Inter-comparison and evaluation of sea ice algorithms: towards further  identification of challenges and optimal approach using passive microwave  observations

Ivanova, N. P.; Pedersen, Leif Toudal; Tonboe, Rasmus; Kern, Stefan; Heygster, Georg; Lee, Thomas; Sørensen, Atle; Saldo, Roberto; Dybkjær, Gorm; Brucker, Ludovic; Shokr, Mohammed

doi:10.5194/tc-9-1797-2015

Cited by 242 publications

(325 citation statements)

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“…Two algorithms have been selected in combination based on the evaluation in Andersen et al (2007), the Bristol over ice, and the Bootstrap in frequency mode over open water. An independent evaluation of algorithms in Ivanova et al (2015) pointed at the same two algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…The overall accuracy of the SMMR total ice concentrations is estimated to be ±7 % (Gloersen et al, 1992). During summer the uncertainties are larger than during winter (Ivanova et al, 2015).…”

Section: Introductionmentioning

confidence: 90%

“…It occurs when the satellite is not exactly oriented (Poe et al, 2008). Simulations show that because of the large footprints (see next section for footprint sizes) compared to the typical geolocation errors of the SSM/I (about ±5 km, Hollinger et al, 1990), the ice concentration uncertainty due to geolocation errors is small and neglected here. There may be regions along the ice edge and along coastlines where the geolocation errors may be significant.…”

Section: The Sea Ice Concentration Uncertaintiesmentioning

confidence: 99%

“…The next update is planned for autumn 2016. It will include development from the ESA sea ice climate change initiative project working towards improved sea ice climate record methodologies (Ivanova et al, 2015).…”

Section: Future Workmentioning

confidence: 99%

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The EUMETSAT sea ice concentration climate data record

et al. 2016

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Abstract. An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of microwave radiometer data from NASA's Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from October 1978 to April 2015 and updates and further developments are planned for the next phase of the project. The methodology for computing the sea ice concentration uses (1) numerical weather prediction (NWP) data input to a radiative transfer model for reduction of the impact of weather conditions on the measured brightness temperatures; (2) dynamical algorithm tie points to mitigate trends in residual atmospheric, sea ice, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new sea ice concentration uncertainty algorithm has been developed to estimate the spatial and temporal variability in sea ice concentration retrieval accuracy. A comparison to US National Ice Center sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate sea ice concentrations between open water and 100 % ice. The sea ice concentration climate data record is available for download at www.osi-saf.org, including documentation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Section: The Sea Ice Concentration Uncertaintiesmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

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The EUMETSAT sea ice concentration climate data record

et al. 2016

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“…Those algorithms present different advantages and drawbacks depending on frequency, spatial resolution, atmospheric effects, physical temperature, and others. According to Ivanova et al (2015), the first source of error in the computation of sea ice concentration is the sensitivity to changes in the physical temperature of sea ice, in particular for those algorithms that use measurements between 10 and 37 GHz. They identify atmospheric water vapour and cloud liquid water as the second source of error except for algorithms at 89 GHz, where it becomes the dominant error.…”

Section: Introductionmentioning

confidence: 99%

New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

et al. 2017

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Abstract. Monitoring sea ice concentration is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea ice concentration have some limitations, for instance the impact of the atmosphere, the physical temperature of ice, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea ice, remarkably sea ice thickness. However, the potential of satellite Lband observations for obtaining sea ice concentration had not yet been explored.In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice concentration. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea ice and seawater. In addition, the brightness temperatures of 100 % sea ice and 100 % seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea ice concentration, leading to a more robust estimate.After analysing the full year 2014 in the entire Arctic, we have found that the sea ice concentration obtained with our method is well determined as compared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF) dataset. However, when thin sea ice is present (ice thickness 0.6 m), the method underestimates the actual sea ice concentration.Our results open the way for a systematic exploitation of SMOS data for monitoring sea ice concentration, at least for specific seasons. Additionally, SMOS data can be synergistically combined with data from other sensors to monitor panArctic sea ice conditions.

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The phenology of Arctic Ocean surface warming

Steele

Dickinson

2016

JGR Oceans

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In this work, we explore the seasonal relationships (i.e., the phenology) between sea ice retreat, sea surface temperature (SST), and atmospheric heat fluxes in the Pacific Sector of the Arctic Ocean, using satellite and reanalysis data. We find that where ice retreats early in most years, maximum summertime SSTs are usually warmer, relative to areas with later retreat. For any particular year, we find that anomalously early ice retreat generally leads to anomalously warm SSTs. However, this relationship is weak in the Chukchi Sea, where ocean advection plays a large role. It is also weak where retreat in a particular year happens earlier than usual, but still relatively late in the season, primarily because atmospheric heat fluxes are weak at that time. This result helps to explain the very different ocean warming responses found in two recent years with extreme ice retreat, 2007 and 2012. We also find that the timing of ice retreat impacts the date of maximum SST, owing to a change in the ocean surface buoyancy and momentum forcing that occurs in early August that we term the Late Summer Transition (LST). After the LST, enhanced mixing of the upper ocean leads to cooling of the ocean surface even while atmospheric heat fluxes are still weakly downward. Our results indicate that in the near‐term, earlier ice retreat is likely to cause enhanced ocean surface warming in much of the Arctic Ocean, although not where ice retreat still occurs late in the season.

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Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Cited by 242 publications

References 50 publications

The EUMETSAT sea ice concentration climate data record

The EUMETSAT sea ice concentration climate data record

New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

The phenology of Arctic Ocean surface warming

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