The fine spatial resolution of laser altimeters makes them potentially valuable to oceanography studying features at mesoscale, close to land, and in the marginal ice zone. To fulfill this promise, we must understand laser sea state bias (SSB). SSB occurs in the measurement of sea surface height in the presence of waves when the altimeter observations are preferentially influenced by particular parts (e.g., wave troughs) of the wave-covered surface. Radar altimeters have received considerable attention relating radar SSB to wave properties and wind speed. Comparatively, little attention has been devoted to the SSB of laser altimeters, and the studies of laser SSB which have been done have led to indeterminate or ambiguous results even as to sign. Here, we find that to make changes in satellite dynamic ocean topography (DOT) from the Ice, Clouds, and Land Elevation Satellite (ICESat) period, 2004–2009, to the CryoSat-2 period, 2011–2015, consistent with hydrography plus ocean bottom pressure in the subarctic Greenland and Norwegian seas, we need to correct the ICESat DOT for SSB. On average, ICESat SSB is –18% of significant wave height in excess of 1.7 m.

Model and observational evidence has shown that ocean current speeds in the Beaufort Gyre have increased and recently stabilized. Because these currents rival ice drift speeds, we examine the potential for the Beaufort Gyre's shift from a system in which the wind drives the ice and the ice drives a passive ocean to one in which the ocean often, in the absence of high winds, drives the ice. The resultant stress exerted on the ocean by the ice and the resultant Ekman pumping are reversed, without any change in average wind stress curl. Through these curl reversals, the ice‐ocean stress provides a key feedback in Beaufort Gyre stabilization. This manuscript constitutes one of the first observational studies of ice‐ocean stress inclusive of geostrophic ocean currents, by making use of recently available remote sensing data.

A budget of meteoric water (MW = river runoff, net precipitation minus evaporation, and glacial meltwater) over four regions of the Arctic Ocean is constructed using a simple box model, regional precipitation-evaporation estimates from reanalysis data sets, and estimates of import and export fluxes derived from the literature with a focus on the 2003–2008 period. The budget indicates an approximate/slightly positive balance between MW imports and exports (i.e., no change in storage); thus, the observed total freshwater increase observed during this time period likely resulted primarily from changes in non-MW freshwater components (i.e., increases in sea ice melt or Pacific water and/or a decrease in ice export). Further, our analysis indicates that the MW increase observed in the Canada Basin resulted from a spatial redistribution of MW over the Arctic Ocean. Mean residence times for MW were estimated for the Western Arctic (5–7 years), Eastern Arctic (3–4 years), and Lincoln Sea (1–2 years). The MW content over the Siberian shelves was estimated (~14,000 km³) based on a residence time of 3.5 years. The MW content over the entire Arctic Ocean was estimated to be ≥ 44,000 km³. The MW export through Fram Strait consisted mostly of water from the Eastern Arctic (3237 ± 1370 km³ yr^-1) whereas the export through the Canadian Archipelago was nearly equally derived from both the Western Arctic (1182 ± 534 km³ yr^-1) and Lincoln Sea (972 ± 391 km³ yr^-1).

In this work, we explore the seasonal relationships (i.e., the phenology) between sea ice retreat, sea surface temperature (SST), and atmospheric heat fluxes in the Pacific Sector of the Arctic Ocean, using satellite and reanalysis data. We find that where ice retreats early in most years, maximum summertime SSTs are usually warmer, relative to areas with later retreat. For any particular year, we find that anomalously early ice retreat generally leads to anomalously warm SSTs. However, this relationship is weak in the Chukchi Sea, where ocean advection plays a large role. It is also weak where retreat in a particular year happens earlier than usual, but still relatively late in the season, primarily because atmospheric heat fluxes are weak at that time. This result helps to explain the very different ocean warming responses found in two recent years with extreme ice retreat, 2007 and 2012. We also find that the timing of ice retreat impacts the date of maximum SST, owing to a change in the ocean surface buoyancy and momentum forcing that occurs in early August that we term the Late Summer Transition (LST). After the LST, enhanced mixing of the upper ocean leads to cooling of the ocean surface even while atmospheric heat fluxes are still weakly downward. Our results indicate that in the near-term, earlier ice retreat is likely to cause enhanced ocean surface warming in much of the Arctic Ocean, although not where ice retreat still occurs late in the season.

