Guest Post by Willis Eschenbach
The Inter-Tropical Convergence Zone from space
A while ago I started studying the question of the amplification of the tropical tropospheric temperature with respect to the surface. After months of research bore fruit, I started writing a paper. My intention was to have it published in a peer-reviewed journal. I finished the writing about a week ago.
During that time, I also wrote and published The Temperature Hypothesis here on WUWT. This got me to thinking about science, and about how we establish scientific facts. In climate science, the peer review process is badly broken. Among other problems, it is often an “old boy” system where very poor work is waved through. In common with other sciences, turnaround of ideas in journals takes weeks. Under pressure to publish, journals often do only the most cursory examination of the papers.
Upon reflection, I have decided to try a different way to examine the truth content of my paper. This is to invite all of the authors whose work I discuss, and other interested scientists of all stripes, to comment on the paper and on whether they can find any flaws in it. To facilitate the process I have provided all of the code and data that I used to do the analysis.
To make this process work will require cooperation. First, I ask for science and science only. No discussions of motives. No ad homs. No generalizations to larger spheres. No asides. No disrespect, we can be gentlemen and gentlewomen. No comments on politics, CO2, or AGW, no snowball earth. This thread has one purpose only, to establish whether my ideas stand: to either attack and destroy the ideas I put forth in the paper below, or to provide evidence and data to support the ideas I put forth below.
People think science is a cooperative endeavor. It is not. It is a war of ideas. An idea is put out, and scientists gather round to attack it and disprove it. Sometimes, other scientists may support and defend it. The more fierce the attack, the better … because if it can withstand the strongest attack, it is more likely true. When your worst scientific enemies and greatest disbelievers can’t show that you are wrong, your ideas are taken as scientific fact. (Until your ideas in turn are perhaps overthrown). Science is a blood sport, but all of the attack and parry is historically done in private. I propose to bring it out in public, and I offer my contribution below as the first victim.
Second, I will insist on a friendly, appreciative attitude towards the contributions of others. We are interested in working together to advance our primitive knowledge of how the climate works. We are doing that by trying to tear my ideas down, to disprove them, to find errors in them. To make this work we must do this with respect for the people involved and the ideas they put forwards. You don’t have to smash the guy to smash the idea, in fact it reduces your odds.
Third, no anonymous posting, please. If you are think your ideas are scientifically valid, please put your name on them.
With that in mind, I’d like to invite any and all of the following authors whose work I discuss below, to comment on and/or tear to shreds this study.
J. S. Boyle, J. R. Christy, W. D. Collins, K. W. Dixon, D. H. Douglass, C. Doutriaux, M. Free, Q. Fu, P. J. Gleckler, L. Haimberger, J. E. Hansen, G. S. Jones, P. D. Jones, T. R. Karl, S. A. Klein, J. R. Lanzante, C. Mears, G. A. Meehl, D. Nychka, B. D. Pearson, V Ramaswamy, R. Ruedy, G. Russell, B. D. Santer, G. A. Schmidt, D. J. Seidel, S. C. Sherwood, S. F. Singer, S. Solomon, K. E. Taylor, P. W. Thorne, M. F. Wehner, F. J. Wentz, and T. M. L. Wigley
(Man, all those 34 scientists on that side … and on this side … me. I’d better attack quick, while I have them outnumbered … )
I also invite anyone who has evidence, logic, theory, or data to disprove or to support my analysis to please contribute to the thread. Because at the end of this process, where I have exposed my ideas and the data and code to the attacks of anyone and everyone who can find flaws with them, I will have my own answer. If no one is able to disprove or find flaws in my analysis, I will consider it to be established science until someone can do so. Whether you consider it established science is up to you. However, it is certainly a more rigorous process than peer-review, and anyone who disagrees has had an opportunity to do so.
I see something in this nature, a web-based process, as the future of science. We need a place where scientific ideas can be brought up, discussed and debated by experts from all over the world, and either shot down or provisionally accepted in real time. Consider this an early experiment in that regard.
Three months to comment on a Journal paper is so 20th century. I’m amazed that the journals haven’t done something akin to this on the web, with various restrictions on reviewers and participants. Nothing sells journals like blood, and scientific blood is no different from any other.
So without further ado, here is my paper. Tear it apart or back it up, enjoy, ask questions, that’s science.
My best to everyone.
w.
A New Amplification Metric
ABSTRACT: A new method is proposed for exploring the amplification of the tropical tropospheric temperature with respect to the surface. The method looks at the change in amplification with the length of the record. The method is used to reveal the similarities and differences between the HadAT2 balloon, UAH MSU satellite, RSS MSU satellite, RATPAC balloon, AMSU satellite, NCEP Reanalysis, and five climate model datasets In general, the observational datasets (HadAT2, RATPAC, and satellite datasets) agree with each other. The climate model and reanalysis datasets disagree with the observations. They also disagree with each other, with no two being alike.
Background
“Amplification” is the term used for the general observation that in the tropics, the tropospheric temperatures at altitude tend to vary more than the surface temperature. If the surface temperature goes up by a degree, the tropical temperature aloft often goes up by more than a degree. If surface and tropospheric temperatures were to vary by exactly the same amount, the amplification would be 1.0. If the troposphere varies more than the surface, the amplification will be greater than one, and vice versa.
There are only a limited number of observational datasets of tropospheric temperatures. These include the HadAT2 and RATPAC weather balloon datasets, and two versions of the Microwave Sounding Unit (MSU) satellite data. At present the satellite record is about thirty years long, and the two balloon datasets are about 50 years in length.
Recently there has been much discussion of the the Santer et al. 2005, Douglass et al. 2007, and the Santer et al. 2008 papers on tropical tropospheric amplification. The issue involved is posed by Santer et al. 2005 in their abstract:
The month-to-month variability of tropical temperatures is larger in the troposphere than at the Earth’s surface. This amplification behavior is similar in a range of observations and climate model simulations, and is consistent with basic theory. On multi-decadal timescales, tropospheric amplification of surface warming is a robust feature of model simulations, but occurs in only one observational dataset [the RSS dataset]. Other observations show weak or even negative amplification. 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.
Metrics of Amplification
Santer 2005 utilizes two different metrics of amplification, viz:
We examine two different amplification metrics: RS(z), the ratio between the temporal standard deviations of monthly-mean tropospheric and TS anomalies, and Rβ(z), the ratio between the multi-decadal trends in these quantities.
Neither of these metrics, however, measures the amount of the amplification at altitude which is related to the surface variations. Ratios of standard deviations merely measure the size of the swings. They cannot indicate whether one is an amplified version of the other. The same is true of trend ratios. All they can show is the size of the difference, not whether the surface and atmosphere are actually correlated.
