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The creation and curation of environmental data present numerous challenges and rewards. In this study, we reflect on the maturity of freely available glacier data sets (inventories and changes), as well as on related demands by data providers, data users, and data repositories in-between. The amount of glacier data has increased significantly over the last two decades, especially as remote-sensing techniques have developed quickly. The portfolio of observed parameters has increased as well, which presents new challenges for international data centers, and fosters new expectations from users.We assess the services of the Global Terrestrial Network for Glaciers (GTN-G) as the central organization for standardized data on glacier distribution and changes. Within GTN-G, different glacier data sets are consolidated under one umbrella, and the glaciological community supports this service by actively contributing their data sets and by providing strategic guidance via an Advisory Board. To assess each GTN-G data set, we present a maturity matrix and summarize achievements, challenges, and future ambitions.Most challenges can only be overcome in a financially secure setting for data services and with the help of international standardization. Therefore, dedicated support and long-term commitment for certified data repositories build the basis for the successful democratization of data. In the field of glacier data, this balancing act has so far been successfully achieved through joint collaboration between data repositories, data providers, and data users. However, we also note an unequal allotment of funds for data creation and projects using the data, and data curation. Considering the importance of glacier data to answering numerous key societal questions (from water availability to global sea-level rise), this imbalance needs to be adjusted. In order to guarantee the continuation and success of GTN-G in the future, basic funding schemes, flexible adaptation measures, and regular evaluations are required.

Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. In this study we use observational data to evaluate the contribution of the trend to the skill of persistence-based statistical forecasts of monthly and seasonal ice extent on the pan-Arctic and regional scales. We focus on the Beaufort Sea for which the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the winter and summer trends during the 1990s. Persistence-based statistical forecasts of the Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. In only a few regions does September ice extent correlate significantly with antecedent ice anomalies in the same region more than 2 months earlier. The springtime “predictability barrier” in regional forecasts based on persistence of ice extent anomalies is not reduced by the inclusion of several decades of pre-satellite data. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea's ice extent as far back as July explains about 20 % of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead time of a month or two.

Across High Asia, the amount, timing, and spatial patterns of snow and ice melt play key roles in providing water for downstream irrigation, hydropower generation, and general consumption. The goal of this paper is to distinguish the specific contribution of seasonal snow versus glacier ice melt in the major basins of High Mountain Asia: Ganges, Brahmaputra, Indus, Amu Darya, and Syr Darya. Our methodology involves the application of MODIS-derived remote sensing products to separately calculate daily melt outputs from snow and glacier ice. Using an automated partitioning method, we generate daily maps of (1) snow over glacier ice, (2) exposed glacier ice, and (3) snow over land. These are inputs to a temperature index model that yields melt water volumes contributing to river flow. Results for the five major High Mountain Asia basins show that the western regions are heavily reliant on snow and ice melt sources for summer dry season flow when demand is at a peak, whereas monsoon rainfall dominates runoff during the summer period in the east. While uncertainty remains in the temperature index model applied here, our approach to partitioning melt from seasonal snow and glacier ice is both innovative and systematic and more constrained than previous efforts with similar goals.

Analog archival data can supplement modern digital research, but only if those data are preserved, described, and migrated to appropriate formats. The National Snow and Ice Data Center (NSIDC) at the University of Colorado Boulder (CU) is responsible for managing, archiving, and disseminating cryospheric and polar data. The clear majority of these data are digital, but the NSIDC also houses a collection of historical archival materials that include measurements related to the earth's glaciated regions prior to the development of modern instrumentation. Their formats, however, are not conducive to contemporary analysis, rendering them ostensibly “lost” to research. This paper describes a series of efforts to provide access to these collections that date back to their original acquisition, as long ago as the mid-nineteenth century, with focus primarily on activities over the last 15 years. The most recent effort was funded by the Council on Library & Information Resources and won the 2016 International Data Rescue Award. The intent is to highlight key challenges, and our proposed own solutions to those challenges, in designing a digitization project centered on providing online access to analog data in glaciological, geomorphological, and related research.

