Gavin Schmidt confirms model excursions

Gavin Schmidt asks at RealClimate: “How should one make graphics that appropriately compare models and observations?” and goes on to reconstruct John Christy’s updated comparison between climate models and satellite temperature measurements. The reconstruction was cited here by Simon in response to Gary Kerkin’s reference to Christy’s graph ( – h/t Richard Cumming, Gary Kerkin and Simon for references).

There’s been a lot of blather about Christy’s telling graph and heavy criticism here from Schmidt — but the graph survives. In taking all the trouble to point out where Christy is wrong, even going so far as to provide an alternative graph, Schmidt amazingly fails to alter the impression gained from looking at it. Even in his reconstruction the model forecasts still soar way above the observations.

So Christy’s graph is true. What else does it need to say to sentence the models to fatal error and irrelevance? Are our policy makers listening?

Though he alters the baseline and other things, the graphic clearly reveals that since about 1998 most climate models have continued an excursion far above the actual surface temperatures. This failure for nearly 20 years to track actual temperatures reveals serious faults with the models and strongly suggests the greenhouse hypothesis itself is in deep trouble.

The continued refusal of those in charge of the models to announce that there’s a problem, to talk about it or explain what they’re doing to fix it, illustrates the steadfastly illusory nature of the alarm the models — and only the models — underpin. When the models are proven wrong like this yet tales of alarm don’t stop, we can be sure that the alarm is not rooted in reality.

Gavin Schmidt's 'improved' version of John Christy's iconic graph comparing climate model output with real-world temperatures

Gavin Schmidt’s ‘improved’ version of John Christy’s iconic graph comparing climate model output with real-world temperatures

Here’s Christy’s updated graph, as he presented it to the U.S. House Committee on Science, Space & Technology on 2 Feb 2016.

John Christy's updated graph as presented to the US House of Representatives committee last February

Christy comments that “the Russian model (INM-CM4) was the only model close to the observations.”

Gavin Schmidt’s confirmation of the essence of Christy’s criticism of the models signals deep problems with Schmidt’s continued (stubborn?) reliance on the models. Christy’s presentation goes on:

The information in this figure provides clear evidence that the models have a strong tendency to over-warm the atmosphere relative to actual observations. On average the models warm the global atmosphere at a rate 2.5 times that of the real world. This is not a short-term, specially-selected episode, but represents the past 37 years, over a third of a century. This is also the period with the highest concentration of greenhouse gases and thus the period in which the response should be of largest magnitude.

Richard Cumming has several times recently said (on different grounds) that “we are witnessing the abject failure of the anthropogenic global warming theory.” I can only agree with him.

96 Thoughts on “Gavin Schmidt confirms model excursions

  1. Richard C (NZ) on July 19, 2016 at 9:13 am said:

    The El Nino spike at the beginning of 2016 was as good as it gets for Schmidt, now he’s looking at his own GISTEMP cooling rapidly. That was the El Nino Schmidt claimed for man-made climate change leaving the El Nino contribution only 0.07 C:

    Schmidt estimated El Niño was responsible for 0.07C of the above-average warming we saw in 2015.

    https://www.carbonbrief.org/analysis-how-much-did-el-nino-boost-global-temperature-in-2015

    Egg on face now.

  2. Richard C (NZ) on July 19, 2016 at 9:59 am said:

    >”Christy comments that “the Russian model (INM-CM4) was the only model close to the observations.”

    I inquired from John what that model was when he first started compiling these graphs. He didn’t know why INM-CM4 was different. Others have looked into it though:

    INMCM4 (Russian Academy of Sciences) in Judith Curry’s post: ‘Climate sensitivity: lopping off the fat tail’

    There is one climate model that falls within the range of the observational estimates: INMCM4 (Russian). I have not looked at this model, but on a previous thread RonC makes the following comments.

    “On a previous thread, I showed how one CMIP5 model produced historical temperature trends closely comparable to HADCRUT4. That same model, INMCM4, was also closest to Berkeley Earth and RSS series.

    Curious about what makes this model different from the others, I consulted several comparative surveys of CMIP5 models. There appear to be 3 features of INMCM4 that differentiate it from the others.”

