scholarly journals Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea: Importance of dynamical and thermodynamic melting processes

2018 ◽  
Author(s):  
Takuya Nakanowatari ◽  
Jun Inoue ◽  
Kazutoshi Sato ◽  
Laurent Bertino ◽  
Jiping Xie ◽  
...  
Keyword(s):  
Sea Ice ◽  
Lead Time ◽  
Early Summer ◽  
Prediction Skill ◽  
Ice Thickness ◽  
Dynamical Process ◽  
Sea Ice Thickness ◽  
East Siberian Sea ◽  
Medium Range ◽  
Maritime Navigation

Abstract. Accelerated retreat of Arctic Ocean summertime sea ice has focused attention on the potential use of the Northern Sea Route (NSR), for which sea ice thickness (SIT) information is crucial for safe maritime navigation. This study evaluated the medium-range (lead time below 10 days) forecast skill of SIT distribution in the East Siberian Sea (ESS) in early summer (June–July) based on the TOPAZ4 ice ocean data assimilation system. Comparison of the operational model SIT data to all available observations (in situ and satellite) showed that the TOPAZ4 reanalysis reproduces the observed seasonal cycle and the rates of advance and melting of SIT in the ESS, with average bias of approximately ±20 cm. Pattern correlation analysis of the SIT forecast data over 4 years (2013–2016) reveals that the early summer SIT distribution is skillfully predicted for a lead time of up to 3 days, but that the prediction skill drops abruptly after the 4th day, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations. For longer lead times (> 4 days), the thermodynamic melting process takes over, which makes most of the remaining prediction skill. In July 2014, during which an ice-blocking incident occurred, relatively thick SIT (approximately 150 cm) was simulated over the ESS, which is consistent with the reduction of vessel speed. These results suggest that TOPAZ4 sea ice information has a great potential for practical applications in summertime maritime navigation via the NSR.

scholarly journals Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system

2018 ◽  
Vol 12 (6) ◽  
pp. 2005-2020 ◽  
Author(s):  
Takuya Nakanowatari ◽  
Jun Inoue ◽  
Kazutoshi Sato ◽  
Laurent Bertino ◽  
Jiping Xie ◽  
...  
Keyword(s):  
Sea Ice ◽  
Lead Time ◽  
Early Summer ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ocean Data Assimilation ◽  
East Siberian Sea ◽  
Medium Range ◽  
Maritime Navigation ◽  
Data Assimilation System

Abstract. Accelerated retreat of Arctic Ocean summertime sea ice has focused attention on the potential use of the Northern Sea Route (NSR), for which sea ice thickness (SIT) information is crucial for safe maritime navigation. This study evaluated the medium-range (lead time below 10 days) forecast of SIT distribution in the East Siberian Sea (ESS) in early summer (June–July) based on the TOPAZ4 ice–ocean data assimilation system. A comparison of the operational model SIT data with reliable SIT estimates (hindcast, satellite and in situ data) showed that the TOPAZ4 reanalysis qualitatively reproduces the tongue-like distribution of SIT in ESS in early summer and the seasonal variations. Pattern correlation analysis of the SIT forecast data over 3 years (2014–2016) reveals that the early summer SIT distribution is accurately predicted for a lead time of up to 3 days, but that the prediction accuracy drops abruptly after the fourth day, which is related to a dynamical process controlled by synoptic-scale atmospheric fluctuations. For longer lead times ( >  4 days), the thermodynamic melting process takes over, which contributes to most of the remaining prediction accuracy. In July 2014, during which an ice-blocking incident occurred, relatively thick SIT ( ∼  150 cm) was simulated over the ESS, which is consistent with the reduction in vessel speed. These results suggest that TOPAZ4 sea ice information has great potential for practical applications in summertime maritime navigation via the NSR.

scholarly journals Seasonal prediction and predictability of regional Antarctic sea ice

2021 ◽  
pp. 1-68
Author(s):  
Mitchell Bushuk ◽  
Michael Winton ◽  
F. Alexander Haumann ◽  
Thomas Delworth ◽  
Feiyu Lu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Heat Content ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
The Arctic ◽  
Ice Thickness ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice

AbstractCompared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales.

scholarly journals Interseasonal Connections between the Timing of the Stratospheric Final Warming and Arctic Sea Ice

2020 ◽  
Vol 33 (8) ◽  
pp. 3079-3092 ◽  
Author(s):  
Michael E. Kelleher ◽  
Blanca Ayarzagüena ◽  
James A. Screen
Keyword(s):  
Sea Ice ◽  
Polar Vortex ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Surface Circulation ◽  
East Siberian Sea ◽  
Circulation Patterns ◽  
Stratospheric Final Warming ◽  
Arctic Sea

AbstractConnections across seasons in atmospheric circulation and sea ice have long been sought to advance seasonal prediction. This study presents a link between the springtime stratosphere and Arctic sea ice in summer through autumn. The polar stratospheric vortex dominates the winter stratosphere before breaking down each spring, which is called the stratospheric final warming, as solar radiation returns to the pole. Interannual variability of this breakdown is dynamically driven, leading to different springtime tropospheric and surface circulation patterns. To examine the different impacts of delayed and early final warmings, a multimodel composite was generated from selected CMIP5 models. Additionally, regressions were performed on JRA-55 against an index of springtime polar vortex strength. In both the multimodel composites and reanalysis regressions, significant anomalies in sea ice thickness persist several months following an anomalous timing of the final warming. A later final warming or stronger springtime polar stratospheric vortex leads to negative sea ice thickness anomalies in the East Siberian Sea and positive anomalies in the Beaufort Sea in comparison with an earlier final warming or weaker polar vortex. The spring polar stratospheric vortex is related to spring polar surface circulation patterns. The winds associated with this pattern induce anomalous sea ice motion, moving ice from the East Siberian Sea toward the Beaufort Sea. Reduced sea ice in the East Siberian Sea is linked to anomalous warmth over this region in autumn. Our results suggest that the timing of the stratospheric final warming exerts an influence on the tropospheric circulation and sea ice through autumn, which has implications for seasonal climate prediction.

