Dynamical Core in Atmospheric Model Does Matter in the Simulation of Arctic Climate

Jun, Sang‐Yoon; Choi, Seok Reyol; Kim, Baek-Min

doi:10.1002/2018gl077478

Cited by 11 publications

(13 citation statements)

References 74 publications

(57 reference statements)

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“…Some modeling studies have provided supporting evidence for this connection and the importance of Arctic sea ice loss 6–8 , whereas others have argued that Arctic sea ice loss has only small impacts on mid-latitude climate 9–11 . Therefore, large uncertainty exists among simulation models/studies 12,13 , while the underestimation and/or model dependency of the sea ice impacts are suggested as a possible cause 14,15 .…”

Section: Introductionmentioning

confidence: 99%

Memory effects of Eurasian land processes cause enhanced cooling in response to sea ice loss

et al. 2019

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Amplified Arctic warming and its relevance to mid-latitude cooling in winter have been intensively studied. Observational evidence has shown strong connections between decreasing sea ice and cooling over the Siberian/East Asian regions. However, the robustness of such connections remains a matter of discussion because modeling studies have shown divergent and controversial results. Here, we report a set of general circulation model experiments specifically designed to extract memory effects of land processes that can amplify sea ice–climate impacts. The results show that sea ice–induced cooling anomalies over the Eurasian continent are memorized in the snow amount and soil temperature fields, and they reemerge in the following winters to enhance negative Arctic Oscillation-like anomalies. The contribution from this memory effect is similar in magnitude to the direct effect of sea ice loss. The results emphasize the essential role of land processes in understanding and evaluating the Arctic–mid-latitude climate linkage.

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Section: Introductionmentioning

confidence: 99%

Memory effects of Eurasian land processes cause enhanced cooling in response to sea ice loss

et al. 2019

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“…Another possible cause is that all the tunable parameters in physics schemes are set up for the optimal behavior of the KIM. Notably, different dynamical cores can display diverse simulated responses via different diffusive behaviors, especially at high latitudes (Jun et al., 2018), even though the general circulations in atmospheric models are mainly derived from physical parameterizations representing diabatic and sub‐grid scale processes.…”

Section: Resultsmentioning

confidence: 99%

Seasonal Performance of a Nonhydrostatic Global Atmospheric Model on a Cubed‐Sphere Grid

Kim

Koo

Yoo

et al. 2021

Earth and Space Science

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A new operational weather forecast model, KIM, is evaluated using a seasonal simulation framework focusing on large-scale characteristics. Systematic model biases are comparable between the KIM and conventional spectral model under conditions employing the same physics package. Seasonal precipitation and turbulent fluxes over oceans need to be improved by a better representation of atmosphere-ocean interaction.

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“…The first uses the spectral element (SE) 5 dynamical core on a cubed-sphere grid system with a horizontal resolution of 16 by 16 elements on one face and four collocation points on one element edge (e.g., named 'ne16np4', approximately 2º at the equator). The other uses the finite volume (FV) dynamical core on a latitude-longitude grid system with 91-latitudinal and 144-longitudinal grid points (e.g., named 'fv19') (Jun et al 2018). The FV dynamical core is the present defaulting dynamical core in the Community Earth System Model (CESM) and is being used for the CESM's contributions to the Intergovernmental Panel on Climate Change 10 (IPCC) 5th assessment report (Collins et al, 2004;Denis et al, 2012).…”

Section: Dynamical Coresmentioning

confidence: 99%

“…Several ongoing debates on the linkage issue, such as 'warm Arctic-cold continents (WACC)', is a good example of the considerable uncertainty that persists among models relevant to the Arctic climate system (Honda et al 2009;Liu et al 2012;Sun et al 2015;Chen et al 2018) . 25 Among the issues of uncertainty in the Arctic climate system, recent studies suggest the choice of a dynamical core to improve the forecasting quality in the Arctic regions (Jun et al 2018). The dynamical core resolves the fluid motion governing atmospheric dynamics on numerical equations based on the features of a grid structure (Harris and Lin 2013).…”

Section: Introductionmentioning

confidence: 99%

“…For example, the use of the cubed-sphere grid 30 in the dynamical core considerably enhances the computational efficiency, resolving pole singularity issues, compared to the latitude-longitude grid formation. Jun et al (2018) raised the issue of assessing the influence of existing dynamical cores underlying the grid formation of the Arctic region in a global simulation compared to the various global climate simulations. The use of different dynamical cores significantly affects the simulations of Arctic winter climate and linked teleconnection in the mid-latitudes.…”

Section: Introductionmentioning

confidence: 99%

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Altered sub-seasonal predictability of Community Atmosphere Model 5 (CAM5) in CESM 1.2.1 by the choices of dynamical core

Kim

Jun

et al. 2020

Preprint

Self Cite

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Abstract. This study investigates the prediction skill of sub-seasonal prediction models that vary based on the choice of two dynamical cores: the finite volume (FV) dynamical core on a latitude-longitude grid system and the spectral element (SE) dynamical core on a cubed-sphere grid system. Recent research showed that the SE dynamical core on a uniform grid system increases parallel scalability and removes the need for polar filters for mitigating uncertainty in climate prediction, particularly for the Arctic region. However, it still remains questionable whether the choice of dynamical cores can actually yield significant changes in prediction skill. To tackle this issue, we implemented a sub-seasonal prediction model based on the Community Atmospheric Model version 5 by incorporating the above two dynamical cores with virtually the same physics schemes. Sub-seasonal prediction skills of the SE dynamical core and FV dynamical core are verified with ERA-Interim reanalysis during the early winter (November–December) and the late winter (January–February) from 2001/2002 to 2017/2018. The prediction skills of two different dynamical cores were significantly different regardless of the similar physics scheme. In the ocean, the predictability of the SE dynamical core is similar to that of the FV dynamical core, mostly because our simulation configuration imposes the same boundary and initial conditions at the surface. Notable differences in the one-month predictability between the two cores are observed for the wintertime Arctic and mid-latitudes, particularly over North America and Eurasia continents. With a one-month lead, the SE dynamical core exhibited higher predictability over North America in late winter (r ≈ 0.45 in SE, r ≈ 0.10 in FV) whereas the FV dynamical core showed relatively higher predictability in East Asia and Eurasia in early winter (r ≈ 0.15 in SE, r ≈ 0.43 in FV). Therefore, we conclude that caution is needed when selecting the dynamical cores of sub-seasonal prediction models. Partially, these differences can be ascribed to the different manifestations of Arctic-mid-latitude linkage in the two dynamical cores; the SE dynamical core captures warmer Arctic and colder mid-latitudes relatively better than the FV dynamical core.

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Dynamical Core in Atmospheric Model Does Matter in the Simulation of Arctic Climate

Cited by 11 publications

References 74 publications

Memory effects of Eurasian land processes cause enhanced cooling in response to sea ice loss

Memory effects of Eurasian land processes cause enhanced cooling in response to sea ice loss

Seasonal Performance of a Nonhydrostatic Global Atmospheric Model on a Cubed‐Sphere Grid

Altered sub-seasonal predictability of Community Atmosphere Model 5 (CAM5) in CESM 1.2.1 by the choices of dynamical core

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