In order to measure whether one dataset is an amplified version of another, the simplest measure is the slope of the ordinary least squares regression line. This measures how much one temperature varies in relation to another.
Despite a variety of searches, I was unable to find published studies showing that the “amplification behavior is similar in a range of observations and climate model simulations” as Santer et al. 2005 states. To investigate whether the tropical amplification is “robust” at various timescales, I decided to calculate the tropical and global amplification (average slope of the regression line) at all time scales between three months and thirty years or more for a variety of temperature datasets. I started with the UAH and the RSS versions of the satellite record. Next I looked at the HadAT2 balloon (radiosonde) dataset. The results are shown in Figure 1.
To measure the amplification, for every time interval (e.g. 5 months) I calculated the amplification of all contiguous 5-month periods in the entire dataset. In other words, I exhaustively sub-sampled the full record for all possible contiguous 5-month periods. I took the average of the results for each time period. Details of the method are given in the Supplementary Online Material (SOM) Sections 2 & 3 below.
I plotted the results as a curve which shows the average amplification for each of the various time periods from three months to thirty years (the length of the MSU datasets). This shows the “temporal evolution” of amplification, how it changes as we look at longer and longer timescales. I show the results at a variety of pressure levels in Figure 1. In general, at all pressure levels, short term (3 – 48 month) amplifications are much smaller than longer term amplifications.
Figure 1. Change of amplification with length of observation. Left column is amplification in the tropics (20N/S), and the right column is global amplification. T2 and TMT are middle troposphere measurements. T2LT and TLT are lower troposphere. Starting date is January 1979. Shortest period shown is amplification over three months. Effective weighted altitudes (from the RSS weighting curves) are about 450 hPa for the lower altitude TLT (~ 6.5 km) and 350 hPa (~ 8 km) for the higher altitude TMT.
Tropical Observations 1979-2008
Figure 1(a). UAH and RSS satellite data. This was the first analysis done. It confirmed the sensitivity of this temporal evolution method, as it shows a clear difference between the two versions (RSS and MSU) of the MSU satellite data. Both of the datasets (UAH and RSS) are quite close in the first half of the record. They diverge in the second half.
The higher and lower altitude amplifications are very similar in both the RSS and the UAH versions. 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. Because of this, I have used only the lower altitude T2LT (UAH) and TLT (RSS) datasets in the remainder of this analysis.
Figure 1(b). HadAT2 radiosonde data. (Note the difference in vertical scale.) Despite the widely discussed data problems with the radiosonde data, this shows a clear picture of amplification increasing with altitude to 200 hPa, and decreasing above that. It confirms the existence of the tropical tropospheric “hot spot”, where the amplification is large. It conforms with the result expected from theoretical consideration of the effect of lapse rate. It also shows remarkable internal consistency. The amplification increases steadily with altitude up to 200 hPa, and decreases steadily with altitude above that. Note that the 1998 El Nino is visible as a “bump” in the records at about month 240. It is also visible in the satellite record, in Fig. 1(a).
Figure 1(c). HadAT2, overlaid with MSU satellite data. Same vertical scale as (a). The satellite and balloon data all agree in the first half of the record. In the latter half, the fit is much better for the UAH satellite data analysis than the corresponding RSS analysis. Note that the amplification of the UAH version is a good fit with the 500 hPa level of the HadAT2 data. This agrees with the theoretical effective weighted altitude of the T2LT measurement.
Global Observations 1979-2008
Figure 1(d). Global UAH and RSS satellite data. Note difference in vertical scale. There is little amplification at the global level.
Again, the UAH and RSS records are similar in the short term but not the long term.
Figure 1(e). Global HadAT2 radiosonde data. Note difference in vertical scale. Here we see that the amplification is clearly a tropical phenomenon. We do not see significant amplification at any level.
Figure 1(f). Global HadAT2, overlaid with MSU satellite data. Same vertical scale as (d). Once again, the satellite and balloon data all agree in the first half of the record. In the latter half, again the UAH analysis generally agrees with the observations. And again, the RSS version is a clear outlier.
Both in the tropics and globally, amplification of the levels above 850 hPa start low. After that they show a slow increase. The greatest amplification occurs at 8-10 years. After that, they all (except RSS) show a gradual decrease up to the 30 years end of the record.
Having seen the agreement between the UAH T2LT and the HadAT2 datasets, I next compared the tropical RATPAC data with the HadAT2 data. The RATPAC data is annual and quarterly. I averaged the HadAT2 data annually and quarterly to match. They are shown in Fig. 2. Note that these are fifty-year datasets, a much longer timespan than Fig. 1.
Figure 2. RATPAC and HadAT2 Tropical Amplification, 3 years to 50 years. Left column is annual data. Right column is quarterly data.
There is very close agreement between the HadAT2 and the RATPAC datasets. The annual version shows a number of levels of pressure altitude. The quarterly version averages the troposphere down into two levels, one from 850-300 hPh, and one from 300-100 hPa. Both annual and quarterly RATPAC versions agree well with HadAT2.
Before going further, let me draw some conclusions about tropical amplification from Figs. 1 & 2.
1. Three of the four observational datasets (HadAT2, RATPAC, and UAH MSU) are in surprisingly close agreement. The fourth, the RSS MSU dataset, is a clear outlier. The very close correspondence between HadAT2 and RATPAC at all levels gives increased confidence that the observations are dependable. This is reinforced by the good agreement in Figs. 1(c) and 1(f) between the UAH MSU and the HadAT2 500 hPa level amplifications, both in the tropics and globally.
2. Figure 1(c) clearly shows the theoretically predicted “tropical hot spot”. It is most pronounced at 200 hPa at about 8-10 years. At its peak the 200 hPa level has an amplification of about 2. However, this gradually decays over time to a long-term (fifty year) amplification of about 1.5.
3. The lowest level, 850 hPa, has a short-term amplification of just under 1. It gradually increases over time to an amplification of about 1. It varies very little with the length of observations. RATPAC and HadAT2 are in excellent agreement regarding the amplification at the 850 hPa level.
4. The amplification of the next two levels (700 and 500 hPa) are quite similar. The higher level (500 hPa) has slightly greater amplification than the lower. Again, both datasets (RATPAC and HadAT2) agree very closely. This is supported by the UAH MSU satellite data, which agrees with the 500 hPa level of both the other datasets.
5. The amplification of the 300 and 200 hPa levels are also quite similar. The amplification of the higher level (200 hPa) exceeds that of the next lower level in the early part of the record. However, the 200 hPa amplification decreases over time more than that of the lower level (300 hPa). This accelerated long-term decay in amplification is also seen in the 150 and 100 hPa levels.