    1.INMCM4 has the lowest CO2 forcing response at 4.1K for 4XCO2. That is 37% lower than multi-model mean

    2.INMCM4 has by far the highest climate system inertia: Deep ocean heat capacity in INMCM4 is 317 W yr m22 K-1, 200% of the mean (which excluded INMCM4 because it was such an outlier)

    3.INMCM4 exactly matches observed atmospheric H2O content in lower troposphere (215 hPa), and is biased low above that. Most others are biased high.

    So the model that most closely reproduces the temperature history has high inertia from ocean heat capacities, low forcing from CO2 and less water for feedback.

    Definitely worth taking a closer look at this model, it seems genuinely different from the others.

    http://judithcurry.com/2015/03/23/climate-sensitivity-lopping-off-the-fat-tail/

    Also, from Stephens et al (2012) :

    Figure 2a The change in energy fluxes expressed as flux sensitivities (Wm−2 K−1) due to a warming climate.
    (top graph – TOA)
    http://www.nature.com/ngeo/journal/v5/n10/fig_tab/ngeo1580_F2.html

    For all-sky OLR, INM-CM4 is the only model with a positive sensitivity (system heat gain). It also has the highest positive clear sky OLC-C sensitivity (heat gain).

    Basically, INM-CN4 is the only model exhibiting system heat gain from an outgoing longwave radiation forcing.

  3. Richard C (NZ) on July 19, 2016 at 1:12 pm said:

    ‘Climate Models are NOT Simulating Earth’s Climate – Part 4’

    Posted on March 1, 2016 by Bob Tisdale

    Alternate Title: Climate Models Undermine the Hypothesis of Human-Induced Global Warming

    […]

    THE PECULIARITY

    In the real world, according to the hypothesis of human induced global warming, if the Earth had a negative energy imbalance (that is, the outgoing energy was greater than incoming), wouldn’t global surfaces be cooling? They aren’t in the 5 models with the negative energy imbalances.

    See Figure 4.

    In fact, regardless of whether the climate models are showing the extremely high positive top-of-the-atmosphere energy imbalances or showing negative imbalances, all of the models show global surface warming. In other words, global surface warming is not dependent on a positive energy imbalance in 5 of the climate models used by the IPCC for their 5th Assessment Report.

    More>>>>>
    https://bobtisdale.wordpress.com/2016/03/01/climate-models-are-not-simulating-earths-climate-part-4/

    # # #

    The earth’s energy balance is the IPCC’s primary criteria for climate change whether natural cause or anthropogenic theory. A positive imbalance is a system heat gain, a negative imbalance is a system heat loss. But contrary to IPCC’s climate change criteria, even the climate models exhibiting a negative imbalance also exhibit a system heat gain.

    The IPCC omitted to mention this in AR5 Chapter 9 Evaluating Climate Models i.e. it is not necessarily what the IPCC says that matters, the biggest issues lie in what they don’t say (is that incompetent negligence or willful negligence?).

    It was left to Bob Tisdale to bring the issue to light. Thanks Bob.

  4. Richard Treadgold on July 19, 2016 at 1:25 pm said:

    !

    if the Earth had a negative energy imbalance (that is, the outgoing energy was greater than incoming), wouldn’t global surfaces be cooling? They aren’t in the 5 models with the negative energy imbalances.

    Wow. Thanks for highlighting this. We also have it on the very good authority of M. Mann that the models don’t replicate earth’s climate. Multiple streams of evidence now invalidate the models.

  5. Richard C (NZ) on July 19, 2016 at 1:44 pm said:

    >”The continued refusal of those in charge of the models to announce that there’s a problem,……”

    To be fair, the IPCC announced the problem in Chapter 9 Box 9.2. They even offer 3 or 4 reasons why the models are wrong. Main ones are that model sensitivity to CO2 is too high (CO2 forcing is excessive) and neglect of natural variation. The latter what sceptcs had been saying for years previous to AR5 and the IPCC finally had to concede.

    We didn’t read the news headlines about that though.

    Thing is, if the smoothed observation data does not rise above flat over the next 3, 4, 5, 6, 7 years (give them 5 yrs), CO2 forcing is not just excessive, it is superfluous.

