Climate Audit

Steve Mosher provides the following recipe for getting GISS Model E results:

Ok getting ModelE data

Start here data.giss.nasa.gov/

See the link for climate simulations of 1880-2003. click that

data.giss.nasa.gov/modelE/transient/climsim.html

Here you will see the link to the paper and all the readme I know of

now to get the data look at table 1.

See line #4. ALL FORCINGS. These are the similautions that include all forcings
( line 1-3 contain individual forcings, like GHG only for example, or volcano)

On the left hand side of the table you will see links for the forcing. On the RIGHT
you will see a list of RESPONSES.

select “lat time”

data.giss.nasa.gov/modelE/transient/Rc_jt.1.11.html

Now you will see a pull down menu.

See the first box. Quantity? Pick surface temp ( there are others as well )

Mean Peroid. pick 1 month to get rid of the running mean

Time interval: pick what you like.

base peroid. I selected 1961-1990 because I wanted to compare ModelE to hadcrut.

Output. Formatted page with download links

Show plot and then get the data

data.giss.nasa.gov/work/modelEt/time_series/work/tmp.4_E3Af8aeM20_1_1880_2003_1961_1990-L3AaeoM20D/LTglb.txt

Update: John Christy has sent in the following showing GISS Model E versus UAH. noting his regret that GISS had not reported their run a bit further out.

Here at Climate Audit, we occasionally try to solve mysteries that have vexed climate scientists for years. On a previous occasion, we helped UCAR locate the mysterious civilization of Chile, on another occasion the lost city of Wellington NZ and, most recently, helped NASA find the lost city of Cobija, Bolivia. Today we’ll help the climate science community identify the provenance of a graphic shown below, that was produced in 1990 by a mysterious organization known to insiders as IPCC.

IPCC 1990 Figure 7c.

At his blog last year, William Connolley, obviously impressed with then recent CA success in locating Chile and Wellington NZ, appealed for help in solving this outstanding puzzle, specifically challenging me to identify its provenance for the climate science community, even accusing me of being “curiously uninterested in the source [of this graphic], or lack thereof” and worried that I was “ignoring” the provenance of this graphic, leaving the wider community to fend for itself.

One of his readers, obviously of the view that it was not necessarily my responsibility to sort out this particular IPCC conundrum, asked Connolley why he didn’t just determine who the IPCC author was and ask him:

Where does the 1990 graph come from then? I presume the IPCC author didn’t just hand draw it? Who is the IPCC lead author responsible, and if he is still alive, couldn’t one just ask?

This seems like a logical method, but Connolley was uninterested:

AFAIK it was hand drawn - it looks like it was. Its certainly not a computer plot. Things were free-and-easy back in ‘90 I suppose (thought as far as I know this is the only graph from the ‘90 report that does not have a good source) -W

At wikipedia, Connolley expounded further on the lack of a clear source for this graphic:

A schematic (non-quantitative) curve was used to represent temperature variations over the last 1000 years in chapter 7. The vertical temperature scale was labelled as “Temperature change (°C)” but no numerical labels were given; it could be taken to imply that temperature variations of the MWP and LIA were each of the order of 0.5 °C from the temperature around 1900. The section specifically states recent climate changes were in a range of probably less than 2 °C. The 1990 report noted that it was not clear whether all the fluctuations indicated were truly global (p 202). The graph had no clear source (it resembles figure A9(d) from the 1975 NAS report, which is sourced to Lamb, 1966), and disappeared from the 1992 supplementary report.

To say that I was “uninterested” in its source was untrue. This is the sort of thing that usually interests me, but I can’t solve every climate science puzzle instantaneously and on demand. On an earlier occasion, I posted up an extended excerpt from IPCC 1990, which is not available to many readers. However, I didn’t then know the source of the IPCC 1990 graphic, just as I don’t know how Mann calculated the MBH99 confidence intervals or many other small climate science mysteries.

Today I’m pleased to report that I think that I can report a solution to this small mystery, which I propose below. Read the rest of this entry »

Four of the past 5 months are “all-time” records for Southern Hemisphere sea ice anomalies, “unprecedented” since the data set began in 1979 as shown below:

On a global basis, world sea ice in April 2008 reached levels that were “unprecedented” for the month of April in over 25 years. Levels are the third highest (for April) since the commencement of records in 1979, exceeded only by levels in 1979 and 1982. This continues a pattern established earlier in 2008, as global sea ice in March 2008 was also the third highest March on record, while January 2008 sea ice was the second highest January on record. It was also the second highest single month in the past 20 years (second only to Sept 1996).

