Numerical experiments are conducted to project arctic sea ice responses to varying levels of future anthropogenic warming and climate variability over 2010–2050. A summer ice-free Arctic Ocean is likely by the mid-2040s if arctic surface air temperature (SAT) increases 4 deg C by 2050 and climate variability is similar to the past relatively warm two decades. If such a SAT increase is reduced by one-half or if a future Arctic experiences a range of SAT fluctuation similar to the past five decades, a summer ice-free Arctic Ocean would be unlikely before 2050. If SAT increases 4 deg C by 2050, summer ice volume decreases to very low levels (10–37% of the 1978–2009 summer mean) as early as 2025 and remains low in the following years, while summer ice extent continues to fluctuate annually. Summer ice volume may be more sensitive to warming while summer ice extent more sensitive to climate variability. The rate of annual mean ice volume decrease relaxes approaching 2050. This is because, while increasing SAT increases summer ice melt, a thinner ice cover increases winter ice growth. A thinner ice cover also results in a reduced ice export, which helps to further slow ice volume loss. Because of enhanced winter ice growth, arctic winter ice extent remains nearly stable and therefore appears to be a less sensitive climate indicator.

The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of –0.57 m decade^-1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

The connection between sea ice variability and cloud cover over the Arctic seas during autumn is investigated by analyzing the 40-yr ECMWF Re-Analysis (ERA-40) products and the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) Polar Pathfinder satellite datasets. It is found that cloud cover variability near the sea ice margins is strongly linked to sea ice variability. Sea ice retreat is linked to a decrease in low-level cloud amount and a simultaneous increase in midlevel clouds. This pattern is apparent in both data sources. Changes in cloud cover can be explained by changes in the atmospheric temperature structure and an increase in near-surface temperatures resulting from the removal of sea ice. The subsequent decrease in static stability and deepening of the atmospheric boundary layer apparently contribute to the rise in cloud level. The radiative effect of this change is relatively small, as the direct radiative effects of cloud cover changes are compensated for by changes in the temperature and humidity profiles associated with varying ice conditions.

A model study has been conducted of the unprecedented retreat of arctic sea ice in the summer of 2007. It is found that preconditioning, anomalous winds, and ice-albedo feedback are mainly responsible for the retreat. Arctic sea ice in 2007 was preconditioned to radical changes after years of shrinking and thinning in a warm climate. During summer 2007 atmospheric changes strengthened the transpolar drift of sea ice, causing more ice to move out of the Pacific sector and the central Arctic Ocean where the reduction in ice thickness due to ice advection is up to 1.5 m more than usual. Some of the ice exited Fram Strait and some piled up in part of the Canada Basin and along the coast of northern Greenland, leaving behind an unusually large area of thin ice and open water. Thin ice and open water allow more surface solar heating because of a much reduced surface albedo, leading to amplified ice melting. The Arctic Ocean lost additional 10% of its total ice mass in which 70% is due directly to the amplified melting and 30% to the unusual ice advection, causing the unprecedented ice retreat. Arctic sea ice has entered a state of being particularly vulnerable to anomalous atmospheric forcing.

We conduct experiments with an ice-ocean model to answer the question whether and to what degree unusually clear skies during the summer of 2007 contributed to the record sea ice extent minimum in the Arctic Ocean during September of 2007. Anomalously high pressure over the Beaufort Sea during summer 2007 appears associated with a strong negative cloud anomaly. This anomaly is two standard deviations below the 1980–2007 average established from a combination of two different satellite-based records. Cloud anomalies from the MODIS sensor are compared with anomalies from the NCEP/NCAR reanalysis and are found in good agreement in spatial patterns and magnitude. However, these experiments establish that the negative cloud anomaly and increased downwelling shortwave flux from June through August did not contribute substantially to the record sea ice extent minimum. This finding eliminates one aspect of the unusual weather that may have contributed to the record minimum.

Ensemble predictions of arctic sea ice in spring and summer 2008 have been carried out using an ice-ocean model. The ensemble is constructed by using atmospheric forcing from 2001 to 2007 and the September 2007 ice and ocean conditions estimated by the model. The prediction results show that the record low ice cover and the unusually warm ocean surface waters in summer 2007 lead to a substantial reduction in ice thickness in 2008. Up to 1.2 m ice thickness reduction is predicted in a large area of the Canada Basin in both spring and summer of 2008, leading to extraordinarily thin ice in summer 2008. There is a 50% chance that both the Northern Sea Route and the Northwest Passage will be nearly ice free in September 2008. It is not likely there will be another precipitous decline in arctic sea ice extent such as seen in 2007, unless a new atmospheric forcing regime, significantly different from the recent past, occurs.

