Why John Christy’s Missing Hotspot Matters

By Eric Worrall – Re-Blogged From http://www.WattsUpWithThat.com

One thing which struck me about the recent climate science hearing is how little attention was paid to Dr. John Christy’s demonstration of a flawed climate model prediction – the missing Tropospheric hotspot.

A flawed prediction does not automatically mean the models are totally wrong – but it is a strong indicator that something isn’t right.

German garden gnome

Consider the primary observation. The world has warmed since the mid 1850s, and for the sake of argument lets assume that the world has warmed since the mid 1930s.

Given that warming, you could propose a number of different theories for the cause of that warming, for example;

1. Chaotic shifts in ocean currents or solar influences have influenced global temperature.
2. Anthropogenic CO2 emissions have caused global temperature to rise
3. Gnomes are lighting fires under the polar icecaps.

All three of the theories proposed above can potentially explain the primary observation – the world is warming, and heating is more pronounced in polar regions.

How do you eliminate the incorrect theories?

The way you eliminate incorrect theories is to test other non-trivial secondary predictions. It is easy to create a theory which explains global warming – even my Gnome theory does that. What is more difficult is to create a theory which coherently explains other observable phenomena, or better still predicts observations which haven’t been attempted yet.

For example, there are simple tests for the presence of Gnomes lighting fires under the polar icecaps. You could dig holes and try to find the Gnomes. If you don’t find any Gnomes, you cannot conclusively prove they don’t exist – the Gnomes might be very good at evading attempts at discovery. But failure to find Gnomes, or failure to find evidence of extensive efforts to light fires under the polar icecaps, should allow you to conclude that the Gnome theory is very unlikely to be correct.

How do you test the Anthropogenic CO2 theory? Just as the Gnomes lighting fires theory predicts the existence of Gnomes and extensive fire pits under the polar ice caps, so the Anthropogenic CO2 theory predicts various observations.

We could simply wait 50 years and see if global temperatures go crazy, but it would be nice to know whether the theory is correct before we all cook. So we need a non trivial secondary observation which we can test here and now.

One of the key predicted observations of anthropogenic CO2 climate theory is the existence of an equatorial tropospheric hotspot.

The hotspot prediction is easy to understand. The atmosphere is thicker, reaches higher into space over the equator than the poles, due to centrifugal force of the Earth’s spin. Centrifugal force is greater at the equator than the poles, so air, including CO2, tends to pile up higher into space over the equator.

The equator also receives more sunlight.

If the buildup of greenhouse gasses is trapping significantly more heat, the effect on the atmosphere should be most pronounced where the sunlight is strongest and the greenhouse blanket is thickest.

But nobody has yet managed to unequivocally detect that predicted hotspot.

Various theories have been advanced to explain the missing hotspot.

For example one theory is the balloon measurements are not being analysed correctly, so the hotspot is there, but it is evading detection unless you properly homogenise the data.

In my opinion this theory is undermined by satellite measurements which confirm the un-homogenised balloon measurements. This confirmation of un-homogenised balloon measurements casts doubt on the data homogenisation process which led to the alleged detection of the hotspot.

Another theory I have seen mentioned is that the hotspot is there, but the effect is not pronounced enough to be detectable as yet. More plausible in my opinion than the instrument anomaly theory, but this proposition verges intriguingly close to an admission that anthropogenic global warming is not a big deal.

Whatever the reason, the absence of a pronounced hotspot is or should be as much of an embarrassment to the Anthropogenic CO2 theory, as the absence of fire pits and captured Gnomes is an embarrassment to the Gnome theory.

Does the absence of a tropospheric equatorial hotspot mean anthropogenic climate models are unequivocally wrong?

The answer is no.

There are plenty of examples of scientific theories which were slightly wrong, which didn’t fully explain observations, which were later found to be mostly right.

