Alternate Title: Climate Science Community Continues to Lose Sight of Reality
SkepticalScience is promoting the Holland and Bruyère (2013) paper Recent Intense Hurricane Response to Global Climate Change as proof positive that hypothetical human-induced global warming has caused more intense hurricanes. See Dana Nuccitelli’s post New Research Shows Humans Causing More Intense Hurricanes. My Figure 1 is Figure 1 from Holland and Bruyère (2013).
Figure 1
The abstract of Holland and Bruyère (2013) begins:
An Anthropogenic Climate Change Index (ACCI) is developed and used to investigate the potential global warming contribution to current tropical cyclone activity. The ACCI is defined as the difference between the means of ensembles of climate simulations with and without anthropogenic gases and aerosols. This index indicates that the bulk of the current anthropogenic warming has occurred in the past four decades, which enables improved confidence in assessing hurricane changes as it removes many of the data issues from previous eras.
That’s right; referring to Figure 1, Holland and Bruyère (2013) created an index by subtracting the multi-model mean of climate models forced by natural factors (variations in solar activity and volcanic aerosols) from the mean of the simulations that are also forced with anthropogenic factors like manmade greenhouse gases—as if the two types of model simulations and their difference represent reality. They then used that model-based index, with little to no basis in the real world, for comparisons to hurricane activity at various hurricane strengths.
Hurricane activity is influenced by tropical sea surface temperatures. Yet, we know climate models cannot simulate sea surface temperatures over the past 31 years, which is included in the 1975 to 2010 period studied by Holland and Bruyère (2013). Refer to the post here for a model-data comparison of satellite-era sea surface temperature anomalies. And we’ve also discussed for 4 years how ocean heat content data and satellite-era sea surface temperature data indicate the oceans warmed naturally. Refer to the illustrated essay “The Manmade Global Warming Challenge” [42MB]. The models are obviously flawed.
Hurricane activity is also influenced by the El Niño-Southern Oscillation (ENSO). There are fewer Atlantic hurricanes during El Niño years due to the increase in wind shear there. On the other hand, there’s an increase in the intensity of eastern tropical Pacific cyclones during El Niño years. See Table 1, which is from the NOAA Weather Impacts of ENSO webpage.
Table 1
Does Holland and Bruyère (2013) consider ENSO? No. The words El Niño and La Niña do not appear in the paper, and ENSO appears only once, when they’re discussing the reason for the use of 5-year smoothing.
All variance numbers use the 5-years smoothed annual time series to remove ENSO type variability.
Can climate models simulate ENSO? The answer is also no. Refer to the post Guilyardi et al (2009) “Understanding El Niño in Ocean-Atmosphere General Circulation Models: progress and challenges”.
Guilyardi et al (2009) includes:
Because ENSO is the dominant mode of climate variability at interannual time scales, the lack of consistency in the model predictions of the response of ENSO to global warming currently limits our confidence in using these predictions to address adaptive societal concerns, such as regional impacts or extremes (Joseph and Nigam 2006; Power et al. 2006).
The multidecadal variability of the sea surface temperatures in the North Atlantic is called the Atlantic Multidecadal Oscillation or AMO. There are numerous papers that discuss the influence of the Atlantic Multidecadal Oscillation on hurricane activity. In fact, the NOAA Frequently Asked Questions About the Atlantic Multidecadal Oscillation (AMO) includes the question Does the AMO influence the intensity or the frequency of hurricanes (which)? Their answer reads:
The frequency of weak-category storms – tropical storms and weak hurricanes – is not much affected by the AMO. However, the number of weak storms that mature into major hurricanes is noticeably increased. Thus, the intensity is affected, but, clearly, the frequency of major hurricanes is also affected. In that sense, it is difficult to discriminate between frequency and intensity and the distinction becomes somewhat meaningless.
The AMO began its multidecadal rise in temperature in the mid-1970s. See Figure 2. By focusing their analysis on the period of 1975 to 2010, Holland and Bruyère (2013) appear to be, in part, attempting to blame manmade greenhouse gases for an increase in activity that’s already been attributed to the natural variability of the AMO.
