All the curves that are fit to print

Regarding the latest UAH and RSS global temperature data plots Dave B writes: “…could you post a best-fit, to be fair? I don’t have the technology.”

Sometimes I’m tempted to tell people to do the work themselves, after all, I’m overloaded as it is. But, it is the 4th of July weekend, and I’m stuck here in the smoky toasty Sacramento Valley babysitting a bunch of servers until my chief tech support guy comes back from vacation, so what the heck.

I’m not sure what he’s implying by “fair” but it has been my experience that no matter what you put in a graph, or how you graph it, somebody will find fault with it.  Below are raw data overlaid with 1st order and 5th order curve fits to show long and short term trends.

Click for large plots

And “to be fair”, and to make everyone happy/angry here is the last 11 years, when the warming trend flattened.

Click for a larger image

Have at it lads. :-)

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77 thoughts on “All the curves that are fit to print

  1. You can say it’s fair to say, “the long term trend is up, the short term trend is down”. The short term trend is a leading indicator, the long term will follow, unless we start warming.

  2. “fair”

    Demands a pun, given the vqrious ways it gets used in the daily forecasts and such.

    But I’m not up to it.

    I do thing science doesn’t have much use for the word except as a way-point between “bad” and “good”.

    But it does seem that the the graphs here provide an underpinning for know-nothings like me that say the Earth by definition has been warming since the bottom of the last “ice age” minimum and will continue to do so until it begins (or began) the next cyclic cooling interval.

  3. For a layman, could you explain the difference between 1st order and 5th order curve fit in a 30 year period? Obvoiuosly, the 1st takes a longer time frame, but is it 5 times longer?

  4. Thanks Anthony; sterling work as usual; I’d send money, but I’m from Australia and the greens are in charge here and we are going to show the world the meaning of self-sacrifice in respect of AGW as soon as Garnaut hands down his report; so I’m going to be broke and I’m putting money away for the lean times ahead; I’ve even got 2 mice running on little treadmills powering my light bulbs; but the mice are hopeless; I think it’s their names ; windy and solaris; no base effort.

    I’ve often wondered about the long term trend being up and I’m not going to revisit the trend corruption of base periods, the Great Pacific Climate Event, PDO oscillations and historic revision of data; just looking at your graphs though; if you remove ’98 as an outlier the downward trend is pronounced, and this downward trend would be even more pronounced the further back you begin your graph. Which makes any analysis of what caused the ’98 aberration essential; conventional wisdom is that it was an exceptional El Nino year; but that is tautological; no doubt the SOI was -ve and correlated with El Nino, as did the other indices, but what made it so extreme? I’m wondering whether the Pinutabo after-effects were not having a final say. Pinutabo was in 91-92, and your graph shows the traditional post-volcano cooling; but part of the eruption was higher atmosphere particulate which would have intercepted UV and X-rays; once that particulate settled, the UV and X-ray and their heat would have been free to reach the lower atmosphere and surface; just in time to collaborate with the ’98 El Nino. That being the case the ’98 spike should be removed, along with the relatively minor post Pinutabo cooling.

  5. Hansen postulated global cooling first, before big Al sucked him into the warming camp, where his investments are. Now, for the long view, why is no one saying that we are actually in a very long term cooling trend, and warming is just a temporary glitch? The opposite argument sure gets a lot of traction with nothing more than some impenetrable climate models which seem to have less and less connection to reality. The refusal to admit COO rises after warming occurs, and is not the cause of warming is just one problem.

    Still no more cycle 24 sunspots. Hope everyone has their warm clothes ready.

  6. Thanks Anthony,

    As I said, I’m a layman to this. I meant no offense by asking you to be “fair”.

    This helps me, I appreciate it.

    Happy Independance Day!

  7. Of course to be fair, you really need to do the curve fit to temperature data in geologic time, with data in increments of say 100 years over maybe a million years :-). Yes, I watched Prof Carter’s lecture series, very good I might add.

    Curve fitting, it refers to the order of the polynomial used to give the best fit of a smoothed curve to noisy data, like temperature samples. Most good spreadsheet programs do it automatically for you, some will select the best fit automatically, without the excess oscillation you may get by selecting too high an order polynomial.

    Yes it’s true, Hansen was the original ‘cooling guy’ back in the 70s, he just found more money in warming. One of the schemes put forward back then, was to cover the ice caps with black carbon to melt them and stave off the glaciers.

  8. “It is the 4th of July weekend, and I’m stuck here in the smoky toasty Sacramento Valley babysitting a bunch of servers until my chief tech support guy comes back from vacation…”

    Change it to past tense and add, “when in came a set of the sweetest curves you ever saw…”

    And a great sequel is well on its way.

  9. I used to annoy Warmers by pointing out that Earth’s climate is always warming and it’s always cooling. It just depends on the time frame you are measuring over.

    So the argument that you need to measure climate over x versus y years is spurious.

    So how do we determine if the Earth’s climate is warming?

    One answer to this question is you treat the data as a continuous data stream. Whenever it goes down it’s cooling and whenever it goes up it’s warming. You can then say climate data is noisy* and the data has to go up or down for some period in order to ensure we are not just seeing noise.

    How long that period is, is determined by how noisy the data is.

    * I would argue there is no reason climate data should be noisy, and there is no real evidence for the claimed noisyness of the data. It looks to me like an ad hoc explanation to save the Forcings Model. Because if the climate really is bouncing around the way the graphs above say it is then the Forcings Model and all the climate models are just junk.

    Which is not to say the models aren’t junk for other reasons.

  10. It seems to me that the fitting of a polynomial curve to this data is inappropriate. Although you can get a polynomial to fit almost any data, it has no meaning other than it seems to fit. My preference for this kind of trending is to use an exponential moving mean that weights the most recent data heavily and earlier data less. The result is a curve that tends to follow the current change in the data which is what we want in this case whether it is going up or down.

    Love your site. Read it every day.

  11. George M,

    Sorry if the last posting came out terse; I was trying to quote and, well, things didn’t quite work out.

    Anwya, you said :”Hansen postulated global cooling first”. Do you have a reference for that, please? Not that it’s any biggie; people are allowed to change their minds.

    Thanks,

  12. First or fifth order curve fits have nothing to do with time frames. A first order curve fit is one of the form y=mx+b. You need at least two points to make such a fit (which would be a wast of time). A fifth order curve fit is of the form y=ax^5+bx^4+cx^3+dx^2+ex+f. You need at least six data points (ie, xy) to perform that. A first order curve fit is a linear (straight) line. A fifth order fit is useful for making a smooth fit through a lot of data, but has little physical meaning (ie, try to project it beyond the last point in Anthony’s graph and it might project down to -infinity or swing around and go to +positive infinity. Now, Anthony isn’t trying to pull the wool over anyone’s eyes by doing that – its just that making a nice smooth fit using a fifth order is easy and fast in excel.

