Indicators of Arctic Sea Ice Bistability in Climate Model Simulations and Observations

2014 ◽  
Author(s):  
Ian Eisenman
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Model Simulations ◽  
Arctic Sea ◽  
Climate Model Simulations

The relative roles of Arctic and Antarctic sea-ice loss in the response to greenhouse warming

2021 ◽  
Author(s):  
Stephanie Hay ◽  
Paul Kusnher
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Arctic Sea ◽  
Climate Model Simulations ◽  
Arctic Sea Ice Loss

<p>Antarctic sea ice has gradually increased in extent over the forty-year-long satellite record, in contrast with the clear decrease in sea-ice extent seen in the Arctic over the same time period. However, state-of-the-art climate models ubiquitously project Antarctic sea-ice to decrease over the coming century, much as they do for Arctic sea-ice. Several recent years have also seen record low Antarctic sea-ice. It is therefore of interest to understand what the climate response to Antarctic sea-ice loss will be. </p><p>We have carried out new fully coupled climate model simulations to assess the response to sea-ice loss in either hemisphere separately or coincidentally under different albedo parameter settings to determine the relative importance of each. By perturbing the albedo of the snow overlying the sea ice and the albedo of the bare sea ice, we obtain a suite of simulations to assess the linearity and additivity of sea-ice loss. We find the response to sea-ice loss in each hemisphere exhibits a high degree of additivity, and can simply be decomposed into responses due to loss in each hemisphere separately. We find that the response to Antarctic sea-ice loss exceeds that of Arctic sea-ice loss in the tropics, and that Antarctic sea-ice loss leads to statistically significant Arctic warming, while the opposite is not true.</p><p>With these new simulations and one in which CO<sub>2</sub> is instantaneously doubled , we can further characterize the response to sea-ice loss from each hemisphere using an extension to classical pattern scaling that includes three controlling parameters. This allows us to simultaneously compute the sensitivity patterns to Arctic sea-ice loss, Antarctic sea-ice loss, and to tropical warming. The statistically significant response to Antarctic sea-ice loss in the Northern Hemisphere extratropics is found to be mediated by tropical warming and small amounts of Arctic sea-ice loss.</p>

scholarly journals How much should we believe correlations between Arctic cyclones and sea ice extent?

2017 ◽  
Vol 11 (6) ◽  
pp. 3023-3034 ◽  
Author(s):  
Jamie G. L. Rae ◽  
Alexander D. Todd ◽  
Edward W. Blockley ◽  
Jeff K. Ridley
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Reanalysis Data ◽  
Arctic Sea Ice ◽  
Sea Ice Extent ◽  
Sea Level Pressure ◽  
Temporal Sampling ◽  
Arctic Sea ◽  
Climate Model Simulations ◽  
Arctic Sea Ice Extent

Abstract. This paper presents an investigation of the robustness of correlations between characteristics of Arctic summer cyclones and September Arctic sea ice extent. A cyclone identification and tracking algorithm is run for output from 100-year coupled climate model simulations at two resolutions and for 30 years of reanalysis data, using two different tracking variables (mean sea-level pressure, MSLP; and 850 hPa vorticity) for identification of the cyclones. The influence of the tracking variable, the spatial resolution of the model, and spatial and temporal sampling on the correlations is then explored. We conclude that the correlations obtained depend on all of these factors and that care should be taken when interpreting the results of such analyses. Previous studies of this type have used around 30 years of reanalysis and observational data, analysed with a single tracking variable. Our results therefore cast some doubt on the conclusions drawn in those studies.

Reconstructing Arctic Sea Ice Over the Common Era Using Data Assimilation

2021 ◽  
pp. 1-55
Author(s):  
M. Kathleen Brennan ◽  
Gregory J. Hakim
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Temperature Sensitive ◽  
Sea Ice Extent ◽  
Model Experiments ◽  
Proxy Records ◽  
Arctic Sea ◽  
Climate Model Simulations ◽  
Arctic Sea Ice Extent

AbstractArctic sea-ice decline in recent decades has been dramatic, however few long-term records of Arctic sea ice exist to put such a decline in context. Here we employ an ensemble Kalman filter data assimilation approach to reconstruct Arctic sea-ice concentration over the last two millennia by assimilating temperature-sensitive proxy records with ensembles drawn from last millennium climate model simulations. We first test the efficacy of this method using pseudo-proxy experiments. Results showgood agreement between the target and reconstructed total Arctic sea-ice extent (R2 value and coefficient of efficiency values of 0.51 and 0.47 for perfect model experiments, and 0.43 and 0.43 for imperfect-model experiments). Imperfect-model experiments indicate that the reconstructions inherit some bias from the model prior. We assimilate 487 temperature-sensitive proxy records with two climate model simulations to produce two gridded reconstructions of Arctic sea ice over the last two millennia. These reconstructions show good agreement with satellite observations between 1979–1999 CE for total Arctic sea-ice extent with an R2 and coefficient of efficiency of about 0.60 and 0.50, respectively, for both models. Regional quantities derived from these reconstructions show encouraging similarities with independent reconstructions and sea-ice sensitive proxy records from the Barents, Baffin Bay and East Greenland seas. The reconstructions show a positive trend in Arctic sea-ice extent between around 750–1820 CE, and increases during years with large volcanic eruptions that persist about 5 years. Trend analysis of total Arctic sea-ice extent reveals that for time periods longer than 30 years, the satellite era decline in total Arctic sea-ice extent is unprecedented over the last millennium.

Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period 

2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Volume Growth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Concentration ◽  
Marginal Seas ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration

<p><span>We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.</span></p>

Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

2019 ◽  
Vol 32 (5) ◽  
pp. 1361-1380 ◽  
Author(s):  
J. Ono ◽  
H. Tatebe ◽  
Y. Komuro
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Drastic Reduction ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

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