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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

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., EOR, doi:10.1175/BAMS-D-23-0163.1, 2024.

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22 Apr 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.

Closure of Earth's global seasonal cycle of energy storage

Johnson, G.C., F.W. Landerer, N.G. Loeb, J.M. Lyman, M. Mayer, A.L.S. Swann, and J. Zhang, "Closure of Earth's global seasonal cycle of energy storage," Surv. Geophys., EOR, doi:10.1007/s10712-023-09797-6, 2023.

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18 Jul 2023

The global seasonal cycle of energy in Earth's climate system is quantified using observations and reanalyses. After removing long-term trends, net energy entering and exiting the climate system at the top of the atmosphere (TOA) should agree with the sum of energy entering and exiting the ocean, atmosphere, land, and ice over the course of an average year. Achieving such a balanced budget with observations has been challenging. Disagreements have been attributed previously to sparse observations in the high-latitude oceans. However, limiting the local vertical integration of new global ocean heat content estimates to the depth to which seasonal heat energy is stored, rather than integrating to 2000 m everywhere as done previously, allows closure of the global seasonal energy budget within statistical uncertainties. The seasonal cycle of energy storage is largest in the ocean, peaking in April because ocean area is largest in the Southern Hemisphere and the ocean's thermal inertia causes a lag with respect to the austral summer solstice. Seasonal cycles in energy storage in the atmosphere and land are smaller, but peak in July and September, respectively, because there is more land in the Northern Hemisphere, and the land has more thermal inertia than the atmosphere. Global seasonal energy storage by ice is small, so the atmosphere and land partially offset ocean energy storage in the global integral, with their sum matching time-integrated net global TOA energy fluxes over the seasonal cycle within uncertainties, and both peaking in April.

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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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