[1] We present our best estimate of the thickness and volume of the Arctic Ocean ice cover from 10 Ice, Cloud, and land Elevation Satellite (ICESat) campaigns that span a 5-year period between 2003 and 2008. Derived ice drafts are consistently within 0.5 m of those from a submarine cruise in mid-November of 2005 and 4 years of ice draft profiles from moorings in the Chukchi and Beaufort seas. Along with a more than 42% decrease in multiyear (MY) ice coverage since 2005, there was a remarkable thinning of $0.6 m in MY ice thickness over 4 years. In contrast, the average thickness of the seasonal ice in midwinter ($2 m), which covered more than two-thirds of the Arctic Ocean in 2007, exhibited a negligible trend. Average winter sea ice volume over the period, weighted by a loss of $3000 km 3 between 2007 and 2008, was $14,000 km 3 . The total MY ice volume in the winter has experienced a net loss of 6300 km 3 (>40%) in the 4 years since 2005, while the first-year ice cover gained volume owing to increased overall area coverage. The overall decline in volume and thickness are explained almost entirely by changes in the MY ice cover. Combined with a large decline in MY ice coverage over this short record, there is a reversal in the volumetric and areal contributions of the two ice types to the total volume and area of the Arctic Ocean ice cover. Seasonal ice, having surpassed that of MY ice in winter area coverage and volume, became the dominant ice type. It seems that the near-zero replenishment of the MY ice cover after the summers of 2005 and 2007, an imbalance in the cycle of replenishment and ice export, has played a significant role in the loss of Arctic sea ice volume over the ICESat record.

Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice-ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981-2010: National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP-U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice-ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R 5 0.82). The trend in the total ice volume in September is greatest with MERRA (24.1 3 10 3 km 3 decade 21 ) and least with CFSR (22.7 3 10 3 km 3 decade 21 ).

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Navy submarines in the Arctic Ocean routinely obtain observations from an upward-looking sonar of the draft of the sea ice cover overhead. Draft data are now publicly available from some 40 cruises from 1975 to 2000 covering over 120 000 km of track in roughly the central half of the Arctic Ocean. To apply these observations to geophysics, error estimates are needed. This paper assesses how well the correction of the data during normal processing accounts for the major sources of error in the draft data from U.S. Navy submarines and what errors remain in the data. The error treated is the error for the average draft over tens of kilometers. The following sources of error are considered: measurement precision error; errors in identifying open water (as ice of zero draft); sound speed error; errors caused by variable sonar footprint size, by uncontrolled gain and thresholds, and by ship’s trim; and differences between data from analog charts and digitally recorded data. The bias with respect to the actual draft is +29 cm and is important both for knowing the actual ice draft and for comparing drafts from submarines with thicknesses in models and with draft, thickness, or freeboard estimated by other vehicles and technologies. The standard deviation is 25 cm. This number estimates the repeatability and comparability of draft measurements by U.S. Navy submarines and is important for examining the submarine data for regional and temporal variation. These errors are tolerable for an operational data source with a signal many meters in amplitude.

Time sequences of surface based measurements of passive microwave emission from growing saline ice reported by Wensnahan et al. (1993) are used to explore the possibility of developing a satellite based sea ice concentration algorithm which solves for the presence of thinner ice. It is shown that two classes of thinner ice can be distinguished from mixtures of open water (OW), first‐year (FY) ice, and multiyear (MY) ice. The two classes do not necessarily correspond to specific World Meteorological Organization ice types; rather, newly formed ice represents a brief transition spectrum between OW and thin ice. Newly formed ice appears to be optically thick at 37 and 90 GHz and has a relatively dry surface. The thin ice spectrum occurs when the ice is greater than 4 cm thick and appears to result from the accumulation of brine at the surface of the ice. Thin ice has a relatively stable spectrum characterized by high brightness temperatures, a near‐zero spectral gradient at vertical polarization, and a large difference between vertical and horizontal polarizations. Supervised principal component analysis (PCA) was done of laboratory data using 10 channels of passive data: vertical and horizontal polarization at 6.7, 10, 19, 37, and 90 GHz. Analyses were also done on subsets of the laboratory data at 6.7 to 37 GHz as well as 19 to 90 GHz, representing the scanning multichannel microwave radiometer (SMMR) and special sensor microwave imager (SSM/I) satellite frequencies, respectively. Using all of the channels or the SMMR subset makes it possible to solve for mixtures of OW and FY, MY, newly formed and thin ice but with large errors. However, any four of these scene types can be distinguished with reasonable accuracy. The SSM/I frequencies allow determination of at most four of these scene types but with moderate errors. PCA was used in a case study of SSM/I data from the Bering Sea for April 2, 1988. Winds from the north formed thin ice areas which the NASA Team algorithm interprets as large amounts of OW and MY ice. With PCA, these same areas are interpreted as 20–30% OW near the lee shores but otherwise as consisting almost entirely of thin ice. We conclude that thin ice can be detected using satellite data. However, questions remain as to how the thin ice spectrum varies with environmental conditions, how it evolves to that of FY, and how this evolution affects the predicted concentrations of thin ice.

Measurements of arctic sea‐ice draft have been taken by Navy submarines for nearly five decades. The data are in two inherently different forms, analog paper charts and digitally recorded data. “Raw” analog drafts digitized from paper charts are biased toward thicker ice by over 30 cm compared with the digital drafts. This is due to the coarser temporal resolution of the paper charts compared the digital data. We examine coincident analog and digital data to determine how they can be made equivalent in mean draft and draft distribution (the histogram of draft vs. fractional frequency of observation). Image processing techniques are used to thin vertical features in the scanned chart images; this produces a “final” analog mean draft that is essentially unbiased (2 ± 6 cm) relative to the digital mean and final draft distributions that are in good agreement.

The results of multispectral passive microwave observations (6.7 to 90‐GHz) are presented from the cruises of the FS Polarstern in the Weddell Sea from July to December 1986. This paper includes primarily the analysis of radiometric observations taken at ice station sites. Averaged emissivity spectra for first‐year (FY) ice were relatively constant throughout the experiment and were not statistically different from FY ice signatures in the Arctic. Detailed ice characterization was carried out at each site to compare the microwave signatures of the ice with the physical properties. Absorption optical depths of FY ice were found to be sufficiently high that only the structure in the upper portions of the ice contributed significantly to interstation emissivity variations. The emissivities at 90‐GHz, e(90), had the greatest variance. Both e(90) at vertical polarization and GRe(90,18.7) (defined as [ev(90)‐ev(18.7)]/ev[(90)+ev(18.7)]) depended on the scattering optical depth which is a function of the snow grain diameter and layer thickness. The variance showed a latitude dependence and is probably due to an increase in the strength of snow metamorphism nearer the northern edge of the ice pack. The contribution of variations of near‐surface brine volume to the emissivity was not significant over the range of values encountered at the station sites. Emissivity spectra are presented for a range of thin ice types. Unsupervised principal component analysis produced three significant eigenvectors and showed a separation among four different surface types: open water, thin ice, FY ice, and FY ice with a thick snow cover. A comparison with SMMR satellite data showed that averaged ice concentrations derived from the ship's ice watch log were consistent with the satellite concentrations. The surface based emissivities for FY ice were also compared with emissivities calculated from scanning multichannel microwave radiometer (SMMR) satellite radiances. Best agreement was found at 6.7 and 10‐GHz, while at 18 and 37‐GHz, SMMR emissivities were slightly lower than surface based results. For the three lower frequencies agreement was found within a confidence limit of 95% and for 37‐GHz within about 90%.