A recent weakening of winter temperature association between Arctic and Asia

Arctic warming and its association with the mid-latitudes have been hot topic over the past two decades. Although many studies have explored these issues it is not clear that how their linkage has changed over time. The results show that winter low tropospheric temperatures in Asia experienced two phases over the past two decades. Phase I (2007/2008 to 2012/2013) was characterized by a warm Arctic and cold Eurasia, and phase II by a warm Arctic and warm Eurasia (2013/2014 to 2018/2019). A strengthened association in winter temperature between the Arctic and Asia occurred during phase I, followed by a weakened linkage during phase II. Simulation experiments forced by observed Arctic sea ice variability largely reproduce observed patterns, suggesting that Arctic sea ice loss contributes to phasic (or low-frequency) variations in winter atmosphere and make the Arctic-Asia temperature association fluctuate over time. The weakening of the Arctic-Asia linkage post-2012/2013 was associated with amplified and expanded Arctic warming. The corresponding anomalies in SLP resembled a positive phase North Atlantic Oscillation (NAO) during phase II. This study implies that the phasic warm Arctic-cold Eurasia and warm Arctic-warm Eurasia patterns would alternately happen in the context of Arctic sea ice loss, which increase the difficulty to correctly predict Asian winter temperature.

A Robust Arctic Amplification Factor Throughout the Last 21,000 Years

Arctic amplification (AA), a phenomenon that a larger change in temperature near the Arctic areas than the Northern Hemisphere average in the past 100+ years, has significant impacts on mid-latitude weather and climate, and therefore is of great concern in current climate projections. Previous studies suggest a wide range of AA factors from 1.0 to 12.5 using either the 20th century observations or climate model hindcasts. In the present paper, we explore the diversity of AA factor in a long-term transient simulation covering the past glacial-to-interglacial years. It is shown that the natural AA phenomenon is essentially linked with North Atlantic sea ice changes through ice-albedo feedback with a narrowed and robust AA factor of 2.5±0.8 throughout the last 21,000 years. Current observed AA phenomenon is a mixed result combining sea ice melting induced AA mode with GHGs induced global uniform warming, and thus has an AA factor slightly less than 2.5. In the future, as Arctic sea ice gradually melts off, we speculate that AA phenomenon might fade off accordingly and the AA factor will decline close to 1.0 in 1-2 centuries. Our findings provide new evidence for better understanding the range of AA factor and associated key physical processes, and provide new insights for AA’s projection in current anthropogenic warming climate.

Anomalous Arctic Sea Ice Melting Linked to Recent Warming Amplification

This study investigates the mechanism of seasonal sea ice variation and recent warming amplification. Seasonal temperature changes in the vertical structure reveal that the autumn and winter seasons are warming more than summer. The thermodynamic processes of sea-ice-air interactions via the heat flux component have been studied. The summer Arctic Sea ice has receded by half (∼52%), producing excessive heat. This sea ice loss plays a significant role in determining the heat exchange between the ocean and atmosphere in the following season. During a warm season, the ocean heats up due to incident solar radiation. As a result, delayed ice growth and atmospheric warming occur. Sea ice and heat flux feedbacks explain a large part of Arctic atmospheric warming. These abrupt changes are closely coupled to accelerated Arctic Sea ice loss and atmospheric warming, which are still uncertain.

The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China: 2020/21 La Niña and 2020 Arctic Sea Ice Loss

Several consecutive extreme cold events impacted China during the first half of winter 2020/21, breaking the low-temperature records in many cities. How to make accurate climate predictions of extreme cold events is still an urgent issue. The synergistic effect of the warm Arctic and cold tropical Pacific has been demonstrated to intensify the intrusions of cold air from polar regions into middle-high latitudes, further influencing the cold conditions in China. However, climate models failed to predict these two ocean environments at expected lead times. Most seasonal climate forecasts only predicted the 2020/21 La Niña after the signal had already become apparent and significantly underestimated the observed Arctic sea ice loss in autumn 2020 with a 1–2 month advancement. In this work, the corresponding physical factors that may help improve the accuracy of seasonal climate predictions are further explored. For the 2020/21 La Niña prediction, through sensitivity experiments involving different atmospheric-oceanic initial conditions, the predominant southeasterly wind anomalies over the equatorial Pacific in spring of 2020 are diagnosed to play an irreplaceable role in triggering this cold event. A reasonable inclusion of atmospheric surface winds into the initialization will help the model predict La Niña development from the early spring of 2020. For predicting the Arctic sea ice loss in autumn 2020, an anomalously cyclonic circulation from the central Arctic Ocean predicted by the model, which swept abnormally hot air over Siberia into the Arctic Ocean, is recognized as an important contributor to successfully predicting the minimum Arctic sea ice extent.

