Abstract David Andrew (Drew) Rothrock III lived during a period of vigorous scientific research in the Earth Sciences, from the International Geophysical Year to the era of satellites and high‐speed computer modeling. Drew made fundamental contributions to Arctic science, helping to lay the theoretical foundations for modeling the movement and thickness of sea ice, and later championing the acquisition and use of satellite and submarine data to test and improve those models, and to quantify changes in sea‐ice thickness over time. He was a founding member of the Polar Science Center at the University of Washington in Seattle, where he led major research projects, contributed his expertise to agency panels and working groups, and launched the careers of young scientists through his mentorship.

Davis Strait is a primary gateway for freshwater exchange between the Arctic and North Atlantic Oceans including freshwater contributions from west Greenland and Canadian Arctic Archipelago glacial melt. Data from six years (2004-10) of continuous measurements collected by a full-strait moored array and concurrent high-resolution Seaglider surveys are used to estimate volume and liquid freshwater transports through Davis Strait, with respective annual averages of -1.6 +/- 0.5 Sverdrups (Sv; 1 Sv equivalent to 10(6) m(3) s(-1)) and -93 +/- 6 mSv (negative sign indicates southward transport). Sea ice export contributes an additional -10 +/- 1 mSv of freshwater transport, estimated using satellite ice area transport and moored upward-looking sonar ice thickness measurements. Interannual and annual variability of the net transports are large, with average annual volume and liquid freshwater transport standard deviations of 0.7 Sv and 17 mSv and with interannual standard deviations of 0.3 Sv and 15 mSv. Moreover, there are no clear trends in the net transports over the 6-yr period. However, salinity in the upper 250 m between Baffin Island and midstrait decreases starting in September 2009 and remains below average through August 2010, but appears to return to normal by the end of 2010. This freshening event, likely caused by changes in arctic freshwater storage, is not apparent in the liquid freshwater transport time series due to a reduction in southward volume transport in 2009-10. Reanalysis of Davis Strait mooring data from the period 1987-90, compared to the 2004-10 measurements, reveals less arctic outflow and warmer, more saline North Atlantic inflow during the most recent period.

A coupled sea ice-ocean model is developed to quantify the sea ice response to changes in atmospheric and oceanic forcing in the Bering Sea over the period 1970-2008. The model captures much of the observed spatiotemporal variability of sea ice and sea surface temperature (SST) and the basic features of the upper-ocean circulation in the Bering Sea. Model results suggest that tides affect the spatial redistribution of ice mass by up to 0.1 m or 15% in the central-eastern Bering Sea by modifying ice motion and deformation and ocean flows. The considerable interannual variability in the pattern and strength of winter northeasterly winds leads to southwestward ice mass advection during January-May, ranging from 0.9 x 10(12) m(3) in 1996 to 1.8 x 10(12) m(3) in 1976 and averaging 1.4 x 10(12) m(3), which is almost twice the January-May mean total ice volume in the Bering Sea. The large-scale southward ice mass advection is constrained by warm surface waters in the south that melt 1.5 x 10(12) m(3) of ice in mainly the ice-edge areas during January-May, with substantial interannual variability ranging from 0.94 x 10(12) m(3) in 1996 to 2.0 x 10(12) m(3) in 1976. Ice mass advection processes also enhance thermodynamic ice growth in the northern Bering Sea by increasing areas of open water and thin ice. Ice growth during January-May is 0.90 x 10(12) m(3) in 1996 and 2.1 x 10(12) m(3) in 1976, averaging 1.3 x 10(12) m(3) over 1970-2008. Thus, the substantial interannual variability of the Bering Sea ice cover is dominated by changes in the wind-driven ice mass advection and the ocean thermal front at the ice edge. The observed ecological regime shifts in the Bering Sea occurred with significant changes in sea ice, surface air temperature, and SST, which in turn are correlated with the Pacific decadal oscillation over 1970-2008 but not with other climate indices: Arctic Oscillation, North Pacific index, and El Nino-Southern Oscillation. This indicates that the PDO index may most effectively explain the regime shifts in the Bering Sea.

