scholarly journals Attributing Causes of 2015 Record Minimum Sea-Ice Extent in the Sea of Okhotsk

2017 ◽  
Vol 30 (12) ◽  
pp. 4693-4703 ◽  
Author(s):  
Seungmok Paik ◽  
Seung-Ki Min ◽  
Yeon-Hee Kim ◽  
Baek-Min Kim ◽  
Hideo Shiogama ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
External Forcing ◽  
Sea Ice Extent ◽  
The Sea Of Okhotsk ◽  
The North

In 2015, the sea ice extent (SIE) over the Sea of Okhotsk (Okhotsk SIE) hit a record low since 1979 during February–March, the period when the sea ice extent generally reaches its annual maximum. To quantify the role of anthropogenic influences on the changes observed in Okhotsk SIE, this study employed a fraction of attributable risk (FAR) analysis to compare the probability of occurrence of extreme Okhotsk SIE events and long-term SIE trends using phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations performed with and without anthropogenic forcing. It was found that because of anthropogenic influence, both the probability of extreme low Okhotsk SIEs that exceed the 2015 event and the observed long-term trends during 1979–2015 have increased by more than 4 times (FAR = 0.76 to 1). In addition, it is suggested that a strong negative phase of the North Pacific Oscillation (NPO) during midwinter (January–February) 2015 also contributed to the 2015 extreme SIE event. An analysis based on multiple linear regression was conducted to quantify relative contributions of the external forcing (anthropogenic plus natural) and the NPO (internal variability) to the observed SIE changes. About 56.0% and 24.7% of the 2015 SIE anomaly was estimated to be attributable to the external forcing and the strong negative NPO influence, respectively. The external forcing was also found to explain about 86.1% of the observed long-term SIE trend. Further, projections from the CMIP5 models indicate that a sea ice–free condition may occur in the Sea of Okhotsk by the late twenty-first century in some models.

scholarly journals How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent?

2012 ◽  
Vol 6 (5) ◽  
pp. 3539-3573 ◽  
Author(s):  
V. Zunz ◽  
H. Goosse ◽  
F. Massonnet
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Climate Models ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Positive Trend ◽  
External Forcing ◽  
Sea Ice Extent ◽  
The One ◽  
The Impact

Abstract. Observations over the last 30 yr have shown that the sea ice extent in the Southern Ocean has slightly increased since 1979. Mechanisms responsible for this positive trend have not been well established yet and climate models are generally unable to simulate correctly this expansion. In this study, we focus on two related hypotheses that could explain the misrepresentation of the positive trend in sea ice extent by climate models: an unrealistic internal variability and an inadequate initialization of the system. For that purpose, we analyze the evolution of sea ice around the Antarctic simulated by 24 different general circulation models involved in the 5th Coupled Model Intercomparison Project (CMIP5). On the one hand, historical simulations, driven by external forcing and initialized without observations, are examined. They provide information about the mean state, the variability and the trend in sea ice extent simulated by each model. On the other hand, decadal prediction experiments, driven by external forcing and initialized with some observed fields, allow us to assess the impact of the representation of the observed initial state on the quality of model predictions. Our analyses show that CMIP5 models respond to the forcing, including the one induced by stratospheric ozone depletion, by reducing the sea ice cover in the Southern Ocean. Some simulations display an increase in sea ice extent. However, models strongly overestimate the variability of sea ice extent and the initialization methods currently used in models do not improve systematically the simulated trends in sea ice extent. On the basis of those results, a critical role of the internal variability in the observed increase in the sea ice extent in the Southern Ocean could not be ruled out but current models results appear inadequate to test more precisely this hypothesis.

scholarly journals Records of sea-ice extent and air temperature at the Sea of Okhotsk from an ice core of Mount Ichinsky, Kamchatka

2011 ◽  
Vol 52 (58) ◽  
pp. 44-50 ◽  
Author(s):  
Sumito Matoba ◽  
Takayuki Shiraiwa ◽  
Akane Tsushima ◽  
Hirotaka Sasaki ◽  
Yaroslav D. Muravyev
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Thermohaline Circulation ◽  
Ice Core ◽  
Solar Irradiation ◽  
Sea Ice Extent ◽  
Arctic Oscillation Index ◽  
High Productivity ◽  
The Sea Of Okhotsk ◽  
Ice Production

