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

The algorithms used in the SIC CDRs are evolutions of those already applied in the previous version of the EUMETSAT OSI SAF CDR (OSI-409).

“In many cases, these algorithms are evolutions of those already applied in the previous version of the EUMETSAT OSI SAF CDR (OSI-409, Tonboe et al., 2016).”

• cites Tonboe et al., 2016 sourcePartly supported

  Section 2.3 describes the EUMETSAT record’s hybrid Bristol–Bootstrap algorithm, supporting the claim that the record uses established sea-ice concentration algorithms. The References name OSI-409 and OSI-409a, but the paper does not say which algorithms OSI-409 used or establish that the algorithms in later SIC CDRs evolved from it. (LLM, conf=0.80)

  Experimental — accuracy not yet measured

Observational uncertainty is the main source of uncertainty for estimating at which level of global warming the Arctic will lose its summer sea-ice cover.

“Niederdrenk and Notz (2018) found that observational uncertainty is the main source of uncertainty for estimating at which level of global warming the Arctic will lose its summer sea-ice cover.”

• cites Niederdrenk and Notz (2018) sourceSupported

Substantial differences exist between existing estimates of sea-ice concentration from various algorithms.

“This is, for example, reflected by substantial differences between existing estimates of sea-ice concentration from various algorithms (e.g.Ivanova et al., 2015;Comiso et al., 2017b).”

• cites Ivanova et al., 2015 sourceSupported
• cites Comiso et al., 2017bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Satellite-retrieved records of Arctic and Antarctic sea-ice concentration differ widely in their estimates of a specific sea-ice concentration on a given day in a given region.

“Satellite-retrieved records of Arctic and Antarctic sea-ice concentration differ widely in their estimates of a specific sea-ice concentration on a given day in a given region (e.g.Ivanova et al., 2015;Comiso et al., 2017a).”

• cites Ivanova et al., 2015 sourceSupported
• cites Comiso et al., 2017aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Locations for which the NT value is greater than 95 % are an acceptable representation of 100 % ice for selecting closed-ice training samples.

“As in Tonboe et al. (2016), the CI training sample is based on the results of the NASA Team (NT) algorithm (Cavalieri et al., 1984): locations for which the NT value is greater than 95 % are used as a representation of 100 % ice (Kwok, 2002).”

• cites Cavalieri et al., 1984 sourceNot checked — no full text of the source
• cites Kwok, 2002 sourceCould not verify

  The Data Sets and Approach section identifies the NASA Team algorithm, but does not describe a >95% threshold or closed-ice training samples. The Results and Discussion and Conclusions discuss uncertainty in winter-pack ice concentrations, without assessing whether NT values above 95% can represent 100% ice. Searches for the threshold and training-sample criterion found no such passage in our copy. (LLM, conf=0.76)

  Experimental — accuracy not yet measured

Values of K were tuned using a footprint simulator and selected cloud-free scenes of the marginal ice zone imaged by MODIS as described in Tonboe et al. (2016).

“Values of K were tuned using a footprint simulator and selected cloud-free scenes of the marginal ice zone imaged by the Moderate-Resolution Imaging Spectroradiometer (MODIS) as described in Tonboe et al. (2016).”

• cites Tonboe et al. (2016) sourcePartly supported

  Section 1.4 describes selecting cloud-free MODIS scenes with different ice concentrations, and Section 2.4.2 describes using them in a satellite-footprint simulation. Those sections use the simulation to estimate smearing uncertainty; they do not say that values of K were tuned with it. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

A complete, 100% cover of thin sea ice will be retrieved with a lower concentration, depending on the thickness.

“A complete, 100 % cover of thin sea ice will be retrieved with a lower concentration, depending on the thickness (Ivanova et al., 2015).”

• cites Ivanova et al., 2015 sourceSupported

All SIC algorithms based on passive microwave data are very sensitive to melt-pond water on top of the ice.

“All SIC algorithms based on the passive microwave data are very sensitive to melt-pond water on top of the ice (Kern et al., 2016).”

• cites Kern et al., 2016 sourcePartly supported

  The Conclusions report that all eight algorithms investigated underestimated sea-ice concentration when melt ponds covered 40% of the ice. But the paper does not establish that all passive-microwave SIC algorithms are *very* sensitive: Section 4.3.3 says Near90_lin is sensitive only to some degree, and the Conclusions identify Bootstrap_p and NASA Team as having the lowest sensitivity. Page numbers were not supplied. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

Our algorithms do not rely on such a channel.

“Our algorithms do not rely on such a channel (Sect.3.4.2).”

The spatial resolution of the channels needed for the SIC algorithms is similar for the three coarse-resolution sensor series with about 70×45 km iFoV for 19 GHz and 38×30 km for 37 GHz.

“Although not identical, the spatial resolution of the channels needed for the SIC algorithms is similar for the three coarse-resolution sensor series (SMMR, SSM/I, and SSMIS) with about 70×45 km instantaneous field-of-view (iFoV) diameters for the 19 GHz frequency channels, and 38 × 30 km for the 37 GHz ones (Table 2).”

All three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration.

“We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration.”

SICCI-25km and SICCI-50km are based on medium-resolution (15-25 km) PMW satellite data available from June 2002 onwards.

“The second and third CDRs are called SICCI-25km and SICCI-50km.They are based on medium-resolution (15-25 km) PMW satellite data available from June 2002 onwards.”

Wintertime, monthly averaged maps of SIC ucorr exhibit systematic errors at high-concentration ranges, especially visible in the central Arctic Ocean.

“Wintertime, monthly averaged maps of SIC ucorr exhibit systematic errors at high-concentration ranges, which are especially visible in the central Arctic Ocean.”

By varying θ the optimization process samples several planes and eventually returns the optimal angles θOW and θCI that respectively define the BOW and BCI algorithms.

