APL Home
APL-UW Home

Jobs
About
Campus Map
Contact
Intranet

Ron Lindsay

Senior Principal Physicist

Email

lindsay@apl.washington.edu

Phone

206-543-5409

Biosketch

Ron Lindsay is interested in how the sea ice in the Arctic moves, grows, and decays in response to changing environmental conditions and how the changes in the ice pack are impacting the atmosphere above. To pursue these research themes he uses a wide variety of in situ and remote sensing data and numerical models. In situ data is from ice camps, buoys, submarines, and moorings, while remote sensing data is from many different sensors. He is particularly interested in collecting, comparing, and utilizing ice thickness measurements and has compiled a public data set of measurements from submarines, moorings, aircraft, and satellites. In support of these interests he has joined the IceBridge science team to help direct a NASA program to monitor ice thickness from aircraft. He has conducted extensive analyses of the output of the retrospective Polar Science Center sea ice model to determine how, where, and why the ice pack is rapidly changing. The model is also the basis for a statistical predictive scheme he has developed for forecasting the ice extent months in advance, either for the Arctic as a whole or for specific regions. Finally, he is developing a capability for modeling the response of the atmosphere to changing pack ice conditions in order to understand the extent to which the heat absorbed in the open water areas in the summer slows the growth of ice in the winter. Ron has been conducting Arctic research for over 35 years and has been with the Polar Science Center since 1988.

Department Affiliation

Polar Science Center

Education

B.S. Physics, University of California at Davis, 1968

M.S. Atmospheric Sciences, University of Washington, 1976

Projects

RADARSAT Geophysical Processor System at the Polar Science Center

 

Bering Strait: Pacific Gateway to the Arctic

The Bering Strait is the only Pacific gateway to the Arctic. Since 1990, under various funding, APL-UW has been measuring properties of the Pacific inflow using long-term in situ moorings, supported by annual cruises. Data, papers, cruise reports, plans, and results are available.

 

The Fate of Summertime Arctic Ocean Heating: A Study of Ice-Albedo Feedback on Seasonal to Interannual Time Scales

The main objective of this study is to determine the fate of solar energy absorbed by the arctic seas during summer, with a specific focus on its impact on the sea ice pack. Investigators further seek to understand the fate of this heat during the winter and even beyond to the following summer. Their approach is use a coupled sea ice–ocean model forced by atmospheric reanalysis fields, with and without assimilation of satellite-derived ice and ocean variables. They are also using satellite-derived ocean color data to help determine light absorption in the upper ocean.

 

More Projects

Seasonal Ensemble Forecasts of Arctic Sea Ice

Project investigators aim to improve upon the existing seasonal ensemble forecasting system and use the system to predict sea ice conditions in the arctic and subarctic seas with lead times ranging from two weeks to three seasons. Investigators will develop seasonal ensemble forecasts based on an enhanced synthesis of an ice–ocean model, forcing data, assimilation data, and validation data. Improvement of model physics will target some of the sea ice processes that are particularly sensitive in a warming Arctic with a thinning ice cover.

 

Publications

2000-present and while at APL-UW

Evaluation of seven different atmospheric reanalysis products in the Arctic

Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, "Evaluation of seven different atmospheric reanalysis products in the Arctic," J. Clim., 27, 2588-2606, doi:10.1175/JCLI-D-13-00014.1, 2014.

More Info

1 Apr 2014

Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice%u2013ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981–2010: National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP–U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice–ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R = 0.82). The trend in the total ice volume in September is greatest with MERRA (–4.1 ± 103 km3 decade-1) and least with CFSR (–2.7 ± 103 km3 decade-1).

Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

Kurtz, N.T., S.L. Farrell, M. Studinger, N. Galin, J.P. Harbeck, R. Lindsay, V.D. Onana, B. Panzer, and J.G. Sonntag, "Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data," The Cryosphere, 7, 1035-1056, doi:10.5194/tc-7-1035-2013, 2013.

More Info

4 Jul 2013

The study of sea ice using airborne remote sensing platforms provides unique capabilities to measure a wide variety of sea ice properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in sea ice properties. In this paper we describe methods for the retrieval of sea ice thickness, freeboard, and snow depth using data from a multi-sensor suite of instruments on NASA's Operation IceBridge airborne campaign. We assess the consistency of the results through comparison with independent data sets that demonstrate that the IceBridge products are capable of providing a reliable record of snow depth and sea ice thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing IceBridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved sea ice thickness. Lastly, we present results for the 2009 and 2010 IceBridge campaigns, which are currently available in product form via the National Snow and Ice Data Center.

The impact of an intense summer cyclone on 2012 Arctic sea ice retreat

Zhang, J., R. Lindsay, A. Schweiger, and M. Steele, "The impact of an intense summer cyclone on 2012 Arctic sea ice retreat," Geophys. Res. Lett., 40, 720-726, doi:10.1002/grl.50190, 2013.

More Info

25 Jan 2013

This model study examines the impact of an intense early August cyclone on the 2012 record low Arctic sea ice extent. The cyclone passed when Arctic sea ice was thin and the simulated Arctic ice volume had already declined ~40% from the 2007–2011 mean. The thin sea ice pack and the presence of ocean heat in the near surface temperature maximum layer created conditions that made the ice particularly vulnerable to storms. During the storm, ice volume decreased about twice as fast as usual, owing largely to a quadrupling in bottom melt caused by increased upward ocean heat transport. This increased ocean heat flux was due to enhanced mixing in the oceanic boundary layer, driven by strong winds and rapid ice movement. A comparison with a sensitivity simulation driven by reduced wind speeds during the cyclone indicates that cyclone-enhanced bottom melt strongly reduces ice extent for about two weeks, with a declining effect afterwards. The simulated Arctic sea ice extent minimum in 2012 is reduced by the cyclone, but only by 0.15 x 106 km2 (4.4%). Thus without the storm, 2012 would still have produced a record minimum.

