Several sea ice concentration (SIC) algorithms have been developed for passive microwave data (PMW). They differ in the usage of, e.g., frequencies and polarizations of the PMW data () or the usage of static or dynamic tie points (; ; ). From the resulting datasets, it is apparent that the Arctic sea ice extent (SIE) in September has been decreasing at a rate of about 12 % per decade since the launch of modern satellite multi-frequency microwave radiometers in 1978 (; ). This negative sea ice trend in the Northern Hemisphere started in the 1970s (; ), though regional trends can differ, as seen for example within the Barents Sea (). In the Antarctic there are large regional differences in SIE trends, but, until recently, the overall trend was positive due to sea ice dynamics (; ). This has changed, however, in the last decade as a result of several record lows, and, as such, overall trends have shifted to a more homogeneous pattern () and are now slightly negative in summer, November to February (NDJF). Until recently, the slightly positive trend was believed to be part of the long-term natural variability that overshadowed the effects of global warming starting in the 1960s (; ; ; ; ; ). In order to fully understand the drivers of sea ice variability, extending the sea ice data record backwards in time is essential.

Globally, SIE information prior to the satellite data record came largely from ice charts and ship observations. While there have been efforts to include these data in long-term assessments of sea ice change, the data are typically provided at relatively coarse spatial and temporal resolutions (1° grid) (), interpolating in both time and space (). Only satellite-based datasets offer the ability to cover both hemispheres at improved spatial and temporal resolutions and generally show consistency in processing methods (). The SIC derived from NIMBUS 5 Electrically Scanning Microwave Radiometer (ESMR) data was previously processed by . Here, we apply a new processing method that is comparable to the EUMETSAT/ESA CCI SIC record from 1978 and onwards (see ; ). Compared to , this method reduces atmospheric noise regionally over both ice and water surfaces and uses the pre-processed data to develop a SIC algorithm calibration that is effective in removing both instrument drift and offsets. Seasonal sea ice signature variations are removed by using dynamical tie points. Lastly, the algorithm calculates temporally and spatially varying uncertainty estimates. The ESMR SIC data are presented on the same grid and with the same masking as the EUMETSAT/ESA CCI record, which makes these two records directly comparable. This and the modern processing chain mentioned above warrant the reprocessing presented in this article.

In Sect. 2, the satellite and reanalysis data are described, including the formatting and initial filtering of the data. Section 3 introduces the radiative transfer model (RTM) used for the atmospheric correction, while Sect. 4 describes the dynamical tie points, the SIC algorithm with uncertainty estimations, the land-spillover method, and data flags assigned during post-processing. In Sect. 5, the resulting SIC dataset is presented and compared to other datasets. Finally, Sect. 6 consists of a discussion, and Sect. 7 provides the conclusions of this work.

The NIMBUS 5 ESMR instrument was a cross-track scanner that measured at 78 scan positions perpendicular to the flight track with a maximum incidence angle of about 64° on both sides (). No direct observations at nadir were made; the closest positions were at ±0.7°. Its near-circular orbit had a height of about 1112 km and an inclination of 81°. The phased-array antenna dimensions were 85.5×83.3 cm, and the spatial resolution was about 25 km near nadir, increasing to about 160×45 km at the edges of the swath (). The full swath was about 3100 km, with the varying incidence angle and the spatial resolution giving very good (unprecedented) daily coverage in polar regions with no gaps, i.e., no pole holes. The ESMR onboard the NIMBUS 5 satellite was a one-channel 19.35 GHz horizontally polarized microwave radiometer operating from 11 December 1972 until 16 May 1977 (1617 d) with some interruptions (see the list of days with missing files in Appendix A2). Due to a hot-load anomaly, there are major data gaps between March to May and again in August 1973. Another major data gap occurs from 3 June until 14 September 1975 because the ground segment was used for receiving NIMBUS 6 data instead (). When operation was resumed in September 1975, the instrument was only operated approximately every other day. From late 1976 to the end of the mission, operation was highly irregular. The last file in the dataset is from 16 May 1977. The data have recently been made available online by NASA in the original tape archive format (TAP files).

The radiative transfer model (RTM) was developed specifically for atmospheric noise reduction and is comparable to the RTMs used in and but with the addition that this ESMR RTM (Eq. ) can be applied for different incidence angles over both ocean and ice. The RTM takes as input the atmospheric columnar water vapor V [mm or kg m-2], the 10 m wind speed W [m s-1], the atmospheric columnar cloud liquid water L [mm or kg m-2], the sea surface temperature Ts [K], the ice emitting layer temperature Ti [K], the sea ice concentration cice [0–1], and the incidence angle [°]. In return, it simulates the top-of-the-atmosphere 19.35 GHz TB at horizontal polarization: 5TB=RTM(V,W,L,Ts,Ti,cice,θ).

