scholarly journals Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions

2020 ◽  
Vol 14 (7) ◽  
pp. 2469-2493 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Dirk Notz ◽  
Leif Toudal Pedersen ◽  
Rasmus Tonboe
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Group Iii ◽  
Melt Pond ◽  
Ice Surface ◽  
Group I ◽  
Ice Concentration ◽  
Group Ii

Abstract. 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-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 European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently 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 overestimate MODIS Arctic average SIC by 5 %–10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates 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 between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation 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 of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to 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 melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role – particularly for groups I and II – and recommend conducting further research in this field.

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

2020 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Dirk Notz ◽  
Leif Toudal Pedersen ◽  
Rasmus Tage Tonboe
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Group Iii ◽  
Melt Pond ◽  
Ice Surface ◽  
Group I ◽  
Ice Concentration ◽  
Group Ii

Abstract. 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-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 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 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 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 melt-pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the un-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 research in this field.

scholarly journals Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

2019 ◽  
Vol 13 (12) ◽  
pp. 3261-3307 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Dirk Notz ◽  
Leif Toudal Pedersen ◽  
Rasmus Tage Tonboe ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Data Sets ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Group Iii ◽  
Group I ◽  
Ice Concentration ◽  
Group Ii ◽  
The Antarctic

Abstract. 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 for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from −0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and −3.7 % (ASI), but similar for the Antarctic, −5.4 % and −5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends.

scholarly journals Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations

2019 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Dirk Notz ◽  
Leif Toudal Pedersen ◽  
Rasmus Tage Tonboe ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Data Sets ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Group I ◽  
Ice Concentration ◽  
Data Products ◽  
Group Ii ◽  
The Antarctic

Abstract. Accurate sea-ice concentration (SIC) data are a pre-requisite to reliably monitor the polar sea-ice covers. Over the last four decades, many algorithms have been developed to retrieve the SIC from satellite microwave radiometry, some of them applied to generate long-term data products. We report on results of a systematic inter-comparison of ten global SIC data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global winter-time near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the observed inter-product consistency and differences of the inter-comparison results. Group I consists of data sets using the self-optimizing EUMETSAT-OSISAF – ESA-CCI algorithms. Group II includes data using the NASA-Team 2 and Comiso-Bootstrap algorithms, and the NOAA-NSIDC sea-ice concentration climate data record (CDR). The standard NASA-Team and the ARTIST Sea Ice (ASI) algorithms are put into a separate group III because of their often quite diverse results. Within group I and II evaluation results and intra-product differences are mostly very similar. For instance, among group I products, SIA agrees within ±100 000 km2 in both hemispheres during maximum and minimum sea-ice cover. Among group II products, satellite- minus ship-based SIC differences agree within ±0.7 %. Standing out with large negative differences to other products and evaluation data is the standard NASA-Team algorithm, in both hemispheres. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to the 100 % reference SIC with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic only range from −0.2  to +0.9 %. The standard deviation is smaller in the Arctic for the quoted group I products: 1.9 % to 2.9 % and Antarctic: 2.5 % to 3.1 %, than for group II products: Arctic: 3.6 % to 5.0 %, Antarctic: 4.5 % to 5.4 %. Products of group I exhibit larger overall satellite- minus ship-based SIC differences than group II in both hemispheres. However, compared to group II, group I products’ standard deviations are smaller, correlations higher and evaluation results are less sensitive to seasonal changes. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to re-construct the un-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for, e.g., surface heat-flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and suggest advancing studies about the influence of these weather filters on SIA and SIE time-series and their trends.

scholarly journals The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations

2016 ◽  
Vol 10 (5) ◽  
pp. 2217-2239 ◽  
Author(s):  
Stefan Kern ◽  
Anja Rösel ◽  
Leif Toudal Pedersen ◽  
Natalia Ivanova ◽  
Roberto Saldo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Sea Ice Concentration ◽  
Melt Pond ◽  
Surface Fraction ◽  
Brightness Temperatures ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Ice Floes

Abstract. Sea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction. We derive gridded daily sea-ice concentrations from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between ice floes, and the ice-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea-ice concentration, which is the melt-pond fraction plus the ice-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-ice concentrations are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near90GHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-ice concentration by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea ice with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt-pond fraction of 20 %. In presence of real open water between ice floes, the sea-ice concentration is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-ice concentration and by 20 % across all algorithms at 80 % sea-ice concentration. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are more sensitive to melt ponds could be optimized more easily because the influence of unknown snow and sea-ice surface property variations is less pronounced.