    The other “curve fits” that are often discussed are actually statistical deconstructions of wave forms, and assume that temperature follows a complex combination of long and short waves that are combined. The waves are assumed to have a basis in reality in that they could be caused by sun spots or some other periodic influence, and, unlike a fifth (or even first order curve fit) can be projected into the future. THey are used often in financial and other types of forecasting. Those types of deconstructions and take more time (and a good stats package like “R” – climateaudit.org discusses R quite extensively and is a good place to go if you want to see how stats are applied. The R CRAN site is another…

    You can do the same – I believe that a few posts back someone made a link to all the data for the last few decades in excel. THere are a lot of good quickie instructions on the web as to how to do the first and other curve fits using excel. I strongly encourage those who don’t know how to fit data (or even how to graph it!) to learn…

    Oh, and Neilo – don’t be lazy. It took me three seconds to find the IBD article that confirms what George M stated. Hanson has apparently been trying to find the limelight since the 70’s….

    http://www.investors.com/editorial/editorialcontent.asp?secid=1501&status=article&id=275267681833290

    Happy Independence Day!

  13. “For a layman, could you explain the difference between 1st order and 5th order curve fit in a 30 year period? Obvoiuosly, the 1st takes a longer time frame, but is it 5 times longer?”

    It’s simply the best polynomial fit to the data. A linear trend is in the form of y = mx + b (first order polynomial). A second order polynomial has an x-squared term in it, and the curve will have a maximum of one inflection point. A third order will include a cubed term and have up to two inflection points, and so on. The fifth order term will have up to four inflection points. The increased number of inflections allows a curve that will better adapt to changes in trends, though it’s somewhat of a dubious exercise to add too many terms, because at some point all you are doing is duplicating the data. The problem with using things like a fifth order polynomial is demonstrated by the last segment of the graph. While the curve certainly fits recent history well, a logical extension of the curve provides illogical long-term results. Though, to be fair, many would argue that using a linear trend does the same.

  14. Bob Zorunkle,

    If memory serves, first-order and fifth-order refer to the degree of polynomial to which the data has been fitted. Both first-order and fifth-order curves provided by Anthony encompass ALL the data.

    A first-order curve fitting is an assumption that the data are following a straight line and that deviations from that line are just random variations. This strikes me as a fantastically stupid assumption on something that is coupled, nonlinear and chaotic, like climate. But hey, most members of the media don’t even understand the concept of slope.

    A fifth-order curve is one of the form fx5 + ex4 + dx3 + cx2 + bx + a, and is much more robust for fitting temperature trends.

  15. To give Anthony a devil of a headache this 4th, here’s a link that will have AGW types wailing, with much grinding and gnashing of their teeth. This guy actually explains the physics very much the way I know them, and is very careful in describing how CO2 affects global temperature. So, when someof my buddies get all rightous on me and say “Admit that increased CO2 increases temperature” I have to say that is correct. I even have had to grind my teeth when I’ve been reading Jim Manzi (note to Jim – being rich doesn’t always make you right…) when he says we should just admit that CO2 increases temperature and move the subject onto the economics. I feel we can argue the economics on energy quite well on their own…

    Anyway, here’s the link…

    http://brneurosci.org/co2.html

    And here’s another to make the wind worshipers (I’ve written briefly here before about the false economics of free wind being cheap – it isn’t for a variety of reasons). This article uses very basic data and shows how wind can’t fill the void – its too unreliable. Read it – its brutal…

    http://www.theregister.co.uk/2008/07/03/wind_power_needs_dirty_pricey_gas_backup_report/

    I will admit that cheap solar may make a difference (see my post from a week or so ago…) but it will need pumped storage to make it effective.

  16. Bob, in this case, first order and fifth order refer to polynomials of the form:
    y=a0 + a1*x**1 + … + an*x**n
    which describe a best fit curve to the data. A first order polynomial is a line.

  17. I had a thought {happens from time to time} while looking at these graphs — they show what has happened, not what will happen. So opposing positions can look at the graphs and take solace that their position has been ‘proven’. The graphs don’t explain why there’s been warming and cooling, though the red line suggests that perhaps a cooling period has begun. And in ten or twenty years, should the blue line also go negative, will the AGW proponents start trotting out temperature charts dating to just after the end of the LIA? Of course, should the cooling get really serious with another brief LIA, there are sure to be folk foolish enough to suggest we can ‘do something’ to change the climate. And the beat goes on…

  18. Thanks for the graphs, Anthony. It’s pretty clear that anyone looking at the first two graphs back in 2004 would think things were getting warmer.

    I sure hope all the fuss will slack off, and people will start thinking about what they will do long-term to handle climate change, both colder and warmer. It’s a good start that more people are paying attention, and more detail is being developed on the potential effects of both warming and cooling. Long range contingency planning, efforts to better inform our representatives, and acquiring more knowledge on these topics would be a lot more helpful than knee-jerk political bashing.
    Tnx again – Tim (www.timprosserfuturing.wordpress.com)

  19. Why not a 200 day moving average? That would show more of the short term moves while smoothing out the peaks and valleys. The 5th order isn’t bad, at least it shows a little bit of the variation. (just eyeballing it, it looks like about 1,000 day moving average, but I’m guessing)

    With a little bit of logic, I think I can demonstrate that the first order curve fit is of no analytical value. It appears to be simply a straight line fit through the available data. Here’s the problem: the slope of that line is dependant on the starting point, which was a completely arbitrary decision by the observors. I realize that that is all of the data we have for these measurements, but the underlying temperature function stretches back to time immemorial. The choice of starting point was a human decision that has no relationship to the underlying function. When an arbitrary decision by the obersvor which has no relationship to the underlying function can dramatically skew the results of the curve fit, then that curve fit tells you next to nothing about the underlying function. The fact that the 11 year fit shows a very different slope illustrates this point.

    Moving averages may not tell you much about the underlying long term function, but at least they show you relative movement over a defined time period, which seems quite appropriate when dealing with a cyclical phenomena.

  20. neilo:

    On July 9, 1971, the Post published a story headlined “U.S. Scientist Sees New Ice Age Coming.” It told of a prediction by NASA and Columbia University scientist S.I. Rasool. The culprit: man’s use of fossil fuels.

    The Post reported that Rasool, writing in Science, argued that in “the next 50 years” fine dust that humans discharge into the atmosphere by burning fossil fuel will screen out so much of the sun’s rays that the Earth’s average temperature could fall by six degrees.

    Sustained emissions over five to 10 years, Rasool claimed, “could be sufficient to trigger an ice age.”

    Aiding Rasool’s research, the Post reported, was a “computer program developed by Dr. James Hansen,” who was, according to his resume, a Columbia University research associate at the time.

    So what about those greenhouse gases that man pumps into the skies? Weren’t they worried about them causing a greenhouse effect that would heat the planet, as Hansen, Al Gore and a host of others so fervently believe today?

