Comparison of sea ice concentration derived from small-scale ice motion and interpretation of spaceborne passive microwave radiometry
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sea ice radiometry↗
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Antarctic sea ice concentrations inferred from Nimbus 5 ESMR and Landsat imagery
Landsat Multispectral Scanner near-infrared band images (MSS-7) are used to calculate Antarctic ice concentration values and these results are quantitatively compared to those derived from Nimbus 5 electrically scanning microwave radiometer (ESMR) brightness temperature data. The set of images includes regions near the ice edge and near the continental boundary during late winter or spring. The percentage of ice cover is derived from the microwave data, using an algorithm that incorporates the brightness temperature emissivity and interpolated climatological physical temperatures and that is not dependent on spatial resolution. Cloud-free Landsat images are used to provide an alternative determination of large-scale ice concentrations, but this method is limited in its ability to resolve individual ice flows, especially near the ice edge. A proportional classification procedure is applied to Landsat data based on observed reflection and the results are correlated with similarly-derived ESMR ice concentration values. The corresponding ice concentration values are found to agree to + or - 15%, with the largest uncertainties occurring in regions of low ice concentration.
Comiso, J. C.↗
Global maps of sea ice concentration, age and surface temperature derived from Nimbus-7 scanning multichannel microwave radiometer data - A case study
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Gloersen, P.↗
Derivation of sea ice concentration, age and surface temperature from multispectral microwave radiances obtained with the Nimbus-7 scanning multichannel microwave radiometer
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Gloersen, P.↗
Effects of weather on the retrieval of sea ice concentration and ice type from passive microwave data
Effects of wind, water vapor, and cloud liquid water on ice concentration and ice type calculated from passive microwave data are assessed through radiative transfer calculations and observations. These weather effects can cause overestimates in ice concentration and more substantial underestimates in multi-year ice percentage by decreasing polarization and by decreasing the gradient between frequencies. The effect of surface temperature and air temperature on the magnitudes of weather-related errors is small for ice concentration and substantial for multiyear ice percentage. The existing weather filter in the NASA Team Algorithm addresses only weather effects over open ocean; the additional use of local open-ocean tie points and an alternative weather correction for the marginal ice zone can further reduce errors due to weather. Ice concentrations calculated using 37 versus 18 GHz data show little difference in total ice covered area, but greater differences in intermediate concentration classes. Given the magnitude of weather-related errors in ice classification from passive microwave data, corrections for weather effects may be necessary to detect small trends in ice covered area and ice type for climate studies.
Maslanik, J. A.↗
Inter-relationship between subtropical Pacific sea surface temperature, Arctic sea ice concentration, and North Atlantic Oscillation in recent summers
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Young-Kwon Lim↗
The Surface Albedo of Sea Ice in CMIP6 and the Implications for the Surface Albedo Feedback
The Arctic has experienced rapid sea ice loss and a substantial decline in surface albedo, significantly impacting its radiation budget. Climate models from the Coupled Model Intercomparison Project (CMIP6) reproduce these changes. However, inconsistencies remain among models regarding the magnitude, spatial distribution, and seasonal patterns of Arctic surface albedo evolution. This study investigates these discrepancies by comparing model outputs with observation from the Clouds and the Earth's Radiant Energy System (CERES). We develop a decomposition method to assess the contributions of sea ice albedo, sea ice concentration, and sea ice extent to Arctic surface albedo. Over land, differences in snow cover account for the substantial inter-model spread in surface albedo, while over the ocean, sea ice albedo, concentration, and extent all contribute. Comparisons between CMIP6 and the Atmospheric Model Intercomparison Project (AMIP) simulations, which use identically prescribed sea ice concentrations, reveals considerable inter-model spread in Arctic Ocean surface albedo due to differences in sea ice albedo. Applying the decomposition method to projections shows that models predicting larger decreases in sea ice concentration and extent, especially in the Central Arctic, exhibit lower surface albedo and stronger sea ice albedo feedback. Beyond 2050, Arctic Ocean surface albedo decline is mainly influenced by sea ice extent indicating that the retreat of the ice edge is the most important process to constrain the surface albedo feedback. This study provides insights into factors contributing to the spread and changes in Arctic surface albedo and the associated sea ice albedo feedback.
