Walter N. Meier

Arctic Sea Ice Extent Plummets in 2007

Arctic sea ice declined rapidly to unprecedented low extents in the summer of 2007, raising concern that the Arctic may be on the verge of a fundamental transition toward a seasonal ice cover. Arctic sea ice extent typically attains a seasonal maximum in March and minimum in September. Over the course of the modern satellite record (1979 to present), sea ice extent has declined significantly in all months, with the decline being most pronounced in September. By mid-July 2007, it was clear that a new record low would be set during the summer of 2007.

Whither Arctic sea ice? A clear signal of decline regionally, seasonally and extending beyond the satellite record

Abstract 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.

Sea ice response to an extreme negative phase of the Arctic Oscillation during winter 2009/2010

[3] The long‐term annual mean Arctic SLP field features an anticyclone centered over the northern Beaufort Sea, known as the Beaufort Sea High (BSH), and a trough of low pressure extending from the Icelandic Low northeastward into the Kara Sea. Associated surface winds drive the clockwise Beaufort Gyre ice motion, and the Transpolar Drift Stream (TDS), representing ice motion from the Siberian coast across the Arctic and then into the North Atlantic through Fram Strait. When the winter AO is in its positive mode, SLP over the Icelandic Low region and extending into the Arctic is anomalously low and the BSH is weak, promoting a cyclonic (counter‐clockwise) sea ice circulation anomaly. This is expressed as decreased ice transport from the Beaufort Sea westward across the dateline into the Chukchi Sea, increased ice transport out of the Arctic Ocean through Fram Strait, and increased transport of ice away from the Siberian coast, leaving open water areas that foster new iceformation[Rigoretal.,2002].Bypromotingmorethinice in spring, the positive AO sets the stage for negative summer ice extent anomalies. Conversely, during a negative AO phase, SLP is above normal over the Arctic, most prominent in the vicinity of the Icelandic Low. Ice motion tends to have an anticyclonic (clockwise) anomaly. Ice flow through Fram Strait is reduced, and the Beaufort Gyre is stronger, leading to enhanced ice transport from the western to the eastern Arctic where ice thickens by ridging and rafting against the Siberian coast. The stronger Beaufort Gyre also sequesters and thickens ice in the Canada Basin. Collectively, these processes favor survival of sea ice through summer.

Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in arctic peripheral seas

An accurate representation of sea ice concentration is valuable to operational ice analyses, process studies, model inputs, and detection of long-term climate change. Passive microwave imagery, such as from the Special Sensor Microwave/Imager (SSM/I), are particularly valuable for monitoring of sea ice conditions because of their daily, basin-scale coverage under all sky conditions. SSM/I-derived sea ice concentration estimates using four common algorithms [Bootstrap (BT), Cal/Val (CV), NASA Team (NT), and NASA Team 2 (N2)] are compared with concentrations computed from Advanced Very High Resolution Radiometer (AVHRR) visible and infrared imagery. Comparisons are made over approximately an eight-month period in three regions of the Arctic and focus on areas near the ice edge where differences between the algorithms are likely to be most apparent. The results indicate that CV and N2 have the smallest mean error relative to AVHRR. CV tends to overestimate concentration, while the other three algorithms underestimate concentration. NT has the largest underestimation of nearly 10% on average and much higher in some instances. In most cases, mean errors of the SSM/I algorithm were significantly different from each other at the 95% significance level. The BT algorithm has the lowest error standard deviation, but none of the considered algorithms was found to have statistically significantly different error standard deviations in most cases. This indicates that spatial resolution is likely a limiting factor of SSM/I in regions near the ice edge in that none of the algorithms satisfactorily resolve mixed pixels. Statistical breakdowns by season, region, ice conditions, and AVHRR scene generally agree with the overall results. Representative case studies are presented to illustrate the statistical results.

Reductions in Arctic sea ice cover no longer limited to summer

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.

Error analysis and assimilation of remotely sensed ice motion within an Arctic sea ice model

New sea ice motion fields available from remotely sensed data are potentially useful for assessing and improving models of the polar ice pack. Here we investigate the error characteristics of the observed ice motions relative to drifting buoys and a dynamic-thermodynamic ice model. A data assimilation approach is then used to assess the potential of the motion data for reducing model biases, as well as the potential of the model to serve as an interpolation tool to generate improved ice motion data sets. Special Sensor Microwave/Imager (SSM/I) derived and model simulated ice motions for the years 1988 through 1993 are compared with ice displacement observations from drifting buoys. Variability and biases are summarized for seasonal and regional means. SSM/I motions are assimilated into the model using an optimal interpolation method that accounts for the modeled and SSM/I motion error variances and the number and distribution of the SSM/I motions. Modeled and SSM/I-derived motions are found to have comparable mean errors, with some notable regional and seasonal differences. Assimilation substantially reduces the error standard deviation and improves the correlation of the simulated motions relative to the buoy observations, but some biases remain. In the model framework used here, assimilation of the SSM/I data substantially alters average ice thickness in some regions of the Arctic and affects ice mass outflow through the Fram Strait but has a small effect on mean ice concentration. The assimilation yields an increase in the spatial and temporal variability in ice deformation. The observations are particularly suited for improving the simulation of specific synoptic events, where substantial differences can occur between simulated and observed ice transport.