The authors examine the natural variability of the arctic climate system simulated by two very different models: the Geophysical Fluid Dynamics Laboratory (GFDL) global climate model, and an area-averaged model of the arctic atmosphere-sea ice-upper-ocean system called the polar cap climate model, the PCCM. A 1000-yr integration of the PCCM is performed in which the model is driven by a prescribed, stochastic atmospheric energy flux convergence (D), which has spectral characteristics that are identical to the spectra of the observed D. The standard deviation of the yearly mean sea ice thickness from this model is 0.85 m; the mean sea ice thickness is 3.1 m. In contrast, the standard deviation of the yearly averaged sea ice thickness in the GFDL climate model is found to be about 6% of the climatological mean thickness and only 24% of that simulated by the PCCM. A series of experiments is presented to determine the cause of these disparate results. First, after changing the treatment of sea ice and snow albedo in the (standard) PCCM model to be identical thermodynamically to that in the GFDL model, the PCCM is driven with D from the GFDL control integration to demonstrate that the PCCM model produces an arctic climate similar to that of the GFDL model. Integrations of the PCCM are then examined in which the different prescriptions of the sea ice treatment (GFDL vs standard PCCM) and D (GFDL vs observed) are permutated. The results indicate that unarguable improvements in the treatment of sea ice in the GFDL climate model should amplify significantly the natural variability in this model. The authors present calculations that indicate the variability in the sea ice thickness is extremely sensitive to the spectrum of the atmospheric energy flux convergence. Specifically, the differences between the GFDL and observed D at timescales shorter than 3 yr are shown to have a significant impact on the sea ice variability on all timescales. A conservative best estimate for the amplitude of the natural variability in the arctic sea ice volume is presented; this estimate is a significant fraction (about 25%) of the mean sea ice thickness. The results suggest that most of the global climate models that have been used to evaluate climate change may also have artificially quiescent natural variability in the Arctic.

This paper synthesizes our understanding of the Arctic's large‐scale freshwater cycle. It combines terrestrial and oceanic observations with insights gained from the ERA‐40 reanalysis and land surface and ice‐ocean models. Annual mean freshwater input to the Arctic Ocean is dominated by river discharge (38%), inflow through Bering Strait (30%), and net precipitation (24%). Total freshwater export from the Arctic Ocean to the North Atlantic is dominated by transports through the Canadian Arctic Archipelago (35%) and via Fram Strait as liquid (26%) and sea ice (25%). All terms are computed relative to a reference salinity of 34.8. Compared to earlier estimates, our budget features larger import of freshwater through Bering Strait and larger liquid phase export through Fram Strait. While there is no reason to expect a steady state, error analysis indicates that the difference between annual mean oceanic inflows and outflows (∼8% of the total inflow) is indistinguishable from zero. Freshwater in the Arctic Ocean has a mean residence time of about a decade. This is understood in that annual freshwater input, while large (∼8500 km3), is an order of magnitude smaller than oceanic freshwater storage of ∼84,000 km3. Freshwater in the atmosphere, as water vapor, has a residence time of about a week. Seasonality in Arctic Ocean freshwater storage is nevertheless highly uncertain, reflecting both sparse hydrographic data and insufficient information on sea ice volume. Uncertainties mask seasonal storage changes forced by freshwater fluxes. Of flux terms with sufficient data for analysis, Fram Strait ice outflow shows the largest interannual variability.

The RADARSAT Geophysical Processor System (RGPS) has produced a wealth of data on Arctic sea ice motion, deformation, and thickness with broad geographical coverage and good temporal resolution. These data provide unprecedented spatial detail of the structure and evolution of the sea ice cover. The broad purpose of this study was to take advantage of the strengths of the RGPS data set to investigate sea ice kinematics and thickness, which affect the climate through their influence on ice production, ridging, and transport (i.e. mass balance); heat flux to the atmosphere; and structure of the upper ocean mixed layer. The objectives of this study were to: (1) Explain the relationship between the discontinuous motion of the ice cover and the large-scale, smooth wind field that drives the ice; (2) Characterize the sea ice deformation in the Arctic at different temporal and spatial scales, and compare it with deformation predicted by a state-of-theart ice/ocean model; and (3) Compare RGPS-derived sea ice thickness with other data, and investigate the thinning of the Arctic sea ice cover as seen in ULS data obtained by U.S. Navy submarines. We briefly review the results of our work below, separated into the topics of sea ice deformation and sea ice thickness. This is followed by a list of publications, meetings and presentations, and other activities supported under this grant. We are attaching to this report copies of all the listed publications. Finally, we would like to point out our community service to NASA through our involvement with the ASF User Working Group and the RGPS Science Working Group, as evidenced in the list of meetings and presentations below.

