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 Dynamical Link between the Barents–Kara Sea Ice and the Arctic Oscillation

2016 ◽  
Vol 29 (14) ◽  
pp. 5103-5122 ◽  
Author(s):  
Xiao-Yi Yang ◽  
Xiaojun Yuan ◽  
Mingfang Ting
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Arctic Oscillation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Mean Flow ◽  
Stratospheric Polar Vortex ◽  
Arctic Sea ◽  
Barents And Kara Seas ◽  
The Arctic Oscillation

Abstract The recent accelerated Arctic sea ice decline has been proposed as a possible forcing factor for midlatitude circulation changes, which can be projected onto the Arctic Oscillation (AO) and/or North Atlantic Oscillation (NAO) mode. However, the timing and physical mechanisms linking AO responses to the Arctic sea ice forcing are not entirely understood. In this study, the authors suggest a connection between November sea ice extent in the Barents and Kara Seas and the following winter’s atmospheric circulation in terms of the fast sea ice retreat and the subsequent modification of local air–sea heat fluxes. In particular, the dynamical processes that link November sea ice in the Barents and Kara Seas with the development of AO anomalies in February is explored. In response to the lower-tropospheric warming associated with the initial thermal effect of the sea ice loss, the large-scale atmospheric circulation goes through a series of dynamical adjustment processes: The decelerated zonal-mean zonal wind anomalies propagate gradually from the subarctic to midlatitudes in about one month. The equivalent barotropic AO dipole pattern develops in January because of wave–mean flow interaction and firmly establishes itself in February following the weakening and warming of the stratospheric polar vortex. This connection between sea ice loss and the AO mode is robust on time scales ranging from interannual to decadal. Therefore, the recent winter AO weakening and the corresponding midlatitude climate change may be partly associated with the early winter sea ice loss in the Barents and Kara Seas.

scholarly journals Summertime changes in climate extremes over the peripheral Arctic regions after a sudden sea ice retreat

2021 ◽  
Author(s):  
Steve Delhaye ◽  
Thierry Fichefet ◽  
François Massonnet ◽  
David Docquier ◽  
Rym Msadek ◽  
...  
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Climate Extremes ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Maximum Surface ◽  
Temperature And Precipitation ◽  
Arctic Regions ◽  
Arctic Sea

Abstract. The retreat of Arctic sea ice is frequently considered as a possible driver of changes in climate extremes in the Arctic and possibly down to mid-latitudes. However, it is unclear how the atmosphere will respond to a near-total retreat of summer Arctic sea ice, a reality that might occur in the foreseeable future. This study explores this question by conducting sensitivity experiments with two global coupled climate models run at two different horizontal resolutions to investigate the change in temperature and precipitation extremes during summer over peripheral Arctic regions following a sudden reduction in summer Arctic sea ice cover. An increase in frequency and persistence of maximum surface air temperature is found in all peripheral Arctic regions during the summer when sea ice loss occurs. For each million km2 of Arctic sea ice extent reduction, the absolute frequency of days exceeding the surface air temperature of the climatological 90th percentile increases by ~4 % over the Svalbard area, and the duration of warm spells increases by ~1 day per month over the same region. Furthermore, we find that the 10th percentile of surface daily air temperature increases more than the 90th percentile, leading to a weakened diurnal cycle of surface air temperature. Finally, an increase in extreme precipitation, which is less robust (statistically speaking) than the increase in extreme temperatures, is found in all regions in summer. These findings suggest that a sudden retreat of summer Arctic sea ice clearly impacts the extremes in maximum surface air temperature and precipitation over the peripheral Arctic regions with the largest influence over inhabited islands such as Svalbard or Northern Canada. Nonetheless, even with a large sea ice reduction in regions close to the North Pole, the local precipitation response is relatively small compared to internal climate variability.

scholarly journals Response of the Wintertime Northern Hemisphere Atmospheric Circulation to Current and Projected Arctic Sea Ice Decline: A Numerical Study with CAM5

2014 ◽  
Vol 27 (1) ◽  
pp. 244-264 ◽  
Author(s):  
Yannick Peings ◽  
Gudrun Magnusdottir
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Northern Hemisphere ◽  
Numerical Study ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Late Winter ◽  
Stratospheric Polar Vortex ◽  
Sea Ice Decline ◽  
Arctic Sea

Abstract The wintertime Northern Hemisphere (NH) atmospheric circulation response to current (2007–12) and projected (2080–99) Arctic sea ice decline is examined with the latest version of the Community Atmospheric Model (CAM5). The numerical experiments suggest that the current sea ice conditions force a remote atmospheric response in late winter that favors cold land surface temperatures over midlatitudes, as has been observed in recent years. Anomalous Rossby waves forced by the sea ice anomalies penetrate into the stratosphere in February and weaken the stratospheric polar vortex, resulting in negative anomalies of the northern annular mode (NAM) that propagate downward during the following weeks, especially over the North Pacific. The seasonality of the response is attributed to timing of the phasing between the forced and climatological waves. When sea ice concentration taken from projections of conditions at the end of the twenty-first century is prescribed to the model, negative anomalies of the NAM are visible in the troposphere, both in early and late winter. This response is mainly driven by the large warming of the lower troposphere over the Arctic, as little impact is found in the stratosphere in this experiment. As a result of the thermal expansion of the polar troposphere, the westerly flow is decelerated and a weak but statistically significant increase of the midlatitude meanders is identified. However, the thermodynamical response extends beyond the Arctic and offsets the dynamical effect, such that the stronger sea ice forcing has limited impact on the intensity of cold extremes over midlatitudes.

