scholarly journals Uncertainties in Arctic Sea Ice Thickness Associated with Different Atmospheric Reanalysis Datasets Using the CICE5 Model

Atmosphere ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 361
Author(s):  
Su-Bong Lee ◽  
Baek-Min Kim ◽  
Jinro Ukita ◽  
Joong-Bae Ahn
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Reanalysis Data ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Volume ◽  
Arctic Sea

Reanalysis data are known to have relatively large uncertainties in the polar region than at lower latitudes. In this study, we used a single sea-ice model (Los Alamos’ CICE5) and three sets of reanalysis data to quantify the sensitivities of simulated Arctic sea ice area and volume to perturbed atmospheric forcings. The simulated sea ice area and thickness thus volume were clearly sensitive to the selection of atmospheric reanalysis data. Among the forcing variables, changes in radiative and sensible/latent heat fluxes caused significant amounts of sensitivities. Differences in sea-ice concentration and thickness were primarily caused by differences in downward shortwave and longwave radiations. 2-m air temperature also has a significant influence on year-to-year variability of the sea ice volume. Differences in precipitation affected the sea ice volume by causing changes in the insulation effect of snow-cover on sea ice. The diversity of sea ice extent and thickness responses due to uncertainties in atmospheric variables highlights the need to carefully evaluate reanalysis data over the Arctic region.

Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

2019 ◽  
Vol 32 (5) ◽  
pp. 1361-1380 ◽  
Author(s):  
J. Ono ◽  
H. Tatebe ◽  
Y. Komuro
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Drastic Reduction ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

scholarly journals Record high Pacific Arctic seawater temperatures and delayed sea ice advance in response to episodic atmospheric blocking

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tsubasa Kodaira ◽  
Takuji Waseda ◽  
Takehiko Nose ◽  
Jun Inoue
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
The Arctic ◽  
Atmospheric Blocking ◽  
Sea Ice Extent ◽  
The Pacific ◽  
Arctic Sea ◽  
Highly Correlated

AbstractArctic sea ice is rapidly decreasing during the recent period of global warming. One of the significant factors of the Arctic sea ice loss is oceanic heat transport from lower latitudes. For months of sea ice formation, the variations in the sea surface temperature over the Pacific Arctic region were highly correlated with the Pacific Decadal Oscillation (PDO). However, the seasonal sea surface temperatures recorded their highest values in autumn 2018 when the PDO index was neutral. It is shown that the anomalous warm seawater was a rapid ocean response to the southerly winds associated with episodic atmospheric blocking over the Bering Sea in September 2018. This warm seawater was directly observed by the R/V Mirai Arctic Expedition in November 2018 to significantly delay the southward sea ice advance. If the atmospheric blocking forms during the PDO positive phase in the future, the annual maximum Arctic sea ice extent could be dramatically reduced.

scholarly journals The potential of sea ice leads as a predictor for summer Arctic sea ice extent

2018 ◽  
Vol 12 (12) ◽  
pp. 3747-3757 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Strong Relationship ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Leads ◽  
Arctic Sea Ice Extent

Abstract. The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for summer Arctic sea ice extent forecast using a recently developed daily sea ice lead product retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Our results show that July pan-Arctic sea ice extent can be predicted from the area of sea ice leads integrated from midwinter to late spring, with a prediction error of 0.28 million km2 that is smaller than the standard deviation of the observed interannual variability. However, the predictive skills for August and September pan-Arctic sea ice extent are very low. When the area of sea ice leads integrated in the Atlantic and central and west Siberian sector of the Arctic is used, it has a significantly strong relationship (high predictability) with both July and August sea ice extent in the Atlantic and central and west Siberian sector of the Arctic. Thus, the realistic representation of sea ice leads (e.g., the areal coverage) in numerical prediction systems might improve the skill of forecast in the Arctic region.

scholarly journals The role of moisture transport for precipitation in the inter-annual and inter-daily fluctuations of the Arctic sea ice extension

2019 ◽  
Vol 10 (1) ◽  
pp. 121-133 ◽  
Author(s):  
Luis Gimeno-Sotelo ◽  
Raquel Nieto ◽  
Marta Vázquez ◽  
Luis Gimeno
Keyword(s):  
Sea Ice ◽  
Moisture Transport ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Melting ◽  
Sea Ice Extent ◽  
Moisture Sources ◽  
Arctic Sea