“By varying θ the optimization process samples several planes and eventually returns the optimal angles θ OW and θ CI that respectively define the B OW and B CI algorithms.”

Some low-concentration TB data will also be detected as OW by the GR3719v test.

“Some low-concentration T B data (not shown, but falling between H and (A-D), closer to H) will also be detected as OW by the GR3719v test.”

The optimum rotation angle for CI cases is generally not the same as that corresponding to the BRI plane.

“Likewise, the optimum rotation angle for CI cases is generally not the same as that corresponding to the BRI plane.”

In between these two extremes, the B_CI(T) values oscillate between being below and over the SIC = 100 % line.

“In between these two extremes, the B CI (T ) values oscillate between being below and over the SIC = 100 % line.”

The minimum of the blue and red curves are not achieved at the same angle.

“The minimum of the blue and red curves are not achieved at the same angle.”

Most of the OW TB data are flagged as OW by the GR3719v test, as expected.

“Most of the OW T B data (grey triangle symbols) are thus flagged as OW, as expected.”

A novel correction scheme implemented as part of the second algorithm iteration effectively mitigates most of these systematic errors over the basin.

“A novel correction scheme is implemented as part of the second algorithm iteration (Fig. 2) that effectively mitigates most of these systematic errors over the basin.”

The optimum rotation angle for OW cases is generally not exactly at θ = 0° (BFM).

“Figure 4 (right panel) also shows that the optimum rotation angle for OW cases is generally not exactly at θ = 0 • (BFM).”

Our new algorithm is a generalization of BRI.

“Our new algorithm is a generalization of BRI.”

In the (19V, 37V) diagram, the GR3719v = T isolines are steeper than the consolidated ice line (A-D).

“In the (19V , 37V ) diagram of Fig. 3, the GR3719v = T isolines are steeper than the consolidated ice line (A-D).”

Despite using all the sensors, some data gaps still appear in the daily SIC maps, especially in the early part of the data record (late 1970s to mid-1990s).

“Despite using all the sensors, some data gaps still appear in the daily SIC maps, especially in the early part of the data record (late 1970s to mid-1990s).”

In the raw_ice_conc_values variable, both negative and positive values appear that correspond to the intrinsic retrieval noise level of the SIC algorithm before the OWF is applied.

“Both negative (blue) and positive (red) values appear that correspond to the intrinsic retrieval noise level of the SIC algorithm before the OWF is applied.”

The atmospheric noise reduction step does not much improve the OW standard deviation in the SH at the beginning of the OSI-450 period for the SMMR instrument (1979-1987).

“It is noteworthy that the atmospheric noise reduction step does not much improve the OW standard deviation in the SH at the beginning of the OSI-450 period for the SMMR instrument (1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987).”

On Fig. 5 (right) there is an overall increase of the DAL value from the Canadian Arctic Archipelago (multiyear ice) across the pole and towards the Laptev and Kara seas (first-year ice).

“On Fig. 5 (right) we observe an overall increase of the DAL value from the Canadian Arctic Archipelago (multiyear ice) across the pole and towards the Laptev and Kara seas (first-year ice).”

Atmospheric correction does yield a positive impact on the accuracy of OW SICs during the SMMR era.

“Even if not as large as later in the time series, atmospheric correction does yield a positive impact on the accuracy of OW SICs during the SMMR era.”

SICCI-25km achieves roughly the same OW standard deviation as OSI-450.

“SICCI-25km (red) achieves roughly the same OW standard deviation as OSI-450.”

The correspondence in the transitions of DAL values with the contour lines of sea-ice age is very good.

“The correspondence in the transitions of DAL values with the contour lines of sea-ice age is very good”

The algorithm uncertainty is typically between 2 % and 3 % SIC.

“The algorithm uncertainty is typically between 2 % and 3 % SIC.”

A value of K = 1 was found to yield good results for all three CDRs.

“A value of K = 1 was found to yield good results for all three CDRs.”

The total uncertainty, which is the sum (in variance) of σ_algo and σ_smear, is dominated by σ_smear.

“The total uncertainty, which is the sum (in variance) of σ algo and σ smear (Sect.3.5), is dominated by σ smear .”

• AssumptionThe authors state this as an assumption: The total uncertainty σ_tot is given by two uncertainty components, σ_algo and σ_smear.

The standard deviations resulting from uncorrected TB data are consistently above those for corrected data by about 3 % to 4 % on average, depending on the season and hemisphere.

“The impact of the explicit correction of brightness temperature for atmospheric noise effects is also clearly visible in Fig. 8, since the standard deviations resulting from uncorrected T B data (thin solid lines) are consistently above those for corrected data (thick solid lines) by about 3 % to 4 % on average, depending on the season and hemisphere.”

Large off-range SIC >100 % raw values are non-physical and are set to exactly 100 % in variable ice_conc.

“These raw values are non-physical (like the blue-shaded SIC <0 % values) and are set to exactly 100 % in variable ice_conc.”

The algorithms implemented in the three CDRs achieve zero bias for all instruments and both hemispheres on a daily basis.

“From Fig. 8, it is easy to see that the algorithms implemented in the three CDRs achieve zero bias (dotted lines along the y = 0 axis) for all instruments and both hemispheres on a daily basis.”

The seasonal variability is larger from the uncorrected data, especially in the NH.

“The seasonal variability is also larger from the uncorrected data, especially in the NH.”

All three CDRs exhibit a negative bias, i.e. a modal SIC <100%.

“All three CDRs exhibit a negative bias, i.e. a modal SIC <100 %.”

For sea ice, SIC distributions around 100% are almost identical for OSI-450 and SICCI-25km for both hemispheres.

