R. W. Lindsay

Seasonal forecasts of the September 2012 Arctic sea ice thickness and extent are conducted starting from 1 June 2012. An ensemble of forecasts is made with a coupled ice‐ocean model. For the first time, observations of the ice thickness are used to correct the initial ice thickness distribution to improve the initial conditions. Data from two airborne campaigns are used: NASA Operation IceBridge and SIZONet. The model was advanced through April and May using reanalysis data from 2012 and for June–September it was forced with reanalysis data from the previous seven summers. The ice extent in the corrected runs averaged lower in the Pacific sector and higher in the Atlantic sector compared to control runs with no corrections. The predicted total ice extent is 4.4 +/− 0.5 M km², 0.2 M km² less than that made with the control runs but 0.8 M km² higher than the observed September extent.

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 pus... Abstract 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 pus...

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.

Ice concentration is a critical parameter of the polar marine environment because of the large effect sea ice has on the surface albedo and heat exchange between the atmosphere and the ocean. Simulations of the energy exchange processes in models would benefit if the ice concentration were represented more accurately. Reanalysis simulations that use historical wind and temperature fields may develop erroneous ice concentration estimates; these can be corrected by using observed ice concentration fields. The ice concentration assimilation presented here is a new method based on nudging the model ice concentration toward the observed concentration in a manner that emphasizes the ice extent and minimizes the effect of observational errors in the interior of the pack. The nudging weight is a nonlinear function of the difference between the model and the observed ice concentration. The simulated ice extent is improved with the assimilation of ice concentration but is not identical to the observed extent. The simulated ice draft is compared to that measured by upward-looking sonars on submarines and moorings. Significant improvements in the ice draft comparisons are obtained with assimilation of ice concentration alone and even more with assimilation of both ice concentration and ice velocity observations.

Recent observations of summer Arctic sea ice over the satellite era show that record or near-record lows for the ice extent occurred in the years 2002–05. To determine the physical processes contributing to these changes in the Arctic pack ice, model results from a regional coupled ice–ocean model have been analyzed. Since 1988 the thickness of the simulated basinwide ice thinned by 1.31 m or 43%. The thinning is greatest along the coast in the sector from the Chukchi Sea to the Beaufort Sea to Greenland. It is hypothesized that the thinning since 1988 is due to preconditioning, a trigger, and positive feedbacks: 1) the fall, winter, and spring air temperatures over the Arctic Ocean have gradually increased over the last 50 yr, leading to reduced thickness of first-year ice at the start of summer; 2) a temporary shift, starting in 1989, of two principal climate indexes (the Arctic Oscillation and Pacific Decadal Oscillation) caused a flushing of some of the older, thicker ice out of the basin and an increase in the summer open water extent; and 3) the increasing amounts of summer open water allow for increasing absorption of solar radiation, which melts the ice, warms the water, and promotes creation of thinner first-year ice, ice that often entirely melts by the end of the subsequent summer. Internal thermodynamic changes related to the positive ice–albedo feedback, not external forcing, dominate the thinning processes over the last 16 yr. This feedback continues to drive the thinning after the climate indexes return to near-normal conditions in the late 1990s. The late 1980s and early 1990s could be considered a tipping point during which the ice–ocean system began to enter a new era of thinning ice and increasing summer open water because of positive feedbacks. It remains to be seen if this era will persist or if a sustained cooling period can reverse the processes.

A new Lagrangian sea ice model for the Arctic Ocean has been developed to accommodate the assimilation of integral measures of ice displacement over periods of days or weeks. The model is a two-layer (thick ice and open water) dynamic model with 600–700 cells spaced at roughly 100 km. The force balance equation is solved for each cell with standard wind and water stress terms, a Coriolis term, and an internal ice stress term. The internal stress is found using a viscous–plastic rheology and an elliptical yield curve. The strain rate is determined by the “Smoothed Particle Hydrodynamics” formalism, which determines the spatial derivatives of the velocity by a weighted summation of the velocities of adjacent cells. A length scale of 150 km is used. The model is driven with observed geostrophic winds and climatological-mean ocean currents. Ice growth and melt are found from a seasonal- and thickness-based lookup table because the current focus is on the model dynamics. The model ice velocity simulat... Abstract A new Lagrangian sea ice model for the Arctic Ocean has been developed to accommodate the assimilation of integral measures of ice displacement over periods of days or weeks. The model is a two-layer (thick ice and open water) dynamic model with 600–700 cells spaced at roughly 100 km. The force balance equation is solved for each cell with standard wind and water stress terms, a Coriolis term, and an internal ice stress term. The internal stress is found using a viscous–plastic rheology and an elliptical yield curve. The strain rate is determined by the “Smoothed Particle Hydrodynamics” formalism, which determines the spatial derivatives of the velocity by a weighted summation of the velocities of adjacent cells. A length scale of 150 km is used. The model is driven with observed geostrophic winds and climatological-mean ocean currents. Ice growth and melt are found from a seasonal- and thickness-based lookup table because the current focus is on the model dynamics. The model ice velocity simulat...

