scholarly journals Seasonal Changes in Arctic Cooling After Single Mega Volcanic Eruption

2021 ◽  
Vol 9 ◽  
Author(s):  
Bin Liu ◽  
Chen Zhao ◽  
Ling Zhu ◽  
Jian Liu
Keyword(s):  
Heat Transfer ◽  
Solar Radiation ◽  
Heat Source ◽  
Sea Ice ◽  
Seasonal Changes ◽  
Volcanic Eruption ◽  
Rapid Expansion ◽  
The Arctic ◽  
Winter Cooling ◽  
Arctic Summer

To investigate the pure long-term influence of single mega volcanic eruption (SMVE) of universal significance on Arctic temperature changes in summer and winter, the Samalas eruption in Indonesia which is the largest eruption over the past millennium is selected as an ideal eruption for simulation study based on Community Earth System Model. The significant Arctic cooling lasts for 16 years after the Samalas eruption. The obvious Arctic cooling shifts from summer to winter, and this seasonal change of cooling after the SMVE only exists in the high-latitude Arctic region. The cooling range in Arctic summer is larger than that in winter during the first 2 years, due to the strong weakening effect of volcanic aerosol on summer incident solar radiation and the snow-ice positive feedback caused by the rapid expansion of summer sea ice, while the winter sea ice in the same period doesn’t increase obviously. Starting from the third year, the Arctic winter cooling is more intense and lasting than summer cooling. The direct weakening effect of aerosol on solar radiation, which is the main heat source in Arctic summer, is greatly weakened during this period, making summer cooling difficult to sustain. However, as the main heat source in Arctic winter, the sea surface upward longwave radiation, sensible heat, and latent heat transport still maintain a large decrease. Furthermore, sea ice expansion and albedo increase result in the decrease in solar radiation and heat absorbed and stored by the ocean in summer. And the isolation effect of sea ice expansion on air-sea heat transfer in winter during this period makes the heat transfer from the ocean to the atmosphere correspondingly reduce in winter, thus intensifying the Arctic winter cooling. Additionally, the Arctic Oscillation (AO) changes from the negative phase to the positive phase in summer after the SMVE (such as Samalas), while it is reversed in winter. This phase change of AO is also one of the reasons for the seasonal changes in Arctic cooling.

Increasing winter conductive heat transfer in the Arctic sea-ice-covered areas: 1979–2014

2017 ◽  
Vol 16 (6) ◽  
pp. 1061-1071 ◽  
Author(s):  
Xieyu Fan ◽  
Haibo Bi ◽  
Yunhe Wang ◽  
Min Fu ◽  
Xuan Zhou ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Conductive Heat Transfer ◽  
The Arctic ◽  
Conductive Heat ◽  
Arctic Sea

Total sea ice concentration retrieval from the SSM/I 85.5 GHz channels during the arctic summer

1997 ◽  
Vol 62 (1) ◽  
pp. 63-76 ◽  
Author(s):  
Dan Lubin ◽  
Caren Garrity ◽  
RenéO. Ramseier ◽  
Robert H. Whritner
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Arctic Summer

scholarly journals The Impact of Arctic Winter Infrared Radiation on Early Summer Sea Ice

2015 ◽  
Vol 28 (15) ◽  
pp. 6281-6296 ◽  
Author(s):  
Hyo-Seok Park ◽  
Sukyoung Lee ◽  
Yu Kosaka ◽  
Seok-Woo Son ◽  
Sang-Woo Kim
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Interannual Variability ◽  
Radiative Forcing ◽  
Heat Content ◽  
The Arctic ◽  
Upper Ocean ◽  
Upper Ocean Heat Content ◽  
The Arctic Ocean ◽  
Arctic Summer

Abstract The Arctic summer sea ice area has been rapidly decreasing in recent decades. In addition to this trend, substantial interannual variability is present, as is highlighted by the recovery in sea ice area in 2013 following the record minimum in 2012. This interannual variability of the Arctic summer sea ice area has been attributed to the springtime weather disturbances. Here, by utilizing reanalysis- and satellite-based sea ice data, this study shows that summers with unusually small sea ice area are preceded by winters with anomalously strong downward longwave radiation over the Eurasian sector of the Arctic Ocean. This anomalous wintertime radiative forcing at the surface is up to 10–15 W m−2, which is about twice as strong than that during the spring. During the same winters, the poleward moisture and warm-air intrusions into the Eurasian sector of the Arctic Ocean are anomalously strong and the resulting moisture convergence field closely resembles positive anomalies in column-integrated water vapor and tropospheric temperature. Climate model simulations support the above-mentioned findings and further show that the anomalously strong wintertime radiative forcing can decrease sea ice thickness over wide areas of the Arctic Ocean, especially over the Eurasian sector. During the winters preceding the anomalously small summer sea ice area, the upper ocean of the model is anomalously warm over the Barents Sea, indicating that the upper-ocean heat content contributes to winter sea ice thinning. Finally, mass divergence by ice drift in the preceding winter and spring contributes to the thinning of sea ice over the East Siberian and Chukchi Seas, where radiative forcing and upper-ocean heat content anomalies are relatively weak.

