scholarly journals Assessing the controllability of Arctic sea ice extent by sulfate aerosol geoengineering

2015 ◽  
Vol 42 (4) ◽  
pp. 1223-1231 ◽  
Author(s):  
L. S. Jackson ◽  
J. A. Crook ◽  
A. Jarvis ◽  
D. Leedal ◽  
A. Ridgwell ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Sulfate Aerosol ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

scholarly journals Sources of multi-decadal variability in Arctic sea ice extent

2012 ◽  
Vol 7 (3) ◽  
pp. 034011 ◽  
Author(s):  
J J Day ◽  
J C Hargreaves ◽  
J D Annan ◽  
A Abe-Ouchi
Keyword(s):  
Sea Ice ◽  
Decadal Variability ◽  
Arctic Sea Ice ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

scholarly journals Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs

2014 ◽  
Vol 8 (1) ◽  
pp. 1383-1406 ◽  
Author(s):  
P. J. Hezel ◽  
T. Fichefet ◽  
F. Massonnet
Keyword(s):  
Sea Ice ◽  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the Radiative Concentration Pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following the peak radiative forcing in 2044 in all 9 models. RCP4.5 demonstrates continued summer Arctic sea ice decline due to continued warming on longer time scales. These two scenarios imply that summer sea ice extent could begin to recover if and when radiative forcing from greenhouse gas concentrations were to decrease. In RCP8.5 the Arctic Ocean reaches annually ice-free conditions in 7 of 9 models. The ensemble of simulations completed under the extended RCPs provide insight into the global temperature increase at which sea ice disappears in the Arctic and reversibility of declines in seasonal sea ice extent.

scholarly journals December 2016: Linking the Lowest Arctic Sea-Ice Extent on Record with the Lowest European Precipitation Event on Record

2019 ◽  
Vol 100 (1) ◽  
pp. S43-S48 ◽  
Author(s):  
Juan C. Acosta Navarro ◽  
Pablo Ortega ◽  
Javier García-Serrano ◽  
Virginie Guemas ◽  
Etienne Tourigny ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Precipitation Event ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

scholarly journals Mechanisms for low-frequency variability of summer Arctic sea ice extent

2015 ◽  
Vol 112 (15) ◽  
pp. 4570-4575 ◽  
Author(s):  
Rong Zhang
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Low Frequency ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Low Frequency Variability ◽  
Sea Ice Decline ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

Satellite observations reveal a substantial decline in September Arctic sea ice extent since 1979, which has played a leading role in the observed recent Arctic surface warming and has often been attributed, in large part, to the increase in greenhouse gases. However, the most rapid decline occurred during the recent global warming hiatus period. Previous studies are often focused on a single mechanism for changes and variations of summer Arctic sea ice extent, and many are based on short observational records. The key players for summer Arctic sea ice extent variability at multidecadal/centennial time scales and their contributions to the observed summer Arctic sea ice decline are not well understood. Here a multiple regression model is developed for the first time, to the author’s knowledge, to provide a framework to quantify the contributions of three key predictors (Atlantic/Pacific heat transport into the Arctic, and Arctic Dipole) to the internal low-frequency variability of Summer Arctic sea ice extent, using a 3,600-y-long control climate model simulation. The results suggest that changes in these key predictors could have contributed substantially to the observed summer Arctic sea ice decline. If the ocean heat transport into the Arctic were to weaken in the near future due to internal variability, there might be a hiatus in the decline of September Arctic sea ice. The modeling results also suggest that at multidecadal/centennial time scales, variations in the atmosphere heat transport across the Arctic Circle are forced by anticorrelated variations in the Atlantic heat transport into the Arctic.

Northern Alaskan land surface response to reduced Arctic sea ice extent

2011 ◽  
Vol 38 (9-10) ◽  
pp. 2099-2113 ◽  
Author(s):  
Matthew E. Higgins ◽  
John J. Cassano
Keyword(s):  
Sea Ice ◽  
Land Surface ◽  
Arctic Sea Ice ◽  
Sea Ice Extent ◽  
Surface Response ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

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.

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