The graph below shows the monthly anomaly (aggregating NH and SH), collating information from sidads.colorado.edu/DATASETS/NOAA/G02135.

Figure 2. Monthly anomaly sea ice area.

As suggested by a reader, here’s the same information with each monthly series plotted as a separate line (April-solid; January - dotted.) The surge in anomaly area in 2008 is not limited to a single month, but is consistent for all 4 months to date (and for the YTD average).

At Cryosphere Today, they provide the following scientific description of recent sea ice changes:

You’ve heard Al Gore comment that the “Earth has a fever”? It may also have major tooth decay.

They provide an animation showing declining sea ice to 2007 lows, but not the subsequent recovery in 2008:

Peruse an archive of map displays of the atmospheric and radiative climatic conditions leading up to the record setting Northern Hemisphere sea ice minimum of 2007: sea ice autopsy

Instead of perhaps celebrating the dramatic recent increase in sea ice, they complain that there has been a loss of “multiyear sea ice”.

I’ve uploaded my collation of the NOAA data to www.climateaudit.org/data/ice/seaice.dat .

UODATE: NOAA reported high March 2008 SH sea ice here.

One of the issues in play in criticisms of Douglass et al 2007 pertained to their use of RAOBCORE 1.2 rather than RAOBCORE 1.4.

As an editorial comment, since some critics of Climate Audit seem to feel that I bear some personal responsibility for defending this paper, I was not a co-author of Douglass et al nor I did not provide advice on it. I had not posted on it or reviewed it or even read it until a few days ago. Nor did I have any personal familiarity with radiosonde data sets. Nor had I followed the realclimate discussion of this article until a few days ago. I posted up a few days ago on tropical troposphere temperatures because of Ross McKitrick’s T3 concept and I merely did a simple plot of tropical troposphere temperature.

My proximate interest in this paper arose because this post prompted commentary on Douglass et al., including a statistical issue, previously raised by Gavin Schmidt (which I had not followed at the time), which was raised here by Beaker, which caught my interest. The idea of a climate scientist making a gross statistical error is something that would obviously not come as a total surprise to me, though I remain unconvinced that the particular issue advanced by Schmidt and endorsed by Beaker, concerning multi-model means, rises much above a play on words. In fact, my impression is it is more likely that Schmidt has committed the error, by confusing the real world with the output of a model, something that anthropologists have observed as something of an occupational hazard for climate modelers. (See discussion of Truth Machines here.)

The issues concerning radiosonde trends are more substantial, though Schmidt’s commentary is more oriented to proving a gotcha than a careful commentary on real issues pertaining to this data.

RAOBCORE is a re-analysis of radiosonde data by Leopold Haimberger and associates. RAOBCORE 1.2 was published in April 2007, though presumably available in preprint prior to that. Douglass et al 2007 was submitted in May 2007, when the ink was barely dry on the publication of RAOBCORE 1.2. Nonetheless, Schmidt excoriates Douglass et al for using RAOBCORE 1.2.

To date, RAOBCORE 1.4 has not been published in a peer-reviewed journal, though a discussion has been submitted (Haimberger et al 2008) and is currently online at Haimberger’s site. It was announced in Jan 2007 with Haimberger’s website stating that it used the “more conservative ERA-40 bg modification”. “Conservative”. I must say that I dislike the use of such adjectives by climate scientists. Dendros talk about “conservative” standardization, never about “liberal” standardization. Another adjective that sets my teeth on edge is “rigorous” as in a “rigorous statistical procedure”. Inevitably, such procedures are anything but.

RAOBCORE 1.4 data is online in a MSU gridded format at ftp://raobcore:empty@srvx6.img.univie.ac.at/v1_4/grid2.5_invd_1_6, with 24 different data sets covering combinations of 4 layers: tls=Lower Stratosphere (MSU4), tts=Troposphere-Stratosphere (MSU3), tmt=Mid-Troposphere (MSU2), tlt=Lower Troposphere; 3 versions: bg, tm and tmcorr; and two times: midnight (00) and noon (12). I’ve written a short program to extract this data and have made monthly time series for the tropics for all versions.

The underlying concept of the RAOBCORE re-analysis is to apply changepoint algorithms to detect inhomogeneities in the radiosonde record and there seems to be plenty of evidence that inhomogeneities are a real problem. So CA readers that are concerned about inhomogeneities in the surface record should not take the radiosonde record as written in stone, merely because they like the answer. Uncertainties in this record seem just as serious, if not more serious than uncertainties in the surface record.

