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Data Science Blog > Student Works > Data Study on El Niño and La Niña

Data Study on El Niño and La Niña

Bin Fang
Posted on Jul 23, 2016
The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Introduction

El Niño (Warm) and La Niña (cool) are two extreme events that particularly happen in tropical pacific region. El Niño has both negative and positive global impacts, especially in U.S. Western coast and Southeast U.S., In contrast with El Niño, La Niña normally appears associated with cold extreme weather. Data shows the two events are expected to bring above-average precipitation, higher risk of wildfires, more hurricane activities, higher risk of flooding, etc., to U.S. during Fall-Winter-Spring.

The five weather variables between 1980-2015 are averaged from three months of each season. The averaged anomalies for each variable were calculated by subtracting the long-term averaged data from each single year. In order to better summarize the weather anomalies, two typical regions: Eastern U.S. (25-45o N, 65-85 o W) and Western U.S. (30-50 o N, 115-125 o W) were divided and studied separately. The two winters, which correspond two extreme El Niño and La Niña effect years respectively from 1988-1989 and 1997-1998 were highlighted for analyzing and map visualization.

 

 

Data Study on El Niño and La Niña

 

Data Visualization and Exploration

a. Time series analysis of averaged anomalies of U.S. East/West coast

The time series plots demonstrate anomalies of the selected variables. It seems El Niño is usually associated with higher temperature, greater rainfall and higher vegetation transpiration activity, while La Niña shows opposite trends. The other two variables snow cover and soil wetness are lagged to respond to the aforementioned variables, probably due to contributions from other factors, e.g., air pressure.

 

Data Study on El Niño and La Niña

Data Study on El Niño and La Niña

b. Analysis of spatial distribution pattern

By analyzing the spatial distribution pattern of the variables in the El Niño affected winter of 1997-1998, we may know that for the temperature, Central West and Northeastern U.S. experienced abnormally high temperature ranging 4-8 oC, while 2 oC lower from normal record in Central Mexico. Great precipitation occurred in California and Southern and Eastern U.S., which was up to 50-200 kg/m2 higher than normal especially in Northern California and Florida. The snow cover map shows in New England region is a bit higher than normal. Southeast U.S. and Texas are observed of higher vegetation transpiration activities than usual from the map.

map1

The results of La Niña affected winter in 1988-1999 show that the entire U.S. east coast was experiencing above-normal high temperature of approximately 2 oC, while west coast was experiencing cool temperature, which was up to 5 oC. The whole North America had drought trend, while central south has greater rainfall than normal. Another interesting event was the vegetation transpiration in Florida was observed 2.5-5.5 kg/m2 higher than normal.

map2

Conclusion

From the analyses of anomalies of five land surface variables: temperature, precipitation, snow cover, soil wetness and vegetation transpiration, it can be found that temperature and precipitation are directly related to El Niño/La Niña effects. El Niño is more positively related to temperature and precipitation while La Niña is more negatively related to the two variables. South and East Coast U.S. as well as California tend to be easier affected by the two extreme events.

About Author

Bin Fang

With a multi-disciplinary background in earth science, electrical engineering and satellite technology, Bin has spent more than ten years in scientific research and teaching in university and research institute. His previous study aimed to integrate and interpret remote...
View all posts by Bin Fang >

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