“For sea ice, we find almost identical SIC distributions around 100 % for OSI-450 and SICCI-25km for both hemispheres (Fig. 11a, b, and d, e).”

Without exception, mean SIC total uncertainties are smaller than 2 standard deviations of the retrieval errors.

“Also without exception, mean SIC total uncertainties are smaller than 2 standard deviations of the retrieval errors (not shown).”

For open water, SIC distributions around 0% are quite similar for all three CDRs for both hemispheres.

“For open water, we find quite similar SIC distributions around 0 % for all three CDRs for both hemispheres (Fig. 10).”

SICCI-25km curves are also well below 15%.

“The SICCI-25km curves are also well below 15 %.”

A spatial sampling of 25 km does not fully represent the true spatial resolution of the OSI-450 product since the footprints of the SMMR, SSM/I, and SSMIS channels used by the algorithms are coarser.

“However, a spatial sampling of 25 km does not fully represent the true spatial resolution of the product since the footprints of the SMMR, SSM/I, and SSMIS channels used by the algorithms are coarser (Table 2).”

For sea ice SIC=100%, mean SIC total uncertainties for OSI-450 (∼3%) are smaller than those for SICCI-25km (∼3.5%) in both hemispheres.

“For sea ice, SIC = 100 % (Fig. 12c, d), we find that mean SIC total uncertainties for OSI-450 (∼ 3 %) are smaller than those for SICCI-25km (∼ 3.5 %) in both hemispheres.”

For SICCI-50km mean SIC total uncertainties are smaller than for the other two CDRs - particularly in the Northern Hemisphere, at ∼2%.

“For SICCI-50km mean SIC total uncertainties are smaller than for the other two CDRs -particularly in the Northern Hemisphere, at ∼ 2 % (Fig. 12c).”

During summer, SIC distribution is skewed to negative SIC for all CDRs except OSI-450 in the Antarctic.

“During summer (red curves) we find SIC distribution to be skewed to negative SIC for all CDRs except OSI-450 in the Antarctic.”

Distributions are generally more narrow for SICCI-50km than for the other two CDRs.

“Distributions are generally more narrow for SICCI-50km than for the other two CDRs.”

Seasonal variations are visible especially at the beginning of the time series for NH cases, when typical winter values are around 7.5% SIC and peak to 10% SIC in summer.

“Seasonal variations are visible especially at the beginning of the time series for NH cases, when typical winter values are around 7.5 % SIC and peak to 10 % SIC in summer.”

For OSI-450 and SICCI-25km, mean SIC total uncertainties are smaller than 1 standard deviation of the retrieval errors.

“For OSI-450 and SICCI-25km, mean SIC total uncertainties are smaller than 1 standard deviation of the retrieval errors.”

Without exception the precision is better (smaller standard deviation) in summer than winter.

“Without exception the precision is better (smaller standard deviation) in summer than winter.”

OSI-450 and SICCI-25km have very similar accuracies with a mean SIC of 0% or -0.2% during summer and of ∼0.5% during winter in both hemispheres.

“Figure 12 (a and b, black crosses) illustrates the very similar accuracies for OSI-450 and SICCI-25km with a mean SIC of 0 % or -0.2 % during summer and of ∼ 0.5 % during winter in both hemispheres.”

All three CDRs achieve a rather stable detection of true SIC mostly below the 15% SIC threshold commonly used to define SIE.

“All in all, we note that all three CDRs achieve a rather stable detection of true SIC mostly below the 15 % SIC threshold commonly used to define SIE.”

Without exception mean SIC total uncertainties exceed 1 standard deviation of the retrieval errors.

“Without exception mean SIC total uncertainties exceed 1 standard deviation of the retrieval errors (compare black and blue bars in Fig. 12a, b).”

For SICCI-50km, mean SIC total uncertainties are comparable to or larger than 1 standard deviation of the retrieval errors.

“For SICCI-50km, mean SIC total uncertainties are comparable to (Fig. 12c) or larger than (Fig. 12d) 1 standard deviation of the retrieval errors.”

The standard deviation of the mean SIC (precision) ranges between 1% and 2% for all three CDRs.

“The standard deviation of the mean SIC (black bars), i.e. the precision, ranges between 1 % and 2 % for all three CDRs.”

SICCI-50km offers the smallest SIC standard deviation of the mean (best precision) of ∼2% and ∼3% for the Arctic and Antarctic.

“In addition, SICCI-50km also offers the smallest SIC standard deviation of the mean (black bars), i.e. the best precision, of ∼ 2 % and ∼ 3 % for the Arctic and Antarctic.”

The dynamic tuning of the OWF as implemented here secures a quite stable level for the minimum detectable true SIC, of the order of 10% SIC, on average well below the 15% SIC threshold commonly used for defining sea-ice extent.

“We note that the dynamic tuning of the OWF as implemented here secures a quite stable level for the minimum detectable true SIC, of the order of 10 % SIC, on average well below the 15 % SIC threshold commonly used for defining sea-ice extent.”

An analysis of the temporal consistency of the OWF also revealed that the dynamic tuning of the OWF does not perform as optimally on the SICCI-50km CDR than on SICCI-25km and OSI-450.

“An analysis of the temporal consistency of the open-water filter (Sect.4.2.1) also revealed that our dynamic tuning of the OWF does not perform as optimally on the SICCI-50km CDR than on SICCI-25km and OSI-450.”

The product has a long-term stable zero bias arising from the dynamical retuning of the tie points.

“We find in particular that our product has a long-term stable zero bias arising from the dynamical retuning of the tie points.”

The algorithm and the products are embedded into an operational context.

“Second, the algorithm and the products are embedded into an operational context.”

The total uncertainty σ tot is dominated by the σ smear contribution.

“σ tot is dominated by the σ smear contribution.”

