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.

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.

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>

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

Testing for Dynamical Dependence: Application to the Surface Mass Balance Over Antarctica

2020 ◽  
Author(s):  
Quentin Dalaiden ◽  
Stephane Vannitsem ◽  
Hugues Goosse
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Mass Balance ◽  
Information Transfer ◽  
Climate Models ◽  
Weddell Sea ◽  
Surface Mass Balance ◽  
Sea Ice Concentration ◽  
Surface Mass ◽  
Ice Concentration

<p>Dynamical dependence between key observables and the surface mass balance (SMB) over Antarctica is analyzed in two historical runs performed with the MPI‐ESM‐P and the CESM1‐CAM5 climate models. The approach used is a novel method allowing for evaluating the rate of information transfer between observables that goes beyond the classical correlation analysis and allows for directional characterization of dependence. It reveals that a large proportion of significant correlations do not lead to dependence. In addition, three coherent results concerning the dependence of SMB emerge from the analysis of both models: (i) The SMB over the Antarctic Plateau is mostly influenced by the surface temperature and sea ice concentration and not by large‐scale circulation changes; (ii) the SMB of the Weddell Sea and the Dronning Maud Land coasts are not influenced significantly by the surface temperature; and (iii) the Weddell Sea coast is not significantly influenced by the sea ice concentration.</p>

scholarly journals Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models

2016 ◽  
Vol 105 ◽  
pp. 60-70 ◽  
Author(s):  
O. Lecomte ◽  
H. Goosse ◽  
T. Fichefet ◽  
P.R. Holland ◽  
P. Uotila ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Surface Wind ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic

Polynya area and frequency in the Weddell Sea in CMIP6 climate models

2020 ◽  
Author(s):  
Martin Mohrmann ◽  
Céline Heuzé ◽  
Sebastiaan Swart
Keyword(s):  
Sea Ice ◽  
Deep Water ◽  
Climate Models ◽  
Numerical Models ◽  
Open Water ◽  
Weddell Sea ◽  
Threshold Method ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration

<p>The presence of polynyas has a large effect on air-sea fluxes and deep water production, therefore impacting climate-relevant properties such as heat and carbon exchange between the atmosphere and ocean interior. One of the key areas of deep water formation is in the Weddell Sea, where much attention has recently been placed in the reoccurance of the open ocean Maud Rise polynya. In this study, two methods are presented to track the number, area and spatial distribution of polynyas with a focus on the Weddell Sea. The analysis is applied to a set of 10 Coupled Model Intercomparison Project phase 6 (CMIP6) models and to satellite sea ice concentration data. The first approach is a sea ice threshold method applied to the CMIP6 sea ice data at the original model grid. Open water areas surrounded by sea ice are classified as polynyas. Without requiring any remapping or interpolation, this method preserves the area information of all grid cells and is well suited to compute the combined area of the polynyas in the Weddell Sea. The second approach makes use of an image analysis technique to outline areas with low sea ice concentration. This method is preferable for counting the absolute number of polynyas and obtaining statistical information about their position. Satellite sea ice concentration is used as a reference to compare the performance of the models representing polynya area and to indicate model biases in the location of polynyas. All analyzed CMIP6 models show coastal polynyas, while only about half of the models regularly form open water polynyas. The resolution (about one degree for most models) sets a limit for the number of the polynyas in the numerical models.</p>

scholarly journals Changes in atmospheric variability in a glacial climate and the impacts on proxy data: a model intercomparison

2009 ◽  
Vol 5 (3) ◽  
pp. 489-502 ◽  
Author(s):  
F. S. R. Pausata ◽  
C. Li ◽  
J. J. Wettstein ◽  
K. H. Nisancioglu ◽  
D. S. Battisti
Keyword(s):  
Sea Level ◽  
North Atlantic ◽  
Climate Models ◽  
Atmospheric Variability ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
The North ◽  
Surface Climate ◽  
Leading Mode ◽  
The North Atlantic Oscillation

Abstract. Using four different climate models, we investigate sea level pressure variability in the extratropical North Atlantic in the preindustrial climate (1750 AD) and at the Last Glacial Maximum (LGM, 21 kyrs before present) in order to understand how changes in atmospheric circulation can affect signals recorded in climate proxies. In general, the models exhibit a significant reduction in interannual variance of sea level pressure at the LGM compared to pre-industrial simulations and this reduction is concentrated in winter. For the preindustrial climate, all models feature a similar leading mode of sea level pressure variability that resembles the leading mode of variability in the instrumental record: the North Atlantic Oscillation (NAO). In contrast, the leading mode of sea level pressure variability at the LGM is model dependent, but in each model different from that in the preindustrial climate. In each model, the leading (NAO-like) mode of variability explains a smaller fraction of the variance and also less absolute variance at the LGM than in the preindustrial climate. The models show that the relationship between atmospheric variability and surface climate (temperature and precipitation) variability change in different climates. Results are model-specific, but indicate that proxy signals at the LGM may be misinterpreted if changes in the spatial pattern and seasonality of surface climate variability are not taken into account.

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