The seasonal evolution of sea ice loss in the Beaufort Sea during 1979–2012 is examined, focusing on differences between eastern and western sectors. Two stages in ice loss are identified: the Day of Opening (DOO) is defined as the spring decrease in ice concentration from its winter maximum below a value of 0.8 areal concentration; the Day of Retreat (DOR) is the summer decrease below 0.15 concentration. We consider three aspects of the subject, i.e., (i) the long-term mean, (ii) long-term linear trends, and (iii) interannual variability. We find that in the mean, DOO occurs earliest in the eastern Beaufort Sea (EBS) owing to easterly winds which act to thin the ice there, relative to the western Beaufort Sea (WBS) where ice has been generally thicker. There is no significant long-term trend in EBS DOO, although WBS DOO is in fact trending toward earlier dates. This means that spatial differences in DOO across the Beaufort Sea have been shrinking over the past 33 years, i.e., these dates are becoming more synchronous, a situation which may impact human and marine mammal activity in the area. Retreat dates are also becoming more synchronous, although with no statistical significance over the studied time period. Finally, we find that in any given year, an increase in monthly mean easterly winds of ~1 m/s during spring is associated with earlier summer DOR of 6–15 days, offering predictive capability with 2–4 months lead time.

The albedo of Arctic sea ice is calculated from summertime output of twentieth century Community Climate System Model v.4 (CCSM4) simulations. This is compared with an empirical record based on the generalized observations of the summer albedo progression along with melt onset dates determined from remote sensing. Only the contributions to albedo from ice, snow, and ponds are analyzed; fractional ice area is not considered in this assessment. Key factors dictating summer albedo evolution are the timing and extent of ponding and accumulation of snow. The CCSM4 summer sea ice albedo decline was found, on average, to be less pronounced than either the empirical record or the CLARA-SAL satellite record. The modeled ice albedo does not go as low as the empirical record, nor does the low summer albedo last as long. In the model, certain summers were found to retain snow on sea ice, thus inhibiting ice surface melt and the formation or retention of melt ponds. These "frozen" summers were generally not the summers with the largest spring snow accumulation, but were instead summers that received at least trace snowfall in June or July. When these frozen summers are omitted from the comparison, the model and empirical records are in much better agreement. This suggests that the representation of summer Arctic snowfall events and/or their influence on the sea ice conditions are not well represented in CCSM4 integrations, providing a target for future model development work.

Storm-track analysis is applied to the meridional winds at 10 m and 850 hPa for the winters of 1999–2006. The analysis is focused on the North Atlantic and North Pacific Ocean basins and the Southern Ocean spanning the region south of the Indian Ocean. The spatial patterns that emerge from the analysis of the 850-hPa winds are the typical free-tropospheric storm tracks. The spatial patterns that emerge from the analysis of the surface winds differ from the free-tropospheric storm tracks. The spatial differences between the surface and free-tropospheric storm tracks can be explained by the influence of the spatial variability in the instability of the atmospheric boundary layer. Strongly unstable boundary layers allow greater downward mixing of free-tropospheric momentum (momentum mixing), and this may be the cause of the stronger surface storm tracks in regions with greater instability in the time mean.

Principal component analysis suggests that the basin-scale variability that is reflected in the storm-track signature is the same for the free-tropospheric and surface winds. Separating the data based on the boundary layer stability shows that the surface storm track has a local maximum in the region of maximum instability, even when there is no local maximum in the free-tropospheric storm track above the region. The spatial patterns of the surface storm tracks suggest a positive feedback for storm development as follows: 1) an existing storm generates strong free-tropospheric wind variability, 2) the momentum mixing of the unstable boundary layers acts to increase the ocean–atmosphere energy fluxes, and 3) the fluxes precondition the lower atmosphere for subsequent storm development.

Two research cruises were conducted in the summers of 1998 and 1999 as part of the North Pacific Acoustic Laboratory (NPAL) project. The cruises' objective was to test the theory that predicts acoustic fluctuations from the internal wave sound speed or temperature fluctuations. Here we discuss the in situ profile measurements of temperature, salinity, and derived sound speed taken with conductivity temperature density (CTD) instruments dropped off the side of the ships as they steamed between the NPAL Acoustic Thermometry of Ocean Climate source off Kauai and a billboard receiving array on Sur Ridge off Point Sur, California. The first cruise, IW98, was aboard the University of Hawaii research vessel Moana Wave. The second cruise, IW99, was aboard the R/V Melville.

