Patterns of Asian Winter Climate Variability and Links to Arctic Sea Ice

2015 ◽  
Vol 28 (17) ◽  
pp. 6841-6858 ◽  
Author(s):  
Bingyi Wu ◽  
Jingzhi Su ◽  
Rosanne D’Arrigo
Keyword(s):  
Sea Ice ◽  
East Asia ◽  
Barents Sea ◽  
Winter Precipitation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atmospheric Variability ◽  
Winter Climate ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

Abstract This paper describes two dominant patterns of Asian winter climate variability: the Siberian high (SH) pattern and the Asia–Arctic (AA) pattern. The former depicts atmospheric variability closely associated with the intensity of the Siberian high, and the latter characterizes the teleconnection pattern of atmospheric variability between Asia and the Arctic, which is distinct from the Arctic Oscillation (AO). The AA pattern plays more important roles in regulating winter precipitation and the 850-hPa meridional wind component over East Asia than the SH pattern, which controls surface air temperature variability over East Asia. In the Arctic Ocean and its marginal seas, sea ice loss in both autumn and winter could bring the positive phase of the SH pattern or cause the negative phase of the AA pattern. The latter corresponds to a weakened East Asian winter monsoon (EAWM) and enhanced winter precipitation in the midlatitudes of the Asian continent and East Asia. For the SH pattern, sea ice loss in the prior autumn emerges in the Siberian marginal seas, and winter loss mainly occurs in the Barents Sea, Labrador Sea, and Davis Strait. For the AA pattern, sea ice loss in the prior autumn is observed in the Barents–Kara Seas, the western Laptev Sea, and the Beaufort Sea, and winter loss only occurs in some areas of the Barents Sea, the Labrador Sea, and Davis Strait. Simulation experiments with observed sea ice forcing also support that Arctic sea ice loss may favor frequent occurrence of the negative phase of the AA pattern. The results also imply that the relationship between Arctic sea ice loss and winter atmospheric variability over East Asia is unstable, which is a challenge for predicting the EAWM based on Arctic sea ice loss.

scholarly journals Polar cod in jeopardy under the retreating Arctic sea ice

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mats Brockstedt Olsen Huserbråten ◽  
Elena Eriksen ◽  
Harald Gjøsæter ◽  
Frode Vikebø
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Keystone Species ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Biophysical Model ◽  
The Arctic ◽  
Polar Cod ◽  
Arctic Sea ◽  
Shelf Sea

Abstract The Arctic amplification of global warming is causing the Arctic-Atlantic ice edge to retreat at unprecedented rates. Here we show how variability and change in sea ice cover in the Barents Sea, the largest shelf sea of the Arctic, affect the population dynamics of a keystone species of the ice-associated food web, the polar cod (Boreogadus saida). The data-driven biophysical model of polar cod early life stages assembled here predicts a strong mechanistic link between survival and variation in ice cover and temperature, suggesting imminent recruitment collapse should the observed ice-reduction and heating continue. Backtracking of drifting eggs and larvae from observations also demonstrates a northward retreat of one of two clearly defined spawning assemblages, possibly in response to warming. With annual to decadal ice-predictions under development the mechanistic physical-biological links presented here represent a powerful tool for making long-term predictions for the propagation of polar cod stocks.

scholarly journals Mechanisms causing reduced Arctic sea ice loss in a coupled climate model

2012 ◽  
Vol 6 (4) ◽  
pp. 2653-2687 ◽  
Author(s):  
A. E. West ◽  
A. B. Keen ◽  
H. T. Hewitt
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Historical Record ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Slight Reduction ◽  
Coupled Climate Model ◽  
Arctic Sea ◽  
Ensembles Project ◽  
Arctic Sea Ice Loss

Abstract. The fully-coupled climate model HadGEM1 produces one of the most accurate simulations of the historical record of Arctic sea ice seen in the IPCC AR4 multi-model ensemble. In this study, we examine projections of sea ice decline out to 2030, produced by two ensembles of HadGEM1 with natural and anthropogenic forcings included. These ensembles project a significant slowing of the rate of ice loss to occur after 2010, with some integrations even simulating a small increase in ice area. We use an energy budget of the Arctic to examine the causes of this slowdown. A negative feedback effect by which rapid reductions in ice thickness north of Greenland reduce ice export is found to play a major role. A slight reduction in ocean-to-ice heat flux in the relevant period, caused by changes in the MOC and subpolar gyre in some integrations, is also found to play a part. Finally, we assess the likelihood of a slowdown occurring in the real world due to these causes.

scholarly journals Evaluating Impacts of Recent Arctic Sea Ice Loss on the Northern Hemisphere Winter Climate Change