scholarly journals Snow Depth and Air Temperature Seasonality on Sea Ice Derived From Snow Buoy Measurements

2021 ◽  
Vol 8 ◽  
Author(s):  
Marcel Nicolaus ◽  
Mario Hoppmann ◽  
Stefanie Arndt ◽  
Stefan Hendricks ◽  
Christian Katlein ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Weddell Sea ◽  
The Arctic ◽  
Validation Data ◽  
Antarctic Sea Ice

Snow depth on sea ice is an essential state variable of the polar climate system and yet one of the least known and most difficult to characterize parameters of the Arctic and Antarctic sea ice systems. Here, we present a new type of autonomous platform to measure snow depth, air temperature, and barometric pressure on drifting Arctic and Antarctic sea ice. “Snow Buoys” are designed to withstand the harshest environmental conditions and to deliver high and consistent data quality with minimal impact on the surface. Our current dataset consists of 79 time series (47 Arctic, 32 Antarctic) since 2013, many of which cover entire seasonal cycles and with individual observation periods of up to 3 years. In addition to a detailed introduction of the platform itself, we describe the processing of the publicly available (near real time) data and discuss limitations. First scientific results reveal characteristic regional differences in the annual cycle of snow depth: in the Weddell Sea, annual net snow accumulation ranged from 0.2 to 0.9 m (mean 0.34 m) with some regions accumulating snow in all months. On Arctic sea ice, the seasonal cycle was more pronounced, showing accumulation from synoptic events mostly between August and April and maxima in autumn. Strongest ablation was observed in June and July, and consistently the entire snow cover melted during summer. Arctic air temperature measurements revealed several above-freezing temperature events in winter that likely impacted snow stratigraphy and thus preconditioned the subsequent spring snow cover. The ongoing Snow Buoy program will be the basis of many future studies and is expected to significantly advance our understanding of snow on sea ice, also providing invaluable in situ validation data for numerical simulations and remote sensing techniques.

Winter arctic sea-ice cover variability and the prediction of spring vegetation growth over Eurasia

2020 ◽  
Author(s):  
Liuqing Ji ◽  
Ke Fan
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Air Temperature ◽  
Prediction Models ◽  
Surface Air Temperature ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Hybrid Scheme ◽  
Coupled Modes ◽  
Previous Season

<p align="justify">The changes in Eurasian vegetation not only have important effects on regional climate, but also have effects on global temperatures and the carbon cycle<span>. </span>In this study, the interannual linkage between spring vegetation <span>growth</span> over Eurasia and winter sea-ice cover over the Barents Sea (SICBS), as well as the <span>prediction of spring Euraisan vegetation </span>are investigated. The Normalized Difference Vegetation Index (NDVI) derived from the advanced very high resolution radiometer is used as the proxy of vegetation <span>growth</span>. During 1982–2015, the winter SICBS is significantly correlated with the spring NDVI over Eurasia (NDVIEA). The positive (negative) winter SICBS anomalies tend to increase (decrease) the spring NDVIEA. The increased winter SICBS corresponds to higher winter surface air temperature and soil temperature over most parts of Eurasia, and in turn, corresponds to less winter snow cover and less snow water equivalent. The persistent less and thinner snow cover from winter to spring over Eurasia, especially over Western and Central Siberia, tends to induce increased surface air temperature through decreased surface albedo and less snowmelt latent heat. Subsequently, the increased surface air temperature corresponding to increased SICBS contributes to higher vegetation <span>growth</span> over Eurasia in spring and vice versa. <span>Based on this linkage, s</span>easonal predictions of spring NDVI over Eurasia are explored by applying the year-to-year increment approach. The prediction models were developed based on the coupled modes of singular value decomposition analyses between Eurasian NDVI and climate factors. One synchronous predictor, the spring surface air temperature from the NCEP<span>’</span>s Climate Forecast System (SAT-CFS), and three previous-season predictors (winter SICBS, winter sea surface temperature over the equatorial Pacific (SSTP), and winter North Atlantic Oscillation (NAO) were chosen to develop four single-predictor schemes: the SAT-CFS scheme, SICBS scheme, SSTP scheme, and NAO scheme. Meanwhile, a statistical scheme that involves the three previous-season predictors (i.e., SICBS, SSTP, and NAO) and a hybrid scheme that includes all four predictors are also proposed. To evaluate the prediction skills of the schemes, one-year-out cross-validation and independent hindcast results are analyzed, revealing the hybrid scheme as having the best prediction skill in terms of both the spatial pattern and the temporal variability of spring Eurasian NDVI.</p>

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.