    “They found no need to worry about the carbon dioxide fuel-burning puts in the atmosphere,” the Post said in the story, which was spotted last week by Washington resident John Lockwood, who was doing research at the Library of Congress and alerted the Washington Times to his finding.

    From this article.

  21. Out of interest (and I’ve seen it before but just can’t find it anywhere) what is the current “background” warming level?

    I seem to remember it is somewhere in the region of 0.6 degrees centigrade per century since the “end” of the last ice-age (end is in inverted commas as I’m was always taught in my geology lectures that we are still in an ice-age as we have icecaps – it is just a warm period inbetween cold ones).

  22. Short term, long term, and really long term . . .
    I’m a fan of eustatic sea level data, in part for its inherent smoothing and detailed long view. The temperature graphs of recent are very short term. If one looks at the entire present interglacial, the sea level data shows that things peaked around 6,000 years go. If the starting points of trends analysis begin a few thousand years ago rather than a hundred years ago, it is pretty clear the long-term trend is cooler. This is consistent with the behavior of the previous interglacial (Eemian) where it warms to the era’s highs early in the period, then gradually cools only drop suddenly at the end of the interglacial. Following this notion, it is unlikely that the world will return to the highs of the Medieval Climate Optimum, but increasingly possible that a protracted cooling will follow the late 20th Century highs.

  23. Higher order polynomial regression curve fits are great for smoothing data, and to see trends within the scope of that data. But if you have ever tried to use them to extrapolate outside that scope, you will know the perils of that exercise. Once you exceed the boundary conditions of that fit, all bets are off, and the higher order the polynomial, the less useful it is for predictions beyond the scope of the data, especially when the so-called “dependent variable” (the y axis value) has little dependence on the “independent variable” (the x-axis).

    Heck, even first-order regressions are subject to this limitation. Just because you can get a line or curve to fit doesn’t prove a causal relationship.

    My $.02
    DaveK

  24. Brendan.
    We have wind here in Denmark (around 20% of electricity needs are supplied that way) and there is nothing wrong with it at all, as long as it doesn ‘t “stand alone”. In Denmark we have swedish hydro-electric, natural gas and of course oil as back-up, so no problem. Wind is also looking more and more economical compared to the rising oil prices, so don’t knock it.

    Question to all:
    Isn’t all this talk about “surface temperature” a waste of time. Surely the only
    temperature that effects climate is the temperature (not at the surface) of the oceans, as witnessed by PDO, AMO, El nino etc. ???

    Surely surface temperatures are too sporadic to gain any real meaning on climate from them ??

  25. Just because you can get a line or curve to fit doesn’t prove a causal relationship.

    But it is indicative of one.

    While, the failure of a line or curve to fit, as in the the IPCCs climate predictions, is conclusive proof of no causal relationship.

  26. Much as I love to see the temperatures giving grief to the alarmists, we might find the temperatures start to turn upwards soon. According to http://www.cdc.noaa.gov/people/klaus.wolter/MEI/mei.html we are probably coming to the end of the La Nina. If we go into an El Nino this can mean a rapid rise in temperatures. Of course if we are in a period like that between 1950 and 1975 when La Ninas seems to follow one after the other, then we may be in for a prolonged period of lower temperatures.

  27. The Engineer:

    I don’t agree that surface temperature is a waste of time, since it’s what most people key off of and understand about global warming. The time spent illustrating and discussing that there has been little to no warming for 11+ years will eventually pay off.

    I do agree that surface temperature fluctuations are periodic and driven in the most part by oceanic oscillations: the AMO and ENSO, but not the PDO, which is not the simple residual of global SST subtracted from the North Pacific SST, north of 20N. The magnitude of the “North Pacific Residual” is more on the order of the AMO, but the timing of its oscillation is different than the AMO, and the PDO for that matter. The PDO is an aftereffect of ENSO, or at least one paper describes it as such. In “ENSO-Forced Variability of the Pacific Decadal Oscillation”, Newman et al state in the conclusions, “The PDO is dependent upon ENSO on all timescales.” Refer to:
    http://www.cdc.noaa.gov/people/gilbert.p.compo/Newmanetal2003.pdf

    I’ve illustrated and discussed the difference between the PDO and the North Pacific Residual here:
    http://bobtisdale.blogspot.com/2008/06/common-misunderstanding-about-pdo.html

    I have 17 posts on Smith and Reynolds SST data, including instructions on how to download it from NOMADS, over on my blogspot. For the Smith and Reynolds posts, I’ve tried to keep my AGW skepticism to myself and to report only what I found.
    http://bobtisdale.blogspot.com/2008/06/smith-and-reynolds-sst-posts.html

    Regards

  28. ‘WWS’ asked for a 200 day (roughly 6 month) moving average… As we say round here, ‘Yer ’tis:

    http://www.woodfortrees.org/plot/hadcrut3vgl/from:1979/offset:-0.15/mean:6/plot/gistemp/from:1979/offset:-0.24/mean:6/plot/uah/mean:6/plot/rss/mean:6

    REPLY: Just a note to readers, Paul has created an exceptional web resource at http://www.woodfortrees.org which is in the blogroll on the main page. If you wonder whata certain graph of temps or other variables looks like, be sure to try this page and it’s easy interactive menu system for generating graphs.

  29. Bob Tisdale.
    Thanks for the information. I’ll get onto it during the weekend.
    My main point was that its the sea that drives the climate, not land.
    The sea stores energy for long periodes of time, the earth warms
    or cools very quickly.

    Trying to compare, or average temperatures in the desert with temperatures
    in the antartic, makes no sense to me at all. Climatologists are attempting
    to compare thousands of different situations that exist today with thousands of different situations that existed yesterday in chaotic systems
    and then trying to extrapolate to some kind of average figure in a stable system. The noise must be almost suffocating.

  30. Another simplified approach to explanation of “first and fifth order” curve fits to data: A first order fit (linear) results in a straight line through the data points. A second order fit (quadratic) allows a smooth arc through the data points. A third order fit (cubic) allows the arc to change direction (up or down) one time — creating a simple “wave shaped” line through the data points. A fourth order fit (quartic) allows the wave to change direction one extra time as it passes through the data. A fifth order fit allows the wave to change direction one more time than does a fourth order. And so on — as the “order” increases, the number of individual “waves” (ups and downs) in the fitted curve increases.

    I like a cubic fit myself (third order fit) — for data that is obviously non linear, subject to change in direction at any time, and when I don’t know the underlying physical reality. The cubic allows the “curvature” to reverse direction so beats a quadratic (second order fit), in my mind. Nonetheless, the cubic remains conservative compared to higher orders — being less likely to exaggerate curvature at the endpoints of the data.

    By comparison to a cubic fit (third order fit), the outcome of a linear fit (first order fit) depends too greatly on the time interval chosen (though all “curve fitting” can suffer from that). Nonetheless, linear is useful to clearly illustrate a simple point to an audience of varied academic background (I suppose that’s why so many linear fits are seen in the “global warming” popular literature).

