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>

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 The observed recent surface air temperature development across Svalbard and concurring footprints in local sea ice cover

2020 ◽  
Vol 40 (12) ◽  
pp. 5246-5265 ◽  
Author(s):  
Sandro Dahlke ◽  
Nicholas E. Hughes ◽  
Penelope M. Wagner ◽  
Sebastian Gerland ◽  
Tomasz Wawrzyniak ◽  
...  
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Ice Cover ◽  
Temperature Development

scholarly journals On the Origin of the Surface Air Temperature Difference between the Hemispheres in Earth's Present-Day Climate

2013 ◽  
Vol 26 (18) ◽  
pp. 7136-7150 ◽  
Author(s):  
Georg Feulner ◽  
Stefan Rahmstorf ◽  
Anders Levermann ◽  
Silvia Volkwardt
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Air Temperature ◽  
Ocean Circulation ◽  
Temperature Difference ◽  
Greenhouse Gas Emission ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
Polar Regions ◽  
Additional Contribution

Abstract In today's climate, the annually averaged surface air temperature in the Northern Hemisphere (NH) is 1°–2°C higher than in the Southern Hemisphere (SH). Historically, this interhemispheric temperature difference has been attributed to a number of factors, including seasonal differences in insolation, the larger area of (tropical) land in the NH, the particularities of the Antarctic in terms of albedo and temperature, and northward heat transport by ocean circulation. A detailed investigation of these factors and their contribution to the temperature difference, however, has to the authors' knowledge not been performed so far. Here the origin of the interhemispheric temperature difference is traced using an assessment of climatological data and the observed energy budget of Earth as well as model simulations. It is found that for the preindustrial climate the temperature difference is predominantly due to meridional heat transport in the oceans, with an additional contribution from the albedo differences between the polar regions. The combination of these factors (that are to some extent coupled) governs the evolution of the temperature difference over the past millennium. Since the beginning of industrialization the interhemispheric temperature difference has increased due to melting of sea ice and snow in the NH. Furthermore, the predicted higher rate of warming over land as compared to the oceans contributes to this increase. Simulations for the twenty-first century show that the interhemispheric temperature difference continues to grow for the highest greenhouse gas emission scenarios due to the land–ocean warming contrast and the strong loss of Arctic sea ice, whereas the decrease in overturning strength dominates for the more moderate scenarios.

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 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.

scholarly journals Implications of all season Arctic sea-ice anomalies on the stratosphere

2012 ◽  
Vol 12 (5) ◽  
pp. 12423-12451
Author(s):  
D. Cai ◽  
M. Dameris ◽  
H. Garny ◽  
T. Runde
Keyword(s):  
Sea Ice ◽  
Boundary Conditions ◽  
Air Temperature ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Time Slice ◽  
Wave Radiation ◽  
Arctic Sea ◽  
The Impact

Abstract. In this study the impact of a substantially reduced Arctic sea-ice cover on the lower and middle stratosphere is investigated. For this purpose two simulations with fixed boundary conditions (the so-called time-slice mode) were performed with a Chemistry-Climate Model. A reference time-slice with boundary conditions representing the year 2000 is compared to a second sensitivity simulation in which the boundary conditions are identical apart from the polar sea-ice cover, which is set to represent the years 2089–2099. Three features of Arctic air temperature response have been identified which are worth to be discussed in detail. Firstly, tropospheric mean polar temperatures increase up to 7 K during winter. This warming is primarily driven by changes in outgoing long-wave radiation. Secondly, temperatures decrease significantly in the summer stratosphere caused by a decline in outgoing short-wave radiation, accompanied by a characteristic increase of ozone mixing ratios. Thirdly, there are short periods of statistical significant temperature anomalies in the winter stratosphere probably driven by modified planetary wave activity. Both the internal as well as the inter-annual variability of Arctic sea-ice content is related to Arctic climatic fields like surface air temperature, sea level pressure or precipitation, which are analogue with the variability of the Arctic Oscillation (AO)-index. In this study significant changes in the AO-index are detected in the course of winter. Neutral phases of AO appear more often. As expected, the dominating dynamical response of the stratosphere during winter turned out to be consistent to alterations in the tropospheric AO, although it is not statistically significant most of the time.

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