Here is a recent candid judgment on the reliability of confidence intervals in MBH99:

Jim Bouldin | March 8, 2013 at 5:39 pm | Reply

Those uncertainty ranges are essentially worthless–they don’t mean anything.

http://thelukewarmersway.wordpress.com/2013/03/07/can-you-dig-it/comment-page-1/#comment-2456

]]>Wang, try this:

http://bishophill.squarespace.com/blog/2008/8/11/caspar-and-the-jesus-paper.html

]]>‘FOIA\documents\mbh98-osborn\TREE\ITRDB\NOAMER’ this directories and other files:

BACKTO_1000

BACKTO_1000-CENSORED

BACKTO_1000-FIXED

BACKTO_1100-CENSORED

BACKTO_1200-CENSORED

BACKTO_1300

BACKTO_1300-CENSORED

BACKTO_1400

BACKTO_1400-CENSORED

BACKTO_1400-FIXED

BACKTO_1450

BACKTO_1600

BACKTO_1750

BACKTO_500

BACKTO_800 ]]>

>I have just read this lettter – and I think it is crap. I am sick to

>death of Mann stating his reconstruction represents the tropical

>area just because it contains a few (poorly temperature

>representative ) tropical series. He is just as capable of

>regressing these data again any other “target” series , such as the

>increasing trend of self-opinionated verbage he has produced over

>the last few years , and … (better say no more)

>Keith

Cook to Briffa in 1051638938.txt ”

|”I come more from the “cup half-full” camp when it comes to the MWP, maybe yes, maybe no, but it is too early to say what it is. Being a natural skeptic, I guess you might lean more towards the MBH camp, which is fine as long as one is honest and open about evaluating the evidence (I have my doubts about the MBH camp). We can always politely(?) disagree given the same admittedly equivocal evidence.

I should say that Jan should at least be made aware of this reanalysis of his data.

Admittedly, all of the Schweingruber data are in the public domain I believe, so that should not be an issue with those data. I just don’t want to get into an open critique of the Esper data because it would just add fuel to the MBH attack squad. They tend to work in their own somewhat agenda-filled ways. We should also work on this stuff on our

own, but I do not think that we have an agenda per se, other than trying to objectively understand what is going on.

Cheers,

Ed”

http://www.americanthinker.com/2009/12/understanding_climategates_hid.html

**Steve:** I wish that he’d paid more attention to the analyses by Jean S and myself which are more precise.

p.s. Gabi: when do you and Tom plan to publish your NH reconstruction that now goes back

about 1500 years or so? It would be nice to have more independent reconstructions.

Dear Phil and Gabi,

I’ve attached a cleaned-up and commented version of the matlab code that I wrote for

doing the Mann and Jones (2003) composites. I did this knowing that Phil and I are

likely to have to respond to more crap criticisms from the idiots in the near future, so

best to clean up the code and provide to some of my close colleagues in case they want

to test it, etc. Please feel free to use this code for your own internal purposes, but

don’t pass it along where it may get into the hands of the wrong people.

In the process of trying to clean it up, I realized I had something a bit odd, not

necessarily wrong, but it makes a small difference. It seems that I used the ‘long’ NH

instrumental series back to 1753 that we calculated in the following paper:

* Mann, M.E., Rutherford, S., Bradley, R.S., Hughes, M.K., Keimig, F.T., [1]Optimal

Surface Temperature Reconstructions using Terrestrial Borehole Data, Journal of

Geophysical Research, 108 (D7), 4203, doi: 10.1029/2002JD002532, 2003.

(based on the sparse available long instrumental records) to set the scale for the

decadal standard deviation of the proxy composite. Not sure why I used this, rather than

using the CRU NH record back to 1856 for this purpose. It looks like I had two similarly

named series floating around in the code, and used perhaps the less preferable one for

setting the scale.

Turns it, this has the net effect of decreasing the amplitude of the NH reconstruction

by a factor of 0.11/0.14 = 1.29.