scholarly journals Trends and regime shifts in climatic conditions and river runoff in Estonia during 1951–2015

2017 ◽  
Vol 8 (4) ◽  
pp. 963-976 ◽  
Author(s):  
Jaak Jaagus ◽  
Mait Sepp ◽  
Toomas Tamm ◽  
Arvo Järvet ◽  
Kiira Mõisja
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Large Scale ◽  
Regime Shifts ◽  
The Arctic ◽  
Dry Period ◽  
Snow Cover Duration ◽  
Large Scale Atmospheric Circulation

Abstract. Time series of monthly, seasonal and annual mean air temperature, precipitation, snow cover duration and specific runoff of rivers in Estonia are analysed for detecting of trends and regime shifts during 1951–2015. Trend analysis is realised using the Mann–Kendall test and regime shifts are detected with the Rodionov test (sequential t-test analysis of regime shifts). The results from Estonia are related to trends and regime shifts in time series of indices of large-scale atmospheric circulation. Annual mean air temperature has significantly increased at all 12 stations by 0.3–0.4 K decade−1. The warming trend was detected in all seasons but with the higher magnitude in spring and winter. Snow cover duration has decreased in Estonia by 3–4 days decade−1. Changes in precipitation are not clear and uniform due to their very high spatial and temporal variability. The most significant increase in precipitation was observed during the cold half-year, from November to March and also in June. A time series of specific runoff measured at 21 stations had significant seasonal changes during the study period. Winter values have increased by 0.4–0.9 L s−1 km−2 decade−1, while stronger changes are typical for western Estonia and weaker changes for eastern Estonia. At the same time, specific runoff in April and May have notably decreased indicating the shift of the runoff maximum to the earlier time, i.e. from April to March. Air temperature, precipitation, snow cover duration and specific runoff of rivers are highly correlated in winter determined by the large-scale atmospheric circulation. Correlation coefficients between the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices reflecting the intensity of westerlies, and the studied variables were 0.5–0.8. The main result of the analysis of regime shifts was the detection of coherent shifts for air temperature, snow cover duration and specific runoff in the late 1980s, mostly since the winter of 1988/1989, which are, in turn, synchronous with the shifts in winter circulation. For example, runoff abruptly increased in January, February and March but decreased in April. Regime shifts in annual specific runoff correspond to the alternation of wet and dry periods. A dry period started in 1964 or 1963, a wet period in 1978 and the next dry period at the beginning of the 21st century.

scholarly journals Stationary Wave Interference and Its Relation to Tropical Convection and Arctic Warming

2016 ◽  
Vol 29 (4) ◽  
pp. 1369-1389 ◽  
Author(s):  
Michael Goss ◽  
Steven B. Feldstein ◽  
Sukyoung Lee
Keyword(s):  
Sea Ice ◽  
Stationary Wave ◽  
Warm Pool ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Boreal Winter ◽  
Destructive Interference ◽  
Constructive Interference ◽  
Sea Ice Concentration ◽  
Stratospheric Polar Vortex

Abstract The interference between transient eddies and climatological stationary eddies in the Northern Hemisphere is investigated. The amplitude and sign of the interference is represented by the stationary wave index (SWI), which is calculated by projecting the daily 300-hPa streamfunction anomaly field onto the 300-hPa climatological stationary wave. ERA-Interim data for the years 1979 to 2013 are used. The amplitude of the interference peaks during boreal winter. The evolution of outgoing longwave radiation, Arctic temperature, 300-hPa streamfunction, 10-hPa zonal wind, Arctic sea ice concentration, and the Arctic Oscillation (AO) index are examined for days of large SWI values during the winter. Constructive interference during winter tends to occur about one week after enhanced warm pool convection and is followed by an increase in Arctic surface air temperature along with a reduction of sea ice in the Barents and Kara Seas. The warming of the Arctic does occur without prior warm pool convection, but it is enhanced and prolonged when constructive interference occurs in concert with enhanced warm pool convection. This is followed two weeks later by a weakening of the stratospheric polar vortex and a decline of the AO. All of these associations are reversed in the case of destructive interference. Potential climate change implications are briefly discussed.

scholarly journals Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

2016 ◽  
Author(s):  
Kwang-Yul Kim ◽  
Benjamin D. Hamlington ◽  
Hanna Na ◽  
Jinju Kim
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
The Arctic ◽  
Turbulent Heat ◽  
Arctic Amplification ◽  
Ice Melting ◽  
Downward Longwave Radiation

Abstract. Sea ice melting is proposed as a primary reason for the Artic amplification, although physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice melting in the Arctic Ocean and the Arctic amplification. While sea ice melting is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains to be thin in winter only in the Barents-Kara Seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice melting warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be ice free. A 1 % reduction in sea ice concentration in winter leads to ~ 0.76 W m−2 increase in upward heat flux, ~ 0.07 K increase in 850 hPa air temperature, ~ 0.97 W m−2 increase in downward longwave radiation, and ~ 0.26 K increase in surface air temperature. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort Seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara Seas and Laptev, East Siberian, Chukchi, and Beaufort Seas.

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