6. Between 700 and 200 hPa, amplification rises to a peak at around 8-10 years, and declines after that.
Observations and Models
Because it is the most detailed of the observational records, I will take the HadAT2 as a comparison standard. Fig. 3(a) shows the full length (50 year) HadAT2 record. Figures 3(b) to (e) show the outputs from five climate models. These models were selected at random. They are simply the first five models I found to investigate.
In Fig. 3(a) the decline in the observed amplification at the 200 hPa level seen in the shorter 30 year record in Fig. 1(c) continues unabated to the end of the 50 year record. The 200 hPa amplification crosses over the 300 hPa level and keeps declining. The models in Fig. 3(b-f), however, show something completely different.
Figure 3. Three month to fifty year amplification for HadAT2 and for the output of various computer models.
The model results shown in Figs 3(b) to (e) were quite unexpected. It was not a surprise that the models disagreed with observations. It was a surprise that the model results varied so widely among themselves. The atmospheric amplification at the various pressure levels are very different in each of the models
In the observations, the greatest amplification is at 200 hPa at around eight to ten years. Only one of the models, the GISSE-R, Fig. 3(d), reproduces that slow buildup of amplification. Unfortunately, the model amplification continues to increase from there on to the end of the record, the opposite of the observations.
The observed amplification at all levels except 850 hPa peaks at 8-10 years and then decreases over time. This again is opposite to the models. Amplification in all levels above 850 hPa of all of the models either stay level, or they increase over time.
In the observations, amplification increases steadily with altitude from 850 hPa to 200 hPa. Not a single model showed that result. All of the models examined showed one or more inversions, instead of the expected steady increase in amplification with altitude shown in the observations.
The 850 hPa observations start slightly below 1, and gradually increase to 1. Only one model, BCCR (c), correctly reproduced this lowest level.
Variability of Observations and Models
To investigate the natural variability in the amplification of both observations and models, I looked at thirty-year subsets of the various 50-year datasets. Figure 4 shows the amplification of thirty-year subsets of the datasets and model output. This shows how much variability there is in thirty year records. I show subsets taken at 32 month intervals, with the earliest ones at the back of the stack.
Once again, there are some conclusions that can be drawn from first looking at the observations, which are shown in Fig 4 (a).
1. The relationship between the various layers is maintained in all of the subsets. The lowest level (850 hPa) is always at the bottom. It is invariably below (smaller amplification than) the rest of the levels up to 200 hPa. The 700/500 hPa pair are always very close, with the higher almost always having the greater amplification. The 200 hPa level is above the 300 hPa level for all of the early part of the record. This order, with amplification steadily increasing with altitude up to 200 hPa, holds true for every one of the thirty-year subsets of the observations.
2. The 700 hPa amplification is never less than the 850 hPa amplification. As one goes lower, so does the other. They cross only at the short end of the time scale.
3. At all but the 850 hPa level, amplification peaks at somewhere around 8-10 years, and subsequently generally declines from that peak.
4. The amplification at 200 hPa is much larger than at 300 hPa at short (decade) timescales, but decreases faster than the 300 hPa amplification.
5. There is a surprising amount of variation in these thirty-year overlapping subsets. This implies that the satellite record is still too short to provide more than a snapshot of the variation in amplification.
Figure 4. Evolution of amplification in thirty year subsets of fifty-year datasets. The interval between subsets is 32 months. Fig. 4(a) is observations (HadAT2). The rest are model hindcasts.
The most obvious difference between the models and the observations is that most of the models have much less variability than the observations.
The next apparent difference is that the amplification in the models trend level or upwards with time, while the observed amplifications generally trend downwards.
Next, the pairings of levels are different. The only model which has the same pairings as the observations (700/500 and 300/200 hPa) is the HadCM3 model. And even in that model, both pairs are reversed from the observations. The other models have 700 hPa paired with (and often below) the 850 hPa level.
There is a final oddity in the model results. The short term (say four year) amplification at 200 hPa is very different in the various models. But at thirty years the models converge to a range of around 1.5 to 1.9 or so. This has led to a mistaken idea that the models reveal a “robust” long term amplification.
DISCUSSION
Having examined the changes in amplification over time for both observations and models, let us return to re-examine, one by one, the issues involved as stated at the beginning:
The month-to-month variability of tropical temperatures is larger in the troposphere than at the Earth’s surface.
This is clearly true. There is an obvious tropical “hot spot” of high amplification in the upper troposphere. It peaks at a pressure altitude of 200 hPa at about 8-10 years.
This amplification behavior is similar in a range of observations and climate model simulations, and is consistent with basic theory.
This amplification behavior is in fact similar in a range of observations. However, it is very dissimilar in a range of climate model simulations. While the observations are consistent with basic theory (amplification increasing with altitude up to 200 hPa), the climate models are inconsistent with that basic theory (higher levels often have lower amplitude than lower levels).
On multi-decadal timescales, tropospheric amplification of surface warming is a robust feature of model simulations, but occurs in only one observational dataset [the RSS dataset].
There are no “robust” features of amplification in the model simulations. They have very little in common. They are all very different from each other.
Multi-decadal amplification decreases gradually over the 50-year observational record. Three of the observation datasets (UAH, RATPAC, and HadAT2) all agree with each other in this regard. The RSS dataset is the outlier among the observations, staying level over time. This RSS behavior is similar to several of the models, which also stay level over time. One possible explanation of the RSS difference is that I understand it uses computer climate modeling to inform a portion of its analysis of the underlying MSU data.
Other observations show weak or even negative amplification.
None of the tropical observational datasets above 850 hPa show “negative amplification” (amplification less than 1) at timescales longer than a few years. On the other hand, all of the observational datasets show negative amplification at short timescales, as do all of the models.
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.
The problem is not that the observations fail to capture the long term trends. It is that every model disagrees with every other model, as well as disagreeing with the observations.
It appears that different physical mechanisms do indeed control the amplification at different timescales. The models match the observations in part of this, in that amplification starts out low and then rises to a peak over a period of years. In most of the models examined to date, however, this happens on a much shorter time scale (months to a few years) compared with observations (8-10 years).
However, the models seem to be missing a longer-term mechanism which leads to long-term decrease in amplification. I suspect that the problem is related to the steady racheting up of the model temperature by CO2, which increases the longer term amplification and leads to upward trends. Whatever the cause may be, however, that behaviour is not seen in the observations, which decrease over time.
Conclusions
1. Temporal evolution of amplification appears to be a sensitive metric of the state of the atmosphere. It shows similar variations in the various balloon and satellite datasets with the exception of the RSS MSU satellite temperature dataset.