Sea ice and atmospheric potential predictability in coupled GCMs

2021 ◽  
Author(s):  
Daniela Flocco ◽  
Ed Hawkins ◽  
Leandro Ponsoni ◽  
François Massonnett ◽  
Daniel Feltham ◽  
...  
Keyword(s):  
Sea Ice ◽  
Initial Conditions ◽  
Complete Information ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Potential Predictability ◽  
Sea Ice Thickness

<p>Assimilation of sea ice concentration satellite products has successfully been used to initialize sea ice models and coupled NWP systems. Sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. We have examined the potential for sea ice thickness observations to improve forecast skill on timescales from days to a year ahead in two state-of-the-art coupled GCMs.</p><p>Here we examine the influence of Arctic sea-ice thickness observations on the potential predictability of the sea-ice and atmospheric circulation using idealised ‘data denial’ experiments. We perform paired sets of ensembles with the HadGEM3 and EC-Earth GCMs using different initial conditions retrieved from present-day control runs.</p><p>One set of ensembles start with complete information about the sea-ice conditions and is treated as “truth”, and one set has degraded sea ice information. We investigate how the pairs of ensembles, all started in January, predict the subsequent evolution of the sea-ice state, sea level pressure and circulation within the Arctic with the aim of quantifying the value of sea-ice observations for improving predictions.</p><p>We show that accurate initialization of sea ice thickness improves the model prediction skill during the first month of simulation and that several sea ice state and atmospheric variables present a re-emergence of skill in September. Prediction skill of several oceanic variables is also observed. The two models present a good agreement in terms of the regions where they show either a skill gain or loss.</p>

scholarly journals Revisiting Trans-Arctic Maritime Navigability in 2011–2016 from the Perspective of Sea Ice Thickness

2021 ◽  
Vol 13 (14) ◽  
pp. 2766
Author(s):  
Xiangying Zhou ◽  
Chao Min ◽  
Yijun Yang ◽  
Jack C. Landy ◽  
Longjiang Mu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Transit Time ◽  
Open Water ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
East Siberian Sea ◽  
Transportation Accessibility ◽  
Sea Ice Edge ◽  
Arctic Navigation

Arctic navigation has become operational in recent decades with the decline in summer sea ice. To assess the navigability of trans-Arctic passages, combined model and satellite sea ice thickness (CMST) data covering both freezing seasons and melting seasons are integrated with the Arctic Transportation Accessibility Model (ATAM). The trans-Arctic navigation window and transit time are thereby obtained daily from modeled sea ice fields constrained by satellite observations. Our results indicate that the poorest navigability conditions for the maritime Arctic occurred in 2013 and 2014, particularly in the Northwest Passage (NWP) with sea ice blockage. The NWP has generally exhibited less favorable navigation conditions and shorter navigable windows than the Northern Sea Route (NSR). For instance, in 2013, Open Water (OW) vessels that can only safely resist ice with a thickness under 15 cm had navigation windows of 47 days along the NSR (45% shorter than the 2011–2016 mean) and only 13 days along the NWP (80% shorter than the 2011–2016 mean). The longest navigation windows were in 2011 and 2015, with lengths of 103 and 107 days, respectively. The minimum transit time occurred in 2012, when more northward routes were accessible, especially in the Laptev Sea and East Siberian Sea with the sea ice edge retreated. The longest navigation windows for Polar Class 6 (PC6) vessels with a resistance to ice thickness up to 120 cm reached more than 200 days. PC6 vessels cost less transit time and exhibit less fluctuation in their navigation windows compared with OW vessels because of their ice-breaking capability. Finally, we found that restricted navigation along the NSR in 2013 and 2014 was related to the shorter periods of navigable days in the East Siberian Sea and Vilkitskogo Strait, with local blockages of thick ice having a disproportionate impact on the total transit. Shorter than usual navigable windows in the Canadian Arctic Archipelago and Beaufort Sea shortened the windows for entire routes of the NWP in 2013 and 2014.

scholarly journals Seasonal sea ice forecast skills and predictability of the KMA's GloSea5

2018 ◽  
Author(s):  
Byoung Woong An ◽  
Sang Min Lee ◽  
Pil-Hun Chang ◽  
KiRyong Kang ◽  
Yoon Jae Kim
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Freshwater Flux ◽  
Potential Predictability ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
East Siberian Sea ◽  
Arctic Sea

Abstract. Ensemble sea ice forecasts of the Arctic Ocean conducted with the Korea Meteorological Administration's coupled global seasonal forecast system (GloSea5) is verified. To investigate the temporal and spatial characteristics of the seasonal projection of Arctic sea ice extent and thickness, a set of ensemble potential predictability is assessed. It shows significance for all lead months except anomalous around East Siberian Sea, Chukchi Sea and Beaufort Sea during summer months. However, during the radipdly thawing and freezing season, initial states lose its predictability and increase uncertainties in the prediction. The probability skill metrics show the summer sea ice prediction which strongly depends on the sea ice thickness interacting with the accuracy of the snow depth. We found the forecast skill is determined primarily by the timing of sea ice drift (i.e., Beaufort Gyre and Transpolar drift) and sea ice formation by freshwater flux in the East Siberian Sea. Therefore, capturing the sea ice thickness state effectively is the key process for skillful estimation of Arctic sea ice. In spite of the uncertainties in atmospheric conditions, this system provides skillful Arctic seasonal sea ice extent predictions up to six months.

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