Evaluation of Arctic Sea-ice Cover and Thickness Simulated by MITgcm

Zheng, Fei; Sun, Yue; Yang, Qinghua; Mu, Longjiang

doi:10.1007/s00376-020-9223-6

Cited by 16 publications

(8 citation statements)

References 50 publications

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“…The model performance is evaluated against the observed sea ice extents from the National Snow and Ice Data Center (NSIDC). We find that the model can capture the interannual and seasonal variability of sea ice extent (Figures S1–S3), indicating the reliability of the sea ice simulation in the model . The sea ice model successfully reproduces both interannual and seasonal variations of the sea ice extent .…”

Section: Methodsmentioning

confidence: 66%

“…We find that the model can capture the interannual and seasonal variability of sea ice extent (Figures S1−S3), indicating the reliability of the sea ice simulation in the model. 43 The sea ice model successfully reproduces both interannual and seasonal variations of the sea ice extent. 43 Moreover, the model generates ice concentration and thickness distributions that closely resemble observed values.…”

Section: Sea Ice Modelmentioning

confidence: 94%

“…43 The sea ice model successfully reproduces both interannual and seasonal variations of the sea ice extent. 43 Moreover, the model generates ice concentration and thickness distributions that closely resemble observed values. Notably, the simulations demonstrate a significant correlation (p < 0.01) with observations in both March and September, as indicated by a correlation coefficient ranging from 0.83 to 0.95.…”

Section: Sea Ice Modelmentioning

confidence: 94%

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Modeling the Mercury Cycle in the Sea Ice Environment: A Buffer between the Polar Atmosphere and Ocean

Huang,

Wang,

Yuan

et al. 2023

Environ. Sci. Technol.

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Sea ice (including overlying snow) is a dynamic interface between the atmosphere and the ocean, influencing the mercury (Hg) cycling in polar oceans. However, a large-scale and process-based model for the Hg cycle in the sea ice environment is lacking, hampering our understanding of regional Hg budget and critical processes. Here, we develop a comprehensive model for the Hg cycle at the ocean–sea ice–atmosphere interface with constraints from observational polar cryospheric data. We find that seasonal patterns of average total Hg (THg) in snow are governed by snow thermodynamics and deposition, peaking in springtime (Arctic: 5.9 ng/L; Antarctic: 5.3 ng/L) and minimizing during ice formation (Arctic: 1.0 ng/L, Antarctic: 0.5 ng/L). Arctic and Antarctic sea ice exhibited THg concentration peaks in summer (0.25 ng/L) and spring (0.28 ng/L), respectively, governed by different snow Hg transmission pathways. Antarctic snow-ice formation facilitates Hg transfer to sea ice during spring, while in the Arctic, snow Hg is primarily moved through snowmelt. Overall, first-year sea ice acts as a buffer, receiving atmospheric Hg during ice growth and releasing it to the ocean in summer, influencing polar atmospheric and seawater Hg concentrations. Our model can assess climate change effects on polar Hg cycles and evaluate the Minamata Convention’s effectiveness for Arctic populations.

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Section: Methodsmentioning

confidence: 66%

Section: Sea Ice Modelmentioning

confidence: 94%

Section: Sea Ice Modelmentioning

confidence: 94%

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Modeling the Mercury Cycle in the Sea Ice Environment: A Buffer between the Polar Atmosphere and Ocean

Huang,

Wang,

Yuan

et al. 2023

Environ. Sci. Technol.

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“…While viscous-plastic rheologies with an elliptical yield curve and normal flow rule have been employed for many years in GCMs including MITgcm [e.g. 39,40], it should be noted that they produce unphysical fracture angles. This is why current development efforts aim at using rheologies which result in better agreements of small-scale sea ice features with observations [e.g.…”

Section: Sea Ice Dynamics Componentmentioning

confidence: 99%

The role of dynamic sea ice in a simplified general circulation model used for palaeoclimate studies