2. The wide difference between the individual models was unexpected. It was also surprising that none of them show steadily increasing amplification with altitude up to 200 hPa, as basic theory suggests and observations confirm Instead, levels are often flipped, with higher levels (below 200 hPa) having less amplification than lower levels.
3. From this, it appears that we have posed the wrong question regarding the comparison of models and data. The question is not why the observations do not show amplification at long timescales. The real question is why model amplification is different, both from observations and from other models, at almost all timescales.
4. 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. We do not know enough about either the climate or the models to make that claim.
5. Observationally, amplification varies even at “climate length” time scales. The thirty year subsets of the observations showed large changes over time. Climate is ponderous and never at rest. Clearly there are amplification cycles and/or variations at play with long timescales.
6. While at first blush this analysis only applies to temperatures in the tropical troposphere, it would not be surprising to find this same kind of amplification behavior (different amplification at different timescales) in other natural phenomena. The concept of amplification, for example, is used in “adjusting” temperature records based on nearby stations. However, if the relationship (amplification) between the stations varies based on the time span of the observations, this method could likely be improved upon.
Additional Information
The Supplementary Online Material contains an analysis of amplification in the AMSU satellite dataset and the NCEP Reanalysis dataset. It also contains the data, the data sources, notes on the mathematical methods used, and the R function and R program used to do the analyses and to create the graphics used in this paper.
References
Douglass DH, Christy JR, Pearson BD, Singer SF. 2007. A comparison of tropical temperature trends with model predictions. International Journal of Climatology 27: Doi:10.1002/joc.1651.
Santer BD, et al. 2005. Amplification of surface temperature trends and variability in the tropical atmosphere. Science 309: 1551–1556.
Santer BD et al. 2008. Consistency of modelled and observed temperature trends in the tropical troposphere. Int. J. Climatol. 28: 1703–1722
SUPPLEMENTARY ONLINE MATERIAL
SOM Section 1. Further investigations.
AMSU dataset
A separate dataset from a single AMSU (Advanced Microwave Sounding Unit) on the NOAA-15 satellite is maintained at <http://discover.itsc.uah.edu/amsutemps/>. Although the dataset is short, it has the advantage of not having any splices in the record. The amplification of that dataset is shown in Fig. S2.
Figure S-1 Short-term Global Amplification of AMSU satellite data, MSU data, and HadAT2 data . The dataset is only ten years long.
Figure S-1(a). AMSU satellite data. This is a curious dataset. The 400 hPa level is a clear and dubious outlier. The distinctive “duck’s head facing right” shape of the second half of the 900 and 600 hPa levels is similar, while that of the 400 hPa level is quite different. It is very doubtful that one level would be that different from the levels above and below it. This was a strong indication of some unknown error with the 400 hPa data that is affecting the longer term amplification.
Figure S-1(b). Adjusted and unadjusted AMSU satellite data. To attempt to correct this error, I added a simple linear trend to the 400 hPa level. I did not adjust it until the amplification was right. Instead, I adjusted it until its long-term trend ended up proportionally between the long-term trends of the layers above and below. This gave the adjusted version (light green) of the amplification.
Figure S-1(c). Adjusted AMSU satellite data. After the adjustment of the 400 hPa trend, the amplifications fit together well. Curiously, despite being adjusted by a linear trend, the shape of the latter half of the 400 hPa level has changed. Now it has the “duck’s head facing right” shape of the 600 and 900 hPa levels. This unexpected change supports the idea that there is indeed an error in the trend of the 400 hPa data.
Figure S-1(d). Interpolated AMSU satellite data. Unfortunately, the referenced levels in the two datasets are at different pressure altitudes than HadAT2. To compare the AMSU to HadAT2, we need to interpolate. Fortunately, the HadAT2 dataset range (850 to 200) fits neatly inside the AMSU range (900 to 150). This, along with the similar and close nature of the signals at various levels, allows for linear interpolation to give the equivalent AMSU amplification at the HadAT2 levels. The interpolated version is shown.
Figure S-1(e). HadAT2 and RSS/UAH satellite data. The global observational data over this short period (ten years) is scattered. Also, the HadAT2 data has a more jagged and variable shape. We may be seeing the effects of the paucity of the observations. In the short-term (this is ten years and less) the RSS and UAH amplification records are quite similar. As before, both are close to the 500 hPa HadAT2 amplification.
Figure S-1(f). AMSU and HadAT2 data. Close, but not a good match. The 200, 700, and 850 hPa levels match, but 300 and 500 hPa are quite different. Overall, the satellite data seems more reliable. The AMSU data shows a gradual change in amplification with altitude. The HadAT2 data is bunched and sometimes inverted.
My conclusions from the AMSU dataset are:
1. It contains an error, which appears to be a linear trend error, in the 400 hPa level.
2. Other than that, it is the most internally coherent of the observational datasets.
3. It points up the weakness of using short (one decade) subsets of the HadAT2 dataset.
NCEP REANALYSIS
One of the attempts to provide a spatially and temporally complete global dataset despite having limited observational data is the NCEP reanalysis dataset. Figure S-2 compares the temporal evolution of amplification of HadAT2 and NCEP Reanalysis output
Figure S-2 Evolution of amplification of HadAt2 and NCAR. Left column is amplification from 3 months to 50 years, right column is amplification of 30 year subsets of the 50 year datasets. The interval between the individual realizations in the right column is 32 months.
The NCEP reanalysis data in Fig. S-2 (b) shows a fascinating pattern. The three middle levels (700,500, and 300 hPa) are close to the HadAT2 observations. The 300 hPa levels agree extremely well. And while the 700 and 500 hPa levels are flipped in NCEP, still they are in the right location and are very close to the observed values.
But at the same time, the amplification of the lowest and highest levels are way off the rails. The 850 hPa amplification starts at 1, and just keeps rising. And the 200 hPa amplification starts out reasonably, but then takes a big jump with a peak around thirty years. That seems doubtful.
The observation that there are problems at the lowest and highest levels is reinforced by the analysis of the variation of thirty year subsets in the right column of Fig. S-2. These show the 200 hPa amplification varying wildly over all of the different 30 year datasets. In one of the thirty year subsets the 200 hPa amplification dips down to almost touch the highest 850 hPa line. There is clearly something wrong with the NCEP output at the 200 hPa level.
In addition, in the full NCEP record shown in Fig. S2(b) and all of the 30 year subsets shown in Fig. S2(d), the lowest level (850 hPa) increases steadily over time. After about 20 years it has more amplification than either of the 700 and 500 hPa levels. This behavior is not seen in either the observations or any of the models.
