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Jinlun Zhang

Senior Principal Oceanographer






Dr. Zhang is interested in understanding how air-ice-ocean interaction in polar oceans affects polar and global climate. He investigates properties of polar air-ice-ocean systems using large- scale sea ice and ocean models. His recent work has focused on examining the evolution of the sea ice cover and the upper ocean in the Arctic in response to a significant climate change recently observed in the northern polar ocean.

He has developed a coupled global ice-ocean model to study the responses of sea ice to different conditions of surface heat fluxes and the effects of sea ice growth/decay on oceanic thermohaline circulation. He is also interested in developing next-generation sea ice models which capture anisotropic nature of ice dynamics. Dr. Zhang joined the Laboratory in 1994

Department Affiliation

Polar Science Center


B.S. Shipbuilding & Ocean Engineering, Harbin Shipbuilding Engineering Institute, China, 1982

M.S. Ship Fluid Dynamics & Ocean Engineering, China Ship Scientific Research Center, 1984

Ph.D. Ice and Ocean Dynamics, Thayer School of Engineering, Dartmouth College, 1993


Changing Sea Ice and the Bering Sea Ecosystem

Part of the BEST (Bering Sea Ecosystem Study) Project, this study will use high-resolution modeling of Bering Sea circulation to understand past change in the eastern Bering climate and ecosystem and to predict the timing and scope of future change.


The Arctic Ocean Model Intercomparison Project (AOMIP): Synthesis and Integration

The AOMIP science goals are to validate and improve Arctic Ocean models in a coordinated fashion and investigate variability of the Arctic Ocean and sea ice at seasonal to decadal time scales, and identify mechanisms responsible for the observed changes. The project's practical goals are to maintain and enhance the established AOMIP international collaboration to reduce uncertainties in model predictions (model validation and improvements via coordinated experiments and studies); support synthesis across the suite of Arctic models; organize scientific meetings and workshops; conduct collaboration with other MIPs with a special focus on model improvements and analysis; disseminate findings of AOMIP effort to broader communities; and train a new generation of ocean and sea-ice modelers.


The Impact of Changes in Arctic Sea Ice on the Marine Planktonic Ecosystem- Synthesis and Modeling of Retrospective and Future Conditions

This work will investigate the historical and contemporary changes of arctic sea ice, water column, and aspects of the marine ecosystem as an integrated entity, and project future changes associated with a diminished arctic ice cover under several plausible warming scenarios.


More Projects

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.


Variability and Trends in Antarctic Sea Ice

This project will investigate, through modeling and data assimilation, the historical evolution of the Antarctic sea ice–ocean system from 1979 to the present to enhance our understanding of the large-scale changes that have occurred in the sea ice and the upper ocean in response to changes in atmospheric circulation. Project investigators aim to identify key mechanisms, interactions, and linkages between the sea ice, the upper ocean, and the atmosphere that may explain the observed regional changes in Antarctic sea ice and the observed overall increase in Antarctic sea ice extent since 1979.


Changing Seasonality of the Arctic: Alteration of Production Cycles and Trophic Linkages in Response to Changes in Sea Ice and Upper Ocean Physics

This project will investigate future changes in the seasonal linkages and interactions among arctic sea ice, the water column, and the marine production cycles and trophic structure as an integrated system. This is a collaborative project led by Jinlun Zhang with Mike Steele, Univ. of WA, Y. Spitz, Oregon State Univ., C. Ashjian, Woods Hole, and R. Campbell, Univ. of Rhode Island.


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.



2000-present and while at APL-UW

Circulation in the Eastern Bering Sea: Inferences from a 2-km-resolution model

Durski, S.M., A. Kurapov, J. Zhang, and G.G. Panteleev, "Circulation in the Eastern Bering Sea: Inferences from a 2-km-resolution model," Deep Sea Res. II, 134, 48-64, doi:10.1016/j.dsr2.2015.02.002, 2016.

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1 Dec 2016

A 2-km-resolution model of the eastern Bering Sea is developed to capture dynamical processes on the scale of the Rossby radius of deformation on tidal to seasonal time scales. The model spans the region from 178°E to the Alaskan coast and from roughly 50° to 66°N, including the Aleutian Islands in the south and the Bering Strait in the north. The high resolution throughout ensures that the mesoscale dynamics of significant subregions of the domain, such as the Aleutian Island passes, Bering Sea slope, and the shelf canyons, are captured simultaneously without the concern for loss of interconnectivity between regions. Simulations are performed for the ice-free season (June–October) of 2009, with tidal and atmospheric forcing. The model compares favorably with observations from AVHRR and Envisat satellites, Argo drifters, and Bering Sea shelf moorings. The mesoscale dynamics of the mixing and exchange flow through the eastern Aleutian Island passes, which exhibit strong diurnal and two-week variability, are well represented. The two-week oscillation in volume flux through the largest of these passes, Amukta Pass, is found to be out of phase with the transport through the neighboring passes (e.g., Seguam and Samalga passes). Mesoscale structure is also found to be ubiquitous along the mixing front of the cold pool. Structures at the scale of O(20 km) persist and play a role in determining the pattern of erosion of the water mass as the shelf warms and mixes. On the Bering Sea shelf, tidal motions are dominant, and variability on the horizontal scale of the first-mode internal tide develops (O(30 km)) from the shelf break to the onshore edge of the Bering shelf cold pool.

Multi-model seasonal forecast of Arctic sea-ice: Forecast uncertainty at pan-Arctic and regional scales

Blanchard-Wrigglesworth, E., and 13 others, including J. Zhang, "Multi-model seasonal forecast of Arctic sea-ice: Forecast uncertainty at pan-Arctic and regional scales," Clim. Dyn., EOR, doi:10.1007/s00382-016-3388-9, 2016.

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13 Oct 2016

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

Modeling the seasonal evolution of the Arctic sea ice floe size distribution

Zhang, J., H. Stern, B. Hwang, A. Schweiger, M. Steele, M. Stark, and H.C. Graber, "Modeling the seasonal evolution of the Arctic sea ice floe size distribution," Elem. Sci. Anth., 4, doi:10.12952/journal.elementa.000126, 2016

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13 Sep 2016

To better simulate the seasonal evolution of sea ice in the Arctic, with particular attention to the marginal ice zone, a sea ice model of the distribution of ice thickness, floe size, and enthalpy was implemented into the Pan-arctic IceOcean Modeling and Assimilation System (PIOMAS). Theories on floe size distribution (FSD) and ice thickness distribution (ITD) were coupled in order to explicitly simulate multicategory FSD and ITD distributions simultaneously. The expanded PIOMAS was then used to estimate the seasonal evolution of the Arctic FSD in 2014 when FSD observations are available for model calibration and validation.

Results indicate that the simulated FSD, commonly described equivalently as cumulative floe number distribution (CFND), generally follows a power law across space and time and agrees with the CFND observations derived from TerraSAR-X satellite images. The simulated power-law exponents also correlate with those derived using MODIS images, with a low mean bias of 2%. In the marginal ice zone, the modeled CFND shows a large number of small floes in winter because of stronger winds acting on thin, weak first-year ice in the ice edge region. In mid-spring and summer, the CFND resembles an upper truncated power law, with the largest floes mostly broken into smaller ones; however, the number of small floes is lower than in winter because floes of small sizes or first-year ice are easily melted away. In the ice pack interior there are fewer floes in late fall and winter than in summer because many of the floes are welded together into larger floes in freezing conditions, leading to a relatively flat CFND with low power-law exponents.

The simulated mean floe size averaged over all ice-covered areas shows a clear annual cycle, large in winter and smaller in summer. However, there is no obvious annual cycle of mean floe size averaged over the marginal ice zone. The incorporation of FSD into PIOMAS results in reduced ice thickness, mainly in the marginal ice zone, which improves the simulation of ice extent and yields an earlier ice retreat.

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Early ice retreat and ocean warming may induce copepod biogeographic boundary shifts in the Arctic Ocean

Feng, Z., R. Ji, R.G. Campbell, C.J. Ashjian, and J. Zhang, "Early ice retreat and ocean warming may induce copepod biogeographic boundary shifts in the Arctic Ocean," J. Geophys. Res., 121, 6137-6158, doi:10.1002/2016JC011784, 2016.

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1 Aug 2016

Early ice retreat and ocean warming are changing various facets of the Arctic marine ecosystem, including the biogeographic distribution of marine organisms. Here an endemic copepod species, Calanus glacialis, was used as a model organism, to understand how and why Arctic marine environmental changes may induce biogeographic boundary shifts. A copepod individual-based model was coupled to an ice-ocean-ecosystem model to simulate temperature- and food-dependent copepod life history development. Numerical experiments were conducted for two contrasting years: a relatively cold and normal sea ice year (2001) and a well-known warm year with early ice retreat (2007). Model results agreed with commonly known biogeographic distributions of C. glacialis, which is a shelf/slope species and cannot colonize the vast majority of the central Arctic basins. Individuals along the northern boundaries of this species' distribution were most susceptible to reproduction timing and early food availability (released sea ice algae). In the Beaufort, Chukchi, East Siberian, and Laptev Seas where severe ocean warming and loss of sea ice occurred in summer 2007, relatively early ice retreat, elevated ocean temperature (about 1–2°C higher than 2001), increased phytoplankton food, and prolonged growth season created favorable conditions for C. glacialis development and caused a remarkable poleward expansion of its distribution. From a pan-Arctic perspective, despite the great heterogeneity in the temperature and food regimes, common biogeographic zones were identified from model simulations, thus allowing a better characterization of habitats and prediction of potential future biogeographic boundary shifts.

An inverse modeling study of circulation in the Eastern Bering Sea during 2007–2010

Panteleev, G., M. Yaremchuk, O. Francis, P.J. Stabeno, T. Weingartner, and J. Zhang, "An inverse modeling study of circulation in the Eastern Bering Sea during 2007–2010," J. Geophys. Res., 121, 3970-3989, doi:10.1002/2015JC011287, 2016.