I’ve done a quick assessment of the data, which has primarily involved figuring out how to download the data (which only goes to end 2006) and plotting the net adjustments in RAOBCORE 1.4 to the original data. (I haven’t located RAOBCORE 1.2 online yet.)

The difficulty that arises is that the recommended adjustments are typically of the same order of magnitude as the underlying trend and, in one case, larger than the underlying trend, such that the sign of the adjusted trend is different from the raw trend. First here is a figure showing the net adjustments for the tropics in deg C for the 4 levels (going high to low). In each case, the adjustments are implemented primarily in the 1985-2000 period, so one is not dealing with the far past. All records end in 2006 are not fully up-to-date.

Figure 1. RAOBCORE (tropics) adjustments for 4 levels 1957-2006. Black - midnight; blue- noon.

Next here is a figure showing the original and RAOBCORE 1.4 trends for the tropics for the 4 levels (version 1.2 is not shown). The sign in the MSU3 level is reversed by the adjustment process.

For completeness, here are plots showing the original and adjusted versions for the 4 levels.

It is evident from the above plots that the RAOBCORE adjustments are the same order of magnitude as the trend that people are seeking to determine.

Reference:
Haimberger L., 2007: Homogenization of Radiosonde Temperature Time Series Using Innovation Statistics. J. Climate, 20,1377- 1403 (April 2007) url

Last year, we noted the insolent and unresponsive answers by IPCC chapter 6 Lead Authors to Review Comments in connection with the Hockey Stick reconstructions. Under IPCC policies, Review Editors have important obligations to ensure responsiveness of Chapter Authors (see policies discussed here). The comments by Review Editors were not put online by IPCC, but, after some effort, David Holland managed to obtain the Review Comments for WG1 and WG2. While a few WG2 Review Editors made substantive comments, WG1 review editor comments proved to be a few-sentence form letter in all but one case (chapter 6 Review Editor Mitchell noting outstanding controversy in connection with the Hockey Stick.)

It seemed inconceivable that these form letters were the entire corpus of the Review Editor contributions, given their important obligations in the IPCC process. Holland accordingly pressed Mitchell for any supplementary information, reports pertaining to his duties as IPCC Review Editor. Even though IPCC policies state clearly that all comments will be retained for 5 years:

All written expert, and government review comments will be made available to reviewers on request during the review process and will be retained in an open archive in a location determined by the IPCC Secretariat on completion of the Report for a period of at least five years.

Mitchell replied that he had no kept “any” working papers and that he was not required to do so.

For my own part, I have not kept any working papers. There is no requirement to do so, given the extensive documentation already available from IPCC.

In the modern day and age, it seemed inconceivable that Mitchell could have discharged his duties without any trace or ripple in the electronic pond and accordingly, on April 1, 2008, Holland submitted an FOI request asking for all emails to and from Dr Mitchell in his capacity as IPCC Review Editor, with a turn of phrase that unfortunately was construed as limiting the request to emails concerning the HS. Once again, he has been essentially stonewalled. Although Mitchell’s final terse Review Editor report referred to outstanding issues in connection with the HS, according to the Met Office, Mitchell either never corresponded with any IPCC chapter author or IPCC official about these misgivings during the course of the IPCC review process or subsequently destroyed the relevant correspondence. Read the rest of this entry »

David Douglass writes in: Read the rest of this entry »

Last year, Ross McKitrick proposed the ironic idea of a “T3 Tax” in which carbon tax levels were related to observed temperature increases in the tropical troposphere. Temperature increases in the tropical troposphere are, as I understand it, a distinctive “fingerprint” for carbon dioxide forcing. Apparent discrepancies between a lack of warming in satellite data and surface warming have been a battleground issue for many years. In one of the most recent surveys of the matter in 2006, the U.S. CCSP proclaimed that the issue had been put to rest:

Previously reported discrepancies between the amount of warming near the surface and higher in the atmosphere have been used to challenge the reliability of climate models and the reality of human induced global warming. Specifically, surface data showed substantial global-average warming, while early versions of satellite and radiosonde data showed little or no warming above the surface. This significant discrepancy no longer exists because errors in the satellite and radiosonde data have been identified and corrected. New data sets have also been developed that do not show such discrepancies.

In this respect, the March 2008 satellite data for the tropics is pretty interesting. The graph below shows UAH (black) and RSS (red) for the tropics (both divided by 1.2 to synchronize to the surface variations - an adjustment factor that John Christy said to use in an email). I also collated the most recent CRU gridded data and calculated a tropical average for 20S to 20N, shown in green. All series have been centered on a common interval.

