Abstract Title: Signatures of Climatic Phenomena in Climate Networks: El Niño and La Niña events.

Abstract Submitted to: OCEAN SCIENCES

Abstract Text:

We construct a climate network based on surface air temperature data to identify distinctly signatures of climatic phenomena such as El Niño and La Niña events which trigger many climatic disruptions around the globe with severe economic and ecological consequences.

Climate networks are used to forecast various critical climate phenomena, such as the
monsoon, the North Atlantic Oscillation, El Niño events. Here we use correlation networks
constructed out of the surface air temperature data (we use reanalysis data of the daily near-surface air temperature (1000 hPa) from the ’NCEP/ NCAR reanalysis 1 project' (1979-2019 (https://psl.noaa.gov/)). We pick 725 grid points (7.5˚*12.5˚ resolution) for the entire globe and 105 grid points (2.5˚*2.5˚ resolution) for El Niño 3.4 region. We use a correlation network constructed out of the surface air temperature of time series of different locations in a global grid to analyze El Niño and La Niña phenomena. We obtained a monthly correlation of surface air temperature data during the arrival of the El Niño or La Niña events the event, both for the entire globe and the El Niño basin region, and constructed the heat map. This is performed by calculating the Pearson correlation coefficient, for all possible pairs of nodes, and calculating the average over the days of the given month. These values are used as the elements of the grid nodes adjacency matrix, which is then visualized as a heat map. We can also use the correlation matrix to calculate quantifiers like the fraction of links with correlation values above a certain threshold and transitivity. The entropy defined by –ln (no. of links with a correlation above a given threshold / no. of total links).
The correlation matrix of the network shows a structure which has distinct characteristics for El Niño events, La Niña, and a period of no events. We also identify the signature of the El Niño and La Niña oscillations in the heat map of the system and justify our heat map with the quantifiers. We hope these quantifiers can be further used for the prediction of
climatic events.