Newtonian gravity mostly explains the orbit of the planets, but some observations don’t match the theory. For example, Newtonian predictions of the orbit of Mercury do not match observations. Mercury is very close to the sun, much closer than the Earth. That close to a massive body like the Sun, Einstein’s General Relativity becomes important. Relativistic effects cause Mercury’s orbit to diverge from Newtonian predictions of what its orbit should be.

This deviation from theoretical predictions does not mean Newtonian theory is broken, in this case it simply means the Newtonian theory is incomplete. Unless you need extreme precision, for example when creating a global positioning satellite system, the tiny perturbations introduced by Einstein’s theory are not significant enough to worry about.

But a flawed prediction is not something which should be ignored. Sometimes when you don’t find any gnomes at the bottom of the garden, you should stop digging holes.

As for the theory that chaotic shifts in ocean currents or solar influences control the climate, the evidence for this seems to be a mixed bag.

Suggestions that the eleven year solar cycle affects climate are convincingly disputed by Willis. If the powerful eleven year solar cycle doesn’t do anything to the climate, why would longer solar cycles have any effect?

On the other hand, there appears to be growing evidence solar modulation of cosmic rays may have a significant effect on atmospheric chemistry.

In my opinion, the short answer is we simply don’t know what drives the climate. More research is required, without premature efforts to formulate policy around theories which clearly do not explain all the key observations.

CONTINUE READING –>

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10 thoughts on “Why John Christy’s Missing Hotspot Matters

  1. The hot spot has been found for over a decade, in at least 10 studies dating back to at least 2004. The satellite analysis from the NOAA group (Fu et al.) was one of the first to show the hot spot. Why does your lot keep claiming otherwise?

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    • I’m familiar with a UNSW study by Steve Sherwood which, although he says he didn’t use models, his description actually shows it was from a model (“This method, in effect, performs a multiple linear regression of the data onto a structural model that includes both natural variability, trends, and time-changing instrument biases, thereby avoiding estimation biases inherent in traditional homogenization methods.” http://iopscience.iop.org/1748-9326/10/5/054007). Using a model to prove what actually is happening in the real world is a non no, so although he says he found a smaller hotspot than anticipated, he says it’s there. Not.

      Also, it is well known that ENSO affects satellite & radiosonde readings, so periods when ENSOs occur need to account for the ENSO perturbations. (Please see: https://wattsupwiththat.com/2016/09/22/study-tropical-hotspot-fingerprint-of-global-warming-doesnt-exist-in-the-real-world-data/.)

      Of course, if somebody actually observed the hotspot (no models or opinion please) and if they accounted for ENSO effects, then I’d be very interested in seeing the paper. Thanks for the heads up.

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      • Thank you for your polite response. I will respond in kind.

        First, Sherwood’s 2015 paper is just one recent example of a paper that found the hot spot. As I said in my first comment, the hot spot has been found since at least 2004 in Fu et al.’s satellite data analysis. Here are some recent papers on this subject, from different data sources:

        Radiosondes: “Internal variability in simulated and observed tropical tropospheric temperature trends”
        Radiosondes: “New estimates of tropical mean temperature trend profiles from zonal mean historical radiosonde and pilot balloon wind shear observations”
        Satellite data: “Comparing tropospheric warming in climate models and satellite data”
        Satellite data: “Removing diurnal cycle contamination in satellite-derived tropospheric temperatures: understanding tropical tropospheric trend discrepancies”
        Re-analyses: “Estimating low-frequency variability and trends in atmospheric temperature using ERA-Interim”
        Re-analyses: Figure 7: “Detection and analysis of an amplified warming of the Sahara Desert”

        Second, WUWT is not a credible science source. I do not understand why you claim you want no “opinion”, when you cite a website that cares more about opinion than facts.

        Third, scientists already account for ENSO when looking at temperature. They do so using the multivariate ENSO index, or MEI.