Figure 1. Tropic (20S-20N) temperatures in [anomaly] deg C. All data shown to March 2008. Script for calculations is given in #19 below. Reference periods for original data converted to reference period 1979-1997 here.

There have only been a few months in the past 30 years which have been as cold in the tropical troposphere as March 2008 four months in the 1988-1989 La Nina. At present, there is no statistically significant trend for the MSU version. The data set has very high autocorrelation (but I note that autocorrelation doesn’t represent the spikes very well.)

Obviously each fluctuation is unique - I presume that we’ll see some sort of behavior in the next 18 months like after the 1988-1989 Nina - so that one can reasonably project that the long-term “trend” as at the end of 2009 will be a titch lower than the trend as calculated today.

While RSS and UAH move together, there is a slight drift upwards in RSS relative to UAH and there’s still a slight trend in the RSS numbers. There’s a third data set (Vinnikov - Maryland) which is not kept up to date, which has trends higher than either. Even CRU is now reporting tropical temperatures at surface that are below average during this period.

I draw no conclusions from this other than some claims about the statistical significance of trends need to be examined. The autocorrelation of the data set is very high; although I’m not in a position to pronounce on the matter, the concerns expressed by Cohn and Lins about long-term persistence seem highly pertinent to the sort of patterns that one sees here. Some readers may note a graphic in summer 2005 .

realclimate discusses the issue up to Dec 2007 here. Since then, cooling has been 0.3-0.4 deg C in UAH and MSU.

UPDATE: This post has occasioned references to Douglass et al 2007. Here is Table IIa from that paper.

Anthony has two interesting reports on his NCDC visit. Take a look.

UC and Hu McCulloch have been carrying on a very illuminating discussion of statistical issues relating to calibration , with UC, in particular, drawing attention to the approach of Brown (1982) towards establishing confidence intervals in calibration problems.

In order to apply statistical theory of regression , you have to regress the effect Y against the cause X. You can’t just regress a cause X against a bunch of effects Y, which is what Wilson did in Kyrgyzstan and occurs all too frequent in paleoclimate, without a proper consideration of the effect of the inverse procedure.

Calibration deals with the statistical situation where Y is a “proxy” for X and where you want to estimate X, given Y. It’s the kind of statistics that dendros should be immersing themselves in, but which they’ve totally disregarded, using instead procedures for estimating confidence intervals that cannot be supported under any statistical theory - a practice unfortunately acquiesced in by IPCC AR4, hardly enhancing their credibility on these matters.

The starting point in Brown (1982) is the following:

Perhaps the simplest approach is that of joint sampling. It is easy to see that given α, β, σ, X, X’, the joint sampling distribution of is such that:

is standard normal. Note that this standard normal does not involve any of the conditioning parameters α, β, σ, X, X’ so that probability statements are also true unconditionally and, in particular, over repetitions of (Y,X) where both Y and X are allowed to vary.

I dare say that many readers may find that this statement is a fairly big first bite and that it may not be as obvious to them as to Brown’s audience.

However, this particular result is derived in chapter 1 of a standard textbook, Draper and Smith, Applied Regression Analysis (1981). I worked through this chapter in detail and found the exercise very helpful. Its’ approach is, in turn, derived from E.J. Williams (1959), Regression Analysis, chapter 6. In some fields, while people “move on”, they try to at least achieve results that survive the test of time.

The key strategy in the univariate cases is to draw curves enclosing the 100(1-γ) confidence intervals for y given x. These are quadratic in x. Illustrations are given in Draper and Smith Figures 1.11 and 1.12 and Williams Figure 6.2. The equation for the confidence interval curves is:

where t is the 100(1-γ) t-statistic for the relevant degrees of freedom, for the calibration set and the others are usual estimators.

This can be transformed to a quadratic equation in x. The strategy in these texts for estimating fiducial limits on x given y is to draw a horizontal line at y, determine the intersections with the two confidence interval curves and take the x-values of the intersection as the upper and lower fiducial limits, with the estimate being calculated from the fitted linear equation:

In a “well behaved case”, the upper confidence limit is on the upper quadratic and the lower confidence limit is on the lower quadratic and the estimate is between the upper and lower confidence intervals. However, if the roots to the quadratic are complex, there are no solutions to the equation, which means that any value of x falls within the confidence limits permitted from the data. Another related pathological case arises when both the “upper” and “lower” confidence intervals are on the same side of the estimate.

In these cases, if one examines the regression fit in univariate calibration, one finds that there was no statistically significant fit and in effect could not be statistically differentiated from zero. This is a point that UC has been emphasizing in recent posts.

