Consistency and discrepancy in the atmospheric response to
Arctic sea-ice loss across climate models

Screen, James A.; Deser, Clara; Smith, Doug; Zhang, Xiangdong; Blackport, Russell; Kushner, Paul J.; Oudar, Thomas; McCusker, Kelly E.; Sun, Lantao

doi:10.1038/s41561-018-0059-y

Cited by 322 publications

(343 citation statements)

References 84 publications

(163 reference statements)

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“…However, over the Arctic Ocean, despite the little change in SIE and SIC, there is still a SAT response in the 2CiceASO experiment. This is consistent with previous coupled model experiments using a range of models and experiment protocols (Screen et al, 2018). This is likely attributed to the reductions in ice thickness in the 2CiceASO experiment, as Labe et al (2018) and Lang et al (2017) find similar SAT responses to imposed reductions in sea ice thickness.…”

Section: Temperature Responsesupporting

confidence: 90%

“…Sea ice albedo reduction has been previously used to examine the impacts of sea ice loss on the climate (Blackport & Kushner, 2016Scinocca et al, 2009); however, the modifications could be unphysical (Screen et al, 2018) and result in an unrealistic seasonal cycle with too much ice loss in summer and too little ice loss in winter . Sea ice albedo reduction has been previously used to examine the impacts of sea ice loss on the climate (Blackport & Kushner, 2016Scinocca et al, 2009); however, the modifications could be unphysical (Screen et al, 2018) and result in an unrealistic seasonal cycle with too much ice loss in summer and too little ice loss in winter .…”

Section: Model and Experimentsmentioning

confidence: 99%

“…There is growing evidence that sea ice loss, and the warming it causes, has the potential to impact the atmospheric circulation in midlatitudes, particularly in winter (Cohen et al, 2014;Vavrus, 2018). While there is disagreement between such experiments run with different models and different experimental designs, some common responses have emerged (Screen et al, 2018). While there is disagreement between such experiments run with different models and different experimental designs, some common responses have emerged (Screen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Influence of Arctic Sea Ice Loss in Autumn Compared to That in Winter on the Atmospheric Circulation

Blackport

Screen

2019

Geophysical Research Letters

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There is growing evidence that Arctic sea ice loss affects the large‐scale atmospheric circulation. Some studies suggest that reduced autumn sea ice may be a precursor to severe midlatitude winters. Here we use coupled ocean–atmosphere model experiments to investigate the extent to which the winter atmospheric circulation response to Arctic sea ice loss is driven by sea ice loss in preceding months. We impose different seasonal cycles of sea ice by using various combinations of sea ice albedo parameters. Year‐round sea ice loss causes an equatorward migration of the eddy‐driven jet and a shift toward the negative phase of the North Atlantic Oscillation in winter. However, these circulation changes are not found when sea ice is reduced only in late summer and autumn, despite high‐latitude warming persisting into the winter. Our results imply that the winter atmospheric circulation response to sea ice loss is primarily driven by sea ice loss in winter rather than in autumn.

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Section: Temperature Responsesupporting

confidence: 90%

Section: Model and Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of Arctic Sea Ice Loss in Autumn Compared to That in Winter on the Atmospheric Circulation

Blackport

Screen

2019

Geophysical Research Letters

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“…Although the Arctic sea ice cover (SIC) is the least in late September, the significant decrease in winter sea ice and the more frequent extreme cold weather in mid-latitudes in winter make it necessary to focus on the winter Arctic SIC change and associated processes [7][8][9]. Recently, many studies have addressed the mechanism of SIC change in winter [10,11]. The roles of radiation and sensible heat [8,12], moisture transport [13,14], wind-driven sea ice drifting [15,16] and other factors [17] have been examined in many previous studies.…”

Section: Introductionmentioning

confidence: 99%

Effects of Northern Hemisphere Atmospheric Blocking on Arctic Sea Ice Decline in Winter at Weekly Time Scales