Analyzing Variations in the Association of Eurasian Winter–Spring Snow Water Equivalent and Autumn Arctic Sea Ice

Because Eurasian snow water equivalent (SWE) is a key factor affecting the climate in the Northern Hemisphere, understanding the distribution characteristics of Eurasian SWE is important. Through empirical orthogonal function (EOF) analysis, we found that the first and second modes of Eurasian winter SWE present the distribution characteristics of an east–west dipole and north–south dipole, respectively. Moreover, the distribution of the second mode is caused by autumn Arctic sea ice, with the distribution of the north–south dipole continuing into spring. As the sea ice of the Barents–Kara Sea (BKS) decreases, a negative-phase Arctic oscillation (AO) is triggered over the Northern Hemisphere in winter, with warm and humid water vapor transported via zonal water vapor flux over the North Atlantic to southwest Eurasia, encouraging the accumulation of SWE in the southwest. With decreases in BKS sea ice, zonal water vapor transport in northern Eurasia is weakened, with meridional water vapor flux in northern Eurasia obstructing water vapor transport from the North Atlantic, discouraging the accumulation of SWE in northern Eurasia in winter while helping preserve the cold climate of the north. The distribution characteristics of Eurasian spring SWE are determined primarily by the memory effect of winter SWE. Whether analyzed through linear regression or support vector machine (SVM) methods, BKS sea ice is a good predictor of Eurasian winter SWE.

Seasonal Variability in the Mechanisms Behind the 2020 Siberian Heatwaves

Record breaking heatwaves and wildfires immersed Siberia during the boreal spring of 2020 following an anomalously warm winter. Springtime heatwaves are becoming more common in the region, with statistically significant trends in the frequency, magnitude, and duration of heatwave events over the past four decades. Mechanisms by which the heatwaves occur and contributing factors differ by season. Winter heatwave frequency is correlated with the atmospheric circulation, particularly the Arctic Oscillation, while the frequency of heatwaves during the spring months is highly correlated with aspects of the land surface including snow cover, albedo, and latent heat flux. Idealized AMIP-style experiments are used to quantify the contribution of suppressed Arctic sea ice and snow cover over Siberia on the atmospheric circulation, surface energy budget, and surface air temperature in Siberia during the winter and spring of 2020. Sea ice concentration contributed to the strength of the stratospheric polar vortex and Arctic Oscillation during the winter months, thereby influencing the tropospheric circulation and surface air temperature over Siberia. Warm temperatures across the region resulted in an earlier than usual recession of the winter snowpack. The exposed land surface contributed to up to 20% of the temperature anomaly during the spring through the albedo feedback and changes in the ratio of the latent and sensible heat fluxes. This, in combination with favorable atmospheric circulation patterns, resulted in record breaking heatwaves in Siberia in the spring of 2020.

Antarctic sea ice types from active and passive microwave remote sensing

Polar sea ice is one of the Earth’s climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014 and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly of MYI. Due to differences in physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently, no method has existed to monitor the distribution and temporal development of Antarctic ice types, particularly MYI throughout the freezing season and on decadal time scales. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The first circumpolar, long-term time series of Antarctic sea ice types; MYI, first-year ice and young ice is being established, so far covering years 2013–2019. Qualitative comparison with synthetic aperture radar data, with charts of the development stage of the sea ice, and with Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval for the first time allows insight into the evolution and dynamics of Antarctic sea ice types. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors.

FEATURES OF SPATIAL-TIME VARIABILITY OF TOTAL TURBULENT HEAT FLUXES AT THE OCEAN-ATMOSPHERE INTERFACE IN THE ATLANTIC

The features of the spatial distribution of climate values and the coefficients of linear trends of total tur-bulent heat fluxes are revealed, based on NCEP/NCAR reanalysis data for 1950–2020 for the Atlantic Ocean. Variability of total turbulent heat fluxes is investigated on scales of more than 10 and more than 30 years. It is shown that the trends of average annual total heat fluxes significant at 95% level in most part of the Atlantic Ocean area are negative (except for the western parts of anticyclonic gyres and area of arctic sea ice edge). It is confirmed that the maxima of the low-frequency variability of the total heat fluxes correspond to important energy-active zones of the Atlantic, they are North Atlantic deep-water mass formation region, ice edge zone in the north of the North Atlantic and the Atlantic sector of the Arc-tic Ocean.