    This is the acid test for the GCMs. The AGW theory having already failed its acid test by the IPCC’s own primary climate change criteria (earth’s energy balance measured at TOA), see this article for that:

    IPCC Ignores IPCC Climate Change Criteria – Incompetence or Coverup?
    https://dl.dropboxusercontent.com/u/52688456/IPCCIgnoresIPCCClimateChangeCriteria.pdf

  6. Richard Treadgold on July 19, 2016 at 1:49 pm said:

    Yes, good points. Of course I’d seen the AR5 comments but the penny didn’t drop when I was writing this — though there’s still been no rush to a press conference to announce an investigation. I’m still coming to grips with that “IPCC ignores” article.

  7. Richard C (NZ) on July 19, 2016 at 3:59 pm said:

    >”…..there’s still been no rush to a press conference to announce an investigation”

    No investigation but at least someone is trying to take natural multi-decadal variation (MDV) out of the models -obs comparison. I’ve intoned on this here at CCG at great length so not much here. Basically, models don’t model ENSO or MDV so both must be removed from observations before comparing to model runs.

    Kosaka and Xie are back on this after their first attempt which I thought was reasonable (I think I recall), I would point out that there is already a body of signal analysis literature that extracts the MDV signal from GMST – it’s not that difficult. What happens then though is that there’s a miss-attribution of the residual secular trend (ST, a curve) to the “anthropogenic global warming signal”. It becomes obvious that the ST is not CO2-forced when the ST is compared to the CO2-forced model mean. The model mean is much warmer than the ST in GMST.

    Problem now is: Kosaka and Xie’s latest effort below is nut-case bizarre:

    ‘Claim: Researchers create means (a model) to monitor anthropogenic global warming in real time’
    https://wattsupwiththat.com/2016/07/18/claim-researchers-create-means-a-model-to-monitor-anthropogenic-global-warming-in-real-time/

    This is their graph:

    This graph shows observed global mean surface temperature (GMST) based on three datasets (black curves in degree C), and the new estimates of anthropogenic global warming (AGM). The simulated GMST change without considering tropical Pacific internal variability is plotted as reference (white curve with blue shading indicating the uncertainty). CREDIT Nature Geoscience
    https://wattsupwiththat.files.wordpress.com/2016/07/agw-real-time-scripps.jpg

    [Please excuse my shouting here]
    THEY END UP WITH MORE agw WARMING THAN THE ACTUAL OBSERVATIONS FROM 1995 ONWARDS

    Shang-Ping Xie says this:

    “Most of the difference between the raw data and new estimates is found during the recent 18 years since 1998,” said Xie. “Because of the hiatus, the raw data underestimate the greenhouse warming.”

    No Shang-Ping, the greenhouse gas warming is grossly over estimated, including in your new method.

    Kosaka and Xie are either astoundingly stupid themselves, or they think everyone else is.

  8. Richard C (NZ) on July 19, 2016 at 4:24 pm said:

    From 1980 on, Kosaka and Xie’s graph is no different to Schmidt’s or Christy’s.

  9. Richard Treadgold on July 19, 2016 at 5:00 pm said:

    the raw data underestimate the greenhouse warming

    Confirmation bias makes them forget the supremacy of data over hypothesis.

  10. Simon on July 19, 2016 at 8:08 pm said:

    You should probably explain to casual readers what TMT actually is, as it is not obvious.
    Temperature measurements in the mid-troposphere are considerably less understood than the surface.
    Note that the CMIP5 runs are projections not predictions. The comparison would have been much fairer if the models were retrospectively re-run with known solar + volcanic forcings and ENSO outcomes.
    There is as yet insufficient evidence to say that the models are biased and there may yet be issues with satellite observation degradation.

  11. Richard Treadgold on July 19, 2016 at 9:51 pm said:

    Simon,

    Well, you’ve just done so; thanks. Whether you call them predictions or projections doesn’t matter—they were wrong. Volcanic forcings I agree with. But the fact that models cannot predict solar variation or ENSO influences is very significant, for without those, you’ll never have an accurate picture of our temperatures, wouldn’t you say? You can scarcely say “we don’t do ENSO” and still expect us to uproot all the energy sources we rely on for our prosperity.

    Insufficient evidence to show model bias? That’s not the point; there’s plenty of evidence that they’re wrong. I’m not too worried about satellite degradation; if they prove unreliable, the balloon observations will do.

    I’m still interested in your response to my questions about the papers cited by Renwick and Naish in the slide featured in the previous post:

    Do you fully support these papers, with their methodologies and assumptions? Do you accept their conclusions with no reservations whatsoever?