• cites Fig. 7Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

In open-water conditions, the uncertainties provided in the CDR product files overestimate the observed retrieval noise by a couple of SIC percent (in terms of standard deviation).

“In open-water conditions, the uncertainties provided in the CDR product files overestimate the observed retrieval noise by a couple of SIC percent (in terms of standard deviation).”

The reduction in variability from the ice curve correction comes in addition to the correction for systematic errors (e.g. underestimation north of Canadian Arctic Archipelago).

“This reduction in the variability comes in addition to the correction for the systematic errors (e.g.underestimation north of Canadian Arctic Archipelago; see left panel for which the ice curve correction was designed).”

Over 100% SIC, there is a close correspondence between the reported uncertainty and the observed retrieval noise for both hemispheres.

“Over 100 % SIC, there is a close correspondence between the reported uncertainty and the observed retrieval noise for both hemispheres.”

The self-tuning and self-optimizing algorithms also allowed consistent processing of SIC CDRs from the AMSR-E and AMSR2 instruments.

“The self-tuning and self-optimizing algorithms also allowed consistent processing of SIC CDRs from the AMSR-E and AMSR2 instruments.”

In open-water regions (outside the 15% SIC contour), the reduction in variability is even larger (3%-4% SIC) than over closed-ice regions.

“In the open-water regions of Fig.5(outside the 15 % SIC contour), the reduction in variability (centre panel) is even larger (3 %-4 % SIC) than over closed-ice regions.”

Over open water, the retrieval accuracy of SICCI-50km is as good as 1% to 1.5% SIC (1 standard deviation).

“Over open water, the retrieval accuracy of SICCI-50km is as good as 1 % to 1.5 % SIC (1 standard deviation).”

All products contain per-pixel information on uncertainties arising from the algorithm itself at every time step (e.g. sensor noise or residual geophysical noise) and the smearing uncertainty from spatial remapping.

“All products contain per-pixel information on uncertainties arising from the algorithm itself at every time step (e.g.sensor noise or residual geophysical noise) and the smearing uncertainty from spatial remapping.”

At every time step, the final products include per-pixel information on the impact of possible filtering.

“For example, at every time step they include per-pixel information on the impact of possible filtering.”

A limited number of regions show no improvement or slight degradation from the ice curve correction.

“A limited number of regions show no improvement (white colour) or slight degradation.”

In regions covered with sea ice (>=70% SIC), the variability at high concentration is rather consistently reduced by about 1%-2% SIC by the ice curve correction.

“In the regions covered with seaice (> = 70 % SIC), the shades of light blue indicate that the variability at high concentration is rather consistently reduced by about 1 %-2 % SIC by the ice curve correction”

Evaluation results reveal very similar accuracies in the northern and southern hemispheres, even though the sea-ice conditions can be very different.

“Our evaluation results reveal very similar accuracies in the northern and southern hemispheres, even though the sea-ice conditions can be very different.”

The final products contain substantial information on the impact of the various processing steps.

“First, the final products contain substantial information on the impact of the various processing steps.”

The atmospheric correction step on average increases SIC over open-water regions close to the sea-ice edge, e.g. in East Greenland Sea, Barents Sea, and in Labrador Sea.

“From the left panel, it appears that the atmospheric correction step on average increases SIC (shades of red) over open-water regions close to the sea-ice edge, e.g. in East Greenland Sea, Barents Sea, and in Labrador Sea.”

All three SIC CDRs were developed with their primary application as climate-data records in mind, putting very narrow constraints on the permissible long-term drift of the records.

“In particular, they were all developed with their primary application as climate-data records in mind, putting very narrow constraints on the permissible long-term drift of the records.”

Both the bias correction of large-scale climate models and the extrapolation of observed relationships between forcing and sea-ice coverage can only be carried out robustly if observational uncertainty is sufficiently small.

“This is because both the bias correction of large-scale climate models and the extrapolation of observed relationships between forcing and sea-ice coverage can only be carried out robustly if observational uncertainty is sufficiently small.”

Our algorithms do not require stringent calibration thanks to using dynamic tuning.

“since our algorithms do not require such stringent calibration thanks to using dynamic tuning (Sect.3.3).”

There is no continuous data record of brightness temperatures in the vicinity of the water vapour absorption line (22.235GHz).

“There is thus no continuous data record of brightness temperatures in the vicinity of the water vapour absorption line (22.235GHz).”

The reliability and long-term stability of existing sea-ice concentration records is often not clear.

“However, the reliability and long-term stability of existing records is often not clear.”

Integrated over the entire Arctic, differences between satellite-retrieved records accumulate up to a 20 % uncertainty in the long-term trends of sea-ice extent and sea-ice area.

“Integrated over the entire Arctic, these differences accumulate up to a 20 % uncertainty in the long-term trends of sea-ice extent and seaice area (Comiso et al., 2017b)”

• cites Comiso et al., 2017bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Unavailability of ERA-Interim data prior to 1979 made it impractical to use the earliest period of SMMR data (October to December 1978).

“Unavailability of ERA-Interim data prior to 1979 made it impractical to use the earliest period of SMMR data (October to December 1978).”

The underlying algorithms provide the capability to adjust to the climatological changes in the above-mentioned noise sources.

“(c) the capability to adjust to the climatological changes in the above-mentioned noise sources”

iFoV diameters are not a measure of the true footprint of an individual measured pixel.

“It is noteworthy that iFoV diameters, as reported in Table 2 and at several online resources, such as the WMO OSCAR Space-based Capabilities database, are not a measure of the true footprint of an individual measured pixel.”

The albedo of the polar oceans is strongly influenced by sea-ice concentration.

“For example, the albedo of the polar oceans is strongly influenced by sea-ice concentration (e.g.Brooks, 1925)”

• cites Brooks, 1925 sourceNot checked — no full text of the source

Information on sea-ice concentration is key to the vast majority of approaches for understanding the changing sea-ice cover of our planet.

