Role of synoptic eddy feedback on polar climate responses to the anthropogenic forcing

Kug, Jong‐Seong; Choi, Do Il; Jin, Fei‐Fei; Kwon, Won‐Tae; Ren, Hong‐Li

doi:10.1029/2010gl043673

Cited by 28 publications

(16 citation statements)

References 30 publications

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“…Many studies have tried to understand the underlying physical mechanisms that are responsible for the AA, including local feedback associated with the decline of sea ice extent [Serreze et al, 2009;Overland and Wang, 2010;Screen and Simmonds, 2010], atmospheric and oceanic transports [Graversen et al, 2008;Kug et al, 2010;Screen et al, 2011;Alexeev and Jackson, 2013;Koenigk and Brodeau, 2014;Rudeva and Simmonds, 2015], and feedbacks associated with changes in temperature, clouds, water vapor, snow cover, and vegetation [Liu et al, 2008;Graversen and Wang, 2009;Derksen and Brown, 2012;Jeong et al, 2012Jeong et al, , 2014Pithan and Mauritsen, 2014]. Among them, the Arctic sea ice is known to be a primary physical contributor to the AA, because it controls incoming solar radiation in association with the surface albedo feedback and the heat exchange between the cold atmosphere and the warmer, ice-covered ocean.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Arctic warming to sea ice concentration

et al. 2016

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We examine the sensitivity of Arctic amplification (AA) to background sea ice concentration (SIC) under greenhouse warming by analyzing the data sets of the historical and Representative Concentration Pathway 8.5 runs of the Coupled Model Intercomparison Project Phase 5. To determine whether the sensitivity of AA for a given radiative forcing depends on background SIC state, we examine the relationship between the AA trend and mean SIC on moving 30 year windows from 1960 to 2100. It is found that the annual mean AA trend varies depending on the mean SIC condition. In particular, some models show a highly variable AA trend in relation to the mean SIC clearly. In these models, the AA trend tends to increase until the mean SIC reaches a critical level (i.e., 20–30%), and the maximum AA trend is almost 3 to 5 times larger than the trend in the early stage of global warming (i.e., 50–60%, 60–70%). However, the AA trend tends to decrease after that. Further analysis shows that the sensitivity of AA trend to mean SIC condition is closely related to the feedback processes associated with summer surface albedo and winter turbulent heat flux in the Arctic Ocean.

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Section: Introductionmentioning

confidence: 99%

Sensitivity of Arctic warming to sea ice concentration

et al. 2016

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“…Relatively large warming of T2m in the higher latitude is attributed to feedbacks associated with changes in snow cover and sea ice (Serreze et al 2007;Comiso et al 2008;Soden et al 2008;Graversen and Wang 2009;Kumar et al 2010). Also the horizontal transport of globally increased latent heat into the Arctic is also known to induce the enhanced warming near the poles (Alexeev et al 2005;Cai 2005;Langen and Alexeev 2007;Kug et al 2010), which is mainly confined in the lower layer of the atmosphere since the air is statistically stable in the higher latitude. Such latitudinal distributions of T2m are also shown in Fig.…”

Section: Winter Climate Changes Under Rcpsmentioning

confidence: 99%

Winter climate changes over East Asian region under RCP scenarios using East Asian winter monsoon indices

2016

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that resemble the composite strong winter monsoon pattern are simulated more or less weakly in CMIP5 compared to the observation. However, the regressed fields of sea level pressure, surface air temperature, 500-hPa geopotential height, and 300-hPa zonal wind are well established with pattern correlations above 0.83 between CMIP5 and observation data. The differences between RCPs and Historical indicate strong warming, which increases with latitude, ranging from 1 to 5 °C under RCP4.5 and from 3 to 7 °C under RCP8.5 in the East Asian region. The anomalous southerly winds generally become stronger, implying weaker EAWMs in both scenarios. These features are also identified with fields regressed onto the indices in RCPs. The future projections reveal that the interannual variability of the indices will be maintained with an intensity similar to that of the present. The correlation between monsoon indices and Arctic Oscillation increases over time. On the other hand, the correlation between monsoon indices and North Atlantic Oscillation decreases.

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“…In the future, climate models project increased moisture transport into the Arctic [e.g., Kug et al, 2010;Bengtsson et al, 2011]. Focusing only on the zonal-mean transport, studies show that thermodynamic effects contribute more than dynamics to this increased transport [e.g., Skific et al, 2009aSkific et al, , 2009bSkific and Francis, 2013].…”

Section: Transient Moisture Transport Into the Arctic (Across 60mentioning

confidence: 99%

Extreme moisture transport into the Arctic linked to Rossby wave breaking

2015

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The transport of moisture into the Arctic is tightly connected to midlatitude dynamics. We show that the bulk of the transient poleward moisture transport across 60°N is driven by extreme transport (fluxes greater than the 90th percentile) events. We demonstrate that these events are closely related to the two types of Rossby wave breaking (RWB)—anticyclonic wave breaking (AWB) and cyclonic wave breaking (CWB). Using a RWB tracking algorithm, we determine that RWB can account for approximately 68% of the extreme poleward moisture transport by transients across 60°N in winter and 56% in summer. Additional analysis suggests that the seasonality of such RWB‐related moisture transport is determined approximately equally by (1) the magnitude of transport (which is largely a function of the background moisture gradient) and (2) the frequency of RWB occurrence. The seasonality of RWB occurrence is, in turn, tied to the seasonal variation of the latitude of the jet streams—AWB‐related (CWB‐related) transport occurs more frequently when the jet is shifted poleward (equatorward). The interannual variability of RWB‐related transport across 60°N in winter is shown to be strongly influenced by climate variability captured by the El Niño/Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). In the positive (negative) phase of ENSO, AWB transports less (more) moisture through the Bering Strait and CWB transports more (less) through western Canada. In the positive (negative) phase of the NAO, AWB transports more (less) moisture through the Norwegian Sea and CWB transports less (more) along the west coast of Greenland. These results highlight that low‐frequency climate variability outside of the polar regions can influence the Arctic water vapor by modulating extreme synoptic transport events.

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Role of synoptic eddy feedback on polar climate responses to the anthropogenic forcing

Cited by 28 publications

References 30 publications

Sensitivity of Arctic warming to sea ice concentration

Sensitivity of Arctic warming to sea ice concentration

Winter climate changes over East Asian region under RCP scenarios using East Asian winter monsoon indices

Extreme moisture transport into the Arctic linked to Rossby wave breaking

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