Arctic sea ice data from a variety of historical sources have been synthesized into a database extending back to 1850 with monthly time‐resolution. The synthesis procedure includes interpolation to...

Barry, a giant in climate and cryospheric sciences, pioneered the archival of computer data and traveled the world to share his vision with others.

Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. However, the strong negative trend of sea ice coverage in recent decades complicates the evaluation of statistical skill by inflating the correlation of interannual variations of pan-Arctic and regional ice extent. In this study we provide a quantitative evaluation of the contribution of the trend to the predictive skill of monthly and seasonal ice extent on the pan-Arctic and regional scales. We focus on the Beaufort Sea where the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the trend during the 1990s in most of the Arctic subregions. The Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical predictability out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea’s ice extent as far back as July explains about 20 % of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead time of a month or two.

September open water fraction in the Arctic is analyzed using the satellite era record of ice concentration (1979–2017). Evidence is presented that three breakpoints (shifts in the mean) occurred in the Pacific sector, with higher amounts of open water starting in 1989, 2002, and 2007. Breakpoints in the Atlantic sector record of open water are evident in 1971 in longer records, and around 2000 and 2011. Multiple breakpoints are also evident in the Canadian and Russian halves. Statistical models that use detected breakpoints of the Pacific and Atlantic sectors, as well as models with breakpoints in the Canadian and Russian halves and the Arctic as a whole, outperform linear trend models in fitting the data. From a physical standpoint, the results support the thesis that Arctic sea ice may have critical points beyond which a return to the previous state is less likely. From an analysis standpoint, the findings imply that de-meaning the data using the breakpoint means is less likely to cause spurious signals than employing a linear detrend.

This study presents the improvement in ice edge error within the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System – ACNFS) went into operations with a horizontal resolution of ~ 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the Navy's sea ice forecasting systems.

Passive microwave sensors have produced a 35 year record of sea-ice concentration variability and change. Operational analyses combine a variety of remote-sensing inputs and other sources via manual integration to create high-resolution, accurate charts of ice conditions in support of navigation and operational forecast models. One such product is the daily Multisensor Analyzed Sea Ice Extent (MASIE). The higher spatial resolution along with multiple input data and manual analysis potentially provide more precise mapping of the ice edge than passive microwave estimates. However, since MASIE is based on an operational product, estimates may be inconsistent over time due to variations in input data quality and availability. Comparisons indicate that MASIE shows higher Arctic-wide extent values throughout most of the year, largely because of the limitations of passive microwave sensors in some conditions (e.g. surface melt). However, during some parts of the year, MASIE tends to indicate less ice than estimated by passive microwave sensors. These comparisons yield a better understanding of operational and research sea-ice data products; this in turn has important implications for their use in climate and weather models.

Analysis of weekly sea ice charts produced by the U.S. National Ice Center from 1976 to 2007 indicates large interannual variations in the averaged winter landfast ice extent around the Arctic Basin. During the 32-yr period of the record, landfast ice cover was relatively extensive from the early to mid-1980s but since then has declined in many coastal regions of the Arctic, particularly after the early 1990s. While the Barents, Baltic, and Bering Seas show increases in landfast ice area, the overall change for the Northern Hemisphere is negative, about −12.27 (±2.8) × 103 km2 yr−1, or −7 (±1.5)% decade−1 relative to the long-term mean. Except in a few coastal regions, the seasonal duration of landfast ice is shorter overall, particularly in the Laptev, East Siberian, and Chukchi Seas. The decreased winter landfast ice extent is associated with some notable changes in ice growth and melt patterns, in particular the slowed landfast ice expansion during fall and early winter since 1990. The observed changes in Arctic landfast ice could have profound impacts on the Arctic coasts. The challenge is to understand and project the responses of the whole coastal ecosystem to changing ice cover and Arctic warming.

