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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

Improving arctic sea ice seasonal outlook by ensemble prediction using an ice–ocean model

Yang, Q., L. Mu, X. Wu, J. Liu, F. Zheng, J. Zhang, and C. Li, "Improving arctic sea ice seasonal outlook by ensemble prediction using an ice–ocean model," Atmos. Res., 227, 14-23, doi:10.1016/j.atmosres.2019.04.021, 2019.

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1 Oct 2019

An ensemble based Sea Ice Seasonal Prediction System (SISPS) is configured towards operationally predicting the Arctic summer sea ice conditions. SISPS runs as a pan-Arctic sea ice–ocean coupled model based on Massachusetts Institute of Technology general circulation model (MITgcm). A 4-month hindcast is carried out by SISPS starting from May 25, 2016. The sea ice–ocean initial fields for each ensemble member are from corresponding restart files from an ensemble data assimilation system that assimilates near-real-time Special Sensor Microwave Imager Sounder (SSMIS) sea ice concentration, Soil Moisture and Ocean Salinity (SMOS) and CryoSat-2 ice thickness. An ensemble of 11 time lagged operational atmospheric forcing from the National Center for Environmental Prediction (NCEP) climate forecast system model version 2 (CFSv2) is used to drive the ice–ocean model. Comparing with the satellite based sea ice observations and reanalysis data, the SISPS prediction shows good agreement in the evolution of sea ice extent and thickness, and performs much better than the CFSv2 operational sea ice prediction. This can be largely attributed to the initial conditions that we used in assimilating the SMOS and CryoSat-2 sea ice thickness data, thereafter reduces the initial model bias in the basin wide sea ice thickness, while in CFSv2 there is no sea ice thickness assimilation. Furthermore, comparisons with sea ice predictions driven by deterministic forcings demonstrate the importance of employing an ensemble approach to capture the large prediction uncertainty in Arctic summer. The sensitivity experiments also show that the sea ice thickness initialization that has a long-term memory plays a more important role than sea ice concentration and sea ice extent initialization on seasonal sea ice prediction. This study shows a good potential to implement Arctic sea ice seasonal prediction using the current configuration of ensemble system.

Arctic sea ice volume variability over 1901–2010: A model-based reconstruction

Schweiger, A.J., K.R. Wood, and J. Zhang, "Arctic sea ice volume variability over 1901–2010: A model-based reconstruction," J. Clim., 32, 4731-4753, doi:10.1175/JCLI-D-19-0008.1, 2019.

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1 Aug 2019

PIOMAS-20C, an Arctic sea ice reconstruction for 1901–2010, is produced by forcing the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) with ERA-20C atmospheric data. ERA-20C performance over Arctic sea ice is assessed by comparisons with measurements and data from other reanalyses. ERA-20C performs similarly with respect to the annual cycle of downwelling radiation, air temperature, and wind speed compared to reanalyses with more extensive data assimilation such as ERA-Interim and MERRA. PIOMAS-20C sea ice thickness and volume are then compared with in situ and aircraft remote sensing observations for the period of ~1950–2010. Error statistics are similar to those for PIOMAS. We compare the magnitude and patterns of sea ice variability between the first half of the twentieth century (1901–40) and the more recent period (1980–2010), both marked by sea ice decline in the Arctic. The first period contains the so-called early-twentieth-century warming (ETCW; ~1920–40) during which the Atlantic sector saw a significant decline in sea ice volume, but the Pacific sector did not. The sea ice decline over the 1979–2010 period is pan-Arctic and 6 times larger than the net decline during the 1901–40 period. Sea ice volume trends reconstructed solely from surface temperature anomalies are smaller than PIOMAS-20C, suggesting that mechanisms other than warming, such as changes in ice motion and deformation, played a significant role in determining sea ice volume trends during both periods.

Estimation of turbulent heat flux over leads using satellite thermal images

Qu, M., X. Pang, X. Zhao, J. Zhang, Q. Ji, and P. Fan, "Estimation of turbulent heat flux over leads using satellite thermal images," Cryosphere, 13, 1565-1582, doi:10.5194/tc-13-1565-2019, 2019.

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4 Jun 2019

Sea ice leads are an important feature in pack ice in the Arctic. Even covered by thin ice, leads can still serve as prime windows for heat exchange between the atmosphere and the ocean, especially in the winter. Lead geometry and distribution in the Arctic have been studied using optical and microwave remote sensing data, but turbulent heat flux over leads has only been measured on-site during a few special expeditions. In this study, we derive turbulent heat flux through leads at different scales using a combination of surface temperature and lead distribution from remote sensing images and meteorological parameters from a reanalysis dataset. First, ice surface temperature (IST) was calculated from Landsat-8 Thermal Infrared Sensor (TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images using a split-window algorithm; then, lead pixels were segmented from colder ice. Heat flux over leads was estimated using two empirical models: bulk aerodynamic formulae and a fetch-limited model with lead width from Landsat-8. Results show that even though the lead area from MODIS is a little larger, the length of leads is underestimated by 72.9% in MODIS data compared to TIRS data due to the inability to resolve small leads. Heat flux estimated from Landsat-8 TIRS data using bulk formulae is 56.70% larger than that from MODIS data. When the fetch-limited model was applied, turbulent heat flux calculated from TIRS data is 32.34% higher than that from bulk formulae. In both cases, small leads accounted for more than a quarter of total heat flux over leads, mainly due to the large area, though the heat flux estimated using the fetch-limited model is 41.39% larger. A greater contribution from small leads can be expected with larger air–ocean temperature differences and stronger winds.

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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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