Climate change and North Atlantic Oscillation (NAO) index: Can entropy reveal a non-natural variability trend in NAO index?

The North Atlantic Oscillation (NAO) index describes the hemispheric meridional oscillation of atmospheric masses above near Iceland and the subtropical Atlantic regions. In specific, NAO index indicates the differences in atmospheric pressure patterns between the two regions. Strong positive NAO index values relate to significant pressure differences, exposing US East and Northern Europe to warmer weather conditions, and Southern Europe to colder weather conditions. Negative NAO index values indicate weaker pressure differences, exposing US East and Northern Europe to cold weather, and Southern Europe to warm weather. A significant portion of the Atlantic sector climate is associated with NAO index and its variability. Historically, NAO index values were in a positive trend between 1970s and 1980s. Highest positive values were reported in the early 1990s. By that time, it was suggested that NAO index positive trends contributed significantly to the global warming signal. Most recently, research outputs from climate model predictions suggest that NAO index values will be more at the positive phase as a result of strong global warming signal. In a warmer climate the overall number of storms is predicted to decrease but storms will be more intense. However, more research is needed to understand variability trends in NAO index and to what extent they might be attributed to climate change impacts. Hence referred in here as NAO natural and non-natural, or else, climate change related variability trends. This study investigated the NAO variability trends by use of Singular Spectrum Analysis (SSA) and SSA based Entropy index. SSA is a statistical mechanics tool used to study the non-linear behavioral characteristics in complex geophysical, meteorological and climatic systems, monitored by time series data. The main objective of this analysis is to reveal the evolution of the NAO index dynamical system and convey information about the changing dynamics of the system. By use of SSA Entropy based index, the chaotic behavior of NAO index is studied. An SSA entropy based chaotic descriptor might entail information of the non-natural variability trend for NAO index values. Also the same descriptor might prove capable of defining a historical milestone of when this NAO variability trend started changing in an unpredictable- non natural- way owed to climate change forcing factors. NAO Index data are extracted from Climatic Research Unit, University of East Anglia from 1979-2018. SSA Phase space reconstruction by method of delays has been applied first to characterize the statistical and chaotic behavior of NAO patterns, by calculating variability and inconsistency descriptors. Phase space reconstruction allows analyzing time series data within the dynamics systems theory context. Following that, reconstructed attractor from the NAO observed time series allowed to build an approximation of the unknown observed states. Results revealed a highly variable and inconsistent behavior in NAO patterns over time. SSA Entropy based index investigation is currently underway to further understand the nature of inconsistency revealed in NAO patterns. Further research is expected to establish wind and wave storm patterns connections with NAO index patterns, through transfer dynamics concepts.