Observations from passive microwave satellite sensors have provided a continuous and consistent record of sea ice extent since late 1978. Earlier records, compiled from ice charts and other sources exist, but are not consistent with the satellite record. Here, a method is presented to adjust a compilation of pre-satellite sources to remove discontinuities between the two periods and create a more consistent combined 59-yr time series spanning 1953–2011. This adjusted combined time series shows more realistic behavior across the transition between the two individual time series and thus provides higher confidence in trend estimates from 1953 through 2011. The long-term time series is used to calculate linear trend estimates and compare them with trend estimates from the satellite period. The results indicate that trends through the 1960s were largely positive (though not statistically significant) and then turned negative by the mid-1970s and have been consistently negative since, reaching statistical significance (at the 95% confidence level) by the late 1980s. The trend for September (when Arctic extent reaches its seasonal minimum) for the satellite period, 1979–2011 is −12.9% decade−1, nearly double the 1953–2011 trend of −6.8% decade−1 (percent relative to the 1981–2010 mean). The recent decade (2002–2011) stands out as a period of persistent decline in ice extent. The combined 59-yr time series puts the strong observed decline in the Arctic sea ice cover during 1979–2011 in a longer-term context and provides a useful resource for comparisons with historical model estimates.

September open water fraction in the Arctic is analyzed using the satellite era record of ice concentration (1979–2014). This analysis suggests that there is a statistically significant breakpoint (shift in the mean) and increase in the variance around 1988 and another breakpoint around 2007 in the Pacific sector. These structural breaks are robust to the choice of algorithm used for deriving sea ice concentration from satellite data, and are also apparent in other measures of open water, such as operational ice charts and the record of navigable days from Barrow to Prudhoe Bay. Breakpoints in the Atlantic sector record of open water are evident in 1988 and 2007 but more weakly significant. The breakpoints appear to be associated with concomitant shifts in average ice age, and tend to lead change in Arctic circulation regimes. These results support the thesis that Arctic sea ice may have critical points beyond which a return to the previous state is less likely.

The Arctic sea ice has been pointed to as one of the first and clearest indicators of climate change. Satellite passive microwave observations from 1979 through 2005 now indicate a significant –8.4±1.5% decade–1 trend (99% confidence level) in September sea-ice extent, a larger trend than earlier estimates due to acceleration of the decline over the past 41 years. There are differences in regional trends, with some regions more stable than others; not all regional trends are significant. The largest trends tend to occur in months where melt is at or near its peak for a given region. A longer time series of September extents since 1953 was adjusted to correct biases and extended through 2005. The trend from the longer time series is –7.7±0.6% decade–1 (99%), slightly less than from the satellite-derived data that begin in 1979, which is expected given the recent acceleration in the decline.

This study presents the improvement in the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System – ACNFS) went into operations with a horizontal resolution of ~3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements to both of the Navy's sea ice forecasting systems.

Although certain regions of the Arctic have experienced periods of decadal warming during the last 100 years, recent decades show an ongoing suite of Arctic-wide, interrelated atmospheric, oceanic, terrestrial, and human dimension changes. A major workshop conclusion stresses the necessity for coordination between arctic disciplines to form a continuing and enhanced dataset of arctic change. A major workshop recommendation is to increase the use of past and evolving datasets for understanding arctic change through, for example, an arctic change protocol, an aaaarctic system reanalysis, timely intercalibration of satellite products, and multidisciplinary, multiregional analyses. Understanding of the Arctic can improve change detection due to the roles of vegetation type, sea ice, and other feedbacks in providing a multiyear memory for the climate system.

This work is an examination of potential uses of multiangular remote sensing imagery for mapping and characterizing sea ice and ice sheet surfaces based on surface roughness properties. We use data from the Multi-angle Imaging SpectroRadiometer (MISR) to demonstrate that ice sheet and sea ice surfaces have characteristic angular signatures and that these angular signatures may be used in much the same way as spectral signatures are used in multispectral classification. Three case studies are examined: sea ice in the Beaufort Sea off the north coast of Alaska, the Jakobshavn Glacier on the western edge of the Greenland ice sheet, and a region in Antarctica south of McMurdo station containing glaciers and blue-ice areas. The MISR sea ice image appears to delineate different first-year ice types and, to some extent, the transition from first-year to multiyear ice. The MISR image shows good agreement with sea ice types that are evident in concurrent synthetic aperture radar (SAR) imagery and ice analysis charts from the National Ice Center. Over the Jakobshavn Glacier, surface roughness data from airborne laser altimeter transects correlate well with MISR-derived estimates of surface roughness. In Antarctica, ablation-related blue-ice areas, which are difficult to distinguish from bare ice exposed by crevasses, are easily detected using multiangular data.