For the 1999 winter, this paper examines the behavior of the Bering Sea St. Lawrence Island polynya using a combination of Advanced Very High Resolution Radiometer (AVHRR), RADARSAT synthetic aperture radar (SAR), meteorological data, over‐winter moored upward looking sonars (ULS) and SeaBird salinity/temperature sensors. We define a thermal ice thickness from the AVHRR retrieval of ice surface temperature combined with meteorological observations and a heat flux model. South of the island, we compare the ULS and thermal thicknesses for congelation and frazil ice. When the satellites observe congelation ice over the ULSs, the ULS and thermal ice thicknesses generally agree. When SAR observes Langmuir plumes over the ULSs, which indicate frazil ice formation, the ULSs show scatterers at 5–20 m depths in the water column and the seawater temperatures are either within 0.01°C of freezing or are slightly supercooled. This suggests that during frazil events, crystals either nucleate at depth or are transported to depth by the Langmuir circulation. The combination of the SAR imagery and ULS observations also allow measurement of the pack ice advection velocity, the polynya width and the downwind frazil accumulation thickness, giving widths of 10 to 30 km and thicknesses of 0.1–0.2 m. Substitution of these observed values with the heat flux into the Pease polynya model yields polynya widths that approximately agree with the observed.

[i] During our yearlong participation in the Surface Heat Budget of the Arctic Ocean experiment (SHEBA), we found the measured relative humidity, figured for saturation with respect to ice, to almost always be near 100%. Often, multiple humidity sensors even showed supersaturation. Four months of observations over sea ice in the Antarctic showed the same behavior. These frequent, ubiquitous, and reproducible measurements are too compelling to discount. We hypothesize that the high relative humidity is a consequence of plentiful water vapor given up by leads and polynyas. We thus develop a simple time-dependent vapor budget model that we solve analytically to assess the role of leads in supplying water vapor to the polar atmospheric boundary layer. The solution to that model shows that (1) because the polar marine boundary layer is generally thin, its timescale for reaching moisture equilibrium is much shorter than the timescale of the synoptic processes that tend to disrupt equilibrium, and (2) because they have relatively warm surfaces, open leads and polynyas supply water vapor more rapidly than the surrounding sea ice surface can remove it, despite an open water fractional area that may be only 5%. In concert, the two processes commonly lead to water vapor densities in the boundary layer over sea ice that are near the value for ice saturation.

[1] Satellite data are important for providing the large-scale context of the Surface Heat Budget of the Arctic Ocean (SHEBA) station and for characterizing the spatial variability of the sea ice in its vicinity. The Canadian RADARSAT satellite collected 195 synthetic aperture radar (SAR) images of the SHEBA site over the course of the 1 year drift. The RADARSAT Geophysical Processor System (RGPS) used these images to compute the spatial pattern of ice motion within 100 km of the SHEBA station by tracking features in sequential images. From the ice motion data the divergence and shear of the pack ice are estimated. The divergence is large from November to January, followed by a gradual convergence from February through July. The character of the ice motion changes at the end of July, from piecewise rigid motion to free drift. The ice motion reverts to its winterlike character in late September. Thus the kinematic summer runs from late July to late September. The radar backscatter also goes through seasonal transitions, capturing the abrupt onset of melt (29 May) and freeze-up (15 August). The concentration of multiyear ice is about 94% in the fall, and its backscatter signature remains stable through spring. Multiyear and first-year ice cannot be distinguished during the summer melt season, when the mean backscatter is negatively correlated with the surface air temperature. The thermodynamic summer runs from late May to mid-August.

The pattern of recent surface warming observed in the Arctic exhibits both polar amplification and a strong relation with trends in the Arctic Oscillation mode of atmospheric circulation. Paleoclimate analyses indicate that Arctic surface temperatures were higher during the 20th century than during the preceding few centuries and that polar amplification is a common feature of the past. Paleoclimate evidence for Holocene variations in the Arctic Oscillation is mixed. Current understanding of physical mechanisms controlling atmospheric dynamics suggests that anthropogenic influences could have forced the recent trend in the Arctic Oscillation, but simulations with global climate models do not agree. In most simulations, the trend in the Arctic Oscillation is much weaker than observed. In addition, the simulated warming tends to be largest in autumn over the Arctic Ocean, whereas observed warming appears to be largest in winter and spring over the continents.

