Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records

Lee, Thomas; Sørensen, Atle; Kern, Stefan; Tonboe, Rasmus; Notz, Dirk; Aaboe, Signe; Bell, Louisa; Dybkjær, Gorm; Eastwood, Steinar; Gabarró, Carolina; Heygster, Georg; Killie, Mari Anne; Kreiner, Matilde Brandt; Lavelle, J. W.; Saldo, Roberto; Sandven, Stein; Pedersen, Leif Toudal

doi:10.5194/tc-13-49-2019

Cited by 276 publications

(268 citation statements)

References 53 publications

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“…Out of group III, ASI is similar to group I products while NT SIC under-estimates MODIS Arctic average SIC by 5-10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA 25 between the four groups reported for the summer months by Kern et al (2019). MODIS ISF is systematically over-estimated by all products; NT provides the smallest (up to 25 %) over-estimations, group II and IV products the largest (up to 45 %) over-estimations.…”

supporting

confidence: 76%

“…Like in Kern et al (2019), we consider 10 different sea-ice concentration products which we very briefly summarize in Table 2.1.1. More information about these products is given in Kern et al (2019, Appendix 7.1 -7.6).…”

Section: Sea-ice Concentration Data Setsmentioning

confidence: 99%

“…The products are compared against SIC and net ice-surface fraction (ISF) -SIC minus the per-grid cell melt-pond fraction (MPF) on sea ice -as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-15 going vessels. Like in Kern et al (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the EUMETSAT OSI SAF and ESA CCI algorithms.…”

mentioning

confidence: 99%

“…In Fig. 1 we show the seasonal cycle of the sea-ice concentration algorithm standard error -aka the precision -of the OSI-450, SICCI-25km and SICCI-50km products (Lavergne et al, 2019) for illustration. To summarize: 1) we do not know what the sea-ice concentration algorithms actually measure during summer (actual sea-ice concentration or net sea-ice surface fraction), and whichever they measure the accuracy is poor compared to the winter conditions.…”

mentioning

confidence: 99%

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Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions

Kern¹,

Lee²,

Notz³

et al. 2020

Preprint

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We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice-surface fraction (ISF) -SIC minus the per-grid cell melt-pond fraction (MPF) on sea ice -as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-15 going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the EUMETSAT OSI SAF and ESA CCI algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the NOAA NSIDC SIC climate data record (CDR). Group III consists of ARTIST Sea Ice (ASI) and NASA Team (NT) algorithm products and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find wide-spread positive and negative differences between PMW and MODIS SIC with magnitudes frequently 20 reaching up to 20-25 % for groups I and III and up to 30-35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from Group I products agrees with MODIS within 2-5 % accuracy during the entire melt period from May through September. Group II and IV products over-estimate MODIS Arctic average SIC by 5-10 %. Out of group III, ASI is similar to group I products while NT SIC under-estimates MODIS Arctic average SIC by 5-10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA 25 between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically over-estimated by all products; NT provides the smallest (up to 25 %) over-estimations, group II and IV products the largest (up to 45 %) over-estimations. The spatial distribution of the observed over-estimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence 30 of ice-surface properties other than melt ponds, i.e. wet snow and coarse grained snow / refrozen surface, on PMW observations used in the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for i) the observed differences between PMW SIC and MODIS ISF and for ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high meltpond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the un-35 known amount of melt-pond signatures likely included in the ice tie points plays an important role -particularly for groups I and II -and suggest to conduct further...

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supporting

confidence: 76%

Section: Sea-ice Concentration Data Setsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions

Kern¹,

Lee²,

Notz³

et al. 2020

Preprint

Self Cite

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“…Yet another possibility to determine snow depth is to exploit the different scattering horizons from CryoSat (Kuband) and SARAL/AltiKa (Ka-band) (Guerreiro et al, 2016;Lawrence et al, 2018). The same concept may be applied to the upcoming overlap of CryoSat and ICESat-2 (Lawrence et al, 2018). ESA currently also investigates the Copernicus polaR Ice and Snow Topography ALtimeter (CRISTAL; Kern et al, 2019) as a High Priority Candidate Mission.…”

mentioning

confidence: 99%

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Braakmann-Folgmann

Donlon

2019

The Cryosphere

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Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge.A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath > 1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn-dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger "hole at the pole". Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

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Spatial Damped Anomaly Persistence of the sea-ice edge as a benchmark for dynamical forecast systems

Niraula

Goessling

2021

Preprint

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Accelerated sea ice loss and the possibility of ice-free summers in the Arctic has increased the interest in potential human activities in the far North (Stephenson et al., 2011). To address the planning and safety concerns associated with this, government and private agencies need better predictions of sea ice at subseasonal to seasonal timescales (Jung et al., 2016). Over the past few years, many operational centers are already starting to provide such forecasts with longer lead times, although the skill of these forecasts-and how to assess the skill in the first place-is still under question (Smith et al., 2015).There are numerous metrics to measure and quantify the accuracy of a forecast against observation, or "true" conditions, depending on the variable in question (Wilks, 2019). Whether or not forecasts are considered skillful depends not only on the metric to measure the forecast error, but also what benchmark is used to measure skill against. The skill of the forecast produced by a particular forecast system can be compared against that of an earlier version of the same system (e.g., Balan-Sarojini et al., 2019), a different forecast system (e.g., Zampieri

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Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records

Cited by 276 publications

References 53 publications

Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions

Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Spatial Damped Anomaly Persistence of the sea-ice edge as a benchmark for dynamical forecast systems

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