J. C. Comiso

Deriving long‐term time series of sea ice cover from satellite passive‐microwave multisensor data sets

We have generated consistent sea ice extent and area data records spanning 18.2 years from passive-microwave radiances obtained with the Nimbus 7 scanning multichannel microwave radiometer and with the Defense Meteorological Satellite Program F8, F11, and F13 special sensor microwave/imagers. The goal in the creation of these data was to produce a long-term, consistent set of sea ice extents and areas that provides the means for reliably determining sea ice variability over the 18.2-year period and also serves as a baseline for future measurements. We describe the method used to match the sea ice extents and areas from these four multichannel sensors and summarize the problems encountered when working with radiances from sensors having different frequencies, different footprint sizes, different visit times, and different calibrations. A major obstacle to adjusting for these differences is the lack of a complete year of overlapping data from sequential sensors. Nonetheless, our procedure reduced ice extent differences during periods of sensor overlap to less than 0.05% and ice area differences to 0.6% or less.

Variability of antarctic sea ice: and changes in carbon dioxide.

A definitive long-term decrease in the extent of antarctic sea ice is not detectable from 9 years (1973 to 1981) of year-round satellite observations and limited prior data. Regional interannual variability is large, with sea ice decreasing in some regions while increasing in others. A significant decrease in overall ice extent during the mid-1970s, previously suggested to reflect warming induced by carbon dioxide, has not been maintained. In particular, the extent of ice in the Weddell Sea region has rebounded after a large decrease concurrent with a major oceanographic anomaly, the Weddell polynya. Over the 9 years, the trends are nearly the same in all seasons, but for periods of 3 to 5 years, greater winter ice maxima are associated with lesser summer ice minima. The decrease of the mid-1970s was preceded by an increase in ice extent from 1966 to 1972, further indicating the presence of cyclical components of variation that obscure any long-term trends that might be caused by a warming induced by carbon dioxide.

A recent sea-ice retreat west of the Antarctic Peninsula

Satellite passive microwave data show a record decrease in sea ice extent in the Bellingshausen Sea from mid-1988 through early 1991. The change coincides with more southerly surface winds, increased cyclonic activity and rising surface air temperatures, which reached historic highs along the west coast of the Antarctic Peninsula in 1989. Preceded by high ice cover in 1986–87, the retreat was most evident during summer in the formerly perennial sea-ice field over the continental shelf. Ocean heat storage probably contributed to the persistence and coastal propagation of this anomaly.

Spatial distribution of trends and seasonally in the hemispheric sea ice covers: 1978–1996

We extend earlier analyses of a 8.8-year sea ice data set that described the local seasonal variations and trends in each of the hemispheric sea ice covers to the recently merged 18.2-year sea ice record from four satellite instruments. The seasonal cycle characteristics remain essentially the same as for the shorter time series, but the local trends are markedly different, in some cases reversing sign. The sign reversal reflects the lack of a consistent long-term trend and could be the result of localized long-term oscillations in the hemispheric sea ice covers. By combining the separate hemispheric sea ice records into a global one, we have shown that there are statistically significant net decreases in the sea ice coverage on a global scale. The change in the global sea ice extent is −0.01 ± 0.003 × 106 km2 per decade. The decrease in the areal coverage of the sea ice is only slightly smaller, so that the difference in the two, the ice-free areas within the packs, has no statistically significant change.

Linked trends in the south Pacific sea ice edge and Southern Oscillation Index

Previous work have shown that sea ice variability in the South Pacific is associated with extratropical atmospheric anomalies linked to the Southern Oscillation (SO). Over a 32 year period (1982–2013), our study shows that the trend in Southern Oscillation Index (SOI) is also able to quantitatively explain the trends in sea ice edge, drift, and surface winds in this region. On average two thirds of the winter ice edge trend in this sector, linked to ice drift and surface winds, could be explained by the positive SOI trend, thus subjecting the ice edge to strong decadal SO variability. If this relationship holds, the negative SOI trend prior to the recent satellite era suggests that ice edge trends opposite to that of the recent record over a similar time scale. Significant low-frequency ice edge trends, linked to the natural variability of SO, are superimposed upon any trends expected of anthropogenic forcing.

