Drivers of Eurasian spring snow cover variability

2020 ◽  
pp. 1
Author(s):  
Taotao Zhang ◽  
Tao Wang ◽  
Yutong Zhao ◽  
Chaoyi Xu ◽  
Yingying Feng ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Wave Train ◽  
Surface Air Temperature ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atmospheric Teleconnection ◽  
The North ◽  
Sst Anomalies

AbstractThe variability of spring snow cover over Eurasia can have notable impacts on the current and following season climate, but the causes of it are poorly understood. This study investigates the potential drivers and the associated physical processes for the first two empirical orthogonal function (EOF) modes of the Eurasian spring snow cover variability during 1967-2018, which are characterized by a continent-wide coherent pattern and a west-east dipole structure, respectively. Analyses show that the spring surface air temperature and snowfall are the direct factors influencing the two modes. We further examined the contributions to the snow cover variability of atmospheric teleconnection patterns, sea surface temperature (SST) anomalies, and variations of Arctic sea ice during spring. The results indicate that circulation anomalies associated with the Arctic Oscillation, Polar–Eurasia, and West Pacific patterns can partly explain the formation of the EOF1 mode, while the EOF2 mode has a close relationship with the East Atlantic–Western Russia pattern. In addition, a horseshoe like monopole structure of SST anomalies over the North Atlantic plays an important role in regulating the EOF2 mode, by inducing a wave train circulation. Moreover, the EOF2 mode is also affected by anomalous circulations induced by the sea ice anomalies in the Barents–Kara Seas. An empirical model using these drivers satisfactorily reproduced the temporal variations of the two EOF modes, implying that our results can substantially improve comprehension of the variability of Eurasian spring snow cover.

Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

2021 ◽  
Author(s):  
Vladimir Semenov ◽  
Tatiana Matveeva
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The North ◽  
Arctic Sea ◽  
Ice Concentration

<p>Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20<sup>th</sup> century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model’s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20<sup>th</sup> century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km<sup>2</sup> in September and 0.7 million km<sup>2</sup> in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21<sup>st</sup> century.</p>

scholarly journals Effects of Arctic sea ice in autumn on extreme cold events over the Tibetan Plateau in the following winter: possible mechanisms

2021 ◽  
Author(s):  
Miao Bi ◽  
Qingquan Li ◽  
Song Yang ◽  
Dong Guo ◽  
Xinyong Shen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Wave Train ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
The Tibetan Plateau ◽  
The North ◽  
Arctic Sea ◽  
Cold Events ◽  
Extreme Cold

AbstractExtreme cold events (ECEs) on the Tibetan Plateau (TP) exert serious impacts on agriculture and animal husbandry and are important drivers of ecological and environmental changes. We investigate the temporal and spatial characteristics of the ECEs on the TP and the possible effects of Arctic sea ice. The daily observed minimum air temperature at 73 meteorological stations on the TP during 1980–2018 and the BCC_AGCM3_MR model are used. Our results show that the main mode of winter ECEs over the TP exhibits the same spatial variation and interannual variability across the whole region and is affected by two wave trains originating from the Arctic. The southern wave train is controlled by the sea ice in the Beaufort Sea. It initiates in the Norwegian Sea, and then passes through the North Atlantic Ocean, the Arabian Sea, and the Bay of Bengal along the subtropical westerly jet stream. It enters the TP from the south and brings warm, humid air from the oceans. By contrast, the northern wave train is controlled by the sea ice in the Laptev Sea. It originates from the Barents and Kara seas, passes through Lake Baikal, and enters the TP from the north, bringing dry and cold air. A decrease in the sea ice in the Beaufort Sea causes positive potential height anomalies in the Arctic. This change enhances the pressure gradient between the Artic and the mid-latitudes, leading to westerly winds in the northern TP, which block the intrusion of cold air into the south. By contrast, a decrease in the sea ice in the Laptev Sea causes negative potential height anomalies in the Artic. This change reduces the pressure gradient between the Artic and the mid-latitudes, leading to easterly winds to the north of the TP, which favors the southward intrusion of cold polar air. A continuous decrease in the amount of sea ice in the Beaufort Sea would reduce the frequency of ECEs over the TP and further aggravate TP warming in winter.

scholarly journals Ocean–Atmosphere State Dependence of the Atmospheric Response to Arctic Sea Ice Loss

2017 ◽  
Vol 30 (5) ◽  
pp. 1537-1552 ◽  
Author(s):  
Joe M. Osborne ◽  
James A. Screen ◽  
Mat Collins
Keyword(s):  
Sea Ice ◽  
North Pacific ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
The North ◽  
Arctic Sea ◽  
Ocean Atmosphere ◽  
Arctic Sea Ice Loss ◽  
The North Pacific ◽  
Sst Anomalies

