Subject: Re: [Fwd: sorry to take your time up, but really do need a scrub of this singer/christy/etc effort]
Date: Sat, 29 Dec 2007 22:10:30 +0100
Cc: John.Lanzante@noaa.gov, "Thomas.R.Karl" <Thomas.R.Karl@noaa.gov>, carl mears <firstname.lastname@example.org>, "David C. Bader" <email@example.com>, "'Dian J. Seidel'" <firstname.lastname@example.org>, "'Francis W. Zwiers'" <email@example.com>, Frank Wentz <firstname.lastname@example.org>, Karl Taylor <email@example.com>, Melissa Free <Melissa.Free@noaa.gov>, "Michael C. MacCracken" <firstname.lastname@example.org>, "'Philip D. Jones'" <email@example.com>, Sherwood Steven <firstname.lastname@example.org>, Steve Klein <email@example.com>, 'Susan Solomon' <firstname.lastname@example.org>, "Thorne, Peter" <email@example.com>, Tim Osborn <firstname.lastname@example.org>, Tom Wigley <email@example.com>
I have attached the tropical mean trend profiles, now for the period
RAOBCORE versions show much more upper tropospheric heating for this
period, RICH shows slightly more heating.
Note also stronger cooling of unadjusted radiosondes in stratospheric
layers compared to 1999-2004.
Just for information I have included also zonal mean trend plots for the
unadjusted radiosondes (tm), RAOBCORE v1.4 (tmcorr) and RICH (rgmra)
I do not suggest that these plots should be included but some of you
maybe want to know about the spatial coherence
of the zonal mean trends. It is interesting to see the lower
tropospheric warming minimum in the tropics in all three plots,
which I cannot explain. I believe it is spurious but it is remarkably
robust against my adjustment efforts.
Meridional resolution is 10 degrees.
As you can imagine, the tropical upper tropospheric heating maximum at
5S and the cooling in the unadjusted radiosondes at 5N are
based on very few long records in these belts. 2-3 in 5S, about 5 in 5N.
Best regards and I wish you all a happy new year.
Ben Santer wrote:
> Dear Leo,
> The Figure that you sent is extremely informative, and would be great
> to include in a response to Douglass et al. The Figure clearly
> illustrates that the "structural uncertainties" inherent in
> radiosonde-based estimates of tropospheric temperature change are much
> larger than Douglass et al. have claimed. This is an important point
> to make.
> Would it be possible to produce a version of this Figure showing
> results for the period 1979 to 1999 (the period that I've used for
> testing the significance of model-versus-observed trend differences)
> instead of 1979 to 2004?
> With best regards, and frohes Neues Jahr!
> Leopold Haimberger wrote:
>> Dear all,
>> I have attached a plot which summarizes the recent developments
>> concerning tropical radiosonde temperature datasets and which could
>> be a candidate to be included in a reply to Douglass et al.
>> It contains trend profiles from unadjusted radiosondes,
>> HadAT2-adjusted radiosondes, RAOBCORE (versions 1.2-1.4) adjusted
>> and from radiosondes adjusted with a neighbor composite method (RICH)
>> that uses the break dates detected with RAOBCORE (v1.4) as metadata.
>> RAOBCORE v1.2,v1.3 are documented in Haimberger (2007), RAOBCORE v1.4
>> and RICH are discussed in the manuscript I mentioned in my previous
>> Latitude range is 20S-20N, only time series with less than 24 months
>> of missing data are included. Spatial sampling of all curves is the
>> same except HadAT which contains less stations that meet the 24month
>> criterion. Sampling uncertainty of the trend curves is ca.
>> +/-0.1K/decade (95% percentiles estimated with bootstrap method).
>> RAOBCORE v1.3,1.4 and RICH are results from ongoing research and
>> warming trends from radiosondes may still be underestimated.
>> The upper tropospheric warming maxima from RICH are even larger (up
>> to 0.35K/decade, not shown), if only radiosondes within the tropics
>> (20N-20S) are allowed as reference for adjustment of tropical
>> radiosonde temperatures. The pink/blue curves in the attached plot
>> should therefore not be regarded as upper bound of what may be
>> achieved with plausible choices of reference series for homogenization.