More Publications

Observed increases in Bering Strait oceanic fluxes from the Pacific to the Arctic from 2001 to 2011 and their impacts on the Arctic Ocean water column

Woodgate, R.A., T.J. Weingartner, and R. Lindsay, "Observed increases in Bering Strait oceanic fluxes from the Pacific to the Arctic from 2001 to 2011 and their impacts on the Arctic Ocean water column," Geophys. Res. Lett., 39, doi:10.1029/2012GL054092,2012.

More Info

1 Dec 2012

Mooring data indicate the Bering Strait throughflow increases ~50% from 2001 (~0.7 Sv) to 2011 (~1.1 Sv), driving heat and freshwater flux increases. Increase in the Pacific-Arctic pressure-head explains two-thirds of the change, the rest being attributable to weaker local winds. The 2011 heat flux (~5 x 1020J) approaches the previous record high (2007) due to transport increases and warmer lower layer (LL) temperatures, despite surface temperature (SST) cooling. In the last decade, warmer LL waters arrive earlier (1.6 ± 1.1 days/yr), though winds and SST are typical for recent decades. Maximum summer salinities, likely set in the Bering Sea, remain remarkably constant (~33.1 psu) over the decade, elucidating the stable salinity of the western Arctic cold halocline. Despite this, freshwater flux variability (strongly driven by transport) exceeds variability in other Arctic freshwater sources. Remote data (winds, SST) prove insufficient for quantifying variability, indicating interannual change can still only be assessed by in situ year-round measurements.

Seasonal forecasts of Arctic sea ice initialized with observations of ice thickness

Lindsay, R., C. Haas, S. Hendricks, P. Hunkeler, N. Kurtz, J. Paden, B. Panzer, J. Sonntag, J. Yungel, and J. Zhang, "Seasonal forecasts of Arctic sea ice initialized with observations of ice thickness," Geophys. Res. Lett., 39, doi:10.1029/2012GL053576, 2012.

More Info

1 Nov 2012

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.

Recent changes in the dynamic properties of declining Arctic sea ice: A model study

Zhang, J., R. Lindsay, A. Schweiger, and I. Rigor, "Recent changes in the dynamic properties of declining Arctic sea ice: A model study," Geophys. Res. Lett., 39, doi:10.1029/2012GL053545, 2012.

More Info

30 Oct 2012

Results from a numerical model simulation show significant changes in the dynamic properties of Arctic sea ice during 2007–2011 compared to the 1979–2006 mean. These changes are linked to a 33% reduction in sea ice volume, with decreasing ice concentration, mostly in the marginal seas, and decreasing ice thickness over the entire Arctic, particularly in the western Arctic. The decline in ice volume results in a 37% decrease in ice mechanical strength and 31% in internal ice interaction force, which in turn leads to an increase in ice speed (13%) and deformation rates (17%). The increasing ice speed has the tendency to drive more ice out of the Arctic. However, ice volume export is reduced because the rate of decrease in ice thickness is greater than the rate of increase in ice speed, thus retarding the decline of Arctic sea ice volume. Ice deformation increases the most in fall and least in summer. Thus the effect of changes in ice deformation on the ice cover is likely strong in fall and weak in summer. The increase in ice deformation boosts ridged ice production in parts of the central Arctic near the Canadian Archipelago and Greenland in winter and early spring, but the average ridged ice production is reduced because less ice is available for ridging in most of the marginal seas in fall. The overall decrease in ridged ice production contributes to the demise of thicker, older ice. As the ice cover becomes thinner and weaker, ice motion approaches a state of free drift in summer and beyond and is therefore more susceptible to changes in wind forcing. This is likely to make seasonal or shorter-term forecasts of sea ice edge locations more challenging.

Evaluation of Arctic sea ice thickness simulated by Arctic Ocean Model Intercomparison Project models

Johnson, M.A., A.Y. Proshutinsky, Y. Aksenov, A.T. Nguyen, R. Lindsay, C. Hass, J. Zhang, N. Diansky, R. Kwok, W. Maslowski, S. Hakkinen, I. Ashik, and B. de Cuevas, "Evaluation of Arctic sea ice thickness simulated by Arctic Ocean Model Intercomparison Project models," J. Geophys. Res, 117, doi:10.1029/2011JC007257, 2012.

More Info

15 Mar 2012

Six AOMIP model simulations are compared with estimates of sea ice thickness derived from pan-arctic satellite freeboard measurements (2004-2008), airborne electromagnetic measurements (2001-2009), ice-draft data from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea (1992- 2008) and from submarines (1975-2000), drill hole data from the Arctic basin, Laptev and East Siberian marginal seas (1982-1986) and coastal stations (1998-2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than ~2 m and underestimate the thickness of ice measured thicker than about ~2 m. In the regions of flat immobile land-fast ice (shallow Siberian Seas with depths less than 25-30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than four times and more than one standard deviation, respectively. The models do not reproduce conditions of fast-ice formation and growth. Instead, the modeled fast-ice is replaced with pack ice which drifts, generates ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the ECCO2 and UW models.

Uncertainty in modeled Arctic sea ice volume

Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, and R. Kwok, "Uncertainty in modeled Arctic sea ice volume," J. Geophys. Res., 116, doi:10.1029/2011JC007084, 2011.

