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The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km 2 , respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km 2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at 10.5285/34a15b96f1134d9e95b9e486d74e49cf. [ABSTRACT FROM AUTHOR]

The Electrically Scanning Microwave Radiometer (ESMR) instrument on board the NIMBUS 5 satellite was a one channel microwave radiometer measuring the 19.35 GHz horizontally polarised brightness temperature (TB) from Dec. 11, 1972 to May. 16. 1977. The original tape archive data in swath projection have recently been made available online by NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though ESMR was a predecessor of modern multi frequency radiometers, there are still parts of modern processing methodology which can be applied to the data to derive the sea ice extent globally. Here we have reprocessed the entire data set using a modern processing methodology, that includes implementation of preprocessing filtering, dynamical tie-points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include re-sampling to daily grids, land-spill-over correction, application of climatological masks, setting of processing flags and estimation of sea ice extent, monthly means and estimation of trends. This sea ice dataset derived from NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid 1970s. To make a consistent analysis of the sea ice development through time easier, the same grid and landmask as for EUMETSAT's OSI-SAF SMMR based sea ice CDR have been used for our ESMR dataset. SIC uncertainties have been included for further ease of comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) derived from the same ESMR dataset, with mean differences of 240.000 and 590.000 km², respectively. The largest differences reach up to 2 million km², when comparing monthly sea ice extents. Such large differences cannot be explained by the different grids and landmasks of the datasets alone, and must therefore also result from the difference in data filtering and algorithms, such as the dynamical tiepoints and atmospheric correction. The new ESMR SIC data set has been released as part of the ESA Climate Change Initiative Programme (ESA CCI) and is publicly available at: http://dx.doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023). [ABSTRACT FROM AUTHOR]

The Arctic region is responding heavily to climate change, and yet, the air temperature of ice-covered areas in the Arctic is heavily under-sampled when it comes to in situ measurements, resulting in large uncertainties in existing weather and reanalysis products. This paper presents a method for estimating daily mean clear-sky 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model, which has been tuned against in situ observations to estimate daily mean T2m based on clear-sky satellite ice surface skin temperatures. The daily satellite-derived T2m product includes estimated uncertainties and covers the Arctic sea ice and the Greenland Ice Sheet during clear skies for the period 2000–2009, provided on a 0.25 ∘ regular latitude–longitude grid. Comparisons with independent in situ measured T2m show average biases of 0.30 and 0.35 ∘ C and average root-mean-square errors of 3.47 and 3.20 ∘C for land ice and sea ice, respectively. The associated uncertainties are verified to be very realistic for both land ice and sea ice, using in situ observations. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic and is independent of numerical weather prediction model input. Therefore, it provides an important supplement to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison of T2m derived from satellite and ERA-Interim/ERA5 estimates shows that the satellite-derived T2m validates similar to or better than ERA-Interim/ERA5 against in situ measurements in the Arctic. [ABSTRACT FROM AUTHOR]

Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and the impact on the Ka- and Ku-band radar scattering horizon or the "track point" (i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (>0.1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka- and Ku-band dual-frequency CRISTAL radar altimeter missions. [ABSTRACT FROM AUTHOR]

The observational uncertainty in sea ice concentration estimates from remotely sensed passive microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarization from standard output of an Earth System Model. To evaluate sources of uncertainties when applying ARC3O, we compare brightness temperatures simulated by applying ARC3O on three assimilation runs of the MPI Earth System Model (MPI-ESM), assimilated with three different sea ice concentration products, with brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that the simulated and observed brightness temperatures differ up to 10 K in the period between October and June, depending on the region and the assimilation run. We show that these discrepancies between simulated and observed brightness temperature can be attributed mainly to the underlying observational uncertainty in sea ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea ice concentration and the melt pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable of realistically translating the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization. [ABSTRACT FROM AUTHOR]

We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of 6.9 GHz. We investigate the influence of simplifying assumptions for the representation of sea ice vertical properties on the simulation of microwave brightness temperatures. We do so in a one-dimensional setup, using a complex 1D thermodynamic sea ice model and a 1D microwave emission model. We find that realistic brightness temperatures can be simulated in cold conditions from a simplified linear temperature profile and a simplified salinity profile as a function of depth in the ice. These realistic brightness temperatures can be obtained based on profiles interpolated to as few as five layers. Most of the uncertainty resulting from the simplifications is introduced by the simplification of the salinity profiles. In warm conditions, the simplified salinity profiles lead to brine volume fractions that are too high in the subsurface layer. To overcome this limitation, we suggest using a constant brightness temperature for the ice during warm conditions and treating melt ponds as water surfaces. Finally, in our setup, we cannot assess the effect of wet snow properties. As periods of snow with intermediate moisture content, typically occurring in spring and fall, locally last for less than a month, our approach allows one to estimate realistic brightness temperatures at 6.9 GHz from climate model output for most of the year. [ABSTRACT FROM AUTHOR]