Patrick C Taylor↗
EOS Aqua AMSR-E Arctic Sea-Ice Validation Program: Arctic2006 Aircraft Campaign Flight Report
In March 2006, a coordinated Arctic sea-ice validation field campaign using the NASA Wallops P-3B aircraft was successfully completed. This campaign was the second Alaskan Arctic field campaign for validating the Earth Observing System (EOS) Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. The first campaign was completed in March 2003. The AMSR-E, designed and built by the Japanese Space Agency for NASA, was launched May 4, 2002 on the EOS Aqua spacecraft. The AMSR-E sea-ice products to be validated include sea-ice concentration, sea-ice temperature, and snow depth on sea ice. The focus of this campaign was on the validation of snow depth on sea ice and sea-ice temperature. This flight report describes the suite of instruments flown on the P-3, the objectives of each of the six flights, the Arctic regions overflown, and the coordination among satellite, aircraft, and surface-based measurements.
Cavalieri, D. J.↗
Data report on variations in the composition of sea ice during MIZEX/East'83 with the Nimbus-7 SMMR
Data acquired with the scanning multichannel microwave radiometer (SMMR) on board the Nimbus-7 satellite for a six-week period including the 1983 MIZEX in Fram Strait were analyzed with the use of a previously developed procedure for calculating sea ice concentration, multiyear fraction, and ice temperature. These calculations can compared with independent observations made on the surface and from aircraft in order to check the validity of the calculations based on SMMR data. The calculation of multiyear fraction, which was known earlier to be invalid near the melting point of sea ice, was of particular interest during this period. The indication of multiyear ice was found to disappear a number of times, presumably corresponding to freeze/thaw cycles which occurred in this time period. Both grid-print maps and grey-scale images of total sea ice concentration and multiyear sea ice fraction for the entire period are included.
Gloersen, P.↗
DMSP-SSM/1 NASA algorithm validation using primarily LANDSAT and secondarily DMSP and/or AVHRR visible and thermal infrared satellite imagery
The approach to the DMSP SSMI (Defense Meteorological Satellite Program; Special Sensor Microwave Imager) sea-ice validation effort is to demonstrate a quantitative relationship between the SSMI-derived sea ice parameters and those same parameters derived from other data sets including visible and infrared satellite imagery, aerial photographic and high-resolution microwave aircraft imagery. The question to be addressed is to what accuracy (relative to these other observations) can the following ice parameters be determined: (1) position of the ice boundary; (2) total sea ice concentration; and (3) multiyear sea ice concentration. Specific tasks include: (1) a study of the interrelationship of surface information content and sensor spatial and spectral resolution in order to establish relationships between ice surface features and the manner in which they are expressed in the satellite observations; and (2) apply these relationships to map the sea ice features which can be used to evaluate NASA's proposed SSM/1 sea ice algorithms. Other key points to be addressed include the accuracy to which these parameters can be determined in different regions (marginal ice zone such as Bering Sea, Arctic ocean, such as Beaufort Sea); the accuracy of these parameters for different seasons; the accuracy of the algorithms weather filter under different weather conditions; and the effectiveness of the 85.5 GHz channels to locate the ice edge.
Steffen, K.↗
Observational Evidence of a Hemispheric-wide Ice-ocean Albedo Feedback Effect on Antarctic Sea-ice Decay
The effect of ice-ocean albedo feedback (a kind of ice-albedo feedback) on sea-ice decay is demonstrated over the Antarctic sea-ice zone from an analysis of satellite-derived hemispheric sea ice concentration and European Centre for Medium-Range Weather Forecasts (ERA-40) atmospheric data for the period 1979-2001. Sea ice concentration in December (time of most active melt) correlates better with the meridional component of the wind-forced ice drift (MID) in November (beginning of the melt season) than the MID in December. This 1 month lagged correlation is observed in most of the Antarctic sea-ice covered ocean. Daily time series of ice , concentration show that the ice concentration anomaly increases toward the time of maximum sea-ice melt. These findings can be explained by the following positive feedback effect: once ice concentration decreases (increases) at the beginning of the melt season, solar heating of the upper ocean through the increased (decreased) open water fraction is enhanced (reduced), leading to (suppressing) a further decrease in ice concentration by the oceanic heat. Results obtained fi-om a simple ice-ocean coupled model also support our interpretation of the observational results. This positive feedback mechanism explains in part the large interannual variability of the sea-ice cover in summer.