More Info

1 Sep 2011

Uncertainty in the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) Arctic sea ice volume record is characterized. A range of observations and approaches, including in situ ice thickness measurements, ICESat retrieved ice thickness, and model sensitivity studies, yields a conservative estimate for October Arctic ice volume uncertainty of 1.35 x 10^3 km^3 and an uncertainty of the ice volume trend over the 1979-2010 period of 1.0 x 10^3 km^3 decade^-1. A conservative estimate of the trend over this period is ~2.8 x 10^3 km^3 decade^-1. PIOMAS ice thickness estimates agree well with ICESat ice thickness retrievals (<0.1 m mean difference) for the area for which submarine data are available, while difference outside this area are larger. PIOMAS spatial thickness patterns agree well with ICESat thickness estimates with pattern correlations of above 0.8. PIOMAS appears to overestimate thin ice thickness and underestimate thick ice, yielding a smaller downward trend than apparent in reconstructions from observations. PIOMAS ice volume uncertainties and trends are examined in the context of climate change attribution and the declaration of record minima. The distribution of 32 year trends in a preindustrial coupled model simulation shows no trends comparable to those seen in the PIOMAS retrospective, even when the trend uncertainty is accounted for. Attempts to label September minima as new record lows are sensitive to modeling error. However, the September 2010 ice volume anomaly did in fact exceed the previous 2007 minimum by a large enough margin to establish a statistically significant new record.

Thinning of arctic sea ice

Lindsay, R, "Thinning of arctic sea ice," in Encyclopedia of Snow, Ice, and Glaciers. Dordrecht: Springer, 2011.

1 Jul 2011

Arctic sea-ice melt in 2008 and the role of solar heating.

Perovich, D.K., J.A. Richter-Menge, K.F. Jones, B. Light, B.C. Elder, C. Polashenski, D. Laroche, T. Markus, and R. Lindsay, "Arctic sea-ice melt in 2008 and the role of solar heating." Ann. Glaciol., 52, 355-359, 2011.

More Info

1 Jun 2011

There has been a marked decline in the summer extent of Arctic sea ice over the past few
decades. Data from autonomous ice mass-balance buoys can enhance our understanding of this decline. These buoys monitor changes in snow deposition and ablation, ice growth, and ice surface and bottom melt. Results from the summer of 2008 showed considerable large-scale spatial variability in the amount of surface and bottom melt. Small amounts of melting were observed north of Greenland, while melting in the southern Beaufort Sea was quite large. Comparison of net solar heat input to the ice and heat required for surface ablation showed only modest correlation. However, there was a strong correlation between solar heat input to the ocean and bottom melting. As the ice concentration in the Beaufort Sea region decreased, there was an increase in solar heat to the ocean and an increase
in bottom melting.

Solar partitioning in a changing Arctic sea-ice cover

Perovich, D.K., K.F. Jones, B. Light, H. Eicken, T. Markus, J. Stroeve, and R. Lindsay, "Solar partitioning in a changing Arctic sea-ice cover," Ann. Glaciol., 52, 192-196, 2011.

More Info

1 Jan 2011

The summer extent of the Arctic sea-ice cover has decreased in recent decades and there have been alterations in the timing and duration of the summer melt season. These changes in ice conditions have affected the partitioning of solar radiation in the Arctic atmosphere-ice-ocean system. The impact of sea-ice changes on solar partitioning is examined on a pan-Arctic scale using a 25 km x 25 km Equal-Area Scalable Earth Grid for the years 1979-2007. Daily values of incident solar irradiance are obtained from NCEP reanalysis products adjusted by ERA-40, and ice concentrations are determined from passive microwave satellite data. The albedo of the ice is parameterized by a five-stage process that includes dry snow, melting snow, melt pond formation, melt pond evolution, and freeze-up. The timing of these stages is governed by the onset dates of summer melt and fall freeze-up, which are determined from satellite observations. Trends of solar heat input to the ice were mixed, with increases due to longer melt seasons and decreases due to reduced ice concentration. Results indicate a general trend of increasing solar heat input to the Arctic ice-ocean system due to declines in albedo induced by decreases in ice concentration and longer melt seasons. The evolution of sea-ice albedo, and hence the total solar heating of the ice-ocean system, is more sensitive to the date of melt onset than the date of fall freeze-up. The largest increases in total annual solar heat input from 1979 to 2007, averaging as much as 4%a-1, occurred in the Chukchi Sea region. The contribution of solar heat to the ocean is increasing faster than the contribution to the ice due to the loss of sea ice.

New unified sea ice thickness climate data record

Lindsay, R., "New unified sea ice thickness climate data record," Eos Trans. AGU, 91, 405-406, doi:10.1029/2010EO440001, 2010.

More Info

2 Nov 2010

With the recent dramatic record-low ice extent of 2007 and with the third-lowest extent having been recorded in 2010, the changing Arctic climate, and particularly the rapidly changing sea ice cover, is often in the news. The climate models of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report forecast that rising Arctic temperatures and the reduction of sea ice will be the earliest and strongest indications of global warming. However, these models generally underestimate the observed rate of change in summer ice cover over the past 3 decades.

The 2007 Bering Strait oceanic heat flux and anomalous Arctic sea-ice retreat

Woodgate, R.A., T. Weingartner, and R. Lindsay, "The 2007 Bering Strait oceanic heat flux and anomalous Arctic sea-ice retreat," Geophys. Res. Lett., 37, doi:10.1029/2009GL041621, 2010.