This may explain part of what perplexed Gabi when she was comparing w/ the instrumental

series. I’ve attached the version of the reconstruction where the NH is scaled by the

CRU NH record instead, as well as the Matlab code which you’re welcome to try to use

yourself and play around with. Basically, this increases the amplitude of the

reconstruction everywhere by the factor 1.29. Perhaps this is more in line w/ what Gabi

was estimating (Gabi?)

Anyway, doesn’t make a major difference, but you might want to take this into account in

any further use of the Mann and Jones series…

Phil: is this worth a followup note to GRL, w/ a link to the Matlab code?

Mike

p.s. Gabi: when do you and Tom plan to publish your NH reconstruction that now goes back

about 1500 years or so? It would be nice to have more independent reconstructions

published in the near future! Maybe I missed this? Thanks…

% COMPOSITENH”

%

% (c) 2003, M.E. Mann

%

% THIS ROUTINE PERFORMS A RECONSTRUCTION OF NORTHERN HEMISPHERE

% MEAN ANNUAL TEMPERATURE BASED ON A WEIGHTED COMPOSITE OF LONG-TERM TEMPERATURE

% PROXY RECORDS SCALED AGAINST THE INSTRUMENTAL HEMISPHERIC MEAN TEMPERATURE

% SERIES, AS USED IN THE FOLLOWING TWO PUBLICATIONS:

%

%

% Jones, P.D., Mann, M.E., Climate Over Past Millennia, Reviews of Geophysics,

% 42, RG2002, doi:10.1029/2003RG000143, 2004

%

% Mann, M.E., Jones, P.D., Global Surface Temperatures over the Past two Millennia,

% Geophysical Research Letters,

% 30 (15), 1820, doi: 10.1029/2003GL017814, 2003

%

%

% 1. READ IN INSTRUMENTAL RECORD

%

% Read in CRU instrumental NH mean temeperature record (1856-2003)

load nh.dat;

yearinstr=nh(:,1);

% calculate both warm-season and annual means

warmseason=(nh(:,5)+nh(:,6)+nh(:,7)+nh(:,8)+nh(:,9)+nh(:,10))/6;

annualmean=nh(:,14);

% use annual mean record in this analysis

nhmean=annualmean;

%

% 2. READ IN PREVIOUSLY PUBLISHED PROXY-RECONSTRUCTIONS OF NH ANNUAL MEAN

% RECONSTRUCTIONS AND FORM APPROPRIATELY SCALED COMPOSITE

%

% Read in Mann et al (1998), Crowley and Lowery (2000), and Jones et al (1998)

% NH temperature reconstructions

load nhem-millennium.dat;

load crowleylowery.dat;

load joneshemisrecons.dat;

nhmbh=nhem_millennium(1:981,2);

nhjones=joneshemisrecons(1:981,2);

nhcl=crowleylowery(1:981,2);

yearmillen=nhem_millennium(1:981,1);

% since some reconstructions are only decadally resolved, smooth each on

% decadal timescales through use of a lowpass filter with cutoff at

% f=0.1 cycle/year. Based on use of the filtering routine described in:

%

% Mann, M.E., On Smoothing Potentially Non-Stationary Climate Time Series,

% Geophysical Research Letters, 31, L07214, doi: 10.1029/2004GL019569, 2004.

%

% using ‘minimum norm’ constraint at both boundaries for all time series

nhsmooth=lowpass(nhmean,0.10,0,0);

nhmbhsmooth=lowpass(nhmbh,0.10,0,0);

nhjonessmooth=lowpass(nhjones,0.10,0,0);

nhclsmooth=lowpass(nhcl,0.10,0,0);

% Mann et al (1998) already calibrated in terms of hemispheric annual mean temperature, but

% reference mean has to be adjusted to equal that of the instrumental series

% over the 1856-1980 overlap period (which uses a 1961-1990 reference period)

admbh=mean(nhsmooth(1:125))-mean(nhmbhsmooth(857:981));

newmbh=nhmbhsmooth+admbh;