        Fourth, there is nothing inherently wrong with using a model to show what’s happening in the real world. For example, scientists use orbital mechanics models to show how satellites behave. Sherwood seemed to have used the model for the purpose of homogenization via kriging. Plenty of other scientists have used models for homogenization, including the satellite teams for RSS, NOAA, and UW. The UW validated their model-based homogenization using observational data. Please see this paper for more on that:

        “Removing diurnal cycle contamination in satellite-derived tropospheric temperatures: understanding tropical tropospheric trend discrepancies”

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      • OK, so here is what I’ve found:

        Re: “Internal variability…” (http://onlinelibrary.wiley.com/doi/10.1002/2017GL073798/full) – They say, “We explore the extent to which internal variability can reconcile discrepancies between observed and simulated warming in the upper tropical troposphere….” So, this doesn’t find a hotspot. They compare the CMIP5 model ‘average’ to the actual data and say that the models are ‘consistent’ with the data. In other words, they couldn’t outright discard the models as being too far wrong. That’s not the same as proving a hotspot.

        Re: “New estimates of tropical mean…” (http://onlinelibrary.wiley.com/doi/10.1002/2014JD022664/pdf) – “wind data can be used to estimate zonal mean temperature trends in the tropics using the thermal wind relationship.” So, what they say is that, since they couldn’t find the hotspot directly by using the temperature data, they used an estimate of the temps through the wind data. In effect, they used a model of how wind relates to temp to generate their data set. Why not use the actual temp data? Because it didn’t give the answer they wanted.

        Re: “Comparing tropospheric warming in climate models and satellite data” (http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0333.1) – They say that previous analyses find a 3 times over-estimation of the hotspont. What they do is blend stratospheric cooling (above 100,000 feet) into the tropospheric record (under 50,000 feet), then cherrypick only 6 new & improved models (why not CMPI5?), and voila, 5 out of 6 model output become only 1.7 times overestimates.

        Re: “Removing diurnal cycle contamination in satellite-derived tropospheric temperatures…” (http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00767.1) – There are two satellite data sets, UAH and RSS. The algorithm used by UAH was found to have a small bias because of diurnal drift, and a correction was made years ago by UAH. Now, both UAH & RSS agree. This study seems to make a second diurnal drift adjustment to the data. Then they ‘homogenize’ (adjust) the data and compare the result to models. Then they say that, after multiple adjustments (why?) the massaged data now is close enough to the models to say the data and models are ‘in accord.’

        Re: “Re-analyses: “Estimating…” (http://onlinelibrary.wiley.com/doi/10.1002/qj.2317/abstract) – They say, ‘…how closely ERA-Interim fits the upper-air data it assimilates, the bias adjustments it infers….’ They are using a reanalysis (another term for model) to infer an adjustment that they want to perform on th actual data. I don’t understand this continuous effort tochange the data to conform to the models!

        I asked for data, but it looks like what you posted is models and adjustments to the actual data.

        Finally, you say that WUWT – a blog which has received MANY awards, one of which is Best Science Blog – is a fluff opinion site. I expect that you have never read anything on WUWT. In reality, WUWT provides data on which opinions are expressed. They even post occasional Alarmist items (with critiques of course). They don’t descend into personal attacks, but generally stick with showing that Alarmist views are wrong, using data and logic. They allow comments from all comers, and (seldom) block anybody except for foul language flames. On the occasions that I have visited Alarmist sites, I have been underwhelmed by the lack of data, logic, and good manners – and they don’t allow opposing views in comments. Please, do yourself a favor and visit WUWT, read bunch of articles, and then decide.

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  2. You misrepresent scientific research to a mind-boggling rate. Why? Are you doing it intentionally? Misrepresentations like your’s are why scientifically-minded people get fed up with responding to your lot.

    Re: “So, this doesn’t find a hotspot.”
    You left this out of your quote-mine:
    “The simulated AMPLIFICATION of surface warming aloft in the troposphere is consistent with observations, and the linear correlation between surface and simultaneous tropospheric warming trends decreases with trend length.”
    So no, they did find the hotspot. You just conveniently left that fact out.

