I stared at these graphs for a long time trying to understand them before. I could see the shape wasn’t right like the wrong curve was on the wrong graph, now it makes much more sense.

I came back today after my m08 reconstruction of the reconstruction showed how important these series are.

Hu: nicely done. Now, can anyone tell if the actual data or the interpolated data was used to regress against instrumental temperatures?

Thanks, Craig! As for your question, see my Comment #23 on the Proxy Screening by Correlation thread. In their SI, MZHBMRN08 indicate that they first interpolated the actual multi-year data to annual frequency, then ran an unspecified filter (perhaps Butterworth) on it to iron out fluctuations less than 20 years or so, and then computed their correlations. They used 15-2 = 13 DOF to interpret their full sample correlations, on the assumption that they had about 15 independent observations on each series after this filter. But when, (as with Steve’s example of Punta Laguna) there were in fact only 9 or 10 observations to start with, this would greatly understate the critical value, and so greatly overstate the significance of the correlation.

In Loehle and McCulloch (2008), the low-frequency data were likewise first interpolated and then smoothed (with a 29-year centered mean), but since each series had already been calibrated to temperature by their authors, this procedure did not affect the validity of these calibrations. My 95% CI’s for the mean of the up to 18 series did use t critical values that assumed that the variance of each series about the global mean was computed from about 60 independent tridecadal observations, but the effect of DOF on t critical values is much weaker than the effect of DOF on R2 critical values.

As I mention in the comment linked above, in order to make correct inference about any calibration of the MZHBMRN08 series, the handful of actual observations for each series should have been regressed on the corresponding (local or global) temperatures. Far fewer than 484 would have appeared significant, and this is particularly true of the non-dendro subset.

stat=as.matrix( details[381:384, c(22:24,32:35)]);stat=round(t(stat),3) ;stat
stat=rbind(stat,array(NA,dim=c(3,4) ) )
dimnames(stat)[[2]]=as.character(details$id)[381:384]
row.names(stat)[8:10]=c(“emu1850_1996″,”emu1896_1995″,”emu1850_1949″)

for (k in 1:4) { year=mann[[380+k]]$year
temp1=(year>=1850)&(year=1896)&(year=1850)&(year< =1949);
fred=ts.union(gridcell,ts(mann[[k+380]]$proxy,start=year[1]))
stat[8:10,k]= round( c( cor(fred[temp1,],use=use0)[1,2], cor(fred[temp2,],use=use0)[1,2],cor(fred[temp3,],use=use0)[1,2] ),3)
}
stat
curtis_1996_d13c curtis_1996_d13cpyro curtis_1996_d18o curtis_1996_d18opyro
r1850_1995 NA 0.397 0.627 NA
r1896_1995 NA 0.599 0.869 NA
r1850_1949 NA 0.516 0.747 NA
rtable.r1850_1995 0.132 0.329 0.432 -0.179
rtable.r1850_1995lf 0.261 0.397 0.627 -0.270
rtable.r1896_1995 -0.213 0.363 0.581 -0.188
rtable.r1896_1995lf -0.293 0.599 0.869 -0.281
emu1850_1996 0.135 0.419 0.622 -0.004
emu1896_1995 -0.206 0.354 0.565 -0.182
emu1850_1949 0.097 0.422 0.645 0.199

`

##TEMPERATURE CORRELATION
#spot check Curtis
# id start end r1850_1995 r1850_1949 r1896_1995
#381 curtis_1996_d13c -1597 1993 NA NA NA
#382 curtis_1996_d13cpyro -1597 1993 0.3971 0.5158 0.5987
#383 curtis_1996_d18o -1597 1993 0.6273 0.7467 0.8693
#384 curtis_1996_d18opyro -1597 1993 NA NA NA

#Mann PRoxy Data
#download.file(“http://data.climateaudit.org/data/mann.2008/mann.tab”,”temp.dat”,mode=”wb”)
#load(“temp.dat”); length(mann) #[1] 1209
#returns list of 1209 tables with columns headed year, proxy, count
#might change this to time series format at a later stage
load(“d:/climate/data/mann.2008/mann.tab”)

#MAnn Proxy Info #list of 1209 series
load(“d:/climate/data/mann.2008/details.tab”)

#HAdcru3 Temperature Data
library(ncdf)
v< -open.ncdf("d:/climate/data/gridcell/hadcrut3/HadCRUT3.nc")
instr <- get.var.ncdf( v, v$var[[1]]) # 1850 2006
#dim(instr)# [1] 72 36 1883
#this is organized in 72 longitudes from -177.5 to 177.5 and 36 latitudes from -87.5 to 87.5

read.hadcru3<-function (lat,long){
j<- 19+ floor(lat/5 +.01);
i<- 37+ floor(long/5 +.01);
gridcell3<-instr[i,j,]
index<-length(gridcell3)%%12
gridcell3<-c(gridcell3,rep(NA,12-index) )
close.ncdf(v)
read.hadcru31850)
yuc[temp,]
# depth BP year O18.C_ilosvayi C13.C_ilosvayi O18.P_coronatus C13.P_coronatus
#1 0 -43.000 1993.0 -0.56 -4.93 -2.36 -4.97
#2 1 -34.940 1985.3 -0.54 -4.64 NA NA
#3 4 -10.754 1962.1 -0.27 -5.05 NA NA
#4 7 13.432 1939.0 -0.29 -5.06 NA NA
#5 9 29.556 1923.6 NA NA -1.54 -5.07
#6 10 37.618 1915.8 NA NA -2.98 -6.53
#7 12 53.741 1900.4 NA NA 0.13 -4.34
#8 13 61.802 1892.7 -0.98 -4.83 -2.21 -6.45