Temperature corrected bootstrap algorithm

A temperature corrected bootstrap algorithm has been developed using Nimbus-7 Scanning Multichannel Microwave Radiometer data in preparation to the upcoming AMSR instrument aboard ADEOS and EOS-PM. The procedure first calculates the effective surface emissivity using emissivities of ice and water at 6 GHz and a mixing formulation that utilizes ice concentrations derived using the current bootstrap algorithm but using brightness temperatures from 6 GHz and 37 GHz channels. These effective emissivities are then used to calculate surface ice temperatures which in turn are used to convert the 18 GHz and 37 GHz brightness temperatures to emissivities. Ice concentrations are then derived using the same technique as with the bootstrap algorithm but using emissivities instead of brightness temperatures. The results show significant improvements in areas where ice temperature is expected to vary considerably such as near the continental areas in the Antarctic, where the ice temperature is colder than average, and in marginal ice-zones.

Antarctic sea ice passive microwave signatures during summer and autumn

The microwave signatures of Antarctic sea ice during the summer and autumn of 1992 are examined using SSM/I data in conjunction with ERS-1 SAR data and observations from an ice station in the Western Weddell Sea region. The period from February through April is observed to be critical in terms of monitoring sea ice cover with passive microwave sensors because of surface effects (e.g., melt, slush and flooding) that may cause large fluctuations in the signature of sea ice during the period. The concentrations calculated using reference brightness temperatures normally used for winter data are considerably lower than those observed in the field and those derived from the SAR data. Reference temperatures more appropriate for the summer ice data were inferred and provided more compatible ice concentrations. In late summer and autumn, freezing conditions begin to dominate and the brightness temperatures of sea ice, still different from those of winter, reflect those primarily of refrozen slush over thick ice, young ice, and new ice.<<ETX>>

The Detection Of New Sea Ice And Surface Temperature Using Advanced Very High Resolution Radiometer And Special Sensor Microwave/imager Satellite Data

The accurate and consistent discrimination of thin ice versus open water (and thicker ice) is a key to the realistic parameterization of heat, moisture and turbulence fluxes between atmosphere and ocean, and salt rejection and overall mass balance, in polar regions. NOAA Advanced Very High Resolution Radiometer12 (AVHRR/2) data are used to characterize surface phenomena (including physical temperatures from the thermal infrared data). The problem of thin ice is examined by combining cluster analysis techniques with physical surface information derived from the infrared data. An iterative procedure that utilizes the special attributes of both multi-spectral datasets is used to establish consistent methods of characterizing the ice cover, especially in extensive new ice regions. Preliminary results indicate that cloud-free AVHRR data are a valuable source of sea ice information.

Application of neural networks for identification of sea ice coverage and movements from satellite imagery

Visible and infrared (0.67, 0.8, 3.7, 11.0, and 12.0 /spl mu/m) imagery from the Advanced Very High Resolution Radiometer (AVHRR) aboard NOAAs operational meteorological satellite provides a high resolution (1 km/spl times/1 km) measurement and unique signatures for the identification of sea ice coverage and movements in polar regions. A backpropagation trained artificial neural network (BP ANN) algorithm is developed and applied for the classification of several sea surface conditions, including open water, grease, young ice, and multi-year thick ice coverage from the multispectral information of AVHRR satellite imagery. The trained BP ANN classifier is applied to images covering same region at successive days in order to investigate the changing of the physical status and the movement of ice blocks. An associated neural network based on Hopfield network architecture is investigated to determine the movement of ice coverage at two consecutive time series measurements from classified images. This network is derived from the cross-correlation analysis through minimization of the least mean square error between two images. Displacement and motion at each pixel can be obtained from the output values of the neural network.<<ETX>>

Ice regimes in the Antarctic and the Arctic using satellite multichannel passive microwave data

Monthly and weekly brightness temperature data from the various SSMI channels, especially the 85 GHz channel, show the existence of ice covered areas associated with persistent brightness temperatures. The physical characteristics of the ice cover in each of these areas are studied for possible association with different regimes of the sea ice cover. In general, the ice cover can be divided into different ice regimes that are associated with extensive new ice formation, young ice regions, first year ice areas with or without thick snow cover and a few types of multiyear ice areas. Unsupervised identification of the regimes is done with multichannel cluster analysis. The interpretation of the different regimes is based mainly on extensive field measurements, radiative transfer calculations, comparisons with other satellite data, and time series development of the surface ice cover from early fall through winter. The key to proper identification of some of this region is the use of the 85 GHz data, which is the most sensitive to surface effects among the SSM/I channels.<<ETX>>