AbstarctThe Sea of Okhotsk is the southernmost area in the Northern Hemisphere where seasonal sea ice is produced every year. The formation of sea ice drives thermohaline circulation in the Sea of Okhotsk, and this circulation supports the high productivity in the region. However, recent reports have indicated that sea-ice production in the Sea of Okhotsk is decreasing, raising concern that the decreased sea ice will affect not only circulation but also biological productivity in the sea. To reconstruct climatic changes in the Sea of Okhotsk region, we analyzed an ice core obtained from Ichinskaya Sopka (Mount Ichinsky), Kamchatka. We assumed that the remarkable negative peaks of δD in the ice core were caused by expansion of sea ice in the Sea of Okhotsk. Melt feature percentage (MFP), which indicates summer snowmelt, showed high values in the 1950–60s and the mid-1990s–2000s. The high MFP in the 1950–60s was assumed to be caused by an increase in cyclone activity reaching Kamchatka during a negative period of the Pacific Decadal Oscillation index, and that in the 1990–2000s may reflect the increase in solar irradiation during a positive period of the summer Arctic Oscillation index.

scholarly journals Constraining Uncertainties in CMIP5 Projections of September Arctic Sea Ice Extent with Observations

2020 ◽  
Vol 33 (4) ◽  
pp. 1487-1503 ◽  
Author(s):  
Daniel Senftleben ◽  
Axel Lauer ◽  
Alexey Karpechko
Keyword(s):  
Sea Ice ◽  
Model Uncertainty ◽  
Coupled Model ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Mitigation Strategies ◽  
Sea Ice Extent ◽  
The Past ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

AbstractIn agreement with observations, Earth system models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulate a decline in September Arctic sea ice extent (SIE) over the past decades. However, the spread in their twenty-first-century SIE projections is large and the timing of the first ice-free Arctic summer ranges from 2020 to beyond 2100. The uncertainties arise from three sources (internal variability, model uncertainty, and scenario uncertainty), which are quantified in this study for projections of SIE. The goal is to narrow uncertainties by applying multiple diagnostic ensemble regression (MDER). MDER links future projections of sea ice extent to processes relevant to its simulation under present-day conditions using data covering the past 40 years. With this method, we can reduce model uncertainty in projections of SIE for the period 2020–44 by 30%–50% (0.8–1.3 million km2). Compared to the unweighted multimodel mean, the MDER-weighted mean projects an about 20% smaller SIE and an earlier near-disappearance of Arctic sea ice by more than a decade for a high–greenhouse gas scenario. We also show that two different methods estimating internal variability in SIE differ by 1 million km2. Regardless, the total uncertainties in the SIE projections remain large (up to 3.5 million km2, with irreducible internal variability contributing 30%) so that a precise time estimate of an ice-free Arctic proves impossible. We conclude that unweighted CMIP5 multimodel-mean projections of Arctic SIE are too optimistic and mitigation strategies to reduce Arctic warming need to be intensified.

scholarly journals New Perspectives on Observed and Simulated Antarctic Sea Ice Extent Trends Using Optimal Fingerprinting Techniques*

2015 ◽  
Vol 28 (4) ◽  
pp. 1543-1560 ◽  
Author(s):  
William Richard Hobbs ◽  
Nathaniel L. Bindoff ◽  
Marilyn N. Raphael
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Ross Sea ◽  
Internal Variability ◽  
Ice Cover ◽  
Natural Variability ◽  
Forced Response ◽  
Cmip5 Models ◽  
Sea Ice Extent ◽  
Optimal Fingerprinting

Abstract Using optimal fingerprinting techniques, a detection analysis is performed to determine whether observed trends in Southern Ocean sea ice extent since 1979 are outside the expected range of natural variability. Consistent with previous studies, it is found that for the seasons of maximum sea ice cover (i.e., winter and early spring), the observed trends are not outside the range of natural variability and in some West Antarctic sectors they may be partially due to tropical variability. However, when information about the spatial pattern of trends is included in the analysis, the summer and autumn trends fall outside the range of internal variability. The detectable signal is dominated by strong and opposing trends in the Ross Sea and the Amundsen–Bellingshausen Sea regions. In contrast to the observed pattern, an ensemble of 20 CMIP5 coupled climate models shows that a decrease in Ross Sea ice cover would be expected in response to external forcings. The simulated decreases in the Ross, Bellingshausen, and Amundsen Seas for the autumn season are significantly different from unforced internal variability at the 95% confidence level. Unlike earlier work, the authors formally show that the simulated sea ice response to external forcing is different from both the observed trends and simulated internal variability and conclude that in general the CMIP5 models do not adequately represent the forced response of the Antarctic climate system.

