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© 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-means Simulated annealing Total Variance Alternative clusters Local minimum Global minimum
© Crown copyright 2007 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
© Crown copyright 2007 Pie slices not clusters
© Crown copyright 2007 Cluster centroids don’t match
© Crown copyright 2007 Cluster stability Best estimate of global minimum variance Clusters stable to removal of data?
© Crown copyright 2007 Cluster stability method - schematic Start with full set of data Form clustersGo back to full data setRemove half of the dataForm clustersPair up clusters with originals Count the days that match up
© Crown copyright 2007 Stability measure Repeat analysis 100 times Ratio of days that match to total days Stability change with number of clusters Optimum number?
© Crown copyright 2007 JF cluster stability JF 1900-1949 (blue) 1950-1999 (red)
© Crown copyright 2007 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!
© Crown copyright 2007 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
© Crown copyright 2007 Multidecadal variability in time series
© Crown copyright 2007 Negative summer NAO July / August – summer NAO / AMO links Positive summer NAO
© Crown copyright 2007 Clusters match JA EOF1 series (black)
© Crown copyright 2007 AMO index / cluster frequencies
© Crown copyright 2007 November / December – links to IPO?
© Crown copyright 2007 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
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