Satellite‐observed drop of Arctic sea ice growth in winter 2015–2016

Ricker, Robert; Hendricks, Stefan; Girard-Ardhuin, Fanny; Kaleschke, Lars; Lique, Camille; Tian-Kunze, Xiangshan; Nicolaus, Marcel; Krumpen, Thomas

doi:10.1002/2016gl072244

Cited by 58 publications

(59 citation statements)

References 47 publications

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“…Scenebased lead fraction maps have different characteristics from altimeter-based lead fraction maps due to different sensors, algorithms, and spatial resolutions but showed similar spatial distribution. The results of the lead fraction maps are consistent 25 with the findings of recent studies (Tilling et al, 2015;Ricker et al, 2017;Kim et al, 2017). The lead dynamics based on monthly lead fraction maps were examined with the Arctic Atmospheric and oceanic circulations.…”

Section: Discussionsupporting

confidence: 82%

“…Warm and moist air from the Atlantic Ocean strongly intruded into the Arctic, weakening sea ice growth. Furthermore, the high lead fraction in the Beaufort Sea in February to April 2016 was 5 attributable to the high ice drift speed (Ricker et al, 2017). …”

Section: Lead Dynamicsmentioning

confidence: 97%

“…5). Kim et al (2017) and Ricker et al (2017) explained a plausible reason for the reduction in sea ice growth. Warm and moist air from the Atlantic Ocean strongly intruded into the Arctic, weakening sea ice growth.…”

Section: Lead Dynamicsmentioning

confidence: 99%

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Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Lee¹,

Kim²,

Im³

2017

Preprint

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Abstract. Arctic sea ice leads play a major role in exchanging heat and momentum between the Arctic atmosphere and ocean 10 as well as in the retrieval of sea ice thickness. Although leads cover only a small portion of the Arctic Ocean, they affect the heat budget in the Arctic region considerably. In this study, we propose a waveform mixture analysis to detect leads, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture analysis adopts the concept of linear mixture analysis that is widely used in the field of hyperspectral image analysis. This lead detection method based on the waveform mixture analysis was evaluated with high resolution (250m) MODIS images and showed better 15 performance in detecting leads than previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters, as it directly uses L1B waveform data unlike the existing threshold-based methods.Monthly lead fraction maps derived by the waveform mixture analysis were produced, which show a strong inter-annual variability of recent sea ice cover during 2006-2011 excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from the literature, resulting in similar spatiotemporal patterns. 20

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Section: Discussionsupporting

confidence: 82%

Section: Lead Dynamicsmentioning

confidence: 97%

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Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Lee¹,

Kim²,

Im³

2017

Preprint

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“…The mechanism presented in this paper complements earlier studies of Steele and Ermold (2015), Polyakov et al (2017), Ricker et al (2017) investigating the declining ice cover in the eastern Eurasian Basin. Here we highlight the importance of winter ice dynamics for sea ice anomalies of thickness, volume and extent in addition to atmospheric processes acting on the ice cover in winter and summer.…”

Section: Discussionsupporting

confidence: 69%

“…The interaction between surface winds and warm sea surface temperatures in areas from which the ice has already retreated were recently investigated by Steele and Ermold (2015). During winter, anomalous high temperatures reduce sea ice growth of firstyear ice, resulting in a thinner ice cover at the end of April (Ricker et al, 2017). In addition, enhanced winter ventilation of the ocean reduces sea ice formation at a rate now comparable to losses from atmospheric thermodynamic forcing (Polyakov et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Winter sea ice export from the Laptev Sea preconditions the local summer sea ice cover and fast ice decay

Itkin

Krumpen

2017

The Cryosphere

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Abstract. Ice retreat in the eastern Eurasian Arctic is a consequence of atmospheric and oceanic processes and regional feedback mechanisms acting on the ice cover, both in winter and summer. A correct representation of these processes in numerical models is important, since it will improve predictions of sea ice anomalies along the Northeast Passage and beyond. In this study, we highlight the importance of winter ice dynamics for local summer sea ice anomalies in thickness, volume and extent. By means of airborne sea ice thickness surveys made over pack ice areas in the southeastern Laptev Sea, we show that years of offshore-directed sea ice transport have a thinning effect on the late-winter sea ice cover. To confirm the preconditioning effect of enhanced offshore advection in late winter on the summer sea ice cover, we perform a sensitivity study using a numerical model. Results verify that the preconditioning effect plays a bigger role for the regional ice extent. Furthermore, they indicate an increase in volume export from the Laptev Sea as a consequence of enhanced offshore advection, which has far-reaching consequences for the entire Arctic sea ice mass balance. Moreover we show that ice dynamics in winter not only preconditions local summer ice extent, but also accelerate fast-ice decay.

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Modeling Arctic sea‐ice algae: Physical drivers of spatial distribution and algae phenology

et al. 2017

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Algae growing in sea ice represent a source of carbon for sympagic and pelagic ecosystems and contribute to the biological carbon pump. The biophysical habitat of sea ice on large scales and the physical drivers of algae phenology are key to understanding Arctic ecosystem dynamics and for predicting its response to ongoing Arctic climate change. In addition, quantifying potential feedback mechanisms between algae and physical processes is particularly important during a time of great change. These mechanisms include a shading effect due to the presence of algae and increased basal ice melt. The present study shows pan‐Arctic results obtained from a new Sea Ice Model for Bottom Algae (SIMBA) coupled with a 3‐D sea‐ice–ocean model. The model is evaluated with data collected during a ship‐based campaign to the Eastern Central Arctic in summer 2012. The algal bloom is triggered by light and shows a latitudinal dependency. Snow and ice also play a key role in ice algal growth. Simulations show that after the spring bloom, algae are nutrient limited before the end of summer and finally they leave the ice habitat during ice melt. The spatial distribution of ice algae at the end of summer agrees with available observations, and it emphasizes the importance of thicker sea‐ice regions for hosting biomass. Particular attention is given to the distinction between level ice and ridged ice. Ridge‐associated algae are strongly light limited, but they can thrive toward the end of summer, and represent an additional carbon source during the transition into polar night.

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Satellite‐observed drop of Arctic sea ice growth in winter 2015–2016

Cited by 58 publications

References 47 publications

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Winter sea ice export from the Laptev Sea preconditions the local summer sea ice cover and fast ice decay

Modeling Arctic sea‐ice algae: Physical drivers of spatial distribution and algae phenology

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