The EUMETSAT sea ice concentration climate data record

Tonboe, Rasmus; Eastwood, Steinar; Lee, Thomas; Sørensen, Atle; Rathmann, Nicholas; Dybkjær, Gorm; Pedersen, Leif Toudal; Høyer, Jacob L.; Kern, Stefan

doi:10.5194/tc-10-2275-2016

Cited by 99 publications

(111 citation statements)

References 34 publications

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“…Along with the Goddard products, a relatively new resource for sea ice estimates is the EUMETSAT OSI-SAF Version 1.2 product. Like the Goddard products, both are based on passive microwave inputs over the time period beginning in 1979 as described by Tonboe et al [2016a] and Eastwood et al [2015]. It uses a combination of two algorithms: the Bootstrap (frequency mode only, i.e., using 19 and 37 GHz vertical polarization brightness temperatures) and the Bristol algorithm [Smith, 1996].…”

Section: Eumetsat Osi-saf Productmentioning

confidence: 99%

“…As with SB2, tie points are derived daily based on TBs in100% ice and 100% open water regions. But unlike SB2, the OSI-SAF algorithm the dynamical tie points are based on data from a 615 day window running average as described by Tonboe et al [2016a]. Also, unlike SB2 there is no explicit intersensor calibration done; it is assumed that the daily tie point derivation accounts for any calibration differences between sensors.…”

Section: Eumetsat Osi-saf Productmentioning

confidence: 99%

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Variability and trends in the Arctic Sea ice cover: Results from different techniques

2017

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Variability and trend studies of sea ice in the Arctic have been conducted using products derived from the same raw passive microwave data but by different groups using different algorithms. This study provides consistency assessment of four of the leading products, namely, Goddard Bootstrap (SB2), Goddard NASA Team (NT1), EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI‐SAF 1.2), and Hadley HadISST 2.2 data in evaluating variability and trends in the Arctic sea ice cover. All four provide generally similar ice patterns but significant disagreements in ice concentration distributions especially in the marginal ice zone and adjacent regions in winter and meltponded areas in summer. The discrepancies are primarily due to different ways the four techniques account for occurrences of new ice and meltponding. However, results show that the different products generally provide consistent and similar representation of the state of the Arctic sea ice cover. Hadley and NT1 data usually provide the highest and lowest monthly ice extents, respectively. The Hadley data also show the lowest trends in ice extent and ice area at −3.88%/decade and −4.37%/decade, respectively, compared to an average of −4.36%/decade and −4.57%/decade for all four. Trend maps also show similar spatial distribution for all four with the largest negative trends occurring at the Kara/Barents Sea and Beaufort Sea regions, where sea ice has been retreating the fastest. The good agreement of the trends especially with updated data provides strong confidence in the quantification of the rate of decline in the Arctic sea ice cover.

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Section: Eumetsat Osi-saf Productmentioning

confidence: 99%

Section: Eumetsat Osi-saf Productmentioning

confidence: 99%

Variability and trends in the Arctic Sea ice cover: Results from different techniques

2017

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“…The total Arctic sea‐ice volume, an important parameter for the Earth's energy and water budget, can be assessed by combining observations of sea‐ice concentration and thickness across the entire Arctic region. While consistent pan‐Arctic satellite retrievals exist for sea‐ice concentration since the late 1970s (e.g., Cavalieri et al, ; Tonboe et al, ), satellite retrievals of Arctic sea‐ice thickness only started in the early 2000s (Kwok et al, ). Sea‐ice thickness retrievals are commonly based on freeboard observations obtained from satellite radar or laser altimeters.…”

Section: Introductionmentioning

confidence: 95%

Retrievals of Arctic Sea‐Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability

Bunzel

Notz

Pedersen

2018

Geophysical Research Letters

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We estimate the uncertainty of satellite‐retrieved Arctic sea‐ice thickness, sea‐ice volume, and their trends stemming from the lack of reliable snow‐thickness observations. To do so, we simulate a Cryosat2‐type ice‐thickness retrieval in an ocean‐model simulation forced by atmospheric reanalysis, pretending that only freeboard is known as model output. We then convert freeboard to sea‐ice thickness using different snow climatologies and compare the resulting sea‐ice thickness retrievals to each other and to the real sea‐ice thickness of the reanalysis‐forced simulation. We find that different snow climatologies cause significant differences in the obtained ice thickness and ice volume. In addition, we show that Arctic ice‐volume trends derived from ice‐thickness retrievals using any snow‐depth climatology are highly unreliable because the estimated trend in ice volume can strongly be influenced by the neglected interannual variability in snow volume.

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“…This training is necessary to define the PDFs for the three classes (clear, cloud, ice) and uses an empirical approach. To generate the PDFs, the EUMETSAT Climate SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data (CLARA-A2) dataset [33] and the EUMETSAT OSI SAF CDR on sea ice concentration [34] have been used. CLARA-A2 consists of both re-calibrated AVHRR channel data and cloud/ice masking products.…”

Section: High Latitude Ice Maskingmentioning

confidence: 99%

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

et al. 2018

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Abstract:Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST). We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of multi-decadal global SST information. The Bayesian scheme calculates a probability of clear-sky for each image pixel, conditional on the satellite observations and prior probability. We compare the cloud detection performance to the operational Clouds from AVHRR Extended algorithm (CLAVR-x), as a measure of improvement from reduced cloud-related errors. To do this we use sea surface temperature differences between satellite retrievals and in situ observations from drifting buoys and the Global Tropical Moored Buoy Array (GTMBA). The Bayesian scheme reduces the absolute difference between the mean and median SST biases and reduces the standard deviation of the SST differences by ∼10% for both daytime and nighttime retrievals. These reductions are indicative of removing cloud contaminated outliers in the distribution, as these fall only on one side of the distribution forming a cold tail. At a probability threshold of 0.9 typically used to determine a binary cloud mask for SST retrieval, the Bayesian mask also reduces the robust standard deviation by ∼5-10% during the day, in comparison with the operational cloud mask. This shows an improvement in the central distribution of SST differences for daytime retrievals.

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The EUMETSAT sea ice concentration climate data record

Cited by 99 publications

References 34 publications

Variability and trends in the Arctic Sea ice cover: Results from different techniques

Variability and trends in the Arctic Sea ice cover: Results from different techniques

Retrievals of Arctic Sea‐Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

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