    Re: “Why not use the actual temp data?”
    They use both temperature data AND wind data. They use the wind data as FURTHER confirmation of the temperature data, not as a replacement. This is clearly stated in the paper:
    “Global mean temperature trends for the period 1979–2005 in the tropical belt […] inferred EITHER
    from zonal wind vertical wind shear and temperature […]”

    Re: “What they do is blend stratospheric cooling”
    No, they REMOVE stratospheric cooling from the tropospheric measurements. Since at least 2004, scientists have known that satellite measurements measurements of tropospheric temp were contaminated by stratospheric cooling. RSS, NOAA, and UW now correct for that; UAH still refuses to. You also left this out of your discussion of the paper:
    ” It is shown that amplification of tropical warming between the lower and mid-to-upper troposphere is now in close agreement in the average of 37 climate models and in one updated satellite record.”
    So they found the hotspot as well.

    Re: “There are two satellite data sets, UAH and RSS.”
    No, there are at least FOUR satellite data analyses, as I showed you: UAH, RSS, NOAA, and UW. You also conveniently left out how table 4 shows the hotspot, as noted in the text of the paper:
    “Our amplification factor over the tropics is consistent with tropical tropospheric amplification implied by models, which is approximately 1.4–1.6 […]”

    Re: “Now, both UAH & RSS agree.”
    No, they don’t. UAH is the OUTLIER among the 4 analyses, as shown in the very paper you’re discussing, a paper that was co-authored by the RSS group. The RSS team has repeatedly pointed out that UAH is the outlier. For example, in their paper:
    “Stratospheric temperature changes during the satellite era”

    Re: “Then they ‘homogenize’ (adjust)”
    UAH homogenizes their data as well. What do you think the warm target factor correction, diurnal drift correction, etc. are? They’re homogenization.

    Re: “after multiple adjustments (why?)”
    They clearly state why; please read the paper. The correction is for diurnal drift, and UW’s correction is better validated than UAH’s correction.

    Re: “They are using a reanalysis (another term for model)”
    No, that is not what a reanalysis is. A reanalyses pools data from different sources. Even John Christy is not uninformed enough to say that a reanalysis is a model, since he cites reanalyses as being observational datasets, not models. See page 6 of: https://science.house.gov/sites/republicans.science.house.gov/files/documents/HHRG-115-SY-WState-JChristy-20170329.pdf

    Please let me know when you’re willing to discuss scientific research, without misrepresenting the research

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    • One point of disagreement between us is the term reanalysis. My understanding of it is, they plug observed data into a model as a starting point, as is supported by (CAGW friendly) WIKI “The technique of data assimilation is therefore used to produce an analysis of the initial state, which is a best fit of the numerical model to the available data, taking into account the errors in the model and the data.” (https://en.wikipedia.org/wiki/Meteorological_reanalysis). So, once I see the words model or reanalysis, bogus bells go off for me.

      Another is with the term consistent with. In statistics, you might analyze two data sets to see if it is likely they were drawn from the same population (or not). Two data sets always will show differences even if drawn from the same data set, but you try to see if they are ‘close enough’ to each other. The results may be displayed as, at the 95% confidence level, we couldn’t say they are from different sources. They could be very different, but so long as they are within that 95% confidence interval, we can’t discard that possibility – they are consistent with each other. You use the term to mean close enough, while I look at it as, ‘why not use the original data?’ That’s a big disagreement in our points of view.

      Also, since GISS/NOAA constantly/continuously make adjustments to data which already has been adjusted several times, I look at their output with suspicion. The land surface temps for the US over the last century received significant adjustments 5 times over a recent six year period – all in the same direction to make the trend look more like global warming. Science is science, but scientists are people – subject to ideological and political biases. It’s all too easy for an adjustment to ‘confirm’ what you expected.

      A good example (in another field) is the CPI adjustments. BLS has changed the way they massage the consumer price data several times during the last two generations – during both Democrat and Republican administrations- to make the results look better for he people in power. Economist John Williams at http://www.ShadowStats.com has continued to assemble the CPI as it used to be calculated in 1980 and 1990, and the differences with the current methodology speak loudly of the intellectual corruption involved. The point in this is that I have become sensitized to the term ‘adjustment,’ especially whenever the government is involved, although private people/businesses also do it.

