GISS "raw" station data – before and after

Let loose the lawyers and we will have the Revenge of the Chads. See: Chad — From waste product to headline

Friends:
I have previously posted this on WUWT but it seems desirable to post it again here.
It demonstrates that 6 years ago The Team knew the estimates of average global temperature (mean global temperature, MGT) were worthless and they acted to prevent publication of proof of this.
The most important email among those hacked (?) from CRU may turn out to be one that I wrote 6 years ago. I had forgotten it but Willis Essenbach found it among the hacked (?) emails and circulated it. I copy it here then explain its meaning and significance.
The email is this.
From: RichardSCourtney@aol.com
To: t.osborn@uea.ac.uk, m.allen1@physics.ox.ac.uk, Russell.Vose@noaa.gov
Subject: Re: Workshop: Reconciling Vertical Temperature Trends
Date: Sun, 23 Nov 2003 18:42:59 EST
Cc: trenbert@cgd.ucar.edu, timo.hameranta@pp.inet.fi, Thomas.R.Karl@noaa.gov, ceforest@mit.edu, sokolov@mit.edu, phstone@mit.edu, ekalnay@atmos.umd.edu, richard.w.reynolds@noaa.gov, christy@atmos.uah.edu, roy.spencer@msfc.nasa.gov, benjie.norris@nsstc.uah.edu, kostya@atmos.umd.edu, Norman.Grody@noaa.gov, Thomas.C.Peterson@noaa.gov, sfbtett@metoffice.com, penner@umich.edu, dian.seidel@noaa.gov, trenbert@ucar.edu, wigley@ucar.edu, pielke@atmos.colostate.edu, climatesceptics@yahoogroups.com, aarking1@jhu.edu, bjorn@ps.au.dk, cfk @lanl.gov, c.defreitas@auckland.ac.nz, cidso@co2science.org, dwojick@shentel.net, douglass@pas.rochester.edu, dkaroly@ou.edu, mercurio@jafar.hartnell.cc.ca.us, fredev@mobilixnet.dk, seitz@rockvax.rockefeller.edu, Heinz.Hug@t-online.de, hughel@comcast.net, jahlbeck@ab
Dear All:
The excuses seem to be becoming desperate. Unjustified assertion that I fail to understand “Myles’ comments and/or work on trying the detect/attribute climate change” does not stop the attribution study being an error. The problem is that I do understand what is being done, and I am willing to say why it is GIGO.
Tim Allen said;
In a message dated 19/11/03 08:47:16 GMT Standard Time, m.allen1@physics.ox.ac.uk writes:
“I would just like to add that those of us working on climate change detection and attribution are careful to mask model simulations in the same way that the observations have been sampled, so these well-known dependencies of nominal trends on the trend-estimation technique have no bearing on formal detection and attribution results as quoted, for example, in the IPCC TAR.”
I rejected this saying: At 09:31 21/11/2003, RichardSCourtney@aol.com wrote:
“It cannot be known that the ‘masking’ does not generate additional spurious trends. Anyway, why assume the errors in the data sets are geographical and not?. The masking is a ‘fix’ applied to the model simulations to adjust them to fit the surface data known to contain spurious trends. This is simple GIGO.”
Now, Tim Osborn says of my comment;
In a message dated 21/11/03 10:04:56 GMT Standard Time, t.osborn@uea.ac.uk writes:
“Richard’s statement makes it clear, to me at least, that he misunderstands Myles’ comments and/or work on trying the detect/attribute climate change.
As far as I understand it, the masking is applied to the model to remove those locations/times when there are no observations. This is quite different to removing those locations which do not match, in some way, with the observations – that would clearly be the wrong thing to do. To mask those that have no observations, however, is clearly the right thing to do – what is the point of attempting to detect a simulated signal of climate change over some part of (e.g.) the Southern Ocean if there are no observations there in which to detect the expected signal? That would clearly be pointless.”
Yes it would. And I fully understand Myles’ comments. Indeed, my comments clearly and unarguably relate to Myles comments. But, as my response states, Myles’ comments do not alter the fact that the masked data and the unmasked data contain demonstrated false trends. And the masking may introduce other spurious trends. So, the conducted attribution study is pointless because it is GIGO. Ad hominem insults don’t change that.
And nor does the use of peer review to block my publication of the facts of these matters.
Richard
The great importance of the matter in the quoted email may not be apparent to some. Therefore, I provide this brief background explanation.
Climate change ‘attribution studies’ use computer models to assess possible causes of global climate change. Known effects that cause climate change are input to a computer model of the global climate system, and the resulting output of the model is compared to observations of the real world. Anthropogenic (i.e. man-made) global warming (AGW) is assumed to be indicated by any rise in average global temperature (mean global temperature, MGT) that occurred in reality but is not accounted by the known effects in the model.
Clearly, any error in determinations of changes to MGT provides incorrect attribution of AGW.
The various determinations of the changes to MGT differ and, therefore, there is no known accurate amount of MGT change. But the erroneous MGT change was being input to the models (garbage in, GI) so the amount of AGW attributed by the studies was wrong (garbage out, GO) because ‘garbage in’ gives ‘garbage out’ (GIGO). The attribution studies that provide indications of AGW are GIGO.