scholarly journals Interannual and decadal variability of Arctic summer sea ice associated with atmospheric teleconnection patterns during 1850-2017

2021 ◽  
pp. 1-89
Author(s):  
Qiongqiong Cai ◽  
Dmitry Beletsky ◽  
Jia Wang ◽  
Ruibo Lei
Keyword(s):  
Sea Ice ◽  
Decadal Variability ◽  
Arctic Sea Ice ◽  
Total Variance ◽  
The Arctic ◽  
Teleconnection Patterns ◽  
Arctic Sea ◽  
Mode 2 ◽  
Interannual And Decadal Variability ◽  
Arctic Summer

AbstractThe interannual and decadal variability of summer Arctic sea ice is analyzed, using the longest reconstruction (1850-2017) of Arctic sea ice extent available, and its relationship with the dominant internal variabilities of the climate system is further investigated quantitatively. The leading empirical orthogonal function (EOF) mode of summer Arctic sea ice variability captures an in-phase fluctuation over the Arctic Basin. The second mode characterizes a sea ice dipolar pattern with out-of-phase variability between the Pacific Arctic and the Atlantic Arctic. Summer sea ice variability is impacted by the major internal climate patterns: the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO) and Dipole Anomaly (DA), with descending order of importance based on the multiple regression analyses. The internal climate variability of the five teleconnection patterns accounts for up to 46% of the total variance in sea ice mode 1 (thermodynamical effect), and up to 30% of the total variance in mode 2 (dynamical effect). Furthermore, the variability of sea ice mode 1 decreased from 46% during 1953-2017 to 28% during 1979-2017, while the variability of mode 2 increased from 11% during 1953-2017 to 30% during 1979-2017. The increasingly greater reduction of Arctic summer sea ice during the recent four decades was enhanced with the positive ice/ocean albedo feedback loop being accelerated by the Arctic amplification, contributed in part by the atmospheric thermodynamical forcing from -AO, +NAO, +DA, +AMO, and –PDO and by the dynamical transpolar sea ice advection and outflow driven by +DA- and +AMO-derived strong anomalous meridional winds. Further analysis, using multiple large ensembles of climate simulations and single-forcing ensembles, indicates that the mode 1 of summer sea ice, dominated by the multidecadal oscillation, is partially a forced response to anthropogenic warming.

scholarly journals A Regional Seasonal Forecast Model of Arctic Minimum Sea Ice Extent: Reflected Solar Radiation vs. Late Winter Coastal Divergence

2021 ◽  
pp. 1
Author(s):  
Rachel Kim ◽  
Bruno Tremblay ◽  
Charles Brunette ◽  
Robert Newton
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Forecast Model ◽  
Ice Cover ◽  
The Arctic ◽  
Late Winter ◽  
Coupled Models ◽  
Sea Ice Extent ◽  
Predictive Skill

AbstractThinning sea ice cover in the Arctic is associated with larger interannual variability in the minimum Sea Ice Extent (SIE). The current generation of forced or fully coupled models, however, have difficulty predicting SIE anomalies from the long-term trend, highlighting the need to better identify the mechanisms involved in the seasonal evolution of sea ice cover. One such mechanism is Coastal Divergence (CD), a proxy for ice thickness anomalies based on late winter ice motion, quantified using Lagrangian ice tracking. CD gains predictive skill through the positive feedback of surface albedo anomalies, mirrored in Reflected Solar Radiation (RSR), during melt season. Exploring the dynamic and thermodynamic contributions to minimum SIE predictability, RSR, initial SIE (iSIE) and CD are compared as predictors using a regional seasonal sea ice forecast model for July 1, June 1 and May 1 forecast dates for all Arctic peripheral seas. The predictive skill of June RSR anomalies mainly originates from open water fraction at the surface, i.e. June iSIE and June RSR have equal predictive skill for most seas. The finding is supported by the surprising positive correlation found between June Melt Pond Fraction (MPF) and June RSR in all peripheral seas: MPF anomalies indicate presence of ice or open water that is key to creating minimum SIE anomalies. This contradicts models that show correlation between melt onset, MPF and the minimum SIE. A hindcast model shows that for a May 1 forecast, CD anomalies have better predictive skill than RSR anomalies for most peripheral seas.