    Of course, as others have said, a curve fit (of any order) is no more than a math exercise (for example, the results cannot be reliably extrapolated to times beyond the existing data) — until one has discovered some underlying reality that explains the direction(s) the curve fit takes. Yet, the curve fit result might suggest those real mechanisms to a person with a broad enough scientific background — so has some scientific value.

    For what its worth.

  31. As a physicist, I deal with fitting data on a daily basis. If I were presented with that data in raw form (not knowing what two variables were plotted) so I wouldn’t superimpose some physics on the numbers, my first thought would definitely not even think about a linear fit. That sort of “noisy” data doesn’t even qualify.
    My next step would be to run an FFT. The numerous peaks and valleys suggest an underlying complex time series. After the FFT one can then decide whether to investigate the data in the high frequency realm; in this case days or months, or look at the longer term trends of years, multiyears, or decades. Again I would let the FFT guide me a bit here: IF the longer frequencies dominant the harmonic spectrum, I would average the data over the shorter time frames and then replot and see what happens.
    The above sequence of analysis assumes I don’t have a clue about the physics, but would be trying to find out some relation. Knowing some of the physics behind the data helps tremendously in sorting through the analysis.

  32. Surely surface temperatures are too sporadic to gain any real meaning on climate from them ??

    Maybe. However, the IPCC AR4 projections focus largely on these. Their main projection over time is for surface temperature. They’ve included such projection in every one of their four reports. The compare data to their own projections in their reports.

    Presumably, we can learn just as much from comparing data to projections of surface temperature as the IPCC thinks we can learn by reading their comparisons.

  33. My favorite long term global temperature graph is here: http://www.longrangeweather.com/global_temperatures.htm

    The current cooling period looks very much like the 1300-1350 period. We can only hope is does not proceed quite like the period from 1350-1600. Brrrr!

    I have great difficulty fitting this graph to a hockey stick, though I might be able to beat it into that shape with a hockey stick.

  34. Mike,

    I agree about the FFT.

    When I tried FFT (just for fun) on global temperatures (various incarnations) and historical monthly Sunspot numbers (one incarnation), global temperature data and sunspots share a lot of frequencies.

    Then, I tried (just for fun), a simple model (i.e. intuitive but unsubstantiated physics) of global temperature as a function of Sunspots, etcetera. The simple model “explained” global temperature variations (on the 6 to 12 year and longer scales) over the last 160 years (with only a couple tenths left to explain). So, FFT are fun and practical to get a possibly meaningful handle on noisy data. About sunspots and global temperature — I have not made up my mind.

  35. Anthony,

    The mindset of climate science is linear or polynomial functions but they represent a limited class of functions and ones that are hardly seen in nonlinear systems. The climate is highly nonlinear and we see things like the PDO which causes a climate shift, not a simple rise or fall. You could achieve just as good a fit to the data with a step function: flat from 1979 to about 1998 and then a second level from about 2000 on connected by a smooth transition at the 1998 El Nino. The rise in the step is about 0.2C. It’s not what the modelers would predict, but so what.

  36. Lucia, knowing that you are a statistician and I am an engineer will
    make this conversation easier.

    While I am aware that someone at some point chose to try and work
    with a global average surface temperature, that very process seems
    to me to be an unnecessary complication of an already chaotic
    process.

    Surely if one chose the temperature of a specific area of land or sea,
    one could eventually eliminate all the noise and compute a true value
    for temperature change for that area.

    If others did the same for different areas, one could start averaging
    those numbers. That would basically be many small programmes
    computing local relationships, instead of one massive computer
    attempting to average chaotic global relationships.

    But of course it couldn’t be that simple !!

  37. Patrick Hadley,

    The latest ENSO ensemble forecasts are projecting ENSO neutral conditions through the Winter of 2009. The dynamic models are projecting slightly positive SST anomalies, while the statistical models are predicting slightly negative SST anomalies, with the average hovering around zero. The probabilistic forecast is 75% ENSO neutral, and 25% El Nino OR La Nina.

    All the curves fit to print, and more, on this subject, will be found here:

    http://iri.columbia.edu/climate/ENSO/currentinfo/QuickLook.html

    and here:

    http://www.cpc.noaa.gov/products/analysis_monitoring/lanina/enso_evolution-status-fcsts-web.pdf

  38. Engineer –
    I love knocking wind. Wind is a backup, and a moderately expensive one at that. My point in showing the article (did you even read it?) is that even if there was a supernetwork of electrical tie ins across Europe, the correlation of wind from the sea to the urals is too strong – and there are too many days during peak demand where you see calms of up to a week or more, making it impossible to rely on wind as your main source of power. I won’t repeat what was said very well in that article – but they even mention the british off shore wind going through extended levels of calm that make them useless.

    If you look at any of the specific positions (ie, “areas”) as Anthony has place here, you would see that its not a simple matter to even eliminate noise there.

    As for tying all the data together, there are multiple good ways to do so, that will not only statistically tie together the suffocating data, but will also give good estimations of standard deviation. They do however take some skill, and if the underlying data is corrupt or has been “changed” your mileage results may vary…. Kriging is perhaps the most common, with a sound underlying basis to it. There is some “art” to it in creating some of the input search parameters, and kriging the whole world using one set of these may not in fact be appropriate… There are others, but that would be the first tool I would reach for. (See the R CRAN pages – as an engineer, I would expect you to be able to pick up these methods fairly rapidly).

    I should mention that, if properly taken, we probably have enough points to apply simple averageing to the data without resorting to kriging. Kriging itslef would not take into account mountains, or seas, or deserts – just the data. You could create a numerical surface and tie together correlations on how you expect the surface to react, but now we are into numerical modeling – and, well, that’s another crazy cat lady house of cats that we could get into…

    FFT has been mentioned, but again, it is just one of several tools. I think we all can agree that curve fits are quick and dirty assessments that have little meaning in reality for this application – although a first order is by far the most appropriate, but not a predictor. FFT’s and some other stats methods are much better, and “honor the data” as Andre Journel used to say…

  39. It looks to me like El Nino is coming back.

    If you watch this 5 month animation, you can see there has been a switch from La Nina to El Nino conditions. More importantly, if you keep speeding this animation up until you can actually see the waves of cold/warm water moving in unison (very interesting actually), one will see that the Trade Winds pattern has now reversed (blowing West to East versus the normal East to West) and this reversal usually leads to El Nino conditions.

    http://www.osdpd.noaa.gov/PSB/EPS/SST/anom_anim.html

  40. Just a brief reminder…

    One of the “shifts” that occurs every 10 years on temperature averages (Normals)
    by the NOAA NCDC is that they compute a “new normal” series of temperatures for each observational climate station, using a 30 year period set on each 10th year (1970, 1980, etc), then curve fit it by some polynomial function to create the “normal” daily highs/lows/averages output for each station. You will have to consult the National Climatic Data Center for the particular function used…