Abstract The Arctic is warming faster than the global average. This disproportionate warming—known as Arctic amplification—has caused significant local changes to the Arctic system and more uncertain remote changes across the Northern Hemisphere midlatitudes. Here, an atmospheric general circulation model (AGCM) is used to test the sensitivity of the atmospheric and surface response to Arctic sea ice loss to the phase of the Atlantic multidecadal oscillation (AMO), which varies on (multi-) decadal time scales. Four experiments are performed, combining low and high sea ice states with global sea surface temperature (SST) anomalies associated with opposite phases of the AMO. A trough–ridge–trough response to wintertime sea ice loss is seen in the Pacific–North American sector in the negative phase of the AMO. The authors propose that this is a consequence of an increased meridional temperature gradient in response to sea ice loss, just south of the climatological maximum, in the midlatitudes of the central North Pacific. This causes a southward shift in the North Pacific storm track, which strengthens the Aleutian low with circulation anomalies propagating into North America. While the climate response to sea ice loss is sensitive to AMO-related SST anomalies in the North Pacific, there is little sensitivity to larger-magnitude SST anomalies in the North Atlantic. With background ocean–atmosphere states persisting for a number of years, there is the potential to improve predictions of the impacts of Arctic sea ice loss on decadal time scales.

scholarly journals Ural Blocking as an Amplifier of the Arctic Sea Ice Decline in Winter

2017 ◽  
Vol 30 (7) ◽  
pp. 2639-2654 ◽  
Author(s):  
Tingting Gong ◽  
Dehai Luo
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Wave Train ◽  
Surface Air Temperature ◽  
Moisture Flux ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Decline ◽  
Arctic Sea ◽  
Sea Ice Reduction

In this paper, the lead–lag relationship between the Arctic sea ice variability over the Barents–Kara Sea (BKS) and Ural blocking (UB) in winter (DJF) ranging from 1979/80 to 2011/12 is examined. It is found that in a regressed DJF-mean field an increased UB frequency (days) corresponds to an enhanced sea ice decline over the BKS, while the high sea surface temperature over the BKS is accompanied by a significant Arctic sea ice reduction. Lagged daily regression and correlation reveal that the growth and maintenance of the UB that is related to the positive North Atlantic Oscillation (NAO+) through the negative east Atlantic/west Russia (EA/WR−) wave train is accompanied by an intensified negative BKS sea ice anomaly, and the BKS sea ice reduction lags the UB pattern by about four days. Because the intensified UB pattern occurs together with enhanced downward infrared radiation (IR) associated with the intensified moisture flux convergence and total column water over the BKS, the UB pattern contributes significantly to the BKS sea ice decrease on a time scale of weeks through intensified positive surface air temperature (SAT) anomalies resulting from enhanced downward IR. It is also found that the BKS sea ice decline can persistently maintain even when the UB has disappeared, thus indicating that the UB pattern is an important amplifier of the BKS sea ice reduction. Moreover, it is demonstrated that the EA/WR− wave train formed by the combined NAO+ and UB patterns is closely related to the amplified warming over the BKS through the strengthening (weakening) of mid-to-high-latitude westerly wind in the North Atlantic (Eurasia).

Seasonal Variability in the Mechanisms Behind the 2020 Siberian Heatwaves

2022 ◽  
pp. 1-44
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Land Surface ◽  
Arctic Oscillation ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Stratospheric Polar Vortex

Abstract 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.

scholarly journals Observed Atmospheric Response to Cold Season Sea Ice Variability in the Arctic

2014 ◽  
Vol 27 (3) ◽  
pp. 1243-1254 ◽  
Author(s):  
Claude Frankignoul ◽  
Nathalie Sennéchael ◽  
Pierre Cauchy
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
North Pacific ◽  
Cold Season ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atlantic Sector ◽  
The North ◽  
The North Atlantic ◽  
Sst Anomalies

Abstract The relation between weekly Arctic sea ice concentrations (SICs) from December to April and sea level pressure (SLP) during 1979–2007 is investigated using maximum covariance analysis (MCA). In the North Atlantic sector, the interaction between the North Atlantic Oscillation (NAO) and a SIC seesaw between the Labrador Sea and the Greenland–Barents Sea dominates. The NAO drives the seesaw and in return the seesaw precedes a midwinter/spring NAO-like signal of the opposite polarity but with a strengthened northern lobe, thus acting as a negative feedback, with maximum squared covariance at a lag of 6 weeks. Statistical significance decreases when SLP is considered in the whole Northern Hemisphere but it increases when North Pacific SIC is included in the analysis. The maximum squared covariance then occurs after 8 weeks, resembling a combination of the NAO response to the Atlantic SIC seesaw and the Aleutian–Icelandic low seesaw-like response to in-phase SIC changes in the Bering and Okhotsk Seas, which is found to lag the North Pacific SIC. Adding SST anomalies to the SIC anomalies in the MCA leads to a loss of significance when the MCA is limited to the North Atlantic sector and a slight degradation in the Pacific and hemispheric cases, suggesting that SIC is the driver of the midwinter/spring atmospheric signal. However, North Pacific cold season SST anomalies also precede a NAO/Arctic Oscillation (AO)-like SLP signal after a shorter delay of 3–4 weeks.