Figure 2
Off topic note: Referring to Figure 1 from Holland and Bruyère (2013), notice how the surface temperature data ends in 1999 in cell b, while the models continue for a number of years beyond then, probably to the 2005 end year of the historic CMIP5 simulations. Apparently, some climate scientists haven’t figured out what assumption a reader is forced to make when he or she sees disparities in the end dates of model-data comparisons—that the models would show very poorly if Holland and Bruyère (2013) had extended the data to the end year of the historic simulations, 2005, or to the end year of their study, which was 2010. Note also that the data begins after the start year of the models, too. In other words, most readers wonder what the authors are hiding and assume the worst.
CLOSING
Holland and Bruyère (2013) appears to be a flawed attempt to counter the findings of the recent (2012) IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX). See the Summary for Policymakers here. The IPCC writes:
There is low confidence in any observed long-term (i.e., 40 years or more) increases in tropical cyclone activity (i.e., intensity, frequency, duration), after accounting for past changes in observing capabilities.
Holland and Bruyère (2013) is yet another peer-reviewed study that relies on climate models as if the models represent reality, when climate models clearly do not. Eventually, the climate science community will have to come to terms with this—possibly not in my lifetime at the rate they’re going. And the portrayers of gloom and doom at SkepticalScience like Dana Nuccitelli somehow find papers like Holland and Bruyère (2013) to be credible. Nothing surprising about that.
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Stephen Richards April 30, 2013 at 5:22 am
Stephen, it would really be great if you could differentiate your text from the quoted text. Even a line of hyphens after the quoted text would help. Ideally, though, learning how to use blockquote would be a plus to your commenting.
Bob Tisdale says:
April 30, 2013 at 7:41 am
There has never been a global temperature “standstill in the eighties and nineties” in “GISSTEMP, HadCRUT and NCDC” data. And there isn’t one now.
========
there is a widespread discontinuity around 1990 that co-incides with the collapse of the soviet union and the abandoning of much of the temperature reporting from siberia. this may be the source of the confusion.
Bob Tisdale says:
April 30, 2013 at 7:41 am
Bob – you are correct that “global temperature” index is never static. However, the whole concept of a single “global temperature” is contrived and not very thermodynamically useful. Moreover, I would love to see the same anomaly plots converted to absolute temperatures – has anyone done this?? Since “global warming” is essentially dominated by radiation heat transfer, absolute temperature is the important parameter – not a temperature anomaly. I seem to recall a plot showing model comparisons in absolute temperatures, and they were all over the place.
it does seem a remarkable coincidence that the period in question, the warming between 1980-2000 that climate scientists “cannot explain” except by CO2, is also the time at which there was a fantastic decline in the number of temperature reporting stations around the world.
doesn’t occam’s razor tells us that the most likely explanation has nothing to do with CO2? rather, what we are looking at is more likely an accounting error. most likely these temperatures do not reflect “record profits”. rather, what we are seeing is a change in the accounting methods, that has not been noted on the financial statements. in business, leaving this off the financial statements, this is called fraud. In climate science it is called “creative accounting”.
Frank K. says:
April 30, 2013 at 8:03 am
I seem to recall a plot showing model comparisons in absolute temperatures, and they were all over the place.
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In terms of absolute temperature, the models are indistinguishable from noise. the total observed warming over the past century is miniscule in comparison to the daily, annual and regional fluctuation that occur naturally.
In effect climate science is the science of reading tea leaves and trying to attach meaning to events that for all intents and purposes are random at the scale of human lifetimes.
In the words of the immortal Willis
“It’s models all the way down.”
cn
Frank K.: Sea surface temperature data (HADISST, ERSST.v3b and Reynolds OI.v2) are all produced in absolute form. The only land surface air temperature dataset that I’m aware of that presents absolute data is the CPC GHCN/CAMS t2m analysis. It starts in 1948. All of the datasets are available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere
Regards
ferd berple says: “this may be the source of the confusion.”
I didn’t misinterpret what Arno wrote.