    We were using the 1960 to 1990 “normals” through 2002, then switched to
    1970 to 2000 “normals” which will be in effect until about 2011 or 2012, when
    another set of “normals” will be issued…

    Looking at the ‘history” of using these 30 year ‘normals,” it becomes obvious that
    as you get away from the 1960-1970 “global cooling” and toward the 1998 warming peak, the normals are going to show substantial “warming”…

    Just a thought for your discussion and analysis…

    -JH-

  41. Pat Lindsay,

    We’ve been all over the question of linear vs. non-linear, step functions, slope shifts, etc. Over a limited time frame like the “satellite era” they will all have pros and cons, especially as indicators of where the trend will be next year, or ten years from now. The reason we get so focused on this, though, is because of a failure of climate “science.” Sometime during the late 1990’s the study of “natural climate variability” fell out of vogue in climate studies. What studies did get published were published with a clear bias toward AGW, or they didn’t get published. I.e., studies of “natural climate variability” were motivated by the goal to quantify it so as to remove it as a source of noise in the data, thus revealing the “real” trend caused by AGW. I don’t think you could get a paper published in the past 20 years without this kind of focus, or at the very minimum a “but this doesn’t disprove AGW” qualifier.

    But anyone with a proper understanding of decadal, bidecadal, and multidecadal variation in climate would hardly be surprised at the recent retreat from the relentless upward “trend” of the late 20th century. You’ll search IPCC AR4 in vain for any kind of serious review of the literature on natural climate variability over these time scales, especially in global temperature. But it is there. And that “real” climate “science” seems taken by surprise ought to be an embarrassment.

    As an economist, I cannot help but draw an analogy to business cycles. They are hard to predict, as to timing. But if anyone tries to argue — and at one time, some did — that business cycles are a thing of the past, I stop taking them seriously. I may not be able to predict when the next recession, or expansion, will occur, by I know they will.

    Similarly with climate variability. There are all kinds of cycles in climate: interannual, intraannual, decadal, multidecadal, centennial, millennial, and so on. The least credible forecast of all is that we are on a relentlessly upward trend. Every upward trend will turn down at some point, and on scales that are roughly, if not exactly, predictable. In the early 21st Century we were due for a downturn on several scales — bidecadal, multidecadal (e.g. PDO, and coming soon, probably more from the NAO), and centennial (here maybe a combination of Gleissburg and terrestrial ocean dynamics).

    We will never get the trends right unless we understand the periodicities involved.

  42. I should mention that although wind is looking more economical as compared to oil, the connection between them currently is weak. That is, wind cannot be a replacement for oil, oil is not used to create electricity (for the most part – only crazy cat ladies use oil to create electricity)… The much waited for electric car will bring its own issues as rare earth prices will begin to rise when battery demands begin to go through the roof… I have mentioned before that I think nuclear based methanol is an appropriate susbtiture fuel, in that our current infrastructure doesn’t need to change at all. I am open to solar if the promised breakthrough from Nanosolar reaches $1/W – right now, solar is a toy, and an expensive one at that.

    Boy, I really thought my sticks would stir up more bees.

  43. Ed Ried,

    Thanks for the link. The simple Sunspot model I mentioned above accounted for volcanic eruptions visually (i.e. where the deviations were below the model fit, I looked for some volcanic eruptions in the same time frame). That one chart has in one place all the scattered volcano data I found in my brief google-quest. I’m keeping the chart for reference.

    Overall, the approach of the linked chart “appeals to my intuition” as a fledgling “climate aficionado”. Of course, “appeals…” does not mean its true — but, I like to start with something intuitive and see if it stands up to progressively closer scrutiny.

    Anthony, yours is a very worthwhile site for information sharing.

  44. The Engineer.

    Lucia, knowing that you are a statistician and I am an engineer will
    make this conversation easier.

    I have a BS. MS. and Ph.D. in Mechanical Engineering. My Ph.D. topic was related to turbulent particulate flows. So, I’m accustomed to the various issues associated with the sorts of random seeming variations one sees in different sorts of transport phenomena.

    If you look carefully, to fairly simple statistical tests, of the sort generally taught in undergraduate engineering courses. :)

    There are reasons I don’t try to immediately apply a fourier transform. Some have to do with the phenomenological explanations suggested by the IPCC.

  45. DaveK (00:04:33) :

    “Higher order polynomial regression curve fits are great for smoothing data, and to see trends within the scope of that data. But if you have ever tried to use them to extrapolate outside that scope, you will know the perils of that exercise. Once you exceed the boundary conditions of that fit, all bets are off, and the higher order the polynomial, the less useful it is for predictions beyond the scope of the data, especially when the so-called “dependent variable” (the y axis value) has little dependence on the “independent variable” (the x-axis).”

    That’s worth restating, so I did. You probably don’t want to put much faith in the left and right ends of the data either.

    It would be amusing to take the same time frame, do a 5th order fit over the middle 2/3s or so of the data but display the curve for the whole time period. I bet it would dissuade people from extrapolating such curves in the future!

  46. Thank you very much for the graphwork, Paul. I have bookmarked your site and will check it often!

    And that moving average graph tells me something very useful – which is that we are at an extremely crucial time period *right* *now*. If we are to remain in the relatively stable temperature regime of the last 8 years or so, then temperatures need stop dropping pretty quickly. If we are to see evidence of long term warming, temperatures need to start heading back up rapidly. However, if over the next 6 months temperatures continue to drop at the same rate as they have the last 6 months, then we will plunge back to levels not seen since the low around 1985 and the mid-70’s, in which case I imagine we will begin to see some dramatic observable confirmation.

    Any one of those outcomes is possible; but over the next 6 months only 1 of them can turn out to be correct.

  47. I’d like to make two basic points:

    1. The logic used to suggest that Mr. Hansen believed in global cooling is incorrect. The evidence is that a man who DID argue in favor of global cooling (Mr. Rasool) used a computer program written by Mr. Hansen. The fact that Mr. Rasool used Mr. Hansen’s program provides zero evidence about Mr. Hansen’s beliefs. Mr. Hansen is no more responsible for the global cooling hypothesis than the manufacturer who supplied the hardware on which Mr. Rasool performed his computations. The statements made here about Mr. Hansen are slanderous. Moreover, they are classic ad hominem attacks: arguing that the AGW hypothesis is incorrect because one of its advocates is an evil person. If Osama bin Laden endorses AGW, that doesn’t make it wrong. If Mother Teresa opposes AGW, that doesn’t make it wrong. Let’s dispense with this National Enquirer gossip and concentrate on the facts.