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12 Jun 2016

A two-way nested 4d-variational data assimilation system is implemented in the Eastern Bering Sea (EBS) to investigate changes in circulation and thermodynamic state for a 3.8 year period. Assimilated observations include data from 19 moorings deployed on the shelf and in the Bering Strait, 1705 hydrographic stations occupied during eight surveys, and remotely sensed sea surface temperature and sea surface height (SSH) data. Validation of the presented 4dVar reanalysis against the output of two sequential data-assimilative systems (the Bering Ecosystem Study ice-ocean Modeling and Assimilation System (BESTMAS) and the Arctic Cap Nowcast-Forecast System (ACNFS)) has shown that the product is more consistent with the observed transports in the Bering Strait and in the EBS interior both in terms of their magnitude and time variability. Analysis of the data-optimized solution quantifies a sequence of wind-forced events that resulted in the anomalous heat and freshwater transports through the Bering Strait, including a 28 day long flow reversal that occurred in November 2009 and carried Siberian Coastal Current water down to the Gulf of Anadyr. Lagrangian study of the Arctic-bound Pacific waters indicates the extreme importance of the cross-shelf exchange along the path of the Bering Slope Current and quantifies the spectrum of residence times for the waters entering EBS through Unimak Pass and through Aleutian passages. Residence times in the EBS cold pool are diagnosed to be 2–3 times longer than those in the surrounding waters.

Spring plankton dynamics in the Eastern Bering Sea, 1971-2050: Mechanisms of interannual variability diagnosed with a numerical model

Banas, N.S., and 9 others, including Zhang, "Spring plankton dynamics in the Eastern Bering Sea, 1971-2050: Mechanisms of interannual variability diagnosed with a numerical model," J. Geophys. Res., 121, 1476-1501, doi:, 2016.

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20 Feb 2016

A new planktonic ecosystem model was constructed for the Eastern Bering Sea based on observations from the 2007–2010 BEST/BSIERP (Bering Ecosystem Study/Bering Sea Integrated Ecosystem Research Program) field program. When run with forcing from a data-assimilative ice-ocean hindcast of 1971–2012, the model performs well against observations of spring bloom time evolution (phytoplankton and microzooplankton biomass, growth and grazing rates, and ratios among new, regenerated, and export production). On the southern middle shelf (57°N, station M2), the model replicates the generally inverse relationship between ice-retreat timing and spring bloom timing known from observations, and the simpler direct relationship between the two that has been observed on the northern middle shelf (62°N, station M8). The relationship between simulated mean primary production and mean temperature in spring (15 February to 15 July) is generally positive, although this was found to be an indirect relationship which does not continue to apply across a future projection of temperature and ice cover in the 2040s. At M2, the leading direct controls on total spring primary production are found to be advective and turbulent nutrient supply, suggesting that mesoscale, wind-driven processes—advective transport and storminess—may be crucial to long-term trends in spring primary production in the southeastern Bering Sea, with temperature and ice cover playing only indirect roles. Sensitivity experiments suggest that direct dependence of planktonic growth and metabolic rates on temperature is less significant overall than the other drivers correlated with temperature described above.

Ecosystem model intercomparison of under-ice and total primary production in the Arctic Ocean

Jin, M., E.E. Popova, J. Zhang, R. Ji, D. Pendleton, Ø. Varpe, A. Yool, and Y.J. Lee, "Ecosystem model intercomparison of under-ice and total primary production in the Arctic Ocean," J. Geophys. Res., 121, 934-948, doi:10.1002/2015JC011183, 2016.

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27 Jan 2016

Previous observational studies have found increasing primary production (PP) in response to declining sea ice cover in the Arctic Ocean. In this study, under-ice PP was assessed based on three coupled ice-ocean-ecosystem models participating in the Forum for Arctic Modeling and Observational Synthesis (FAMOS) project. All models showed good agreement with under-ice measurements of surface chlorophyll-a concentration and vertically integrated PP rates during the main under-ice production period, from mid-May to September. Further, modeled 30-year (1980–2009) mean values and spatial patterns of sea ice concentration compared well with remote sensing data. Under-ice PP was higher in the Arctic shelf seas than in the Arctic Basin, but ratios of under-ice PP over total PP were spatially correlated with annual mean sea ice concentration, with higher ratios in higher ice concentration regions. Decreases in sea ice from 1980 to 2009 were correlated significantly with increases in total PP and decreases in the under-ice PP/total PP ratio for most of the Arctic, but nonsignificantly related to under-ice PP, especially in marginal ice zones. Total PP within the Arctic Circle increased at an annual rate of between 3.2 and 8.0 Tg C/yr from 1980 to 2009. This increase in total PP was due mainly to a PP increase in open water, including increases in both open water area and PP rate per unit area, and therefore much stronger than the changes in under-ice PP. All models suggested that, on a pan-Arctic scale, the fraction of under-ice PP declined with declining sea ice cover over the last three decades.

Accuracy of short-term sea ice drift forecasts using a coupled ice-ocean model

Schweiger, A.J., and J. Zhang, "Accuracy of short-term sea ice drift forecasts using a coupled ice-ocean model," J. Geophys. Res., 120, 7827-7841, doi:10.1002/2015JC011273, 2015.

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1 Dec 2015

Arctic sea ice drift forecasts of 6 h – 9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h – 8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high-resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km x 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast.

Model forecast skill and sensitivity to initial conditions in the seasonal Sea Ice Outlook

Blanchard-Wrigglesworth, E., R.I. Cullather, W. Wang, J. Zhang, and C.M. Bitz, "Model forecast skill and sensitivity to initial conditions in the seasonal Sea Ice Outlook," Geophys. Res. Lett., 42, 8042-8048, doi:10.1002/2015GL065860, 2015.

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16 Oct 2015

We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed –1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

On the evolution of Atlantic Meridional Overturning Circulation Fingerprint and implications for decadal predictability in the North Atlantic

Zhang, J., and R. Zhang, "On the evolution of Atlantic Meridional Overturning Circulation Fingerprint and implications for decadal predictability in the North Atlantic," Geophys. Res. Lett., 42, 5419-5426, doi:10.1002/2015GL064596, 2015.

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16 Jul 2015

It has been suggested previously that the Atlantic Meridional Overturning Circulation (AMOC) anomaly associated with changes in the North Atlantic Deep Water formation propagates southward with an advection speed north of 34°N. In this study, using Geophysical Fluid Dynamics Laboratory Coupled Model version 2.1 (GFDL CM2.1), we show that this slow southward propagation of the AMOC anomaly is crucial for the evolution and the enhanced decadal predictability of the AMOC fingerprint — the leading mode of upper ocean heat content (UOHC) in the extratropical North Atlantic. A positive AMOC anomaly in northern high latitudes leads to a convergence/divergence of the Atlantic meridional heat transport (MHT) anomaly in the subpolar/Gulf Stream region, thus warming in the subpolar gyre (SPG) and cooling in the Gulf Stream region after several years. Recent decadal prediction studies successfully predicted the observed warm shift in the SPG in the mid-1990s. Our results here provide the physical mechanism for the enhanced decadal prediction skills in the SPG UOHC.

Sea ice floe size distribution in the marginal ice zone: Theory and numerical experiments

Zhang, J., A. Schweiger, M. Steele, and H. Stern, "Sea ice floe size distribution in the marginal ice zone: Theory and numerical experiments," J. Geophys. Res., 120, 3484-3498, do:10.1002/2015JC010770, 2015.

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12 May 2015

To better describe the state of sea ice in the marginal ice zone (MIZ) with floes of varying thicknesses and sizes, both an ice thickness distribution (ITD) and a floe size distribution (FSD) are needed. In this work, we have developed a FSD theory that is coupled to the ITD theory of Thorndike et al. (1975) in order to explicitly simulate the evolution of FSD and ITD jointly. The FSD theory includes a FSD function and a FSD conservation equation in parallel with the ITD equation. The FSD equation takes into account changes in FSD due to ice advection, thermodynamic growth, and lateral melting. It also includes changes in FSD because of mechanical redistribution of floe size due to ice ridging and, particularly, ice fragmentation induced by stochastic ocean surface waves. The floe size redistribution due to ice fragmentation is based on the assumption that wave-induced breakup is a random process such that when an ice floe is broken, floes of any smaller sizes have an equal opportunity to form, without being either favored or excluded. To focus only on the properties of mechanical floe size redistribution, the FSD theory is implemented in a simplified ITD and FSD sea ice model for idealized numerical experiments. Model results show that the simulated cumulative floe number distribution (CFND) follows a power law as observed by satellites and airborne surveys. The simulated values of the exponent of the power law, with varying levels of ice breakups, are also in the range of the observations. It is found that floe size redistribution and the resulting FSD and mean floe size do not depend on how floe size categories are partitioned over a given floe size range. The ability to explicitly simulate multicategory FSD and ITD together may help to incorporate additional model physics, such as FSD-dependent ice mechanics, surface exchange of heat, mass, and momentum, and wave-ice interactions.

Future climate change under RCP emission scenarios with GISS ModelE2

Nazarenko, L., et al., including J. Zhang, "Future climate change under RCP emission scenarios with GISS ModelE2," J. Adv. Model. Earth Syst., 7, 244-267, doi:10.1002/2014MS000403, 2015.

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1 Mar 2015

We examine the anthropogenically forced climate response for the 21st century representative concentration pathway (RCP) emission scenarios and their extensions for the period 2101–2500. The experiments were performed with ModelE2, a new version of the NASA Goddard Institute for Space Sciences (GISS) coupled general circulation model that includes three different versions for the atmospheric composition components: a noninteractive version (NINT) with prescribed composition and a tuned aerosol indirect effect (AIE), the TCAD version with fully interactive aerosols, whole-atmosphere chemistry, and the tuned AIE, and the TCADI version which further includes a parameterized first indirect aerosol effect on clouds. Each atmospheric version is coupled to two different ocean general circulation models: the Russell ocean model (GISS-E2-R) and HYCOM (GISS-E2-H). By 2100, global mean warming in the RCP scenarios ranges from 1.0 to 4.5°C relative to 1850%u20131860 mean temperature in the historical simulations. In the RCP2.6 scenario, the surface warming in all simulations stays below a 2°C threshold at the end of the 21st century. For RCP8.5, the range is 3.5%–4.5°C at 2100. Decadally averaged sea ice area changes are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with a larger sensitivity during winter months. By the year 2500, there are complete recoveries of the globally averaged surface air temperature for all versions of the GISS climate model in the low-forcing scenario RCP2.6. TCADI simulations show enhanced warming due to greater sensitivity to CO2, aerosol effects, and greater methane feedbacks, and recovery is much slower in RCP2.6 than with the NINT and TCAD versions. All coupled models have decreases in the Atlantic overturning stream function by 2100. In RCP2.6, there is a complete recovery of the Atlantic overturning stream function by the year 2500 while with scenario RCP8.5, the E2-R climate model produces a complete shutdown of deep water formation in the North Atlantic.