  12. Andy on July 20, 2016 at 8:58 am said:

    There is a big difference between a projection and a prediction. We can predict the weather a few days out, but a projection of what a company or technology might look like in 10 years time is largely speculation and guesswork.

  13. Richard C (NZ) on July 20, 2016 at 9:24 am said:

    Simon

    >”The comparison would have been much fairer if the models were retrospectively re-run with known solar + volcanic forcings and ENSO outcomes.”

    Heh. Except for ENSO (wrong there – see below) you’re just repeating what the IPCC conceded in AR5 Chapter 9 Box 9.2. But they add that CO2 sensitivity is too high i.e. CO2 forcing is excessive, and that “natural variation” (not ENSO) has been neglected. In effect, you are tacitly admitting the models are wrong Simon. You’re not alone, the IPCC do too.

    Volcanics are transitory – see below.

    ENSO is irrelevant and NOT the “natural variation” the IPCC are referring too. Models don’t do ENSO. Therefore a direct models-obs comparison is with ENSO noise smoothed out. When Schmidt does that in his graph (as Christy did) he’ll be 100% certain that 95% of the models are junk.

    “Natural variation” is predominantly due to oceanic oscillations e.g. AMO, PDO/IPO etc, otherwise known as multi-decadal variation (MDV), which distort the Northern Hemisphere temperature profile which then overwhelms GMST. The oscillatory MDV signal is close to a 60 yr trendless sine wave which can be subtracted from GMST so that the residual secular trend (ST) can be compared to to the model mean in Schmidt’s and Christy’s graphs.

    GMST – MDV = ST

    Problem is: the model mean will never conform to the GMST secular trend (ST) as long as the models are CO2 forced. The GMST ST passes through ENSO-neutral data at 2015 from BELOW. 2015 is a crossover date when MDV goes from positive to negative i.e. it is on the MDV-neutral “spline”.

    MDV-neutral “spline” in GMST:
    2045 neutral
    2030 < MDV max negative
    2015 neutral………………………(CO2 RF 1.9 W.m-2 @ 400ppm, ERF 2.33+ W.m-2)
    2000 < MDV max positive
    1985 neutral
    1970 < MDV max negative
    1955 neutral………………………(CO2 forcing uptick begins)
    1940 < MDV max positive
    1925 neutral
    1910 < MDV max negative
    1895 neutral

    Schmidt and Christy's graphs are normalized at the beginning of the satellite era so cannot be used in this exercise. The IPCC shows models vs HadCRUT4 and it is perfectly clear that the models CONFOM do the MDV-neutral spline up until the CO2 forcing uptick kicks in at MDV-neutral 1955.

    From IPCC AR5: CMIP3/5 models vs HadCRUT4 1860 – 2010
    https://tallbloke.files.wordpress.com/2013/02/image1.png

    After 1955, the model mean DOES NOT CONFORM to the MDV neutral spline. Volcanics distort the picture 1955 – 2000 but easy to see that at 2000 when MDV is max positive, the model mean corresponds to observations – it SHOULD NOT. The model mean should be BELOW observations at 2000. Remember that the MDV signal must be subtracted from observations (GMST) in order to compare directly to models.

    Volcanics are transitory so can be ignored in this exercise i.e. just connect the model mean at the end of volcanic distortion (2000) to the mean before the volcanics took effect (1955). Similarly observations are distorted by volcanics but only temporarily.

    So now we can correct your statement Simon:

    “The comparison would have been [correct] if the models were retrospectively re-run with known solar + volcanic forcings [but with much less CO2 forcing (maybe even neglected) and with an MDV signal ADDED IN]”

    Correct now.

  14. Simon on July 20, 2016 at 9:34 am said:

    I absolutely support the general conclusions of these studies. The proportion of climate scientists who support the AGW hypothesis is greater than 95%. Do you concur?
    I am most curious how an non-specialist such as yourself can somehow ‘know the truth’ whereas the scientific consensus has ‘got it wrong’. You might also like to comment how you managed to avoid the Dunning-Kruger effect.
    Note also that ENSO, like weather, is chaotic. We can predict the probability of occurrence, but not when they will occur beyond a limited time horizon. What is important is the trend. An expert would know that.
    Nobody apart from yourself is talking about ‘uprooting energy sources’. I would suggest that it is this concern that is causing your cognitive bias.

