Subject: Re: [Fwd: sorry to take your time up, but really do need a scrub of this singer/christy/etc effort]
Date: Sun, 23 Dec 2007 15:50:17 +0100
Cc: "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>, firstname.lastname@example.org, Sherwood Steven <email@example.com>, Steve Klein <firstname.lastname@example.org>, 'Susan Solomon' <email@example.com>, "Thorne, Peter" <firstname.lastname@example.org>, Tim Osborn <email@example.com>, Tom Wigley <firstname.lastname@example.org>
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 radiosondes
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 email.
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 performed
> consist of pairing an observed time series (UAH or RSS MSU) with each one
> of 49 GCM times series from your "ensemble of opportunity". Significance
> 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 the
> given number of hits one could perform a set of resampling trials by
> treating one of the ensemble members as a surrogate observation. For each
> trial, select at random one of the 49 GCM members to be the "observation".
> From the remaining 48 members draw a bootstrap sample of 49, and perform
> 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 trend?
> The advantage of this is that you would reduce somewhat the autocorrelation
> 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 variability,
> 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 yields
> 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 increase
> as well. I suspect that increasing the number of expected hits would make the
> whole procedure more powerful/efficient in a statistical sense since you
> 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 the
> latest version has considerably more tropospheric warming than I recalled
> 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 submission?
> 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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