I went through all 14 MBH99 proxies and found that they beautifully illustrated the pathologies warned about in these texts.

First here is an example where the calibration graphic in the style of Draper and Smith 1981 has the structure of a “well behaved” calibration. This is for Briffa’s Tornetrask series. The calibration here has been “enhanced” by some questionable prior manipulations by Briffa, who constructed his temperature series by an inverse regression of regional temperature against 4 time series - so the “raw” proxy is not really “raw” any more. In these graphics, I’ve used the average value of the proxy in the 1854-1901 “verification” period as the y-value (everything’s been standardized on 1902-1980). In this case, the fiducial limits for x (temperature) given y are 0.36 deg C, so this looks like a pretty successful calibration (BUT the prior massaging will have to be deconstructed at some point.)

Figure 1. Proxy value (as in other figures) is in SD Units; X-axis in deg C.

Next here is the same style of diagram for the Quelccaya 2 accumulation series, showing a very pretty example of complex roots and no fiducial limits. Examining the original calibration regression, one finds an r^2 of 0.011 (Adjust r^2 of -0.00147) with an insignificant t-statistic of -0.94 for the proxy-temperature relationship. Because the coefficient is not distinguishable from 0, there is no contribution towards calibration from this data.

Here’s a snippet of the corresponding Draper -Smith from Google, which shows enough that you can see that the Quelccaya 2 Accumulation case matches the situation in the Draper Smith Figure 1.12 top panel diagram.

Quelccaya 1 accumulation is also pathological but, in this case, the quadratic solves, but the both the “upper” and “lower” confidence intervals are on the same side of the estimate, as shown below. This calibration also fails standard tests, as the t-statistic is -0.544 (the r^2 is less than 0.01).

Here’s another pretty example of total calibration failure - the morc014 tree ring series. This would make a nice illustration in a statistics text. This has a t-statistic of -0.037 - a value that is low even for random series.

In total, 10 of the 14 series in the MBH99 failed this chapter 1 calibration test. In addition to the above 3 series, other failed series were: the fran010 tree ring series, Quelccaya 1 dO18, Quelccaya 2 dO18 (why are there 4 different Quelccaya series??), a Patagonia tree ring series, the Polar Urals reconstruction and the West Greenland dO18 series.

Only 4 series passed this elementary test. In addition to the highly massaged Tornestrask series, the three were: the Tasmania tree ring series, the NOAMER PC2 and the NOAMER PC1 (AD1000 style.) I guess the Tasmania series teleconnects to NH temperature more than most of the NH tree ring reconstructions. Its calibration results are not strong - the t-statistic is 2.1 and the adjusted r^2 is 0.04.

Now to what we’ve been waiting for: the NOAMER PC series. The NOAMER PC2 (and the AD1000 network is far more dominated by Graybill bristlecones than even the AD1400 network) has the strongest fit. It has a t-statistic of 4.3 and an adjusted r^2 of 0.19, the highest in the network.

And what does this high-correlation reconstruction look like? Not very HS, that’s for sure.

Now what of the NOAMER (Graybill bristlecone) PC1? This is the only MBH99 series that has a HS shape (I’ve flipped the archived series so that it has the expected upward bend). It has a very idiosyncratic appearance in the Draper-Smith style diagram as shown below. The upper and lower limits are on opposite sides of the estimate, but this series yields very broad fiducial limits. The t-statistic here is 1.71, somewhat below statistical significance. The MBH99 “adjustment” of the PC1 has the effect of “improving” its fit to temperature, and thereby increasing its weight in an MBH-style reconstruction.

Moving towards Multivariate Calibration

As we approach the mountain of multivariate calibration, let’s pause and consider the information on fiducial limits from the 4 series that actually calibrated, as summarized in the table below:

Thus, we have the remarkable situation where the 95% fiducial limits for the 4 proxies essentially do not overlap at all (there’s a miniscule overlap between the NOAMER PC2 and Tasmania). It will be interesting to see what happens as one works through a Brown 1982 style calibration. It also illustrates rather nicely the total lack of significance of the majority of proxies.

It’s hard to think how one can purport to derive confidence intervals of a few tenths of a degree, when 10 of 14 proxies don’t calibrate at all and the remaining 4 yield results that are inconsistent in the verification period.

I did these calculations with the MBH “sparse” temperature series since it had a verification value. MBH obviously used temperature PCs for calibration. Even though the two series are highly correlated, the calibrations will be different though I’d expect the patterns to stay pretty similar.