Conclusions from the NCEP reanalysis
1. The 700, 500, and 300 hPa level of the NCEP reanalysis are accurate. The 850 and 200 hPa levels suffer from large problems of unknown origin.
2. Use of the NCEP reanalysis in other work seems inadvisable until the 850 and 200 hPa amplification problems are resolved.
SOM Section 2. Data Sources
KNMI was the source for much of the data. It has a wide range of monthly data and model results that you can subset in various ways. Start at http://climexpknmi.nl/selectfield_co2.cgi?someone@somewhere . It contains both Hadley and UAH data, as well as a few model atmospheric results. My thanks to Geert for his excellent site.
Surface data for all observational datasets is from the CruTEM dataset at http://climexp.knmi.nl/data/icrutem3_hadsst2_0-360E_-20-20N_n.dat
UAH data is at http://www.nsstc.uah.edu/data/msu/t2lt/uahncdc.lt
RSS data is at http://www.remss.com/data/msu/monthly_time_series/
HadAT2 balloon data is at http://hadobs.metoffice.com/hadat/hadat2/hadat2_monthly_tropical.txt
CGCM3.1 model atmospheric data is at http://sparc.seos.uvic.ca/data/cgcm3/cgcm3.shtml in the form of a large (250Mb) NC file.
Data for all other models is from the “ta” and “tas” datasets from the WCRP CMIP3 multi-model database at <https://esg.llnl.gov:8443/home/publicHomePage.do>
In particular, the datafiles used were:
GISSE-R: ta_A1.GISS1.20C3M.run1.nc, and tas_A1.GISS1.20C3M.run1.nc
HadCME: ta_A1_1950_Jan_to_1999_Dec.HadCM3.20c3m.run1.nc, and tas_A1.HadCM3.20c3m.run1.nc
BCCR: ta_A1_2.bccr_bcm2.0.nc, and tas_A1_2.bccr_bcm2.0.nc
INCM3: ta_A1.inmcm3.nc, and tas_A1.inmcm3.nc
As all of these are very large (1/4 to 1/2 a gigabyte) files, I have not included them in the online data. Instead, I have extracted the data of interest and saved this much smaller file with the rest of the online data.
SOM Section 3. Notes on the Function and Code.
The main function that does the calculations and created the graphics is called “amp”.
The input variables to the function, along with their default values are as follows:
datablock=NA : the input data for the function. The function requires the data to be in matrix form. By default the date is in the first column, the surface data in the second column, and the atmospheric data in any remaining columns. If the data is arranged in this way, no other variables are required The function can be called as amp(somedata), as all other variables have defaults.
sourcecols=2 : if the surface data is in some column other than #2, specify the column here
datacols=c(3:ncol(datablock)) : this is the default position for the atmospheric data, from column three onwards.
startrow=1 : if you wish to use some start row other than 1, specify it here.
endrow=nrow(datablock) : if you wish to use some end row other than the last datablock row, specify it here.
newplot=TRUE : boolean, “TRUE” indicates that the data will be plotted on a new blank chart
colors=NA : by default, the function gives a rainbow of colors. Specify other colors here as necessary.
plotb=-2 : the value at the bottom of the plot
plott=2 : the value at the top of the plot
periods_per_year=12 : twelve for monthly data, four for quarterly data, one for annual data
plottitle=”Temporal Evolution of Amplification” : the value of the plot title
plotsub=”Various Data” : the value of the plot subtitle
plotxlab=”Time Interval (months)” : label for the x values
plotylab=”Amplification” : label for the y values
linewidth=1 : width of the plot lines
linetype=”solid” : type of plot lines
drawlegend=TRUE : boolean, whether to draw a legend for the plot
SOM Section 4. Notes on the Method.
An example will serve to demonstrate the method used in the “app” function. The function calculates the amplification column by column. Suppose we want to calculation the amplification for the following dataset, where “x” is surface temperature, “y” is say T200, and each row is one month:
x y
1 4
4 7
3 9
Taking the “x” value, I create the following 3X3 square matrix, with each succeeding column offset vertically by one row. This probably has some kind of special matrix name I don’t know, and an easy way to calculate it. I do it by brute force in the function:
1 4 3
4 3 NA
3 NA NA
I do the same for the “y” value:
4 7 9
7 9 NA
9 NA NA
I also create same kind of 3X3 matrix for x times y, and for x squared.
Then I take the cumulative sums of the columns of the four matrices. These are used in the standard least squares trend formula to give a fifth square matrix:
slope of regression line = (N*sum(xy) – sum(x)*sum(y)) / (N*sum(x^2) – sum(x)^2)
I then average the rows to give me the average amplification at each timescale
This method exhaustively samples to find all contiguous sub-samples of each given length. This means that there will be extensive overlap (samples will not be independent). However, despite the lack of independence, using all available samples improves the accuracy of the method. This can be appreciated by considering a fifty year dataset. There are a number of thirty year contiguous subsets of a fifty year dataset, but if you restrict your analysis to non-overlapping subsets, you only can pick one of them … which way will give the best estimate of the true 30-year amplification?
SOM Section 5. Of Averages and Medians.
The distribution of the short-term amplifications is far from normal. In fact, it is not particularly normal at any scale. This is because the amplification is calculated as the slope of a line, and any slope is the result of a division. When the divisor approaches zero, very large numbers can result. This makes averages (means) inaccurate, particularly at the shorter time scales.
One alternative is the median. The problem with the median is that it is not a continuous function. This limits its accuracy, particularly in small samples. It also makes for a very ugly stair-step kind of graph.
Frustrated by this, I devised a continuous Gaussian mean function which outperforms the mean for some varieties of datasets, and outperforms the median on other datasets. It is usually in between the mean and median in value. In all datasets I tested, it equals or outperforms either the mean or the median.
To create this Gaussian mean I reasoned as follows. Suppose I have three numbers picked at random from some unknown stable distribution, let’s say they are 1,2, and 17. What is my best guess as to the actual underlying true center of the distribution?
Since we don’t know the true distribution, the best guess as to its shape has to be a normal distribution. With such a distribution, if we know the standard deviation, we can iteratively calculate the mean.
To do so, we start by calculating the standard deviation, and picking a number for the estimated mean, say 5. If that is the mean, the numbers (1,2,17) when measured in standard deviation units is (-0.6, -0.5, 1.2). Each of those standard deviations has an associated probability. I figured that the sum of these individual probabilities is proportional to the probability that the mean actually is 5.
I then iteratively adjust the estimated mean to maximize the total probability (the sum of the three individual probabilities. It turns out that the gaussian mean of (1, 2, 17) calculated by my method is 3.8. This compares with an average for the three numbers of 6.7, and a median of 2.
