Relevance of warm air intrusions for Arctic satellite sea ice concentration time series

Rostosky, Philip; Spreen, Gunnar

doi:10.5194/tc-17-3867-2023

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The uncertainty is expressed in terms of SD. It is noted that this uncertainty does not account for individual, spatially varying atmospheric and surface effects as for example discussed in Rückert et al (2023) and Rostosky and Spreen (2023).…”

Section: Amsr2 Sicmentioning

confidence: 99%

Multisensor data fusion of operational sea ice observations

Wang,

Dinessen

et al. 2024

Front. Mar. Sci.

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Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations.

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Section: Amsr2 Sicmentioning

confidence: 99%

Multisensor data fusion of operational sea ice observations

Wang,

Dinessen

et al. 2024

Front. Mar. Sci.

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“…It might stem from higher resolution optical data so that the open water and melting sea ice are no longer subpixel, or, depending on the required date range, PM SIC. Although PM SIC products are compromised in summer in the presence of surface melt, a recent study by Rostosky et al, (2023) suggests that SIC by the National Snow and Ice Data Center (NSIDC) (Meier et al, 2021) performs best even in the presence of surface metamorphosis associated with warm air intrusions. We therefore expect NSIDC SIC to be less affected by the water saturated sea ice right before melt and therefore be potentially applicable up to the melt onset.…”

Section: Evaluation Of the Daily Gridded Mpf Datasetmentioning

confidence: 99%

Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data

Istomina,

Niehaus,

Spreen

2023

Preprint

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Abstract. Melt ponds on the Arctic sea ice affect the radiative balance of the region as they introduce darkening of the sea ice during the Arctic summer. Temporal and spatial extent of the ponding as well as its amplitude reflect the state of the Arctic sea ice and are important for our understanding of the Arctic sea ice change. Remote sensing retrievals of melt pond fraction (MPF) provide information both on the present state of the melt pond development as well as its change throughout the years, which is a valuable information in the context of climate change and Arctic amplification. In this work, we transfer the earlier published Melt Pond Detector remote sensing retrieval (MPD) to the Ocean and Land Colour Instrument (OLCI) data onboard the Sentinel-3 satellite and so complement the existing Medium Resolution Imaging Spectrometer (MERIS) MPF dataset (2002–2011) from Environmental Satellite (ENVISAT) with the recent data (2017–present). To evaluate the bias of the MPF product, comparisons to Sentinel-2 MultiSpectral Instrument (MSI) high resolution satellite imagery are presented, in addition to earlier published validation studies. Both MERIS and OLCI MPD tend to overestimate the small MPFs, which can be attributed to the presence of water saturated snow and sea ice before melt onset. Good agreement for middle range MPF is observed, and the areas of exceptionally high MPF = 100 % are recognized as well. The earlier published MERIS MPFs (2002–2011) were reprocessed using an improved cloud clearing routine and together with the recent Sentinel-3 data provide an internally consistent dataset, which allows to analyse the MPF development in the past 20 years. Although the total summer hemispheric MPF trend is moderate with +0.75 % per decade, the regional weekly MPF trends display pronounced dynamic and range from −10 % to as high as +20 % per decade, depending on the region. We conclude on the following effects: the global Arctic melt onset shifted towards spring by at least 2 weeks, with the melt onset happening in late May in the recent years as compared to early-mid June in the beginning of the dataset. there is a change of the melt onset regime in the recent years, with East Siberian and Laptev Sea dominating the melt onset and not the Beaufort Gyre region as before. the Central Arctic, North Greenland and CAA show signs of increasing first year ice (FYI) fraction in the recent years. The daily gridded MPF averages are available at the webpage of the Institute of Environmental Physics, University of Bremen, as a historic dataset for the ENVISAT data, and as ongoing operational processing for the Sentinel-3 data.

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