More Info

7 Jan 2010

To illuminate the role of Pacific Waters in the 2007 Arctic sea-ice retreat, we use observational data to estimate Bering Strait volume and heat transports from 1991 to 2007. In 2007, both annual mean transport and temperatures are at record-length highs. Heat fluxes increase from 2001 to a 2007 maximum, 5–6 x 1020 J/yr. This is twice the 2001 heat flux, comparable to the annual shortwave radiative flux into the Chukchi Sea, and enough to melt 1/3rd of the 2007 seasonal Arctic sea-ice loss. We suggest the Bering Strait inflow influences sea-ice by providing a trigger for the onset of solar-driven melt, a conduit for oceanic heat into the Arctic, and (due to long transit times) a subsurface heat source within the Arctic in winter. The substantial interannual variability reflects temperature and transport changes, the latter (especially recently) being significantly affected by variability (> 0.2 Sv equivalent) in the Pacific-Arctic pressure-head driving the flow.

Spatial scaling of Arctic sea ice deformation

Stern, H.L., and R.W. Lindsay, "Spatial scaling of Arctic sea ice deformation," J. Geophys. Res., 114, doi:10.1029/2009JC005380, 2009.

More Info

21 Oct 2009

Arctic sea ice deformation arises from spatial gradients in the ice velocity field. This deformation occurs across a wide range of spatial scales, from meters to thousands of kilometers. We analyze 7 years of sea ice deformation data from the RADARSAT Geophysical Processor System (RGPS) covering the western Arctic Ocean. We find that the mean deformation rate is related to the spatial scale over which it is measured according to a power law with exponent ~ –0.2, over a scale range from 10 to 1000 km (e.g., deformation rate doubles for a 30-fold reduction in scale). Both the exponent and the deformation rate have distinct annual cycles. The exponent becomes more negative in summer as the ice pack weakens and internal stresses are not as readily transmitted over long distances. The deformation rate reaches a minimum in late winter when the ice pack is strongest. The deformation also exhibits considerable localization, in which the largest deformation rates are confined to smaller and smaller areas as the scale of measurement decreases.

This supports a model for sea ice based on granular or fracture mechanics. The scaling exponent in the power law relationship tends to be larger in magnitude where the concentration of multiyear ice is low, consistent with a thinner and weaker ice pack. With decreasing multiyear ice in the Arctic and a thinning ice pack, an increase in the deformation rate has already been documented (from buoy data). However, the net effect of several deformation/thickness feedbacks is still uncertain.

Arctic sea ice retreat in 2007 follows thinning trend

Lindsay, R.W., J. Zhang, A. Schweiger, M. Steele, and H. Stern, "Arctic sea ice retreat in 2007 follows thinning trend," J. Climate, 22, 165-176, 2009.

More Info

1 Jan 2009

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.

Relationships between arctic sea ice and clouds during autumn

Schweiger, A., R. Lindsay, S. Vavrus, and J. Francis, "Relationships between arctic sea ice and clouds during autumn," J. Clim., 21, 4799-4810, 2008.

More Info

1 Sep 2008

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.

Halo of ice deformation observed over the Maud Rise seamount

Lindsay, R.W., R. Kwok, L. de Steur, and W. Miere, "Halo of ice deformation observed over the Maud Rise seamount," Geophys. Res. Lett., 35, doi:10.1029/2008GL034629, 2008.

More Info

6 Aug 2008

A distinctive halo of sea ice deformation was observed above the Maud Rise seamount in the eastern Weddell Sea in the winter of 2005. The deformation halo is coincident with a halo of low mean ice concentration that is often observed in the region. Monthly mean ice vorticity estimates for the months July through November reveal the deformation zone most clearly in an arc about 100 km northwest of the seamount where there is a strong gradient in the bathymetry at depths of 3000–5000 m. The deformation was computed from satellite-based ice motion vectors derived from Envisat Synthetic Aperture Radar backscatter images. The deformation halo is evidence of a Taylor cap circulation over the seamount, which has been described and analyzed with modeling studies and concurrent oceanographic observations obtained during an extensive field campaign.

What drove the dramatic retreat of arctic sea ice during summer 2007?

Zhang, J., R. Lindsay, M. Steele, and A. Schweiger, "What drove the dramatic retreat of arctic sea ice during summer 2007?" Geophys. Res. Lett., 35, doi:10.1029/2008GL034005, 2008.

More Info

11 Jun 2008

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.

Did unusually sunny skies help drive the record sea ice minimum of 2007?

Schweiger, A.J., J. Zhang, R.W. Lindsay, and M. Steele, "Did unusually sunny skies help drive the record sea ice minimum of 2007?" Geophys. Res. Lett., 35, doi:10.1029/2008GL033463, 2008.

More Info

30 May 2008

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.

Scaling properties of sea ice deformation from buoy dispersion analysis

Rampal, R., J. Weiss, D. Marsan, R. Lindsay, and H. Stern, "Scaling properties of sea ice deformation from buoy dispersion analysis," J. Geophys. Res., 113, doi:10.1029/2007JC004143, 2008.

More Info

4 Mar 2008

A temporal and spatial scaling analysis of Arctic sea ice deformation is performed over timescales from 3 h to 3 months and over spatial scales from 300 m to 300 km. The deformation is derived from the dispersion of pairs of drifting buoys, using the IABP (International Arctic Buoy Program) buoy data sets. This study characterizes the deformation of a very large solid plate (the Arctic sea ice cover) stressed by heterogeneous forcing terms like winds and ocean currents. It shows that the sea ice deformation rate depends on the scales of observation following specific space and time scaling laws. These scaling properties share similarities with those observed for turbulent fluids, especially for the ocean and the atmosphere. However, in our case, the time scaling exponent depends on the spatial scale, and the spatial exponent on the temporal scale, which implies a time/space coupling. An analysis of the exponent values shows that Arctic sea ice deformation is very heterogeneous and intermittent whatever the scales, i.e., it cannot be considered as viscous-like, even at very large time and/or spatial scales. Instead, it suggests a deformation accommodated by a multiscale fracturing/faulting processes.