“Our focus on sea-ice concentration is to a substantial degree driven by the fact that information on sea-ice concentration is key to the vast majority of approaches for understanding the changing sea-ice cover of our planet.”

The underlying algorithms provide sea-ice concentration estimates with low sensitivity to surface noise including wind roughening of the ocean surface, and variability of sea-ice emissivity and temperature.

“(b) low sensitivity to surface noise including wind roughening of the ocean surface, and variability of sea-ice emissivity and temperature”

The underlying algorithms provide a quantification of the remaining noise at each time step for each pixel.

“(d) a quantification of the remaining noise at each time step for each pixel.”

Building CDRs from this suite of satellite sensors is best achieved if the selected algorithms only use channels that are consistently available throughout the period.

“Building CDRs from this suite of satellite sensors is best achieved if the selected algorithms only use channels that are consistently available throughout the period.”

The underlying algorithms provide sea-ice concentration estimates with low sensitivity to atmospheric noise including liquid water content and water vapour.

“provide sea-ice concentration estimates with (a) low sensitivity to atmospheric noise including liquid water content and water vapour”

This is at the cost of bringing a coarser spatial resolution into the algorithm.

“This is at the cost of bringing a coarser spatial resolution into the algorithm.”

θOW and θCI define more accurate algorithms than BFM and BRI.

“θ OW (blue disc) and θ CI (red disc) thus indeed define more accurate algorithms than BFM and BRI.”

The role of the higher frequencies is to ensure a significant spread of the CI TB samples along the ice line and thus offer a good base for computing vector u with PCA.

“The role of the higher frequencies is to ensure a significant spread of the CI T B samples along the ice line and thus offer a good base for computing vector u with PCA.”

The dynamic training allows us to cope with slightly different frequencies between different instruments.

“(c) cope with slightly different frequencies between different instruments (e.g.SMMR, SSM/I, and AMSR-E all have a different frequency around 19 and 37 GHz; see Table 2)”

The closed-ice scatter also has increased variance directions, e.g. due to ice-type and snow characteristics.

“The closed-ice scatter also has increased variance directions, e.g.due to icetype and snow characteristics.”

This optimization step allows us to cope with the anisotropy of the OW and CI TB samples in the (19V, 37V, 37H) TB space.

“This optimization step allows us to cope with the anisotropy of the OW and CI T B samples in the (19V , 37V , 37H ) T B space.”

The weather filters of Cavalieri et al. (1992) have been used in basically all available SIC CDRs except the earlier EUMETSAT OSI SAF data sets.

“The weather filters (WFs) of Cavalieri et al. (1992) have been used in basically all available SIC CDRs except the earlier EUMETSAT OSI SAF data sets (Andersen et al., 2007;Ton-boe et al., 2016).”

• cites Cavalieri et al. (1992)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Andersen et al., 2007 sourceNot checked — no full text of the source
• cites Ton-boe et al., 2016Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The GR3719v filter will remove true sea ice as well, even in calm weather conditions.

“As seen in Fig. 3, this is not how the GR3719v filter works, as it will remove true sea ice as well, even in calm weather conditions (OW samples below J).”

Our sliding window is made shorter so that tie points react more rapidly to seasonal cycles, e.g. onset of melting.

“Our sliding window is made shorter so that tie points react more rapidly to seasonal cycles, e.g.onset of melting.”

The value of 10 % SIC is chosen to be below the threshold commonly used for defining the sea-ice extent (15 % SIC) to ensure that the weather filter does not interfere when computing the sea-ice extent.

“The value of 10 % SIC is chosen to be below the threshold commonly used for defining the sea-ice extent (15 % SIC) to ensure that the weather filter does not interfere when computing the sea-ice extent.”

Recent investigations confirmed that NT was an acceptable choice for the purpose of selecting closed-ice samples.

“Recent investigations, e.g. during the ESA CCI Sea Ice projects, confirmed that NT was an acceptable choice for the purpose of selecting closed-ice samples.”

The role of the lower vertically polarized channel is to ensure a sufficient dynamic range between OW and CI signatures and thus aim to reduce retrieval noise.

“The role of the lower vertically polarized channel is to ensure a sufficient dynamic range between OW and CI signatures and thus aim to reduce retrieval noise.”

The higher-frequency channels bring a higher spatial resolution to the retrieved SIC.

“They also bring a higher spatial resolution to the retrieved SIC, since higher-frequency channels achieve higher spatial resolution (Table 2).”

By construction, SIC algorithms BFM, BPM, BRI, and the new dynamic algorithms consider that the SIC is exactly 100 % when the input TB falls on the consolidated ice line.

“By construction, SIC algorithms BFM, BPM, BRI, and our new dynamic algorithms consider that the SIC is exactly 100 % when the input T B falls on the consolidated ice line (Fig. 3).”

GR3719v contains information on sea-ice type.

“In addition, GR3719v contains information on sea-ice type (Cavalieri et al., 1984)”

• cites Cavalieri et al., 1984 sourceNot checked — no full text of the source

Because of these anisotropies, the optimal projection plane will generally not be that of BRI.

“Because of these anisotropies, the optimal projection plan will generally not be that of BRI.”

The dynamic training allows us to mitigate sensor drift (if not already mitigated in the FCDR).

“(d) mitigate sensor drift (if not already mitigated in the FCDR)”

Since the exact locations of H, A, and D vary for each instrument, hemisphere, and day in the data record, the threshold T will change during the whole data record, without the need for prescribed values.

“Since the exact locations of H, A, and D vary for each instrument, hemisphere, and day in the data record, our threshold T will change (although slightly) during the whole data record, without the need for prescribed values (such as T = 0.05 for the Cavalieri, 1992 WF).”