Adam

Andres

Rehfeld

2021

Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference

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Observational records provide a strong basis for constraining sea ice models within a narrow range of climate conditions. Given current trends away from these conditions, models need to be tested over a wider range of climate states. The past provides many such examples based on paleoclimate data, including abrupt tipping points. However, the millennial-duration of typical paleoclimatesimulations necessitates balancing the inclusion and sophistication of model processes against computational cost. We investigate the impact on climate mean states and variability of introducing sea ice dynamics into the simplified general circulation model PlaSim-LSG [1-3].Considering the technical constraints of PlaSim-LSG, we choose to integrate a modied version of the MITgcm's dynamical sea ice component [4, 5] into the model setup. We adapt the component to the structure and parallelization scheme of PlaSim-LSG, validate the physical consistency and stability of the component, and evaluate the impact of sea ice dynamics onto the simulated climate from decadal to millennial time scales. Specifically, we compare climatologies, variability and scaling of the extended model to control simulations of the preexisting setups, and quantify how additional sea ice dynamics affect well-known climatic biases of the PlaSim model family.With our extended PlaSim-LSG model we aim at capturing the key small-scale sea ice processes that are important to past climate tipping points while maintaining model efficiency for millennial simulations. Sea ice is a key component of coupled atmosphere-ocean processes that led to large-amplitude, abrupt climate variability in the past [6-8]. Therefore, the extended model can be usedto investigate the role of sea ice for such oscillations. This facilitates the understanding of processes that lead to current mismatches between palaeoclimate data and simulations, and that impact thesimulated surface climate variability [9].References[1] K. Fraedrich et al. Meteorol. Z. 14.3 (2005), 299-304. doi: 10.1127/0941-2948/2005/0043.[2] F. Lunkeit et al. Tech. rep. 2011. url: https://www.mi.uni-hamburg.de/en/arbeitsgruppen/theoretische-meteorologie/modelle/sources/psreferencemanual-1.pdf.[3] H. J. Andres et al. Clim. Past 15.4 (2019), 1621-1646. doi: 10.5194/cp-15-1621-2019.[4] J. Zhang et al. J. Geophys. Res. 102.4 (1997), 412-415.[5] M. Losch et al. Ocean Model. 33.1-2 (2010), 129-144. doi: 10.1016/j.ocemod.2009.12.008.[6] T. M. Dokken et al. Paleoceanography 28.3 (2013), 491-502. doi: 10.1002/palo.20042.[7] G. Vettoretti et al. Geophys. Res. Lett. 43.10 (2016), 5336-5344. doi: 10.1002/2016GL068891.[8] C. Li et al. Quat. Sci. Rev. 203 (2019), 1-20. doi: 10.1016/j.quascirev.2018.10.031.[9] N. Weitzel et al. presented at Fall Meeting AGU. 2020. url: https://agu.confex.com/agu/fm20/webprogram/Paper739241.html.

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“…The Arctic sea ice is a crucial component of the cryosphere, and it has decreased sharply since 1979 due to global warming (Blackport & Screen, 2019; Chen et al., 2023; Chen & Wu, 2018; Chen, Wu, et al., 2020; Ding et al., 2021; Screen & Simmonds, 2010). Substantial studies signified that decreased autumn and winter Arctic sea ice can remarkably regulate climate variability over the Eurasian continent through decreasing meridional temperature gradient (Labe et al., 2020; Peings & Magnusdottir, 2013), stimulating meridional Rossby wave trains propagating from the polar region to Eurasia (Li & Wang, 2013; Li & Wu, 2012; Nakamura et al., 2015), regulating the North Atlantic Oscillation (NAO) and associated Rossby wave trains (Chen et al., 2021), and modulating Arctic Oscillation (AO) induced by the stratospheric pathway (Chen & Wu, 2018; Yang et al., 2023). The linkage between the Arctic and the TP has been highly concerned by the international scientific community, because the glaciers, ice, and snow in these two regions are more abundant than anywhere else in the Northern Hemisphere (Chen, Duan, & Li, 2020; Duan et al., 2022; Gao et al., 2015; Hu et al., 2023a, 2023b; Li et al., 2020; Wu et al., 2023; Xu et al., 2019; You et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Potential Impacts of Winter Arctic Sea Ice on Subsequent Spring Thermal Condition Over the Tibetan Plateau

Yu,

Yang,

Liu

et al. 2024

JGR Atmospheres

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Spring thermal forcing over the Tibetan Plateau (TP) is largely determined by surface sensible heating (SSH), which is an important fuel for atmospheric circulations over subtropics and even the globe. However, insufficient efforts were devoted on exploring the potential influencing factors of spring TP's SSH. In the current study, observational analysis and model results confirm that the sea ice concentration in the north of Greenland can act as a precursor. Specifically, the winter shrinkage of the sea ice concentration can stimulate a meridional Rossby wave train, with a remarkable positive‐negative‐positive pattern of geopotential height anomalies controlling the Arctic, western Eurasia, and the southeastern Mediterranean, respectively. This teleconnection is equivalent barotropic in the whole troposphere, which shifts southeastward from winter to spring, with a significant anomalous anticyclone and increased geopotential height dominating the western TP in spring. The surface northeasterly anomalies in the southeastern flank of the anticyclone decrease the climatological westerlies and weaken the SSH over the central‐southern TP. Existing literature mainly focused on the Arctic‐TP relationship in autumn and winter. Our finding can not only provide a new insight onto the variation of spring thermal condition over the TP, but also deepen our knowledge of cross‐seasonal relationships between the Arctic and the TP with similar snow‐ice albedo feedback.

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Evaluation of Arctic Sea-ice Cover and Thickness Simulated by MITgcm

Cited by 16 publications

References 50 publications

Modeling the Mercury Cycle in the Sea Ice Environment: A Buffer between the Polar Atmosphere and Ocean

Modeling the Mercury Cycle in the Sea Ice Environment: A Buffer between the Polar Atmosphere and Ocean

The role of dynamic sea ice in a simplified general circulation model used for palaeoclimate studies

Potential Impacts of Winter Arctic Sea Ice on Subsequent Spring Thermal Condition Over the Tibetan Plateau

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