2018 ◽  
Vol 45 (7) ◽  
pp. 3255-3263 ◽  
Author(s):  
Fumiaki Ogawa ◽  
Noel Keenlyside ◽  
Yongqi Gao ◽  
Torben Koenigk ◽  
Shuting Yang ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Northern Hemisphere ◽  
Arctic Sea Ice ◽  
Winter Climate ◽  
Winter Climate Change ◽  
Arctic Sea ◽  
Hemisphere Winter ◽  
Arctic Sea Ice Loss ◽  
Northern Hemisphere Winter

Greenhouse-gas contribution to Arctic sea-ice loss

2021 ◽  
Author(s):  
Yeon-Hee Kim ◽  
Seung-Ki Min
Keyword(s):  
Sea Ice ◽  
Greenhouse Gas ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Detection Analysis ◽  
External Forcings ◽  
Arctic Sea ◽  
Intercomparison Project ◽  
Arctic Sea Ice Loss

<p>Arctic sea-ice area (ASIA) has been declining rapidly throughout the year during recent decades, but a formal quantification of greenhouse gas (GHG) contribution remains limited. This study conducts an attribution analysis of the observed ASIA changes from 1979 to 2017 by comparing three satellite observations with the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations using an optimal fingerprint method. The observed ASIA exhibits overall decreasing trends across all months with stronger trends in warm seasons. CMIP6 anthropogenic plus natural forcing (ALL) simulations and GHG-only forcing simulations successfully capture the observed temporal trend patterns. Results from detection analysis show that ALL signals are detected robustly for all calendar months for three observations. It is found that GHG signals are detectable in the observed ASIA decrease throughout the year, explaining most of the ASIA reduction, with a much weaker contribution by other external forcings. We additionally find that the Arctic Ocean will occur ice-free in September around the 2040s regardless of the emission scenario.</p>

The Robustness of Midlatitude Weather Pattern Changes due to Arctic Sea Ice Loss

2016 ◽  
Vol 29 (21) ◽  
pp. 7831-7849 ◽  
Author(s):  
Hans W. Chen ◽  
Fuqing Zhang ◽  
Richard B. Alley
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Coupled System ◽  
Circulation Pattern ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Intrinsic Variability ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

Abstract The significance and robustness of the link between Arctic sea ice loss and changes in midlatitude weather patterns is investigated through a series of model simulations from the Community Atmosphere Model, version 5.3, with systematically perturbed sea ice cover in the Arctic. Using a large ensemble of 10 sea ice scenarios and 550 simulations, it is found that prescribed Arctic sea ice anomalies produce statistically significant changes for certain metrics of the midlatitude circulation but not for others. Furthermore, the significant midlatitude circulation changes do not scale linearly with the sea ice anomalies and are not present in all scenarios, indicating that the remote atmospheric response to reduced Arctic sea ice can be statistically significant under certain conditions but is generally nonrobust. Shifts in the Northern Hemisphere polar jet stream and changes in the meridional extent of upper-level large-scale waves due to the sea ice perturbations are generally small and not clearly distinguished from intrinsic variability. Reduced Arctic sea ice may favor a circulation pattern that resembles the negative phase of the Arctic Oscillation and may increase the risk of cold outbreaks in eastern Asia by almost 50%, but this response is found in only half of the scenarios with negative sea ice anomalies. In eastern North America the frequency of extreme cold events decreases almost linearly with decreasing sea ice cover. This study’s finding of frequent significant anomalies without a robust linear response suggests interactions between variability and persistence in the coupled system, which may contribute to the lack of convergence among studies of Arctic influences on midlatitude circulation.

The Seasonal Atmospheric Response to Projected Arctic Sea Ice Loss in the Late Twenty-First Century

2010 ◽  
Vol 23 (2) ◽  
pp. 333-351 ◽  
Author(s):  
Clara Deser ◽  
Robert Tomas ◽  
Michael Alexander ◽  
David Lawrence
Keyword(s):  
Sea Ice ◽  
High Latitude ◽  
Vertical Structure ◽  
Arctic Sea Ice ◽  
First Century ◽  
The Arctic ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Twenty First Century ◽  
Arctic Sea Ice Loss