How well can the extent of arctic sea ice be predicted for lead periods of up to one year? The forecast ability of a linear empirical model is explored. It uses as predictors historical information about the ocean and ice obtained from an ice–ocean model retrospective analysis. The monthly model fields are represented by a correlation-weighted average based on the predicted ice extent. The forecast skill of the procedure is found by fitting the model over subsets of the available data and then making subsequent projections using independent predictor data. The forecast skill, relative to climatology, for predictions of the observed September ice extent for the pan-arctic region is 0.77 for six months lead (from March) and 0.75 for 11 months lead (from October). The ice concentration is the most important variable for the first two months and the ocean temperature of the model layer with a depth of 200 to 270 m is most important for longer lead times. The trend accounts for 76% of the variance of the pan-arctic ice extent, so most of the forecast skill is realized by determining model variables that best represent this trend. For detrended data there is no skill for lead times of 3 months or more. The forecast skill relative to the estimate from the previous year is lower than the climate-relative skill but it is still greater than 0.45 for most lead times. Six-month predictions are also made for each month of the year and regional three-month predictions are made for 45-degree sectors. The ice-ocean model output significantly improves the predictive skill of the forecast model.

The melt season of the Arctic sea ice cover is greatly affected by the partitioning of the incident solar radiation between reflection to the atmosphere and absorption in the ice and ocean. This partitioning exhibits a strong seasonal cycle and significant interannual variability. Data in the period 1998, 2000–2004 were analyzed in this study. Observations made during the 1997–1998 SHEBA (Surface HEat Budget of the Arctic Ocean) field experiment showed a strong seasonal dependence of the partitioning, dominated by a five-phase albedo evolution. QuikSCAT scatterometer data from the SHEBA region in 1999–2004 were used to further investigate solar partitioning in summer. The time series of scatterometer data were used to determine the onset of melt and the beginning of freezeup. This information was combined with SSM/I-derived ice concentration, TOVS-based estimates of incident solar irradiance, and SHEBA results to estimate the amount of solar energy absorbed in the ice-ocean system for these years. The average total solar energy absorbed in the ice-ocean system from April through September was 900 MJ m^-2. There was considerable interannual variability, with a range of 826 to 1044 MJ m^-2. The total amount of solar energy absorbed by the ice and ocean was strongly related to the date of melt onset, but only weakly related to the total duration of the melt season or the onset of freezeup. The timing of melt onset is significant because the incident solar energy is large and a change at this time propagates through the entire melt season, affecting the albedo every day throughout melt and freezeup.

The low-level atmospheric temperature inversion is a dominant feature of the Arctic atmosphere throughout most of the year. Meteorological stations that provide radiosonde data are sparsely distributed across the Arctic, and therefore provide little information on the spatial distribution of temperature inversions. Satellite-borne sensors provide an opportunity to fill the observational gap. In this study, a 17-yr time series, 1980–96, of clear-sky temperature inversion strength during the cold season is derived from High Resolution Infrared Radiation Sounder (HIRS) data using a two-channel statistical method. The satellite-derived clear-sky inversion strength monthly mean and trends agree well with radiosonde data. Both increasing and decreasing trends are found in the cold season for different areas. It is shown that there is a strong coupling between changes in surface temperature and changes in inversion strength, but that trends in some areas may be a result of advection aloft rather than warming or cooling at the surface.

Winter and spring changes in cloudiness are compared over the arctic seas (ocean areas north of 60°N) from the TOVS (TIROS Operational Vertical Sounder) Polar Pathfinder retrievals and two separate datasets derived from the Advanced Very High Resolution Radiometer (AVHRR). All satellite products exhibit significant decreases in cloud fraction over the arctic seas during winter (December, January, February) on the order of 5% / decade. An equally striking increase in spring (March, April, May) cloudiness is evident from the TOVS Pathfinder (TPP) and the extended AVHRR Polar Pathfinder (APP-x) projects. In the Central Arctic these positive trends can be as large as 15% / decade. Surface observations from the Russian drifting meteorological stations are consistent with satellite-observed changes during the 1980s. Negative trends in spring cloudiness reported by Comiso [2003] are in conflict with these findings. Spring changes in cloudiness are associated with changes in the atmospheric circulation. These dramatic, large-scale changes may have substantial impacts on the surface energy balance.

Products from the TIROS-N Operational Vertical Sounder (TOVS) Polar Pathfinder (Path-P) data set are compared with surface measurements and other satellite remote sensing retrievals during the Surface Heat Balance of the Arctic Ocean (SHEBA) field program (October 1997 to September 1998). The comparison provides estimates of Path-P retrieval uncertainties. Results are placed in the context of the natural variability and timescales of variability to allow potential users to judge the applicability of the data set for their purpose. Results show temperature profiles to be accurate within 3 K, total column precipitable water within 2 mm annually, and surface temperature within 3 K. Uncertainties in temperature retrieval are below "within-season" variability during all times of the year. Uncertainties in water vapor retrieval during winter and summer are slightly below observed variability in those seasons but are well below during spring. Uncertainty in retrieved cloud fraction is highly dependent on the timescale of observations. Cloud fractions from the surface and satellite are well correlated (correlation coefficient > 0.7) at timescales greater than 4 days but show weaker correlation at shorter timescales. Uncertainty in TOVS-retrieved cloud fraction is less than 20% for 5-day averages. In winter, TOVS-retrieved cloud fractions are higher than those reported in standard meteorological observations but match those derived from lidar data. This supports the notion that standard meteorological observations may underestimate cloudiness in winter. Cloud-top temperatures measured from the surface (lidar/radar) are significantly different from those estimated using TOVS and Advanced Very High Resolution Radiometer (AVHRR) radiances, which highlights the fundamental and inherent dissimilarity between these two measurement techniques.