>> Please let me know your comments.
>> I wish you a merry Christmas.
>> With best regards
>> John Lanzante wrote:
>>> Perhaps a resampling test would be appropriate. The tests you have
>>> consist of pairing an observed time series (UAH or RSS MSU) with
>>> each one
>>> of 49 GCM times series from your "ensemble of opportunity".
>>> of the difference between each pair of obs/GCM trends yields a certain
>>> number of "hits".
>>> To determine a baseline for judging how likely it would be to obtain
>>> given number of hits one could perform a set of resampling trials by
>>> treating one of the ensemble members as a surrogate observation. For
>>> trial, select at random one of the 49 GCM members to be the
>>> From the remaining 48 members draw a bootstrap sample of 49, and
>>> 49 tests, yielding a certain number of "hits". Repeat this many
>>> times to
>>> generate a distribution of "hits".
>>> The actual number of hits, based on the real observations could then be
>>> referenced to the Monte Carlo distribution to yield a probability
>>> that this
>>> could have occurred by chance. The basic idea is to see if the observed
>>> trend is inconsistent with the GCM ensemble of trends.
>>> There are a couple of additional tweaks that could be applied to
>>> your method.
>>> You are currently computing trends for each of the two time series
>>> in the
>>> pair and assessing the significance of their differences. Why not first
>>> create a difference time series and assess the significance of it's
>>> The advantage of this is that you would reduce somewhat the
>>> in the time series and hence the effect of the "degrees of freedom"
>>> adjustment. Since the GCM runs are based on coupled model runs this
>>> differencing would help remove the common externally forced
>>> but not internally forced variability, so the adjustment would still be
>>> Another tweak would be to alter the significance level used to assess
>>> differences in trends. Currently you are using the 5% level, which
>>> only a small number of hits. If you made this less stringent you
>>> would get
>>> potentially more weaker hits. But it would all come out in the wash
>>> so to
>>> speak since the number of hits in the Monte Carlo simulations would
>>> as well. I suspect that increasing the number of expected hits would
>>> make the
>>> whole procedure more powerful/efficient in a statistical sense since
>>> would no longer be dealing with a "rare event". In the current
>>> scheme, using
>>> a 5% level with 49 pairings you have an expected hit rate of 0.05 X
>>> 49 = 2.45.
>>> For example, if instead you used a 20% significance level you would
>>> have an
>>> expected hit rate of 0.20 X 49 = 9.8.
>>> I hope this helps.
>>> On an unrelated matter, I'm wondering a bit about the different
>>> versions of
>>> Leo's new radiosonde dataset (RAOBCORE). I was surprised to see that
>>> latest version has considerably more tropospheric warming than I
>>> from an earlier version that was written up in JCLI in 2007. I have a
>>> couple of questions that I'd like to ask Leo. One concern is that if
>>> we use
>>> the latest version of RAOBCORE is there a paper that we can
>>> reference --
>>> if this is not in a peer-reviewed journal is there a paper in
>>> The other question is: could you briefly comment on the differences
>>> in methodology used to generate the latest version of RAOBCORE as
>>> compared to the version used in JCLI 2007, and what/when/where did
>>> changes occur to
>>> yield a stronger warming trend?
>>> Best regards,
>>> On Saturday 15 December 2007 12:21 pm, Thomas.R.Karl wrote:
>>>> Thanks Ben,
>>>> You have the makings of a nice article.
>>>> I note that we would expect to 10 cases that are significantly
>>>> different by chance (based on the 196 tests at the .05 sig level).
>>>> You found 3. With appropriately corrected Leopold I suspect you
>>>> will find there is indeed stat sig. similar trends incl.
>>>> amplification. Setting up the statistical testing should be
>>>> interesting with this many combinations.
>>>> Regards, Tom
Ao. Univ. Prof. Dr. Leopold Haimberger
Institut für Meteorologie und Geophysik, Universität Wien
Althanstraße 14, A - 1090 Wien
Tel.: +43 1 4277 53712
Fax.: +43 1 4277 9537
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