  15. Richard C (NZ) on July 20, 2016 at 9:35 am said:

    ‘A TSI-Driven (solar) Climate Model’

    February 8, 2016 by Jeff Patterson

    “The fidelity with which this model replicates the observed atmospheric CO2 concentration has significant implications for attributing the source of the rise in CO2 (and by inference the rise in global temperature) observed since 1880. There is no statistically significant signal of an anthropogenic contribution to the residual plotted Figure 3c. Thus the entirety of the observed post-industrial rise in atmospheric CO2 concentration can be directly attributed to the variation in TSI, the only forcing applied to the system whose output accounts for 99.5% ( r2=.995) of the observational record.

    How then, does this naturally occurring CO2 impact global temperature? To explore this we will develop a system model which when combined with the CO2 generating system of Figure 4 can replicate the decadal scale global temperature record with impressive accuracy.

    Researchers have long noted the relationship between TSI and global mean temperature.[5] We hypothesize that this too is due to the lagged accumulation of oceanic heat content, the delay being perhaps the transit time of the thermohaline circulation. A system model that implements this hypothesis is shown in Figure 5.”

    “The results (figure 10) correlate well with observational time series (r = .984).”

    http://wattsupwiththat.com/2016/02/08/a-tsi-driven-solar-climate-model/comment-page-1/

    # # #

    Goes a long way towards modeling Multi-Decadal Variation/Oscillation (MDV/MDO).
    Long system lag (“oceanic delay”), well in excess of 70 years depending on TSI input series.
    Cannot be accused of “curve fitting” (but was in comments even so).

    Models CO2 as an OUTPUT. The IPCC’s climate models parameterize CO2 as an INPUT.

  16. Andy on July 20, 2016 at 9:43 am said:

    The proportion of climate scientists who support the AGW hypothesis is greater than 95%.

    What is the AGW hypothesis?

    What hypothesis, or lack thereof, do the other 3-5% subscribe to, to explain whatever phenomenon they assert needs explaining?

  17. Richard C (NZ) on July 20, 2016 at 9:46 am said:

    Simon

    >”I absolutely support the general conclusions of these studies. The proportion of climate scientists who support the AGW hypothesis is greater than 95%. Do you concur?”

    Climate scientists? I don’t think so.

    From Verheggen (2014):

    Gonzalez, G. A. An eco-Marxist analysis of oil depletion via urban sprawl. Environ. Polit. 2006, 15, 515−531.

    Entman, R. M. Improving Newspapers’ Economic Prospects by Augmenting Their Contributions to Democracy. Int. J. Press- Polit. 2010, 15, 104−125.

    Harribey, J. M. The unsustainable heaviness of the capitalist way of development. Pensee 2002, 31 − +.

    Delmelle, E. C.; Thill, J.-C. Urban Bicyclists Spatial Analysis of Adult and Youth Traffic Hazard Intensity. Transp. Res. Record 2008, 31−39.

    Howard, C.; Parsons, E. C. M. Attitudes of Scottish city inhabitants to cetacean conservation. Biodivers. Conserv. 2006, 15, 4335−4356.

    McCright, A. M.; Dunlap, R. E. Cool dudes: The denial of climate change among conservative white males in the United States. Glob. Environ. Change-Human Policy Dimens. 2011, 21, 1163−1172.

    An “eco-Marxist analysis” is a scientific analysis of global warming by climate scientists?

    Get real Simon.

    Similarly for Cook et al. Psychologist José Duarte writes:

    The Cook et al. (2013) 97% paper included a bunch of psychology studies, marketing papers, and surveys of the general public as scientific endorsement of anthropogenic climate change.

    “Psychology studies, marketing papers, and surveys of the general public” are scientific analyses of global warming by climate scientists?

    Get real Simon.

  18. Richard C (NZ) on July 20, 2016 at 9:53 am said:

    Andy

    >”What is the AGW hypothesis?”

    Has never been formally drafted. But we can infer one from the IPCC’s primary climate change criteria:

    The AGW hypothesis: The earth’s energy balance, measured at the top of the atmosphere, moves synchronous with and commensurate with anthropogenic forcing

    Falsified by the IPCC’s cited observations in AR5 Chapter 2.

