ENSO Prediction: The Summer (JJA) Oceanic Nino Index used as a harbinger for El Niño and La Nina Events.

The JJA or Summer Oceanic Nino Index seems to be a predictor for later in the year La Nina's and El Niño's. The JJA ONI at certain thresholds in the negative (La Nina) and the positive (El Niño) can act as a predictor for the definition of ENSO events where the ONI is +/-0.5 consecutively for 5 trimonthly periods or more.

The El Niño Indicator: 100% of the time since 1950 , if the JJA ONI is +0.4 or higher while excluding the years that already have El Niño's occurring through the year (1958, 1969, 1987), then an El Niño will have started by MAM to ASO. Half of the years end up having Strong El Niño Status.

 

 

The La Nina Indicator: 100% of the time since 1950, If an El Niño ends at the beginning of the year, and the ONI for the trimonthly period of JJA is -0.4 or less, then a La Nina will have already started by AMJ to JAS. 5 out of 7 of the La Nina's end up Strong.

 

 

The Biannual Arctic Oscillation and it's association with Influenza Outbreaks since 1950.

The negative mode of the Arctic Oscillation seems to be associated with flu outbreaks. The reason speculated is a -AO allows the polar jet to slow down and allow cold air to flow meridionally south into the mid-latitudes. This cold air effects the population and the Influenza Virus in a way that's favorable for infection.

1. People are more likely to stay in buildings and domisciles, spreading the virus between each other.

2. Your body needs to use energy to heat your body depleting energy away from your immune system.

3. The flu virus is very hardy in the winter cold, it's external shell becomes a barrier to allow it to live in the cold better. Therefore, it stays around in the cold to infect the population.

The literature also equates Influenza with PDO and ENSO modes, but I figured I'd try the AO which fit rather well. The 4 main Flu Outbreaks since 1950 are denoted by the more negative biannual values of the Arctic Oscillation where the most negative value ranks 1st and the most positive value ranks last. The four Influenza Outbreaks rank one, two, four, and sixteen since 1950. It's rather telling that the clustering toward the top of the rank in mainly denoted by Hong Kong, H1N1, & Asian Flu (1,2, and 4 ranked respectively). As for the 16th ranked Russian Flu (which happened to be a late 70's winter), I speculate it may have been dictated by Scandinavian or East Asian/West Russian blocking mechanism versus the Arctic Oscillation, maybe even the TNH teleconnection.

Here is the graphic:

Experimental: An Indicator for March Rockford,ILL / KRFD Snowfall Within The February U.S. Meridional Wind Anomaly Distribution

This is kind of an experiment to see if these type of correlating indicators work as shown by the map. If so, maybe forecasts like this can be used for snowfall, precipitation, and temperature for different cities across the world. This is an example of using the previous months V-Wind across the United States as a harbinger for KRFD Snowfall. Other indicators could be anything from sea-level pressure, SST's, or even stream function on a sigma level. This begs the question about the typical "Correlation With Causation Argument". The March Snowfall Average was calculated using March 1951-2012 Snowfall, which averaged out to 5.5 inches. We'll come back to this to see if it worked or not.......

Research: Constructing a Composite of Teleconnection Indices to Better Forecast Temperature over the Eastern United States

Here is a research paper I did during my Senior Year, it attempts to to combine teleconnections mathematically as a tool for Eastern U.S. Temperature Prediction.

 

Download The PDF: http://www.docdroid.net/file/download/9ftd/constructing-a-composite-of-teleconnection-indices-to-better-forecast-temperature-over-the-eastern-united-states-marinaro-2012.pdf

 

 

Constructing a Composite of Teleconnection Indices to Better Forecast Temperature over the Eastern United States

 

Alan Marinaro 

Department of Geography

Northern Illinois University

DeKalb, IL 60115

  

ABSTRACT: 

The agricultural, industrial, and financial sectors east of the 100W longitude line to the I-95 corridor of the United States, are directly or indirectly susceptible to the affects of surface air temperature. A new modeling regime to improve temperature forecasts would help curb weather risks, crop yields, and energy consumption within the most economically active part of the country. A new index is constructed to better describe the temperature over the eastern half of the country. Northern Hemispheric teleconnections are known to have direct effects on temperatures and 500mb geopotential heights over the United States. Three teleconnections that best describe atmospheric blocking over the eastern U.S. include the East Pacific Oscillation (EPO), North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO). The EPO, NAO, and AO have a direct correlation with 500mb geopotential heights and surface air temperature east of the Rocky Mountains to the I-95 Corridor. Creating a composited & weighted time series from these three teleconnections using multiple regression created a more comprehensive tool to describe, forecast, and model surface temperatures for the eastern U.S. This is known as the Composite Blocking Index (CBI). Comparing the CBI composite time series with its components (EPO, NAO, and AO) individually, it shows a higher correlation with surface temperatures over the eastern U.S. The variance that describes the average surface temperature is increased 11.5% to 18.8% higher annually using the CBI in comparison to the NAO, AO, and EPO individually.

Keywords: Atmospheric Blocking, Teleconnections, North Atlantic Oscillation, & United States Temperatures.

 

 

 

Global Wind Oscillation Analog Forecast for March of 2014, Cold Weather Continues Across Central/Eastern CONUS, La Nina Atmospheric Base State Prevails.

The Global Wind Oscillation is an interesting scheme devised by Klaus Weickmann & Ed Berry. It can detect the Atmosphere in 4 main base states, La Nina, El Nino, +Torque, and -Torque. The GWO also contains a Madden-Julian Oscillation component to detect tropical forcing which affects the mid-latitudes. The Atmosphere is not always coupled with the Oceanic Base State, and currently that is the case. Using GWO data, GLAAM and it's Tendency from February 1st to 19th, This is the current hindcast/forecast for February 2014 and forecast for March 2014 Temperature Wise using the mean of this GWO data compared to past years.

Reference for GWO scheme: http://www.esrl.noaa.gov/psd/map/clim/wb08_revised_final.pdf

GWO Data: http://www.esrl.noaa.gov/psd/map/clim/gwo.data.txt