Circulation in the eastern Bering Sea: Inferences from a 2-km-resolution model

Durski, S.M., A. Kurapov, J. Zhang, and G.G. Panteleev, "Circulation in the eastern Bering Sea: Inferences from a 2-km-resolution model," Deep Sea Res. II, EOR, do:10.1016/j.dsr2.2015.02.002, 2015.

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7 Feb 2015

A 2-km-resolution model of the eastern Bering Sea is developed to capture dynamical processes on the scale of the Rossby radius of deformation on tidal to seasonal time scales. The model spans the region from 178°E to the Alaskan coast and from roughly 50° to 66°N, including the Aleutian Islands in the south and the Bering Strait in the north. The high resolution throughout ensures that the mesoscale dynamics of significant subregions of the domain, such as the Aleutian Island passes, Bering Sea slope, and the shelf canyons, are captured simultaneously without the concern for loss of interconnectivity between regions. Simulations are performed for the ice-free season (June–October) of 2009, with tidal and atmospheric forcing. The model compares favorably with observations from AVHRR and Envisat satellites, Argo drifters, and Bering Sea shelf moorings. The mesoscale dynamics of the mixing and exchange flow through the eastern Aleutian Island passes, which exhibit strong diurnal and two-week variability, are well represented. The two-week oscillation in volume flux through the largest of these passes, Amukta Pass, is found to be out of phase with the transport through the neighboring passes (e.g., Seguam and Samalga passes). Mesoscale structure is also found to be ubiquitous along the mixing front of the cold pool. Structures at the scale of O(20 km) persist and play a role in determining the pattern of erosion of the water mass as the shelf warms and mixes. On the Bering Sea shelf, tidal motions are dominant, and variability on the horizontal scale of the first-mode internal tide develops (O(30 km)) from the shelf break to the onshore edge of the Bering shelf cold pool.

Seasonal ice loss in the Beaufort Sea: Toward synchrony and prediction

Steele, M., S. Dickinson, J. Zhang, and R. Lindsay, "Seasonal ice loss in the Beaufort Sea: Toward synchrony and prediction," J. Geophys. Res., 120, 1118-1132, doi:10.1002/2014JC010247, 2015.

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1 Feb 2015

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.

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.

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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).

Seasonality and long-term trend in Arctic Ocean surface stress in a model

Martin, T., M. Steele, and J. Zhang, "Seasonality and long-term trend in Arctic Ocean surface stress in a model," J. Geophys. Res., 119, 1723-1738, doi:10.1002/2013JC009425, 2014.

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1 Mar 2014

A numerical ocean sea-ice model is used to demonstrate that Arctic sea ice retreat affects momentum transfer into the ocean. A thinner and thus weaker ice cover is more easily forced by the wind, which increases the momentum flux. In contrast, increasing open water reduces momentum transfer because the ice surface provides greater drag than the open water surface. We introduce the concept of optimal ice concentration: momentum transfer increases with increasing ice concentration up to a point, beyond which frictional losses by floe interaction damp the transfer. For a common ice internal stress formulation, a concentration of 80–90% yields optimal amplification of momentum flux into the ocean. We study the seasonality and long-term evolution of Arctic Ocean surface stress over the years 1979–2012. Spring and fall feature optimal ice conditions for momentum transfer, but only in fall is the wind forcing at its maximum, yielding a peak basin-mean ocean surface stress of ~0.08 N/m2. Since 1979, the basin-wide annual mean ocean surface stress has been increasing by 0.004 N/m2/decade, and since 2000 by 0.006 N/m2/decade. In contrast, summertime ocean surface stress has been decreasing at –0.002 N/m2/decade. These trends are linked to the weakening of the ice cover in fall, winter and spring, and to an increase in open water fraction in summer, i.e., changes in momentum transfer rather than changes in wind forcing. In most areas, the number of days per year with optimal ice concentration is decreasing.

Arctic Ocean circulation patterns revealed by GRACE

Peralta-Ferriz, C., J.H. Morison, J.H. Wallace, J.A. Bonin, and J. Zhang, "Arctic Ocean circulation patterns revealed by GRACE," J. Clim., 27, 1445-1468, doi:10.1175/JCLI-D-13-00013.1, 2014.

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1 Feb 2014

Measurements of ocean bottom pressure (OBP) anomalies from the satellite mission Gravity Recovery and Climate Experiment (GRACE), complemented by information from two ocean models, are used to investigate the variations and distribution of the Arctic Ocean mass from 2002 through 2011. The forcing and dynamics associated with the observed OBP changes are explored. Major findings are the identification of three primary temporal–spatial modes of OBP variability at monthly-to-interannual time scales with the following characteristics. Mode 1 (50% of the variance) is a wintertime basin-coherent Arctic mass change forced by southerly winds through Fram Strait, and to a lesser extent through Bering Strait. These winds generate northward geostrophic current anomalies that increase the mass in the Arctic Ocean. Mode 2 (20%) reveals a mass change along the Siberian shelves, driven by surface Ekman transport and associated with the Arctic Oscillation. Mode 3 (10%) reveals a mass dipole, with mass decreasing in the Chukchi, East Siberian, and Laptev Seas, and mass increasing in the Barents and Kara Seas. During the summer, the mass decrease on the East Siberian shelves is due to the basin-scale anticyclonic atmospheric circulation that removes mass from the shelves via Ekman transport. During the winter, the forcing mechanisms include a large-scale cyclonic atmospheric circulation in the eastern-central Arctic that produces mass divergence into the Canada Basin and the Barents Sea. In addition, strengthening of the Beaufort high tends to remove mass from the East Siberian and Chukchi Seas. Supporting previous modeling results, the month-to-month variability in OBP associated with each mode is predominantly of barotropic character.

Modeling the impact of wind intensification on Antarctic sea ice volume

Zhang, J., "Modeling the impact of wind intensification on Antarctic sea ice volume," J. Clim., 27, 202-214, doi:10.1175/JCLI-D-12-00139.1, 2014.

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1 Jan 2014

A global sea ice–ocean model is used to examine the impact of wind intensification on Antarctic sea ice volume. Based on the NCEP/NCAR reanalysis data, there are increases in surface wind speed (0.13% yr-1) and convergence (0.66% yr-1) over the ice-covered areas of the Southern Ocean during the period 1979–2010. Driven by the intensifying winds, the model simulates an increase in sea ice speed, convergence, and shear deformation rate, which produces an increase in ridge ice production in the Southern Ocean (1.1% yr-1). The increased ridged ice production is mostly in the Weddell, Bellingshausen, Amundsen, and Ross Seas where an increase in wind convergence dominates. The increase in ridging production contributes to an increase in the volume of thick ice (thickness > 2 m) in the Southern Ocean, while the volumes of thin ice (thickness < 1 m) and medium thick ice (1 m < thickness < 2 m) remain unchanged over the period 1979–2010. The increase in thick ice leads to an increase in ice volume in the Southern Ocean, particularly in the southern Weddell Sea where a significant increase in ice concentration is observed. The simulated increase in either the thick ice volume (0.91% yr-1) or total ice volume (0.46% yr-1) is significantly greater than other ice parameters (simulated or observed) such as ice extent (0.14–0.21% yr-1) or ice area fraction (0.24–0.28% yr-1), suggesting that ice volume is a potentially strong measure of change.

The great 2012 Arctic Ocean summer cyclone enhanced biological productivity on the shelves

Zhang, J., C. Ashjian, R. Campbell, V. Hill, Y.H. Spitz, and M. Steele, "The great 2012 Arctic Ocean summer cyclone enhanced biological productivity on the shelves," J. Geophys. Res., 119, 297-312, doi:10.1002/2013JC009301, 2014.

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1 Jan 2014

A coupled biophysical model is used to examine the impact of the great Arctic cyclone of early August 2012 on the marine planktonic ecosystem in the Pacific sector of the Arctic Ocean (PSA). Model results indicate that the cyclone influences the marine planktonic ecosystem by enhancing productivity on the shelves of the Chukchi, East Siberian, and Laptev seas during the storm. Although the cyclone's passage in the PSA lasted only a few days, the simulated biological effects on the shelves last 1 month or longer. At some locations on the shelves, primary productivity (PP) increases by up to 90% and phytoplankton biomass by up to 40% in the wake of the cyclone. The increase in zooplankton biomass is up to 18% on 31 August and remains 10% on 15 September, more than 1 month after the storm. In the central PSA, however, model simulations indicate a decrease in PP and plankton biomass. The biological gain on the shelves and loss in the central PSA are linked to two factors. (1) The cyclone enhances mixing in the upper ocean, which increases nutrient availability in the surface waters of the shelves; enhanced mixing in the central PSA does not increase productivity because nutrients there are mostly depleted through summer draw down by the time of the cyclone's passage. (2) The cyclone also induces divergence, resulting from the cyclone's low-pressure system that drives cyclonic sea ice and upper ocean circulation, which transports more plankton biomass onto the shelves from the central PSA. The simulated biological gain on the shelves is greater than the loss in the central PSA, and therefore, the production on average over the entire PSA is increased by the cyclone. Because the gain on the shelves is offset by the loss in the central PSA, the average increase over the entire PSA is moderate and lasts only about 10 days. The generally positive impact of cyclones on the marine ecosystem in the Arctic, particularly on the shelves, is likely to grow with increasing summer cyclone activity if the Arctic continues to warm and the ice cover continues to shrink.

The influence of wind and ice on spring walrus hunting success on St. Lawrence Island, Alaska

Huntington, H.P., G. Noongwook, N.A. Bond, B. Benter, J.A. Snyder, and J. Zhang, "The influence of wind and ice on spring walrus hunting success on St. Lawrence Island, Alaska," Deep-Sea Res. II, 94, 312-322, doi:10.1016/j.dsr2.2013.03.016, 2013.

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1 Oct 2013

St. Lawrence Island Yupik hunters on St. Lawrence Island, Alaska, take hundreds of Pacific walrus (Odobenus rosmarus divergens) each year. The harvest and associated effort (hunting trips taken), however, are variable from year to year and also from day to day, influenced by physical environmental factors among other variables. We used data from 1996 to 2010 to construct generalized additive models (GAMs) to examine several relationships among the variables. Physical factors explained 18% of the variability in harvest in Savoonga and 25% of the variability in effort; the corresponding figures for Gambell were 24% and 32%. Effort alone explained 63% of the harvest in Savoonga and 59% in Gambell. Physical factors played a relatively smaller role in determining hunting efficiency (walrus taken per hunting trip), explaining 15% of the variability in efficiency in Savoonga and 22% in Gambell, suggesting that physical factors play a larger role in determining whether to hunt than in the outcome of the hunt once undertaken.