  19. Richard Treadgold on July 20, 2016 at 10:12 am said:

    Simon,

    I am most curious how an [sic] non-specialist such as yourself can somehow ‘know the truth’ whereas the scientific consensus has ‘got it wrong’.

    You err in supposing I “know the truth”. I simply point out errors in some of these studies, whether those errors have been discovered by others or myself. It’s obvious you have no interest in facing those errors, as you offer no comment about them, and that’s curious. Aren’t you keen to know the truth?

    What consensus? There’s no evidence, given the grievous errors in some of these studies, of a substantial consensus. You’re the one with a cognitive bias, Simon.

    You might also like to comment how you managed to avoid the Dunning-Kruger effect.

    How do you know that I did? But that’s for others to comment on— how could I know?

    Nobody apart from yourself is talking about ‘uprooting energy sources’. I would suggest that it is this concern that is causing your cognitive bias.

    Sorry, Simon, this was roughly expressed. I meant to say replacing traditional, reliable energy sources such as coal, oil and gas responsible for our prosperity with modern wind turbines, solar panels, thermal solar plants, tidal and wave power generation and burning North American wood chips in UK generating stations, while steadfastly avoiding the two leading contenders for emissions-free electricity: hydro and nuclear. Not to mention solving the horrendous scheduling problems of generating with fickle breeze-blown windmills and solar panels that go down every night.

    In brief: uprooting traditional energy sources.

    I meant to say that others certainly are advocating ripping out traditional energy sources. You could start with the Coal Action Network with their slogan “keep the coal in the hole” and a campaign for Fonterra to quit coal and move on to examine the WWF, Greenpeace and the UN. All of those want to end our industrial practices for the sake of the planet.

  20. Richard C (NZ) on July 20, 2016 at 10:26 am said:

    Simon

    >”What is important is the trend.”

    No Simon, the (temperature) trend is not important. It is not even critical. What is critical is the IPCC’s primary climate change criteria:

    “The energy balance of the Earth-atmosphere system…..measured at the top of the atmosphere”

    The critical trend is in this criteria and it is NOT temperature in Kelvin or Celsius, it is the TOA energy imbalance stated in units of W.m-2. The IPCC concede in AR5 Chapter 2 that it is “highly unlikely” that there is a statistically significant trend in the imbalance. If the IPCC’s forcing theory was valid, there SHOULD be a trend because both theoretical CO2 RF and total effective anthro forcing (ERF) is increasing. In AR5 CO2 RF was 1.83 W.m-2 and ERF was 2.33 W.m-2.

    Instead, the IPCC reported that the imbalance was static around 0.6 W.m-2 i.e. their forcing theory is invalid.

    But the IPCC didn’t address the critical discrepancy. Read about that here:

    IPCC Ignores IPCC Climate Change Criteria – Incompetence or Coverup?
    https://dl.dropboxusercontent.com/u/52688456/IPCCIgnoresIPCCClimateChangeCriteria.pdf

    But back to the trend that you seem to think is “important”. John Christy states in the post above:

    “On average the models warm the global atmosphere at a rate 2.5 times that of the real world”

    Well OK, maybe that is important.

  21. Richard C (NZ) on July 20, 2016 at 10:36 am said:

    Crud. Missed a tag after “The energy balance of the Earth-atmosphere system…..measured at the top of the atmosphere”

    I Was using my “slim”, but fast, browser version which doesn’t give me the Edit facility.

  22. Richard C (NZ) on July 20, 2016 at 12:25 pm said:

    >”The oscillatory MDV signal is close to a 60 yr trendless sine wave which can be subtracted from GMST so that the residual secular trend (ST) can be compared to to the model mean…..”

    Bob Tisdale doesn’t grasp this. See his latest:

    ‘June 2016 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Update’
    Bob Tisdale / July 19, 2016
    https://wattsupwiththat.com/2016/07/19/june-2016-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update/

    Scroll down to MODEL-DATA COMPARISON & DIFFERENCE

    The graph below shows a model-data difference using anomalies, where the data are represented by the UKMO HadCRUT4 land+ocean surface temperature product and the model simulations of global surface temperature are represented by the multi-model mean of the models stored in the CMIP5 archive. Like Figure 10, to assure that the base years used for anomalies did not bias the graph, the full term of the graph (1880 to 2013) was used as the reference period.

