Peter Wadhams

Further evidence of ice thinning in the Arctic Ocean

A sea ice thickness profile, obtained in September 1996 from the Eurasian Basin of the Arctic Ocean between Fram Strait and the North Pole, was compared with a profile obtained in the same region in September-October 1976. A decline in mean ice draft of 43% was observed over the 20-year interval, in agreement with changes observed in other parts of the Arctic Ocean by Rothrock et al. [1999].

The freshwater budget of the Arctic Ocean

Preface. Acknowledgements. Summary Poem. Introduction. 1. Oceanic freshwater fluxes in the climate system A. Stigebrandt. 2. Global atmospheric circulation patterns and relationships to Arctic freshwater fluxes J.E. Walsh. 3. Atmospheric components of the Arctic Ocean freshwater balance and their interannual variability R.G. Barry, M.C. Serreze. 4. Hydroclimatology of the Arctic drainage basin L.C. Bowling, P.D. Lettenmaier, B.V. Matheussen. 5. The Arctic Oceans freshwater budget: sources, storage and export E.C. Carmack. 6. The Arctic Ocean freshwater budget of a climate General Circulation Model H. Cattle, D. Cresswell. 7. Atmospheric components of the Arctic Ocean hydrologic budget assessed from Rawinsonde data M.C. Serreze, R.G. Barry. 8. Reanalyses depictions of the Arctic atmospheric moisture budget D.H. Bromwich, R.I. Cullather, M.C. Serreze. 9. Moisture transport to Arctic drainage basins relating to significant precipitation events and cyclogenesis J.R. Gyakum. 10. Atmospheric climate models: simulation of the Arctic Ocean fresh water budget components V.M. Kattsov, J.E. Walsh, A. Rinke, K. Dethloff. 11. Discharge observation networks in Arctic regions: computation of the river runoff into the Arctic Ocean, its seasonality and variability W.E. Grabs, F. Portmann, T. de Couet. 12. Arctic river flow: a review of contributing areas T.D. Prowse, P.O. Flegg. 13. The dynamics of river water inflow to the Arctic Ocean I.A. Shiklomanov, A.I. Shiklomanov, R.B. Lammers, B.J. Peterson, C.J. Vorosmarty. 14. River input of water, sediment, major ions, nutrients and trace metals from Russian territory to the Arctic Ocean V.V. Gordeev. 15. The dispersion of Siberian river flows into coastal waters: meteorological, hydrological and hydrochemical aspects I.P. Semiletov, N.I. Savelieva, G.E. Weller, I.I. Pipko, S.P. Pugach, A.Yu. Gukov, L.N. Vasilevskaya. 16. The variable climate of the Mackenzie River basin: its water cycle and fresh water discharge R.E. Stewart. 17. Arctic estuaries and ice: a positive-negative estuarine couple R.W. Macdonald. 18. Satellite views of the Arctic Ocean freshwater balance D.A. Rothrock, R. Kwok, D. Groves. 19. Tracer studies of the Arctic freshwater budget P. Schlosser, B. Ekwurzel, S. Khatiwala, B. Newton, W. Maslowski, S. Pfirman. 20. Exchanges of freshwater through the shallow straits of the North American Arctic H. Melling. 21. The transformations of Atlantic water in the Arctic Ocean and their significance for the freshwater budget B. Rudels, H.J. Friedrich. 22. Modelling the variability of exchanges between the Arctic Ocean and the Nordic seas R. Gerdes. 23. Sea ice growth, melt and modeling: a survey M. Steele M., G.M. Flato. 24. Fresh water freezing/melting cycle in the Arctic Ocean G.V. Alekseev, L.V. Bulatov, V.F. Zakharov. Subject Index.

Sea-Ice Topography of the Arctic Ocean in the Region 70 degrees W to 25 degrees E