Models smodels. Hmmm.. Smodle. Sodmel. Soldme. Sold ’em on global warming. Works for me.
They also ended Figure 1 at about 2000 so they didn’t have to show actual temperatures running flat since.
How can anyone even pretend to be doing science when they “hide the pause”? How does this get past peer review? *Facepalm*
Changing the data set used for papers invalidates those papers. Anybody got a list of them?
rgbatduke says:
April 30, 2013 at 5:31 am
If I were reviewing a paper that published this figure in particular, I’d have to ask how an a priori model manages to produce coherent dips at 1883, 1903, 1963 along with ENSEMBLE predictions in between with THE SAME SCALE OF NOISE as the actual temperature series.
1883 = Krakatoa
1902 = Santa Maria
1963 = Agung
1982 = El Chichon
1991 = Pinatubo
I presume these are the wiggle matching points which are the test of all GCM’s abilities. They must show appropriate dips at these points to appear credible (even though they are not).
As we know, only major volcanic eruptions and the evil gas CO2 are capable of altering earth’s temperature.
I’m flabbergasted about their inability to understand proportions. The fact that major hurricanes are increasing in proportion to all hurricanes is because the total number of hurricanes is dropping. I’ve got a graph up of the last 30 years which illustrates this quite well.
Will the sophistry ever end from these people?
How can they talk about frequency / intensity of hurricanes without looking at ACE Global ACE
http://www.wunderground.com/hurricane/accumulated_cyclone_energy.asp?basin=gl
Nik Marshall-Blank:
At April 30, 2013 at 9:11 am you ask
I reply.
No, I don’t think there is such a list. But I know that at least one paper was prevented from publication by the frequency and magnitudes of alterations to global temperature data sets. See
http://www.publications.parliament.uk/pa/cm200910/cmselect/cmsctech/memo/climatedata/uc0102.htm
Richard
PS I suspect you may want to read the draft of the paper and it is Appendix B of the item at the link.
rgbatduke April 30, 2013 at 5:31 am:
“… If I were reviewing a paper that published this figure in particular, I’d have to ask how an a priori model manages to produce coherent dips at 1883, 1903, 1963 along with ENSEMBLE predictions in between with THE SAME SCALE OF NOISE as the actual temperature series.”
Could it be the work of a major massage parlor? All models put in dips for an imaginary volcanic cooling based on the date of the eruption.. It can be demonstrated that volcanic cooling, so-called, is nothing more than misidentification of naturally occurring La Nina temperature dips. They don’t get it because they still don’t know that the entire global temperature curve is a concatenation of El Nino peaks with La Nina valleys in between. They match up accurately in temperature curves from all parts of the world as Müller’s data show. He, too did not understand this and considered them noise. To modelers it is noise to be wiped out with a running mean. If a volcanic eruption coincides in time with an El Nino peak the La Nina valley that follows it is immediately recruited for its “volcanic cooling” dip. But then these guys get in trouble if, by chance, the eruption coincides with a La Nina valley. What follows a La Nina valley is an El Nino peak and mysteriously, that volcano will refuse to do any cooling and actually cause a temperature rise. That is what happened to El Chichon and they are still going through contortions to find its non-existent cooling. How come Pinatubo had such a distinct cooling but El Chichon did not? Very simple, Pinatubo eruption just happened to coincide with an El Nino peak and the La Nina that followed it was recruited to serve as its volcanic cooling dip. I explained it all in simple language in my book “What Warming?” two years ago but these big experts do not read anything that their friends did not write. Your point about the scale of noise is of course important. Since what they think of as noise is in large part ENSO oscillations which determine the scale of that “noise” it is easy to match its scale with random noise input to the models. Oh, one more thing. There are exceptions to the regularity of these El Nino peaks, one example of which is the super El Nino of 1998. Its exceptional size is ignored or suppressed in most ground-based temperature curves.
Arno Arrak says: “How come Pinatubo had such a distinct cooling but El Chichon did not? Very simple, Pinatubo eruption just happened to coincide with an El Nino peak and the La Nina that followed it was recruited to serve as its volcanic cooling dip.”