Seasonal predictions of ice extent in the Arctic Ocean

Lindsay, R.W., J. Zhang, A.J. Schweiger, and M.A. Steele, "Seasonal predictions of ice extent in the Arctic Ocean," J. Geophys. Res., 113, doi:10.1029/2007JC004259, 2008.

More Info

29 Feb 2008

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.

What is the trajectory of arctic sea ice?

Stern, H., R. Lindsay, C. Bitz, and P. Hezel, "What is the trajectory of arctic sea ice?" in Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications, edited by E.T. DeWeaver, C.M. Bitz, and B.-L. Tremblay, 175-185 (American Geophysical Union, 2008).

1 Jan 2008

The influence of sea ice on ocean heat uptake in response to increasing CO2

Bitz, C.M., P.R. Gent, R.A. Woodgate, M.M. Holland, and R.A. Lindsay, "The influence of sea ice on ocean heat uptake in response to increasing CO2," J. Clim., 19, 2437-2450, doi:10.1175/JCLI3756.1, 2006.

More Info

1 Jun 2006

Two significant changes in ocean heat uptake that occur in the vicinity of sea ice cover in response to increasing CO2 are investigated with Community Climate System Model version 3 (CCSM3): a deep warming below ~500 m and extending down several kilometers in the Southern Ocean and warming in a ~200-m layer just below the surface in the Arctic Ocean. Ocean heat uptake caused by sea ice retreat is isolated by running the model with the sea ice albedo reduced artificially alone. This integration has a climate response with strong ocean heat uptake in the Southern Ocean and modest ocean heat uptake in the subsurface Arctic Ocean.

The Arctic Ocean warming results from enhanced ocean heat transport from the northern North Atlantic. At the time of CO2 doubling, about 1/3 of the heat transport anomaly results from advection of anomalously warm water and 2/3 results from strengthened inflow. At the same time the overturning circulation is strengthened in the northern North Atlantic and Arctic Oceans. Wind stress changes cannot explain the circulation changes, which instead appear related to strengthened convection along the Siberian shelves.

Deep ocean warming in the Southern Ocean is initiated by weakened convection, which is mainly a result of surface freshening through altered sea ice and ocean freshwater transport. Below about 500 m, changes in convection reduce the vertical and meridional temperature gradients in the Southern Ocean, which significantly reduce isopycnal diffusion of heat upward around Antarctica. The geometry of the sea ice cover and its influence on convection have a strong influence on ocean temperature gradients, making sea ice an important player in deep ocean heat uptake in the Southern Ocean.

Assimilation of ice concentration in an ice–ocean model

Lindsay, R.W., and J. Zhang, "Assimilation of ice concentration in an ice–ocean model," J. Atmos. Ocean. Technol., 23, 742-749, doi:10.1175/JTECH1871.1, 2006.

More Info

1 May 2006

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.

Arctic Ocean ice thickness: Modes of variability and the best locations from which to monitor them

Lindsay, R.W., and J. Zhang, "Arctic Ocean ice thickness: Modes of variability and the best locations from which to monitor them," J. Phys. Oceaongr., 36, 496-506, doi:10.1175/JPO2861.1, 2006.

More Info

1 Mar 2006

Model simulations of Arctic sea ice and ocean systems are used to determine the major spatial and temporal modes of variability in the ice thickness. A coupled ice–ocean model is forced with daily NCEP–NCAR reanalysis surface air pressure and surface air temperature fields for the period 1951–2003 with the analysis of the results performed for the 51-yr period 1953–2003. Ice concentration data and ice velocity data (beginning in 1979) are assimilated to further constrain the simulations to match the observed conditions. The simulated ice thins over the study period with the area of greatest thinning in a band from the Laptev Sea across the Pole to Fram Strait. The thinning rate is greatest since 1988. The major spatial modes of variability were determined with empirical orthogonal functions (EOFs) for the ice thickness within the Arctic Ocean. The first three EOFs account for 30%, 18%, and 15%, respectively, of the annual mean ice thickness variance. The first EOF is a nearly basinwide pattern, and the next two are orthogonal lateral modes. Because of the nonstationary nature of the ice thickness time series, significant changes in the modes are found if a shorter period is analyzed. The second and third principal components are well correlated with the Arctic Oscillation. The model results are also used to simulate an observation system and to then determine optimal mooring locations to monitor the basinwide mean ice thickness as well as the spatial and temporal patterns represented in the EOF analysis. The nonstationary aspect of the ice thickness limits the strength of the conclusions that can be drawn.

The thinning of arctic sea ice, 1988-2003: Have we passed a tipping point?

Lindsay, R.W., and J. Zhang, "The thinning of arctic sea ice, 1988-2003: Have we passed a tipping point?" J. Climate, 18, 4879-4894, doi:10.1175/JCLI3587.1, 2005

More Info

30 Nov 2005

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.

Scale dependence and localization of the deformation of arctic sea ice

Marsan, D., H. Stern, R. Lindsay, and J. Weiss, "Scale dependence and localization of the deformation of arctic sea ice," Phys. Rev. Lett., 93, 17, doi:10.1103/PhysRevLett.93.178501, 2004.

More Info

20 Oct 2004

A scaling analysis of the deformation of Arctic sea ice over a 3-day time period is performed for scales of 10 to 1000 km. The deformation field is derived from satellite radar data; it allows us to study how a very large solid body — the Arctic sea-ice cover — deforms under the action of heterogeneous forcing winds and ocean currents. The deformation is strongly localized at small scales, and can be characterized as multifractal. This behavior is well known for turbulent flows, and is here also observed for a deforming solid. A multiscaling extrapolation to the meter scale (laboratory scale) shows that, at the 3-day time scale, about 15% of the deformation is larger than 10-4 s-1, implying brittle failure, over 0.2% of the total area.