In general there is very little difference between my gaussian mean, the arithmetic mean, and the median. However, it is much better behaved than the mean in non-normal datasets. And unlike the median, it is a continuous function, which gives greater accuracy.
All three options are included in the amp() function, with two of them remarked out, so you can see the effects of each one. The only noticeable difference is that the mean is not very accurate at short time scales, and the gaussian mean and the mean are not discontinuous like the median.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
Willis,
My previous comment was overly simplified. In order for the troposphere to add net heat to the Earth (i.e., have positive amplification), the very short time values of T to the 4th power of the troposphere minus T to the 4th power of Earth have to be examined, so large day to night swings are not symmetrical in their effect. The integral value of this difference then can then be converted to a short time value of average amplification, which then has to be integrated over the desired longer time interval. I don’t think there is much data with the required temporal resolution to do this properly, but even a small amount of data, along with estimated swings for other data, may allow approximate values to be used. Only then do you have the effective amplification. This can then be averaged over longer periods, but not the way you did it. If each point in the short average is less than one, then all longer averages will still be less than one.
JohnV (08:37:51)
At the KNMI web site listed in the paper, you can get the global surface temperature for any combination of latitude and longitude. I used the data at:
http://climexp.knmi.nl/data/icrutem3_hadsst2_0-360E_-20-20N_n.dat
My thanks, I should have included that in the data section. For the models, I used each model’s surface air temperature datasets. See the data section above.
“Robust” to me means that the different models would give the same results. While amplification occurs in all models, the results are very different. The amplification shown by the models have little in common, as Fig. 4 shows.
Glug (09:19:14), thanks for your pertinent questions.
See my discussion of error bars above.
Because I live in the Solomon Islands, the datasets are huge, I collect what I can from hotel broadband when I travel, I have been stymied by the gigantic size of the datasets exceeding my computer’s memory, I don’t have thirty people helping me, and I work full time at my day job …
Having said that, all of those are excellent questions. I can only report that I was surprised by the differences in how the models represent the atmosphere. I would be exceedingly surprised if I had picked five that were totally unrepresentative of the full group.
However, had we but world enough, and time …
Excellent points. I have used individual model runs, as this is the output of the model. To see if the results were different in different runs, I compared two GISSE runs. The temporal evolution of amplification was nearly identical in the two runs. While this was only one test, the results were so close that it was clear that I was measuring something not affected by whether it was run one or run two, something more basic to and characteristic of the model itself.
Analyzing amplification by looking at how it evolves over time is a new method, that as far as I know, I invented, although it is likely it has been done before … so no references there. I referenced the three papers that I was discussing. I would have thought that those three papers have provided all the background needed. What other ideas or statements would you like referenced?
Thank you, I will attend to that. An excellent point.
Robert (09:22:02) :
Agree, apologies, my poor attempt at humor …
Sandy (09:24:10) :
I would have thought so too, but a look at Fig. 4 shows great variety. Since those are thirty year subsamples of a fifty year dataset, it seems that the signal is not swamped for large subsamples.
Peter Taylor (10:03:22) :
My guess is that it is a signal of the temperature thermostat I describe The Thermostat Hypothesis. Since the Earth’s temperature is always trending towards some central value, in the long term the amplification value will trend down. The models, on the other hand, contain no such natural thermostat. They have instead a CO2 ratchet that is guaranteed to raise their temperature over time. This built-in underlying trend leads to a long-term upward trend in amplification.
If I understand your question, my analysis is separate from the question of cycles. Certainly they exist.
timetochooseagain (10:07:08) :
See above.
The amplification can also be measured against the lowest 850 hPa level. This is unaffected by any surface temperature problems. It shows the same decline in amplification in the upper levels over time.
Brian Buerke (10:11:24) :
I discuss uncertainties above, as shown in Fig. 4.
As the title states, we are using different metrics. He is simply comparing the long-term trends. This does not measure whether one is an “amplified” version of the other. I use the slope of the regression line.
None of those are what I would call datasets. They are complex computer analyses, using different algorithms. IUK is bizarre, with the 500, 300, and 200 layers all together at about 1.5, the 700 hPa layer with an amplification of 1, and the 850 hPa layer with an amplification of 0.75. Unlike anything, models or observations. I looked at it a while ago, I could dig it out. I haven’t found RAOBCORE gridded data, just station data. You are right, I probably should have showed IUK, but the paper was so long anyhow.
Onwards …
w
A key message of your analysis (which you note) is that rising CO2 forcing in the models governs the shape of these curves, but the shape does NOT match reality. I agree. Your elegant method of analysis is exactly what is needed to detect this.
A practical matter is that you need lots more citations to look “scientifical”
Excellent paper Willis and your gamble on going public here looks to be paying off, with some good points raised on style and content.
The poor fit between the models and the observations surprised me. I think the modellers need to do much more work on understanding the mechanisms of the amplification process, which is clearly non-linear.
Perhaps a next step could be to try to understand the energy components which drive the system so that a better understanding of the ‘thermostat’ and its operation can be successfully modelled.
Please keep up the good work.
Willis,
A brief note regarding the R source might be in order. It was pretty obvious but I got it wrong first so I’m putting the steps I have done here:
1) > source(‘amp_function_090701.R’);
then
2)> source(‘Amplification_090701.R’);
Also after drawing figure 2(c) but without actually labing it as (c) just “HadAT2 Quarterly Balloon Data,… ” I get the following error:
Error in legend(plotlength * legendh, plotb + (plott – plotb) * legendv, :
‘legend’ is of length 0
I can probably debug this (though I’m quite an R novice so possibly not) but you probably want to fix it for others.
I don’t have much hope for this model of science when the second comment identifies the author as Anthony Watts, instead of the true author, who is using a pen anme based on Godel Escher Bach.
REPLY: So quick to jump to conclusions, so unwilling to ask questions. The issue with me being identified as the author was about a 5 minute window where the author name disappeared from the top of the post when I first published it. Moving the position of the top ITCZ graphic right before publishing resulted in an accidental deletion of the name. It was fixed immediately and I pointed out that I was not the author. Note that it has not recurred. But if you want to use that argument to support your implied view that people that read these things are “not very smart”, be my guest. It is typical fodder of little merit.
As for your claim that Willis is using a pen name derived from ” Godel Escher Bach ” all I can say is “puhleeeeze”. Having corresponded with the man, I say, prove your point. Here’s mine:
Eschenbach has published: See Energy & Environment (E&E). In 2004 (Vol. 15, No. 3)
E&E published a paper on sea level rise at Tuvalu by Willis Eschenbach. He’s described as an amateur scientist and “Construction Manager” for the Taunovo Bay Resort in Fiji. Will your next point be that they didn’t check his name?