      My gut response is, ‘Show me the data! Not the multi-adjusted data, not the reanalyzed data, not the model data, not the consistent with data, but the actual observed data.’ Yes, sometimes (frequently?) individual data points need to be corrected in QA, but once that’s done, then the data must rule.

      So, it seems to me that we have differences of opinion which will keep us from seeing the same reports the same way. We need to reconcile our views on models vs data, reanalysis vs data, consistent with vs data, and other items. I don’t think that what you see is evil – that’s just the interpretation that your mind is set up to accept. I’m not trying to be contrary – I just accept things differently from how you do. Likely, at least one of us is wrong. We’ll see.

      So then, what consequences are we willing to accept if we’re wrong? I’m willing to accept a possible year 2100 result that my great-great-great grandchildren will need to adapt to, while you’re willing to accept a current economic deterioration (as we’ve begun to see these last 8 years, and which Europe has been suffering through for a generation). That’s a pretty big philosophical gap the we likely can’t bridge. I fear for my grandkids, but they already are being taught your way of looking at the world in school.

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  3. Re: “they plug observed data into a model as a starting point, as is supported by (CAGW friendly)”
    “CAGW” is not a thing in mainstream climate science. It’s a straw man made up by contrarians, as shown in:
    “Polluted Discourse: Communication and Myths in a Climate of Denial”
    https://link.springer.com/chapter/10.1007/978-3-319-20161-0_3
    So please drop that strawman. I take it as seriously as creationists offering a “molecules-to-man” straw man of evolutionary biology.

    Re: “So, once I see the words model or reanalysis, bogus bells go off for me.”
    Sorry, but that doesn’t work. Models are regularly and justifiably used in virtually every branch of science. For example, the UAH group uses a model to convert oxygen microwave emissions into temperature measurements. The RSS and NOAA groups use a climate model for their diurnal drift correction. Scientists use astronomical models to predict the orbits of satellites and planets. Biologists use models to explain the spread of pathogens. So no, the word “model” should not send off your “bogus bells”, unless you think virtually every branch of science is bogus (including the branch of science I work in) and unless you want to reject UAH’s analysis as well.

    Re: “Another is with the term consistent with.”
    We’re not talking about consistency between warming trends from models v. observed warming trends. We’re talking about whether the hot spot appears in the observations. And the hot spot clearly appears in the vast majority of the data sets (5 out of 6 of the radiosonde analyses, 3 out of 4 of the satellite analyses [not even counting at least 2 other satellite analyses in which the hot spot appears], 3 out of 4 of the re-analyses).

    Re: “You use the term to mean close enough, while I look at it as, ‘why not use the original data?’ That’s a big disagreement in our points of view.”
    No, it isn’t a point of disagreement, since you don’t use the original data either. You used the HOMOGENIZED data, just like I do. After all, you rely on UAH and RSS, both of whom homogenize their data. Unhomogenized tropospheric data would show little-to-no tropospheric warming, and would be contaminated with stratospheric cooling, issues with diurnal drift, warm factor target bias, etc.

    Re: “My gut response is, ‘Show me the data! Not the multi-adjusted data, not the reanalyzed data, not the model data, not the consistent with data, but the actual observed data.’”
    THEY DO show you that. For example, in the 1990s UAH showed their largely unadjusted, “actual observed data”. They used that data to claim there was no tropospheric warming. That was a disaster for UAH’s credibility, since it turned out that UAH had not appropriately adjusted for diurnal drift. That’s the problem with relying on unadjusted, unhomogenized data: the data will be biased by factors you should have corrected for. That’s why almost no sensible person studying atmospheric temperature, relies on unhomogenized, unadjusted temperatures. Also, this paper I cited for you did give you largely unadjusted data, along with the adjusted data (see the dark blue lines in figures 8 and 9):
    “New estimates of tropical mean temperature trend profiles from zonal mean historical radiosonde and pilot balloon wind shear observations”

    Re: “Likely, at least one of us is wrong. We’ll see.”
    But we did already see. The data is in. Santer, Karl, the NOAA team, the RSS team, the UW team, the ERAI team, the MERRA team, the RAOBCORE team, the UNSW team, the… and I were right: the hot spot exists. So the UAH team and their online acolytes, were wrong.