Correction of water vapour using ERA-Interim data is effective enough in polar regions so that very limited additional screening is triggered by GR2219v when applied after TB correction.

“the correction of water vapour using ERA-Interim data is effective enough in polar regions so that very limited additional screening is triggered by GR2219v when applied after T B correction.”

The dynamic training of our algorithms allows us to adapt to interseasonal and interannual variations of the sea-ice and open-water emissivity.

“The dynamic training of our algorithms allows us to (a) adapt to interseasonal and interannual variations of the sea-ice and open-water emissivity”

The WF is tuned to avoid removing true ice with concentration larger than 10 %, on average.

“Our WF is tuned to avoid removing true ice with concentration larger than 10 %, on average.”

A near 22 GHz channel is not available throughout the satellite time series.

“a near 22 GHz channel is not available throughout the satellite time series (Sect.2.1)”

The dynamic training allows us to cope with different calibration of different instruments in a series, or between different FCDRs.

“(b) cope with different calibration of different instruments in a series, or be-tween different FCDRs”

The WF by Cavalieri et al. (1992) detects all observations with either GR3719v > 0.050 and/or GR2219v > 0.045 as open water.

“The WF by Cavalieri et al. (1992) detects (and consequently forces SIC to 0 %) all observations with either GR3719v > 0.050 and/or GR2219v > 0.045 as open water.”

• cites Cavalieri et al. (1992)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Computing OWFs at Level 2 can help to mitigate the potential impacts of changes in satellite crossing time between different missions.

“Computing OWFs at Level 2 can also help to mitigate the potential impacts of changes in satellite crossing time between different missions.”

The same reasoning can be carried within other 3-D TB spaces, as long as such spaces offer a clustering of the CI conditions along an ice line and sufficient dynamic range between the OW signature and the CI line.

“The same reasoning can, however, be carried within other 3-D T B spaces, as long as such spaces offer a clustering of the CI conditions along an ice line and sufficient dynamic range between the OW signature and the CI line.”

The open-water scatter has increased variance along the directions resulting from weather effects on the emissivity of water.

“The open-water scatter has increased variance along the directions resulting from weather effects (including wind speed, cloud liquid water, and water vapour) on the emissivity of water.”

There cannot be a single SIC algorithm that performs best both on low-concentration and high-concentration conditions.

“This is a clear illustration that there cannot be a single SIC algorithm that performs best both on low-concentration and high-concentration conditions and confirms the strategy already adopted by Comiso (1986), Andersen et al. (2007), and Tonboe et al. (2016) to construct hybrid algorithms.”

The new SIC CDRs introduce explicit land spillover correction of the TB at all used channels and on swath projection.

“Such a method showed limitations and the new SIC CDRs now introduce explicit land spillover correction of the T B at all used channels and on swath projection.”

Sea-ice concentration will be consistently overestimated in coastal regions.

“This means that sea-ice concentration will be consistently overestimated in coastal regions.”

SMMR data for the period October to December 1978 are not included in the OSI-450 CDR because of the unavailability of ERA-Interim data for a correction of the atmospheric influence on TB.

“SMMR data for the period October to December 1978 are not included in the CDR because of the unavailability of ERA-Interim data for a correction of the atmospheric influence on T B (Sect.2.2).”

The novel correction scheme moves the concept of an ice line to an ice curve that more closely follows the B_CI(T) samples along the u axis.

“Our novel correction scheme moves the concept of an ice line to an ice curve that more closely follows the B CI (T ) samples along the u axis.”

The size of the footprint of the 19 GHz channels of the SMMR, SSM/I, and SSMIS instruments is larger than the resolution of the grid used to present the SIC field (25 × 25 km).

“Effect (a) is that the size of the footprint of the 19 GHz channels of the SMMR, SSM/I, and SSMIS instruments is larger than the resolution of the grid used to present the SIC field (25 × 25 km; see Table 1).”

This is expected from the choice of frequency channels, since SICCI-50km uses a C-band (6.9 GHz) channel, while SICCI-25km and OSI-450 use Ku-band (∼19 GHz).

“This is expected from the choice of frequency channels, since SICCI-50km uses a C-band (6.9 GHz) channel, while SICCI-25km and OSI-450 use Ku-band (∼ 19 GHz).”

The monthly varying maximum sea-ice extent climatology implemented in Meier et al. (2017) was used as a basis for the new climatology.

“By the same token, the monthly varying maximum sea-ice extent climatology implemented in Meier et al. (2017) was used as a basis for our own climatology.”

• cites Meier et al., 2017 sourceNot checked — no full text of the source

SICCI-50km achieves a coarser spatial resolution (50 km) due to the limited size of the AMSR-E and AMSR2 antenna.

“However, it achieves a coarser spatial resolution (50 km) due to the limited size of the AMSR-E and AMSR2 antenna.”

The belt of low SIC values bordered by a dark-red region is very probably true low-concentration or thin sea ice that is removed by the OWF at the marginal ice zone.

“This is very probably true low-concentration or thin sea ice that is removed by the OWF at the marginal ice zone.”

The larger expansion of the cleaned climatologies in the Southern Hemisphere (250 km) is to cope with the positive trends in the Southern Hemisphere sea-ice extent.

“The larger expansion in SH is to cope with the positive trends in the SH sea-ice extent (Hobbs et al., 2016).”

• cites Hobbs et al., 2016 sourceNot checked — no full text of the source

The algorithm uncertainty term σ_algo is derived from the accuracy (estimated as statistical variance) of the algorithm for retrieving 0 % (100 %) when applied onto the OW (CI) training data samples.

“This term is derived from the accuracy (estimated as statistical variance) of the algorithm for retrieving 0 % (100 %) when applied onto the OW (CI) training data samples (Sect.3.3).”

