(7) You say

Even in scientific disciplines which are well understood, taking the position when models disagree with observations that “more plausibly” the observations are incorrect is adventurous. In climate science, on the other hand, it is downright risky.

However, if one looks at the context in which Santer et al. made that claim, it seems to be in arguing that they believe that the tropospheric tropical amplification as seen in the models and not so much in the data at multidecadal timescales really is there. And, strangely enough, it seems to me that your analysis seems to confirm this basic fact…In fact, you seem to find that the amplification is there in the data (perhaps a little weaker at the multidecadal timescales than on average in the models although it depends on which model you compare to). So, you seem to chide Santer et al. for their hubris at the same time as you confirm their basic point that the amplification is really there at the multidecadal timescales. One thing that confuses me a bit though is why you seem to see it in the data sets whereas they don’t…I.e., how does your analysis of the data (in the 30-year limit) differ from theirs (besides the fact that you used the corrected UAH data set)? Is it the difference between the length of the data set that you used vs what they used (since they didn’t have 30 years yet); Or, is there some other difference in the definition of the amplifications or what?

The part I disagree with is:

However, if one looks at the context in which Santer et al. made that claim, it seems to be in arguing that they believe that the tropospheric tropical amplification as seen in the models and not so much in the data at multidecadal timescales really is there.

I don’t read it that way at all. They said:

These results suggest that either different physical mechanisms control amplification processes on monthly and decadal timescales, and models fail to capture such behavior, or (more plausibly) that residual errors in several observational datasets used here affect their representation of long-term trends.

Seems pretty clear, they are saying it is “A” or “B”, where “A” is the models don’t capture all the mechanisms, and “B” is the observations are wrong.

Is amplification there at longer timescales? Yes. But it is much more subtle and complex than their analysis suggests. They claim (but never show) that the models agree with the data at (unspecified) short timescales, but not at the longest timescales. In addition, they make the implicit (but totally unfounded) assumption that the amplification is basically constant over time.

This constancy is seen in all of the models … and in none of the datasets. That strongly suggests to me that the models “fail to capture such behavior” … but as always, YMMV.

However, the huge differences in the models definitely mean one thing:

They can’t all be right.

w.

(6) You say “The oddity in Fig 1(a) is that I had expected the amplification at higher altitude (T2 and TMT) to be larger than at the lower altitude (T2LT and TLT) amplification. Instead, the higher altitude record had lower amplification. This suggests a strong stratospheric influence on the T2 and TMT datasets.” I think this is a fairly well-known issue so I think it is not really such an oddity and it is a good place to make reference to some previous literature (the lack of such references being a general weakness of this paper that others have already pointed out).

My problem is that I’m too damn honest. People have busted me a number of times for saying “I was surprised by” … hey, I was surprised, what can I say?

Yes, it is a “well-known issue” … but I was surprised to see how much that well known issue affected the amplification.

And yes, more references are always good, and I plan to add more.

However, there has been very little peer-reviewed publication on the general subject of amplification in general. In addition, this is a new metric, so there is nothing published on e.g. how stratospheric contamination (which I should have referenced) might affect my metric of amplification.

w.

(5) I agree with some of Pamela’s stylistic points but I disagree with her on always using the passive voice…I think that is somewhat “old school” thinking these days and the scientific community has begun to catch up with the rest of the world in rejecting the need to use the passive voice so exclusively. In particular, I have noticed that overuse of the passive voice sometimes even makes it difficult to determine when the author is referring to work that he/she has done versus when the reference is to the work of others. Having written almost all of my papers with co-authors, I am not sure what the guidelines are for using “I” vs “we” though. I kind of think that “we” sounds better, but I know that Physical Review once had a prohibition against the use of “we” for a single-author paper that led an author to once add his dog as a co-author so that he could keep the “we”!

Pamela and others have made excellent points. Being a lone wolf in this one, I loved the addition of the dog …

Joel, thanks for pointing this out. Here’s the record in full:

Update 7 Aug 2005 ****************************

An artifact of the diurnal correction applied to LT
has been discovered by Carl Mears and Frank Wentz
(Remote Sensing Systems). This artifact contributed an
error term in certain types of diurnal cycles, most
noteably in the tropics. We have applied a new diurnal
correction based on 3 AMSU instruments and call the dataset
v5.2. This artifact does not appear in MT or LS. The new
global trend from Dec 1978 to July 2005 is +0.123 C/decade,
or +0.035 C/decade warmer than v5.1. This particular
error is within the published margin of error for LT of
+/- 0.05 C/decade (Christy et al. 2003). We thank Carl and
Frank for digging into our procedure and discovering this
error. All radiosonde comparisons have been rerun and the
agreement is still exceptionally good. There was virtually
no impact of this error outside of the tropics.

I don’t think this is the source of the “negative amplification”, however. I had forgotten that they are not actually measuring amplification, but are simply measuring a ratio of trends. And with or without the adjustment, the UAH MSU trend over 1979-2005 is less than the HadCRUT trend over the same period.

I appreciate the heads-up.

w.