The RTM uses the atmospheric part of the model described in to compute the atmospheric emission, transmissivity, and reflectivity at the sea surface (open water) together with added modules for simulating the sea ice emissivity (Fig. ) and open water reflectivity as a function of incidence angle.

For the sea ice emissivity, a look-up table is produced from a simulation using a combined sea ice thermodynamic and emission model during the Arctic winter on first-year ice. The thermodynamical and emission model setup and the simulations are described in . The emissivities as a function of incidence angle are shown in Fig. , and the look-up table is given in Table .

Sea water permittivity, which is used to estimate the calm sea reflectivity (Eq. ) as a function of temperature, is computed using equation E64 (p. 2046) in . The permittivity is almost invariant with respect to the water salinity at 19 GHz, and a constant value of 34 ppt is used here.

The calm sea (Fresnel) power reflection coefficient, rh, as a function of the relative permittivity, ϵ, and the incidence angle, θ, for a lossy medium is computed using Eq. (1.52) in , i.e., 6rh(θ)=(p-cos⁡(θ))2+q2(p+cos⁡(θ))2+q2, where p and q are abbreviations for: 7p=1(2)(ϵ′r-sin⁡2θ)2+ϵ′′r2)12+(ϵ′r-sin⁡2θ)12 and 8q=1(2)(ϵ′r-sin⁡2θ)2+ϵ′′r2)12-(ϵ′r-sin⁡2θ)12. The relative permittivity ϵr=ϵ′r+ϵ′′rj of the water surface is a complex number. The calm sea emissivity, E0, is then 9E0=1-rh.

The rough water surface emissivity component, EW, is added to the calm sea emissivity, E0, to produce the total sea water emissivity, Ewater. Between ESMR incidence angles of 0 and 63°, the sensitivity of the 19.35 GHz horizontal polarization, EW, to wind speed is an almost linear function Δ(EWTs)ΔW=0.0094θ+0.3 of the incidence angle θ (), i.e., 10EW=W(0.0094θ+0.3)Ts, and 11Ewater=E0+EW, where θ is the incidence angle in degrees, W is the wind speed, and Ts is the sea surface temperature [K].

This combination of E0 and EW follows the procedure described in .

The resulting brightness temperature is a linear combination of the sea water and sea ice emission weighted by the SIC following : 12TB=TBU+τ((1-cice)Ewater×Ts+(1-cice)(1-Ewater)(ΩTBD+τTBC)+ciceEiceTi+cice(1-Eice)(TBD+τTBC)), where TBU is the upwelling brightness temperature from the atmosphere, τ is the atmospheric transmissivity, Ewater is the water surface emissivity, Eice is the sea ice emissivity, Ω is the reflection reduction factor due to water surface roughness, TBD is the downwelling brightness temperature, and TBC is the cosmic background brightness temperature (2.7 K).

EMSR-simulated TB values and emissivities have been compared with other TB values simulated using other RTMs for AMSR (), SMMR (), and SSM/I () for a constant incidence angle of 55°, which is close to the incidence angles of other instruments and RTMs (53–55°). The comparison showed that the TB values of the ESMR RTM are within the range (within approx. 2 K) of values obtained with the other models and therefore seem to be reasonable given the differences in instrument center frequencies and measurement geometries. Note that in the correction procedure, the difference between two simulated TB values is used to minimize model biases. Even if the absolute values of the RTM-simulated TB values were biased, this bias would be removed by taking the difference between two simulated TB values, which is the only part used in the correction.

The RTM is an essential part of the SIC derivation for applying an atmospheric noise reduction. The following section presents the calculations of dynamical tie points and the SIC algorithm along with uncertainty estimations. Lastly, the post-processing, including the land-spillover method and data flag assignment, is described. A flow chart illustrating this processing chain can be found in the ESA CCI ESMR product user guide (PUG) ().

A list of all output variables in the daily SIC files and a short description of them can be seen in Appendix . Examples of monthly means of the SIC and the mean uncertainty can be seen in Figs.  and .

It is worth noting that the coverage in Fig.  is complete and, because of the ESMR's wide swath width of 3100 km and its inclination, the North Pole is covered, in contrast to the satellite microwave radiometers that have followed the NIMBUS 5 ESMR, which have a “pole hole”. The area covered by multi-year ice in the central Arctic has a lower SIC than the first-year ice regions. This is a consequence of the one-channel SIC algorithm, which has inherent ambiguity in SIC, ice type, and emitting layer temperature variations. In Figs. b and b, it can be seen that the uncertainties are largest near the ice edges, as expected. This is due to the resampling uncertainty, which dominates near the ice edge, where the spatial variability of TB is high. Coastal regions also show higher uncertainties for this reason, since the land-spillover correction is first applied to the SIC after the uncertainty estimations.