The differences between Tropical Atmosphere Ocean (TAO) anemometer and QuikSCAT scatterometer winds are analyzed over a period of 3 yr. Systematic differences are expected owing to ocean currents because the anemometer measures absolute air motion, whereas a radar measures the motion of the air relative to the ocean. Monthly averaged collocated wind differences (CWDs) are compared with available near-surface current data at 15-m depth from drifters, at 25-m depth from acoustic Doppler current profilers (ADCPs), and at 10-m depth from current meters and with geostrophic currents at the surface from the TOPEX/Poseidon radar altimeter. Because direct current observations are so sparse, comparisons are also made with climatological currents from these same sources. Zonal CWDs are in good agreement with the zonal current observations, particularly from 2°S to 2°N where there are strong currents and a robust seasonal cycle, with the altimeter-derived anomalous currents giving the best match. At higher latitudes there is qualitative agreement at buoys with relatively large currents. The overall variance of the zonal component of the CWDs is reduced by approximately 25% by subtracting an estimate of the zonal currents. The meridional CWDs are nearly as large as the zonal CWDs but are unpredictable. The mean CWDs show a robust divergence pattern about the equator, which is suggestive of Ekman currents, but with unexpectedly large magnitudes.

Coefficients for estimating climatological zonal surface currents from the altimeter at the TAO buoys are tabulated: the amplitudes and phases for the annual and semiannual harmonics, and a linear regression against the Southern Oscillation index, are combined with the mean from the drifter currents. Examples are shown of the application of these estimators to data from SeaWinds on the Midori satellite. These estimators are also useful for deriving air–sea fluxes from TAO winds.

A solution is presented for correcting the data collected by the digital ice profiling system (DIPS) from the first half of the SCICEX '98 cruise. The ice draft measurements are intrinsi- cally related to the depth of the submarine and were corrupted by faulty measurements of depth. The ship's digital depth detector measured the gross movements of the submarine, but was unresponsive to small changes in depth associated with the natural porpoising of the boat. This porpoising, which is a periodic vertical movement of the submarine of several feet, was transferred to the ice draft data. An independent sensor package, the Icecat2, collected pressure data, which were converted to depth. The DIPS and Icecat2 systems had different clocks. To align them the depth signatures from each system were compared during large, rapid descents of the submarine. A time-dependent time offset between the two clocks was computed. By removing the DIPS depths from the ice draft measurements and replacing them with the depths measured by the Icecat2 system, the ice draft data were corrected.

Satellite-mounted radar scatterometers designed to quantify surface winds over the ocean actually measure the relative motion between the air and the ocean surface. Estimates of the wind stress from conventional surface wind measurements are usually derived neglecting ocean currents. However, when the relative motion is used, the differences in the estimated stress can be as large as 50% near the equator and may even reverse sign during an El Nino. This assertion is supported by the strong relationship between the surface currents measured by the Tropical Atmosphere–Ocean (TAO) array in the Pacific Ocean and the differences between the winds estimated from scatterometer data and those measured by TAO anemometers. The fact that the scatterometer measures relative motion, and not wind alone, makes scatterometer-derived stress a more accurate representation of the boundary condition needed for both atmospheric and oceanic models than stress fields derived neglecting ocean currents.

There was an opportunity to compare 10 months of collocated National Aeronautics and Space Administration scatterometer (NSCAT) wind vectors with those from the Tropical Atmosphere Ocean (TAO) buoy array, located in the tropical Pacific Ocean. Over 5500 data pairs, from nearly 70 buoys, were collocated in the calibration/validation effort for NSCAT. These data showed that the wind speeds produced from the NSCAT-1 model function were low by about 7%–8% compared with TAO buoy winds. The revised model function, NSCAT-2, produces wind speeds with a bias of about 1%. The scatterometer directions were within 20° (rms), meeting accuracy requirements, when compared to TAO data. The mean direction bias between the TAO and the NSCAT vectors (regardless of model function) is about 9° with the scatterometer winds to the right of the TAO winds, which may be due to swell. The statistics of the two datasets are discussed, using component biases in lieu of the speed bias, which is naturally skewed. Using ocean currents and buoy winds measured along the equator, it is shown that the scatterometer measures the wind relative to the moving ocean surface. In addition, a systematic effect of rain on the NSCAT wind retrievals is noted. In all analyses presented here, winds less than 3 m s^-1 are removed, due to the difficulty in making accurate low wind measurements.