© Crown copyright Met Office Stratospheric Influences on the Troposphere Adam Scaife December 2010.
© Crown copyright 2007 Cluster analysis of mean sea level pressure fields and multidecadal...
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Transcript of © Crown copyright 2007 Cluster analysis of mean sea level pressure fields and multidecadal...
© Crown copyright 2007
Cluster analysis of mean sea level pressure fields and multidecadal variability
David Fereday, Jeff Knight, Adam Scaife, Chris Folland, Andreas Philipp 13 March 2007
© Crown copyright 2007
Introduction
Use cluster analysis to examine circulation variability
Are genuine clusters present in MSLP data?
Stability of different numbers of clusters
Multidecadal variability and links with SST
© Crown copyright 2007
Data
EMSLP dataset – daily mean MSLP fields 1850-2003
NAE region – 25°N-70°N, 70°W-50°E
5 degree x 5 degree resolution
© Crown copyright 2007
Methods
Divide data into two month seasons
Seasonally varying climatology removed
Apply cluster analysis to fields in each season separately
Aim is to characterise daily variability – no low pass filtering applied
© Crown copyright 2007
Cluster algorithm
Variant of k-means
Specify number of clusters beforehand
Each field belongs to one cluster
Random initial allocation
Minimise within cluster variance by exchanging fields
© Crown copyright 2007
Simulated annealing
Aim to avoid local minima
k-meansSimulated annealing
Total Variance
Alternative clusters
Local minimum
Globalminimum
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Are there clusters in MSLP fields?
Algorithm produces clusters whether any present or not
If clusters are present, there must be a fixed number of them
Number of clusters is specified beforehand – how is this number decided?
© Crown copyright 2007
Try to find local minima of total within cluster variance
For all but small numbers of clusters, many different alternatives
Local minima
Global minimum
Local minima
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Cluster stability
Best estimate of global minimum variance
Clusters stable to removal of data?
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Cluster stability method - schematic
Start with full set of dataForm clusters Go back to full data set Remove half of the data Form clusters Pair up clusters with originals Count the days that match up
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Stability measure
Repeat analysis 100 times
Ratio of days that match to total days
Stability change with number of clusters
Optimum number?
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Cluster conclusions
Many local minima - no strong clustering
Stability reduced as clusters increase
No optimum number of clusters
Choice of number of clusters is subjective
Clusters are nevertheless useful!
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Multidecadal variability
10 clusters per season
Circulation variability - frequency time series
Variability on many different timescales
Low pass filter (25 year half power)
SST links via regression analysis
HadISST from month before MSLP season
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Negative summer NAO
July / August – summer NAO / AMO links
Positive summer NAO
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Conclusions
No genuine clusters, but clusters still useful
Clusters relate to EOF time series
Reproduce known relationships with SST
Many results – hint at new SST links