Combining physical factors with effort explained 70% of the harvest variability in Savoonga and 66% in Gambell. Although these results indicate that other factors (e.g. fuel prices, socioeconomic conditions) collectively cause a greater share of variability in harvest and effort than ice and wind, at least as indicated by the measures used as predictors in the GAMs, they also suggest that environmental change is also likely to influence future harvest levels, and that climate models that yield appropriately scaled data on ice and wind around St. Lawrence Island may be of use in determining the magnitude and direction of those influences.

CryoSat-2 estimates of Arctic sea ice thickness and volume

Laxon, S.W., K.A. Giles, A.L. Ridout, D.J. Winham, R. Willatt, R. Cullen, R. Kwok, A. Schweiger, J. Zhang, C. Haas, S. Hendricks, P. Krishfield, N. Kurtz, S. Farrell, and M. Davidson, "CryoSat-2 estimates of Arctic sea ice thickness and volume," Geophys. Res. Lett., 40, 732-737, doi:10.1002/grl.50193, 2013.

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28 Feb 2013

Satellite records show a decline in ice extent over more than three decades, with a record minimum in September 2012. Results from the Pan-Arctic Ice-Ocean Modelling and Assimilation system (PIOMAS) suggest that the decline in extent has been accompanied by a decline in volume, but this has not been confirmed by data. Using new data from the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, we generate estimates of ice volume for the winters of 2010/11 and 2011/12. We compare these data with current estimates from PIOMAS and earlier (2003–8) estimates from the National Aeronautics and Space Administration ICESat mission. Between the ICESat and CryoSat-2 periods, the autumn volume declined by 4291 km3 and the winter volume by 1479 km3. This exceeds the decline in ice volume in the central Arctic from the PIOMAS model of 2644 km3 in the autumn, but is less than the 2091 km3 in winter, between the two time periods.

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.

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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.

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.

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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.

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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.

Marginal Ice Zone (MIZ) Program: Science and Experiment Plan

Lee, C.M., et al., "Marginal Ice Zone (MIZ) Program: Science and Experiment Plan," APL-UW TR 1201, Technical Report, Applied Physics Laboratory, University of Washington, Seattle, October 2012, 48 pp.

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9 Oct 2012

The Marginal Ice Zone (MIZ) intensive field program will employ an array of cutting-edge autonomous platforms to characterize the processes that govern Beaufort Sea MIZ evolution from initial breakup and MIZ formation though the course of the summertime sea ice retreat. Instruments will be deployed on and under the ice prior to initial formation of the MIZ along the Alaska coast, and will continue sampling from open water, across the MIZ, and into full ice cover, as the ice edge retreats northward through the summer. The flexible nature of ice-mounted and mobile, autonomous oceanographic platforms (e.g., gliders and floats) facilitates access to regions of both full ice cover and riskier MIZ regions. This approach exploits the extended endurance of modern autonomous platforms to maintain a persistent presence throughout the entire northward retreat. It also takes advantage of the inherent scalability of these instruments to sample over a broad range of spatial and temporal scales.

Circulation on the central Bering Sea shelf, July 2008 – July 2010

Danielson, S.L., T.J. Weingartner, Kn. Aagaard, J. Zhang, and R.A. Woodgate, "Circulation on the central Bering Sea shelf, July 2008 – July 2010," J. Geophys. Res., 117, doi:10.1029/2012JC008303, 2012.

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1 Oct 2012

We examine the July 2008 to July 2010 circulation over the central Bering Sea shelf using measurements at eight instrumented moorings, hindcast winds and numerical model results. At sub-tidal time scales, the vertically integrated equations of motion show that the cross-shelf balance is primarily geostrophic. The along-shelf balance is also mainly geostrophic, but local accelerations, wind stress and bottom friction account for 10-40% of the momentum balance, depending on season and water depth. The shelf exhibits highly variable flow with small water column average vector mean speeds (< 5 cm s-1). Mean/peak speeds in summer (3–6 cm s-1/10–30 cm s-1) are smaller than in winter and fall (6–12 cm s-1/30–70 cm s-1). Low frequency flows (< 1/4 cpd) are horizontally coherent over distances exceeding 200 km. Vertical coherence varies seasonally, degrading with the onset of summer stratification. Because effects of heating and freezing are enhanced in shallow waters, warm summers increase the cross-shelf density gradient and thus enhance northward transport; cold winters with increased ice production and brine rejection increase the (now reversed) cross-shelf density gradient and enhance southward transport. Although the baroclinic velocity is large enough to influence seasonal transports, wind-forced Ekman dynamics are primarily responsible for flow variations. The system changes from strong northward flow (with coastal convergence) to strong southward flow (with coastal divergence) for northerly and easterly winds, respectively. Under northerly and northwesterly winds, nutrient-rich waters flow toward the central shelf from the north and northwest, replacing dilute coastal waters that are carried south and west.

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.

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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.

Process-based coastal erosion modeling for Drew Point, North Slope, Alaska

Ravens, T.M., B.M. Jones, J. Zhang, C.D. Arp, and J.A. Schmutz, "Process-based coastal erosion modeling for Drew Point, North Slope, Alaska," J. Waterw. Port C.-ASCE, 138, 122-130, doi:10.1061/(ASCE)WW.1943-5460.0000106, 2012.

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1 Mar 2012

A predictive coastal erosion/shoreline change model has been developed for a small coastal segment near Drew Point, Beaufort Sea, Alaska. This coastal setting has experienced a dramatic increase in erosion since the early 2000's. The bluffs at this site are 3-4 m tall and consist of ice-wedge bounded blocks of fine-grained sediments cemented by ice-rich permafrost and capped with a thin organic layer. The bluffs are typically fronted by a narrow (5 m wide) beach or none at all. During a storm surge, the sea contacts the base of the bluff and a niche is formed through thermal and mechanical erosion. The niche grows both vertically and laterally and eventually undermines the bluff, leading to block failure or collapse. The fallen block is then eroded both thermally and mechanically by waves and currents, which must occur before a new niche forming episode may begin. The erosion model explicitly accounts for and integrates a number of these processes including: (1) storm surge generation due to wind and atmospheric forcing, (2) erosional niche growth due to wave-induced turbulent heat transfer and sediment transport (using the Kobayashi niche erosion model), and (3) thermal and mechanical erosion of the fallen block. The model was calibrated with historic shoreline change data for one time period (1979-2002) and validated with a later time period (2002-2007).

Towards seasonal prediction of the distribution and extent of cold bottom waters on the Bering Sea shelf

Zhang, J., R. Woodgate, and S. Mangiameli, "Towards seasonal prediction of the distribution and extent of cold bottom waters on the Bering Sea shelf," Deep Sea Res. II, 65-70, 58-71, doi:10.1016/j.dsr2.2012.02.023, 2012.

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21 Feb 2012

A coupled sea ice–ocean model, combined with observational and reanalysis data, is used to explore the seasonal predictability of the distribution and extent of cold bottom waters on the Bering Sea shelf through numerical simulations or statistical analyses. The model captures the spatiotemporal variability of trawl survey observations of bottom water temperature over the period 1970–2009. Of the various winter air–ice–3ocean parameters considered, the interannual variability of the winter on-shelf heat transport across the Bering Sea shelf break, dominated by changes in ocean flow, is most highly correlated with the interannual variability of the bottom layer properties (bottom temperature, and the distribution and extent of cold bottom waters) in spring–summer. This suggests that the winter heat transport may be the best seasonal predictor of the bottom layer properties. To varying degrees, the winter mean simulated sea surface temperature (SST), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis surface air temperature (SAT), simulated and observed sea ice extent, the Bering Strait outflow, and the Pacific Decadal Oscillation are also significantly correlated with the spring–summer bottom layer properties. This suggests that, with varying skill, they may also be useful for statistical seasonal predictions. Good agreement between observations and results of the coupled ice–ocean model suggests also the possibility of numerical seasonal predictions of the bottom layer properties. The simulated field of bottom layer temperature on the Bering Sea shelf on 31 May is a good predictor of the distribution and extent of cold bottom waters throughout late spring and summer. These variables, both in the model and in reality, do not change significantly from June to October, primarily owing to increased upper ocean stratification in late spring due to ice melt and surface warming, which tends to isolate and preserve the cold bottom waters on the shelf. However, the ocean stratification, and hence the isolation effect, is stronger in cold years than in warm years because more ice is available for melting in spring–summer.

Modeling the formation and fate of the near-surface temperature maximum in the Canadian Basin of the Arctic Ocean

Steele, M., W. Ermold, and J. Zhang, "Modeling the formation and fate of the near-surface temperature maximum in the Canadian Basin of the Arctic Ocean," J. Geophys. Res., 116, doi:10.1029/2010JC006803, 2011.

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12 Nov 2011

A numerical model is used to investigate the time and space extent of the near-surface temperature maximum (NSTM) of the Canadian Basin of the Arctic Ocean over the years 2000%u20132009. The NSTM is formed from local summertime absorption of solar radiation which, in some circumstances, descends through the fall and early winter to form a warm subsurface layer just below the winter mixed layer. We find that winter survival of this layer is confined largely to the Beaufort Gyre of the Canadian Basin, where Ekman convergence and downwelling push the summer warm layer down below the winter mixing depth. In recent years, summer stratification has increased, downwelling has accelerated, and the NSTM has warmed as the sea ice cover in the Beaufort Gyre has thinned. The result is a strengthening NSTM which contained enough heat by the end of winter 2007/2008 to melt about 20 cm of sea ice. Northwest of Alaska the model also simulates a second, deeper temperature maximum layer that forms from advection of saltier summer Pacific water. However, this layer is difficult to adequately resolve and maintain given the model's resolution.

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.

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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.

A basin-coherent mode of sub-monthly variability in Arctic Ocean bottom pressure

Peralta-Ferriz, C., J.H. Morison, J.M. Wallace, and J. Zhang, "A basin-coherent mode of sub-monthly variability in Arctic Ocean bottom pressure," Geophys. Res. Lett., 38, doi:10.1029/2011GL048142, 2011.

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22 Jul 2011

A sub-monthly mode of non-tidal variability of ocean bottom pressure (OBP) is observed in a 5-year record of deep-sea bottom pressure at the North Pole. OBP records from other regions in the Arctic show that the North Pole non-tidal mass fluctuation is part of a non-propagating basin-coherent variation that is well represented by the ice-ocean model PIOMAS, with a basin-averaged winter-only RMS of 3.3 cm. Wavelet analysis of the modeled OBP shows that the basin-averaged mass variations are non-stationary and only significant during the winter. The basin-averaged OBP is strongly related to the meridional wind component over the Nordic Seas. The ocean response is consistent with episodic wind forcing driving a northward geostrophic slope current. The mass transport anomaly associated with the mode is significant relative to the annual net mean flow.