2018

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Abstract:In this study, the effects of the Northern Hemisphere atmospheric blocking circulation on Arctic sea ice decline at weekly time scales are examined by defining four key regions based on observational data analysis. Given the regression analysis, the frequently occurring atmospheric patterns related to the sea ice decline in four key sea regions (Baffin Bay, Barents-Kara Seas, Okhotsk Sea and Bering Sea) are found to be Greenland blocking (GB), Ural blocking (UB), western Pacific blocking (PB-W) and eastern Pacific blocking (PB-E), respectively. The results show that the regional blocking frequency is higher (lower) in lower (higher) sea ice winters for each key region. Moreover, composite analysis indicates that blocking evolution is usually accompanied by significant sea ice decline at weekly time scales during the blocking life cycle for each key region. In addition, the intensified surface downward infrared radiation (IR) anomaly and the precipitable water for the entire atmosphere (PWA) in each key region are found to make significant contributions to the positive surface air temperature (SAT) anomaly, which is beneficial for the reduction in sea ice. The approximate quantitative analysis of different surface energy fluxes induced by blocking is also applied. Further analysis shows that the blocking event and the associated changes in SAT and radiation anomalies for each key region lead the sea ice decline by approximately 3~6 days. This result indicates that regional blocking can contribute to regional sea ice decline at weekly time scales through surface warming associated with enhanced water vapor and associated IR variations. Further quantitative estimates indicate that regional blocking can reduce regional sea ice cover (SIC) by 49.6%, 49.4%, 52.2% and 49.5% for Baffin Bay, Barents-Kara Seas, Okhotsk Sea and Bering Sea, respectively, during the blocking life cycle. Finally, a physical process diagrammatic sketch is given to illustrate how blocking affects SIC decline.

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“…In the Arctic, the accelerated reduction of sea ice cover in recent years is also associated with a regional amplification in near‐surface air temperatures (Screen & Simmonds, ; Serreze & Barry, ). These effects may also influence the larger‐scale general circulation (Alexander et al, ; Deser et al, ; Screen et al, ). Appropriate treatment of sea ice characteristics in seasonal forecasting models may then influence NH predictive skill (Jung et al, ); however, there is uncertainty in the causal relationship between Arctic sea ice conditions and midlatitude weather variability (Overland et al, ), in part due to the limited the atmospheric response to sea ice variability in models (Screen et al, ).…”

Section: Seasonal and Subseasonal Forecast Assessmentmentioning

confidence: 99%

GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

et al. 2020

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The Global Modeling and Assimilation Office (GMAO) has recently released a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐2, that represents a substantial improvement in performance and infrastructure over the previous system. The system is described here in detail, and results are presented from forecasts, climate equillibrium simulations, and data assimilation experiments. The climate or equillibrium state of the atmosphere and ocean showed a substantial reduction in bias relative to GEOS‐S2S‐1. The GEOS‐S2S‐2 coupled reanalysis also showed substantial improvements, attributed to the assimilation of along‐track absolute dynamic topography. The forecast skill on subseasonal scales showed a much improved prediction of the Madden‐Julian Oscillation in GEOS‐S2S‐2, and on a seasonal scale the tropical Pacific forecasts show substantial improvement in the east and comparable skill to GEOS‐S2S‐1 in the central Pacific. GEOS‐S2S‐2 anomaly correlations of both land surface temperature and precipitation were comparable to GEOS‐S2S‐1 and showed substantially reduced root‐mean‐square error of surface temperature. The remaining issues described here are being addressed in the development of GEOS‐S2S Version 3, and with that system GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.

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Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models

Cited by 322 publications

References 84 publications

Influence of Arctic Sea Ice Loss in Autumn Compared to That in Winter on the Atmospheric Circulation

Influence of Arctic Sea Ice Loss in Autumn Compared to That in Winter on the Atmospheric Circulation

Effects of Northern Hemisphere Atmospheric Blocking on Arctic Sea Ice Decline in Winter at Weekly Time Scales

GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

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