Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC

Ono, Jun; Tatebe, Hiroaki; Komuro, Yuzo; Nodzu, Masato I.; Ishii, Masayoshi

doi:10.5194/tc-12-675-2018

Cited by 15 publications

(16 citation statements)

References 45 publications

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“…Note that loading due to sea ice and accumulated snow on sea ice are removed from the model sea level height and that the global mean value is eliminated. sistent with previous modeling studies that reported the importance of the well-resolved stratosphere for better simulation of stratospheric variability (e.g., Cagnazzo and Manzini, 2009;Charlton-Perez et al, 2013;Osprey et al, 2013). In December-January, however, MIROC6 still underestimates the frequency of SSW events, which is a common bias in other high-top climate models (e.g., Inatsu et al, 2007;Charlton-Perez et al, 2013;Osprey et al, 2013).…”

Section: Stratospheric Circulationsupporting

confidence: 76%

“…Seasonal to decadal climate predictions are also of significant concern. By initializing prognostic variables in our climate models using observation-based data (Tatebe et al, 2012), significant prediction skills in several specific phenomena, such as the El Niño-Southern Oscillation (ENSO) and the Arctic sea ice extent on seasonal timescales, the Pacific Decadal Oscillation (PDO; Mantua et al, 1997), the Atlantic Multi-decadal Oscillation (AMO; Schlesinger and Ramankutty, 1994), and the tropical transbasin interactions between the Pacific and the Atlantic on decadal timescales, are detected (e.g., Mochizuki et al, 2010;Chikamoto et al, 2015;Imada et al, 2015;Ono et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6

et al. 2019

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Abstract. The sixth version of the Model for Interdisciplinary Research on Climate (MIROC), called MIROC6, was cooperatively developed by a Japanese modeling community. In the present paper, simulated mean climate, internal climate variability, and climate sensitivity in MIROC6 are evaluated and briefly summarized in comparison with the previous version of our climate model (MIROC5) and observations. The results show that the overall reproducibility of mean climate and internal climate variability in MIROC6 is better than that in MIROC5. The tropical climate systems (e.g., summertime precipitation in the western Pacific and the eastward-propagating Madden–Julian oscillation) and the midlatitude atmospheric circulation (e.g., the westerlies, the polar night jet, and troposphere–stratosphere interactions) are significantly improved in MIROC6. These improvements can be attributed to the newly implemented parameterization for shallow convective processes and to the inclusion of the stratosphere. While there are significant differences in climates and variabilities between the two models, the effective climate sensitivity of 2.6 K remains the same because the differences in radiative forcing and climate feedback tend to offset each other. With an aim towards contributing to the sixth phase of the Coupled Model Intercomparison Project, designated simulations tackling a wide range of climate science issues, as well as seasonal to decadal climate predictions and future climate projections, are currently ongoing using MIROC6.

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Section: Stratospheric Circulationsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6

et al. 2019

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“…June nual variability, the temperature and salinity of the Atlantic inflows to the Arctic Ocean show also pronounced decadal to multidecadal variability (Zhang, 2015). This aligns with the concept of different previous studies, which suggest that the decreasing trend in the Arctic sea ice is partially driven by AMO (Park and Latif, 2008;Lindsay et al, 2005;Ding et al, 2014;Yu et al, 2017). Moreover, starting at the beginning of 1990s, the AMO switched to a positive phase, at the same time when the Arctic sea ice extent started its abrupt decline.…”

Section: May Datasupporting

confidence: 90%

“…1 and 5), thus contribut-ing to the skill of our forecast. The OHC anomalies form the North Atlantic flow into the Arctic basin, via advection, and affect the sea ice distribution (Polyakov et al, 2017;Ono et al, 2018). In a recent study, Yu et al (2017) have shown that the leading mode of variability of global sea ice concentration is positively correlated with the AMO and negatively correlated with the PDO.…”

Section: Discussionmentioning

confidence: 99%

September Arctic sea ice minimum prediction – a skillful new statistical approach

et al. 2019

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Abstract. Sea ice in both polar regions is an important indicator of the expression of global climate change and its polar amplification. Consequently, broad interest exists on sea ice coverage, variability and long-term change. However, its predictability is complex and it depends strongly on different atmospheric and oceanic parameters. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we applied a robust statistical model based on different oceanic and atmospheric parameters to calculate an estimate of the September sea ice extent (SSIE) on a monthly timescale. Although previous statistical attempts of monthly/seasonal SSIE forecasts show a relatively reduced skill, when the trend is removed, we show here that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' oceanic and atmospheric conditions. Our statistical model skillfully captures the interannual variability of the SSIE and could provide a valuable tool for identifying relevant regions and oceanic and atmospheric parameters that are important for the sea ice development in the Arctic and for detecting sensitive/critical regions in global coupled climate models with a focus on sea ice formation.

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“…Recent research includesPetty et al (2017),Ono et al (2018),Serreze and Meier (2019),and Ionita et al (2019). Ongoing prediction research forums include the Sea Ice Prediction Network at https://www.…”

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confidence: 99%

Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections

Diebold

Rudebusch

2020

ERWP

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The downward trend in the amount of Arctic sea ice has a wide range of environmental and economic consequences including important effects on the pace and intensity of global climate change. Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century. The best fitting statistical model indicates that overall sea ice coverage is declining at an increasing rate. By contrast, average projections from the CMIP5 global climate models foresee a gradual slowing of Arctic sea ice loss even in scenarios with high carbon emissions. Our long-range statistical projections also deliver probability assessments of the timing of an ice-free Arctic. These results indicate almost a 60 percent chance of an effectively ice-free Arctic Ocean sometime during the 2030s-much earlier than the average projection from global climate models.

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Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC

Abstract: Abstract. To assess the skill of seasonal to inter-annual predictions of the detrended sea ice extent in the Arctic Ocean (SIE AO ) and to clarify the underlying physical processes,

Cited by 15 publications

References 45 publications

Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6

Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6

September Arctic sea ice minimum prediction – a skillful new statistical approach

Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections

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