    Second, on the matter of the most useful curve to which to fit the data: we should rely on Occam’s Razor to guide our considerations. We want to go with the simplest hypothesis. If we represent our data with a polynomial, then the complexity of the polynomial is directly commensurate with the degree of the polynomial. In other words, y=ax+b is simpler than y=ax + b(x**2) + c. Occam’s Razor argues strongly in favor of the first order least squares fit over the fifth-order fit. This works in favor of the AGW hypothesis.

    REPLY: I’m sorry, your story in number one is just absolutely ludicrous. You’d have to assume that they never talked about it or collaborated on it. If Hansen didn’t believe in global cooling at the time then he’d likely not have allowed his connection to it in any way. University research departments are tight centers of collaboration, and if one scientist is in disagreement of others works in the department, they usually don’t collaborate to create a finished product. These two people were just office doors apart, not on other sides of the world where one found a tool to use created by the other and there was minimal collaboration.

    It doesn’t much matter what James Hansen, other commenters, or you believe for that matter, nature will be the final arbiter on the AGW issue.

    There were lots of university meteorology departments that started research into global cooling at the time, so it would be no surprise that Hansen and Rasool were following the same path as many other departments at the time. It is your logic that is flawed.

    – Anthony

  48. I’d like to respond to Basil’s comments about periodicities. I think they represent a common logical error: scanning complex time-sequenced data for patterns. The most obvious examples of this come from financial analysis. There have been lots of voodoo artists who crunched the data trying to predict the markets. They’d fit complex polynomials or various periodic functions to the data, twiddling the coefficients, until they got a good fit. Then they’d base investment decisions on these models. And surprise, surprise, half of them would be right and make money! The other half would lose money and declare that they just needed to make a few more adjustments. The problem here is that they’re playing numerological games, not serious analysis. If you mindlessly fit functions to data, you get garbage. The important thing is to have an underlying mechanism to justify whatever function you attempt to fit to the data.

    Where you can justify a function with a mechanism, then you’re safe to proceed. And sometimes you can use random function-fitting to work backwards in an attempt to discover a mechanism. If your data shows a apparent periodicity, then it’s worthwhile to inquire into the possible causes of that periodicity. If you can’t identify a mechanism, then any attempt to project that periodicity into the future is vulnerable to the possibility that you’re looking at some sort of statistical artifact.

    There are some periodicities in climate whose underlying mechanisms have been worked out. I don’t question them. What I am warning against is numerology as opposed to scientific analysis. GIGO.

  49. Ophie,

    It is your colleagues at “real”climate that are the numerologists.

    It is they that not only are scanning complex time-sequenced data for patterns but also are injecting those patterns into the data for them to find.

    Sorry if this is a bit vitriolic, Anthony, but I’ve just been perusing the Australian “Daft” Garnaut report. I just keep on having this mental image of Emperor Canute, naked, telling the tides to stop. How can people be this stupid? Answer, they are not, they are dishonest.

  50. It is your colleagues at “real”climate that are the numerologists.

    It is they that not only are scanning complex time-sequenced data for patterns but also are injecting those patterns into the data for them to find.

    Fine. So let’s not do it here, OK?

    REPLY: Opie, I’ll be the one to choose what does and does not get discussed here, not you. If you want to be able to make those choices yourself, get your own blog.

    Discussion of the issues about time sequenced data is of interest to a large majority of people here, and it was you that brought up the issue. Thus the issue and discussion is fair game. – Anthony

  51. I am extrapolating from Oph’s views that the AGW models assume that CO2 is the mechanism. From what little I know about scientific investigation of modeling (I know more about the gold standard scientific method, having used it myself), I would think that the group that came up with the AGW model understood that “A” model is built to explain a theory, not test it. If a theory is being tested, a control model (or even better, several competing models) would be advantageous here (I am also extrapolating from Oph’s view that the CO2 theory has not been proven yet and they are still in the testing mode). That would lead me to suggest to the AGW investigation group that they need several models going at the same time with different mechanistic theoretical assumptions for each.

    The research literature is awash with climate theories, not just one climate theory. I don’t understand the eggs in one basket method unless the group is not an investigative group and is instead an agenda group.

  52. Anthony dismisses my argument with speculative comments for which he offers no evidence. For example, he claims that These two people were just office doors apart

    If you know that to be true, then surely you can provide us with their office numbers. Or are you just making it up?

    [REPLY: Same department, same building at Columbia, strong circumstantial evidence of being just doors away. Office numbers? Heh, funny man that Opie. I challenge you to find and present here the department course catalog from Columbia that year. Office numbers will likely be there.]

    f Hansen didn’t believe in global cooling at the time then he’d likely not have allowed his connection to it in any way.

    That’s speculation for which you have neither evidence nor logic. You’re just making it up.

    [REPLY: Oh the logic is there. Speculation perhaps, but supported by cicrcumstances. ]

    if one scientist is in disagreement of others works in the department, they usually don’t collaborate to create a finished product.

    You have no evidence that they collaborated. The evidence you have states that Mr. Rasoor used Mr. Hansen’s program. Scientists grab code from each other all the time. Ever heard of good old SPSS? In my research I took some functions from another fellow who had nothing to do with my project. Borrowing code in part or in entirety does not establish collaboration. You’re fantasizing a connection that is not derivable from the evidence.

    [REPLY: You mean you used his code without even bothering to tell him or ask permission? Seems like plagarization to me.

    The fact that Rasool used code from Hansen says they collaborated at some level, unless you are ready to say that Rasool used it without notice of any kind or even asked for instructions on how to use it. Highly unlikely in a university department envrionment. Again I submit that if Hansen didn’t agree with the research being conducted by Rasool, he probably would not have helped or agreed to provide the Mie scattering code because he would not want his name associated with a global cooling paper.]

    It doesn’t much matter what James Hansen, other commenters, or you believe for that matter, nature will be the final arbiter on the AGW issue.

    Indeed so!

    There were lots of university meteorology departments that started research into global cooling at the time

    Yes, indeed — and do you know what their conclusions were? They pretty much trashed the idea. Remember the NAS, which I mentioned earlier as having never made an erroneous report? In 1975, they issued a report on the question entitled “Understanding Climate Change: A Program for Action”. Basically, they said that “we do not know enough about climate to make predictions”. And by the way, the Rasool paper did NOT predict global cooling — it presented a mechanism by which global cooling was possible.

    [REPLY: “They pretty much trashed the idea.” No, the PDO did that for them in 1978, Nature ruled the day. They were well along in the research for cooling and then nature threw the curve ball, they didn’t know why, they started to look for answers, CO2 was centered upon. PDO wasn’t discovered until much later. Nature leads, research follows, it has always been that way. To suggest that they weren’t on that path in the 70’s and that they came to the conclusion without nature’s lead is just ludicrous on your part. ]

    This whole story about scientists predicting global cooling is a load of lies. It didn’t happen that way.

    [REPLY: Load of lies? To use your own words, “no logic, speculation”. Oh it did happen that way, I was in the middle of it at the time. It’s not lies, its being spun now for the current view. I suppose now you’ll cite the recent Petersen ex post facto paper as “proof”.