Nonstationary lagged relationships between the Arctic and the midlatitudes

2020 ◽  
Author(s):  
Erik W. Kolstad ◽  
James A. Screen ◽  
Marius Årthun
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Cold Temperatures ◽  
The North ◽  
Arctic Sea ◽  
The North Atlantic Oscillation ◽  
Sst Anomalies

<p>Statistical relationships between climate variables are good source of seasonal predictability, but can we trust them to be valid in the future? In two recent papers, we investigated the stationarity of some well-known lagged relationships. The predictors were Arctic sea surface temperatures (SSTs) and sea ice cover during autumn, and the predictands were the North Atlantic Oscillation (NAO) and European temperature in winter. The reason for studying these variables was that in recent decades, reduced sea ice and above-normal SSTs in autumn have often preceded negative NAO conditions and cold temperatures in Northern Europe in the following winter. When we looked further back in time, however, we found that the relationships between SST/ice and NAO/temperatures have been highly changeable and sometimes even the complete opposite to that seen recently. One key finding was that, according to two 20th century reanalyses, the strength of the negative lagged correlation between Barents Sea SST anomalies in fall and European temperature anomalies in winter after 1979 is unprecedented since 1900. An analysis of hundreds of simulations from multiple climate models confirms that the relationships vary with time, just due to natural climate variability. This led us to question the causality and/or robustness of the links between the variables and to caution against indiscriminately predicting wintertime weather based on Arctic sea ice and SST anomalies.</p>

scholarly journals On the Predictability of the Winter Euro-Atlantic Climate: Lagged Influence of Autumn Arctic Sea Ice

2015 ◽  
Vol 28 (13) ◽  
pp. 5195-5216 ◽  
Author(s):  
J. García-Serrano ◽  
C. Frankignoul ◽  
G. Gastineau ◽  
A. de la Cámara
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Wave Train ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
Sea Ice Concentration ◽  
Atlantic Sector ◽  
Empirical Prediction ◽  
The North

Abstract Satellite-derived sea ice concentration (SIC) and reanalyzed atmospheric data are used to explore the predictability of the winter Euro-Atlantic climate resulting from autumn SIC variability over the Barents–Kara Seas region (SIC/BK). The period of study is 1979/80–2012/13. Maximum covariance analyses show that the leading predictand is indistinguishable from the North Atlantic Oscillation (NAO). The leading covariability mode between September SIC/BK and winter North Atlantic–European sea level pressure (SLP) is not significant, indicating that no empirical prediction skill can be achieved. The leading covariability mode with either October or November SIC/BK is moderately significant (significance levels <10%), and both predictor fields yield a cross-validated NAO correlation of 0.3, suggesting some empirical prediction skill of the winter NAO index, with sea ice reduction in the Barents–Kara Seas being accompanied by a negative NAO phase in winter. However, only November SIC/BK provides significant cross-validated skill of winter SLP, surface air temperature, and precipitation anomalies over the Euro-Atlantic sector, namely in southwestern Europe. Statistical analysis suggests that November SIC/BK anomalies are associated with a Rossby wave train–like anomaly across Eurasia that affects vertical wave activity modulating the stratospheric vortex strength, which is then followed by downward propagation of anomalies that impact transient-eddy activity in the upper troposphere, helping to settle and maintain the NAO-like pattern at surface. This stratospheric pathway is not detected when using October SIC/BK anomalies. Hence, only November SIC/BK, with a one-month lead time, could be considered as a potential source of regional predictability.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

scholarly journals Contributors to linkage between Arctic warming and East Asian winter climate

2021 ◽  
Author(s):  
Xinping Xu ◽  
Shengping He ◽  
Yongqi Gao ◽  
Botao Zhou ◽  
Huijun Wang
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Initial Conditions ◽  
Wave Train ◽  
East Asian ◽  
Surface Air Temperature ◽  
General Circulation Models ◽  
The Arctic ◽  
Winter Climate ◽  
Arctic Warming

AbstractPrevious modelling and observational studies have shown discrepancies in the interannual relationship of winter surface air temperature (SAT) between Arctic and East Asia, stimulating the debate about whether Arctic change can influence midlatitude climate. This study uses two sets of coordinated experiments (EXP1 and EXP2) from six different atmospheric general circulation models. Both EXP1 and EXP2 consist of 130 ensemble members, each of which in EXP1 (EXP2) was forced by the same observed daily varying sea ice and daily varying (daily climatological) sea surface temperature (SST) for 1982–2014 but with different atmospheric initial conditions. Large spread exists among ensemble members in simulating the Arctic–East Asian SAT relationship. Only a fraction of ensemble members can reproduce the observed deep Arctic warming–cold continent pattern which extends from surface to upper troposphere, implying the important role of atmospheric internal variability. The mechanisms of deep Arctic warming and shallow Arctic warming are further distinguished. Arctic warming aloft is caused primarily by poleward moisture transport, which in conjunction with the surface warming coupled with sea ice melting constitutes the surface-amplified deep Arctic warming throughout the troposphere. These processes associated with the deep Arctic warming may be related to the forcing of remote SST when there is favorable atmospheric circulation such as Rossby wave train propagating from the North Atlantic into the Arctic.

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