The roles of winds and waves in Arctic sea ice variability

2021 ◽  
Author(s):  
Lettie Roach ◽  
Edward Blanchard-Wrigglesworth ◽  
Cecilia Bitz
Keyword(s):  
Sea Ice ◽  
Prediction Models ◽  
Climate Model ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Near Surface ◽  
Coupled Modelling ◽  
Ocean Surface Waves ◽  
Air Temperatures ◽  
Arctic Sea

<p><span>It is broadly accepted that variability and trends in Arctic sea ice remain poorly simulated even in the most state-of-the-art coupled climate and climate prediction models. Here, we show that a modern coupled climate model (CESM1) is in fact able to reproduce the observed variability and decline in summer sea ice when winds are nudged towards values from reanalysis.<span>  </span>We argue that the nudged-winds framework provides a straightforward way of evaluating models by removing much of the contribution of internal variability, revealing model successes and biases. The results demonstrate the importance of atmospheric circulation in driving interannual variability in sea ice and near-surface air temperatures, particularly in the summer. Finally, we will discuss the potential role of ocean surface waves in driving variability in Arctic sea ice, based on observational analysis and new coupled modelling results.</span></p>

scholarly journals Dynamical and thermodynamical contributions to the mid-latitude atmospheric response to Arctic sea ice decline

2021 ◽  
Author(s):  
Svenya Chripko ◽  
Rym Msadek ◽  
Emilia Sanchez-Gomez ◽  
Laurent Terray ◽  
Laurent Bessières ◽  
...  
Keyword(s):  
North America ◽  
Sea Ice ◽  
Central Asia ◽  
Climate Model ◽  
Temperature Response ◽  
Arctic Sea Ice ◽  
Atmospheric Response ◽  
Near Surface ◽  
Sea Ice Decline ◽  
Arctic Sea

<p>Previous climate model studies have shown that Arctic sea ice decline can solely affect weather and climate at lower latitudes during the cold season. However, the mechanisms beneath this linkage are poorly understood. Whether sea ice loss have had an influence on the lower latitudes climate over the past decades is also uncertain (Barnes and Screen 2015). The goal of this work is to better understand the relative contributions of dyncamical and thermodynamical changes in the atmospheric response to Arctic sea ice loss, which have been suggested to oppose each other (Screen 2017). We conducted two sets of sensitivity transient experiments that allow to isolate the effect of Arctic sea ice decline on the mid-latitudes from other climate forcings, using the climate model CNRM-CM6 (Voldoire et al. 2019) in a coupled configuration or with an atmosphere-only. The first set of experiments, that is part of the European H2020 PRIMAVERA project, consists of a 100-member ensemble in which sea ice albedo is reduced to the ocean value (PERT) in the fully coupled CNRM-CM6, and which is compared to a 1950 control run (CTL) (Haarsma et al. 2016). This yields idealised ice-free conditions in summer and a more moderate sea ice reduction during the following months. The second set of experiments, that is part of the CMIP6 Polar Amplification Model Intercomparison Project (PAMIP, Smith et al. 2019), consists of a 300-member ensemble in which the atmospheric component of CNRM-CM6 is forced by sea ice anomalies associated with a future 2°C warming (FUT) and present day sea surface temperatures (SSTs). These are compared to experiments in which the atmosphere is forced by present-day sea ice conditions (PD) and the same SSTs. To extract the dynamical component of the response in the two sets of experiments, we use a dynamical adjustment method (Deser et al. 2016) based on a regional reconstruction of circulation analogs. We focus on three mid-latitudes regions in which a significant near-surface temperature response has been identified, namely North America, Europe and central Asia. We show that the cooling occurring over central Asia in both sets of experiments is dynamically-induced through an intensification of the Siberian High, and that opposed temperature responses over North America between the two sets of experiments could be explained by opposed dynamical components occurring in response to the imposed Arctic sea ice decline. Finally, we discuss whether different dynamical and thermodynamical contributions in the PAMIP multi-model experiments could explain the multi-model differences in the atmospheric response to sea ice loss.</p>

scholarly journals Seasonal transition dates can reveal biases in Arctic sea ice simulations

