A Winter North Atlantic Oscillation Predictor in the North Pacific ONLY during El Nino Events.

Here is a bit of climate research from the summer of 2004. It ONLY works during El Nino years defined with the 1971-2000 base period Oceanic Nino Index. From here we look to the North Pacific Sea Level Pressure status throughout October to be deterministic of the December-March North Atlantic Oscillation (NAO). October SLP's below 1013-hPa indicate a higher likelihood of a -NAO, while SLP's above indicate a higher likelihood of a +NAO or Neutral NAO.

Now, the problem for the winter 2014-2015 is we don't know if we will have an official El Nino event as defined by the ONI (5 trimonthly periods of 0.5C or higher in Nino Region 3.4). Some models are predicting an El Nino, some are not. Right now, according to climatology I have used, the only type of +ENSO events that really can occur is a weak event, with a very outside chance of moderate.

The Experimental Winter DJF 2014-15 NAO Forecast using Zonal Wind indicators from May & September

This is a NAO forecast based on research done in 2011, whereby certain zonal wind anomalies in the Northern Hemisphere during May and September are used as predictors. It has worked decent during 2 of 3 forecasts from the winters of 2011-12 & 2012-2013, but busted rather harsh during the Winter of 2013-2014.

Figure 1. Tropical Northern Hemisphere 500mb Loading Pattern in +Mode (NOAA/CPC)

In my attempt to try to figure out why, I remembered the extreme North Pacific regime and Tropical Northern Hemisphere (TNH) (Figure 1) blocking in the Winter 2013-2014 was dominant to the point where I believe it may have affected some way the expected Atlantic DJF blocking outcome (NAO). I noticed the 2 times the model failed quite badly was during periods of extremely positive TNH values, as a matter of fact the winters of 2008-2009 and 2013-2014 had the highest DJF TNH values since 1948. This meant a very strong Hudson Bay Low anomaly over Canada and above normal 500-hPa heights from the Gulf of Alaska to just of the coast of California. Out of all the winters with a TNH of +1 S.D., 4 out of 6 had inaccuracies, which was the largest cluster of inaccuracy found in the data. The model's R^2 has dropped from 0.79 to 0.69 due to the 2013-14 forecast failure, but still describes 69% of the NAO.

NAO Forecast Graphic

The Winter of 2014-2015 could very much be driven by North Pacific dominate variables, although that is not a guarantee. If that is the case, this forecast could be offset by a +TNH dominant winter, so take it as an "Experimental" tool not a definite. The NAO is essentially being forecasted as neutral with a value of -0.13 but with error -0.13 +/- 0.2 or -0.33 to 0.07.

Summer U.S. Temperature 2014 - GWO Forecast (From May 2014) Verification

Here is what was forecasted using Global Wind Oscillation Data from May 2014, specifically key on the temperature distribution across the United States in the bottom-right area of the graphic below.

Now compare it to what actually happened, I believe this summer forecast went rather well.

6-Month North Pacific SST Pool Analogs For the Winter of 2014-2015.

Hello everyone, been awhile since I have blogged, been a busy last summer working at the Midwestern Regional Climate Center in Champaign, IL. As for me, I'm back to checking out what the coming winter could possibly be like. Here are the Top 10 Warmest NPAC analogs for the 30N-70N and 180W-130W bounded SST Pool from March to August in comparison to this last period (MAMJJA 2014) with respect to Winter (DJF) 500mb height anomalies.

These analogs are also broken down by ENSO category with respect to U.S. Temperatures during the November through March Period, notice areas of the Midwest, Great Lakes, and Eastern U.S. have a decent shot at seeing a cold winter based solely on past occurrences of ENSO/NPAC combinations. I wouldn't use this as an exact forecast for winter, but as guidance for the upcoming winter according to what the NPAC looks like now. Notice only one analog had a nation wide warm winter (2005-2006). The MEI is used as the metric to categorize ENSO status.

