14:30 〜 14:45
[ACG46-09] Subseasonal-scale atmospheric responses to sea ice reduction over the Arctic in 2020/21 winter
The sea ice area in the Arctic has been rapidly reducing due to the global warming. Previous studies argued that the sea-ice reduction can influence on the mid- to high-latitudes large-scale atmospheric circulation in boreal winter. The most suspected impact of sea-ice loss is on the recent enhancement of Siberian High (high sea-level pressure anomaly), and associated frequent cold air outbreak in the central to East Asia. Nevertheless, the extent of the impact of sea ice reduction is still an open question.
The present study is exploring the atmospheric responses to the sea ice reduction in 2020/21 winter by using the numerical model named NICAM with 14-km resolution. The atmospheric response is assessed through sensitivity experiments; observed sea-ice is prescribed to one set experiments, climatological-mean sea ice to the other. The atmosphere model is initialized with the ensemble product of ERA5, in which the reanalysis fields are slightly perturbed by referring to observational errors. Our primary focus is on the atmospheric response to the sea ice reduction on subseasonal scale (up to 10 days), and its dependence on the initial atmospheric condition.
Our preliminary analysis suggests that the sea ice reduction can yield positive sea-level pressure (SLP) response near the Western Siberia with the lead time of 5 - 8 days. The horizontal scale is meso-alpha to synoptic scale. The SLP response acts to slightly intensify the blocking high that induced intense cold air outbreak over East Asia. Although the amplitude is approximately 5hPa at most, the response is statistically significant when the atmospheric model is initialized with the atmospheric conditions on each date from December 27 to 30th 2020. However, the response vanishes with the initial conditions from December 24 to 26th 2020. Thus, it is likely that the response is sensitive to the atmospheric distribution varying on daily scale and its relation to sea ice distribution.
Acknowledgements: This work was supported by MEXT as “Program for Promoting Researches on the Supercomputer Fugaku” (JPMXP1020200305) (Project ID: hp200128/hp210166/hp220167), and by the Arctic Challenge for Sustainability II (ArCS II) Project (Program Grant Number JPMXD1420318865), and by JSPS KAKENHI Grant Number JP19H05703, and 22H01299.
The present study is exploring the atmospheric responses to the sea ice reduction in 2020/21 winter by using the numerical model named NICAM with 14-km resolution. The atmospheric response is assessed through sensitivity experiments; observed sea-ice is prescribed to one set experiments, climatological-mean sea ice to the other. The atmosphere model is initialized with the ensemble product of ERA5, in which the reanalysis fields are slightly perturbed by referring to observational errors. Our primary focus is on the atmospheric response to the sea ice reduction on subseasonal scale (up to 10 days), and its dependence on the initial atmospheric condition.
Our preliminary analysis suggests that the sea ice reduction can yield positive sea-level pressure (SLP) response near the Western Siberia with the lead time of 5 - 8 days. The horizontal scale is meso-alpha to synoptic scale. The SLP response acts to slightly intensify the blocking high that induced intense cold air outbreak over East Asia. Although the amplitude is approximately 5hPa at most, the response is statistically significant when the atmospheric model is initialized with the atmospheric conditions on each date from December 27 to 30th 2020. However, the response vanishes with the initial conditions from December 24 to 26th 2020. Thus, it is likely that the response is sensitive to the atmospheric distribution varying on daily scale and its relation to sea ice distribution.
Acknowledgements: This work was supported by MEXT as “Program for Promoting Researches on the Supercomputer Fugaku” (JPMXP1020200305) (Project ID: hp200128/hp210166/hp220167), and by the Arctic Challenge for Sustainability II (ArCS II) Project (Program Grant Number JPMXD1420318865), and by JSPS KAKENHI Grant Number JP19H05703, and 22H01299.