In an introductory section we review the physical processes influencing the formation and evolution of melt ponds on sea ice during the Arctic summer. As melt progresses, the changing properties of the surface interact strongly with the surface heat balance. The small interannual variability of the seasonal ice extent suggests an interannual variability of the surface heat balance of ±1 W m−2 or less. The interannual variance of atmospheric forcing represented by the transport of moist static energy into the Arctic is an order of magnitude greater. This appears to contradict the notion of a highly sensitive sea ice cover and emphasizes the need to generate albedo as an important internal variable in interactive models. Observations of melt ponds are needed in order to derive improved relationships between surface albedo and parameters such as the amount of snow, the onset and termination of melting, the ice thickness distribution, and ice deformation. Here classified (National Technical Means) imagery is used to measure melt pond coverage as it evolves over a summer on ice surrounding a drifting buoy. Local variability of pond cover is greatest at the beginning of the melt season, that is, pond coverage from 5% to 50% depending on ice type, as previously found by Russian investigators. An important distinction is found in the temporal change of pond cover: it decreases with time on thick ice, and it increases with time on thin ice (eventually leading to the disappearance of thin ice at the end of summer). An attempt to relate pond coverage to ice concentrations derived from passive microwave data proved unsuccessful.

Leads are relatively linear features in the sea ice cover, which are composed of open water or new, thin ice. Because of their composition, leads impact the ocean/air heat exchange. Automated analysis of leads from sea ice imagery may provide a means of gathering important information about the sea ice cover and its climatic influence. This paper describes: (1) a method for extracting and analyzing leads from ERS-1 synthetic aperture radar (SAR) images classified by ice type and (2) the results of using this method on images of the Beaufort Sea. The methodology consists of identifying potential lead features in the image and measuring their characteristics both before and after using a thinning or skeletonization technique on the features. The measurements obtained using this method include lead area, average width, number of leads in an area, amount of branching, and linearity of the lead. These measurements were analyzed with respect to the time of year and the latitude of the images. Results indicate that the measurements produced by the methodology are consistent with lead measurement distributions that others have found. The results of the study suggest that the methodology is appropriate to study lead characteristics on a large scale.

The Submarine Arctic Science Program, SCICEX, is a federal interagency collaboration among the operational Navy, research agencies, and the marine research community to use nuclear-powered submarines for scientific studies of the Arctic Ocean. The SCICEX program seeks to acquire comprehensive data about Arctic sea ice, water properties, and bathymetry. This project s long term goal is to build a permanent data archive for new and old data collected as part of SCICEX, making the data usable by scientists now and preserving data for the future. Our objective is to support SCICEX in all aspects of data management including archiving, documenting, and making SCICEX data available to a wide scientific community. Programs of the size and complexity of SCICEX often need more in the way of support services, including outreach, a Web presence for the program, and development of higher-level products or tools. We provide these services as well. For fiscal year 2012, specific objectives were to: Create a dedicated SCICEX Web presence where scientists and other users can go to obtain data and read about SCICEX meetings and reports. It is also a place to capture historical information and photographs about the SCICEX program. Continue to find, collect, organize, and to the extent possible provide complete documentation for existing SCICEX data. In particular, work to get all SCICEX bathymetric data archived and discoverable by working with NGDC and the SCICEX bathymetric scientists. Act as a liaison to find and provide homes for data collected under newly active SCICEX missions.

