Climate Extremes of the 2019/2020 Winter in Northern Eurasia: Contributions by the Climate Trend and Interannual Variability Related to the Arctic Oscillation

2021 ◽  
pp. 5-16
Author(s):  
V. N. Kryjov ◽  
Keyword(s):  
Interannual Variability ◽  
Arctic Oscillation ◽  
Global Climate ◽  
Linear Trend ◽  
Climate Extremes ◽  
The Arctic ◽  
Northern Eurasia ◽  
Climate Trend ◽  
Temperature And Precipitation ◽  
The Arctic Oscillation

The 2019/2020 wintertime (December–March) anomalies of sea level pressure, temperature, and precipitation are analyzed. The contribution of the 40-year linear trend in these parameters associated with global climate change and of the interannual variability associated with the Arctic Oscillation (AO) is assessed. In the 2019/2020 winter, extreme zonal circulation was observed. The mean wintertime AO index was 2.20, which ranked two for the whole observation period (started in the early 20th century) and was outperformed only by the wintertime index of 1988/1989. It is shown that the main contribution to the 2019/2020 wintertime anomalies was provided by the AO. A noticeable contribution of the trend was observed only in the Arctic. Extreme anomalies over Northern Eurasia were mainly associated with the AO rather than the trend. However, the AO-related anomalies, particularly air temperature anomalies, were developing against the background of the trend-induced increased mean level.

scholarly journals Where can the Arctic oscillation be reconstructed? Towards a reconstruction of climate modes based on stable teleconnections

2005 ◽  
Vol 1 (1) ◽  
pp. 17-56 ◽  
Author(s):  
G. Lohmann ◽  
N. Rimbu ◽  
M. Dima
Keyword(s):  
Arctic Oscillation ◽  
Ice Core ◽  
The Arctic ◽  
Boreal Winter ◽  
Arctic Oscillation Index ◽  
Climate Modes ◽  
Teleconnection Patterns ◽  
Temperature And Precipitation ◽  
The Arctic Oscillation ◽  
Coral Tree

Abstract. Proxy data can bring observed climate variability of the last 100 years into a long-term context. We identify regions of the Northern Hemisphere where the teleconnection patterns of the Arctic Oscillation are stationary. Our method provides a systematic way to examine optimal sites for the reconstruction of climate modes based on paleoclimatic archives that sensitively record temperature and precipitation variations. We identify the regions for boreal winter and spring that can be used to reconstruct the Arctic Oscillation index in the pre-instrumental period. Finally, this technique is applied to high resolution coral, tree ring, ice core and mollusk shell data to understand proxy-climate teleconnections and their use for climate reconstructions.

scholarly journals Interannual Variability of the North American Cold Air Stream and Associated Synoptic Circulations

2017 ◽  
Vol 30 (23) ◽  
pp. 9575-9590 ◽  
Author(s):  
Yuki Kanno ◽  
John E. Walsh ◽  
Toshiki Iwasaki
Keyword(s):  
United States ◽  
Interannual Variability ◽  
Rocky Mountains ◽  
Arctic Oscillation ◽  
Potential Temperature ◽  
The Arctic ◽  
Boreal Winter ◽  
Stratospheric Polar Vortex ◽  
Cold Air ◽  
The Arctic Oscillation

In boreal winter, the cold air mass (CAM) flux of air with a potential temperature below 280 K forms climatological mean CAM streams in East Asia and North America (NA). This study diagnoses the interannual variability of the NA stream by an analysis of the CAM flux across 60°N between Greenland and the Rocky Mountains. The first empirical orthogonal function (EOF) represents the variations in intensity of the NA stream. When the first principal component (PC1) is highly positive, the central part of the NA stream is intensified, with cold anomalies east of the Rocky Mountains. At the same time, a stratospheric polar vortex tends to split or displace toward NA. PC1 is highly correlated with the tropical Northern Hemisphere pattern, implying that this pattern is associated with the intensity of the NA stream. The second EOF shows a longitudinal shift of the NA stream toward Greenland or the Rocky Mountains. A highly negative PC2 results in a cold anomaly from western Canada to the Midwestern United States and anomalous heavy snowfall in the northeastern United States. PC2 is positively correlated with the Arctic Oscillation, which suggests that the longitudinal position of the NA stream varies with the Arctic Oscillation. These results illustrate how the intensity and location of cold air outbreaks vary with large-scale modes of atmospheric variability, with corresponding implications for the predictability of winter severity in NA.

