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The Smoking Code, part 2

Watts Up With That?
Sunday, Dec 6th, 2009

Climategate Code Analysis Part 2

The Smoking Code, part 2 atomThere are three common issues that have been raised in my previous post that I would like to officially address concerning the CRU’s source code.

If you only get one thing from this post, please get this. I am only making a statement about the research methods of the CRU and trying to show proof that they had the means and intent to falsify data. And, until the CRU’s research results can be verified by a 3rd party, they cannot be trusted.

Here are the four most frequent concerns dealing with the CRU’s source code:

  1. The source code that actually printed the graph was commented out and, therefore, is not valid proof.
  2. No proof exists that shows this code was used in publishing results.
  3. Interpolation is a normal part of dealing with large data sets, this is no different.
  4. You need the raw climate data to prove that foul play occurred.

If anyone can think of something I missed, please let me know.

The source code that actually printed the graph was commented out and, therefore, is not valid proof.

Had I done a better job with my source analysis, I would have found a later revision of the briffa_sep98_d.pro source file (linked to in my previous post) contained in a different working tree which shows the fudge-factor array playing a direct result in the (uncommented) plotting of the data.

Snippit from: harris-tree/briffa_sep98_e.pro (see the end of the post for the full source listing)

;
; APPLY ARTIFICIAL CORRECTION
;
yearlyadj=interpol(valadj,yrloc,x)
densall=densall+yearlyadj
  ;
  ; Now plot them
  ;
  filter_cru,20,tsin=densall,tslow=tslow,/nan
  cpl_barts,x,densall,title='Age-banded MXD from all sites',$
    xrange=[1399.5,1994.5],xtitle='Year',/xstyle,$
    zeroline=tslow,yrange=[-7,3]
  oplot,x,tslow,thick=3
  oplot,!x.crange,[0.,0.],linestyle=1
  ;

Now, we can finally put this concern to rest. 

Interpolation is a normal part of dealing with large data sets, this is no different.

This is partially true, the issue doesn’t lie in the fact that the CRU researchers used interpolation. The issue is the weight of the valadj array with respect to the raw data. valadj simply introduces too large of an influence to the original data to do anything productive with it.

Here is the graph I plotted of the valadj array. When we’re talking about trying to interpret temperature data that grows on the scale of one-tenths of a degree over a period of time, “fudging” a value by 2.5 is going to have a significant impact on the data set.

The Smoking Code, part 2 graph

No proof exists that shows this code was used in publishing results.

Correct! That’s why I am (and always have) taken the following stand: Enough proof exists that the CRU had both the means and intent to intentionally falsify data. This means that all of their research results cannot be trusted until they are verified. Period.

The fact that the “fudge-factor” source code exists in the first place is reason enough for alarm. Hopefully, they didn’t use fudged results in the CRU research results, but the truth is, we just don’t know.

You need the raw climate data to prove that foul play occurred.

This is assuming the raw data are valid, which I maintain that it probably is. Several people question the validity of the climate data gathering methods used by the different climate research institutions, but I am not enough of a climate expert to have an opinion one way or the other. Furthermore, It simply doesn’t matter if the raw climate data are correct or not to demonstrate the extreme bias the valadj array forces on the raw data.

So, the raw data could actually be temperature data or corporate sales figures, the result is the same; a severe manipulation of data.

Full Source Listing

As promised, here is the entire source listing for: harris-tree/briffa_sep98_e.pro