The off-range SIC values are needed to compute temporal averages (e.g. monthly means) to avoid introducing biases if only SIC >= 0 % or SIC =<100 % values enter the averaging.

“The off-range SIC values are also needed to compute temporal averages (e.g.monthly means) to avoid introducing biases if only SIC > = 0 % or SIC = <100 % values enter the averaging.”

The central frequency of the AMSR-E and AMSR2 channels (18.7 GHz) is slightly further away from the water vapour absorption line (∼22 GHz) than the SSM/I and SSMIS channels (19.3 GHz).

“The central frequency of the AMSR-E and AMSR2 channels (18.7 GHz) is slightly further away from the water vapour absorption line (∼ 22 GHz) than the SSM/I and SSMIS channels (19.3 GHz).”

Achieving zero bias despite the changes in central wavelengths and calibrations from one satellite to the next is one of the key advantages of using dynamically tuned algorithms.

“Achieving zero bias despite the changes in central wavelengths and calibrations from one satellite to the next is one of the key advantages of using dynamically tuned algorithms (Sect.3.3).”

The patterns seen in Fig. 7 are representative of the uncertainties of all three CDRs for both hemispheres during winter.

“The patterns seen in Fig. 7 are representative of the uncertainties of all three CDRs for both hemispheres during winter.”

Due to the coarse resolution of the sensors used, especially SMMR, SSM/I, and SSMIS, the TB data are influenced by land emissivity several tens of kilometres away from the coastline.

“Due to the coarse resolution of the sensors used, especially SMMR, SSM/I, and SSMIS (Table 2), the T B data are influenced by land emissivity several tens of kilometres away from the coastline.”

Three effects lead to better accuracies of SIC retrievals at low frequencies.

“Three effects lead to better accuracies of SIC retrievals at low frequencies:”

The threshold of the OWF is tuned dynamically against the daily updated training data samples (by instrument and by hemisphere) to preserve true SIC values down to 10%.

“the threshold of the OWF is tuned dynamically against the daily updated training data samples (thus by instrument and by hemisphere) to preserve true SIC values down to 10 %.”

The negative SICs before correction in those regions are due to the training OW samples being selected in lower-latitude conditions rather than prevailing closer to the ice edge.

“This is due to the training OW samples being selected in lower-latitude conditions (ocean surface, atmosphere conditions) rather than prevailing closer to the ice edge and is also discussed in Sect.4.2.3 when evaluating uncertainties.”

The algorithms implemented in the three CDRs dynamically tune their parameters to yield zero bias and minimum standard deviation of the computed SICs over the open-water and closed-ice training targets.

“As described in Sect.3.2 and 3.3, the algorithms implemented in the three CDRs dy- namically tune their parameters to yield zero bias and minimum standard deviation of the computed SICs (a.k.a.best accuracy) over the open-water (OW) and closed-ice (CI) training targets.”

SICCI-50km is designed to be the most accurate of the three SIC CDRs.

“SICCI-50km is designed to be the most accurate of the three SIC CDRs.”

A waterice separation limit at 10% SIC is necessary to ensure that time series of SIE are not influenced by the OWF and only by the evolution of true SIC.

“A waterice separation limit at 10 % SIC is an ambitious goal but is necessary to ensure that time series of sea ice extent (SIE, usually defined with a threshold of 15 % SIC) are not influenced by the OWF and only by the evolution of true SIC.”

The mismatch in frequency and resolution between the channels used to compute the OWF and those used to compute SIC explains the larger variability in the SICCI-50km time series.

“The mismatch in frequency and resolution between the channels used to compute the OWF and those used to compute SIC explains the larger variability in the SICCI-50km time series in Fig. 9.”

The radiation emitted at the wavelengths of the frequency channels comes from a very thin layer at the surface of the melt pond, which does not enable ocean water (in leads and openings) and meltwater (in ponds) to be distinguished.

“The radiation emitted at the wavelengths of the frequency channels comes from a very thin layer at the surface of the melt pond, which does not enable ocean water (in leads and openings) and meltwater (in ponds) to be distinguished.”

Trends in sea-ice decline (in the NH, especially summer) might have an amplification effect on the overestimation of the uncertainties, as the location for selecting training samples is increasingly further away from the sea-ice edge as decades pass.

“Trends in sea-ice decline (in the NH, especially summer) might thus have an amplification effect on the overestimation of the uncertainties, as the location for selecting training samples is increasingly further away from the sea-ice edge as decades pass.”

The uncertainty variables have larger values in the coastal regions where land spillover effects are detected.

“The uncertainty variables have larger values in the coastal regions where land spillover effects are detected.”

The effect of the OWF is not included in the uncertainty variables, which pertain to the unfiltered (raw) ice concentration values.

“The effect of the OWF is not included in the uncertainty variables, which pertain to the unfiltered (raw) ice concentration values.”

The misinterpretation of meltwater on top of sea ice as open water is not included in the uncertainty variables.

“The misinterpretation of meltwater on top of sea ice as open water is not included in the uncertainty variables (Q.Yang et al., 2016).”

• cites Q.Yang et al., 2016Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Conversely to other CDRs, the channels around 22 GHz are not used for filtering water vapour contamination.

“Conversely to other CDRs (e.g.Meier et al., 2017 and its two algorithm components bootstrap and NasaTeam), the channels around 22 GHz are not used for filtering water vapour contamination.”

• cites Meier et al., 2017 sourceNot checked — no full text of the source

CI groundtruth data from East Antarctica are missing completely because of a lack of SAR image acquisitions.

“We note that CI groundtruth data from East Antarctica are missing completely, however, because of a lack of SAR image acquisitions.”

The effect of thin sea ice is not included in the uncertainty fields of the SIC CDRs.