    Opie, please answer this question: how old are you? ]

  53. Regarding the temperature time series, it is clear that there are a number of variables that have to be factored in to explain it properly.

    First and foremost, there is an ENSO index variable that greatly impacts the global time series. Major El Nino in 1997-98, 1987-88, 2000-2005 and 1983-84, minor La Nina events in 2007-08, 1989 and 2000.

    Secondly, there are volcanoe impacts with Pinatubo in 1991and El Chichon in 1982.

    If one could factor these out in some manner, the time series would have a much different slope etc.

  54. Opie, I’ll be the one to choose what does and does not get discussed here, not you. If you want to be able to make those choices yourself, get your own blog.

    You misunderstand. Robert Wood criticized an error that he perceives at RealClimate. I suggested that the error not be repeated here. My verb “do” referred to the error, not the discussion.

  55. Occam’s razor argues for the simplest but ‘simple’ needs to be clarified. A linear trend, while ‘simpler’ is not always at the top of the list of preferred trend decriptors in nature. Exponential/Logarithmic or periodic curves are more common and hence might be a better fit as per Occam.

    Great blog! I read it often but this is my first post here.

    ~peace~

  56. A linear trend, while ’simpler’ is not always at the top of the list of preferred trend decriptors in nature. Exponential/Logarithmic or periodic curves are more common and hence might be a better fit as per Occam.

    If you have a model that suggests exponential, logarithmic, or periodic functions, then yes, you should apply that model. If you don’t have such a model, and you are inquiring into the first derivative of the function, then a straight line is the simplest function. Do you have a model suggesting some other function?

  57. Brendan: I looked at your link to the work of T.J. Nelson, reading some of it and skimming the rest. He is to be commended for good organization of material, impressive gathering of relevant data, and clear writing. However, his scientific logic has some weak points. See the following quote:

    QUOTE:
    “Between 1900 and 2000, atmospheric CO2 increased from 295 to 365 ppm, while temperatures increased about 0.57 degrees C (using the value cited by Al Gore and others). It is simple to calculate the proportionality constant (call it ‘k’) between the observed increase in CO2 and the observed temperature increase:
    ln (365/295) = k * 0.57
    k = 0.3735
    ln (2) = 0.693 = k*deltaT
    deltaT = 1.85

    This shows that doubling CO2 over its current values should increase the earth’s temperature by about 1.85 degrees C. Doubling it again would raise the temperature another 1.85 degrees C. Since these numbers are based on actual measurements, not models, they include the effects of amplification, if we make the reasonable assumption that the same amplification mechanisms that occurred previously will also occur in a world that is two degrees warmer.”
    END QUOTE

    Several problems: First he is assuming that 0.57 is the actual increase — but UHI is a concern, not to speak of data manipulation.
    Second, he is assuming that all increase is due to CO2, but there could be other causes for temperatures to rise in the 20th century such as land use, solar variation, recovery from the LIA, . . . .
    Third, he would get a different result if he based his calculation on the last 20 years rather than the 100 years in the 20th century. Or the results would be even more scary and dramatic if he took 1900 to 1940 to establish his k.

  58. In two REPLYs to a previous comment of mine, one person flatly rejects my statement that subsequent research “pretty much trashed” the idea of global cooling. Yet I cited an NAS report from 1975 that stated that we don’t know enough to make such predictions. The replying person states that only the discovery of the PDO in 1978 put an end to the global cooling hypothesis, yet the NAS report in 1975 — three years before the discovery of the PDO — said that there was no basis to make predictions of warming or cooling. The facts contradict the claim made by the replier.

    [REPLY: just because NAS issued a report in 1975 doesn’t mean anybody paid attention to it or dropped all the research about it. Global cooling research went on at many universities into the early 1980’s, NOVA did a show about it in 1979.]

    A second replier insists that the global cooling brouhaha did indeed take place as the skeptics claim, and provides as evidence the assertion that he was there. I suggest that a somewhat higher standard of evidence would be appropriate here. Please cite the scientific papers that predicted global cooling. On that question, I did find one interesting tidbit here:

    http://www.skepticalscience.com/argument.php?a=1

    As far as peer reviewed scientific papers in the 1970’s, very few papers (7 in total) predicted global cooling. What surprises is that even in the 1970’s, on the back of 3 decades of global cooling, significantly more papers (42 in total) predicted global warming due to CO2.

    Lastly, you ask how old I am. I’m 58 years old. I assume that you ask the question because you think that I’m some young punk who doesn’t know his arse from a hole in the ground. Well, I was “there” too — in fact, I was in grad school studying physics at the time. And in fact, in my planetary atmospheres course, the instructor spent plenty of time on the evolution of planetary atmospheres, including heating and cooling factors. The starting point was the greenhouse effect, discovered in the 19th century. Venus is the perfect example of the potential magnitude of the effect. My instructor discussed the possibility of the same effect here, and the standard wisdom at that time was that human emissions of CO2 should in fact lead to temperature increases — but that there was no observational evidence to support what the theory predicts. The overall conclusion was “it may well be happening, but we don’t have the data, so we’ll have to wait and see.”

    This from a physics professor who was teaching a course on planetary atmospheres. I think it represents the conventional wisdom in 1974. So you can see that I really do know what I’m talking about when I say that these stories of scientists believing in global cooling are a pack of lies.

    [REPLY: The “pack of lies” which is your opinion is contradicted by at least 7 papers in the 70’s that you cite above. So are you going to claim those papers were lies now? The scientists wrote them but did not believe in what they wrote? As I predicted, in citing skeptical science you inadvertently cited the Peterson 2008 paper, which is spin central. It was written to quash the idea.

    The tendency that you have to use terms on this forum such as “deniers” and “pack of lies” suggests that you are more about passion and less about facts.]

  59. Okay, this “global cooling” discussion is getting way out of hand. Obviously, I’ll refer everyone to RealClimate’s “Global Cooling” article, but another comment is important:

    What the hell does it matter if, in the infancy days of climate science, climatologists mistakingly believed that there was going to be an imminent period of cooling? Seriously, what does it matter? I was cleaning out my basement the other day and I came across a PC World article from the month when the very first 1.0 GHz machines went on the market; in that article, the writers suggested that it wouldn’t be long before we hit the 10 GHz benchmark. Obviously, they were wrong, as we are now fully aware that heat issues prevent clocking of single core CPU’s that high, which is why we have moved to a multi-core paradigm in computing.

    Should the PC World writers from that month forever hold the stigma that they made an erroneous prediction based on a more primitive knowledge base than we have now? Of course not. Yet you’re doing the same thing.

    Referring to the Global Cooling mole is an ad hominem meant to attack the scientists involved in AGW research, and does nothing to refute or further any argument against the AGW theory and its predictions.