NASA's RADARSAT Geophysical Processor System (RGPS) uses sequential synthetic aperture radar (SAR) images to track the trajectories of some 30 000 points on the Arctic sea ice for periods of up to 6 months. Much of the Arctic basin is imaged and tracked every 3 days. The result is a highly detailed picture of how the sea ice moves and deforms. The points are initially spaced 10 km apart and are organized into four-cornered cells. The area and the strain rates are calculated for each cell for each new observation of its corners. The accuracy of the RGPS ice tracking, area changes, and deformation estimates is needed to make the dataset useful for analysis, model validation, and data assimilation. Two comparisons are made to assess the accuracy. The first compares the tracking performed at two different facilities (the Jet Propulsion Laboratory in Pasadena, California, and the Alaska SAR Facility in Fairbanks, Alaska), between which the primary difference is the operator intervention. The error sta... Abstract NASA's RADARSAT Geophysical Processor System (RGPS) uses sequential synthetic aperture radar (SAR) images to track the trajectories of some 30 000 points on the Arctic sea ice for periods of up to 6 months. Much of the Arctic basin is imaged and tracked every 3 days. The result is a highly detailed picture of how the sea ice moves and deforms. The points are initially spaced 10 km apart and are organized into four-cornered cells. The area and the strain rates are calculated for each cell for each new observation of its corners. The accuracy of the RGPS ice tracking, area changes, and deformation estimates is needed to make the dataset useful for analysis, model validation, and data assimilation. Two comparisons are made to assess the accuracy. The first compares the tracking performed at two different facilities (the Jet Propulsion Laboratory in Pasadena, California, and the Alaska SAR Facility in Fairbanks, Alaska), between which the primary difference is the operator intervention. The error sta...

In the polar oceans the ice thickness distribution controls the exchange of heat between the ocean and the atmosphere and determines the strength of the ice. The Surface Heat Budget of the Arctic Ocean (SHEBA) experiment included a year‐long field program centered on a drifting ice station in the Beaufort and Chukchi Seas in the Arctic Ocean from October 1997 through October 1998. Here we use camp observations and develop methods to assimilate ice thickness and open water observations into a model in order to estimate the evolution of the thickness distribution in the vicinity of the camp. A thermodynamic model is used to simulate the ice growth and melt, and an ice redistribution model is used to simulate the opening and ridging processes. Data assimilation procedures are developed and then used to assimilate observations of the thickness distribution. Assimilated observations include those of the thin end of the distribution determined by aircraft surveys of the surface temperature and helicopter photographic surveys and aircraft microwave estimates of the open water fraction. The deformation of the ice was determined primarily from buoy and RADARSAT Geophysical Processor System (RGPS) measurements of the ice velocity. Because of the substantial convergence and ridging observed in the spring and summer, the estimated mean ice thickness increases by 59%, from 1.53 to 2.44 m, over the year in spite of a net thermodynamic ice loss for most multiyear ice.

The deformation of sea ice is an important element of the Arctic climate system because of its influence on the ice‐thickness distribution and on the rates of ice production and melt. New data obtained from the Radarsat Geophysical Processor System (RGPS) using satellite synthetic aperture radar images of the ice offers an opportunity to compare observations of the ice deformation to estimates obtained from models. The RGPS tracks tens of thousands of points, spaced roughly at 10‐km intervals, for an entire season in a Lagrangian fashion. The deformation is computed from cells formed by the tracked points, typically at 3‐day intervals. We used a coupled ice/ocean model with ice‐thickness and enthalpy distributions that covers the entire Arctic Ocean with a 40‐km grid. Model‐only and model‐with‐data‐assimilation runs were analysed. The data assimilation runs were analysed in order to determine the validity of the comparison techniques and to find the comparisons under the best of circumstances, wh... Résumé [Traduit par la rédaction] La déformation des glaces de mer est un élément important du système climatique de l'Arctique, car elle influe sur la répartition de l'épaisseur des glaces et sur leur vitesse de formation et de fonte. De nouvelles données fournies par le système de processeur géophysique de RADARSAT (RGPS) à l'aide d'images de la glace prises par radar à antenne synthétique à bord d'un satellite permettent de comparer la déformation des glaces observée et la déformation évaluée à partir de modèles. Le RGPS suit des dizaines de milliers de points espacés plus ou moins de 10 km et ce, pendant une saison entière et de façon lagrangienne. La déformation est calculée à partir de cellules formées par les points suivis typiquement à des intervalles de trois jours. Nous avons utilisé un modèle couplé glace/océan avec distribution de l'épaisseur et des enthalpies des glaces qui couvre l'ensemble de l'océan Arctique avec une grille de 40 km. Les résultats du modèle seul et du modèle avec assimilat...