Mechanisms of summertime upper Arctic Ocean warming and the effect on sea ice melt

Steele, M., J. Zhang, and W. Ermold, "Mechanisms of summertime upper Arctic Ocean warming and the effect on sea ice melt," J. Geophys. Res., 115, doi:10.1029/2009JC005849, 2010.

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6 Nov 2010

In this study, we use a numerical sea-ice-ocean model to examine what causes summertime upper ocean warming and sea ice melt during the 21st century in the Arctic Ocean. Our first question is, "What causes the ocean to warm in the Pacific Sector during the summer"? We find that about 80% of total heating over this region comes from ocean surface heat flux, with the remaining 20% originating in ocean lateral heat flux convergence. The latter occurs mostly within a few hundred kilometers of the northwest Alaskan coast. In the summer of 2007, the ocean gained just over twice the amount of heat it did over the average of the previous 7 years. Our second question is, "What causes sea ice to melt in the Pacific Sector during summer"? Our analysis shows that top melt dominates total melt early in the summer, while bottom melt (and in particular, bottom melt due to ocean heat transport) dominates later in the summer as atmospheric heating declines. Bottom melt rates in summer 2007 were 34% higher relative to the previous 7 year average. The modeled partition of top versus bottom melt closely matches observed melt rates obtained by a drifting buoy. Bottom melting contributes about 2/3 of total volume melt but is geographically confined to the Marginal Ice Zone, while top melting contributes a lesser 1/3 of volume melt but occurs over a much broader area of the ice pack.

Arctic sea ice response to atmospheric forcings with varying levels of anthropogenic warming and climate variability

Zhang, J., M. Steele, and A. Schweiger, "Arctic sea ice response to atmospheric forcings with varying levels of anthropogenic warming and climate variability," Geophys. Res. Lett., 37, doi:10.1029/2010GL044988, 2010.

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28 Oct 2010

Numerical experiments are conducted to project arctic sea ice responses to varying levels of future anthropogenic warming and climate variability over 2010–2050. A summer ice-free Arctic Ocean is likely by the mid-2040s if arctic surface air temperature (SAT) increases 4 deg C by 2050 and climate variability is similar to the past relatively warm two decades. If such a SAT increase is reduced by one-half or if a future Arctic experiences a range of SAT fluctuation similar to the past five decades, a summer ice-free Arctic Ocean would be unlikely before 2050. If SAT increases 4 deg C by 2050, summer ice volume decreases to very low levels (10–37% of the 1978–2009 summer mean) as early as 2025 and remains low in the following years, while summer ice extent continues to fluctuate annually. Summer ice volume may be more sensitive to warming while summer ice extent more sensitive to climate variability. The rate of annual mean ice volume decrease relaxes approaching 2050. This is because, while increasing SAT increases summer ice melt, a thinner ice cover increases winter ice growth. A thinner ice cover also results in a reduced ice export, which helps to further slow ice volume loss. Because of enhanced winter ice growth, arctic winter ice extent remains nearly stable and therefore appears to be a less sensitive climate indicator.

Modeling the impact of declining sea ice on the Arctic marine planktonic ecosystem

Zhang, J., Y.H. Spitz, M. Steele, C. Ashjian, Carin, R. Campbell, L. Berline, and P. Matrai, "Modeling the impact of declining sea ice on the Arctic marine planktonic ecosystem," J. Geophys. Res., 115, doi:10.1029/2009JC005387, 2010.

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8 Oct 2010

We have developed a coupled 3-D pan-Arctic biology/sea ice/ocean model to investigate the impact of declining Arctic sea ice on the marine planktonic ecosystem over 1988–2007. The biophysical model results agree with satellite observations of a generally downward trend in summer sea ice extent during 1988–2007, resulting in an increase in the simulated photosynthetically active radiation (PAR) at the ocean surface and marine primary productivity (PP) in the upper 100 m over open water areas of the Arctic Ocean. The simulated Arctic sea ice thickness has decreased steadily during 1988–2007, leading to an increase in PAR and PP in sea ice-covered areas. The simulated total PAR in all areas of the Arctic Ocean has increased by 43%, from 146 TW in 1988 to 209 TW in 2007; the corresponding total PP has increased by 50%, from 456 Tg C yr-1 in 1988 to 682 Tg C yr-1 in 2007. The simulated PAR and PP increases mainly occur in the seasonally and permanently ice-covered Arctic Ocean. In addition to increasing PAR, the decline in sea ice tends to increase the nutrient availability in the euphotic zone by enhancing air-sea momentum transfer, leading to strengthened upwelling and mixing in the water column and therefore increased nutrient input into the upper ocean layers from below. The increasing nutrient availability also contributes to the increase in the simulated PP, even though significant surface nutrient drawdown in summer is simulated. In conjunction with increasing surface absorption of solar radiation and rising surface air temperature, the increasing surface water temperature in the Arctic Ocean peripheral seas further contributes to the increase in PP. As PP has increased, so has the simulated biomass of phytoplankton and zooplankton.

Reconstruction and analysis of the Chukchi Sea circulation in 1990-1991

Panteleev, G., D.A. Nechaev, A. Proshutinsky, R. Woodgate, and J. Zhang, "Reconstruction and analysis of the Chukchi Sea circulation in 1990-1991," J. Geophys. Res., 115, doi:10.1029/2009JC005453, 2010.

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24 Aug 2010

The Chukchi Sea (CS) circulation reconstructed for September 1990 to October 1991 from sea ice and ocean data is presented and analyzed. The core of the observational data used in this study comprises the records from 12 moorings deployed in 1990 and 1991 in U.S. and Russian waters and two hydrographic surveys conducted in the region in the fall of 1990 and 1991. The observations are processed by a two-step data assimilation procedure involving the Pan-Arctic Ice-Ocean Modeling and Assimilation System (employing a nudging algorithm for sea ice data assimilation) and the Semi-implicit Ocean Model [utilizing a conventional four-dimensional variational (4D-var) assimilation technique]. The reconstructed CS circulation is studied to identify pathways and assess residence times of Pacific water in the region; quantify the balances of volume, freshwater, and heat content; and determine the leading dynamical factors configuring the CS circulation.

It is found that in 1990–1991 (high AO index and a cyclonic circulation regime) Pacific water transiting the CS toward the Canada basin followed two major pathways, namely via Herald Canyon (Herald branch of circulation, 0.23 Sv) and between Herald Shoal and Cape Lisburne (central branch of circulation and Alaskan Coastal Current, 0.32 Sv). The annual mean flow through Long Strait was negligible (0.01 Sv). Typical residence time of Pacific water in the region varied between 150 days for waters entering the CS in September and 270 days for waters entering in February/March. Momentum balance analysis reveals that geostrophic balance between barotropic pressure gradient and Coriolis force dominated for most of the year. Baroclinic effects were important for circulation only in the regions with large horizontal salinity gradients associated with the fresh Alaskan and Siberian coastal currents and the Cape Lisburne and Great Siberian polynyas. In the polynyas, the baroclinic effects were due to strong salinification and convection processes associated with sea ice formation.

Sea ice response to atmospheric and oceanic forcing in the Bering Sea

Zhang, J., R. Woodgate, and R. Moritz, "Sea ice response to atmospheric and oceanic forcing in the Bering Sea," J. Phys. Oceanogr., 40, 1729-1747, doi:10.1175/2010JPO4323.1, 2010.

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1 Aug 2010

A coupled sea ice–ocean model is developed to quantify the sea ice response to changes in atmospheric and oceanic forcing in the Bering Sea over the period 1970–2008. The model captures much of the observed spatiotemporal variability of sea ice and sea surface temperature (SST) and the basic features of the upper-ocean circulation in the Bering Sea. Model results suggest that tides affect the spatial redistribution of ice mass by up to 0.1 m or 15% in the central-eastern Bering Sea by modifying ice motion and deformation and ocean flows.

The considerable interannual variability in the pattern and strength of winter northeasterly winds leads to southwestward ice mass advection during January–May, ranging from 0.9 x 1012 m3 in 1996 to 1.8 x 1012 m3 in 1976 and averaging 1.4 x 1012 m3, which is almost twice the January–May mean total ice volume in the Bering Sea. The large-scale southward ice mass advection is constrained by warm surface waters in the south that melt 1.5 x 1012 m3 of ice in mainly the ice-edge areas during January–May, with substantial interannual variability ranging from 0.94 x 1012 m3 in 1996 to 2.0 x 1012 m3 in 1976. Ice mass advection processes also enhance thermodynamic ice growth in the northern Bering Sea by increasing areas of open water and thin ice. Ice growth during January–May is 0.90 x 1012 m3 in 1996 and 2.1 x 1012 m3 in 1976, averaging 1.3 x 1012 m3 over 1970–2008. Thus, the substantial interannual variability of the Bering Sea ice cover is dominated by changes in the wind-driven ice mass advection and the ocean thermal front at the ice edge.

The observed ecological regime shifts in the Bering Sea occurred with significant changes in sea ice, surface air temperature, and SST, which in turn are correlated with the Pacific decadal oscillation over 1970–2008 but not with other climate indices: Arctic Oscillation, North Pacific index, and El Nino–Southern Oscillation. This indicates that the PDO index may most effectively explain the regime shifts in the Bering Sea.

Is the dipole anomaly a major driver to record lows in arctic summer sea ice extent?

Wang, J., J. Zhang, E. Watanabe, M. Ikeda, K. Mizobata, J.E. Walsh, X. Bai, and B. Wu, "Is the dipole anomaly a major driver to record lows in arctic summer sea ice extent?" Geophys. Res. Lett., 36, doi:10.1029/2008GL036706, 2009.

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6 Mar 2009

Recent record lows of Arctic summer sea ice extent are found to be triggered by the Arctic atmospheric Dipole Anomaly (DA) pattern. This local, second-leading mode of sea-level pressure (SLP) anomaly in the Arctic produced a strong meridional wind anomaly that drove more sea ice out of the Arctic Ocean from the western to the eastern Arctic into the northern Atlantic during the summers of 1995, 1999, 2002, 2005, and 2007. In the 2007 summer, the DA also enhanced anomalous oceanic heat flux into the Arctic Ocean via Bering Strait, which accelerated bottom and lateral melting of sea ice and amplified the ice–albedo feedback. A coupled ice–ocean model was used to confirm the historical record lows of summer sea ice extent.