“The effect of thin sea ice is not included in the uncertainty fields of the SIC CDRs.”

Some true coastal sea ice might be removed by the coastal correction scheme.

“By the same token, some true coastal sea ice might be removed by the coastal correction scheme.”

Due to many factors (including smooth surface, absence of snow, brine content) concentration of thin sea-ice (<30 cm) is underestimated by most of the PMW SIC algorithms.

“Due to many factors (including smooth surface, absence of snow, brine content) concentration of thin sea-ice (<30 cm) is underestimated by most of the PMW SIC algorithms (Cavalieri, 1994).”

• cites Cavalieri, 1994 sourceNot checked — no full text of the source

The ice_conc variable of the SIC CDRs should thus hold an estimate of 1 minus the open-water fraction in each grid cell, irrespective of whether this water is from lead and openings or ponds.

“The ice_conc variable of the SIC CDRs should thus hold an estimate of 1 minus the open-water fraction in each grid cell, irrespective of whether this water is from lead and openings or ponds.”

This is one of the reasons why the Meier et al. (2017) data set only starts with SSM/I F08 on 9 July 1987 as a fully qualified CDR.

“This is one of the reasons why the Meier et al. (2017) data set only starts with SSM/I F08 on 9 July 1987 as a fully qualified CDR (according to https://nsidc.org/data/g02202, last access: 1 June 2018).”

• cites Meier et al. (2017) sourceNot checked — no full text of the source

A major challenge that all SIC CDR data producers now face is the end of life for the U.S. Defense Meteorological Satellite Program (DMSP).

“Aside from the technical aspects of reliably running the CDR processing chains on a daily basis, a major challenge that all SIC CDR data producers now face is the end of life for the U.S. Defense Meteorological Satellite Program (DMSP), which has been the workhorse for virtually all seaice CDRs since SSM/I F08 in 1987 (Table 2 and Fig. 1).”

• cites Table 2 and Fig. 1Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Embedding the algorithm and products into an operational context establishes clear rules on version tagging, documentation and availability of the underlying code, which allows other researchers to easily build on the work and develop it further.

“but in particular establishes clear rules on version tagging, documentation and availability of the underlying code, which allows other researchers to easily build on our work and to develop it further.”

It is doubtful whether the resulting single CDR would meet the temporal consistency requirements of many climate applications.

“It is in any case doubtful whether the resulting single CDR would meet the temporal consistency requirements of many climate applications.”

Applying the same algorithm to microwave products based on different frequencies and satellites allows users to combine the advantages of the length of the OSI-450 record with the high true spatial resolution of SICCI-25km and/or the low-noise SICCI-50km.

“In particular, applying the same algorithm to microwave products based on different frequencies and satellites allows users to combine the advantages of the length of the record of the OSI-450 product with the high true spatial resolution of the SICCI-25km product and/or the low-noise product SICCI-50km.”

This is explained by the larger mismatch in frequency and resolution between the channels entering the SIC algorithms, and those used in computing the OWF (19 and 37 GHz only).

“This is explained by the larger mismatch in frequency and resolution between the channels entering the SIC algorithms, and those used in computing the OWF (19 and 37 GHz only).”

The decision to produce distinct CDRs, one with SMMR, SSM/I, and SSMIS and the other two with AMSR-E and AMSR2 data, is mainly based on the difference in spatial resolution.

“The decision to produce distinct CDRs, one with SMMR, SSM/I, and SSMIS and the other two with AMSR-E and AMSR2 data, is mainly based on the difference in spatial resolution.”

The reduction in variability in open-water regions is the result of the atmospheric correction step.

“This reduction is the result of the atmospheric correction step, described in Sect.3.4.1.”

This slight overestimation is probably due to the use of a buffer zone outside of the monthly maximum ice climatology extent that dynamically selects the data samples used to train the algorithms and derive uncertainties.

“This slight overestimation is probably due to the use of a buffer zone outside of the monthly maximum ice climatology extent that dynamically selects the data samples used to train the algorithms (Sect.3.3) and derive uncertainties (Sect.3.5).”

The evaluation of uncertainties carried out in this paper provides some initial information on the remaining random per-pixel uncertainty, which can be used as an estimate of observational uncertainty, for example during model evaluation or data assimilation.

“The evaluation of uncertainties carried out in this paper provides some initial information on the remaining random per-pixel uncertainty, which can be used as an estimate of observational uncertainty, for example during model evaluation or data assimilation.”

Long-term consistency is achieved by developing an algorithm that dynamically adjusts to changing environmental conditions and changing satellite sensors.

“Long-term consistency is achieved by developing an algorithm that dynamically adjusts to changing environmental conditions and changing satellite sensors.”

The SIC after ice curve correction is a more accurate description of a nearly 100% ice cover.

“the SIC after correction is a more accurate description of a nearly 100 % ice cover.”

The analysis of the closed-ice (>=70% SIC) region confirms that the ice curve correction works as expected at high-concentration range.

“The analysis of the closedice (> = 70 % SIC) region in Fig.5thus confirms that the ice curve correction works as expected at high-concentration range”

The increase in SIC over open-water regions close to the ice edge is due to the training OW samples being selected in lower-latitude conditions rather than prevailing closer to the ice edge.

“This is due to the training OW samples being selected in lower-latitude conditions (ocean surface, atmosphere conditions) rather than prevailing closer to the ice edge”

EPS-SG MWI implements a different channel frequency for Ka-band: 31.4GHz instead of 36-37 GHz for SSM/IS and AMSRs.

“It is noticeable that EPS-SG MWI implements quite a different channel frequency for Ka-band (26.5-40GHz): 31.4GHz instead of 36-37 GHz for SSM/IS and AMSRs (Table 2).”

• cites Table 2Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.