% need to adjust and scale Jones et al (1998) and Crowley and Lowery (2000)

% reconstructions to match mean and trend of smoothed instrumental series

% over 1856-1980

t1=1856;

t2=1980;

x=(t1:t2)’;

nhlong=nhmean(1:125);

smoothlong=lowpass(nhlong,0.10,0,0);

amean0=mean(smoothlong);

y=smoothlong;

[yc,t,trend0,detrend0,xm,ym] = lintrend(x, y);

%

y=nhclsmooth(t1-999:t2-999);

[yc,t,trendcl,detrendcl,xm,ym] = lintrend(x, y);

%

y=nhjonessmooth(t1-999:t2-999);

[yc,t,trendjones,detrendjones,xm,ym] = lintrend(x, y);

%

multjones=norm(trend0)/norm(trendjones);

adjustedjones=nhjonessmooth*multjones;

offsetjones=amean0-mean(adjustedjones(t1-999:t2-999));

newjones=adjustedjones+offsetjones;

newjones=newjones’;

%

multcl=norm(trend0)/norm(trendcl);

adjustedcl=nhclsmooth*multcl;

offsetcl=amean0-mean(adjustedcl(t1-999:t2-999));

newcl=adjustedcl+offsetcl;

newcl=newcl’;

%

nhlongcompose=0.3333*(newmbh+newjones’+newcl’)’;

%

% 3. READ IN AND PROCESS PROXY TEMPERATURE RECORDS

%

M=8;

load ‘china-series1.dat’

load ‘itrdb-long-fixed.dat’

load ‘westgreen-o18.dat’

load ‘torny.dat’

load ‘chesapeake.dat’

load ‘mongolia-darrigo.dat’

load ‘dahl-jensen-gripbh1yrinterp.txt’

load ‘dahl-jensen-dye3bh1yrinterp.txt’

% read in years

x1=china_series1(:,1);

x2=itrdb_long_fixed(:,1);

x3=westgreen_o18(:,1);

x4=torny(:,1);

x5=chesapeake(:,1);

x6=mongolia_darrigo(:,1);

x7=dahl_jensen_gripbh1yrinterp(:,1);

x8=dahl_jensen_dye3bh1yrinterp(:,1);

% read in proxy values

y1=china_series1(:,2);

y2=itrdb_long_fixed(:,2);

y3=westgreen_o18(:,2);

y4=torny(:,2);

y5=chesapeake(:,2);

y6=mongolia_darrigo(:,2);

y7=dahl_jensen_gripbh1yrinterp(:,2);

y8=dahl_jensen_dye3bh1yrinterp(:,2);

% Store decadal correlation of each proxy record with local available

% overlapping CRU gridpoint surface temperature record (see Mann and Jones, 2003)

corr(1)=0.22;

corr(2)=0.52;

corr(3)=0.75;

corr(4)=0.32;

corr(5)=0.31;

corr(6)=0.40;

corr(7)=0.53;

corr(8)=0.52;

% Estimate Area represented by each proxy record based on latitude of

% record and estimated number of temperature gridpoints represented by record

pi=3.14159;

factor=pi/180.0;

lat(1)=32.5;

dof(1)=4;

lat(2)=37.5;

dof(2)=2;

lat(3)=77;

dof(3)=0.667;

lat(4)=68;

dof(4)=3.5;

lat(5)=37.0;

dof(5)=1.0;

lat(6)=47;

dof(6)=1;

lat(7)=73;

dof(7)=0.667;

lat(8)=65;

dof(8)=0.667;

for j=1:M

area(j)=dof(j)*cos(lat(j)*factor);

end

% determine min and max available years over all proxy records

%

minarray=[min(x1) min(x2) min(x3) min(x4) min(x5) min(x6) min(x7) min(x8)];

maxarray=[max(x1) max(x2) max(x3) max(x4) max(x5) max(x6) max(x7) max(x8)];

tbegin=max(minarray);

tend1=min(maxarray);

tend=max(maxarray);

