 Introduction
o
o Purpose Statement and Explanation
o Hypothesis
 Literature Review
o Extremes
 Bieniek et al. 2014 determined that Alaskan temperature extremes are
largely dependent on large scale circulations.
 Gutowski et al. 2003 found that models produce too few low intensity
precipitation events compared to observations. Additionally, simulated
precipitation decreases more rapidly with increasing intensity than
observations. The models and observations agree fairly well through the
95th percentile, granted the model’s intensity at this frequency tends to be
less due in part to model resolution constraints (Gutowski et al. 2007).
o SOM Extremes
 Alexander et al. 2009 used SOMs to evaluate temperature and
precipitation extreme variability based on sea surface temperatures from
1870-2006.
 Cassano et al. 2006 related Alaskan large scale circulations to extreme
temperature and wind events in Barrow Alaska.
 Cavazos 1999 used SOMs to evaluate extreme precipitation in
northeastern Mexico and southeastern Texas.
 Cavazos 2000 used SOMs to evaluate extreme precipitation in the Balkins
o MISC
 Bieniek et al. 2014 concluded that Alaskan precipitation variation is much
less decadal than Alaskan temperature variation.
 Gutowski et al. 2004 used SOMs, bias scores, and arithmetic scores to
conclude that the south central U.S. has a precipitation deficit in SON and
DJF.
 Cassano et al. 2006 used SOMs to find large scale circulation trends in
polar regions for both JJA and DJF. Also, they compared these to
temperature and precipitation anomalies.
 (Skific et al. 2009, Skific et al. 2013) used SOMs to analyze atmospheric
moisture transport within the arctic.
 Schuenemann and Cassano, 2010 used SOMs to determine that
Greenland’s precipitation is increasing due to a northward storm track
shift and increased moisture availability due to ice melt.
 Finnis et al. 2009 suggests that GCMs underestimate the strength of the
Beaufort High resulting in positive precipitation anomalies over Alaska
using SOMs.
 Ning et al. 2012 demonstrated how statistical downscaling using SOMs
improves the representative precipitation characteristics in Pennsylvania.
 Experimental Methods
o Time Period
 Annual Time Period
 The winter months of December, January, and February
 Total Time Period
 1986 – 2005 to conform to IPCC
o Region of Interest:
 The entire state of Alaska
o Data Sources
 Observational Data:
 NCDC Precipitation is used for comparison with model
precipitation in Frequency vs. Intensity Plots.
 Reanalysis Data:
 ERA-Interim Reanalysis 500 hPa geopotential heights used in
SOM analysis.
o The ERA-Interim underestimates precipitation in high
mountain regions (Bernhardt et al. 2013)
 Model Data:
 CMIP5 models (Taylor et al. 2012)
 Model precipitation used for Frequency vs. Intensity Plots and
SOM analysis:
o ACCESS1
o MIROC-ESM-CHEM
o MIROC-ESM
o MPI-ESM-LR
o HadGEM2-CC
o HadGEM2-ES
 The reason for using these models is that they model sea ice well
according to paper (April 2013 Diary Page)
o Extreme Percentile
 Using the 99th percentile (or 99.5th percentile) not sure for this study. All
precipitation exceeding this percentile is considered extreme.
o Frequency vs. Intensity
 Precipitation events binned every 2.5 mm/day
 Why 2.5 mm/day?
 All events binned every 2.5 mm/day normalized by the total number of
events
 Do I include the zero bin (everything less than 2.5 mm/day) or not?
 The x – axis is precipitation event intensity
 The y – axis is precipitation event frequency
o Widespread Extreme Definition
 Use Simultaneity Plots to determine Widespread Extreme Threshold
 Will it be 50 grid points like Senior Thesis or a different number?
 A widespread extreme is considered multiple grid points so that if an
extreme is occurring, then it is occurring over a large area.
o Common Grid Size
 Using a Common grid of 0.5x0.5 degree. With a common grid we can
evaluate multiple models and observation sets of different resolutions
equally.
o SOM Analysis
 Define what a SOM is
 Trains 500 hPa heights using a combination of all models and ERAInterim
 Produces the climatological frequencies of different 500 hPa height
patterns occurring for different extremes.
 Shows how a pattern can evolve through time given its evolution
in SOM Space.
 Using a 7x5 array size
 Requires a combination of flat sammon map and low qerror
 Experimental Results
o Frequency vs Intensity
o Master SOM for combined models and ERA – Interim
o Climatological Frequencies
o Highest frequency nodes
o Widespread Extreme Frequencies
o Highest extreme frequency nodes
o Evolution of extreme events in SOM Space
 Conclusions
 References
Outlined Subsections
Sources I have no idea what to do with yet:
Carbone, 2014
Carrera et al. 2004
Cassano et al. 2007
Cassola and Wallace, 2007
Cattiaux et al. 2013
Overland et al. 2011
(Diffenbaugh and Giorgi, 2012) maybe use in the introduction
(Dufresne et al. 2013) maybe use in models part of data section
Grimm et al. 2014 summarizes that humans depend on their ecosystems for food, fuel, fiber,
shelter, etc. So, ecosystem modification due to climate change will have a direct impact on the
people that depend on it. (use in intro)
(GuanHua et al. 2013) use citation in introduction
Portmann et al. 2013
Koenigk et al. 2012