The SIC shows interesting sea ice features in the years 1972–1977. One such feature is the Odden ice tongue () extending eastward from the East Greenland Current, which is visible in Fig.  (around 73° N, 0° E), while another feature is the Maud Rise Polynya (), an open water area encircled by sea ice in the Southern Hemisphere, which can be seen in Fig.  (around 65° S, 0° E). Both examples were much larger in extent in the 1970s and occurred more frequently then than they do today (; ; ).

The daily coverage of valid data points that passed all filtering varied a lot through the ESMR's operating period. While there was nearly full coverage for the first few months, it got much worse after the summer of 1975, when the instrument only recorded data every second day. An example of the poor coverage is shown for May 1976 in Fig. .

Monthly averaged SICs are derived to compare our results against other datasets. Only months with 99 % coverage were used in the comparison, i.e., 99 % of all grid points are covered at least once per month. Monthly mean SIEs are calculated from the monthly SICs using the threshold cice>30%. In Fig. , the ESMR dataset (orange line) is shown together with the OSI-SAF climate data record (CDR; blue line) for 1979–2022 () and the sea ice extent derived from the NSIDC's NIMBUS 5 ESMR ice concentration product (green line) (), using the same threshold for all products (cice>30%).

The comparison shows comparable SIE levels around 1980. In general, our ESMR dataset has slightly higher monthly SIE values than the NSIDC's ESMR product, even though the seasonal pattern is the same.

The mean difference between our sea ice extent and that from NSIDC is 0.24 million km2 in the Arctic and 0.59 million km2 in the Antarctic for the whole dataset.

For the Northern Hemisphere, the SIE during the operational period of NIMBUS 5 ESMR (1972 to 1977) seems to have been similar in magnitude to the SIE during the operational period of NIMBUS 7 SMMR (from 1978 to 1987), with the ESMR's minimum extents being slightly higher than the SMMR's ones. In the Southern Hemisphere, the values for the second half of the 1970s seem to have been around the same magnitude as the largest SIE during the 2014/2015 season.

Comparisons between different sea ice products and the new ESMR dataset proved to be more difficult than initially expected, since not only the processing algorithms but also the land masks, map projections, and dataset grids differ. We were not able to find two independent SIC datasets for 1978 onwards and 1972–1977 which share exactly the same land mask.

Thus, it was decided at the beginning of the processing to use the same land mask as the OSI-420 product (1978 onwards) () for our ESMR dataset, i.e., a 25 km equal-area grid (EASE-2 version 2) land mask, to at least ensure a fair comparison between these two datasets. The NSIDC ESMR dataset (green line in Fig. ) used a different land mask with a polar stereographic projection (), but it was still compared to our ESMR dataset.

The difference in SIE is therefore also influenced by the different projections of the data; however, the area difference between the projections is relatively small (only a few thousand km2), so even a re-projection is expected to yield minimal differences compared to the differences caused by the use of different land masks. The comparison of different land masks is complicated by the varying sea ice extent, which exposes more or less land throughout its annual cycle and thus changes the number of grid points affected by the land mask.

The land mask land area differs between the OSI-SAF and NSIDC ESMR land masks. A comparison of land and ocean points between the NSIDC land mask and the OSI-SAF land mask showed a difference for the Northern Hemisphere of 460 000 km2 (north of 60° N), where the NSIDC land mask has more land, while the difference is the opposite and much smaller in the Southern Hemisphere – only 79 000 km2 (south of 60° S) – with the OSI-SAF's land mask having slightly more land. More land points in the land mask result in fewer available grid points for potential sea ice. The difference in Southern Hemisphere sea ice extent is significantly larger and opposite to the expected contrast from the land mask difference. In the Northern Hemisphere, it is not very clear how much of the SIE difference can be accounted for by the land mask or by algorithm differences. However, since the SIE differences vary a lot, and the differences in the Southern Hemisphere clearly cannot be explained by the land mask difference alone, it is likely that most of the SIE differences come from the algorithms and processing methods, such as the atmospheric correction and tie point calculation.

The blue curve in Fig.  is based on OSI-SAF's SIC products OSI-450 and OSI-430-b (). The shown extend corresponds to the SIE of the OSI-420 product () but with a 30 % instead of a 15 % sea ice threshold applied, matching the OSI-402-d sea ice extent product ().

Compared to the more common 15 %, the 30 % threshold was better suited to a comparison between different ESMR SIE datasets due to the relatively high noise level, which can be seen from the total uncertainty in Fig. . The uncertainty algorithm has been applied for easier data assessment and comparison to other datasets.