    In this example, we’re illustrating the model-data differences smoothed with a 61-month running mean filter. (You’ll notice I’ve eliminated the monthly data from Figure 11. Example here. Alarmists can’t seem to grasp the purpose of the widely used 5-year (61-month) filtering, which as noted above is to minimize the variations due to El Niño and La Niña events and those associated with catastrophic volcanic eruptions.)

    Models Minus Data
    https://bobtisdale.files.wordpress.com/2016/07/11-model-data-difference-hadcrut4.png

    [Also, HadCRUT4 vs Models is helpful]
    https://bobtisdale.files.wordpress.com/2016/07/10-model-data-time-series-hadcrut4.png

    The difference now between models and data is almost worst-case, comparable to the difference at about 1910.

    There was also a major difference, but of the opposite sign, in the late 1880s. That difference decreases drastically from the 1880s and switches signs by the 1910s. The reason: the models do not properly simulate the observed cooling that takes place at that time. Because the models failed to properly simulate the cooling from the 1880s to the 1910s, they also failed to properly simulate the warming that took place from the 1910s until the 1940s. (See Figure 12 for confirmation.) That explains the long-term decrease in the difference during that period and the switching of signs in the difference once again. The difference cycles back and forth, nearing a zero difference in the 1980s and 90s, indicating the models are tracking observations better (relatively) during that period. And from the 1990s to present, because of the slowdown in warming, the difference has increased to greatest value ever…where the difference indicates the models are showing too much warming

    Tisdale doesn’t grasp that 1910 was max negative MDV (see GMST “spline” from upthread reproduced below). Of course the models will be at +0.2 above the observations at 1910. The MDV signal must be subtracted from GMST in order to compare to the model mean. Once that’s done the model mean conforms to the MDV-neutral GMST “spline” at 1910.

    MDV-neutral “spline” in GMST:
    2045 neutral
    2030 < MDV max negative
    2015 neutral………………………(CO2 RF 1.9 W.m-2 @ 400ppm, ERF 2.33+ W.m-2)
    2000 < MDV max positive
    1985 neutral
    1970 < MDV max negative
    1955 neutral………………………(CO2 forcing uptick begins)
    1940 < MDV max positive
    1925 neutral
    1910 < MDV max negative
    1895 neutral

    At MDV-neutral 1925 no subtraction of the MDV signal is needed from GMST. At that date the model mean and observations correspond exactly.

    Similar to 1910 but opposite is 1940 which was max positive MDV. Of couse the models will be at -0.1 below the observations. The MDV signal must be subtracted from GMST in order to compare to the model mean. Once that's done the model mean conforms to the MDV-neutral GMST "spline" at 1940.

    At MDV-neutral 1955 no subtraction of the MDV signal is needed from GMST. At that date the model mean and observations correspond exactly.

    After 1955 the picture is distorted by transitory volcanics which Tisdale has "minimised" by 61 month smoothing but there is still much distortion. The cycle prior to 1955 is broken up.

    At MDV-neutral 1985 the models and observations correspond but that is illusory given the volcanic distortion. Still, it is indicative.

    At max MDV positive in 2000, The models SHOULD be BELOW observations BUT THEY ARE NOT. They are +0.05 above observations.

    At MDV-neutral 2015 no subtraction of the MDV signal is needed from GMST. But at that date instead of the model mean and observations corresponding exactly as back in 1925 1955 and 1985 (sort of), the models are +0.17 above observations.

    When data crunchers like Bob Tisdale can't grasp the models-obs apples-to-apples necessity of subtracting the MDV signal from GMST, it is going to take a long long time before the real models-obs discrepancy is realized.

    That discrepancy only begins 1955 (1985 notwithstanding) and is due to CO2 forcing from 1955 onwards.

  23. Richard C (NZ) on July 20, 2016 at 12:50 pm said:

    Michael Mann:

    “I am a climate scientist and have spent much of my career with my head buried in climate model output and observational climate data, trying to tease out the signal of human-caused climate change.”

    http://www.ecowatch.com/right-wing-denial-machine-distorts-climate-change-discourse-1924120031.html

    Apparently Michael Mann “supports the AGW hypothesis” (as per Simon), but he’s having a great deal of difficulty actually applying it to the real world.