I and others attempted to publish a discussion paper that attempted to explain the problems with analyses of MGT. We compared the data and trends of the Jones et al., GISS and GHCN data sets. These teams each provide 95% confidence limits for their results. However, the results of the teams differ by more than double those limits in several years, and the data sets provided by the teams have different trends. Since all three data sets are compiled from the same available source data (i.e. the measurements mostly made at weather stations using thermometers), and purport to be the same metric (i.e. MGT anomaly), this is surprising. Clearly, the methods of compilation of MGT time series can generate spurious trends (where ‘spurious’ means different from reality), and such spurious trends must exist in all but at most one of the data sets.
So, we considered MGT according to two interpretations of what it could be; viz.
(i) MGT is a physical parameter that – at least in principle – can be measured;
or
(ii) MGT is a ‘statistic’; i.e. an indicator derived from physical measurements.
These two understandings derive from alternative considerations of the nature of MGT:
If the MGT is assumed to be the mean temperature of the volume of air near the Earth’s surface over a period of time, then MGT is a physical parameter indicated by the thermometers (mostly) at weather stations that is calculated using the method of mixtures (assuming unity volume, specific heat, density etc). We determined that if MGT is considered as a physical parameter that is measured, then the data sets of MGT are functions of their construction. Attributing AGW – or anything else – to a change that is a function of the construction of MGT is inadmissable.
Alternatively:
If the thermometers (mostly) at weather stations are each considered to indicate the air temperature at each measurement site and time, then MGT is a statistic that is computed as being an average of the total number of thermometer indications. But if MGT is considered to be a statistic then it can be computed in several ways to provide a variety of results, each of different use to climatologists. In such a way, the MGT is similar in nature to a Retail Price Index, which is a statistic that can be computed in different ways to provide a variety of results, each of which has proved useful to economists. If MGT is considered to be a statistic of this type, then MGT is a form of average. In which case, the word ‘mean’ in ‘mean global temperature’ is a misnomer, because although there are many types of average, a set of measurements can only have one mean. Importantly, if MGT is considered to be an indicative statistic then the differences between the values and trends of the data sets from different teams indicate that the teams are monitoring different climate effects. But if the teams are each monitoring different climate effects then each should provide a unique title for their data set that is indicative of what is being monitored. Also, each team should state explicitly what its data set of MGT purports to be monitoring.
Thus, we determined that – whichever way MGT is considered – MGT is not an appropriate metric for use in attribution studies.
However, the compilers of the MGT data sets frequently alter their published data of past MGT (sometimes they have altered the data in each of several successive months). Hence, our paper always contained incorrect MGT data because the MGT data kept changing. The MGT data always changed between submission of the paper and completion of the peer review process. Thus, the frequent changes to MGT data sets prevented publication of the paper.
Whatever you call this method of preventing publication of a paper, you cannot call it science.
But this method prevented publication of information that proved the estimates of MGT and AGW are wrong and the amount by which they are wrong cannot be known.
It should also be noted that there is no possible calibration for the estimates of MGT. The data sets keep changing for unknown (and unpublished) reasons although there is no obvious reason to change a datum for MGT that is for decades in the past. It seems that the compilers of the data sets adjust their data in attempts to agree with each other.
Methods to correct these problems could have been considered 6 years ago if publication of my paper had not been blocked.
Additionally, I point out that the AGW attribution studies are wrong in principle for two reasons.
Firstly, they are ‘argument from ignorance’.
Such an argument is not new. For example, in the Middle Ages experts said, “We don’t know what causes crops to fail: it must be witches: we must eliminate them.” Now, experts say, “We don’t know what causes global climate change: it must be emissions from human activity: we must eliminate them.” Of course, they phrase it differently saying they can’t match historical climate change with known climate mechanisms unless an anthropogenic effect is included. But evidence for this “anthropogenic effect” is no more than the evidence for witches.
Secondly, they use an attribution study to ‘prove’ what can only be disproved by attribution.
In an attribution study the system is assumed to be behaving in response to suggested mechanism(s) that is modelled, and the behaviour of the model is compared to the empirical data. If the model cannot emulate the empirical data then there is reason to suppose that the suggested mechanism is not the cause (or at least not the sole cause) of the changes recorded in the empirical data.
It is important to note that attribution studies can only be used to reject hypothesis that a mechanism is a cause for an observed effect. Ability to attribute a suggested cause to an effect is not evidence that the suggested cause is the real cause in part or in whole. (To understand this, consider the game of Cludo. At the start of the game it is possible to attribute the ‘murder’ to all the suspects. As each piece of evidence is obtained then one of the suspects can be rejected because he/she can no longer be attributed with the murder).
But the CRU/IPCC attribution studies claim that the ability to attribute AGW as a cause of climate change is evidence that AGW caused the change (because they only consider one suspect for the cause although there could be many suspects both known and unknown).
Then, in addition to those two pieces of pure pseudo-science – as my paper demonstrates – the attribution studies use estimates of climate changes that are know to be wrong!
This does not give confidence that the MGT data sets provide reliable quantification of change to global temperature.
Richard