Tracing freshwater anomalies through the air-land-ocean system: A case study from the Mackenzie River Basin and the Beaufort Gyre

Rawlins, M.A., M. Steele, M.C. Serreze, C.J. Vorosmarty, W. Ermold, R.B. Lammers, K.C. McDonald, T.M. Pavelsky, A. Shilomanov, and J. Zhang, "Tracing freshwater anomalies through the air-land-ocean system: A case study from the Mackenzie River Basin and the Beaufort Gyre," Atmos. Oceans, 47, 79-97, doi:10.3137/OC301.2009, 2009.

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1 Mar 2009

Mackenzie River discharge was at a record low in water year (WY) 1995 (October 1994 to September 1995), was near average in WY 1996, and was at a record high in WY 1997. The record high discharge in WY 1997, with above average flow each month, was followed by a record high flow in May 1998, then a sharp decline. Through diagnosing these changing flows and their expression in the Beaufort Sea via synthesis of observations and model output, this study provides insight into the nature of the Arctic's freshwater system.

The low discharge in WY 1995 manifests negative anomalies in P–E and precipitation, recycled summer precipitation, and dry surface conditions immediately prior to the water year. The complex hydrograph for WY 1996 reflects a combination of spring soil moisture recharge, buffering by rising lake levels, positive P–E anomalies in summer, and a massive release of water held in storage by Bennett Dam. The record high discharge in WY 1997 manifests the dual effects of reduced buffering by lakes and positive P–E anomalies for most of the year. With reduced buffering, only modest P–E the following spring led to a record discharge in May 1998. As simulated with a coupled ice–ocean model, the record low discharge in WY 1995 contributed to a negative freshwater anomaly on the Mackenzie shelf lasting throughout the winter of 1995/96. High discharge from July–October 1996 contributed approximately 20% to a positive freshwater anomaly forming in the Beaufort Sea in the autumn of that year. The remainder was associated with reduced autumn/winter ice growth, strong ice melt the previous summer, and positive P–E anomalies over the ocean itself. Starting in autumn 1997 and throughout 1998, the upper ocean became more saline owing to sea ice growth.

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.

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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.

Variability of sea ice simulations assessed with RGPS kinematics

Kwok, R., E.C. Hunke, W. Maslowski, D. Menemenlis, and J. Zhang, "Variability of sea ice simulations assessed with RGPS kinematics," J. Geophys. Res., 113, doi:10.1029/2008JC004783, 2008.

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12 Nov 2008

Sea ice drift and deformation from coupled ice–ocean models are compared with high-resolution ice motion from the RADARSAT Geophysical Processor System (RGPS). In contrast to buoy drift, the density and extent of the RGPS coverage allows a more extensive assessment and understanding of model simulations at spatial scales from ~10 km to near basin scales and from days to seasonal timescales. This work illustrates the strengths of the RGPS data set as a basis for examining model ice drift and its gradients. As it is not our intent to assess relative performance, we have selected four models with a range of attributes and grid resolution. Model fields are examined in terms of ice drift, export, deformation, deformation-related ice production, and spatial deformation patterns. Even though the models are capable of reproducing large-scale drift patterns, variability among model behavior is high.

When compared to the RGPS kinematics, the characteristics shared by the models are (1) ice drift along coastal Alaska and Siberia is slower, (2) the skill in explaining the time series of regional divergence of the ice cover is poor, and (3) the deformation-related volume production is consistently lower. Attribution of some of these features to specific causes is beyond our current scope because of the complex interplay between model processes, parameters, and forcing. The present work suggests that high-resolution ice drift observations, like those from the RGPS, would be essential for future model developments, improvements, intercomparisons, and especially for evaluation of the small-scale behavior of models with finer grid spacing.

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.

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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.

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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.

Ensemble 1-year predictions of Arctic sea ice for the spring and summer of 2008

Zhang, J., M. Steele, R. Lindsay, A. Schweiger, J. Morison, "Ensemble 1-year predictions of Arctic sea ice for the spring and summer of 2008," Geophys. Res. Lett., 35, doi:10.1029/2008GL033244, 2008.

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22 Apr 2008

Ensemble predictions of arctic sea ice in spring and summer 2008 have been carried out using an ice-ocean model. The ensemble is constructed by using atmospheric forcing from 2001 to 2007 and the September 2007 ice and ocean conditions estimated by the model. The prediction results show that the record low ice cover and the unusually warm ocean surface waters in summer 2007 lead to a substantial reduction in ice thickness in 2008. Up to 1.2 m ice thickness reduction is predicted in a large area of the Canada Basin in both spring and summer of 2008, leading to extraordinarily thin ice in summer 2008. There is a 50% chance that both the Northern Sea Route and the Northwest Passage will be nearly ice free in September 2008. It is not likely there will be another precipitous decline in arctic sea ice extent such as seen in 2007, unless a new atmospheric forcing regime, significantly different from the recent past, occurs.

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.

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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.

Arctic Ocean surface warming trends over the past 100 years

Steele, M., W. Ermold, and J. Zhang, "Arctic Ocean surface warming trends over the past 100 years," Geophys. Res. Lett., 35, doi:10.1029/2007GL031651, 2008.

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29 Jan 2008

Ocean temperature profiles and satellite data have been analyzed for summertime sea surface temperature (SST) and upper ocean heat content variations over the past century, with a focus on the Arctic Ocean peripheral seas. We find that many areas cooled up to –0.5°C per decade during 1930–1965 as the Arctic Oscillation (AO) index generally fell, while these areas warmed during 1965–1995 as the AO index generally rose. Warming is particularly pronounced since 1995, and especially since 2000. Summer 2007 SST anomalies are up to 5°C. The increase in upper ocean summertime warming since 1965 is sufficient to reduce the following winter's ice growth by as much as 0.75 m. Alternatively, this heat may return to the atmosphere before any ice forms, representing a fall freeze-up delay of two weeks to two months. This returned heat might be carried by winds over terrestrial tundra ecosystems, contributing to the local heat budget.

The large-scale energy budget of the Arctic

Serreze, M.C., A.P. Barrett, A.G. Slater, M. Steele, J. Zhang, and K.E. Trenberth, "The large-scale energy budget of the Arctic," J. Geophys. Res., 112, doi:10.1029/2006JD008230, 2007.

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14 Jun 2007

This paper synthesizes a variety of atmospheric and oceanic data to examine the large-scale energy budget of the Arctic. Assessment of the atmospheric budget relies primarily on the ERA-40 reanalysis. The seasonal cycles of vertically integrated atmospheric energy storage and the convergence of energy transport from ERA-40, as evaluated for the polar cap (defined by the 70°N latitude circle), in general compare well with realizations from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis over the period 1979–2001. However, shortcomings in top of atmosphere radiation, as compared to satellite data, and the net surface flux, contribute to large energy budget residuals in ERA-40. The seasonal cycle of atmospheric energy storage is strongly modulated by the net surface flux, which is also the primary driver of seasonal changes in heat storage within the Arctic Ocean. Averaged for an Arctic Ocean domain, the July net surface flux from ERA-40 of –100 W m-2 (i.e., into the ocean), associated with sea ice melt and oceanic sensible heat gain, exceeds the atmospheric energy transport convergence of 91 W m-2. During winter (for which budget residuals are large), oceanic sensible heat loss and sea ice growth yield an upward surface flux of 50–60 W m-2, complemented with an atmospheric energy convergence of 80–90 W m-2 to provide a net radiation loss to space of 175–180 W m-2.

Effect of vertical mixing on the Atlantic Water layer circulation in the Arctic Ocean

Zhang, J., and M. Steele, "Effect of vertical mixing on the Atlantic Water layer circulation in the Arctic Ocean," J. Geophys. Res., 112, doi:10.1029/2006JC003732, 2007.

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13 Mar 2007

An ice-ocean model has been used to investigate the effect of vertical mixing on the circulation of the Atlantic Water layer (AL) in the Arctic Ocean. The motivation of this study comes from the disparate AL circulations in the various models that comprise the Arctic Ocean Model Intercomparison Project (AOMIP). It is found that varying vertical mixing significantly changes the ocean's stratification by altering the vertical distribution of salinity and hence the structure of the arctic halocline. In the Eurasian Basin, the changes in ocean stratification tend to change the strength and depth of the cyclonic AL circulation, but not the basic circulation pattern. In the Canada Basin, however, the changes in ocean stratification are sufficient to alter the direction of the AL circulation. Excessively strong vertical mixing drastically weakens the ocean stratification, leading to an anticyclonic circulation at all depths, including both the AL and the upper layer that consists of the surface mixed layer and the halocline. Overly weak vertical mixing makes the ocean unrealistically stratified, with a fresher and thinner upper layer than observations. This leads to an overly strong anticyclonic circulation in the upper layer and an overly shallow depth at which the underlying cyclonic circulation occurs. By allowing intermediate vertical mixing, the model does not significantly drift away from reality and is in a rather good agreement with observations of the vertical distribution of salinity throughout the Arctic Ocean. This realistic ocean stratification leads to a realistic cyclonic AL circulation in the Canada Basin. In order for arctic ice-ocean models to obtain realistic cyclonic AL circulation in the Canada Basin, it is essential to generate an upward concave-shaped halocline across the basin at certain depths, consistent with observations.

Water properties and circulation in Arctic Ocean models

Holloway, G., F. Dupont, E. Golubeva, S. Hakkinen, E. Hunke, M. Jin, M. Karcher, F. Kauker, M. Maltrud, M.A.M. Maqueda, W. Maslowski, G. Platov, D. Stark, M. Steele, T. Suzuki, J. Wang, J. Zhang, "Water properties and circulation in Arctic Ocean models," J. Geophys. Res., 112, doi:10.1029/2006JC003642, 2007.

7 Mar 2007

Origins of the SHEBA freshwater anomaly in the Mackenzie River delta

Steele, M., A. Porcelli, and J. Zhang, "Origins of the SHEBA freshwater anomaly in the Mackenzie River delta," Geophys. Res. Lett., 33, 10.1029/2005GL024813, 2006.

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4 May 2006

The formation of a low salinity anomaly observed in the southern Beaufort Gyre in fall 1997 is examined, using output from a numerical sea ice – ocean climate model. The anomaly forms from locally reduced fall ice growth and from advection of river water. With regard to the latter, we find anomalous northwestward advection of water from the Mackenzie River delta (MRD) during 1997–1999, which fed a low salinity anomaly that circulated and deepened in the Beaufort Gyre until summer 2002, when it dissipated. The MRD salinity anomaly was especially fresh in 1997 because unusually convergent sea ice the previous summer and fall 1996 suppressed fall ice growth. The model shows a high correlation between advection from the MRD and salinity anomalies in the southern Beaufort Gyre until about 2002, when the correlation weakens as local sea ice melt/growth becomes the dominant forcing.

