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

Abstract

The directional dependencies of different climate indices are explored using the Liang-Kleeman information flow in order to disentangle the influence of certain regions over the globe on the development of low-frequency variability of others. Seven key indices (the sea-surface temperature in El-Niño 3.4 region, the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, the North Pacific America pattern, the Arctic Oscillation, the Pacifid Decadal Oscillation, the Tropical North Atlantic index), together with three local time series located in Western Europe (Belgium), are selected. The analysis is performed on time scales from a month to 5 years by using a sliding window as filtering procedure.A few key new results on the remote influence emerge: (i) The Arctic Oscillation plays a key role at short time (monthly) scales on the dynamics of the North Pacific and North Atlantic; (ii) the North Atlantic Oscillation is playing a global role at long time scales (several years); (iii) the Pacific Decadal Oscillation is indeed slaved to other influences; (iv) the local observables over Western Europe influence the variability on the ocean basins on long time scales. These results further illustrate the power of the Liang-Kleeman information flow in disentangling the dynamical dependencies.

The rate of information transfer as a measure of ocean-atmosphere interactions

“…out what happens at interannual and decadal time scales, we take the 12-month and 120-month running mean SST and THF, respectively, and re-compute the rate of information transfer in the 3D case (SST, SST tendency and THF). This approach is similar to the one used in Vannitsem and Liang (2022), who found differences in the rate of information transfer between climate indices depending on the time scale used.…”

“…This novel method has been successfully applied to several climate studies, e.g. causal influences between greenhouse gases and global mean surface temperature (Stips et al, 2016;Jiang et al, 2019), dynamical dependencies between a set of observables and the Antarctic surface mass balance (Vannitsem et al, 2019), soil moisture -air temperature interactions in China (Hagan et al, 2019), prediction of El Niño Modoki (Liang et al, 2021), causal links between climate indices in the North Pacific and Atlantic regions (Vannitsem and Liang, 2022), and identification of potential drivers of Arctic sea-ice changes (Docquier et al, 2022).…”

Impact of tropical teleconnections on the long-range predictability of the atmosphere at midlatitudes: a reduced-order multi-scale model perspective

“…These are often expressed in terms of correlations between the tropical and extratropical regions, suggesting a potential influence on one another. Note however than correlation does not mean causation as already stressed in many recent works on causality analyses [6][7][8][9]. In [9], a new approach of causality detection was used to clarify the transfer of information among different climate indices and in particular ENSO on monthly to decadal time scales.…”

“…Note however than correlation does not mean causation as already stressed in many recent works on causality analyses [6][7][8][9]. In [9], a new approach of causality detection was used to clarify the transfer of information among different climate indices and in particular ENSO on monthly to decadal time scales. It was in particular shown that ENSO has considerable influence on the Pacific North Atlantic pattern, on the Pacific decadal oscillation, on the tropical North Atlantic region and slightly on the Arctic oscillation.…”

Interactions between ocean heat budget terms in HighResMIP climate models measured by the rate of information transfer

“…This novel method has been successfully applied in several climate studies, e.g. causal influences between greenhouse gases and global mean surface temperature (Stips et al, 2016;Jiang et al, 2019), the dynamical dependence between a set of observables and the Antarctic surface mass balance (Vannitsem et al, 2019), soil moisture -air temperature interactions in China (Hagan et al, 2019), prediction of El Niño Modoki (Liang et al, 2021), causal links between climate indices in the North Pacific and Atlantic regions (Vannitsem and Liang, 2022), identification of potential drivers of Arctic sea-ice changes (Docquier et al, 2022b), and ocean-atmosphere interactions (Docquier et al, 2022a).…”