#spot check Curtis
# id start end r1850_1995 r1850_1949 r1896_1995
#381 curtis_1996_d13c -1597 1993 NA NA NA
#382 curtis_1996_d13cpyro -1597 1993 0.3971 0.5158 0.5987
#383 curtis_1996_d18o -1597 1993 0.6273 0.7467 0.8693
#384 curtis_1996_d18opyro -1597 1993 NA NA NA

####
#PLOT COMPARISON
gridcell=ts.annavg( read.hadcru3(lat=details$lat[k],long=details$long[k]) )
k=381; curtis=”C13.C_ilosvayi”
#k=382 ;curtis=”C13.P_coronatus”#Curtis ID #this is C13.P_coronatus
#k=383;curtis=”O18.C_ilosvayi”
#k=384; curtis=”O18.P_coronatus”

mean.obs=mean(gridcell,na.rm=T);sd.obs=sd(gridcell,na.rm=T);
c(mean.obs,sd.obs) # -0.03530296 0.38290608

mean.est=mean(yuc[temp,curtis],na.rm=T); sd.est=sd(yuc[temp,curtis],na.rm=T);
c(mean.est,sd.est) # -4.6383333 0.9693176

y= (yuc[temp,curtis]-mean.est) *sd.obs/sd.est +mean.obs; y
round(y,3)
#[1] -0.166 NA NA NA NA NA NA -0.751 0.272 0.233 0.083 NA 0.118

z=(mann[[k]]$proxy-mean.est)*sd.obs/sd.est+mean.obs;#z

par(mar=c(3,4,2,4))
plot(c(time(gridcell)),gridcell,xlab=”",ylab=”deg C”,axes=FALSE)
axis(side=2,las=1);axis(side=1);box()
points(yuc$year[temp],y,col=2,pch=19,cex=1.2)
lines(mann[[k]]$year,z,lty=1,col=2,lwd=2)
title(paste(“Punta Laguna “,curtis))
at0= -9: -2; at1= at= (at0-mean.est)*sd.obs/sd.est+mean.obs
axis(side=4,at=at1,labels=paste(at0),col=2,las=1)
text(1930,-.7,paste( “r 1896-1995: “,round(details$rtable.r1896_1995lf[k],2)),font=2,pos=4,cex=.8)
text(1930,-.9,paste( “r 1850-1995: “,round(details$rtable.r1850_1995lf[k],2)),font=2,pos=4,cex=.8)
Data=readmann(k)
z=(Data$proxy-mean.est)*sd.obs/sd.est+mean.obs;#z
points(Data$year,z,col=4,pch=19,cex=.1)

This file has 7 columns, 1-3 for depth, age and date, 4-5 for C. ilosvayi, and 6-7 for P.coronatus. For 1985.3, 1972.1 and 1939.0, columns 6-7 are blank, while for 1923.6, 1915.8 and 1900.4, columns 4-5 are blank. Somehow your code has attributed the non-missing values to the wrong two columns, with the result that some values seem to appear out of nowhere, while others just disappear.

Only series 383 (column 4, C.i d18O) and 382 (column 7, P.c d13C) passed the “screening”, and they did that for all 3 periods. These are graphed correctly below as red X’s, with Mann’s annual version as a blue line:

Up to the last real observation (1993), Mann & Co have simply linearly interpolated the raw data to an annual frequency (fair enough), with no dropped or added values.

However, their nonlinear extrapolations to 1998 are truly thin-air fabrications. I doubt that the brief extrapolations affect the results much for these two series, but if the same procedure was followed for other series, there could be a big problem.

A further minor confusion is that in your Sept 3 post, the series are numbered 381-384 in the order of the columns in the WDCP file, when in fact Mann et al have for some reason listed these in their XLS file as numbers 383, 381, 384, and 382, in that order. The proxy numbers in the CA folder /data/images/mann.2008/proxy0381.gif, etc, do correspond the XLS sequence, however. (I was also thrown for a while by the fact that proxies 381-384 appear on lines 382-5 of the XLS file, but this is only because line 1 contains header labels so that proxy 1 appears on line 2 etc.)

Steve: My bad. I had an error in my read-in code. Unusually for WDCP, the table is tab-separated (which I prefer and use myself, but missed). I should have used:
yuc=read.table(url,fill=TRUE,skip=1,sep=”\t”)
Instead I used:
yuc=read.table(url,fill=TRUE,skip=1)
I’ll re-do and re-issue. I allocated series 381-384 to WDCP columns (which I added to the caption and shold have done earlier) as follows. 381 – C13.C_ilosvayi; 382- C13.P_coronatus; 383 – O18.C_ilosvayi; 384 – O18.P_coronatus. This has been done based on the following nomenclature in Mann’s script call ids: curtis_1996_d13c, curtis_1996_d13cpyro, curtis_1996_d18o, curtis_1996_d18opyro. “pyro” is believed to be P. coronatus, leaving the ones with no appelation to be allocated to C. ilosvayi.

Also, what do you mean by this?

“It sure looks like infilling has taken place for this supposedly “original” data (though this would not be the case for annually resolved data.)”

The infilled data is then what, monthly resolved data? Decadal?

Thanks. Great work.

If it’s raining then the streets are wet. The streets are wet.
Therefore, it’s raining.

Consequently: if it is warm then the isotopes are heavy; the isotopes are heavy but is warm?

You can see here that higher humidity levels gives identical results to warm.

Gavin Schmidt is pretending that this was there all along and demanding an apology from one of his readers, who had the temerity to question Schmidt’s untrue assertion that the SI data at the time of his original comment was not infilled.

Your tax dollars at work.