Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

Alekseeva, Tatyana N; Tikhonov, V. I.; Фролов, С. В.; Repina, Irina; Raev, M. D.; Sokolova, Julia; Шарков, Е. А.; Afanasieva, E. V.; Serovetnikov, S. S.

doi:10.3390/rs11212481

Cited by 22 publications

(13 citation statements)

References 53 publications

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“…The larger, compared to the lower frequencies used in most products (see Table 2), weather influence at ∼ 90 GHz frequencies by atmospheric water content and surface wind speed can cause substantial overestimation of the sea-ice concentration over open water and within the ice edge (Kern, 2004;Andersen et al, 2006). Over open water, the weather influence is reduced by combining sea-ice concentrations obtained with Eq.…”

Section: Discussionmentioning

confidence: 99%

“…15 and 16) are shifted so much to the right compared to the red symbols; this is also evident from the larger fraction of data pairs below than above the identity line for sea-ice concentrations below 60 %-80 %. We note that another data set of a different kind of shipbased observations of Arctic sea-ice conditions is available at the Arctic and Antarctic Research Institute (AARI) and has recently been used for a sea-ice concentration algorithm inter-comparison study using an approach different from the one used in our paper (Alekseeva et al, 2019). We might extend our inter-comparison studies to their data set in the future.…”

Section: Observed Differences To Ship-based Observationsmentioning

confidence: 99%

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Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

et al. 2019

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Abstract. We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from −0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and −3.7 % (ASI), but similar for the Antarctic, −5.4 % and −5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends.

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Section: Discussionmentioning

confidence: 99%

Section: Observed Differences To Ship-based Observationsmentioning

confidence: 99%

Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

et al. 2019

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“…We refer to Worby et al (2008) and Hutchings and Orlich (2019) for more details. Shipbased observations of the ice conditions have been used already in the past to evaluate PMW SIC products in the otherwise data sparse Arctic region (e.g., Alekseeva et al, 2019;Kern et al, 2019;Wang et al, 2018;Xie et al, 2013;Spreen et al, 2008). We use observations provided in a standardized format from https://doi.org/10.26050/WDCC/ESACCIPSMVSBSIO, last access date: 28 October, 2019.…”

Section: Ship-based Visual Sea-ice Cover Observationsmentioning

confidence: 99%

Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions

Kern¹,

Lee²,

Notz³

et al. 2020

Preprint

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We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice-surface fraction (ISF) -SIC minus the per-grid cell melt-pond fraction (MPF) on sea ice -as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-15 going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the EUMETSAT OSI SAF and ESA CCI algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the NOAA NSIDC SIC climate data record (CDR). Group III consists of ARTIST Sea Ice (ASI) and NASA Team (NT) algorithm products and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find wide-spread positive and negative differences between PMW and MODIS SIC with magnitudes frequently 20 reaching up to 20-25 % for groups I and III and up to 30-35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from Group I products agrees with MODIS within 2-5 % accuracy during the entire melt period from May through September. Group II and IV products over-estimate MODIS Arctic average SIC by 5-10 %. Out of group III, ASI is similar to group I products while NT SIC under-estimates MODIS Arctic average SIC by 5-10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA 25 between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically over-estimated by all products; NT provides the smallest (up to 25 %) over-estimations, group II and IV products the largest (up to 45 %) over-estimations. The spatial distribution of the observed over-estimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence 30 of ice-surface properties other than melt ponds, i.e. wet snow and coarse grained snow / refrozen surface, on PMW observations used in the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for i) the observed differences between PMW SIC and MODIS ISF and for ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high meltpond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the un-35 known amount of melt-pond signatures likely included in the ice tie points plays an important role -particularly for groups I and II -and suggest to conduct further...

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“…SIT data used here were either observed visually by a group of Arctic and Antarctic Research Institute (AARI) sea ice scientists using the traditional unified methodological principles in accordance with the requirements of the regulatory guidance (AARI, 2011;Alekseeva et al, 2019), or by the so-called shipborne television complex (STK). The STK consists of a high resolution telecamera, a computer for camera control and processing, and a GPS recorder.…”

Section: Shipborne Sea Ice Thickness Observationsmentioning

confidence: 99%

Interannual variability in Transpolar Drift ice thickness and potential impact of Atlantification

Belter

Krumpen

Albedyll

et al. 2020

Preprint

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Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exits the Arctic Ocean through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age and number of freezing degree days. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea, but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate the impact of Atlantification on sea ice growth in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea ice covered Arctic.

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Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

Cited by 22 publications

References 53 publications

Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions

Interannual variability in Transpolar Drift ice thickness and potential impact of Atlantification

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