Abstract. By considering the moisture transport for precipitation (MTP) for a target region to be the moisture that arrives in this region from its major moisture sources and which then results in precipitation in that region, we explore (i) whether the MTP from the main moisture sources for the Arctic region is linked with inter-annual fluctuations in the extent of Arctic sea ice superimposed on its decline and (ii) the role of extreme MTP events in the inter-daily change in the Arctic sea ice extent (SIE) when extreme MTP simultaneously arrives from the four main moisture regions that supply it. The results suggest (1) that ice melting at the scale of inter-annual fluctuations against the trend is favoured by an increase in moisture transport in summer, autumn, and winter and a decrease in spring and, (2) on a daily basis, extreme humidity transport increases the formation of ice in winter and decreases it in spring, summer, and autumn; in these three seasons extreme humidity transport therefore contributes to Arctic sea ice melting. These patterns differ sharply from that linked to the decline on a long-range scale, especially in summer when the opposite trend applies, as ice melt is favoured by a decrease in moisture transport for this season at this scale.

scholarly journals The potential of sea ice leads as a predictor for seasonal Arctic sea ice extent prediction

2018 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Strong Relationship ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Leads ◽  
Arctic Sea Ice Extent

Abstract. The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for seasonal Arctic sea ice extent forecast using a recently developed daily sea ice leads product retrieved from Moderate-Resolution Imaging Spectroradiometer. Our results show that July pan-Arctic sea ice extent can be accurately predicted from the area of sea ice leads integrated from mid-winter to late spring. However, the predictive skills for August and September pan-Arctic sea ice extent are very low. When the area of sea ice leads integrated in the Atlantic and central and west Siberian sector of the Arctic is used, it has a significantly strong relationship (high predictability) with both July and August sea ice extent in the Atlantic and central and west Siberian sector of the Arctic. Thus, the realistic representation of sea ice leads (e.g., the areal coverage) in numerical prediction systems might improve the skill of forecast in the Arctic region.

Record high Pacific Arctic seawater temperatures and delayed sea ice advance in response to episodic atmospheric blocking

2021 ◽  
Author(s):  
Tsubasa Kodaira ◽  
Takuji Waseda ◽  
Takehiko Nose ◽  
Jun Inoue
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
The Arctic ◽  
Atmospheric Blocking ◽  
Sea Ice Extent ◽  
The Pacific ◽  
Arctic Sea ◽  
Highly Correlated

<p>Arctic sea ice is rapidly decreasing during the recent period of global warming. One of the significant factors of the Arctic sea ice loss is oceanic heat transport from lower latitudes. For months of sea ice formation, the variations in the sea surface temperature over the Pacific Arctic region were highly correlated with the Pacific Decadal Oscillation (PDO). However, the seasonal sea surface temperatures recorded their highest values in autumn 2018 when the PDO index was neutral. It is shown that the anomalous warm seawater was a rapid ocean response to the southerly winds associated with episodic atmospheric blocking over the Bering Sea in September 2018. This warm seawater was directly observed by the R/V Mirai Arctic Expedition in November 2018 to significantly delay the southward sea ice advance. If the atmospheric blocking forms during the PDO positive phase in the future, the annual maximum Arctic sea ice extent could be dramatically reduced.</p>

scholarly journals Rapid decline of Arctic sea ice volume: Causes and consequences

2019 ◽  
Author(s):  
Jean-Claude Gascard ◽  
Jinlun Zhang ◽  
Mehrad Rafizadeh
Keyword(s):  
Sea Ice ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
The Past ◽  
Arctic Sea

Abstract. The drastic reduction of the Arctic sea ice over the past 40 years is the most glaring evidence of climate change on Planet Earth. Among all the variables characterizing sea ice, the sea ice volume is by far the most sensitive one for climate change since it is decaying at the highest rate compared to sea ice extent and sea ice thickness. In 40 years the Arctic Ocean has lost about 3/4 of its sea ice volume at the end of the summer season corresponding to a reduction of both sea ice extent and sea ice thickness by half on average. From more than 16 000 km3, 40 years ago, the Arctic sea ice summer minimum dropped down to less than 4000 km3 during the most recent summers. Being a combination of Arctic sea ice extent and sea ice thickness, the Arctic sea ice volume is difficult to observe directly and accurately. We estimated cumulative Freezing-Degree Days (FDD) over a 9 month freezing time period (September to May each year) based on ERA Interim surface air temperature reanalysis over the whole Arctic Ocean and for the past 38 years. Then we compared the Arctic sea ice volume based on sea ice thickness deduced from cumulative FDD with Arctic sea ice volume estimated from PIOMAS (Pan Arctic Ice Ocean Modeling and Assimilation System) and from the ESA CRYOSAT-2 satellite. The results are strikingly similar. The warming of the atmosphere is playing an important role in contributing to the Arctic sea ice volume decrease during the whole freezing season (September to May). In addition, the FDD spatial distribution exhibiting a sharp double peak-like feature is reflecting the Multi Y ear Ice (MYI) versus First Year Ice (FYI) dual disposition typical of the Arctic sea ice cover. This is indicative of a significant contribution from the vertical ocean heat fluxes throughout the ice depending on MYI versus FYI distribution and the snow layer on top of it influencing the surface air temperature accordingly. In 2018 the Arctic MYI vanished almost completely for the first time ever over the past 40 years. The quasi complete disappearance of the Arctic sea ice is more likely to happen in summer within the next 15 years with broad consequences for Arctic marine and terrestrial ecosystems, climate and weather patterns on a planetary scale and globally on human activities.

