scholarly journals Arctic Cloud Response to a Perturbation in Sea Ice Concentration: The North Water Polynya

2021 ◽  
Author(s):  
Emily Monroe ◽  
Patrick C. Taylor ◽  
Linette N. Boisvert
Keyword(s):  
Sea Ice ◽  
Sea Ice Concentration ◽  
The North ◽  
North Water Polynya ◽  
Ice Concentration ◽  
North Water ◽  
Cloud Response

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 Biomarker characterization of the North Water Polynya, Baffin Bay: Implications for local sea ice and temperature proxies

2021 ◽  
Author(s):  
David J. Harning ◽  
Brooke Holman ◽  
Lineke Woelders ◽  
Anne E. Jennings ◽  
Julio Sepúlveda
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Baffin Bay ◽  
The North ◽  
North Water Polynya ◽  
Lipid Biomarker ◽  
North Water ◽  
Order Of Magnitude ◽  
Insight Into

Abstract. The North Water Polynya (NOW, Greenlandic Inuit: Pikialasorsuaq), Baffin Bay, is the largest polynya and one of the most productive regions in the Arctic. This area of thin to absent sea ice is a critical moisture source for local ice sheet sustenance and coupled with the inflow of nutrient-rich Arctic Surface Water, supports a diverse community of Arctic fauna and indigenous people. Although paleoceanographic records can provide critical insight into the NOW’s past behavior, it is critical that we fully understand the modern functionality of the paleoceanographic proxies beforehand. In this study, we analyzed lipid biomarkers, including algal highly-branched isoprenoids and sterols for sea ice extent and pelagic productivity, and algal alkenones and archaeal GDGTs for ocean temperature, in a suite of modern surface sediment samples from within and around the NOW. Our data show that all highly-branched isoprenoids exhibit strong correlations with each other and show highest concentrations within the NOW, which suggests a spring/autumn sea ice diatom source rather than a combination of sea ice and open water diatoms as seen elsewhere in the Arctic. Sterols are also highly concentrated in the NOW and exhibit an order of magnitude higher concentration here compared to sites south of the NOW, consistent with the order of magnitude higher primary productivity observed within the NOW relative to surrounding waters in spring/summer months. Finally, our temperature calibrations for alkenones, GDGTs and OH-GDGTs reduce the uncertainty present in global temperature calibrations, but also identify some additional variables that may be important in controlling their local distribution, such as salinity, nutrients, and dissolved oxygen. Collectively, our datasets provide new insight into the utility of these lipid biomarker proxies in high-latitude settings and will help provide a refined perspective on the Holocene development of the NOW with their application in downcore reconstructions.

scholarly journals Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations

2022 ◽  
Author(s):  
William Gregory ◽  
Julienne Stroeve ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Sea Level ◽  
Arctic Oscillation ◽  
Climate Models ◽  
Sea Ice Concentration ◽  
Sea Level Pressure ◽  
The North ◽  
Coupled Climate Models ◽  
Ice Concentration ◽  
Arctic Summer

Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the proceeding summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer time scales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in northern-hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the Adjusted Rand Index – a method for comparing spatial patterns of variability, and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability of the AO relatively well, although over-estimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean, and under-estimate the variability over the north Africa and southern Europe. They also under-estimate the importance of regions such as the Beaufort, East Siberian and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.

Factors Influencing Simulated Changes in Future Arctic Cloudiness

2011 ◽  
Vol 24 (18) ◽  
pp. 4817-4830 ◽  
Author(s):  
Stephen J. Vavrus ◽  
Uma S. Bhatt ◽  
Vladimir A. Alexeev
Keyword(s):  
Sea Ice ◽  
Radiative Forcing ◽  
Lower Boundary ◽  
Ice Cover ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Climate System Model ◽  
Model Version ◽  
Ice Concentration ◽  
Cloud Response

Abstract This study diagnoses the changes in Arctic clouds simulated by the Community Climate System Model version 3 (CCSM3) in a transient 2 × CO2 simulation. Four experiments—one fully coupled and three with prescribed SSTs and/or sea ice cover—are used to identify the mechanisms responsible for the projected cloud changes. The target simulation uses a T42 version of the CCSM3, in which the atmosphere is coupled to a dynamical ocean with mobile sea ice. This simulation is approximated by a T42 atmosphere-only integration using CCSM3’s atmospheric component [the Community Atmosphere Model version 3 (CAM3)] forced at its lower boundary with the changes in both SSTs and sea ice concentration from CCSM3’s 2 × CO2 run. The authors decompose the combined effect of the higher SSTs and reduced sea ice concentration on the Arctic cloud response in this experiment by running two additional CAM3 simulations: one forced with modern SSTs and the projected sea ice cover changes in CCSM3 and the other forced with modern sea ice coverage and the projected changes in SSTs in CCSM3. The results suggest that future increases in Arctic cloudiness simulated by CCSM3 are mostly attributable to two separate processes. Low cloud gains are primarily initiated locally by enhanced evaporation within the Arctic due to reduced sea ice, whereas cloud increases at middle and high levels are mostly driven remotely via greater meridional moisture transport from lower latitudes in a more humid global atmosphere. The enhanced low cloudiness attributable to sea ice loss causes large increases in cloud radiative forcing during the coldest months and therefore promotes even greater surface warming. Because CCSM3’s Arctic cloud response to greenhouse forcing is similar to other GCMs, the driving mechanisms identified here may be applicable to other models and could help to advance our understanding of likely changes in the vertical structure of polar clouds.

scholarly journals Stochastic Atmospheric Forcing as a Cause of Greenland Climate Transitions

2015 ◽  
Vol 28 (19) ◽  
pp. 7741-7763 ◽  
Author(s):  
Hannah Kleppin ◽  
Markus Jochum ◽  
Bette Otto-Bliesner ◽  
Christine A. Shields ◽  
Stephen Yeager
Keyword(s):  
Sea Ice ◽  
Warm Pool ◽  
Atmospheric Forcing ◽  
Meridional Heat Transport ◽  
Sea Ice Concentration ◽  
Sea Level Pressure ◽  
Climate System Model ◽  
The North ◽  
Ice Concentration ◽  
Precipitation Anomalies

Abstract An unforced simulation of the Community Climate System Model, version 4 (CCSM4), is found to have Greenland warming and cooling events that resemble Dansgaard–Oeschger cycles in pattern and magnitude. With the caveat that only three transitions were available to be analyzed, it is found that the transitions are triggered by stochastic atmospheric forcing. The atmospheric anomalies change the strength of the subpolar gyre, leading to a change in Labrador Sea sea ice concentration and meridional heat transport. The changed climate state is maintained over centuries through the feedback between sea ice and sea level pressure in the North Atlantic. Indications that the initial atmospheric pressure anomalies are preceded by precipitation anomalies in the western Pacific warm pool are discussed. The full evolution of the anomalous climate state depends crucially on the climatic background state.

Sign in / Sign up

Export Citation Format

Share Document