Summer GWO Radii Model, ENSO Status, Cool Summer on Tap for most of country, West Coast Above Normal Temps...

• The global wind oscillation is progressing along in a more neutral atmospheric ENSO phase where negative mountain and frictional torque rules instead of the "La Nina" like anomalies 15-45 days earlier. The GWO has yet to couple with the oceanic ENSO state. The oceanic ENSO state in the next 30-60 days should shift to Weak El Nino status, a much more tamed +ENSO event than many had already forecasted. The whole idea of a runaway El Nino like that of 97/98 or 82/83 did not make much sense looking at trends in NOI, SOI, and sub-surface temperature wise. This idea of a "Super El Nino" is nothing more than hype by some meteorological outlets and a few environmental pundits. That being said, the PDO being above 1.5 the 1st time since 2005 should help propel the oceanic ENSO Region 3.4 status to a likely moderate event of +1.0 to +1.2C by late fall.
• The GWO Radii Model continues to allude to cooler temperatures over the Midsection and Eastern U.S. while the West Coast should be above normal temperature wise during July/August. This summer cool pattern is something we haven't seen since 2009.



Summer of 2014 Forecast Based On The Global Wind Oscillation

It seems the Atmospheric Base State is still in a La Nina like mode, the Equatorial Pacific is quickly transitioning to an El Nino, but sometimes there is a lag in the coupling between the Oceanic ENSO Pattern and Atmospheric Base State. That lag is occurring now and should sort itself out by the end of the Summer. Here is the latest forecast created from Global Wind Oscillation analogs:

Principal Component Analysis of Winter 2013-2014 - Finding the Predominant Modes

Using the predominant signals of the Winter 13-14 (defined by NDJF), Extremely Warm Mean NPAC SST's, Very High Bering Sea/Alaskan Mean 500mb Heights, Very Low S.E. Canada/Hudson Bay Mean 500mb Heights, and High Pressure dominant Sea Level Pressure over the North Central Plains. These 4 Datasets since 1948 were put through Rotated Principle Components Analysis.

Two Principal Components had significant Eigenvalues of 1 or above and explained 79% of the Winter (NDJF) Pattern. The 1st Principle component explained 43.4% and was determined to be the predominant boreal mode of the East Pacific Oscillation (EPO). The 2nd Principle component explained 35.6% and was determined to be the Tropical/Northern Hemiphere (TNH) teleconnection.

 



NEW: Global Wind Oscillation Analog Model OUTPUT, March Still likely to be colder then normal. Atmospheric Base State: +Torque / Ninoesque

I just ran the Global Wind Oscillation analog model. The atmospheric base stateh at the end of February was that of  + Frictional/Mountain Torque, very close to El-Nino-like. I thought maybe you, the reader would like to see some of the process. Hence, here is the FORTRAN Output along with an info graphic. CLICK ON INFOGRAPHIC FOR FULL SIZE or Click: Infographic

Fortran Output:

  AAM-Y  TEND-X  -ANGLE-  -MAG-  Q  Year  M DI-DE #D ATMENSO PHS
  0.548  -0.653  140.022  0.853  2  1958  2 20-28  9 -Torque 7.5
 -0.731   0.363  296.426  0.816  4  1959  2 20-28  9 La Nina 3.5
 -0.536   0.713  323.102  0.892  4  1960  2 20-28  9 +Torque 3.5
 -0.287   0.734  338.678  0.788  4  1961  2 20-28  9 +Torque 4.5
 -1.053   0.222  281.913  1.077  4  1962  2 20-28  9 La Nina 2.5
 -2.507  -0.494  258.842  2.555  3  1963  2 20-28  9 La Nina 2.5
 -0.284   0.550  332.653  0.619  4  1964  2 20-28  9 +Torque 3.5
 -1.268   0.604  295.491  1.404  4  1965  2 20-28  9 La Nina 3.5
 -1.374  -0.141  264.138  1.382  3  1966  2 20-28  9 La Nina 2.5
 -1.399   0.073  273.001  1.401  4  1967  2 20-28  9 La Nina 2.5
 -0.239  -1.663  188.173  1.680  3  1968  2 20-28  9 -Torque 0.5
  1.258   0.588   64.953  1.388  1  1969  2 20-28  9 El Nino 5.5
  1.189   0.277   76.900  1.221  1  1970  2 20-28  9 El Nino 6.5
 -1.819   0.117  273.670  1.823  4  1971  2 20-28  9 La Nina 2.5
 -0.424   0.852  333.525  0.952  4  1972  2 20-28  9 +Torque 3.5
 -0.140  -0.572  193.748  0.589  3  1973  2 20-28  9 -Torque 0.5
 -1.790   0.467  284.612  1.850  4  1974  2 20-28  9 La Nina 2.5
 -1.217  -0.833  235.592  1.475  3  1975  2 20-28  9 La Nina 1.5
 -1.268   0.753  300.719  1.475  4  1976  2 20-28  9 La Nina 3.5
 -1.452  -0.176  263.107  1.463  3  1977  2 20-28  9 La Nina 2.5
  2.571  -0.299   96.631  2.588  2  1978  2 20-28  9 El Nino 6.5
  0.246  -0.053  102.254  0.251  2  1979  2 20-28  9 El Nino 6.5
  0.720  -0.624  130.935  0.953  2  1980  2 20-28  9 El Nino 7.5
  0.149   1.530    5.558  1.537  1  1981  2 20-28  9 +Torque 4.5
  0.101   0.204   26.315  0.228  1  1982  2 20-28  9 +Torque 5.5
  3.389   0.641   79.287  3.449  1  1983  2 20-28  9 El Nino 6.5
 -2.448  -0.474  259.031  2.493  3  1984  2 20-28  9 La Nina 2.5
 -0.561  -0.114  258.472  0.573  3  1985  2 20-28  9 La Nina 2.5
 -0.748  -0.637  229.589  0.982  3  1986  2 20-28  9 La Nina 1.5
  0.993   1.073   42.783  1.462  1  1987  2 20-28  9 +Torque 5.5
  2.002   0.337   80.455  2.030  1  1988  2 20-28  9 El Nino 6.5
 -2.013   0.289  278.166  2.034  4  1989  2 20-28  9 La Nina 2.5
  0.543  -1.457  159.545  1.555  2  1990  2 20-28  9 -Torque 0.5
  0.470   0.908   27.373  1.022  1  1991  2 20-28  9 +Torque 5.5
  0.411  -0.301  126.220  0.510  2  1992  2 20-28  9 El Nino 7.5
  1.183  -0.802  124.135  1.430  2  1993  2 20-28  9 El Nino 7.5
  0.570   1.206   25.305  1.334  1  1994  2 20-28  9 +Torque 5.5
  0.793   0.126   81.007  0.803  1  1995  2 20-28  9 El Nino 6.5
 -0.973   0.203  281.800  0.994  4  1996  2 20-28  9 La Nina 2.5
  0.009   0.646    0.789  0.646  1  1997  2 20-28  9 +Torque 4.5
  2.006   0.517   75.554  2.071  1  1998  2 20-28  9 El Nino 6.5
 -0.853  -1.033  219.550  1.340  3  1999  2 20-28  9 -Torque 1.5
 -1.366  -0.181  262.445  1.378  3  2000  2 20-28  9 La Nina 2.5
  0.524   0.466   48.404  0.701  1  2001  2 20-28  9 El Nino 5.5
  0.958  -0.100   95.961  0.963  2  2002  2 20-28  9 El Nino 6.5
  0.122  -0.409  163.358  0.427  2  2003  2 20-28  9 -Torque 0.5
  1.030  -0.361  109.320  1.091  2  2004  2 20-28  9 El Nino 6.5
  3.264   0.480   81.635  3.300  1  2005  2 20-28  9 El Nino 6.5
  0.429   0.839   27.079  0.942  1  2006  2 20-28  9 +Torque 5.5
 -1.592   0.253  279.040  1.612  4  2007  2 20-28  9 La Nina 2.5
 -1.602  -0.659  247.646  1.732  3  2008  2 20-28  9 La Nina 2.5
 -1.661   0.876  297.793  1.878  4  2009  2 20-28  9 La Nina 3.5
  1.582  -0.692  113.629  1.727  2  2010  2 20-28  9 El Nino 7.5
 -0.471  -0.631  216.741  0.788  3  2011  2 20-28  9 -Torque 1.5
 -1.900  -1.000  242.241  2.147  3  2012  2 20-28  9 La Nina 1.5
  0.400   1.300   17.103  1.360  1  2013  2 20-28  9 +Torque 4.5
  0.643   0.971   33.523  1.165  1  2014  2 20-28  9 +Torque 5.5
                                                                                         