Halo of low ice concentration observed over the Maud Rise seamount

Lindsay, R.W., D.M. Holland, and R.A. Woodgate, "Halo of low ice concentration observed over the Maud Rise seamount," Geophys. Res. Lett., 31, 10.1029/2004GL019831, 2004.

More Info

1 Jul 2004

A distinctive halo of low sea ice concentration has been observed above the Maud Rise seamount in the eastern Weddell Sea. The 300-km circular halo is seen most clearly in the monthly mean ice concentration for the months July through November. The mean was computed from satellite-based passive microwave measurements over a 23-year period. The halo is most distinct in October; even then, however, the mean ice concentration in the halo is just 10% less than in the center, where it is very near 100%. The halo may reflect the existence of a Taylor cap circulation over the seamount or other topographically induced mechanisms.

Increasing exchanges at Greenland-Scotland Ridge and their links with the North Atlantic Oscillation and Arctic Sea Ice

Zhang, J., M. Steele, D.A. Rothrock, and R.W. Lindsay, "Increasing exchanges at Greenland-Scotland Ridge and their links with the North Atlantic Oscillation and Arctic Sea Ice," Geophys. Res. Lett., 31, L09307, 10.1029/2003GL019304, 2004.

More Info

6 May 2004

A global ice-ocean model shows increasing Atlantic water (AW) inflow at the Iceland-Scotland Ridge (ISR) during 1953–2002. As a result, the Greenland-Iceland-Norwegian (GIN) Sea is gaining more heat and salt from the North Atlantic Ocean, while the latter is being freshened mainly by exporting more salt to the GIN Sea. The exchanges of volume, heat, and freshwater at the Greenland-Scotland Ridge (GSR) are strongly correlated with the North Atlantic Oscillation (NAO) and their positive trend is closely linked to the NAO elevation in recent decades. The model confirms observations of decreasing dense water outflow at the Faroe-Scotland Passage since the 1950s. However, the simulated dense water outflow shows an increase at Denmark Strait, at the Iceland-Faroe Ridge, and at the GSR as a whole, owing to an increase in AW inflow that may cause an increase in AW recirculation and deep water production in the GIN Sea. The increase of the ISR heat inflow since 1965 contributes to continued thinning of the arctic sea ice since 1966. The influence of the heat inflow on arctic sea ice lags 2–3 years, which suppresses ice production even when the NAO temporarily shifts to a negative mode. Because of this delay, the decline of arctic sea ice is likely to continue if the inflow continues to increase and if the NAO does not shift to a sustained negative mode.

Comparison of thin ice thickness distributions derived from RADARSAT Geophysical Processor System and advanced very high resolution radiometer data sets

Yu, Y., and R.W. Lindsay, "Comparison of thin ice thickness distributions derived from RADARSAT Geophysical Processor System and advanced very high resolution radiometer data sets," J. Geophys. Res., 108, 10.1029/2001JC000805, 2003.

More Info

26 Dec 2003

Thin ice thickness distributions estimated from advanced very high resolution radiometer (AVHRR) and RADARSAT Geophysical Processor System (RGPS) data sets were compared over the Beaufort Sea and the Canada Basin for the period December 1996 to February 1997. The comparisons show a compelling agreement. High correlations were found in cases where thin ice grew in large, wide leads extending several hundred kilometers. At these large scales, estimates from AVHRR images and RGPS showed similar amounts of thin ice in leads. However, when major surface deformation occurred on small scales (100 m to 10 km), the finer spatial resolution (100 m) of RADARSAT images enabled the RGPS algorithm to derive more thin ice than that of AVHRR. Under such conditions the correlation between the two dropped, and a small negative bias (about 1%) was observed in the estimates from AVHRR. This bias, mostly concentrated at the very thin end of the thickness distribution, caused a further deficit in the AVHRR-derived thin ice growth, roughly 0.2 cm/d. Although the AVHRR and RGPS algorithms treat snowfall differently in the ice thickness calculations, both snow assumptions appear reasonable. However, RGPS may underestimate the thin ice production because of the 3-day sampling interval. With a better understanding of the sources of uncertainty and improved satellite estimates, a combination of these two satellite data could offer a wider coverage of thin ice thickness observations in the Arctic Basin.

The RADARSAT geophysical processor system: Quality of sea ice trajectory and deformation estimates

Lindsay, R.W., and H.L. Stern, "The RADARSAT geophysical processor system: Quality of sea ice trajectory and deformation estimates," J. Atmos. Ocean. Technol., 20, 1333-1347, DOI: 10.1175/1520-0426(2003)020<1333:TRGPSQ>2.0.CO;2, 2003.

More Info

1 Sep 2003

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 standard deviation of the tracking, not including geolocation errors, is 100 m, which is the pixel size of the SAR images. The second comparison is made with buoy trajectories from the International Arctic Buoy Program. The squared correlation coefficient for RGPS and buoy displacements is 0.996. The median magnitude of the displacement differences is 323 m. The tracking errors give rise to error standard deviations of 0.5% day-1 in the divergence, shear, and vorticity. The uncertainty in the area change of a cell is 1.4% due to tracking errors and 3.2% due to resolving the cell boundary with only four points. The uncertainties in the area change and deformation invariants can be reduced substantially by averaging over a number of cells, at the expense of spatial resolution.

Changes in the modeled ice thickness distributions near the Surface Heat Budget of the Arctic Ocean (SHEBA) drifting ice camp

Lindsay, R.W., "Changes in the modeled ice thickness distributions near the Surface Heat Budget of the Arctic Ocean (SHEBA) drifting ice camp," J. Geophys. Res., 108, 10.1029/2001JC000805, 2003.

