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Jinlun Zhang

Senior Principal Oceanographer

Email

zhang@apl.washington.edu

Phone

206-543-5569

Biosketch

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

Education

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

Projects

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

Publications

2000-present and while at APL-UW

Effects of early life history traits and warming on Arctic cod prewinter length and recruitment

David, C.L., J.A. Hutchings, Z. Feng, C. Bouchard, I.D. Alabia, H. Hop, J. Zhang, and R. Ji, "Effects of early life history traits and warming on Arctic cod prewinter length and recruitment," Elem. Sci. Anth., 13, doi:10.1525/elementa.2024.00015, 2025.

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10 Apr 2025

The Arctic cod (Boreogadus saida) is a key species in Arctic marine ecosystems, adapted to extreme seasonality and cold environments. The overwintering survival and recruitment of age-0 Arctic cod heavily depend on achieving a sizable prewinter length (PWL) in their first year. Over the growth period, PWL is influenced by early life history traits, such as hatch date and size-at-hatch, and by environmental conditions, such as temperature and food availability. However, our knowledge of these interacting aspects of Arctic cod ecology is extremely limited. Here we coupled an individual-based transport and bioenergetic model with a sea ice-ocean model and simulated larval dispersal and growth under current environmental conditions. In addition, we tested two alternative scenarios of higher temperatures, with +2°C, and lower daily ration by 25% over the growth period. Our modeled PWL aligned well with field data on age-0 Arctic cod lengths by the end of summer. Largest PWLs resulted from winter spawns and were associated with more days with ice cover and shorter embryonic development. Under the high-temperature scenario, average PWL increased in Baffin Bay, Chukchi Sea, and Laptev Sea but declined in Svalbard, suggesting that a portion of age-0 Arctic cod are currently at their thermal tolerance limit. The recruitment success into the juvenile stage, defined as reaching a juvenile threshold length by the end of summer, was maximized in all winter spawns under the high-temperature scenario but decreased to zero in nearly all April spawns across all regions. Under the low-food scenario, reduced prey availability halved the recruitment success in all regions, indicating potentially severe consequences for future Arctic cod growth and survival. Our study illustrates how much changes in sea ice, temperature, and food availability influence the early development of Arctic cod and could impact their recruitment, highlighting the species’ increasingly uncertain future amid rapid environmental changes in the Arctic.

Predicting September Arctic sea ice: A multi-modal seasonal skill comparison

Bushuk, M., and 60 others including A. Schweiger, M. Steele, and J. Zhang, "Predicting September Arctic sea ice: A multi-modal seasonal skill comparison," Bull. Am. Meteorol. Soc., 105, doi:10.1175/BAMS-D-23-0163.1, 2024.

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1 Jul 2024

This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.

Assessment of Arctic sea ice and surface climate conditions in nine CMIP6 climate models

Henke, M., F. Cassalho, T. Miesse, C.M. Ferreira, J. Zhang, and T.M. Ravens, "Assessment of Arctic sea ice and surface climate conditions in nine CMIP6 climate models," Arctic Antarctic Alpine Res., 55, doi:10.1080/15230430.2023.2271592, 2023.

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31 Dec 2023

The observed retreat and anticipated further decline in Arctic sea ice holds strong climate, environmental, and societal implications. In predicting climate evolution, ensembles of coupled climate models have demonstrated appreciable accuracy in simulating sea-ice area trends throughout the historical period, yet individual climate models still show significant differences in accurately representing the sea-ice thickness distribution. To better understand individual model performance in sea-ice simulation, nine climate models were evaluated in comparison with Arctic satellite and reanalysis-derived sea-ice thickness data, sea-ice area records, and atmospheric reanalysis data of surface wind and air temperature. This assessment found that the simulated spatial distribution of historical sea-ice thickness varies greatly between models and that several key limitations persist among models. Primarily, most models do not capture the thickest regimes of multiyear ice present in the Wandel and Lincoln seas; those that do often possess erroneous positive bias in other regions such as the Laptev Sea or along the Eurasian Arctic Shelf. This analysis provides enhanced understanding of individual model historical simulation performance, which is critical in informing the selection of coupled climate model projections for dependent future modeling efforts.

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In The News

Fact check: NASA did not deny warming or say polar ice has increased since 1979

USA Today, Kate Petersen

NASA researchers have documented the loss of trillions of tons of ice from Earth's poles due to human-driven climate change. Citing published reports from the Polar Science Center and other sources, popular social media memes claiming an increase in polar ice since 1979 are swatted down.

21 Jan 2022

Arctic's 'last ice area' may be less resistant to global warming

The New York Times, Henry Fountain

The region, which could provide a last refuge for polar bears and other Arctic wildlife that depends on ice, is not as stable as previously thought, according to a new study.

1 Jul 2021

Arctic's 'last ice area' shows earlier-than-expected melt

Associated Press, Seth Borenstein

Part of the Arctic is nicknamed the 'Last Ice Area,' because floating sea ice there is usually so thick that it’s likely to withstand global warming for decades. So, scientists were shocked last summer when there was suddenly enough open water for a ship to pass through.

1 Jul 2021

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