2020 ◽  
Author(s):  
Abigail Smith ◽  
Alexandra Jahn ◽  
Muyin Wang
Keyword(s):  
Sea Ice ◽  
Annual Cycle ◽  
Model Evaluation ◽  
Climate Model ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Seasonal Transition ◽  
Climate Model Evaluation ◽  
Arctic Sea ◽  
Break Up

Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, break-up, opening, freeze onset, freeze-up and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1.5–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions of melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

scholarly journals Seasonal transition dates can reveal biases in Arctic sea ice simulations

2020 ◽  
Vol 14 (9) ◽  
pp. 2977-2997
Author(s):  
Abigail Smith ◽  
Alexandra Jahn ◽  
Muyin Wang
Keyword(s):  
Sea Ice ◽  
Annual Cycle ◽  
Model Evaluation ◽  
Climate Model ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Seasonal Transition ◽  
Ice Growth ◽  
Climate Model Evaluation ◽  
Arctic Sea

Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, breakup, opening, freeze onset, freeze-up, and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions for melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability range, indicating the contribution of model differences. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

Decline of the Arctic 'ice factories' delayed by negative feedbacks

2021 ◽  
Author(s):  
Sam Cornish ◽  
Helen Johnson ◽  
Alice Richards ◽  
Yavor Kostov ◽  
Jakob Dörr
Keyword(s):  
Growth Rate ◽  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Growth ◽  
Air Temperatures ◽  
Arctic Sea ◽  
Negative Feedbacks ◽  
Ice Pack

<p>Over the past few decades, Arctic sea ice volume has been decreasing faster in summer than winter; winter sea ice growth has been increasing, helping to restore the ice pack, despite the fact that Arctic warming is most intense in the winter. This raises the questions: why? And for how long can we expect winter ice growth to keep increasing? We pose these questions with a regional focus on the Kara and Laptev seas. These seas are often termed the ice factories of the Arctic because of their outsized contributions to the Arctic sea ice budget, a consequence of their divergent settings. Using the CESM climate model ensemble, we separate out the influence of different levers on ice factory productivity (the ice growth rate), and show that 20th Century and RCP8.5 changes can be skilfully reconstructed by a linear model incorporating 2 m temperature, snow thickness, September sea ice area, total (gross) divergence and ice export. Ocean temperatures, meanwhile, help to explain the timing of the onset of freezing. Increasing air temperatures naturally decrease the growth rate, while positive contributions to growth rate are made by a decreasing September sea ice area, increasing divergence and increasing export. These positive influences are all associated with a thinning, more mobile ice pack: they are negative feedbacks on sea ice loss. In CESM, once the September sea ice area in the Kara-Laptev seas approaches zero, the year-on-year productivity of the ice factories starts to decline. We place these results in the context of observations and discuss the prospects for the productivity of the Arctic Ocean’s ice factories.</p>

Arctic sea ice in CMIP5 climate model projections and their seasonal variability

2017 ◽  
Vol 36 (8) ◽  
pp. 1-8 ◽  
Author(s):  
Fei Huang ◽  
Xiao Zhou ◽  
Hong Wang
Keyword(s):  
Sea Ice ◽  
Seasonal Variability ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Arctic Sea

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>

scholarly journals Late-Twentieth-Century Simulation of Arctic Sea Ice and Ocean Properties in the CCSM4

2012 ◽  
Vol 25 (5) ◽  
pp. 1431-1452 ◽  
Author(s):  
Alexandra Jahn ◽  
Kara Sterling ◽  
Marika M. Holland ◽  
Jennifer E. Kay ◽  
James A. Maslanik ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Motion Field ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ensemble Mean

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.

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