– Anthony
Citations and references in the literature review:
In the literature review section, referencing means that in the body of the paper, when talking about other authors’ works, the reference is immediately tied to the quoted phrase, or paraphrase not in quotes taken from those papers. In-between these direct references you simply supply your own comments in the literature review section. Based on this definition, you have not provided proper citations and references, which leads to confusion on the part of the reader.
Use of quotes:
There is a proper style of quotation marks and indentations related to short quotes and paragraph long quotes. I would also never include an entire abstract of someone else’s work. These formats and styles makes it easy for the reader to understand when the paper is citing someone else and when the paper is talking about your work. Once your review is finished, from then on the paper should only concentrate on your research. It is exceedingly rare to find citations of papers other than your own after the literature review is done.
Red-Marking a poorly written work sample:
Different styles are used for all kinds of written work. For good reason. Without proper style and format, even a fiction work would be hard to read. Again, a style manual is most helpful and makes the scientific offering believable. If you fail at style and format, your work will not be taken seriously, as it will “read” very amateurish. It won’t matter if it is right or wrong. You could be the most intelligent person on Earth but won’t be able to flip hamburgers if you show up for your interview dressed in rags.
On a scale of 0 to 6 with 5 being a passing mark as this is an adult paper:
Ideas and Content: 5 (sufficient to produce several papers)
Organization: 1 (does not show basic organization)
Sentence Fluency: 3 (some sentences are hard to follow and flow is disrupted)
Conventions: 1 (does not show basic use of conventions)
Word choice: 4 (could improve use of standard technical words used in research writing)
Voice: 3 (needs improvement when examining other papers and should use passive voice when referring to his own work)
Willis, the bottom line is that if this paper travels beyond this blog, it will be dismissed out of hand. And that is my final word.
Joel Shore (19:05:33) :
Huh? In what way does this analysis “confirm” the reality of amplification on a multidecadal timescale? It seems to me that it confirms it on all but the multidecadal timescales.
Joel Shore (18:20:50) :
In comments above I explained that RAOBCORE has a KNOWN warm bias. To use it would be inappropriate given the KNOWN problems with it.
You also make much hay of the UAH corrections issue. This has been blathered on about a lot. I don’t feel like getting into it, however at the very least I hope you are aware that research is tending to show UAH as superior (again, I have posts above where you can find this information). You also put some innuendo in about the “stratosphere” “problem” which UAH long ago developed a satisfactory correction for…the publications you presumably wish to see referenced are blatant crap.
Pamela Gray (09:43:26) :
“Willis, the bottom line is that if this paper travels beyond this blog, it will be dismissed out of hand. And that is my final word”
She is 100% right Willis. Like I said before, if you are truly serious about getting this published then you need to follow the established formats in science writing. You will be dismissed outright on the organization of your paper without a word or moment of consideration on the substance of your paper if you chose to stick with your current format.
Getting published is a tough endeavor, you may as well do everything you can to stack the deck in your favor rather then making things more difficult on yourself when you “chose this way of publishing, because [you] have greater latitude” is really shooting yourself in the foot.
Bravo for this attempt at open science. I have not read much of the paper yet,
but will. Here are two comments about the “background” section.
“Vice versa” does not say what you want to say. I think you need a sentence with
“less than” in it. This comment is about presentation, not science.
“Amplification” is a bad term for the subject. It implies a causal connection,
and that has not been established. Perhaps the term is too well established
to be changed. If so, the bias in the terminology should be explained.
Thanks for your current effort, and your previous useful posts.
Pamela Gray (09:43:26), my apologies for the lack of clarity in my response to you. I am not disagreeing with you, you are correct. The paper is not in the scientific style for the reasons you list. After a number of excellent points, which I have clipped and carefully saved in addition to your earlier postings , you say:
Your most clear set of instructions on how I should have written the paper is excellent, and you are quite correct. As much as I might wish it otherwise, we live in a world where, as you point out, scientific style can be more important than scientific substance, and my ideas may well be “dismissed out of hand” regardless of their correctness because of the way they are presented.
I hope that is not your final word, as your previous words have been very useful. Should I choose to re-write this for submission to a journal, I have saved all of your words because they will be extremely helpful to me, either for this or future papers.
My thanks to you for taking the time to fight my ignorance, more later, gotta run, 6:21 am, I’m out the door.
w.
Willis. Bravo, Bravo. Yes, open science is the way to go. The world is in love with the idea of speed and democracy so let’s not worry too much about stuffy formalitities. The ideas are the thing. Open debate. Don’t hide behind the screen. Coffee culture. Guys like Leif Svalgaard are showing the way.
Some very clever people can’t spell. Are they to be disqualified? We will be the poorer for it.
Now to the ideas:
“Ampification”. Are we accepting here that the greenhouse effect is real? What is the mechanism that is supposed to be producing the temperature gain. Is the rate of convection slowing? Is the atmosphere getting more viscous?
At 850hPa there might be seen to be ‘amplification’, but the term is inappropropriate. Release of latent heat drives temperature at this level and so if evaporation from the ocean increases, more energy is released at 850hPa so the temperature rises faster there than at the surface……but only if the surface has the means to limit energy gain that would drive up its temperature. So, the tropical oceans are a case in point because they appear to be almost ‘energy saturated’ . More energy into the system provokes very little temperature gain at the surface but a strong increase in temperature at 850hPa over time. That response may be evident all the way up to 500hPa. Last time I looked the rate of increase at 850hPa was about double that at the surface. Logically, if we wish to monitor the rate of energy gain by the Earth system we get a better idea of what’s happening by observing the change at 850hPa than at the surface.
Now let’s turn our attention to the 200hpa level. Labitzke and Van Loon in their studies of the response of the atmosphere to the sun found plenty of evidence for a response in the stratosphere. Yes. But the important thing to note is that response could be observed into the troposphere and 200hpa was the cut off point. The parameter responsible for this response is ozone that is generated in the upper stratosphere but is well conserved in the lower stratosphere due to low temperature and the dryness of the air. Just remember that in the industrial manufacture of ozone the air is dried by chilling it to minus 80°C. Conditions for conservation gradually deteriorate below the tropopause. But in certain parts of the globe the tropopause ‘folds’. High pressure cells of the subtropics also bring ozone into the troposphere.
So, the presence of ozone in the lower stratosphere/upper troposphere means that we are dealing with a medium that is heated from two sources. Long wave radiation from the Earth is bang on in terms of wave length for the ozone reactivity reaction and ozone also responds to UVB from the sun.
Let’s throw in another variable. If the tropical ocean warms convection increases and outgoing long wave radiation is observed to diminish. So, wherever ozone is present the temperature of the surrounding air will be observed to fall.
