scholarly journals Atmospheric teleconnection processes linking winter air stagnation and haze extremes in China with regional Arctic sea ice decline

2020 ◽  
Vol 20 (8) ◽  
pp. 4999-5017 ◽  
Author(s):  
Yufei Zou ◽  
Yuhang Wang ◽  
Zuowei Xie ◽  
Hailong Wang ◽  
Philip J. Rasch
Keyword(s):  
Sea Ice ◽  
East Asia ◽  
Climate Model ◽  
Teleconnection Pattern ◽  
Arctic Sea Ice ◽  
Transient Eddy ◽  
Regional Ventilation ◽  
Atmospheric Teleconnection ◽  
Upper Troposphere ◽  
Arctic Sea

Abstract. Recent studies suggested significant impacts of boreal cryosphere changes on wintertime air stagnation and haze pollution extremes in China. However, the underlying mechanisms of such a teleconnection relationship remains unclear. Here we use the Whole Atmosphere Community Climate Model (WACCM) to investigate dynamic processes leading to atmospheric circulation and air stagnation responses to Arctic sea ice changes. We conduct four climate sensitivity experiments by perturbing sea ice concentrations (SIC) and corresponding sea surface temperature (SST) in autumn and early winter over the whole Arctic and three subregions in the climate model. The results indicate distinct responses in circulation patterns and regional ventilation to the region-specific Arctic changes, with the largest increase of both the probability (by 132 %) and the intensity (by 30 %) of monthly air stagnation extremes being found in the experiment driven by SIC and SST changes over the Pacific sector of the Arctic (the East Siberian and Chukchi seas). The increased air stagnation extremes are mainly driven by an amplified planetary-scale atmospheric teleconnection pattern that resembles the negative phase of the Eurasian (EU) pattern. Dynamical diagnostics suggest that convergence of transient eddy forcing in the vicinity of Scandinavia in winter is largely responsible for the amplification of the teleconnection pattern. Transient eddy vorticity fluxes dominate the transient eddy forcing and produce a barotropic anticyclonic anomaly near Scandinavia and wave train propagation across Eurasia to the downstream regions in East Asia. The piecewise potential vorticity inversion analysis reveals that this long-range atmospheric teleconnection of Arctic origin takes place primarily via the middle and upper troposphere. The anomalous ridge over East Asia in the middle and upper troposphere worsens regional ventilation conditions by weakening monsoon northwesterlies and enhancing temperature inversions near the surface, leading to more and stronger air stagnation and pollution extremes over eastern China in winter. Ensemble projections based on state-of-the-art climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) corroborate this teleconnection relationship between high-latitude environmental changes and midlatitude weather extremes, though the tendency and magnitude vary considerably among each participating model.

scholarly journals Atmospheric teleconnection processes linking winter air stagnation and haze extremes in China with regional Arctic sea ice decline

2019 ◽  
Author(s):  
Yufei Zou ◽  
Yuhang Wang ◽  
Zuowei Xie ◽  
Hailong Wang ◽  
Philip J. Rasch
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Teleconnection Pattern ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Transient Eddy ◽  
Regional Ventilation ◽  
Atmospheric Teleconnection ◽  
Upper Troposphere ◽  
Arctic Sea

Abstract. Recent studies suggested significant impacts of boreal cryosphere changes on wintertime air stagnation and haze pollution extremes in China. However, the underlying mechanism of such a teleconnection relationship remains unclear. Here we used the Whole Atmosphere Community Climate Model (WACCM) to investigate dynamic processes leading to atmospheric circulation and air stagnation responses to Arctic sea ice changes. We conducted four climate sensitivity experiments by perturbing sea ice concentrations (SIC) and corresponding sea surface temperature (SST) in autumn and early winter over the whole Arctic and three sub-regions in the climate model. The results indicate different responses in the general circulation and regional ventilation to the region-specific Arctic changes, with the largest increase of both the probability (by 120 %) and the intensity (by 32 %) of air stagnation extreme events being found in the experiment driven by SIC and SST changes over the Pacific sector of the Arctic (the East Siberian and Chukchi Seas). The increased air stagnation extreme events are mainly driven by an amplified hemispheric-scale atmospheric teleconnection pattern that resembles the negative phase of the Eurasian (EU) pattern. Dynamical diagnostics suggest that convergence of transient eddy forcing in the vicinity of Scandinavia in winter is largely responsible for the amplification of the teleconnection pattern. Transient eddy vorticity fluxes dominate the transient eddy forcing and produce a barotropic anticyclonic anomaly near Scandinavia and wave-train propagation across Eurasia to the downstream regions in East Asia. The piecewise potential vorticity inversion analysis reveals that this long-range atmospheric teleconnection of the Arctic origin takes place primarily in the middle and upper troposphere. The anomalous ridge over East Asia in the middle and upper troposphere worsens regional ventilation conditions by weakening monsoon northwesterlies and enhancing temperature inversion near the surface, leading to more and stronger air stagnation and pollution extremes over eastern China in winter. Ensemble projections based on the state-of-the-art climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) corroborate this teleconnection relationship between high-latitude environmental changes and middle-latitude weather extremes, though the tendency and magnitude vary considerably among each participating model.

Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period 

2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Volume Growth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Concentration ◽  
Marginal Seas ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration

<p><span>We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.</span></p>

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 How much should we believe correlations between Arctic cyclones and sea ice extent?

2017 ◽  
Author(s):  
Jamie G. L. Rae ◽  
Alexander D. Todd ◽  
Edward W. Blockley ◽  
Jeff K. Ridley
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Reanalysis Dataset ◽  
Sea Ice Extent ◽  
Sea Level Pressure ◽  
Temporal Sampling ◽  
Time Period ◽  
Arctic Sea ◽  
Short Time

Abstract. This paper presents an analysis of Arctic summer cyclones in a climate model and in a reanalysis dataset. A cyclone identification and tracking algorithm is run for output from model simulations at two resolutions, and for the reanalysis, using two different tracking variables (mean sea-level pressure and 850 hPa vorticity) for identification of the cyclones. Correlations between characteristics of the cyclones and September Arctic sea ice extent are investigated, and the influence of the tracking variable, the spatial resolution of the model, and spatial and temporal sampling, on the correlations is explored. We conclude that the correlations obtained depend on all of these factors, and that care should be taken when interpreting the results of such analyses, especially when the focus is on one reanalysis, or output from one model, analysed with a single tracking variable for a short time period.

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