  60. The tendency that you have to use terms on this forum such as “deniers” and “pack of lies” suggests that you are more about passion and less about facts.

    With all due respect, Anthony, many commenters here enjoy lambasting Hansen and other public AGW-related figures, and love to propagate patently false lies about the AGW theory or its proponents. If you are going to outlaw the use of the word “denier” (which, in the case of your posters who refuse to believe that CO2 can act as a GHG, is a most appropriate term), then it would be courteous of you to outlaw the word “alarmist,” as, to use your own words, that term “suggests [the commenter is] more about passion and less about facts.”

    REPLY: The issue with “denier” is it’s relation to the Nazi holocaust. “Alarmist” has no such connotations.

    which, in the case of your posters who refuse to believe that CO2 can act as a GHG, is a most appropriate term”

    Let’s see a show of hands: who deosn’t believe CO2 is a GHG?

    Don’t think you’ll find anybody. Lots of people here doubt CO2 is the sole cause though, inclding me. – Anthony

  61. The “pack of lies” which is your opinion is contradicted by at least 7 papers in the 70’s that you cite above. So are you going to claim those papers were lies now?

    No, they were a small minority. You seem to be assuming that if a single scientist advocated global cooling, then the idea had currency. There are always weirdo ideas floating around the scientific community, largely because (despite the claims of those with a political agenda who are unaware of the truth) scientists are extremely open-minded about scientific issues. If you can make a good case for something, you’ll get it published. And if you can make a compelling case for something that everybody else rejects, then you get the Nobel Prize.

    Remember, there were also 42 papers — six times as many as the global cooling papers — promulgating global warming. If you want to base a case on these papers, then you end up concluding that scientists were 6:1 against global cooling. Therefore, stories that scientists believed in global cooling are a crock.

    The tendency that you have to use terms on this forum such as “deniers” and “pack of lies” suggests that you are more about passion and less about facts.

    Perhaps you did not read my post on this subject, but I used the term “denier” on my first post and as soon as somebody objected to it, I promised not to use the term again — and I have not used it. Meanwhile, many posters here continue to use derogatory terms towards those they disagree with. Are your standards of civility dependent upon the editorial claims of the writer?

    I think “pack of lies” is a correct characterization of a story that is false and yet is often repeated despite proffered evidence that it is false. When people make an honest mistake, they stop doing it once they have been corrected. However, the skeptics keep repeating the global cooling falsehood over and over. I think we’re not talking about an honest mistake here, but rather willful disregard for the facts.

  62. Daniel: I actually agree with this statement:

    What the hell does it matter if, in the infancy days of climate science, climatologists mistakingly believed that there was going to be an imminent period of cooling?

    However, given that the current round of GCMs are unable to predict/foresee/scenarioize, whatever, anything which can be falsified by observations, since the error bounds apparently encompass conditions ranging from current conditions on Mercury to those on Titan, what makes you think that climate science is still not in a state of infancy? Have you not noticed a tendency over the last 20 years to move the bar every year in order to keep a semblance of credibility? Does that not give you pause?

    I’ve been following this issue since about 1981, long before Hansen testified, from back in the days when atmospheric scientists used to publish about negative feedback mechanisms stabilizing climate, and before the current groupthink of positive feedbacks.

    Positive feedbacks are not intuitively likely because if the Earth’s climate was dominated by positive feedbacks it would have reached a bifurcation point and run away millions if not billions of years ago. End of story. The climate system has been essentially stable for 100’s of millions of years through atmospheric changes, orbital fluctuations, and solar irradiance changes. If positive feedbacks dominated the system, we would not be alive today.

  63. Oops, I didn’t see this earlier:

    REPLY: just because NAS issued a report in 1975 doesn’t mean anybody paid attention to it or dropped all the research about it. Global cooling research went on at many universities into the early 1980’s, NOVA did a show about it in 1979.

    You seriously believe that nobody paid attention to an NAS report? Wow… all I can say is… careful… well, I suppose that there were skeptics even then who refused to pay attention to the NAS. But the great majority of scientists hold the NAS in very high esteem. To be invited to be a member of the NAS is a major career plum. So YOU may not have paid attention to the NAS report, but just about everybody else who had any interest in the topic certainly did. Jeez, even *I* remember reading a story about that report.

    As to “global cooling research”, I think that you’re conflating climatology research with global cooling research. Many of the factors that were cited in the original Science paper, such as aerosols, were legitimate subjects of scientific research. Indeed, they are STILL be studied as part of global warming research. The IPCC AR4 report includes 25 pages on aerosols. Are you suggesting that the IPCC believes in global cooling because it is reporting on research on a topic raised in conjunction with global cooling?

    REPLY: No I’m suggesting that I had firsthand knowledge of scientists at universities in the USA that were still pursuing the idea of global cooling as late as about 1981.
    In fact I know of one university researcher who dropped what he was doing in synoptic and mesoscale meteorology after the hard winters in the USA in 1977/78 and stared focusing on global cooling issues.

    The 1975 NAS report is like the IPCC report is this way; some paid attention, some took it with a grain of salt, and other just flat ignore it as “science by consensus”.

    A good example of science by group consensus that turned out to be flat wrong was the consensus of the solar science panel convened by NASA lat year that claimed we’d be seeing the activity of solar cycle 24 by now.

    From this story on space.com where they talk about the opposing views solar scientists have for cycle 24 they offer some opinions. NOAA Space Environment Center scientist Douglas Biesecker, who chaired the panel, said in a statement:

    […] despite the panel’s division on the Sun cycle’s intensity, all members have a high confidence that the season will begin in March 2008

    Well, obviously March 2008 didn’t happen. We are still waiting. So much for that group consensus. Data, calculations, models, expert panels, and even SWAG wasn’t able to predict natures whims.

    Nature rules, nature confounds, nature outwits.

  64. A dumb question about curve fitting, which I’ve been unable to answer using my old math texts or google:

    What’s the general equation for a skewed sine wave?

    Thanks to anyone that can help.

  65. Daniel Rothenberg wrote: “Okay, this “global cooling” discussion is getting way out of hand.”

    I agree, and as I said in the article; “…it has been my experience that no matter what you put in a graph, or how you graph it, somebody will find fault with it. ”

    And look what is had turned into.

    So we all earn a time-out. Thread closed for the weekend, we all have better and more fun things to do.

  66. You can read NASA’s version of Hansen’s change in position here:

    http://www.giss.nasa.gov/research/features/temptracker/

    They seem to minimize his predictions of global cooling, but they write:

    “Hansen marveled at the power of these airborne particles, known as aerosols. If aerosols can reflect and absorb incoming sunlight, what effect could events like Agung’s eruption have on Earth’s surface temperature? To find out, he plugged what was known at the time about aerosols, greenhouse gases, and how Earth absorbs and radiates energy into some physics equations. His results suggested that the aerosols should slightly cool the planet.”

  67. Pingback: Global warming roundup at STRANGE TIMES

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