Under the Office of Naval Research-sponsored Arctic Leads Accelerated Research Initiative (ARI), a data set of Advanced Very High Resolution Radiometer (AVHRR) imagery covering the years 1988 through 1992 is being constructed. Relatively cloud-free imagery is selected from image hardcopies. Each image examined is subjectively ranked on the percentage of each sea or seas it covers, and the cloudiness of the image within each sea. The images are then logged in a spreadsheet. From the spreadsheet, about 20 images per month (for the year 1989) are ordered from the National Oceanic and Atmospheric Administration for processing. The image data are calibrated and mapped to one of two grids, which together cover most of the Arctic at 1 km per pixel. Care has been taken to match the grid and the projection to that of Special Sensor Microwave Imager (SSM/I) data distributed by the National Snow and Ice Data Center (NSIDC). The 1989 data set is complete at this time. Presently, data are distributed to the Remote Sensing Working Group of the ARI. NSIDC will distribute the data set to a wider audience at a later date.

The third symposium on Remote Sensing of Snow and Ice, organized by the International Glaciological Society, took place in Boulder, Colorado, 17–22 May 1992. As part of this meeting a total of 21 papers was presented on snow and ice applications of Advanced Very High Resolution Radiometer (AVHRR) satellite data in polar regions. Also during this meeting a NASA sponsored Workshop was held to review the status of polar surface measurements from AVHRR. In the following we have summarized the ideas and recommendations from the workshop, and the conclusions of relevant papers given during the regular symposium sessions. The seven topics discussed include cloud masking, ice surface temperature, narrow-band albedo, ice concentration, lead statistics, sea-ice motion and ice-sheet studies with specifics on applications, algorithms and accuracy, following recommendations for future improvements. In general, we can affirm the strong potential of AVHRR for studying sea ice and snow covered surfaces, and we highly recommend this satellite data set for long-term monitoring of polar process studies. However, progress is needed to reduce the uncertainty of the retrieved parameters for all of the above mentioned topics to make this data set useful for direct climate applications such as heat balance studies and others. Further, the acquisition and processing of polar AVHRR data must become better coordinated between receiving stations, data centers and funding agencies to guarantee a long-term commitment to the collection and distribution of high quality data.

[1] We present a time series of sea ice extent in the Russian Arctic based on observational sea ice charts compiled by the Arctic and Antarctic Research Institute (AARI). These charts are perhaps the oldest operational sea ice data in existence and show that sea ice extent in the Russian Arctic has generally decreased since the beginning of the chart series in 1933. This retreat has not been continuous, however. For the Russian Arctic as a whole in summer, there have been two periods of retreat separated by a partial recovery between the mid-1950s and mid-1980s. The AARI charts, combined with air temperature records, suggest that the retreat in recent decades is pan-Arctic and year-round in some regions, whereas the early twentieth century retreat was only observed in summer in the Russian Arctic. The AARI ice charts indicate that a significant transition occurred in the Russian Arctic in the mid-1980s, when its sea ice cover began to retreat along with that of the rest of the Arctic. Summertime sea ice extents derived from the AARI data set agree with those derived from passive microwave, including the Hadley Centre's global sea ice coverage and sea surface temperature (HadISST) data set. The HadISST results do not indicate the 1980s transition or the partial recovery that took place before it. The AARI charts therefore add significantly to our understanding of the variability of Arctic sea ice over the last 8 decades, and we recommend their inclusion in future historical data sets of Arctic sea ice.

[1] Satellite passive microwave observations document an overall downward trend in Arctic sea ice extent and area since 1978. While the record minimum observed in September 2002 strongly reinforced this downward trend, extreme ice minima were again observed in 2003 and 2004. Although having three extreme minimum years in a row is unprecedented in the satellite record, attributing these recent trends and extremes to greenhouse gas loading must be tempered by recognition that the sea ice cover is variable from year to year in response to wind, temperature and oceanic forcings.

[1] Arctic sea ice extent and area in September 2002 reached their lowest levels recorded since 1978. These conditions likely resulted from (1) anomalous warm southerly winds in spring, advecting ice poleward from the Siberian coast (2) persistent low pressure and high temperatures over the Arctic Ocean in summer, promoting ice divergence and rapid melt.

This work is a demonstration of potential uses of combined multiangle and multispectral remote sensing imagery for mapping and characterizing seasonal snow cover, sea ice, and ice sheet surfaces. We use data from the Multiangle Imaging Spectroradiometer (MISR) and demonstrate how multiangle data can augment multispectral mapping in unique and valuable ways.