So, what is this ‘amplification’ thing?
I have just put together a post for Climate change1 where I observe a strong response in 200hPa temperature to the seasonal increase in total column ozone in mid winter between 20 and 40° S latitude off the coast of Chile. Looks like this drives the Southern Oscillation.
Every time we have a sudden stratospheric warming at the poles we can observe a jump in total column ozone across the globe. That too affects 200hpa temperature. Just plot the monthly data for 200hpa temperature against sea surface temperature and total column ozone and you will see what I mean. Do this for places chosen to represent low ozone and high ozone atmospheres and see what you get.
So, the variables that determine the temperature of the upper troposphere are numerous. I would expect to see the relationship between 200hpa temperature and surface temperature to change by the month, the year, the place and over time. What it tells us? I dont think we are clever enough to work out what it means. Do the modellers capture all this? Don’t think so.
Just by the way. I love your urbanity and humility and the way you acknowledge a good suggestion. You are a gentleman as well as a scholar.
Willis,
Thanks for responding to my points. I acknowledge that it is difficult, from a technical standpoint, to deal with vast volumes of model output. If you’re limited that by that constraint I’d recommend trying to collaborate with someone who can handle that volume of data as, IMO, it would improve your analysis.
Wrt references, I thought that by including 34 authors you had more than 3 refs. My mistake. I would recommend placing citations in text though.
Willis
Here is a link to a story I was reading that brought your paper to mind. You will note that the pilots reported rain and warm air in the cloud tops at fl390! (39000 feet) http://www.aviationweek.com/aw/blogs/commercial_aviation/ThingsWithWings/index.jsp?plckController=Blog&plckScript=blogScript&plckElementId=blogDest&plckBlogPage=BlogViewPost&plckPostId=Blog%3A7a78f54e-b3dd-4fa6-ae6e-dff2ffd7bdbbPost%3A6edfeb4b-d969-49f5-bdb3-2558c92f6ebb
The temps at that alt are usually quite crisp. An example of clouds convecting heat. But at such high altitudes?
Interesting. Sorry if O/T.
Anthony, it was a joke. Sorry.
Data Smoothing and Spurious Correlation.
Just kidding Willis.
Interesting work. Still reading it.
Regards, Allan
Having been through the peer review process myself, I agree with Pamela Gray; in its present format, this “paper” will never become a paper. Even in a blog like this, you are not likely to get much feedback from scientists, because they don’t bother to read it when it’s not properly referenced and when the methodology is not properly explained. I’m not saying this to discourage you, but to help you improve in this and later work:)
On the actuall content:
-Your use of the term temporal evolution is not actually a temporal evolution. A temporal evolution would be a figure showing how the amplification changes with actuall time, not by the size of your averaging window (I could be mistaken here, as it’s not really clear how you calculate the amplification). The temporal evolution using it’s proper definition would be more interesting in my view than what are shown herein.
-It’s common to use a Gaussian fit to estimate the maximum (that will give you your mean)when one only have a few points. However, there is an inherent bias in that method, called “Peak locking”. This will bias the maximum to lock onto an integer value, and will be evident if you plot a histogram of all your Gaussian values at mean. There is an extensive litterature on this in the field of Particle Image Velocimetry (do a search on PIV and Peak locking).
-State your conclusions in the abstract! Most readers will never read further than that.
-In your SMO 4, the description of the method for estimating the amplification is confusing. If you do not know what a method is called, find it out. Naming methods properly is important for replication, an important constituent of the scientific method. From what I can understand from SOM 4, I could probably do this in five minutes using Matlab, but that’s provided that I actually understand what you are doing.
-Further on SMO 4, state what you are doing in plain language. Are you calculating the linear regression for a running window, then averaging over these windows? And then increasing the window gradually to get what you call the temporal evolution?
-When you are referring to equations in other work, present the equation. This will help the reader.
Finally, it’s not a valid argument to blame the bandwith of your internett connection for not doing the total work. Either do it properly, or don’t do it. You put yourself in a position for being accused of cherry picking if you can’t give a scientific reason for your selection of data.
It’s a good start, but it needs major re-working if it’s going to be considered in any serious journal.
Bill Illis
Those significant time periods were very interesting. Worth its own writeup and thread IMHO.
timetochooseagain says:
The balloon data in Figs. 1 and 2 for the tropics show amplification factors of greater than 1 out to the longest times studied for the 200 hPa and 300 hPa levels. That in a nutshell is the tropical tropospheric amplification at multidecadal timescales.
So, one claim in a 2009 paper by Christy means the data is inappropriate? And, if that is the case, 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? (See http://www.sciencemag.org/cgi/content/full/sci%3B309/5740/1556)
I’m not even saying that the RAOBCORE re-analysisdata should be favored over the raw data but just that we should see what the sensitivity of Willis’s results are to the data set used…and particularly how it is adjusted to correct for any possible trend artifacts at the multidecadal timescales.
I am not making hay over it. And, what I am talking about is a correction that UAH acknowledged needed to be made..and that they now have made… due to a significant error in their analysis. The issue has nothing to do with the issue that you raise of which data set (UAH or RSS) is now superior AFTER UAH made this correction.
I am just explaining to Willis why a statement made in the 2005 Santer paper seems to contradict what he found: The Santer paper was written before the UAH data set was corrected for this major error and hence the data set they used significantly differs from the data set that Willis used and, in particular, the data set prior to the correct actually shows a slightly negative temperature trend in T2LT in the tropics!
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.” I am merely pointing out that it would be most appropriate to reference the previous literature where this was already discussed.
Honestly, I have no idea why you wrote your post except to be argumentative.
By the way, I realize that what you may be confusing the stratospheric issue with is the issue raised by Fu et al. that even the UAH ***LOWER*** tropospheric temperature record may be contaminated somewhat by the cooling in the stratosphere. I don’t know what the current belief is on this claim of Fu et al. but that is a different issue than whether T2 and TMT are so contaminated, about which I think there is little or no controversy.
Joel Shore (14:54:23) : “So, one claim in a 2009 paper by Christy means the data is inappropriate?” YES.
“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…
WRT radiosonde biases, from:
http://www.pas.rochester.edu/~douglass/papers/Published%20JOC1651.pdf
“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.
Mr. Eschenbach-
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.
I wouldn’t call ±0.10 °C/decade “very accurate”, but whatever. Let’s assume Christy and co. are right. Then, Willis could try imposing a + or -0.10 °C/decade change to the trend of the data and see how that affects things. That’s a pretty significant change.
Joel, you propose a very interesting experiment regarding the trend. I’m out of here tomorrow on a week long holiday, but I will definitely run the numbers when I return.
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.