001  1. ;
002  2. ; PLOTS 'ALL' REGION MXD timeseries from age banded and from hugershoff
003  3. ; standardised datasets.
004  4. ; Reads Harry's regional timeseries and outputs the 1600-1992 portion
005  5. ; with missing values set appropriately.  Uses mxd, and just the
006  6. ; "all band" timeseries
007  7. ;****** APPLIES A VERY ARTIFICIAL CORRECTION FOR DECLINE*********
008  8. ;
009  9. yrloc=[1400,findgen(19)*5.+1904]
010 10. valadj=[0.,0.,0.,0.,0.,-0.1,-0.25,-0.3,0.,-0.1,0.3,0.8,1.2,1.7,2.5,2.6,2.6,$
011 11.   2.6,2.6,2.6]*0.75         ; fudge factor
012 12. if n_elements(yrloc) ne n_elements(valadj) then message,'Oooops!'
013 13. ;
014 14. loadct,39
015 15. def_1color,20,color='red'
016 16. plot,[0,1]
017 17. multi_plot,nrow=4,layout='large'
018 18. if !d.name eq 'X' then begin
019 19.   window, ysize=800
020 20.   !p.font=-1
021 21. endif else begin
022 22.   !p.font=0
023 23.   device,/helvetica,/bold,font_size=18
024 24. endelse
025 25. ;
026 26. ; Get regional tree lists and rbar
027 27. ;
028 28. restore,filename='reglists.idlsave'
029 29. harryfn=['nwcan','wnam','cecan','nweur','sweur','nsib','csib','tib',$
030 30.   'esib','allsites']
031 31. ;
032 32. rawdat=fltarr(4,2000)
033 33. for i = nreg-1 , nreg-1 do begin
034 34.   fn='mxd.'+harryfn(i)+'.pa.mean.dat'
035 35.   print,fn
036 36.   openr,1,fn
037 37.   readf,1,rawdat
038 38.   close,1
039 39.   ;
040 40.   densadj=reform(rawdat(2:3,*))
041 41.   ml=where(densadj eq -99.999,nmiss)
042 42.   densadj(ml)=!values.f_nan
043 43.   ;
044 44.   x=reform(rawdat(0,*))
045 45.   kl=where((x ge 1400) and (x le 1992))
046 46.   x=x(kl)
047 47.   densall=densadj(1,kl)     ; all bands
048 48.   densadj=densadj(0,kl)     ; 2-6 bands
049 49.   ;
050 50.   ; Now normalise w.r.t. 1881-1960
051 51.   ;
052 52.   mknormal,densadj,x,refperiod=[1881,1960],refmean=refmean,refsd=refsd
053 53.   mknormal,densall,x,refperiod=[1881,1960],refmean=refmean,refsd=refsd
054 54. ;
055 55. ; APPLY ARTIFICIAL CORRECTION
056 56. ;
057 57. yearlyadj=interpol(valadj,yrloc,x)
058 58. densall=densall+yearlyadj
059 59.   ;
060 60.   ; Now plot them
061 61.   ;
062 62.   filter_cru,20,tsin=densall,tslow=tslow,/nan
063 63.   cpl_barts,x,densall,title='Age-banded MXD from all sites',$
064 64.     xrange=[1399.5,1994.5],xtitle='Year',/xstyle,$
065 65.     zeroline=tslow,yrange=[-7,3]
066 66.   oplot,x,tslow,thick=3
067 67.   oplot,!x.crange,[0.,0.],linestyle=1
068 68.   ;
069 69. endfor
070 70. ;
071 71. ; Restore the Hugershoff NHD1 (see Nature paper 2)
072 72. ;
073 73. xband=x
074 74. restore,filename='../tree5/densadj_MEAN.idlsave'
075 75. ; gets: x,densadj,n,neff
076 76. ;
077 77. ; Extract the post 1600 part
078 78. ;
079 79. kl=where(x ge 1400)
080 80. x=x(kl)
081 81. densadj=densadj(kl)
082 82. ;
083 83. ; APPLY ARTIFICIAL CORRECTION
084 84. ;
085 85. yearlyadj=interpol(valadj,yrloc,x)
086 86. densadj=densadj+yearlyadj
087 87. ;
088 88. ; Now plot it too
089 89. ;
090 90. filter_cru,20,tsin=densadj,tslow=tshug,/nan
091 91. cpl_barts,x,densadj,title='Hugershoff-standardised MXD from all sites',$
092 92.   xrange=[1399.5,1994.5],xtitle='Year',/xstyle,$
093 93.   zeroline=tshug,yrange=[-7,3],bar_color=20
094 94. oplot,x,tshug,thick=3,color=20
095 95. oplot,!x.crange,[0.,0.],linestyle=1
096 96. ;
097 97. ; Now overplot their bidecadal components
098 98. ;
099 99. plot,xband,tslow,$
100100.   xrange=[1399.5,1994.5],xtitle='Year',/xstyle,$
101101.   yrange=[-6,2],thick=3,title='Low-pass (20-yr) filtered comparison'
102102. oplot,x,tshug,thick=3,color=20
103103. oplot,!x.crange,[0.,0.],linestyle=1
104104. ;
105105. ; Now overplot their 50-yr components
106106. ;
107107. filter_cru,50,tsin=densadj,tslow=tshug,/nan
108108. filter_cru,50,tsin=densall,tslow=tslow,/nan
109109. plot,xband,tslow,$
110110.   xrange=[1399.5,1994.5],xtitle='Year',/xstyle,$
111111.   yrange=[-6,2],thick=3,title='Low-pass (50-yr) filtered comparison'
112112. oplot,x,tshug,thick=3,color=20
113113. oplot,!x.crange,[0.,0.],linestyle=1
114114. ;
115115. ; Now compute the full, high and low pass correlations between the two
116116. ; series
117117. ;
118118. perst=1400.
119119. peren=1992.
120120. ;
121121. openw,1,'corr_age2hug.out'
122122. thalf=[10.,30.,50.,100.]
123123. ntry=n_elements(thalf)
124124. printf,1,'Correlations between timeseries'
125125. printf,1,'Age-banded vs. Hugershoff-standardised'
126126. printf,1,'     Region    Full   <10   >10   >30   >50  >100'
127127. ;
128128. kla=where((xband ge perst) and (xband le peren))
129129. klh=where((x ge perst) and (x le peren))
130130. ts1=densadj(klh)
131131. ts2=densall(kla)
132132. ;
133133. r1=correlate(ts1,ts2)
134134. rall=fltarr(ntry)
135135. for i = 0 , ntry-1 do begin
136136.   filter_cru,thalf(i),tsin=ts1,tslow=tslow1,tshigh=tshi1,/nan
137137.   filter_cru,thalf(i),tsin=ts2,tslow=tslow2,tshigh=tshi2,/nan
138138.   if i eq 0 then r2=correlate(tshi1,tshi2)
139139.   rall(i)=correlate(tslow1,tslow2)
140140. endfor
141141. ;
142142. printf,1,'ALL SITES',r1,r2,rall,$
143143.   format='(A11,2X,6F6.2)'
144144. ;
145145. printf,1,' '
146146. printf,1,'Correlations carried out over the period ',perst,peren
147147. ;
148148. close,1
149149. ;
150150. end

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