(===================================Process Complete!======================================)       
          _____                            _____                            _____                  
         /\    \                          /\    \                          /\    \                 
        /::\    \                        /::\    \                        /::\____\                
       /::::\    \                       \:::\    \                      /::::|   |                
      /::::::\    \                       \:::\    \                    /:::::|   |                
     /:::/\:::\    \                       \:::\    \                  /::::::|   |                
    /:::/__\:::\    \                       \:::\    \                /:::/|::|   |                
   /::::\   \:::\    \                      /::::\    \              /:::/ |::|   |                
  /::::::\   \:::\    \            _____   /::::::\    \            /:::/  |::|___|______          
 /:::/\:::\   \:::\    \          /\    \ /:::/\:::\    \          /:::/   |::::::::\    \         
/:::/  \:::\   \:::\____\        /::\    /:::/  \:::\____\        /:::/    |:::::::::\____\        
\::/    \:::\  /:::/    /        \:::\  /:::/    \::/    /        \::/    / ~~~~~/:::/    /        
 \/____/ \:::\/:::/    /          \:::\/:::/    / \/____/          \/____/      /:::/    /         
          \::::::/    /            \::::::/    /                               /:::/    /          
           \::::/    /              \::::/    /                               /:::/    /           
           /:::/    /                \::/    /                               /:::/    /            
          /:::/    /                  \/____/                               /:::/    /             
         /:::/    /                                                        /:::/    /              
        /:::/    /                                                        /:::/    /               
        \::/    /                                                         \::/    /                
         \/____/                                                           \/____/                 
                                                                                                   
(BY: Alan Marinaro - Northern Illinois University - Meteorology Graduate Teachers Assistant)       
                                                                                                   
(             Program: Global Wind Oscillation Monthly & Periodic Analog Model             )       
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      =8MMMMMM==MMMMMMMMMM.    ZMMMM ...===========MMMMMMMMO=. .. .   ...                          
      =MMMMM=?+MMMMMMMMMM . ?MMMMMM  ..===========MM$~MMMMM=.                                      
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 +=MMDDDDDDDNMMMMMMMMMMMMMMNDDDNNDDMDDDDDDNMNDDDDDDDMMMMMMMMMNDDDDDDNMMN=...                       
 .=NMM7.$$$Z$$DMMMMMMMMMMMMMZ.$$$$MMZ?:$$$ZMMZ.Z$$$MMMMMMMMMMMZ.7$$$MMM==                          
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   ..=+MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM++..                            
  ..,=================================================================.                            
  ..,=================================================================.                            



                         

Rockford,Ill Seasonal Snowfall - The Winter of 2013-2014 Ranks 13th Overall w/ 52.1 Inches.

The Current Seasonal Snowfall Rankings for KRFD: It wouldn't take much snow to push Rockford into the Top 5 Seasons.



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