(2) I think that your conclusions are in general overstated based on the results that you show. Where you see the glass half-empty, I see it half-full. I.e., you seemed to consider it some sort of significant problem that you can find any disagreement between the models and the data. In fact, I would be shocked if you couldn’t, particularly given how hard you are trying to develop a metric to test things as diligently as possible. Honestly, when I look at Figs. 3 and 4, I say, “Wow…The models are doing pretty well at getting the most of the basic features correct. Are there discrepancies? Sure…and it would be interesting to further probe these and understand what problems with the models or the data they could be due to. But, the take-away message is that there are many basic features that are quite robust across the data and the models and then also some notable differences in the details.” In essence, I think you are creating a strawman that is “The models are perfect” and then demolishing it by showing that they are not perfect. (Although the extent to which the discrepancy is due to imperfections of the models and the extent to which they are due to limitations or problems with the data are unclear. However, I am willing to imagine that at least some of it is due to imperfection of the models). I think your presentation would seem more balanced if you had more emphasis of the points where the models and observational data do agree rather than only the points where they differ.

I suspect that you are right that my conclusions are somewhat overstated.

But saying that “the models are doing pretty well” is a whole lot different from saying “the models are good enough to make century long forecasts”. To do that, they need to do more than be in the general ballpark.

I’m not the one who is saying “the models are perfect”. That would be the AGW supporters, since it would obviously take a model that is nearly perfect to forecast the evolution of climate over the next century.

Next, I’d be glad to highlight where the models and the observations agree … I just can’t find them. None of the models show the slow increase over 8-10 years to a peak. This is seen in all of the data. None of the models show increasing amplification with height up to 200 hPa and decreasing after that, as theory suggests and as all observation datasets show.

So I’m in a bit of mystery here … what do you see as “the points where the models and observational data do agree”?

(1) You have tried very hard here to push the models until they break…I.e., to find a more sensitive metric that can probe in more detail exactly what the data and models show, and that is great. However, I would suggest that you do the same in regards to the experimental data. Hence, I would recommend looking at, say, the RAOBCORE re-analysis (despite the claims that some may make that it has some bias…After all, there are lots of concerns about the general biases in the radiosonde data set that RAOBCORE is trying to correct). And, you could look how sensitive your result (particularly concerning whether the amplification trend with time interval is up or down at the longer times) is to the overall trend in the data set in order to understand how sensitive things are to possible artifacts in the data set that affect that the overall multidecadal trends without having much effect on the shorter-term variability, since it is the longer term secular trends that are most subject to artifacts in the observational data sets.

I have looked at the RAOBCORE gridded data (see the link a couple of posts upstream). The problem is that there is very little coverage in the tropics. As such, I don’t see how we can conclude much of anything from the RAOBCORE set. Perhaps there is a set with more coverage, but I haven’t found it.

Regarding changes in trends, take a look at the analysis above of the AMSU data. In figure S-1(b), the two graphs are identical except for having different trends. As you suggest, this affects the long-term amplification but not the short-term.

I would say, however, that the gradual decline in the long-terms trends is real rather than an artifact. I say so because it appears in:

1) All layers of the HadCRUT tropical data from 700 hPa to 200 hPa.

2) All layers of the RATPAC tropical data from 700 hPa to 200 hPa.

3) The UAH MSU T2LT and TLT data.

4) All layers of the HadCRUT global data from 700 hPa to 200 hPa.

5) All layers of the RATPAC global data from 700 hPa to 200 hPa.

6) All layers of the RATPAC quarterly tropical data.

7) All layers of the NCEP reanalysis data from 700 to 200 hPa.

On the other hand, this behavior does not appear in a single model.

More to come, thanks for raising the issues,

w.

Your paper first needs to show the physics of what you are doing. As it stands you jump in with an assumption with no justification that I can see, and claim that this means something.

Perhaps I misunderstand your point, but I ascribe no meaning to the amplification behavior of the atmosphere. I am merely trying to understand and measure the phenomenon.

“Amplification” is the term used to describe the fact that the atmospheric temperature varies more than the surface temperature. Does amplification “mean something”? Not that I know of. It is just one of the many atmospheric phenomena that we are trying to understand.

However, the existence, size, and nature of amplification have become a bit of a battleground in the ongoing climate wars. Many claims have been made by both sides about how large it is, and in particular, about whether the models correctly simulate the amplification behavior of the atmosphere.

As such, I decided to investigate the phenomenon to try to clear the air and place the claims on some firm footing. Having done so, I find that the models do a very poor job of simulating the amplification at various levels in the atmosphere. I do not claim that “means” anything either, it just points out another of the models’ many failings.

However, perhaps I am not following your objection, and you could explain further.

My best to you,

w.