In October 1976 a cooperative experiment was made to survey the sea-ice topography in the European sector of the Arctic Ocean. H. M. submarine Sovereign acquired 4000 km of ice draft data by using an upward-looking sonar, while a Canadian Forces aircraft flew along the submarine’s track and acquired 2200 km of ice elevation data by using a laser profilometer. The two types of profile were processed in corresponding 100 km section lengths, and the following statistical analyses and comparisons were made: (i) Probability density functions o f ice draft and elevation. Each distribution shows a peak for young ice and for undeformed multi-year ice. At large ice thicknesses the distributions take the analytical form of a negative exponential. The mean drafts enable two distinct geographical ice regimes to be identified. There is an ‘offshore zone’ of very heavy pressure ridging extending up to 400 km from the coasts of Ellesmere Island and of north Greenland, with mean ice draft in the range 5.0 to 7.5 m , while out in the central Arctic Ocean the mean ice draft is lower (3.9—5.1 m) and the characteristics of the ice cover remain homogeneous over a length scale of 1000 km. The transition between the two regimes is abrupt, taking place in less than 25 km. Data from the same part of the central Arctic taken in March 1971 showed a mean ice draft 0.3 m lower, while data from the central Beaufort Sea showed a mean draft more than 0.8 m lower. (ii) Level ice distributions. Ice with a local gradient of less than 1 in 40 was defined as level ice, and used as an indicator of the quantity and thickness distribution of undeformed (i.e. thermodynamically grown) ice in the Arctic Ocean. The distribution has a mode at 3.0—3.1 m draft, and level-ice percentages are in the range 30—40 (bottom side) and 70—80 (top side) in the offshore zone, and 45—55 (bottom) and 85-95 (top) in the central Arctic. Thus about half of the Arctic ice cover consists of deformed ice. (iii) Pressure ridge spacings. The spacings of ridge keels fit a negative exponential distribution, characteristic of randomness, except at close spacings where there is a deficit of keels (explained as a geometrical effect) and at very large spacings where there is an excess (due to the contribution of polynyas). The distribution of sail spacings exhibits these two effects, but also differs from a random distribution at moderate sail separations. (iv) Ridge elevations and drafts. Keel drafts fit a law of form P(h) d oc exp ( — Ah2) dh, except for an excess of keels at drafts beyond 20 m. There is a positive correlation between mean keel draft and keel frequency. There are 3.5—4.5 keels per kilometre with draft exceeding 9 m in the offshore zone and 2—3 in the central Arctic. Sail elevations fit a law of form P(h) d h x exp ( — Ah) dh, with a positive correlation between mean sail elevation and sail frequency. The sail elevation and ice elevation distributions can be related by assuming that all thick ice is contained in pressure ridges of triangular cross section. (v) Keel—sail comparison. For the 21 corresponding 100 km sections there are positive correlations between mean sail height and mean keel draft, and between keel frequency and sail frequency. From these it is possible to convert a sail distribution (computed from a laser profile) into a keel distribution, enabling sea ice bulk characteristics to be derived from airborne surveys alone. (vi) Leads and polynyas. A lead was defined as a continuous sequence of depth points greater than 5 m long and not exceeding 1 m draft. The number density n(d) of leads per kilometre of width d m fits the power law n(d) — 15 d-2. Exceptionally wide leads were concentrated in the offshore zone and in the marginal ice zone close to the open water of the Greenland Sea.

The Seasonal Ice Zone

The seasonal sea ice zone is defined as the area between the minimum and maximum seasonal ice limits plus the region of the ice margin that is significantly affected by the ice-ocean boundary. This definition includes almost all of the Antarctic ice cover, the ice in the marginal seas around the Arctic and the ice cover overlying the shelves of the Arctic Ocean itself. Such a vast area includes a very large number of ice types. We shall consider three main divisions:

The Effect of the Marginal Ice Zone on the Directional Wave Spectrum of the Ocean

Abstract During the MIZEX-84 experiment in the Greenland Sea in June–July 1984, a cooperative program was carried out between the Scott Polar Research Institute (SPRI) and the Institute of Oceanographic Sciences (IOS) to measure the change in the directional character of the ocean wave spectrum in the immediate vicinity of the ice edge. The aim was to extend one-dimensional spectral measurements made hitherto so as to study in full the processes of reflection and refraction Directional spectrum analysis of these records shows that (i) significant reflection of wave energy occurs at the ice edge (detected using Long-Hasselmann analysis); (ii) within the ice the directional spectrum at high frequencies, where attenuation is rapid, broadens to become almost isotropic; whereas (iii) the directional spectrum at swell frequencies, where the attenuation is slower, becomes initially narrower before broadening more slowly than the high frequency energy. An explanation of these effects is offered in terms of scatte...

Relationship between sea ice freeboard and draft in the Arctic Basin, and implications for ice thickness monitoring

