Persistent artifacts in the NSIDC ice motion data set and their implications for analysis

Szanyi, S.; Lukovich, Jennifer V.; Barber, David G.; Haller, George

doi:10.1002/2016gl069799

Cited by 36 publications

(35 citation statements)

References 29 publications

(46 reference statements)

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“…The different sources of data are combined through a weighting that depends on the accuracy of each dataset. This merged product suffers from artifacts of the method used to incorporate buoy data, especially in terms of ice divergence and convergence (Szanyi et al, 2016). In our paper, we call this product "NSIDC".…”

Section: Sea Ice Drift Speedmentioning

confidence: 99%

Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6

et al. 2017

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Abstract. Sea ice cover and thickness have substantially decreased in the Arctic Ocean since the beginning of the satellite era. As a result, sea ice strength has been reduced, allowing more deformation and fracturing and leading to increased sea ice drift speed. We use the version 3.6 of the global ocean-sea ice NEMO-LIM model (Nucleus for European Modelling of the Ocean coupled to the Louvain-laNeuve sea Ice Model), satellite, buoy and submarine observations, as well as reanalysis data over the period from 1979 to 2013 to study these relationships. Overall, the model agrees well with observations in terms of sea ice extent, concentration and thickness. The seasonal cycle of sea ice drift speed is reasonably well reproduced by the model. NEMO-LIM3.6 is able to capture the relationships between the seasonal cycles of sea ice drift speed, concentration and thickness, with higher drift speed for both lower concentration and lower thickness, in agreement with observations. Model experiments are carried out to test the sensitivity of Arctic sea ice drift speed, thickness and concentration to changes in sea ice strength parameter P * . These show that higher values of P * generally lead to lower sea ice deformation and lower sea ice thickness, and that no single value of P * is the best option for reproducing the observed drift speed and thickness. The methodology proposed in this analysis provides a benchmark for a further model intercomparison related to the relationships between sea ice drift speed and strength, which is especially relevant in the context of the upcoming Coupled Model Intercomparison Project 6 (CMIP6).

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Section: Sea Ice Drift Speedmentioning

confidence: 99%

Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6

et al. 2017

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“…Recent studies have explored the uncertainty in satellite-derived ice drift data (Sumata et al, 2014) and errors introduced by the NSIDC interpolation methodology (Szanyi et al, 2016). We thus also explore the sensitivity of the model results to the input ice drift data by forcing the model with ice drift estimates provided by three 5 additional satellite-derived ice drift products, as described below and summarized in Table 3.…”

Section: Satellite Derived Ice Drift Datamentioning

confidence: 99%

“…The bimodal ice dynamic behaviour around the pole is thought to be spurious considering interpolating issues 15 across the pole hole in the NSIDCv3 drift product (Szanyi et al, 2016), one reason why we did not include this region in our regional analysis. In the following section we assess the sensitivity of our results to the input ice drift dataset, which will provide some further information as to the reliability of these dynamic budget terms.…”

Section: Budget Analysismentioning

confidence: 99%

The NASA Eulerian Snow on Sea Ice Model (NESOSIM): Initial model development and analysis

Petty¹,

Webster²,

Boisvert³

et al. 2018

Preprint

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“…Weekly SID 30 fields from NSIDC were accessed on an Equal Area Scalable Earth (EASE) grid (Brodzik et al, 2012) with 25 km spacing for the period from October 1978 to December 2015. As pointed out by (Szanyi et al, 2016) this ice drift product contains artifacts due to the composition of the ice drift derived from satellite data, observed by in situ buoys and predicted using a simple free 2 The Cryosphere Discuss., https://doi.org/10. 5194/tc-2017-250 Manuscript under review for journal The Cryosphere Discussion started: 15 November 2017 c Author(s) 2017.…”

Section: Sea Ice Driftmentioning

confidence: 89%

A new tracking algorithm for sea ice age distribution estimation

Korosov¹,

Rampal²,

Pedersen³

et al. 2017

Preprint

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Abstract.A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection scheme is presented.The advection scheme accounts for the observed divergence/convergence and freezing/melting of sea ice and predicts consequent generation/loss of new ice. The algorithm uses daily gridded sea ice drift and sea ice concentration products from the Ocean and Sea Ice Satellite Application Facility and produces gridded fractions of sea ice of a specific age. Thus, each grid cell of the output product contains a frequency distribution of sea ice age allowing to apply mean, median, weighted average or

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Persistent artifacts in the NSIDC ice motion data set and their implications for analysis

Cited by 36 publications

References 29 publications

Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6

Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6

The NASA Eulerian Snow on Sea Ice Model (NESOSIM): Initial model development and analysis

A new tracking algorithm for sea ice age distribution estimation

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