Nihashi, Sohey↗
Satellite Data Sets in the Polar Regions
We have generated about two decades of consistently derived geophysical parameters in the polar regions. The key parameters are sea ice concentration, surface temperature, albedo, and cloud cover statistics. Sea ice concentrations were derived from the Scanning Multichannel Microwave Radiometer (SMMR) data and the Special Scanning Cl Microwave Imager (SSM/I) data from several platforms using the enhanced Bootstrap Algorithm for the period 1978 through 1999. The new algorithm reduces the errors associated with spatial and temporal variations in the emissivity and surface temperatures of sea ice. Also, bad data at ocean/land interfaces are identified and deleted in an unsupervised manner. Surface ice temperature, albedo and cloud cover statistics are derived simultaneously from the Advanced Very High Resolution Radiometer (AVHRR) data from 1981 through 1999 and mapped at a higher resolution but the same format as the ice concentration data. The technique makes use these co-registered ice concentration maps to enable cloud masking to be done separately for open ocean, sea ice and land areas. The effect of inversion is minimized by taking into consideration the expected changes in the effect of inversion with altitude, especially in the Antarctic. A technique for ice type regional classification has also been developed using multichannel cluster analysis and a neural network. This provide a means to identify large areas of thin ice, first year ice, and older ice types. The data sets have been shown to be coherent with each other and provide a powerful tool for in depth studies of the currently changing Arctic and Antarctic environment.
Comiso, Josefino C.↗
Cloud Properties and Boundary Layer Stability Above Southern Ocean Sea Ice and Coastal Antarctica
Significant variability in climate predictions originates from the simulated cloud cover over the Southern Ocean. Historically, Southern Ocean cloud and aerosol properties have been less studied than their northern hemisphere counterparts, and cloud-sea-ice interactions over the Southern Ocean also remain largely unexamined. We used data from combined radar, lidar, radiometer, radiosonde, and ERA5 reanalysis profiles to investigate cloud property relationships to cloud temperature, sea-ice concentration, and boundary layer stability. Our findings show correlations between both cloud macrophysical properties and radiative effects and sea-ice concentration, and that the marine atmospheric boundary layer is more stable over higher sea-ice concentrations. Mixed-phase cloud frequency of occurrence was highest over the sea-ice zone at 15%, three times higher than over cold water south of the Antarctic Polar Front. For temperatures greater than –15°C, low-level, single-layer clouds were more likely to precipitate ice if they were coupled to cold-water or sea-ice surfaces than if they were decoupled from these surfaces, with the highest percentage of clouds precipitating ice observed over sea ice. These findings suggest a surface source of ice-nucleating particles at high southern latitudes that increases cloud glaciation probability. We discuss the implications of our results for future studies into the relationship between cloud properties, aerosols, sea ice, and boundary layer stability at high latitudes over the Southern Ocean.
54 ENVIRONMENTAL SCIENCES↗
Modeling of topographic effects on Antarctic sea ice using multivariate adaptive regression splines
The role of seafloor topography in the spatial variations of the southern ocean sea ice cover as observed (every other day) by the Nimbus 7 scanning multichannel microwave radiometer satellite in the years 1980, 1983, and 1984 is studied. Bottom bathymetry can affect sea ice surface characteristics because of the basically barotropic circulation of the ocean south of the Antarctic Circumpolar current. The main statistical tool used to quantify this effect is a local nonparametric regression model of sea ice concentration as a function of the depth and its first two derivatives in both meridional and zonal directions. First, we model the relationship of bathymetry to sea ice concentration in two sudy areas, one over the Maud Rise and the other over the Ross Sea shelf region. The multiple correlation coefficient is found to average 44% in the Maud Rise study area and 62% in the Ross Sea study area over the years 1980, 1983, and 1984. Second, a strategy of dividing the entire Antarctic region into an overlapping mosaic of small areas, or windows is considered. Keeping the windows small reduces the correlation of bathymetry with other factors such as wind, sea temperature, and distance to the continent. We find that although the form of the model varies from window to window due to the changing role of other relevant environmental variables, we are left with a spatially consistent ordering of the relative importance of the topographic predictors. For a set of three representative days in the Austral winter of 1980, the analysis shows that an average of 54% of the spatial variation in sea ice concentration over the entire ice cover can be attributed to topographic variables. The results thus support the hypothesis that there is a sea ice to bottom bathymetry link. However this should not undermine the considerable influence of wind, current, and temperature which affect the ice distribution directly and are partly responsible for the observed bathymetric effects.
