Causal Links Between Arctic Sea Ice and Its Potential Drivers Based on the Rate of Information Transfer

Abstract

Arctic sea ice has substantially changed over the past four decades, with a large decrease in sea‐ice area and volume. The exact causes of these changes are not entirely known. In our study, we make use of the Swedish Meteorological and Hydrological Institute Large Ensemble. This ensemble consists of 50 members realized with the EC‐Earth3 global climate model and covers the period 1970‐2100. We apply for the first time the Liang‐Kleeman information flow method to analyze the cause‐effect relationships between Arctic sea ice and its potential drivers. We show that recent and future changes in Arctic sea ice are mainly driven by air and sea‐surface temperatures and ocean heat transport. Conversely, changes in Arctic sea ice also considerably impact temperature and ocean heat transport. Finally, we find a progressive decrease in the influence of sea‐ice area and volume on air temperature and ocean heat transport through the twenty‐first century.

Observed contribution of Barents-Kara sea ice loss to warm Arctic-cold Eurasia anomalies by submonthly processes in winter

“…To verify the causality relationship between the WACE anomaly and atmospheric circulation forcing as well as BK sea-ice forcing, we calculated the information flow, which assumes linearity (Liang 2014). It has been used in determining the causality between rainfall and moisture divergence (Xu et al 2019) and the main factors affecting Arctic sea ice loss (Docquier et al 2022). For a pair of the time series X 1 and X 2 , the rate of information flow (in nats per unit time) from the latter to the former is given as:…”

Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis

“…Liang ( 2014 ) defined the calculation method of Liang information flow through a rigorously derived formalism and gave a specific calculation expression. The Liang information flow method have been shown to be effective in capturing the causal relation between time series (Liang, 2021 ; Zhang et al, 2021 ), and has been widely applied to a variety of fields in different disciplines, such as quantum mechanics (Yi & Bose, 2022 ), climate science (Cheng & Redfern, 2022 ; Docquier et al, 2022 ; Liang et al, 2021 ), brain electroencephalography (EEG) network (Hristopulos et al, 2019 ). Its advantages include its very effective performance in computing, as well as its accuracy, and, most of all, its universal applicability because of its firm physical ground, and hence many intrinsic properties that make accurate causal discovery possible.…”

Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction

“…A comprehensive study with generic systems has been fulfilled recently, with explicit formulas attained in closed form; see [2] and [1]. These formulas have been validated with benchmark systems such as baker transformation, Hénon map, etc., and have been applied successfully to real world problems in the diverse disciplines such as global climate change (e.g., [3], [4], [5]), dynamic meteorology (e.g., [6]), land-atmosphere interaction (e.g., [7]), data-driven prediction (e.g., [8], [9]), near-wall turbulence (e.g., [10]), neuroscience (e.g., [11], [12]), financial analysis (e.g., [13], [14]), quantum information (e.g, [15]), to name several.…”