accuracy and one with high accuracy), as well as two hypothetical monthly snow depth products (again one with low accuracy and one with high accuracy). On the basis of the per-pixel uncertainty ranges that are provided with the CryoSat-2 SIT, SIFB, and RFB products, the SIT achieves a much better performance for SIV than the SIFB product, while the performance of RFB is more similar to that of SIT. For SNV, the performance of SIT is only low, the performance of SIFB higher and the performance of RFB yet higher. A hypothetical LFB product with low accuracy (20 cm uncertainty) lies in performance between SIFB and RFB for both SIV and SNV. A reduction in the uncertainty of the LFB product to 2 cm yields a significant increase in performance. Combining either of the SIT/freeboard products with a hypothetical snow depth product achieves a significant performance increase. The uncertainty in the snow product matters: A higher accuracy product achieves an extra performance gain. The provision of spatial and temporal uncertainty correlations with the EO products would be beneficial not only for QND assessments, but also for assimilation of the products.


2021 ◽  
Author(s):  
Hiroshi Sumata ◽  
Laura de Steur ◽  
Dmitry Divine ◽  
Olga Pavlova ◽  
Sebastian Gerland

<p><span><span>Fram Strait is the major gateway connecting the Arctic Ocean and the northern North Atlantic Ocean where about 80 to 90% of sea ice outflow from the Arctic Ocean takes place. Long-term observations from the Fram Strait Arctic Outflow Observatory maintained by the Norwegian Polar Institute captured an unprecedented decline<!-- should we somehow add information that this statement is limited to the time since the early 1990s? --><!-- Reply to Sebastian Gerland (2021/01/12, 15:45): "..." I slightly modified the sentence to mention this. --> of sea ice thickness in 2017 – 2018 since comprehensive observations started in the early 1990s. Four Ice Profiling Sonars moored in the East Greenland Current in Fram Strait simultaneously recorded 50 – 70 cm decline of annual mean ice thickness in comparison with preceding years. A backward trajectory analysis revealed that the decline was attributed to an anomalous sea level pressure pattern from 2017 autumn to 2018 summer. Southerly wind associated with a dipole pressure anomaly between Greenland and the Barents Sea prevented southward motion of ice floes north of Fram Strait. Hence ice pack was exposed to warm Atlantic Water in the north of Fram Strait 2 – 3 times longer than the average year, allowing more melt <!-- should also slower freezing or reduced freezing rates mentioned here during winter and spring (in addition to melt in summer and autumn)? --><!-- Reply to Sebastian Gerland (2021/01/12, 15:46): "..." I would like to keep this sentence as it is, since the analysis implies sea ice melt occurred in the vicinity of Fram Strait in winter (probably due to ocean heat flux), though we don’t have direct measurements of 2018 event. This could be an interesting implications of this study, and seeds for further investigation. -->to happen. At the same time, the dipole anomaly was responsible for the slowest observed annual mean ice drift speed in Fram Strait in the last two decades. As a consequence of the record minimum of ice thickness and the slowest drift speed, the sea ice volume transport through the Fram Strait dropped by more than 50% in comparison with the 2010 – 2017 average.</span></span></p>


2011 ◽  
Vol 52 (57) ◽  
pp. 61-68 ◽  
Author(s):  
S. Hendricks ◽  
S. Gerland ◽  
L.H. Smedsrud ◽  
C. Haas ◽  
A.A. Pfaffhuber ◽  
...  

AbstractResults from electromagnetic induction surveys of sea-ice thickness in Storfjorden, Svalbard, reveal large interannual ice-thickness variations in a region which is typically characterized by a reoccurring polynya. the surveys were performed in March 2003, May 2006 and March 2007 with helicopter- and ship-based sensors. the thickness distributions are influenced by sea-ice and atmospheric boundary conditions 2 months prior to the surveys, which are assessed with synthetic aperture radar (SAR) images, regional QuikSCAT backscatter maps and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset. Locally formed thin ice from the Storfjorden polynya was frequently observed in 2003 and 2007 (mean thickness 0.55 and 0.37 m, respectively) because these years were characterized by prevailing northeasterly winds. In contrast, the entire fjord was covered with thick external sea ice in 2006 (mean thickness 2.21 m), when ice from the Barents Sea was driven into the fjord by predominantly southerly winds. the modal thickness of this external ice in 2006 increased from 1.2m in the northern fjord to 2.4 m in the southern fjord, indicating stronger deformation in the southern part. This dynamically thickened ice was even thicker than multi-year ice advected from the central Arctic Ocean in 2003 (mean thickness 1.83 m). the thermodynamic ice thickness of fast ice as boundary condition is investigated with a one-dimensional sea-ice growth model (1DICE) forced with meteorological data from the weather station at the island of Hopen, southeast of Storfjorden. the model results are in good agreement with the modal thicknesses of fast-ice measurements in all years.


2008 ◽  
Vol 35 (6) ◽  
Author(s):  
S. Gerland ◽  
A. H. H. Renner ◽  
F. Godtliebsen ◽  
D. Divine ◽  
T. B. Løyning

2017 ◽  
Vol 122 (2) ◽  
pp. 1497-1512 ◽  
Author(s):  
Jennifer King ◽  
Gunnar Spreen ◽  
Sebastian Gerland ◽  
Christian Haas ◽  
Stefan Hendricks ◽  
...  

2018 ◽  
Vol 12 (8) ◽  
pp. 2569-2594 ◽  
Author(s):  
Thomas Kaminski ◽  
Frank Kauker ◽  
Leif Toudal Pedersen ◽  
Michael Voßbeck ◽  
Helmuth Haak ◽  
...  

Abstract. Assimilation of remote-sensing products of sea ice thickness (SIT) into sea ice–ocean models has been shown to improve the quality of sea ice forecasts. Key open questions are whether assimilation of lower-level data products such as radar freeboard (RFB) can further improve model performance and what performance gains can be achieved through joint assimilation of these data products in combination with a snow depth product. The Arctic Mission Benefit Analysis system was developed to address this type of question. Using the quantitative network design (QND) approach, the system can evaluate, in a mathematically rigorous fashion, the observational constraints imposed by individual and groups of data products. We demonstrate the approach by presenting assessments of the observation impact (added value) of different Earth observation (EO) products in terms of the uncertainty reduction in a 4-week forecast of sea ice volume (SIV) and snow volume (SNV) for three regions along the Northern Sea Route in May 2015 using a coupled model of the sea ice–ocean system, specifically the Max Planck Institute Ocean Model. We assess seven satellite products: three real products and four hypothetical products. The real products are monthly SIT, sea ice freeboard (SIFB), and RFB, all derived from CryoSat-2 by the Alfred Wegener Institute. These are complemented by two hypothetical monthly laser freeboard (LFB) products with low and high accuracy, as well as two hypothetical monthly snow depth products with low and high accuracy.On the basis of the per-pixel uncertainty ranges provided with the CryoSat-2 SIT, SIFB, and RFB products, the SIT and RFB achieve a much better performance for SIV than the SIFB product. For SNV, the performance of SIT is only low, the performance of SIFB is higher and the performance of RFB is yet higher. A hypothetical LFB product with low accuracy (20 cm uncertainty) falls between SIFB and RFB in performance for both SIV and SNV. A reduction in the uncertainty of the LFB product to 2 cm yields a significant increase in performance.Combining either of the SIT or freeboard products with a hypothetical snow depth product achieves a significant performance increase. The uncertainty in the snow product matters: a higher-accuracy product achieves an extra performance gain. Providing spatial and temporal uncertainty correlations with the EO products would be beneficial not only for QND assessments, but also for assimilation of the products.


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