scholarly journals Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

2021 ◽  
Vol 13 (19) ◽  
pp. 3882
Author(s):  
Jiechen Zhao ◽  
Yining Yu ◽  
Jingjing Cheng ◽  
Honglin Guo ◽  
Chunhua Li ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Microwave Imager

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

Inter-comparison of melt pond products with melt/freeze-up dates and sea ice concentration data

2020 ◽  
Author(s):  
Sanggyun Lee ◽  
Julienne Stroeve ◽  
Michel Tsamados
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Solar Heat ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration

&lt;p&gt;&amp;#160;Melt ponds are a dominant feature on the Arctic sea ice surface in summer, occupying up to about 50 &amp;#8211; 60% of the sea ice surface during advanced melt. Melt ponds normally begin to form around mid-May in the marginal ice zone and expand northwards as the summer melt season progresses. Once melt ponds emerge, the scattering characteristics of the ice surface changes, dramatically lowering the sea ice albedo. Since 96% of the total annual solar heat into the ocean through sea ice occurs between May and August, the presence of melt ponds plays a significant role in this transfer of solar heat, influencing not only the sea ice energy balance, but also the amount of light available under the sea ice and ocean primary productivity. Given the importance melt ponds play in the coupled Arctic climate-ecosystem, mapping and quantification of melt pond variability on a Pan-Arctic basin scale are needed. Satellite-based observations are the only way to map melt ponds and albedo changes on a pan-Arctic scale. R&amp;#246;sel et al. (2012) utilized a MODIS 8-day average product to map melt ponds on a pan-Arctic scale and over several years. In another approach, melt pond fraction and surface albedo were retrieved based on the physical and optical characteristics of sea ice and melt ponds without a priori information using MERIS.Here, we propose a novel machine learning-based methodology to map Arctic melt ponds from MODIS 500m resolution data. We provide a merging procedure to create the first pan-Arctic melt pond product spanning a 20-year period at a weekly temporal resolution. Specifically, we use MODIS data together with machine learning, including multi-layer neural network and logistic regression to test our ability to map melt ponds from the start to the end of the melt season. Since sea ice reflectance is strongly dependent on the viewing and solar geometry (i.e. sensor and solar zenith and azimuth angles), we attempt to minimize this dependence by using normalized band ratios in the machine learning algorithms. Each melt pond retrieval algorithm is different and validation ways are different as well producing somewhat dissimilar melt pond results. In this study, we inter-compare melt ponds products from different institutes, including university of Hamburg, university of Bremen, and university college London. The melt pond maps are compared with melt onset and freeze-up dates data and sea ice concentration. The melt pond maps are evaluated by melt pond fraction statistics from high resolution satellite (MEDEA) images that have not been used for the evaluation in melt pond products.&amp;#160;&lt;/p&gt;

scholarly journals The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset

2019 ◽  
Vol 13 (7) ◽  
pp. 2051-2073 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen ◽  
Christian Haas ◽  
Larysa Istomina ◽  
Frank Kauker ◽  
...  
Keyword(s):  
Sea Ice ◽  
Growth Yield ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
Pressure System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Spatial Coverage ◽  
Ice Concentration

Abstract. Observations of sea-ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea-ice concentrations. This yields a merged sea-ice concentration product combining the gap-free spatial coverage of the passive microwave sea-ice concentration and the 1 km resolution of the thermal infrared sea-ice concentration. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea-ice concentration product resolves leads at sea-ice concentrations between 60 % and 90 %. They are not resolved by the coarser passive microwave sea-ice concentration product. The benefit of the merged product is most pronounced during the formation of the polynya. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly below 0 ∘C and thus more than 20 ∘C higher than normal. Two estimates of thermodynamic sea-ice growth yield sea-ice thicknesses of 60 and 65 cm at the end of March in the area opened by the polynya. This differed from airborne sea-ice thickness measurements, indicating that sea-ice growth processes in the polynya are complicated by rafting and ridging. A sea-ice volume of 33 km3 was produced thermodynamically.

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