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    • CAGW, means Catastrophic Anthropogenic Climate Change, but I’m sure you knew that.

      Catastrophic: If it weren’t going to be catastrophic, then all these loony efforts to control it could not even start to be justified.

      Anthropogenic: If it’s not man-made, then it’s natural. But Alarmists always say that it’s the man-made climate effects which are the problem.

      Global Warming: The PR people in the Alarmist camp decided that Global Warming was not a believable term, even though the meat & potatoes of the alarm is that ever-rising temperatures are going to fry us. So, you can call it Climate Change all you want, but we both know the proper term is Global Warming.

      Skeptics use CAGW as a shorthand for these three terms. Are you suggesting that you don’t claim any one of these is correct?

      Then you talk about models. Yes models can be valuable everywhere in science, and are used widely. But, models can be wrong sometimes, (GIGO!), which is why they need to be validated. In Meteorology, they use models extensively with some forecasters showing the difference between the two main models. Even so, everybody admits that their forecasts become unreliable after 5 days. The climate models have never been validated, and in fact, if you compare the individual models and the CMPI5 ‘average’ to the actual observations, there is a big discrepancy – the models run HOT! Please don’t pretend that climate models are reliable just because models in other areas of science are reliable. They are not.

      Adjustments are made all the time, by skeptics and alarmists alike. The question is whether they can be justified or not. From what little I’ve seen, Santer & Karl use the ‘ends justify the means’ theory. GISS also – serially – beats the original data to within an inch of it life. Unless you’re willing to explain why multiple adjustments, always with the same trend increasing bias, is OK, or why adjusting Argo buoy data to match less reliable & older ship intake readings is OK, or why homogenizing a land observation using another land observation from hundreds of miles away is OK, then I respectfully decline to consider alarmist adjustments to the data as anything but crap.

      This is part of the main sticking point between us. You don’t trust skeptic scientists, and I don’t trust alarmist scientists. What should be a science question has become a political question. While your side has a distinct advantage in money and visibility, my side has an advantage in your side’s inability to convince more than a handful of Americans that this issue is of Top 10 importance. If my side just can hold off your side for another 20 years or so, then a couple of things will happen. First, I likely will be dead by then. Second, the disparity between the model projections and the actual data may become so large that even your side will have to admit you’re wrong. And third, if we finally can recover from Obama-itis, maybe the US will have grown enough to accommodate the CAGW silliness & money wasting more easily.

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  4. Re: “If it weren’t going to be catastrophic, then all these loony efforts to control it could not even start to be justified.”
    False. Something doesn’t need to be “catastrophic” in order for people (and the government) to do something about it. For example, deaths in car accidents are not some great catastrophe that will end the human species and the world. Yet governments still mandate that people put seat belts on their children. And HIV is not some monumental catastrophe that will kill off all life. Yet wealthy countries still provide foreign aid to poorer countries in order to combat HIV. Nor is second-hand smoke an earth-shattering catastrophe that spells the end of us all. Yet governments still make regulations limiting smoking in certain locations.
    I know what you’re doing: basically, people like you invent some ill-defined, flexible definition of “catastrophe”. You then claim that AGW is not a “catastrophe”, based on your personal definition of “catastrophe”. Then you fallaciously claim that since AGW is not a “catastrophe”, people don’t need to support policies you don’t like. That’s shown here:
    “Another claim advanced by those who reject the mainstream scientific agreement on climate is that the consensus position consists of a claim of *catastrophic anthropogenic global warming* or the frequently used acronym CAGW [catastrophic anthropogenic global warming] […]. However, CAGW is rarely, if ever, defined or sourced to a mainstream scientific organization or study. Any scientific study’s result, or statement by a researcher, that does not fit a contrarian’s personal, flexible definition of CAGW can therefore be adopted as ostensibly supporting their view and refuting the mainstream, even when such results are actually consistent with the mainstream position on climate […].”
    https://link.springer.com/chapter/10.1007/978-3-319-20161-0_3