scholarly journals Arctic sea-ice conditions and the distribution of solar radiation during summer

1997 ◽  
Vol 25 ◽  
pp. 445-450 ◽  
Author(s):  
Donald K. Perovich ◽  
Walter B. Tucker
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Melt Ponds ◽  
Arctic Ice ◽  
Median Area

Understanding the interaction of solar radiation with the ice cover is critical in determining the heat and mass balance of the Arctic ice pack, and in assessing potential impacts due to climate change. Because of the importance of the ice-albedo feedback mechanism, information on the surface state of the ice cover is needed. Observations of the surface slate of sea ice were obtained from helicopter photography missions made during the 1994 Arctic Ocean Section cruise. Photographs from one flight, taken during the height of the melt season (31 July 1994) at 76° N, 172° W, were analyzed in detail. Bare ice covered 82% of the total area, melt ponds 12%, and open water 6%, There was considerable variability in these area fractions on scales < 1 km2. Sample areas >2 3 km2gave representative values of ice concentration and pond fraction. Melt ponds were numerous, with a number density of 1800 ponds km-2. The melt ponds had a mean area of 62 m2a median area of 14 m2, and a size distribution that was well lit by a cumulative lognormal distribution. While leads make up only a small portion of the total area, they are the source of virtually all of the solar energy input to the ocean.

scholarly journals Acceleration of sea-ice melting due to transmission of solar radiation through ponded ice area in the Arctic Ocean: results of in situ observations from icebreakers in 2006 and 2007

2011 ◽  
Vol 52 (57) ◽  
pp. 249-260 ◽  
Author(s):  
Motoyo Itoh ◽  
Jun Inoue ◽  
Koji Shimada ◽  
Sarah Zimmermann ◽  
Takashi Kikuchi ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Heat Input ◽  
Open Water ◽  
The Arctic ◽  
Upper Ocean ◽  
Ice Melting ◽  
Ice Concentration ◽  
The Arctic Ocean

AbstractSea-ice melting processes were inferred from in situ sea-ice and ocean condition data obtained in the Arctic in summer 2006 and 2007. the relationship between ice concentration observed by on-board ice watches and water temperature showed negative correlations. This implies that as ice concentration decreases, the upper ocean becomes warmer due to greater absorption of solar radiation into open water, which promotes ice melting. However, heating of surface water is significant even in regions that were almost completely ice-covered, suggesting that transmitted solar radiation through the ice is also effective at melting sea ice. A simplified ice–upper-ocean coupled model was applied to examine the effect of heat input from open water, thick ice and thin ice. the ponded thin ice is estimated to transmit approximately three times more solar radiation than ponded thick ice. Model results suggest that transmission of solar radiation through ponded ice amplified the ice-albedo feedback mechanism, particularly in thin ice regions. Recently, the extent of old and thick multi-year ice in the Arctic Ocean has been rapidly reduced. As a result, heat input to the upper ocean through the ice is enhanced and ice melt is further accelerated.

The impact of orbital forcing on the Arctic climate during the Last Interglacial simulated by the IPSL-CM6A-LR model

2020 ◽  
Author(s):  
Marie Sicard ◽  
Masa Kageyama ◽  
Pascale Braconnot ◽  
Sylvie Charbit
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Climate ◽  
The Arctic ◽  
Orbital Forcing ◽  
Last Interglacial ◽  
Arctic Sea ◽  
The Impact

<p>The Last Interglacial (129 – 116 ka BP) is a time period with a strong orbital forcing which leads to a different seasonal and latitudinal distribution of insolation compared to the present. In particular, these changes amplify the Arctic climate seasonality. They induce warmer summers and colder winters in the high latitudes of the Northern Hemisphere. Such surface conditions favour a huge retreat of the arctic sea ice cover.<br>In this study, we try to understand how this solar radiation anomaly spreads through the surface and impacts the seasonal arctic sea ice. Using IPSL-CM6A-LR model outputs, we decompose the surface energy budget to identify the role of atmospheric and oceanic key processes beyond 60°N and its changes compared to pre-industrial. We show that solar radiation anomaly is greatly reduced when it reaches the Earth’s surface, which emphasizes the role of clouds and water vapor transport.<br>The results are also compared to other PMIP4-CMIP6 model simulations. We would like to thank PMIP participants for producing and making available their model outputs.</p>

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