% initialize proxy data matrix

notnumber = -9999;

for j=1:M

for i=1:minarray(j)-1

time(i)=i;

mat(i,j)=notnumber;

end

for i=minarray(j):tend

time(i)=i;

end

for i=minarray(j):maxarray(j)

if (j==1) mat(i,j)=y1(i-minarray(j)+1);

end

if (j==2) mat(i,j)=y2(i-minarray(j)+1);

end

if (j==3) mat(i,j)=y3(i-minarray(j)+1);

end

if (j==4) mat(i,j)=y4(i-minarray(j)+1);

end

if (j==5) mat(i,j)=y5(i-minarray(j)+1);

end

if (j==6) mat(i,j)=y6(i-minarray(j)+1);

end

if (j==7) mat(i,j)=y7(i-minarray(j)+1);

end

if (j==8) mat(i,j)=y8(i-minarray(j)+1);

end

end

% added in Jones and Mann (2004), extend series ending between

% 1980 calibration period end and 2001 boundary by persistence of

% last available value through 2001

for i=maxarray(j)+1:tend

if (j==1) mat(i,j)=y1(maxarray(j)-minarray(j)+1);

end

if (j==2) mat(i,j)=y2(maxarray(j)-minarray(j)+1);

end

if (j==3) mat(i,j)=y3(maxarray(j)-minarray(j)+1);

end

if (j==4) mat(i,j)=y4(maxarray(j)-minarray(j)+1);

end

if (j==5) mat(i,j)=y5(maxarray(j)-minarray(j)+1);

end

if (j==6) mat(i,j)=y6(maxarray(j)-minarray(j)+1);

end

if (j==7) mat(i,j)=y7(maxarray(j)-minarray(j)+1);

end

if (j==8) mat(i,j)=y8(maxarray(j)-minarray(j)+1);

end

end

end

time=time’;

data=[time mat];

% decadally lowpass of proxy series at f=0.1 cycle/year as described earlier

for j=1:M

unfiltered=mat(minarray(j):tend,j);

filt=lowpass(unfiltered,0.1,0,0);

for i=1:minarray(j)-1

filtered(i,j)=mat(i,j);

end

for i=minarray(j):tend

filtered(i,j)=filt(i-minarray(j)+1);

end

end

% standardize data

% first remove mean from each series

for j=1:M

icount=0;

amean(j)=0;

for i=1:tend

if (filtered(i,j)>notnumber)

icount=icount+1;

amean(j)=amean(j)+filtered(i,j);

end

end

amean(j)=amean(j)/icount;

end

% now divide through by standard deviation

for j=1:M

icount=0;

asum=0;

for i=1:tend

if (filtered(i,j)>notnumber)

asum=asum+(filtered(i,j)-amean(j))^2;

icount=icount+1;

end

end

sd(j)=sqrt(asum/icount);

for i=1:tend

standardized(i,j)=filtered(i,j);

if (mat(i,j)>notnumber)

standardized(i,j)=(filtered(i,j)-amean(j))/sd(j);

end

end

end

%

% 4. Calculate NH mean temperature reconstruction through weighted (and

% unweighted) composites of the decadally-smoothed proxy indicators

%

% impose weighting scheme for NH mean composite

for j=1:M

% weighting method 1: weight each proxy series by approximate area

% weighting method 2: weight each proxy series by correlation between

% predictor and local gridpoint series over available overlap period

% during calibration interval

% weighting method 3: weight each proxy series by correlation between

% predictor and NH mean series over calibration interval:

% weightlong(j)=lincor(nhlong,standardized(1856:1980,j));

% weighting method 4: combine 1 and 3

% weighting method 5: combine 1 amd 2 (this is the ‘standard’ weighting

% scheme chosen by Mann and Jones (2003)

% use standard weighting scheme

weight(j)=corr(j)*area(j);

end

% perform reconstructions based on:

% (1) the 6 proxy temperature records available over interval AD 200-1980

% (2) all 8 proxy temperature records available over interval AD 553-1980

istart0=200;

istart1=200;

istart2=553;

nseries1=0;

nseries2=0;

weightsum1=0;

weightsum2=0;

for j=1:M

if (istart1>=minarray(j))

nseries1=nseries1+1;

weightsum1=weightsum1+weight(j);

end

if (istart2>=minarray(j))

nseries2=nseries2+1;

weightsum2=weightsum2+weight(j);

end

end

% calculate composites through 1995 (too few series available after that date)

% As discussed above, persistence is used to extend any series ending

% between 1980 and 1995 as described by Jones and Mann (2004).

tend=1995;

for i=istart1:tend

unweighted1(i)=0;

unweighted2(i)=0;

weighted1(i)=0;

weighted2(i)=0;

for j=1:M

if (istart1>=minarray(j))

unweighted1(i)=unweighted1(i)+standardized(i,j);

weighted1(i)=weighted1(i)+weight(j)*standardized(i,j);

end

if (istart2>=minarray(j))

unweighted2(i)=unweighted2(i)+standardized(i,j);

weighted2(i)=weighted2(i)+weight(j)*standardized(i,j);

end

end

end

unweighted1=unweighted1/nseries1;

unweighted2=unweighted2/nseries2;

weighted1=weighted1/weightsum1;

weighted2=weighted2/weightsum2;

unweighted1(1:istart1-1)=0;

unweighted2(1:istart2-1)=0;

weighted1(1:istart1-1)=0;

weighted2(1:istart2-1)=0;

% scale composite to have same variance as decadally-smoothed instrumental

% NH series

% Mann and Jones (2003) and Jones and Mann (2004) used for this purpose

% the extended (1753-1980) NH series used in:

% Mann, M.E., Rutherford, S., Bradley, R.S., Hughes, M.K., Keimig, F.T.,

% Optimal Surface Temperature Reconstructions using Terrestrial Borehole Data,

% Journal of Geophysical Research, 108 (D7), 4203, doi: 10.1029/2002JD002532, 2003.

% That series has a decadal standard deviation sd=0.1123

% If instead, the 1856-2003 CRU instrumental NH mean record is used, with

% a decadal standard deviation of sd=0.1446, the amplitude of the reconstruction

% increases by a factor 1.29 (this scaling yields slightly lower verification

% scores)

load nhem-long.dat

nhemlong=nhem_long(:,2);

longsmooth=lowpass(nhemlong,0.10,0,0);

sd0=std(longsmooth);

% use weighted (rather than unweighted) composite in this case

series1=weighted1;

% center composites on 1856-1980 calibration period

y=series1(t1:t2)’;

amean1=mean(series1(t1:t2));

compseries1=series1(t1:t2)-amean1;

mult1=sd0/std(compseries1);

% scale composite to standard deviation of instrumental series and re-center

% to have same (1961-1990) zero reference period as CRU NH instrumental

% temperature record

adjusted1=series1*mult1;

offset1=amean0-mean(adjusted1(t1:t2));

compose1=adjusted1+offset1;

compose1=compose1′;

series2=weighted2;

y=series2(t1:t2)’;

amean2=mean(series2(t1:t2));

compseries2=series2(t1:t2)-amean2;

mult2=sd0/std(compseries2);

adjusted2=series2*mult2;

offset2=amean0-mean(adjusted2(t1:t2));

compose2=adjusted2+offset2;

compose2=compose2′;

%

% 5. UNCERTAINTY ESTIMATION, AND STATISTICAL VERIFICATION

%

% estimate uncertainty in reconstruction

% nominal (white noise) unresolved calibration period variance

calibvar=lincor(smoothlong,compose1(t1:t2))^2;

uncalib=1-calibvar;

sdunc=sd0*sqrt(uncalib);