scholarly journals Predicting Summer Arctic Sea Ice Concentration Intraseasonal Variability Using a Vector Autoregressive Model*

2016 ◽  
Vol 29 (4) ◽  
pp. 1529-1543 ◽  
Author(s):  
Lei Wang ◽  
Xiaojun Yuan ◽  
Mingfang Ting ◽  
Cuihua Li
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Forecast Skill ◽  
The Arctic ◽  
Var Model ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Vector Autoregressive ◽  
Arctic Sea ◽  
Ice Concentration

Abstract Recent Arctic sea ice changes have important societal and economic impacts and may lead to adverse effects on the Arctic ecosystem, weather, and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A vector autoregressive (VAR) model is evaluated for predicting the summertime (May–September) daily Arctic sea ice concentration on the intraseasonal time scale, using only the daily sea ice data and without direct information of the atmosphere and ocean. The intraseasonal forecast skill of Arctic sea ice is assessed using the 1979–2012 satellite data. The cross-validated forecast skill of the VAR model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times of ~20–60 days, especially over northern Eurasian marginal seas and the Beaufort Sea. The daily forecast of ice concentration also leads to predictions of ice-free dates and September mean sea ice extent. In addition to capturing the general seasonal melt of sea ice, the model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intraseasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice can be predicted statistically with reasonable skill at the intraseasonal time scales given the small signal-to-noise ratio of daily data.

Features of changes in the sea ice and snow cover extents associated with temperature changes in the Northern and Southern Hemispheres in recent decades.

2021 ◽  
Author(s):  
Maria Parfenova ◽  
Igor I. Mokhov
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Snow Cover ◽  
Reanalysis Data ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Arctic Sea ◽  
Northern And Southern Hemispheres

<p>Quantitative estimates of the relationship between the interannual variability of Antarctic and Arctic sea ice and changes in the surface temperature in the Northern and Southern Hemispheres using satellitedata, observational data and reanalysis data for the last four decades (1980-2019) are obtained. The previously noted general increase in the Antarctic sea ice extent (up to 2016) (according to satellite data available only since the late 1970s), happening simultaneously with global warming and rapid decrease in the Arctic sea ice extent, is associated with the regional manifestation of natural climate fluctuations with periods of up to several decades. The results of correlation and crosswavelet analysis indicate significant coherence and negative correlation of hemispheric surface temperature with not only Arctic,but also Antarctic sea ice extent in recent decades.</p><p>Seasonal and regional peculiarities of snow cover sensitivity to temperature regime changes in the Northern Hemisphere are noted with an assessment of changes in recent decades. Peculiarities of snow cover variability in Eurasia and North America are presented. In particular, the peculiarities of changes in snow cover during the autumn seasons are noted.</p>

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

2020 ◽  
Author(s):  
Wang Yangjun ◽  
Liu Kefeng ◽  
Shan Yulong ◽  
Zhang Ren
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Similarity Index ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Volume ◽  
Arctic Sea ◽  
The Future

Abstract. This study proposes adaptive forecasting through exponential re-weighting based on the Structural Similarity Index Measure (AFTER-SSIM) algorithm to evaluate the performance of global climate models from the Coupled Model Intercomparison Project (CMIP5) under different emission scenarios during 2006 to 2018, attempting to reduce the uncertainty among them. The SSIM approach uses a loss function to obtain more information on the spatial distribution between model outputs and observed data, where the genetic algorithm (GA) is used to optimise the parameters of both seasonal cycles and long-term trends of sea ice concentration and sea ice thickness. The re-weighting mechanism of the AFTER-SSIM algorithm guarantees a performance improvement in sea ice volume simulations as new information is added. Finally, the ranked models have been combined to estimate the future Arctic sea ice volume and navigation possibility through the Arctic Northern Sea Route. Results show that the proposed algorithm reduces the uncertainty among models, sea ice volume will continue to shrink in the future, and the open periods for 1A super vessels are likely to reach to five months ranging from August to December in 2030.

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