Melt pond development over one summer in the Beaufort Sea was monitored with high resolution satellite data. These data resolve individual melt ponds, and were used to obtain pond coverage measurements for ice floes. If pond coverage is known, floe albedo can be estimated. Pond coverage ranged from 30 to 40% at the beginning of melt to about 15% at freezeup. The range in coverage from floe to floe on any day was about 10%.

Estimates of the recent decline in Arctic Ocean summer sea ice extent can vary due to differences in sea ice data sources, in the number of years used to compute the trend, and in the start and end years used in the trend computation. Compounding such differences, estimates of the relative decline in sea ice cover (given in percent change per decade) can further vary due to the choice of reference value (the initial point of the trend line, a climatological baseline, etc.). Further adding to the confusion, very often when relative trends are reported in research papers, the reference values used are not specified or made clear. This can lead to confusion when trend studies are cited in the press and public reports.

Summer sea ice in the Arctic has shown a significant downward trend of 8% per decade since the late 1970s, leading to a reduction of approximately 20% in sea ice extent in September (when the annual minimum occurs) (Stroeve et al., 2005). The past three summers (2002–2004) have been among the lowest on record, and 2002 was the extreme minimum. Despite decreasing summer extents, the sea ice extent has typically rebounded to near-normal levels during the winter season, yielding an annual average trend of only −3%. This is not surprising since as temperatures drop below freezing, sea ice quickly forms.

In September 2002, Arctic sea ice extent reached a minimum unprecedented in 24 years of satellite passive microwave observations, and almost certainly unmatched in 50 years of charting Arctic ice [Serreze et al., 2003]. Again, in September 2003, ice retreated to an unusually low extent, almost equaling the previous year's minimum (Figure l). The Sea Ice Index (http://nsidc.org/data/seaice_index/), an easy-to-use source of information on sea ice trends and anomalies, assists in observing these minima. The Sea Ice Index is intended for both researchers and the scientifically inclined general public.

Satellite-borne synthetic aperture radar (SAR) is fast becoming an indispensable tool for operational sea-ice analysis. In anticipation of this, the Naval Research Laboratory and the National Oceanic and Atmospheric Administration established a data link between the Alaska SAR Facility (ASF) in Fairbanks and the Navy/NOAA Joint Ice Center in Suitland, Md. The Joint Ice Center is the national center for ice analysis and forecasting. At the NRL Remote Sensing Applications Branch, we are working to ensure that SAR and other satellite data are used to best advantage for ice analysis by putting together appropriate software tools for manipulating images, developing algorithms for automated image analysis, and combining sensor data in ways that increase confidence in the final product, usually an ice type and concentration map. This article gives a brief look at some recent work in each of these areas.

The U.S. National Ice Center requires a robust multiyear ice classification algorithm that can handle variability in backscatter across uncalibrated 500 km RADARSAT scenes. ERS-1 images are used to test a dynamic threshold algorithm. The best results were obtained with a window size of 32, delta of 0.8, Fisher partition criterion and closing.

The ocean surface wind field is observed from space operationally using scatterometry. The European Space Agency's (ESAs) ERS-1 satellite scatterometer routinely produces a wind product that is assimilated into forecast models. Scatterometry cannot give accurate wind estimates close to land, however, because the field of view of a spaceborne scatterometer is on the order of 50 km. Side lobe contamination, due to the large contrast in backscatter between land and water, compounds the problem. Synthetic aperture radar (SAR) can provide wind speed and direction estimates on a finer scale, so that high-resolution wind fields can be constructed near shore. An algorithm has been developed that uses the spectral expression of wind in SAR imagery to estimate wind direction and calibrated backscatter to estimate wind strength. Three versions, based on C-band scatterometer algorithms, are evaluated for accuracy in potential operational use. Algorithm estimates are compared with wind measurements from buoys in the Gulf of Alaska, Bering Strait, and off the Pacific Northwest coast by using a data set of 61 near-coincident buoy and ERS-1 SAR observations. Representative figures for the accuracy of the algorithm are /spl plusmn/2 m/s for wind speed and /spl plusmn/37/spl deg/ for wind direction at a 25-km spatial resolution.