scholarly journals Self-Organizing Maps of Atmospheric Circulation and Interannual Variability of Hydrometeorological Fields in the Arctic

2020 ◽  
Author(s):  
E. E. Lemeshko ◽  
Keyword(s):  
Interannual Variability ◽  
Atmospheric Circulation ◽  
Atmospheric Pressure ◽  
Arctic Oscillation ◽  
Sea Surface ◽  
The Arctic ◽  
Self Organizing Maps ◽  
Arctic Oscillation Index ◽  
The Arctic Oscillation ◽  
Self Organizing

The article suggests the use of a nonlinear method of data analysis based on a neural network – an algorithm of Kohonen self-organizing maps for the task of typing the atmospheric surface circulation in the Arctic. Based on the construction of self-organizing surface pressure maps, the seasonal and interannual variability of atmospheric circulation in the Arctic for the period 1979–2018 is studied. Several modes were distinguished: cyclonic, two anticyclonic, and three mixed types. Indices of seasonal and annual repeatability of self-organizing atmospheric pressure maps are introduced, which allow us to study the temporal variability of atmospheric circulation modes and a composite method is proposed for calculating connected maps of other hydrometeorological parameters. The regimes of variability of the area of sea ice distribution and sea surface temperature depending on the type of atmospheric circulation are highlighted. Depending on the type of wind regime, there is a change in the area of sea ice distribution due to the variability of the flows of warm Atlantic waters into the Arctic Ocean. The characteristic types of sea surface temperature variability in the Barents Sea are identified, which are modulated by cyclonic / anticyclonic regimes of atmospheric circulation in the region and are an indicator of heat advection by the Atlantic waters. The interrelation is established of the repeatability index of self-organizing atmospheric pressure maps characterizing the types of atmospheric circulation with the variability of the Arctic Oscillation Index. The revealed regularities of the change in the types of cyclonic-anticyclonic atmospheric circulation are manifested in the interannual variability of the introduced repeatability index of selforganizing atmospheric pressure maps, which is a development of the Arctic Oscillation Index, improves understanding of the atmospheric climate circulation regimes in the Arctic.

scholarly journals Snowfall distribution and its response to the Arctic Oscillation: an evaluation of HighResMIP models in the Arctic using CPR/CloudSat observations

2019 ◽  
Vol 12 (8) ◽  
pp. 3759-3772 ◽  
Author(s):  
Manu Anna Thomas ◽  
Abhay Devasthale ◽  
Tristan L'Ecuyer ◽  
Shiyu Wang ◽  
Torben Koenigk ◽  
...  
Keyword(s):  
General Circulation ◽  
Arctic Oscillation ◽  
Climate Models ◽  
Global Climate ◽  
General Circulation Models ◽  
Specific Pattern ◽  
Surface Radiation ◽  
Radiation Budget ◽  
The Arctic ◽  
The Arctic Oscillation

Abstract. A realistic representation of snowfall in general circulation models (GCMs) of global climate is important to accurately simulate snow cover, surface albedo, high-latitude precipitation and thus the surface radiation budget. Hence, in this study, we evaluate snowfall in a range of climate models run at two different resolutions by comparing to the latest estimates of snowfall from the CloudSat Cloud Profiling Radar over the northern latitudes. We also evaluate whether the finer-resolution versions of the GCMs simulate the accumulated snowfall better than their coarse-resolution counterparts. As the Arctic Oscillation (AO) is the prominent mode of natural variability in the polar latitudes, the snowfall variability associated with the different phases of the AO is examined in both models and in our observational reference. We report that the statistical distributions of snowfall differ considerably between the models and CloudSat observations. While CloudSat shows an exponential distribution of snowfall, the models show a Gaussian distribution that is heavily positively skewed. As a result, the 10th and 50th percentiles, representing the light and median snowfall, are overestimated by up to factors of 3 and 1.5, respectively, in the models investigated here. The overestimations are strongest during the winter months compared to autumn and spring. The extreme snowfall represented by the 90th percentiles, on the other hand, is positively skewed, underestimating the snowfall estimates by up to a factor of 2 in the models in winter compared to the CloudSat estimates. Though some regional improvements can be seen with increased spatial resolution within a particular model, it is not easy to identify a specific pattern that holds across all models. The characteristic snowfall variability associated with the positive phase of AO over Greenland Sea and central Eurasian Arctic is well captured by the models.

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