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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


2000-present and while at APL-UW

Using modelled prey to predict the distribution of a highly mobile marine mammal

Pendleton, D.E., E.E. Holmes, J. Redfern, and J. Zhang, "Using modelled prey to predict the distribution of a highly mobile marine mammal," Divers. Distrib., 26, 1612-1626, doi:10.1111/ddi.13149, 2020.

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1 Nov 2020

Species distribution models (SDMs) are a widely used tool to estimate and map habitat suitability for wildlife populations. Most studies that model marine mammal density or distributions use oceanographic proxies for marine mammal prey. However, proxies could be a problem for forecasting because the relationships between the proxies and prey may change in a changing climate. We examined the use of model‐derived prey estimates in SDMs using an iconic species, the western Arctic bowhead whale (Balaena mysticetus).

We used Biology Ice Ocean Modeling and Assimilation System (BIOMAS) to simulate ocean conditions important to western Arctic bowhead whales, including important prey species. Using both static and dynamic predictors, we applied Maxent and boosted regression tree (BRT) SDMs to predict bowhead whale habitat suitability on an 8‐day timescale. We compared results from models that used bathymetry with those that used only BIOMAS simulated variables.

The best model included bathymetry and BIOMAS variables. Inclusion of dynamic variables in SDMs produced predictions that reflected temporal dynamics evident from the survey data. Bathymetry was the most influential variable in models that included that variable. Zooplankton was the most important variable for models that did not include bathymetry. Models with bathymetry performed slightly better than models with only BIOMAS derived variables.

Bathymetry and modelled zooplankton were the most important predictor variables in bowhead whale distribution models. Our predictions reflected within‐year variability in bowhead whale habitat suitability. Using modelled prey availability, rather than oceanographic proxies, could be important for forecasting species distributions. Predictor variables used in our study were derived from a biophysical ocean model with demonstrated ability to project future ocean conditions. A natural next step is to use output from our biophysical ocean model to understand the effects of Arctic climate change.

Episodic extrema of surface stress energy input to the western Arctic Ocean contributed to step changes of freshwater content in the Beaufort Gyre

Zhong, W., J. Zhang, M. Steele, J. Zhao, and T. Wang, "Episodic extrema of surface stress energy input to the western Arctic Ocean contributed to step changes of freshwater content in the Beaufort Gyre," Geophys. Res. Lett., 46, 12,173-12,182, doi:10.1029/2019GL084652, 2019.

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16 Nov 2019

The recent dramatic decline of sea ice in the western Arctic Ocean changes the transfer of momentum across the ice‐ocean boundary layer. The surface stress energy input through the surface geostrophic current in the Beaufort Gyre (BG) based on a numerical model is 0.03 mW/m2 in 1992––2004 versus 0.23 mW/m2 in 2005–2017. This energy input is primarily concentrated over the southern Canada Basin and the Chukchi Sea. It is 1.38 x 1016 J in observations versus 4.90 × 1016 J in the model in the BG during 2003–2014. We find that some well‐known freshwater changes in the BG over 1992–2017 resulted from episodic extrema of energy input in 2007, 2012, and 2016. In particular, most of the energy input in 2007 was transformed into potential energy (57%) which resulted in a new state of freshwater budget. Our study suggests that as of 2016, the BG had not yet reached a saturated freshwater state. Our results provide a way to predict the future changes of BG freshwater content.

Spatiotemporal variability of sea ice in the Arctic's last ice area

Moore, G.W.K., A. Schweiger, J. Zhang, and M. Steele, "Spatiotemporal variability of sea ice in the Arctic's last ice area," Geophys. Res. Lett., 46, 11,237-11,243, doi:10.1029/2019GL083722, 2019.

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28 Oct 2019

The Arctic Ocean's oldest and thickest sea ice lies along the ~2,000 km arc from the western Canadian Arctic Archipelago to the northern coast of Greenland. Climate models suggest that this region will be the last to lose its perennial ice cover, thus providing an important refuge for ice‐dependent species. However, remarkably little is known about the climate or characteristics of the sea ice in this remote and inhospitable region. Here, we use the Pan‐Arctic Ice Ocean Modeling and Assimilation System model to show that the ice cover in the region is very dynamic, with changes occurring at a rate twice that of the Arctic Ocean as a whole. However, there are some differences in the changing nature of the ice cover between the eastern and western regions of the Last Ice Area, which include different timing of the annual minimum in ice thickness as well as distinct ice motion patterns associated with ice thickness extrema.

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In The News

February's big patch of open water off Greenland? Not global warming, says new analysis

UW News, Hannah Hickey

In February 2018, a vast expanse of open water appeared in the sea ice above Greenland, a region that normally has sea ice well into the spring. The big pool of open water in the middle of the ice, known as a polynya, was a scientific puzzle.

18 Dec 2018

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

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