That’s two works of fiction from you on this thread, Arno. There was no La Niña following the 1991/92 El Niño. If you understood ENSO as you claim, you would know this. Not every El Niño spawns a trailing La Niña. Here’s a link to a graph of NINO3.4 sea surface temperature anomalies:
http://bobtisdale.files.wordpress.com/2013/04/nino3-4-monthly1.png
Do you see a La Niña following the 1991/92 El Niño? There’s a minor dip in temperature in 1992/93 but it never got close to La Niña levels.
The reason Mount Pinatubo had a distinct dip and rebound was because it was a much larger volcanic eruption than El Chichon, and Mount Pinatubo was also counteracting a much weaker El Niño than El Chichon in 1982.
Arno Arrak says: “There are exceptions to the regularity of these El Nino peaks, one example of which is the super El Nino of 1998. Its exceptional size is ignored or suppressed in most ground-based temperature curves.”
It is? More nonsense from you, Arno. Please show us a one of the “ground-based temperature curves” that shows no 1997/98 El Niño. Here are GISS LOTI, HADCRUT4 and NCDC data. All of them show a very distinct response to the 1997/98 El Niño:
http://bobtisdale.files.wordpress.com/2013/04/04-comparison1.png
In fact, the first time anyone sees a global temperature anomaly curve, they ask, What caused that spike in 1997/98?
As one poster put it a couple of days ago on a different blog post here, it is not about “climate skepticism” versus “climate alarmism”. It is pure and simple about science. Good science as it should be practiced, versus bad science as we get delivered.
The problem is deeper and needs throughout analysis. I’ve seen some good starts – if I correctly remember there was a reproducibility project. Any paper that could not be reproduce is nothing until it gets reproduced.
I would further say that any paper which does not come accompanied with the raw data and the clear description and information how the data is being “prepared” is again nothing. Zero, nada.
Then again, any public funded paper should be stored on a public server with access for the public.
I think these discussions about climate science and climate skepticism are only the start of a bigger conversation towards ensuring proper standards for science. I’ve seen enough of bad science done to suit the narrative for grant money.
And of course then come the grants. Who gets the grants for a study, and this should be a very interesting conversation. Maybe this should be more democratically done for public money?
Nevertheless some changes need to come.
The clowns are linking not only more intense hurricanes to anthropogenic climate change, but even volcanism! Incredible but true.
And where does the anthropogenic gas forcing bring the models with another quarter increase in this century (2000-2013)? How convenient that the measured temperature ceased so early. Why the data is truncated such way, who accepted it? “Hiding the decline” again.
Interesting that there is still a lot of money to waste on such “studies”.
Still no trace of ENSO, PDO, AMO in natural climate forcing reconstructions. Where did all the research money go ? How can it be that amateurs have a better understanding of climate than paid position holders ?
And for those reading through the comments, Joe Bastardi stopped by the cross post at my blog. His comment begins, “Are these guys serious…”
http://bobtisdale.wordpress.com/2013/04/30/on-holland-and-bruyere-2013-recent-intense-hurricane-response-to-global-climate-change/#comment-11065
Rule one for climate science , when the models and reality differ in value its reality that is in error .
Bottom line all their efforts are put into producing the ‘right result ‘ not the correct one , so how they get it means nothing to them .
We would have very, very few Cat 4,5 hurricanes if it wasn’t for the GHGs we have added.
That doesn’t explain why there was so many of them before the Net Anthro forcing became positive around 1970.
And it doesn’t explain why there are so few Cat 4,5 hurricanes today/lately.
The only explanation is the authors faked the numbers (which is what we find everytime someone goes into depth on one of these “we’ve found global warming” studies – you can set your watch by it).
Yet another Waper (What if pAPER). Where are the facts?
AMO is the signal you get when you leave our a linear trend (such as a warming trend). Bob -as usual – is wrong
AMO is the signal you get when you leave our a linear trend (such as a warming trend). Bob -as usual – is wrong . http://www.nature.com/ncomms/journal/v2/n2/fig_tab/ncomms1186_F1.html