Causal links between Arctic sea ice and its potential drivers based on the rate of information transfer

Abstract

Model projections show a more or less rapid continuation of this ongoing process depending on the greenhouse gas emission scenario, with likely summer ice-free Arctic conditions (September Arctic sea-ice area lower than 1 million km 2 ) occurring before 2050 (Arthun et al., 2021; SIMIP Community, 2020).Recent changes in Arctic sea ice have been linked to both anthropogenic global warming (Notz & Stroeve, 2016) and climate internal variability (Swart et al., 2015). However, the exact drivers influencing sea-ice loss and their respective contributions are not fully understood. Both atmospheric (Ding et al., 2017) and ocean (Carmack et al., 2015) processes play a role in Arctic sea-ice changes. Typically, changes in near-surface air temperature strongly control the variability in Arctic sea-ice area over short time scales (Olonscheck et al., 2019), while ocean heat transport has a stronger impact on longer time scales (Onarheim et al., 2015).The influence of atmospheric and ocean processes on Arctic sea ice is usually quantified via correlation and regression analyses, including or not a lag (

Dynamical dependencies at monthly and interannual time scales in the Climate system: Study of the North Pacific and Atlantic regions

“…This approach should also be used in the context of climate model runs in order to validate the dependencies that are accounted for by the models, and at the same time to confirm the dependencies that have been isolated here on short time series, using long climate model runs. One advantage of using climate models is that ensembles can be used that could further provide temporal information on the changes of dependencies like for instance in the recent applications of Hagan et al (2019); Docquier et al (2021), even under transient dynamics.…”

Dynamical Dependencies at Monthly and Interannual Time Scales in the Climate System: Study of the North Pacific and Atlantic Regions

“…This approach should also be used in the context of climate model runs in order to validate the dependencies that are accounted for by the models, and at the same time to confirm the dependencies that have been isolated here on short time series, using long climate model runs. One advantage of using climate models is that ensembles can be used that could further provide temporal information on the changes of dependencies like for instance in the recent applications of Hagan et al (2019); Docquier et al (2021), even under transient dynamics.…”