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.

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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.

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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

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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.

Effect of sea ice rheology in numerical investigations of climate

Zhang, J., and D.A. Rothrock, "Effect of sea ice rheology in numerical investigations of climate," J. Geophys. Res., 110, 10.1029/2004JC002599, 2005

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31 Aug 2005

Plastic sea ice rheologies that employ teardrop and parabolic lens yield curves and allow varying biaxial tensile stresses have been developed. These rheologies, together with the previously developed ellipse and Mohr-Coulomb-ellipse rheologies, are implemented in a thickness and enthalpy distribution sea ice model to examine the rheological effect in numerical investigations of arctic climate. The teardrop, lens, and ellipse rheologies obey a normal flow rule and result in a two-peak shear stress distribution. The first peak is at the zero shear stress; the second is near 16,000 N m-1 for the ellipse and two lens rheologies and near 30,000 N m-1 for the two teardrop rheologies. The location of the second peak depends on the fatness of the yield curve and the amount of biaxial tensile stress allowed. In contrast, the Mohr-Coulomb-ellipse rheology, based on Coulombic friction failure, does not tend to create the second peak. The incorporation of biaxial tensile stress tends to increase ice thickness in most of the Arctic. A fatter yield curve tends to increase the frequency of large shear stresses. An increased frequency of large shear stresses, in conjunction with the inclusion of biaxial tensile stress, tends to reduce ice speed and ice export, to enhance ice ridging in the Arctic interior, and to reduce ice ridging in the coastal areas, which has a significant impact on arctic spatial ice mass distribution and the total ice budget. The teardrop rheologies reduce spatial bias of modeled ice draft against submarine observations more than others. By changing ice motion, deformation, and thickness the choice of plastic rheology also considerably affects the simulated surface energy exchanges, particularly in the Arctic marginal seas.

Arctic Ocean sea ice volume: What explains its recent depletion?

Rothrock, D.A., and J. Zhang, "Arctic Ocean sea ice volume: What explains its recent depletion?" J. Geophys. Res., 110, 10.1029/2004JC002282, 2005.

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4 Jan 2005

Various observations and model results point to an arctic sea ice cover that was extraordinarily thin in the 1990s. This thin ice cover was caused by a strengthened cyclonic circulation of wind and ice and by unusual warmth of springtime air temperatures. Here modeled sea ice volume is decomposed into two components: first, a dynamic or wind-forced response to interannually varying winds but a fixed annual cycle of air temperature and second, a thermally forced solution responding only to interannually varying temperatures. Over the 52-year simulation from 1948 to 1999 these two components have a similar range and variance; the wind-forced component has no substantial trend, but the temperature-forced component has a significant downward trend of –3% per decade. Total ice volume shows a trend of –4% per decade. Export slightly exceeds production over the simulation. Annual export and production can differ from each other and from year to year by ±30%. This behavior seems to characterize an ice cover highly constrained by interannual variations in forcing and not in balance. The bulk (two thirds) of volume loss from the 1960s to the 1990s is a result of a striking thinning of undeformed ice. The remainder of the volume loss is due to thinning of ridged ice and reduced concentrations. The central Arctic Ocean and particularly the East Siberian Sea suffer the greatest losses (of up to 2 m); the ice north of the Canadian archipelago also thinned since the 1960s by ~0.5 m.

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.

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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.

Comparing modeled streamfunction, heat and freshwater content in the Arctic Ocean

Steiner, N., G. Holloway, R. Gerdes, S. Hakkinen, D. Holland, M. Karcher, F. Kauker, W. Maslowski, A. Proshutinsky, M. Steele, and J. Zhang, "Comparing modeled streamfunction, heat and freshwater content in the Arctic Ocean," Ocean Modelling, 6, 265-284, doi:10.1016/S1463-5003(03)00013-1, 2004.

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1 Mar 2004

Within the framework of the Arctic Ocean Model Intercomparison Project results from several coupled sea ice–ocean models are compared in order to investigate vertically integrated properties of the Arctic Ocean. Annual means and seasonal ranges of streamfunction, freshwater and heat content are shown. For streamfunction the entire water column is integrated. For heat and freshwater content integration is over the upper 1000 m. The study represents a step toward identifying differences among model approaches and will serve as a base for upcoming studies where all models will be executed with common forcing. In this first stage only readily available outputs are compared, while forcing as well as numerical parameterizations differ.

The intercomparison shows streamfunctions differing in pattern and by several Sverdrups in magnitude. Differences occur as well for the seasonal range, where streamfunction is subject to large variability.

Annual mean heat content, referenced to 0°C, in the Canada Basin varies from –3.5 to +1.8 GJ m-2 among the models, representing both colder and warmer solutions compared to the climatology. Seasonal range is highest in regions with seasonal or no ice cover.

Corresponding freshwater content, referenced to 34.8 ppt, shows differences most obviously in the Beaufort Sea and Canada Basin where maximum values vary between 6 and 24 m for the individual models. Maxima in the seasonal range are related to river inflow.

In the current stage of the project, applied windstress contributes significantly to the differences. However differences due to model resolutions and model parameterizations can already be detected.

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.

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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.

Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates

Zhang, J.L., D.A. Rothrock, "Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates," Mon. Weather Rev., 131, 845-861, 2003.

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1 May 2003

A parallel ocean and ice model (POIM) in generalized orthogonal curvilinear coordinates has been developed for global climate studies. The POIM couples the Parallel Ocean Program (POP) with a 12-category thickness and enthalpy distribution (TED) sea ice model. Although the POIM aims at modeling the global ocean and sea ice system, the focus of this study is on the presentation, implementation, and evaluation of the TED sea ice model in a generalized coordinate system. The TED sea ice model is a dynamic thermodynamic model that also explicitly simulates sea ice ridging. Using a viscous plastic rheology, the TED model is formulated such that all the metric terms in generalized curvilinear coordinates are retained. Following the POP's structure for parallel computation, the TED model is designed to be run on a variety of computer architectures: parallel, serial, or vector. When run on a computer cluster with 10 parallel processors, the parallel performance of the POIM is close to that of a corresponding POP ocean-only model. Model results show that the POIM captures the major features of sea ice motion, concentration, extent, and thickness in both polar oceans. The results are in reasonably good agreement with buoy observations of ice motion, satellite observations of ice extent, and submarine observations of ice thickness. The model biases are within 8% in Arctic ice motion, within 9% in Arctic ice thickness, and within 14% in ice extent in both hemispheres. The model captures 56% of the variance of ice thickness along the 1993 submarine track in the Arctic. The simulated ridged ice has various thicknesses, up to 20 m in the Arctic and 16 m in the Southern Ocean. Most of the simulated ice is 1–3 m thick in the Arctic and 1–2 m thick in the Southern Ocean. The results indicate that, in the Atlantic–Indian sector of the Southern Ocean, the oceanic heating, mainly due to convective mixing, can readily exceed the atmospheric cooling at the surface in midwinter, thus forming a polynya. The results also indicate that the West Spitzbergen Current is likely to bring considerable oceanic heat (generated by lateral advection and vertical convection) to the Odden ice area in the Greenland Sea, an important factor for an often tongue-shaped ice concentration in that area.

The arctic ice thickness anomaly of the 1990s: A consistent view from observations and models

Rothrock, D.A., J. Zhang, and Y. Yu, "The arctic ice thickness anomaly of the 1990s: A consistent view from observations and models," J. Geophys. Res., 108, 10.1029/2001JC001208, 2003.

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18 Mar 2003

Observations of sea-ice draft from submarine cruises in much of the Arctic Ocean show that the ice cover was unusually thin in the mid-1990s. Here we limit our examination to digitally recorded draft data from eight cruises spanning the years 1987 to 1997 and find a decrease of about 1 m over the 11-year span. Comparisons of our modeled draft with observed draft show good agreement in the temporal change. Comparing average draft over entire cruises, the RMS discrepancy between modeled and observed draft is 0.3 m and the correlation is 0.98. Agreement in the spatial patterns of draft is somewhat lower; the RMS discrepancy of 50-km averages of draft is 0.7 m and the correlation is 0.73. We review reports of interannual variations of ice thickness or volume from other model studies. All models agree that thickness decreased by between 0.6 and 0.9 m from 1987 to 1996. Our model shows a modest recovery in thickness from 1996 to 1999. For the 1950s, 1960s, and 1970s, models tend to disagree on the size and to a lesser extent the timing or phase of interannual variations.

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.

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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.

Multinational effort studies differences among Arctic Ocean models

Proshutinsky, A., M. Steele, J. Zhang, G. Holloway, N. Steiner, S. Hakkinen, D. Holland, R. Gerdes, C. Koeberle, M. Karcher, M. Johnson, W. Maslowski, Y. Zhang, W. Hilber, and J. Wang, "Multinational effort studies differences among Arctic Ocean models," Eos Trans. AGU, 82, 643-644, doi:10.1029/01EO00365, 2001.

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18 Dec 2001

The Arctic Ocean is an important component of the global climate system. The processes occurring in the Arctic Ocean affect the rate of deep and bottom water formation in the convective regions of the high North Atlantic and influence ocean circulation across the globe. This fact is highlighted by global climate modeling studies that consistently show the Arctic to be one of the most sensitive regions to climate change. But an identification of the differences among models and model systematic errors in the Arctic Ocean remains unchecked, despite being essential to interpreting the simulation results and their implications for climate variability. For this reason, the Arctic Ocean Model Intercomparison Project (AOMIP), an international effort, was recently established to carry out a thorough analysis of model differences and errors. The geographical focus of this effort is shown in Figure 1.

A thickness and enthalpy distribution sea-ice model

Zhang, J., and D.A. Rothrock, "A thickness and enthalpy distribution sea-ice model," J. Phys. Oceanogr., 31, 2986-3001, 2001.

