Oliver Thewalt

    Oliver Thewalt

    Theoretical Physics | Quantum Biology | Dark Matter Research Cluster

    El Nino Events - La Nina

    Climate network suggests enhanced El Niño global impacts in localized areas 


    Excerpt:

     We construct directed and weighted climate networks based on near surface air temperature to investigate the global impacts of El Nino and La Nina. We find that regions which are characterized by higher positive or negative network in weighted links, are exhibiting stronger correlations with the El Nino basin and are warmer or cooler during El Nino or La Nina periods. These stronger in-weighted activities are found to be concentrated in localized areas, as compared to non-El Nino periods, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Nino (La Nina) event to another; still some El Nino (La Nina) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Nino or La Nina events and also may be applied in the investigation of other climate variables.

    Zitat:” The El Ni˜no phenomenon strongly impacts the local climate and also remote regions including North  America [27], Australia [28, 29], Europe [30], the South China Sea, the Indian Ocean, and the tropical North Atlantic [31]. It can lead to warming, enhanced rain in some regions and droughts in other regions, decline in fishery, famine, plagues, political and social unrest, and economic changes. El Ni˜no is a coupled ocean-atmosphere phenomenon which has been linked to internal oceanic Kelvin and Rossby tropical wave activity and to the wind activity above the equatorial Pacific Ocean. There are several indices that quantify the El Ni˜no activity, including the Ni˜no 3.4 index and the Oceanic Ni˜no Index (ONI), which is NOAA’s primary indicator for monitoring El Ni˜no and La Ni˜na. ONI is the running three-month mean sea surface temperature (SST) anomaly for the Ni˜no 3.4 region (i.e., 5◦N − 5 ◦S, 120◦ − 170◦W); here we refer to this region as the El Ni˜no Basin (ENB). When the ONI exceeds 0.5 ◦C for at least five consecutive months, the corresponding year is considered to be an El Ni˜no year. The higher the ONI is, the stronger the El Ni˜no. Similarly La Ni˜na is determined to occur when the ONI drops below the −0.5 ◦C anomaly for at least five consecutive months. Presently, we have just undergone one of the strongest El Ni˜no events since 1948.

     

     

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    The constructed climate network enabled us not only to obtain a map of the global impacts of a given El Ni˜no, but also to study the local impacts of El Ni˜no in specific regions. These are achieved for the first time by using our new approach. In addition, using only previous events’ data, our results confirm most of the regions that were affected during the recently concluded El Ni˜no [33]. In the present study, we identify warming and cooling regions which are influenced by the ENB by measuring each node’s strength according to the weights of the “in”- links outgoing from the ENB. We find that during El Ni˜no/La Ni˜na, a large fraction of the globe is not influenced by the events, but the regions that are influenced are significantly more affected by the ENB during El Ni˜no/La Ni˜na than in normal years. Our results also indicate that the El Ni˜no/La Ni˜na events influence different regions with different magnitudes during different events; still by determining the network community structure, our results suggest that similarities exist among some of the El Ni˜no (La Ni˜na) events.”