Arctic sea-ice change: a grand challenge of climate science

Over the period of modern satellite observations, Arctic sea-ice extent at the end of the melt season (September) has declined at a rate of >11% per decade, and there is evidence that the rate of decline has accelerated during the last decade. While climate models project further decreases in sea- ice mass and extent through the 21st century, the model ensemble mean trend over the period of instrumental records is smaller than observed. Possible reasons for the apparent discrepancy between observations and model simulations include observational uncertainties, vigorous unforced climate variability in the high latitudes, and limitations and shortcomings of the models stemming in particular from gaps in understanding physical process. The economic significance of a seasonally sea-ice-free future Arctic, the increased connectivity of a warmer Arctic with changes in global climate, and large uncertainties in magnitude and timing of these impacts make the problem of rapid sea-ice loss in the Arctic a grand challenge of climate science. Meaningful prediction/projection of the Arctic sea-ice conditions for the coming decades and beyond requires determining priorities for observations and model development, evaluation of the ability of climate models to reproduce the observed sea-ice behavior as a part of the broader climate system, improved attribution of the causes of Arctic sea-ice change, and improved understanding of the predictability of sea-ice conditions on seasonal through centennial timescales in the wider context of the polar climate predictability.

Recent changes in the Arctic melt season

Abstract Melt-season duration, melt-onset and freeze-up dates are derived from Satellite passive microwave data and analyzed from 1979 to 2005 over Arctic Sea ice. Results indicate a Shift towards a longer melt Season, particularly north of Alaska and Siberia, corresponding to large retreats of Sea ice observed in these regions. Although there is large interannual and regional variability in the length of the melt Season, the Arctic is experiencing an overall lengthening of the melt Season at a rate of about 2 weeks decade–1. In fact, all regions in the Arctic (except for the central Arctic) have Statistically Significant (at the 99% level or higher) longer melt Seasons by >1 week decade–1. The central Arctic Shows a Statistically Significant trend (at the 98% level) of 5.4 days decade–1. In 2005 the Arctic experienced its longest melt Season, corresponding with the least amount of Sea ice Since 1979 and the warmest temperatures Since the 1880s. Overall, the length of the melt Season is inversely correlated with the lack of Sea ice Seen in September north of Alaska and Siberia, with a mean correlation of –0.8.

The Changing Arctic Cryosphere and Likely Consequences: An Overview

The Arctic cryosphere is a critically important component of the earth system, affecting the energy balance, atmospheric and ocean circulation, freshwater storage, sea level, the storage, and release of large quantities of greenhouse gases, economy, infrastructure, health, and indigenous and non-indigenous livelihoods, culture and identity. Currently, components of the Arctic cryosphere are subjected to dramatic change due to global warming. The need to document, understand, project, and respond to changes in the cryosphere and their consequences stimulated a comprehensive international assessment called “SWIPA”: Snow, Water, Ice, Permafrost in the Arctic. Some of the extensive key SWIPA chapters have been summarized and made more widely available to a global audience with multi-disciplinary interests in this Special Report of Ambio. In this article, an overview is provided of this Special Report in the context of the more detailed and wider scope of the SWIPA Report. Accelerated changes in major components of the Arctic cryosphere are documented. Evidence of feedback mechanisms between the cryosphere and other parts of the climate system are identified as contributing factors to enhanced Arctic warming while the growing importance of Arctic land-based ice as a contributor to global sea-level rise is quantified. Cryospheric changes will result in multifaceted and cascading effects for people within and beyond the Arctic presenting both challenges and opportunities.

Comparison of sea-ice extent and ice-edge location estimates from passive microwave and enhanced-resolution scatterometer data

Abstract Passive microwave sea-ice concentration fields provide some of the longest-running and most consistent records of changes in sea ice. Scatterometry-based sea-ice fields are more recently developed data products, but now they provide a record of ice conditions spanning several years. Resolution enhancement techniques applied to scatterometer fields provide much higher effective resolutions (~10 km) than are available from standard scatterometer and passive microwave fields (25–50 km). Here we examine ice-extent fields from both sources and find that there is general agreement between scatterometer data and passive microwave fields, though scatterometer estimates yield substantially lower ice extents during winter. Comparisons with ice-edge locations estimated from AVHRR imagery indicate that enhanced scatterometer data can sometimes provide an improved edge location, but there is substantial variation in the results, depending on the local conditions. A blended product, using both scatterometer and passive microwave data, could yield improved results.