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1 Oct 2001

The theory of sea ice thickness distribution developed by Thorndike et al. has been extended to include sea ice enthalpy distribution. The extended theory conserves both ice mass and thermal energy, in the form of the heat stored in the ice, by jointly solving a thickness-distribution equation and an enthalpy-distribution equation. Both equations have been implemented in a one-dimensional dynamic thermodynamic sea-ice model with 12 ice thickness categories following the numerical procedure of Hibler. The implementation of the enthalpy-distribution equation allows the sea-ice model to account for any changes in the ice thermal energy induced by sea ice processes. As a result, the model is able to conserve not only the ice mass but also its thermal energy in the presence of ice advection, growth, melting, and ridging. Conserving ice thermal energy in a thickness-distribution sea ice model improves the prediction of ice growth, summer ice melt in particular, and therefore ice thickness. Inability to conserve the thermal energy by not implementing the enthalpy-distribution equation, compounded with an effect of the surface albedo feedback, causes the model to underestimate ice thickness by up to 11% under various conditions of thermal and mechanical forcing. This indicates the importance of conserving energy in numerical investigations of climate.

Thin ice impacts on surface salt flux and ice strength: Inferences from advanced very high resolution radiometer

Yu, Y., D.A. Rothrock, and J. Zhang, "Thin ice impacts on surface salt flux and ice strength: Inferences from advanced very high resolution radiometer," J. Geophys. Res., 106, 13,975-13,988, doi:10.1029/2000JC000311, 2001.

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15 Jul 2001

Temperatures and albedos derived from satellite imagery are combined with a thermodynamic ice model to estimate thin ice thickness distributions over the Beaufort and the northern Greenland seas. The study shows that thin ice (thinner than 1 m) occupied over half the area in the seasonal ice zones in November and December of 1990 but dropped significantly in April 1991; the Beaufort Shelf showed the largest seasonal change. The aggregate properties of surface salt flux and compressive ice strength, both strongly dependent on the thin end of the ice thickness distribution, were estimated with data from advanced very high resolution radiometer to reveal the spatial and temporal variations over a large scale. The salt flux from growing thin ice was 1–2 orders of magnitude larger on the Beaufort and Greenland Shelves than in the deep basins. On the shelves, flux from thin ice accounted for over 90% of the total surface salt budget. These satellite-derived estimates provided detailed spatial information on salt flux, which can be of great use in studies of surface patterns of salinity forcing and shelf-basin interaction. Also revealed by the satellite data was the wide range in values of compressive strength, which affects how freely the ice cover can deform. Strengths were low in early winter and in seasonal ice zones and nearly doubled in spring. Both thin ice fraction and compressive ice strength estimated from satellite imagery were in good agreement with those simulated by a coupled ice-ocean model.

Recent changes in arctic sea ice: The interplay between ice dynamics and thermodynamics

Zhang, J., D.A. Rothrock, and M. Steele, "Recent changes in arctic sea ice: The interplay between ice dynamics and thermodynamics," J. Climate, 13, 3099-3114, 2000.

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1 Sep 2000

It is well established that periods of high North Atlantic oscillation (NAO) index are characterized by a weakening of the surface high pressure and surface anticyclone in the Beaufort Sea and the intensification of the cyclonic circulation in the eastern Arctic Ocean. The response of Arctic sea ice to these atmospheric changes has been studied with a thickness distribution sea-ice model coupled to an ocean model. During a period of high NAO, 1989–96, the model shows a substantial reduction of ice advection into the eastern Arctic from the Canada Basin, and an increase of ice export through Fram Strait, both of which tend to deplete thick ice in the eastern Arctic Ocean and enhance it in the western Arctic, in an uneven dipolar pattern we call the East–West Arctic Anomaly Pattern (EWAAP). From the period 1979–88 with a lower-NAO index to the period 1988–96 with a high-NAO index, the simulated ice volume in the eastern Arctic drops by about a quarter, while that in the western Arctic increases by 16%. Overall, the Arctic Ocean loses 6%. The change from 1987 to 1996 is even larger — a loss of some 20% in ice volume for the whole Arctic. Both the model and satellite data show a significant reduction in ice extent in the eastern Arctic and in the Arctic Ocean as a whole.

There are corresponding changes in open water and therefore in ice growth, which tend to moderate the anomaly, and in lateral melting, which tends to enhance the anomaly. During the high NAO and strong EWAAP period, 1989%u201396, the eastern (western) Arctic has more (less) open water and enhanced (reduced) winter ice growth, so ice growth stabilizes the ice cover. On the other hand, the increased (decreased) open water enhances (reduces) summer melt by lowering (increasing) albedo in the eastern (western) Arctic. The nonlinearity of ice%u2013 albedo feedback causes the increased summer melt in the eastern Arctic to dominate the thermodynamic response and to collaborate with the ice advection pattern to enhance the EWAAP during high NAO.

Modeling arctic sea ice with an efficient plastic solution

Zhang, J., and D.A. Rothrock, "Modeling arctic sea ice with an efficient plastic solution," J. Geophys. Res., 105, 3325-3338, 2000.

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15 Feb 2000

A computationally efficient numerical method is developed for solving sea ice momentum equations that employ a nonlinear viscous-plastic rheology. The method is based on an alternating direction implicit (ADI) technique that involves a direct solution of the momentum equations. This method is therefore more computationally efficient than those employing an iterative procedure in solving the equations. The ADI method for modeling sea ice dynamics is dynamically consistent since it rapidly approaches a viscous-plastic solution described by the sea ice rheology. With different model configurations of varying spatial resolutions and decreasing time step intervals the ADI method converges to the same viscous-plastic solution as another numerical method that uses a line successive relaxation procedure to solve the ice momentum equations. This indicates that the ADI method is also numerically consistent. The approximateness of numerical solutions of sea ice, resulting from coarse model resolutions in time, is addressed. It is found that a significant bias, up to 10% or more, in the solution is likely to occur for a typical but coarse time step interval. This indicates that an assessment of the numerically created bias from a crude time integration may be necessary when model data comparisons are performed. In addition, suggestions are given for selecting appropriate time step intervals to enhance numerical accuracy in model applications.

In The News

Arctic sea ice volume, now tracking record low, stars in data visualization

UW News and Information, Hannah Hickey

The Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) combines weather observations, sea-surface temperature and satellite pictures of ice coverage to compute ice volume and then compares that with on-the-ground measurements. PIOMAS ice numbers starred in an animated graphic posted this week by a climate scientist at the University of Reading.

7 Jul 2016

UW researchers attend sea ice conference — above the Arctic Circle

UW News and Information, Hannah Hickey

University of Washington polar scientists are on Alaska’s North Slope this week for the 2016 Barrow Sea Ice Camp. Supported by the National Science Foundation, the event brings together U.S.-based sea ice observers, satellite experts and modelers at various career stages to collect data and discuss issues related to measuring and modeling sea ice. The goal is to integrate the research community in order to better observe and understand the changes in Arctic sea ice.

1 Jun 2016

Antarctic sea ice his 35-year record high Saturday

The Washington Post Blogs, Jason Samenow

Antarctic sea ice has grown to a record large extent for a second straight year, baffling scientists seeking to understand why this ice is expanding rather than shrinking in a warming world.

23 Sep 2013

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Stronger winds explain puzzling growth of sea ice in Antarctica

UW News and Information, Hannah Hickey

Much attention is paid to melting sea ice in the Arctic. But less clear is the situation on the other side of the planet. Despite warmer air and oceans, there's more sea ice in Antarctica now than in the 1970s — a fact often pounced on by global warming skeptics. The latest numbers suggest the Antarctic sea ice may be heading toward a record high this year.

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17 Sep 2013

A University of Washington researcher says the reason may lie in the winds. A new modeling study to be published in the Journal of Climate shows that stronger polar winds lead to an increase in Antarctic sea ice, even in a warming climate.

"The overwhelming evidence is that the Southern Ocean is warming," said author Jinlun Zhang, an oceanographer at the UW Applied Physics Laboratory. "Why would sea ice be increasing? Although the rate of increase is small, it is a puzzle to scientists."

His new study shows that stronger westerly winds swirling around the South Pole can explain 80 percent of the increase in Antarctic sea ice volume in the past three decades.

The polar vortex that swirls around the South Pole is not just stronger than it was when satellite records began in the 1970s, it has more convergence, meaning it shoves the sea ice together to cause ridging. Stronger winds also drive ice faster, which leads to still more deformation and ridging. This creates thicker, longer-lasting ice, while exposing surrounding water and thin ice to the blistering cold winds that cause more ice growth.

In a computer simulation that includes detailed interactions between wind and sea, thick ice — more than 6 feet deep — increased by about 1 percent per year from 1979 to 2010, while the amount of thin ice stayed fairly constant. The end result is a thicker, slightly larger ice pack that lasts longer into the summer.

"You've got more thick ice, more ridged ice, and at the same time you will get more ice extent because the ice just survives longer," Zhang said.

When the model held the polar winds at a constant level, the sea ice increased only 20 percent as much. A previous study by Zhang showed that changes in water density could explain the remaining increase.

"People have been talking about the possible link between winds and Antarctic sea ice expansion before, but I think this is the first study that confirms this link through a model experiment," commented Axel Schweiger, a polar scientist at the UW Applied Physics Lab. "This is another process by which dynamic changes in the atmosphere can make changes in sea ice that are not necessarily expected."

The research was funded by the National Science Foundation.

Still unknown is why the southern winds have been getting stronger. Some scientists have theorized that it could be related to global warming, or to the ozone depletion in the Southern Hemisphere, or just to natural cycles of variability.

Differences between the two poles could explain why they are not behaving in the same way. Surface air warming in the Arctic appears to be greater and more uniform, Zhang said. Another difference is that northern water is in a fairly protected basin, while the Antarctic sea ice floats in open oceans where it expands freely in winter and melts almost completely in summer.

The sea ice uptick in Antarctica is small compared with the amount being lost in the Arctic, meaning there is an overall decrease in sea ice worldwide.

Many of the global climate models have been unable to explain the observed increase in Antarctic sea ice. Researchers have been working to improve models to better reproduce the observed increase in sea ice there and predict what the future may bring.

Eventually, Zhang anticipates that if warmer temperatures come to dominate they will resolve the apparent contradiction.

"If the warming continues, at some point the trend will reverse," Zhang said.

European satellite confirms UW numbers: Arctic Ocean is on thin ice

UW News and Information, Hannah Hickey

The September 2012 record low in Arctic sea-ice extent was big news, but a missing piece of the puzzle was lurking below the ocean's surface. What volume of ice floats on Arctic waters? And how does that compare to previous summers?

13 Feb 2013

On thin ice: As Arctic Ocean warms, a scramble to understand its weather

Christian Science Monitor, Pete Spotts

Increasing summer ice melt in the Arctic Ocean could shift global weather patterns and make polar waters more navigable. But scientists say forecasting Arctic ice and weather remains a massive challenge.

12 Feb 2013

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

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

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

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