Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record

Peng, Ge; Steele, Michael; Bliss, Angela C.; Meier, Walter N.; Dickinson, S.M.

doi:10.3390/rs10091328

Cited by 43 publications

(35 citation statements)

References 33 publications

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“…A conceptual diagram of the stages of annual sea ice evolution at a location is shown in figure 1, with acronyms defined in table 1. This work expands upon the study by Peng et al (2018), which examines the pan-Arctic seasonal evolution of the sea ice retreat/advance cycle dates and periods. Here we focus on the regional variability of seasonal dates and periods for domains defined in figure 2 and examine change in the seasonal ice zone (SIZ; the area in which sea ice melts out after the winter maximum sea ice extent) to quantify the annual extent of summer sea ice retreat.…”

Section: Introductionmentioning

confidence: 93%

Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record

Bliss

Steele

Peng

et al. 2019

Environ. Res. Lett.

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The seasonal evolution of Arctic sea ice can be described by the timing of key dates of sea ice concentration (SIC) change during its annual retreat and advance cycle. Here, we use SICs from a satellite passive microwave climate data record to identify the sea ice dates of opening (DOO), retreat (DOR), advance (DOA), and closing (DOC) and the periods of time between these events. Regional variability in these key dates, periods, and sea ice melt onset and freeze-up dates for 12 Arctic regions during the melt seasons of 1979-2016 is investigated. We find statistically significant positive trends in the length of the melt season (outer ice-free period) for most of the eastern Arctic, the Bering Sea, and Hudson and Baffin Bays with trends as large as 11.9 d decade −1 observed in the Kara Sea. Trends in the DOR and DOA contribute to statistically significant increases in the length of the open water period for all regions within the Arctic Ocean ranging from 3.9 to 13.8 d decade −1 . The length of the ice retreat period (DOR−DOO) ranges from 17.1 d in the Sea of Okhotsk to 41 d in the Greenland Sea. The length of the ice advance period (DOC−DOA) is generally much shorter and ranges from 17.9 to 25.3 d in the Sea of Okhotsk and Greenland Sea, respectively. Additionally, we derive the extent of the seasonal ice zone (SIZ) and find statistically significant negative trends (SIZ is shrinking) in the Sea of Okhotsk, Baffin Bay, Greenland Sea, and Barents Sea regions, which are geographically open to the oceans and influenced by reduced winter sea ice extent. Within regions of the Arctic Ocean, statistically significant positive trends indicate that the extent of the SIZ is expanding as Arctic summer sea ice declines.

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Section: Introductionmentioning

confidence: 93%

Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record

Bliss

Steele

Peng

et al. 2019

Environ. Res. Lett.

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“…Our retrieval (SD-EDNN) and the retrieval from the University of Bremen (SD-UB) do not support this and have a reduced regional difference in snow depth in June due to the decrease of snow depth from Spring to June. Such discrepancy might be in part due to that W99 is based on data back to the 1950s when MYI coverage and snow depth was larger than recent decades with rapid Arctic environmental changes, i.e., amplified warming, lengthened melting, and decreased sea ice [64].…”

Section: Comparison With Other Retrieved Snow Depth Datamentioning

confidence: 99%

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

et al. 2019

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The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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“…The data are available daily, 1979-2015, at a 25 km by 25 km nominal grid spacing. The time series of daily SIC at each grid cell were used to identify the date of sea ice advance (DOA) when SIC increases to 15% for the first time following the sea ice extent (SIE) minima after Peng et al (2018). Regional mean DOAs for Baffin Bay were obtained from Bliss et al (2018, in review).…”

Section: Passive Microwave Recordsmentioning

confidence: 99%

Greenland Ice Sheet late-season melt: Investigating multi-scale drivers of K-transect events

Ballinger¹,

Mote²,

Mattingly³

et al. 2019

Preprint

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Abstract. One consequence of recent Arctic warming is an increased occurrence and longer seasonality of above-freezing air temperature episodes. There is significant disagreement in the literature concerning potential physical connectivity between high-latitude open water duration proximate to the Greenland Ice Sheet (GrIS) and unseasonal (i.e. late summer and autumn) GrIS melt events. Here, a new date of sea ice advance (DOA) product is used to determine the occurrence of Baffin Bay sea ice growth along Greenland's west coast for the 2011–2015 period. For the unseasonal melt period preceding the DOA, northwest Atlantic Ocean and atmospheric conditions are analyzed and linked to unseasonal melt events observed at a series of on-ice automatic weather stations (AWS) along the K-transect in southwest Greenland. Mesoscale and synoptic influences on the above and below freezing surface air temperature events are assessed through analyses of AWS wind, pressure, and humidity observations. These surface observations are further compared against Modèle Atmosphérique Régional (MAR), Regional Atmospheric Climate Model (RACMO2), and ERA-Interim reanalysis fields to understand the airmass origins and (thermo)dynamic drivers of the melt events. Results suggest that the K-transect late season, ablation zone melt events are strongly affected by ridging atmospheric circulation patterns that transport warm, moist air from the sub-polar North Atlantic toward west Greenland. While thermal conduction and advection off south Baffin Bay open waters impact coastal air temperatures, consistent with previous studies, marine air incursions from Baffin Bay onto the ice sheet are obstructed by barrier flows and the pressure gradient-driven katabatic regime along the western GrIS margin.

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Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record

Cited by 43 publications

References 33 publications

Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record

Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

Greenland Ice Sheet late-season melt: Investigating multi-scale drivers of K-transect events

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