Your paper first needs to show the physics of what you are doing. As it stands you jump in with an assumption with no justification that I can see, and claim that this means something. You have not shown why your analysis shows feedback. You only show the ratio of the long term relative levels of variation. Since the ground is a boundary, vertical convection is restricted. At altitude, large vertical convection currents can change the temperature much more, so I don’t see how looking just at the relative variation amplitude shows anything other than large vertical convection effects, which you would expect in the tropics. The whole idea of a “hot” spot is that the integrated effect of the temperature at altitude is hotter than it would be for no amplification, not that it varies up and down more. I do not see how your analysis addresses that issue.

http://homepage.mac.com/williseschenbach/.Pictures/rich_gridded.jpg

http://homepage.mac.com/williseschenbach/.Pictures/raobcore_gridded.jpg

w.

http://homepage.mac.com/williseschenbach/.Pictures/Amp_Models_multiamp_Two.jpg

As you can see, my previous prediction, viz:

So I’d be surprised to find that all of the rest of the models were very similar to the observations.

was 100% correct. Not a one of these looks like the observations.

More later,

w.

No good?

w.

REPLY: That works, you may want to repost the link with explantion – A

w.

REPLY: Post them to flickr or tinypic and then link with a URL
- Anthony

Also, after two years overseas I’m moved back to Nowherica now, so I’ll have fast internet. And I have a new computer with larger memory. So I plan to continue downloading and analyzing the models. I would note, however, that I have already done one model from about a third of the modeling groups (some of which use two different versions of a given model, like GISSE-H and GISSE-R variants.) So I’d be surprised to find that all of the rest of the models were very similar to the observations.

Anyhow, I’m off on holiday, back soon with more responses and more models.

My thanks to all for your review of the paper,

w.

I applaud your approach to review. A thought came to mind as to how you, and most others, are approaching your research. It seems that everyone is trying to measure the Earth’s “pulse”, each by grabbing a different appendage, and discovering how different things are, tactilely.

It leaves some “holes” as far as potential systems are concerned.

Just a thought, but your’s are much more impressive than my snipes.

Good luck. (and I only have one person left to call,on our other subject, the others are no longer with us. depressing.

I wish you well, no need to respond, concentrate on the others, please.

“Several investigators revised the radiosonde datasets
to reduce possible impacts of changing instrumentation
and processing algorithms on long-term trends. Sherwood
et al. (2005) have suggested biases arising from daytime
solar heating. These effects have been addressed by
Christy et al. (2007) and by Haimberger (2006). Sherwood
et al. (2005) suggested that, over time, general
improvements in the radiosonde instrumentation, particularly
the response to solar heating, has led to negative
biases in the daytime trends vs nighttime trends in unadjusted
tropical stations. Christy et al. (2007) specifically
examined this aspect for the tropical tropospheric layer
and indeed confirmed a spuriously negative trend component
in composited, unadjusted daytime data, but also discovered
a likely spuriously positive trend in unadjusted
nighttime measurements. Christy et al. (2007) adjusted
day and night readings using both UAH and RSS satellite
data on individual stations. Both RATPAC and HadAT2
compared very well with the adjusted datasets, being
within ±0.05 °C/decade, indicating that main cooling
effect of the radiosonde changes were evidently detected
and eliminated in both. Haimberger (2006) has also studied
the daytime/nighttime bias and finds that ‘The spatiotemporal
consistency of the global radiosonde dataset
is improved by these adjustments and spurious large daynight
differences are removed.’ Thus, the error estimates stated by Free et al. (2005); Haimberger (2006), and
Coleman and Thorne (2005) are quite reasonable, so that
the trend values are very likely to be accurate within
±0.10 °C/decade.”

References:
Christy JR, Norris WB, Spencer RW, Hnilo JJ. 2007. Tropospheric
temperature change since 1979 from tropical radiosonde and satellite
measurements. Journal of Geophysical Research-Atmospheres 112:
D06102, DOI:10.1029/2005JD006881.
Coleman H, Thorne PW. 2005. HadAT: An update to 2005 and development
of the dataset website. Data at .
Free M, Seidel DJ, Angell JK. 2005. Radiosonde Atmospheric
Temperature Products for Assessing Climate (RATPAC): a new
dataset of large-area anomaly time series. Journal of Geophysical
Research 110: D22101, DOI:10.1029/2005/D006169.
Haimberger L. 2006. Homogenization of radiosonde temperature time
series using innovation statistics. Journal of Climate 20: 1377–1402.
Sherwood SC, Lanzante JR, Meyer CL. 2005. Radiosonde daytime
biases and late – 20th century warming. Science 309: 1556–1559.

The Radiosonde data appear to be very accurate, thank you.

“why use the original radiosonde data that is known to have cool bias artifacts due to better shielding of the temperature sensor from the sun over time?”

Who is???? I’ve heard about this somewhere before though, and I remember there being more to it than that. But I know that the “raw data” were very different from the present data…Some older papers claimed that, like satellites originally did, that there was no trend at all. This was subsequently corrected. But there are certainly important discussions about what biases do and do not still remain. I’m going to investigate this further.

“Take up the point with Willis since he is the one who states (correctly in my view) that “this suggests a strong stratospheric influence on the T2 and TMT datasets.””

Your “view” is, frankly, totally erroneous.

“Honestly, I have no idea why you wrote your post except to be argumentative.”

I have no idea why you responded then…