More Info

19 Jun 2003

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.

Assimilation of ice motion observations and comparisons with submarine ice thickness data

Zhang, J., D.R. Thomas, D.A. Rothrock, R.W. Lindsay, Y. Yu, and R. Kwok, "Assimilation of ice motion observations and comparisons with submarine ice thickness data," J. Geophys. Res., 108, 10.1029/2001JC001041, 2003.

More Info

3 Jun 2003

Aided by submarine observations of ice thickness for model evaluation, we investigate the effects of assimilating buoy motion data and satellite SSM/I (85 Ghz) ice motion data on simulation of Arctic sea ice. The sea-ice model is a thickness and enthalpy distribution model and is coupled to an ocean model. Ice motion data are assimilated by means of optimal interpolation. Assimilating motion data, particularly from drifting buoys, significantly improves the modeled ice motion, reducing the error to 0.04 m s-1 from 0.07 m s-1 and increasing the correlation with observations to 0.90 from 0.66. Without data assimilation, the modeled ice moves too slowly with excessive stoppage. Assimilation leads to more robust ice motion with substantially reduced stoppage, which in turn leads to strengthened ice outflow at Fram Strait and enhanced ice deformation everywhere. Enhanced deformation doubles the production of ridged ice to an Arctic Ocean average of 0.77 m yr-1, and raises the amount of ridged ice to half the total ice volume per unit area of 2.58 m. Assimilation also significantly alters the spatial distribution of ice mass and brings the modeled ice thickness into better agreement with the thickness observed in four recent submarine cruises, reducing the error to 0.66 m from 0.76 m, and increasing the correlation with observations to 0.65 from 0.45. Buoy data are most effective in reducing model errors because of their small measurement error. SSM/I data, because of their more complete spatial coverage, are helpful in regions with few buoys, particularly in coastal areas. Assimilating both SSM/I and buoy data combines their individual advantages and brings about the best overall model performance in simulating both ice motion and ice thickness.

Air-sea interaction in the presence of the arctic pack ice

Lindsay, R.W., and A.P. Makshtas, "Air-sea interaction in the presence of the arctic pack ice," in Arctic Environment Variability in the Context of Global Change, edited by L.P. Bobylev, K.Y. Kondratyev, K. Kondrashin, and O.M. Johannessen, 203-236 (Springer Verlag, 2003).

1 Jun 2003

Sea-ice deformation rates from satellite measurements and in a model

Lindsay, R.W., J. Zhang, and D.A. Rothrock, "Sea-ice deformation rates from satellite measurements and in a model," Atmos. Ocean, 41, 35-47, doi:10.3137/ao.410103 , 2003.

More Info

1 Mar 2003

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, when many buoy measurements are available for assimilation. This step is necessary because the RGPS and model data differ in spatial and temporal sampling characteristics. The assimilated data included buoy motion and Special Sensor Microwave/Imager (SSM/I)-derived ice motion. The Pacific half of the Arctic Basin was analysed for a 10-month period in 1997 and 1998. Comparisons of ice velocity observations to the modelled velocities showed excellent agreement from the model-with-data-assimilation run but poorer agreement for the model-only run. At a scale of 320 km, the deformation from the data assimilation run was in modest agreement with the observations but where many buoys were available for assimilation the agreement was quite good. Both model runs showed poor agreement during summer. Comparisons of the deformation distribution functions suggest why the agreements were poor even though the velocity agreements were good. Decreasing the ice strength parameter in the model improved the deformation comparisons for the model-only runs.

Ice deformation near SHEBA

Lindsay, R.W., "Ice deformation near SHEBA," J. Geophys. Res., 107, doi:10.1029/2000JC000445, 2002.

More Info

1 Oct 2002

The deformation rate of sea ice is a key parameter for determining the evolution of the ice thickness distribution. It determines the rate of new ice formation through opening and the rate of ridging through closing and shear. An extensive suite of ground-based and satellite-based measurements of ice motion is used to construct a daily time series of the ice velocity and deformation in the vicinity of the Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp that is suitable for forcing a model of the ice thickness distribution. The velocity is interpolated to a square grid that remains centered on the camp, has a spacing of 25 km, is 400 km on a side, and is determined for a 371-day period from 2 October 1997 to 7 October 1998. Velocity measurements from buoys, Advanced Very High Resolution Radiometer (AVHRR), Special Sensor Microwave/Imager (SSMI), and Radarsat Geophysical Processing System (RGPS) are merged using optimal interpolation and a Kalman filter approach. The deformation rate is taken directly from the RGPS measurements when available. The daily total deformation rate measured on a scale of 100 km near the camp averaged 2.21% d-1, and the standard deviation was 1.78% d-1. The divergence was positive in the early winter and negative through most of the spring and summer. There were two major opening/closing events, one in January and one at the end of July. The net divergence over the year was very near zero. The vorticity indicated a net rotation of 87° over the year, with the winter showing strong anticyclonic turning and the summer showing strong cyclonic turning.

Validation of TOVS Path-P data during SHEBA

Schweiger, A.J., R.W. Lindsay, J.A. Francis, J. Key, J.M. Intrieri, and M.D. Shupe, "Validation of TOVS Path-P data during SHEBA," J. Geophys. Res., 107, 8041, doi:10.1029/2000JC000453, 2002.

More Info

28 Sep 2002

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.

Surface heat budget of the Arctic Ocean

Uttal, T., and 27 others including R.E. Moritz, H.L. Stern, A. Heiberg, J.H. Morison, and R.W. Lindsay, "Surface heat budget of the Arctic Ocean," Bull. Amer. Meteor. Soc., 83, 255-275, 2002.