A large number of ESMR data are currently filtered out, and the 99 % threshold for inclusion in the monthly timeline in particular filters out the second half of the ESMR data, where large data gaps occurred, as seen in Fig. .

The filters worked as expected and removed erroneous TB values from the raw data. A reprocessing of the data to rescue more of the currently filtered out data points from the 20 % of ESMR data files is planned. Two approaches will be used to improve the data selection during the next processing. One approach consists of making changes to the set of filters, i.e., performing an adjustment of the filter thresholds, testing different filters, and the possible incorporation of an incidence angle dependency into the data selection. The other approach is to recalibrate some of the erroneous data files that have shown some systematic offsets to rescue whole swaths.

To reduce the uncertainty caused by atmospheric noise, the brightness temperatures were corrected with an RTM that used several atmospheric parameters from NWP (ERA5) data, such as water vapor and wind, as input. This correction showed a consistent reduction of the standard deviation of the brightness temperatures for water points in both hemispheres, as can be seen from Fig. . The correction was less steady over ice surfaces, since the RTM did not describe all relevant processes related to the snow and ice, which are the main noise source over sea ice. Such processes include sea ice deformation, the creation of leads or ridges, and changes in the snow layer, e.g., changes in snow depth, snow density, and grain size but also melting and refreezing, which influences scattering and emission processes inside the layer. Atmospheric noise caused by, e.g., water vapor and cloud liquid water influences the TB over sea ice, but its influence is smaller: only around one-third of the total noise (). By correcting for atmospheric effects with ERA5 data, we might have introduced some noise into the angular dependency of the SIC due to the use of an incident-angle-dependent emissivity in the RTM (Fig.  and Table ).

To avoid biases from the RTM and the NWP data, dynamical tie points have been used, which also calibrate the algorithm for seasonal variations and instrument drift. However, we currently use mean tie points that are independent of the incident angle. Therefore, a possible improvement to a future version of the dataset might be accomplished by using angle-dependent tie points instead.

Even after filtering the data for obvious errors, it is clear that there are still issues with the absolute calibration of the instrument (). For example, after the hot-load anomaly in 1973, the ocean TB in the Southern Hemisphere is several kelvin below the TB level before the anomaly, and in 1976 there is a dip in May and June followed by a sharp increase in TB (). Low-frequency (with timescales ≥ days) TB variations and regional variations at hemispherical scales are compensated for by the dynamical SIC algorithm tie points ().

In spite of data gaps and calibration issues, the experimental NIMBUS satellite program was very successful. Applying modern processing methodologies, including dynamical tie points and atmospheric noise reduction of the TB values, reduces the noise over both ice and open water consistently. This newly processed ESMR sea ice dataset extends the existing sea ice climate data record (CDR) with an important period from the 1970s. This extension of the SIE record contributes to the United Nations Sustainable Development Goals (SDGs) related to climate change by providing more observations for longer-term assessments of Arctic and Antarctic sea ice changes.

The newly processed ESMR data have been released through the ESA CCI Open Data Portal: https://climate.esa.int/en/odp/#/project/sea-ice (last access: 8 March 2024) and at DOI: 10.5285/34a15b96f1134d9e95b9e486d74e49cf ().

In this paper, we have presented a new SIC dataset covering the period 1972–1977, obtained using the ESMR data from the NIMBUS 5 satellite. The dataset consists of resampled daily NetCDF files for the Northern and Southern hemispheres. The SIC, associated uncertainties, and processing flags are included in the dataset. The uncertainties follow the same principles as those of the EUMETSAT SIC CDR, including both algorithm and resampling uncertainties. The choice to use the same land mask, spatial grid, and projection as for EUMETSAT's SIC CDR make comparisons between the time periods easier.

A comparison to NSIDC's ESMR SIC product and the OSI-SAF CDR showed that the seasonal pattern is very similar to NSIDC's ESMR SIC product, but our product shows systematically larger SIE values which can not be explained by differences between land masks alone. For the Northern Hemisphere, our SIE values match the levels in the 1980s from the OSI-SAF CDR with the same land mask, while values in the Southern Hemisphere in the 1970s are larger than those in the 1980s.

Compatibility with the EUMETSAT's SIC CDR was achieved by using a similar processing chain. The processing included atmospheric noise reduction with the use of an RTM and the ERA5 atmospheric data, which lowered the standard deviation of the TBs consistently. Additionally, dynamical tie points were used to avoid biases from the RTM and NWP data as well as to adjust for seasonal variability and instrument biases. To ensure better data assessment in analysis and models and easier comparison to other datasets, temporally and regionally varying uncertainty estimates have been included in our ESMR dataset.