We have confirmed our earlier finding that the probability density function (pdf) of ice freeboard in the Arctic Ocean can be converted to a pdf of ice draft by applying a simple coordinate transformation based on the measured mean draft and mean elevation. This applies in each of six 50-km sections (north of Greenland) of joint airborne laser and submarine sonar profile obtained along nearly coincident tracks from the Arctic Basin north of Greenland and tested for this study. Detailed differences in the shape of the pdf can be explained on the basis of snow load and can, in principle, be compensated by the use of a more sophisticated freeboard-dependent transformation. The measured “density ratio” R (actually mean draft/mean elevation ratio) for each section was found to be consistent over all sections tested, despite differences in the ice regime, indicating that a single value of R might be used for measurements done in this season of the year. The mean value 〈R〉 from all six sections is 7.89; on the assumption that all six values are drawn from the same population, the standard deviation is 0.55 for a single 50-km section, and thus 0.22 for 300 km of track. In attempting to infer ice draft from laser-measured freeboard, we would therefore expect an accuracy of about ±28 cm in 50 km of track (if mean draft is about 4 m) and about ±11 cm in 300 km of track; these accuracies are compatible with the resolution of predictions from numerical models. A simple model for the variability of R with season and with mean ice thickness gives results in reasonable agreement with observations. They show that although there is a large seasonal variability due to snow load, there is a stable period from November to April when the variability is chiefly dependent on the mean ice thickness alone. Thus, in principle, R can be mapped over the Arctic Ocean as a basis for interpreting survey data. Better field data are needed on the seasonal and spatial variability of three key quantities: area-averaged snow load, mean density of first-year and multiyear ice (including the effect of ridging with these two ice regimes), and density of near-surface water.

Exploring Arctic Transpolar Drift During Dramatic Sea Ice Retreat

The Arctic is undergoing significant environmental changes due to climate warming. The most evident signal of this warming is the shrinking and thinning of the ice cover of the Arctic Ocean. If the warming continues, as global climate models predict, the Arctic Ocean will change from a perennially ice-covered to a seasonally ice-free ocean. Estimates as to when this will occur vary from the 2030s to the end of this century. One reason for this huge uncertainty is the lack of systematic observations describing the state, variability, and changes in the Arctic Ocean.

Measurements beneath an Antarctic ice shelf using an autonomous underwater vehicle

The cavities beneath Antarctic ice shelves are among the least studied regions of the World Ocean, yet they are sites of globally important water mass transformations. Here we report results from a mission beneath Fimbul Ice Shelf of an autonomous underwater vehicle. The data reveal a spatially complex oceanographic environment, an ice base with widely varying roughness, and a cavity periodically exposed to water with a temperature significantly above the surface freezing point. The results of this, the briefest of glimpses of conditions in this extraordinary environment, are already reforming our view of the topographic and oceanographic conditions beneath ice shelves, holding out great promises for future missions from similar platforms.

A new view of the underside of Arctic sea ice

The Autosub-II autonomous underwater vehicle (AUV), operating off NE Greenland in August 2004, obtained the first successful swath sonar measurements under sea ice, showing in unprecedented detail the three-dimensional nature of the under-ice surface. The vehicle, operated from RRS James Clark Ross, obtained more than 450 track-km of under-ice multibeam data. We show imagery from first- and multiyear ice, including young ridges, old hummocks and undeformed melting ice. In addition, we show how the combination of other on-board sensors enabled the vehicle to obtain detailed information about seabed topography, water structure and current fields in an exploratory mode within a region which is seldom visited because of difficult year-round ice conditions. This included identification of a new current regime in the Norske Trough.

Top/bottom multisensor remote sensing of Arctic sea ice

The Arctic sea ice cover has been studied using near simultaneous observations by passive and active (synthetic aperture radar, SAR) microwave sensors, upward looking and sidescan sonars, a lidar profilometer, and an infrared sensor. Data from two aircraft and a submarine over an approximately 100 km track of central Arctic sea ice were registered and analyzed to evaluate the characteristics of the ice cover and the utility of each sensor in ice studies. The results of comparative and correlation analyses are as follows. The probability density functions of ice draft from sonar and elevation from lidar were found to be almost identical when isostasy is taken into account. This result suggests that the basic ice thickness distribution can be derived from the surface topography measurements alone. Reasonable correlation was found between SAR backscatter and ice draft (or elevation) especially when scales were adjusted such that 15–20 SAR pixels were averaged. However, surface roughness derived directly from standard deviations in the lidar elevation data was found to be poorly correlated to the SAR backscatter. These results indicate that the SAR values are affected more by scattering from the ice than from the snow-covered surface. The active and passive microwave sensors are shown to generally complement each other as the two sensors are especially sensitive to different physical properties of the sea ice. Undeformed first-year ice showed low backscatter values but high brightness temperatures while some multiyear ice showed high backscatter values and low brightness temperature. However, surfaces identified as multiyear ice by the passive system have a large spread in the unaveraged SAR backscatter, indicating limitations when using a one-channel SAR for ice type identification at the highest resolution. Also, ridged ice identified by sonar and SAR data covers a large range of passive microwave emissivity, suggesting considerable variability in the age and salinity of this type of ice. Significant variations (about 0.11) in the minimum emissivity of consolidated multiyear ice are observed in different regions of the Arctic using the high-resolution (30 m) passive microwave data. This suggests that regional variations in texture and scattering characteristics of multiyear ice in the Arctic are present, likely influenced by different histories of formation of the ice in different regions.