De Veaux, Richard D.↗
Satellite Remote Sensing: Passive-Microwave Measurements of Sea Ice
Satellite passive-microwave measurements of sea ice have provided global or near-global sea ice data for most of the period since the launch of the Nimbus 5 satellite in December 1972, and have done so with horizontal resolutions on the order of 25-50 km and a frequency of every few days. These data have been used to calculate sea ice concentrations (percent areal coverages), sea ice extents, the length of the sea ice season, sea ice temperatures, and sea ice velocities, and to determine the timing of the seasonal onset of melt as well as aspects of the ice-type composition of the sea ice cover. In each case, the calculations are based on the microwave emission characteristics of sea ice and the important contrasts between the microwave emissions of sea ice and those of the surrounding liquid-water medium.
Parkinson, Claire L.↗
Impact Studies of a 2 C Global Warming on the Arctic Sea Ice Cover
The possible impact of an increase in global temperatures of about 2 C, as may be caused by a doubling of atmospheric CO2, is studied using historical satellite records of surface temperatures and sea ice from late 1970s to 2003. Updated satellite data indicate that the perennial ice continued to decline at an even faster rate of 9.2 % per decade than previously reported while concurrently, the surface temperatures have steadily been going up in most places except for some parts of northern Russia. Surface temperature is shown to be highly correlated with sea ice concentration in the seasonal sea ice regions. Results of regression analysis indicates that for every 1 C increase in temperature, the perennial ice area decreases by about 1.48 x 10(exp 6) square kilometers with the correlation coefficient being significant but only -0.57. Arctic warming is estimated to be about 0.46 C per decade on average in the Arctic but is shown to be off center with respect to the North Pole, and is prominent mainly in the Western Arctic and North America. The length of melt has been increasing by 13 days per decade over sea ice covered areas suggesting a thinning in the ice cover. The length of melt also increased by 5 days per decade over Greenland, 7 days per decade over the permafrost areas of North America but practically no change in Eurasia. Statistically derived projections indicate that the perennial sea ice cover would decline considerably in 2025, 2035, and 2060 when temperatures are predicted by models to reach the 2 C global increase.
Comiso, Josefino C.↗
The effects of preconditioning on the summer sea ice thickness evolution during MOSAiC
The central role of sea ice within the Arctic climate system is clear, as is the importance of sea ice properties in the Arctic Ocean’s evolution and response to climate change. Less clear is how the history of the sea ice floe (in winter and spring) precondition the summer thickness evolution. Accurate accounting for the collective influences of processes that affect the summer sea ice thickness evolution is necessary for determining sea ice survivability and the rate of future sea ice loss. In this study, we use a satellite-derived sea ice parcel database co-located with the MOSAiC floe to explore the factors that precondition the summer sea ice thickness evolution. First. we compare the >500 collocated satellite-tracked sea ice parcels that intersected the MOSAiC drift track with observations to evaluate the satellite-derived surface energy budget and surface properties, finding agreement with correlations >0.9. Approximately 60% of the satellite tracked parcels survived the summer melt season. Secondly, we explore the spread in sea ice thickness and survivability across the satellite-tracked parcels, finding contributions from spring/summer surface albedo evolution, surface temperature, clouds, and parcel sea ice concentration. The satellite tracked sea ice parcel analysis provides insights into the spatial coherence of the factors that influence sea ice thickness evolution.
Arctic↗
An Integrated Examination of AMSR2 Products over Ocean
Integrated processing and analysis are presented for oceanic retrievals of precipitation, sea ice concentration, columnar water vapor and cloud water, sea surface temperature, and near surface wind from the advanced microwave scanning radiometer-2 (AMSR2) sensor. By developing a common algorithmic framework and permitting iterative interaction between historically separate science algorithms, ambiguous and contradictory retrieval results are minimized. The integration also serves to improve each algorithm individually, decreasing reliance on ancillary datasets. Case studies are presented that exemplify both ongoing challenges for these retrievals and potential research uses of such an integrated satellite product. Two cases are presented each for precipitation at high latitudes and marginal sea ice detection, with analysis supplemented by satellite data from CloudSat profiles and Himawari-8 imagery. Detection and retrieval of snowfall remains a challenge, while light rainfall detection can be aided by a variational algorithm. Sea ice concentrations below 20% cause disagreements, but the algorithms otherwise agree well on the sea ice edge. Potential mitigation strategies for ambiguous areas of light rainfall and marginal sea ice are discussed. The analysis demonstrates potential avenues for future algorithm development, but also some physical limitations of remote sensing with the AMSR2 frequencies.
variational methods↗