Abstract The authors investigate the atmospheric response to projected Arctic sea ice loss at the end of the twenty-first century using an atmospheric general circulation model (GCM) coupled to a land surface model. The response was obtained from two 60-yr integrations: one with a repeating seasonal cycle of specified sea ice conditions for the late twentieth century (1980–99) and one with that of sea ice conditions for the late twenty-first century (2080–99). In both integrations, a repeating seasonal cycle of SSTs for 1980–99 was prescribed to isolate the impact of projected future sea ice loss. Note that greenhouse gas concentrations remained fixed at 1980–99 levels in both sets of experiments. The twentieth- and twenty-first-century sea ice (and SST) conditions were obtained from ensemble mean integrations of a coupled GCM under historical forcing and Special Report on Emissions Scenarios (SRES) A1B scenario forcing, respectively. The loss of Arctic sea ice is greatest in summer and fall, yet the response of the net surface energy budget over the Arctic Ocean is largest in winter. Air temperature and precipitation responses also maximize in winter, both over the Arctic Ocean and over the adjacent high-latitude continents. Snow depths increase over Siberia and northern Canada because of the enhanced winter precipitation. Atmospheric warming over the high-latitude continents is mainly confined to the boundary layer (below ∼850 hPa) and to regions with a strong low-level temperature inversion. Enhanced warm air advection by submonthly transient motions is the primary mechanism for the terrestrial warming. A significant large-scale atmospheric circulation response is found during winter, with a baroclinic (equivalent barotropic) vertical structure over the Arctic in November–December (January–March). This response resembles the negative phase of the North Atlantic Oscillation in February only. Comparison with the fully coupled model reveals that Arctic sea ice loss accounts for most of the seasonal, spatial, and vertical structure of the high-latitude warming response to greenhouse gas forcing at the end of the twenty-first century.

scholarly journals Internal Variability in Projections of Twenty-First-Century Arctic Sea Ice Loss: Role of the Large-Scale Atmospheric Circulation

2014 ◽  
Vol 27 (2) ◽  
pp. 527-550 ◽  
Author(s):  
Justin J. Wettstein ◽  
Clara Deser
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Wave Train ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Rossby Wave Train ◽  
Arctic Sea Ice Loss

Abstract Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate System Model, version 3 (CCSM3) ensemble for the period 2000–61. Each member is subject to an identical greenhouse gas emissions scenario and differs only in the atmospheric model component's initial condition. September Arctic sea ice extent trends during 2020–59 range from −2.0 × 106 to −5.7 × 106 km2 across the 39 ensemble members, indicating a substantial role for internal variability in future Arctic sea ice loss projections. A similar nearly threefold range (from −7.0 × 103 to −19 × 103 km3) is found for summer sea ice volume trends. Higher rates of summer Arctic sea ice loss in CCSM3 are associated with enhanced transpolar drift and Fram Strait ice export driven by surface wind and sea level pressure patterns. Over the Arctic, the covarying atmospheric circulation patterns resemble the so-called Arctic dipole, with maximum amplitude between April and July. Outside the Arctic, an atmospheric Rossby wave train over the Pacific sector is associated with internal ice loss variability. Interannual covariability patterns between sea ice and atmospheric circulation are similar to those based on trends, suggesting that similar processes govern internal variability over a broad range of time scales. Interannual patterns of CCSM3 ice–atmosphere covariability compare well with those in nature and in the newer CCSM4 version of the model, lending confidence to the results. Atmospheric teleconnection patterns in CCSM3 suggest that the tropical Pacific modulates Arctic sea ice variability via the aforementioned Rossby wave train. Large ensembles with other coupled models are needed to corroborate these CCSM3-based findings.

scholarly journals Simulated Atmospheric Response to Regional and Pan-Arctic Sea Ice Loss

2017 ◽  
Vol 30 (11) ◽  
pp. 3945-3962 ◽  
Author(s):  
James A. Screen
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Arctic Sea Ice ◽  
Ecological Impacts ◽  
The Arctic ◽  
Stratospheric Polar Vortex ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

Abstract The loss of Arctic sea ice is already having profound environmental, societal, and ecological impacts locally. A highly uncertain area of scientific research, however, is whether such Arctic change has a tangible effect on weather and climate at lower latitudes. There is emerging evidence that the geographical location of sea ice loss is critically important in determining the large-scale atmospheric circulation response and associated midlatitude impacts. However, such regional dependencies have not been explored in a thorough and systematic manner. To make progress on this issue, this study analyzes ensemble simulations with an atmospheric general circulation model prescribed with sea ice loss separately in nine regions of the Arctic, to elucidate the distinct responses to regional sea ice loss. The results suggest that in some regions, sea ice loss triggers large-scale dynamical responses, whereas in other regions sea ice loss induces only local thermodynamical changes. Sea ice loss in the Barents–Kara Seas is unique in driving a weakening of the stratospheric polar vortex, followed in time by a tropospheric circulation response that resembles the North Atlantic Oscillation. For October–March, the largest spatial-scale responses are driven by sea ice loss in the Barents–Kara Seas and the Sea of Okhotsk; however, different regions assume greater importance in other seasons. The atmosphere responds very differently to regional sea ice losses than to pan-Arctic sea ice loss, and the response to pan-Arctic sea ice loss cannot be obtained by the linear addition of the responses to regional sea ice losses. The results imply that diversity in past studies of the simulated response to Arctic sea ice loss can be partly explained by the different spatial patterns of sea ice loss imposed.

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