More Info

1 Feb 2002

A summary is presented of the Surface Heat Budget of the Arctic Ocean (SHEBA) project, with a focus on the field experiment that was conducted from October 1997 to October 1998. The primary objective of the field work was to collect ocean, ice, and atmospheric datasets over a full annual cycle that could be used to understand the processes controlling surface heat exchanges—in particular, the ice-albedo feedback and cloud-radiation feedback. This information is being used to improve formulations of arctic ice-ocean-atmosphere processes in climate models and thereby improve simulations of present and future arctic climate. The experiment was deployed from an ice breaker that was frozen into the ice pack and allowed to drift for the duration of the experiment. This research platform allowed the use of an extensive suite of instruments that directly measured ocean, atmosphere, and ice properties from both the ship and the ice pack in the immediate vicinity of the ship. This summary describes the project goals, experimental design, instrumentation, and the resulting datasets. Examples of various data products available from the SHEBA project are presented.

Arctic sea-ice albedo derived from RGPS-based ice thickness estimates

Lindsay, R.W., "Arctic sea-ice albedo derived from RGPS-based ice thickness estimates," Ann. Glaciol., 30, 225-229, doi:10.3189/172756401781818103, 2001.

More Info

1 Jan 2001

The RADARSAT geophysical processor system (RGPS) uses sequential synthetic aperture radar images of Arctic sea ice taken every 3 days to track a large set of Lagrangian points over the winter and spring seasons. The points are the vertices of cells, which are initially square and 10 km on a side, and the changes in the area of these cells due to opening and closing of the ice are used to estimate the fractional area of a set of first-year ice categories. The thickness of each category is estimated by the RGPS from an empirical relationship between ice thickness and the freezing degree-days since the formation of the ice. With a parameterization of the albedo based on the ice thickness, the albedo may be estimated from the first-year ice distribution. We compute the albedo for the first spring processed by the RGPS, the early spring of 1997. The data include most of the Beaufort and Chukchi Seas. We find that the mean albedo is 0.79 with a standard deviation of 0.04, with lower albedo values near the edge of the perennial ice zone. The biggest source of error is likely the assumed rate of snow accumulation on new ice.

In The News

Cyclone did not cause 2012 record low for Arctic sea ice

UW News and Information, Hannah Hickey

"The Great Arctic Cyclone of August 2012," is thought by some to have led to the historic sea ice minimum reached in mid-September 2013. UW research suggests otherwise.

31 Jan 2013

Study finds arctic cyclone had insignificant impact on 2012 ice retreat

The New York Times, Andrew C. Revkin

A new modeling study by the Applied Physics Laboratory at the University of Washington, replaying last summer%u2019s Arctic Ocean ice conditions with and without the storm, shows that the short-term influence of all that ice churning probably played almost no role in the final ice retreat in September.

31 Jan 2013

How do they do it? Predictions are in for arctic sea ice low point

UW News and Information, Nancy Gohring

Researchers are working hard to improve their ability to more accurately predict how much Arctic sea ice will remain at the end of summer. It's an important exercise because knowing why sea ice declines could help scientists better understand climate change and how sea ice is evolving.

14 Aug 2012

More News Items

Arctic sea ice: Claims it has recovered miss the big picture

The Washington Post, Jason Samenow and Brian Jackson

Perhaps you've heard Arctic sea ice extent has fully recovered after nearly setting record low levels in September, 2011. Sea ice extent is a one-dimensional measure of Arctic ice. Sea ice volume, which is estimated each month at the University of Washington, shows levels well below normal.

16 May 2012

Arctic ice hits second-lowest level, US scientists say

BBC News

Sea ice cover in the Arctic in 2011 has passed its annual minimum, reaching the second-lowest level since satellite records began, US scientists say.

16 Sep 2011

NSIDC: Arctic sea ice extent second lowest; NOAA: 8th warmest August globally

Washington Post, James Samenow

While NSIDC's estimate of the minimum extent is second lowest on record, some instruments/algorithms are suggesting a new record low. And University of Washington's estimate for Arctic sea ice volume - which takes into account the ice thickness - is lowest on record.

15 Sep 2011

Arctic sea ice volume reaches record low for second straight year

Washington Post, James Samenow

Arctic sea ice continues a long-term melting trend, setting new record lows for both volume and extent. The University of Washington estimates August sea ice volume was 62% below the 1979-2010 average.

14 Sep 2011

Extent of Arctic summer sea ice at record low level

Christian Science Monitor, Pete Spotts

Researchers at the University of Washington's Polar Science Center note that in 2010 the volume of summer sea ice fell to a record low. Volume takes into account ice thickness, as well as extent.

10 Sep 2011

July Arctic sea ice melts to record low extent, volume

The Washington Post, Jason Samenow

The impacts of a sweltering July extended well beyond the eastern two-thirds of the continental U.S. Both the extent and volume of ice in the Arctic were lowest on record for the month according to data and estimates from the National Snow and Ice Data Center and APL-UW's Polar Science Center.

8 Aug 2011

Arctic sea ice reaches annual low

Naturenews.com, Amber Dance

Floating pack of 4.52 million square kilometres fails to break record shrinkage of last year when unusually warm weather melted much of the ice, and persistent winds drove what was left together into a 4.13-million-square-kilometre area, well below the 1979%u20132000 average of nearly 7 million square kilometres.

17 Sep 2008

North Pole ever closer to having no ice

The Seattle Post-Intelligencer, Lisa Stiffler

For Arctic expert Ignatius Rigor, this is one bet he'd rather lose. He figured he was safe in his wager with fellow polar gurus that the area of ice would have shrunk to a record low this summer, beating last year's astonishing disappearing act.

16 Sep 2008

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
Close

 

Close

 

Close