scholarly journals Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6 based ocean–sea ice model setup for the North Sea and Baltic Sea

2017 ◽  
Author(s):  
Per Pemberton ◽  
Ulrike Löptien ◽  
Robinson Hordoir ◽  
Anders Höglund ◽  
Semjon Schimanke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
North Sea ◽  
Ice Thickness ◽  
The Baltic Sea ◽  
Sea Ice Extent ◽  
The North ◽  
The North Sea ◽  
The Baltic

Abstract. The Baltic Sea is a seasonally ice covered marginal sea in northern Europe with intense wintertime ship traffic and a sensitive ecosystem. Understanding and modeling the evolution of the sea-ice pack is important for climate effect studies and forecasting purposes. Here we present and evaluate the sea-ice component of a new NEMO–LIM3.6 based ocean–sea ice setup for the North Sea and Baltic Sea region. The setup includes a new depth-based fast ice parametrization for the Baltic Sea. The evaluation focuses on long-term statistics, from a 45-year long hindcast, although short-term daily performance is also briefly evaluated. Different sea-ice metrics such as sea-ice extent, concentration and thickness are compared to the best available observational dataset to identify model biases. Overall the model agrees well with the observations in terms of the long-term mean sea-ice extent and thickness. The variability of the annual maximum Baltic Sea ice extent is well in line with the observations but the 1961–2006 trend is underestimated. Based on the simulated ice thickness distribution we estimate the undeformed and deformed ice thickness and concentration in the Baltic Sea, which compares reasonably well with observations. We conclude that the new North Sea/Baltic Sea ocean–sea ice setup is well suited for further climate studies and sea ice forecasts.

scholarly journals Excitation of seiches in ice at port water area of the sea of Okhotsk

2019 ◽  
Vol 486 (4) ◽  
pp. 475-479
Author(s):  
G. I. Dolgikh ◽  
D. P. Kovalev ◽  
P. D. Kovalev
Keyword(s):  
Numerical Simulation ◽  
Spectral Analysis ◽  
Dynamic System ◽  
Sea Of Okhotsk ◽  
Water Area ◽  
External Forcing ◽  
Sea Waves ◽  
Wave Processes ◽  
The Sea Of Okhotsk

Long term observations of sea waves with one second discreteness in the port harbor of Sea of Okhotsk (Sakhalin island) at a depth of about two meters under the ice were carried out using autonomous wave recorders in 2009-2017. Spectral analysis of the data showed the presence of several significant peaks on the periods from 2 to 15 seconds in the spectra for the moments of strong swell at sea. These peaks are caused by wave processes that are generated due to the nonlinear transformation of the swell coming in ice. The numerical simulation of the reaction of the dynamic system - the water area described by the Duffing equation, depending on the parameters included in the equation and determined from experimental observations, is carried out. It is shown, including using the Poincare mapping that the amplitude of external forcing has the greatest influence on the transition of the system to chaos.

scholarly journals An Initial Assessment of Antarctic Sea Ice Extent in the CMIP5 Models

2013 ◽  
Vol 26 (5) ◽  
pp. 1473-1484 ◽  
Author(s):  
John Turner ◽  
Thomas J. Bracegirdle ◽  
Tony Phillips ◽  
Gareth J. Marshall ◽  
J. Scott Hosking
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Initial Assessment ◽  
Cmip5 Models ◽  
Full Time ◽  
Model Intercomparison ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Intercomparison Project ◽  
The Mean

Abstract This paper examines the annual cycle and trends in Antarctic sea ice extent (SIE) for 18 models used in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that were run with historical forcing for the 1850s to 2005. Many of the models have an annual SIE cycle that differs markedly from that observed over the last 30 years. The majority of models have too small of an SIE at the minimum in February, while several of the models have less than two-thirds of the observed SIE at the September maximum. In contrast to the satellite data, which exhibit a slight increase in SIE, the mean SIE of the models over 1979–2005 shows a decrease in each month, with the greatest multimodel mean percentage monthly decline of 13.6% decade−1 in February and the greatest absolute loss of ice of −0.40 × 106 km2 decade−1 in September. The models have very large differences in SIE over 1860–2005. Most of the control runs have statistically significant trends in SIE over their full time span, and all of the models have a negative trend in SIE since the mid-nineteenth century. The negative SIE trends in most of the model runs over 1979–2005 are a continuation of an earlier decline, suggesting that the processes responsible for the observed increase over the last 30 years are not being simulated correctly.

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