% note: this is the *nominal* white noise uncertainty in the reconstruction

% a spectral analysis of the calibration residuals [as discussed briefly in

% Mann and Jones, 2003] indicates that a peak at the multidecadal timescale

% that exceeds the white noise average residual variance by a factor of

% approximately 6. A conservative estimate of the standard error in the

% reconstruction thus inflates the nominal white noise estimate “sdunc” by a

% factor of sqrt(6)

sdlow = sdunc*sqrt(6)

% calculate long-term verification statistics for reconstruction

% use composite of Mann et al (1998)/Crowley and Lowery (2000)/Jones et al (1998)

% and AD 1600-1855 interval

overlapcomp=nhlongcompose(1:981);

% work with longer reconstruction (back to AD 200)

overlaprecon=compose1(1000:1980)’;

%overlaprecon=compose2(1000:1980)’;

%calculate verification R^2

series11=overlaprecon(601:856);

series22=overlapcomp(601:856);

verifrsq=lincor(series11,series22)^2

% calculate verification RE

var1=0.0;

var2=0.0;

var3=0.0;

var4=0.0;

var5=0.0;

am0=0.0;

% insure convention of zero mean over calibration interval

for i=857:981

am0=am0+overlapcomp(i);

end

am0=am0/125;

for i=601:856

var1=var1+(overlapcomp(i)-am0)^2;

var2=var2+(overlapcomp(i)-overlaprecon(i))^2;

end

verifRE=1-var2/var1

http://www.eastangliaemails.com/emails.php?eid=423&filename=1092167224.txt

]]>First was the revelation that Chapter 8 of the 1996 IPCC report had been doctored after a peer review in Madrid to hype a claim of a “discernable human influence” on the atmosphere. The nature of that doctoring was made public by Fredrick Seitz in a WSJ Op-Ed: “A Major Deception on Global Warming” http://www.sepp.org/Archive/controv/ipcccont/Item05.htm

The second was after publication of a paper in Nature, “A Search For Human Influences on the Thermal Structure of the Atmosphere”, that claimed that observed data from radiosondes confirmed the global warming computer models. That was subsequently blown up by Pat Michaels and Paul Knappenberger who showed that Santer and crew used a subset (can I say “cherry picked”?) of the radiosonde data set, and that the full data set showed their claim was bogus. See http://www.john-daly.com/sonde.htm

Lead Authors of 1996 IPCC AR2 Chapter 8: B. Santer, T. Wigley, T. Barnett, E. Anyamba. Authors of 1996 Nature paper: B. Santer, T. Wigley, P. Jones, J. Mitchell, A. Oort, R. Stoufer. Any of these look familiar?

]]>http://www.nature.com/nature/journal/v462/n7273/full/462545a.html

One should compare the editorial to the facts presented in this post. Notice the Nature editor (Ziemelis) in Aug 2004 suggests that all the information was provided above and beyond the call of duty. That same approach is extended in the current Nature editorial, however when you look at the facts the assertions do not hold true.

If all you had to go on, it would be on one side is the editorial staff of (what used to be) one of the most important scientific journals in the world. A published article in Nature would go on the top of the list any young professor would submit to his tenure committee. Why would you question Nature? Any mainstream academic doing so would be committing career suicide. It appears that Nature got so use to the god-like status afforded it, that it made the mistake of thinking it was true. On the other side you have Steve M daring to question authority. Nature would have you believe that because Steve is an outsider, a non-academic he is unqualified to participate in the debate. But what they fail to realize is that it is because Steve is an outsider that he is free to ask these questions. They can not destroy his career, but they can deny him access to data and use the pulpit to belittle him.

And these emails as described in this posting are a complete vindication of those who have been asking for years for the simple scientific propriety of supporting data. Which brings us back to the current Nature editorial. You will see in the days ahead two approaches from those defending “the debate is over” climate science. One will be to continue to stonewall, cover up and resort to authority. The Nature editorial epitomizes this approach. The other will be the Monbiot approach, where stanch defenders will realize that they are on the wrong side of what they love about science and publicly apologize for their complicity in restricting the debate.

]]>