The Community Climate System Model (CCSM) has been created to represent the principal components of the climate system and their interactions. Development and applications of the model are carried out by the U.S. climate research community, thus taking advantage of both wide intellectual participation and computing capabilities beyond those available to most individual U.S. institutions. This article outlines the history of the CCSM, its current capabilities, and plans for its future development and applications, with the goal of providing a summary useful to present and future users.The initial version of the CCSM included atmosphere and ocean general circulation models, a land surface model that was grafted onto the atmosphere model, a sea-ice model, and a "flux coupler" that facilitates information exchanges among the component models with their differing grids. This version of the model produced a successful 300-yr simulation of the current climate without artificial flux adjustments. The model was then used to perform a coupled simulation in which the atmospheric CO2 concentration increased by 1% per year.In this version of the coupled model, the ocean salinity and deep-ocean temperature slowly drifted away from observed values, A subsequent correction to the roughness length used for sea ice significantly reduced these errors. An updated version of the CCSM was used to perform three simulations of the twentieth century's climate, and several projections of the climate of the twenty-first century,The CCSM's simulation of the tropical ocean circulation has been significantly improved by reducing the background vertical diffusivity and incorporating an anisotropic horizontal viscosity tensor. The meridional resolution of the ocean model was also refined near the equator. These changes have resulted in a greatly improved simulation of both the Pacific equatorial undercurrent and the surface countercurrents. The interannual variability of the sea surface temperature in the central and eastern tropical Pacific is also more realistic in simulations with the updated model.Scientific challenges to be addressed with future versions of the CCSM include realistic simulation of the whole atmosphere, including the middle and upper atmosphere, as well as the troposphere, simulation of changes in the chemical composition of the atmosphere through the incorporation of an integrated chemistry model; inclusion of global, prognostic biogeochemical components for land, ocean, and atmosphere; simulations of past climates, including times of extensive continental glaciation as well as times with little or no ice; studies of natural climate variability on seasonal-to-centennial timescales; and investigations of anthropogenic climate change. In order to make such studies possible, work is under way to improve all components of the model. Plans call for a new version of the CCSM to be released in 2002. Planned studies with the CCSM will require much more computer power than is currently available.

We examine sea ice deformation in the Beaufort Sea as computed by the Geophysical Processor System from sequential SAR images, together with coincident estimates of the geostrophic wind and its gradients from the International Arctic Buoy Program. Grid cells of deforming ice at the 5 km scale tend to align in linear features of 100 km or more, separating regions of rigid body motion. We relate the principal axis of shear in the ice to the angle between the linear deformation feature and the ice velocity difference across the feature. We also find a strong relation between the principal axes of shear in the ice and in the wind.

A central objective of the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment was to provide a comprehensive observational test for single-column models of the atmosphere-sea ice-ocean system over the Arctic Ocean. For single-column modeling, one must specify the time-varying tendencies due to horizontal and vertical advection of air through the column. Due to the difficulty of directly measuring these tendencies, it was decided for SHEBA to obtain them from short-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) global forecast model, into which SHEBA rawinsonde and surface synoptic observations were routinely assimilated. The quality of these forecasts directly affects the reliability of the derived advective tendencies. In addition, the ECMWF-forecast thermodynamic and cloud fields, and radiative and turbulent fluxes present an illuminating comparison of the SHEBA observations with a state-of-the-art global numerical model. The authors compare SHEBA soundings, cloud and boundary layer observations with the ECMWF model output throughout the SHEBA year. They find that above the boundary layer, the model was faithful to the SHEBA rawinsonde observations and maintained a proper long-term balance between advective and nonadvective tendencies of heat and moisture. This lends credence to use of the ECMWF-predicted advective tendencies for single-column modeling studies. The model-derived cloud properties and precipitation (which were not assimilated from observations) are compared with cloud radar, lidar, microwave radiometer, surface turbulent and radiative measurements, and basic surface meteorology. The model s slab sea-ice model led to large surface temperature errors and insufficient synoptic variability of temperature. The overall height distribution of cloud was fairly well simulated (though somewhat overestimated) in all seasons, as was precipitation. However, the model clouds typically had a much higher ratio of cloud ice to cloud water than suggested by lidar depolarization measurements, and a smaller optical depth, leading to monthly biases of up to 50 W m^(-2) in the monthly surface downwelling longwave and shortwave radiation. Further biases in net radiation were due to the inaccurate model assumption of constant surface albedo. Observed turbulent sensible and latent heat fluxes tended to be small throughout SHEBA. During high-wind periods during the winter, the ECMWF model predicted sustained downward heat fluxes of up to 60 W m^(-2), much higher than observed. A detailed comparison suggests that this error was due to both inadequate resolution of the 31-level model and a deficient parameterization of sea-ice thermodynamics.