    Re: “Please don’t pretend that climate models are reliable just because models in other areas of science are reliable. They are not.”
    You said: “So, once I see the words model or reanalysis, bogus bells go off for me.”
    I’m pointing out that if you were consistent in your logic, then you’d object to models in other topics as well. And I highly doubt that you know whether the climate models are reliable, since you don’t seem to read the scientific research in which the climate models are tested. After all, if you had read that research, then you’d know that model-based predictions have been confirmed over and over and over… I could easily list 10+ non-trivial, accurate, model-based predictions in climate science. Would that convince you? I doubt it.

    Re: “The question is whether they can be justified or not. From what little I’ve seen, Santer & Karl use the ‘ends justify the means’ theory.”
    Not this baseless conspiracy theory again. Karl et al.’s homogenization was validated. Please read:
    “Assessing recent warming using instrumentally homogeneous sea surface temperature records”

    Re: “This is part of the main sticking point between us. You don’t trust skeptic scientists, and I don’t trust alarmist scientists.”
    You’ve failed to show that scientists are “alarmists”. Like “CAGW”, “alarmist” is just another term you’re abusing. See the discussion of William Freudenburg and Violetta Muselli here:
    “Climate change skepticism and denial: An introduction”
    Those researchers show how the IPCC isn’t alarmist, since the IPCC is more likely to under-estimate climate change’s effects, as opposed to over-stating them. That’s confirmed in:
    “Reexamining climate change debates: Scientific disagreement or scientific certainty argumentation methods (SCAMs)?”
    “Climate change prediction: Erring on the side of least drama?”
    https://www.scientificamerican.com/article/how-the-ipcc-underestimated-climate-change/
    Also see the following for how the IPCC tends to use non-alarmist language:
    “The language of denial: Text analysis reveals differences in language use between climate change proponents and skeptics”
    Anyway, my issue isn’t with “skeptic scientists.” My issue is with scientists who lie and/or make claims that are contradicted by overwhelming and/or offer transparently fallaciously. That’s why I distrust Christy, Curry, Spencer, Lindzen, etc. It’s not about them being “skeptics”, since I don’t think they’re really scientific skeptics.

    Re: “What should be a science question has become a political question.”
    Which is mostly your side’s fault. Many political conservatives have made anthropogenic climate change a political issue, much as many other conservatives did with human evolution, the age of the universe/Earth, HPV vaccination, the efficacy of condoms with respect to HPV, health risks of smoking / second-hand smoking (and the addictiveness of smoking), ozone depletion by CFCs, acid rain, the risks of opting out of childhood vaccination, the risks of parents not putting seatbelts on children, lead in water (and paint and gasoline), the usefulness of embryonic stem cell research, etc. Given this track-record among many conservatives, it’s no surprise that so many conservatives on your side misrepresent climate science for political reasons. It’s what they’ve done for other scientific topics after all. But if past is prologue, then your side is going to lose on this, much as they lost on the other aforementioned topics (with the possible exception of stem cell research funding being cut).

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    • That you are OK with destroying our Economy, and likely causing millions of people around the world to die from Energy Starvation, even if Global Warming is not Catastrophic, is unconscionable!

      I expect that you don’t think that people in relatively rich Europe are suffering today from Energy Poverty, but the reality is that European leaders’ push for windmills and solar arrays has pushed up the cost of Electricity to their subjects by three times. But those leaders aren’t done yet.

      And, you want to bring that insanity to the US? Even if the problem that you imagine is not at a Catastrophic level? How can somebody who wants to save future generations be so heartless to people living in the world today?

      Please, let’s agree to disagree. Read my blog or not – your choice. But I won’t be giving the likes of you any additional space.

      Like

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