    That’s if his use of the phrase “trying to tease out” is anything to go by.

  24. Alexander K on July 20, 2016 at 1:58 pm said:

    I am not a scientist of any kind, but learned many years ago that data gained from careful and thorough observation trumps models every time.
    While I have earned a living doing many things at various times, I have found that engineers work from a philosophy that I can agree with. Scientists not so much, particularly ‘Climate Scientists’.

  25. Richard C (NZ) on July 20, 2016 at 5:58 pm said:

    Schmidt’s latest:

    When it comes to what caused the record highs of 2016, “we get about 40 per cent of the record above 2015 is due to El Nino, and 60 per cent is due to other factors”, Schmidt said.

    http://www.smh.com.au/environment/climate-change/june-sets-another-global-temperature-record-extending-a-blazingly-hot-year-20160719-gq9eo4.html

    No mention of what the “other factors” are. Note the plural.

    And no mention that the temperature spike peaking in Feb 2016 was only in the Northern Hemisphere.

  26. Gary Kerkin on July 20, 2016 at 7:43 pm said:

    Richard C. “When it comes to what caused the record highs of 2016, “we get about 40 per cent of the record above 2015 is due to El Nino, and 60 per cent is due to other factors”, Schmidt said.”

    When I eyeball the graphs presented by Christy and Spencer it looks to me as though the fractions are the other way round i.e. 40% is due to other factors and 60% is due to El Niño. Seems to be equally true of both 1998 and 2015.

    “Other factors” means, I presume, those factors which have caused the rise in temperature since the Little Ice Age.

  27. Gary Kerkin on July 20, 2016 at 8:28 pm said:

    I have been watching this thread throughout the day but because I’ve been trotting backwards and forwards from Hutt Hospital experiencing the modern technological miracle of a camera capsule which has been peering at the inside of my gut, it is only now that I’ve had a chance to comment.

    A couple of points seem very clear to me. One is that associated with trying to compare reality with simulations and, in particular the Schmidt criticisms of the comparisons of Christy and Spencer. I commented on this in the previous post, highlighting the carping nature of the criticisms. These have been reinforced by the comments of both Richards today. As has been pointed out, even if the parameters are manipulated as described by Schmidt, we are still left with the indisputable conclusion that the output of the models do not match reality. This has also been commented on by Tisdale, as pointed out by Richard C. Despite what RC sees as deficiencies in Tisdale’s number crunching, the difference is clear.

    The other point relates to semantics. Andy comments that there is a difference between “projection” and “prediction”. If you Google the terms together you’ll get 26,000,000+ results. A cursory look at some that try to draw a distinction yields little useful information. As far as I can see the terms are used very loosely and I think that is really because they mean much the same thing. For example a dictionary definition of “projection” is “an estimate or forecast of a future situation based on a study of present trends”, and a dictionary definition of “prediction” is “a thing predicted; a forecast”. Can you really identify a difference between the two definitions? I can’t.

    The IPCC chooses to draw the following distinction (http://www.ipcc-data.org/guidelines/pages/definitions.html):

    “Projection”
    “The term ‘projection” is used in two senses in the climate change literature. In general usage, a projection can be regarded as any description of the future and the pathway leading to it. However, a more specific interpretation has been attached to the term ‘climate projection” by the IPCC when referring to model-derived estimates of future climate.”

    “Forecast/Prediction”
    “When a projection is branded ‘most likely’ it becomes a forecast or prediction. A forecast is often obtained using deterministic models, possibly a set of these, outputs of which can enable some level of confidence to be attached to projections.”

    So the IPCC has tried to change the definition of “projection” so that it is tied to the models and it has said it will become a “prediction” when the “projection” is the most likely outcome.

    There seems to me to be something slightly oxymoronic in this and I really have to wonder if it isn’t an attempt to obfuscate what they are portraying. Is there anyone who doesn’t believe that weather forecasts (read “predictions”) are not now made without the assistance of computer models? In what way, then, do they differ in nature from predictions made from climate models? Other than the latter are based on an hypothesis of AGW and with assumptions which, from the comparisons discussed here, are not exactly up to snuff, while the former are presumably based on atmospheric physics and, probably, linear or non-linear modelling of the parameters associated with the atmospheric physics.

    Can we now claim the new oxymoron “climate model”?

  28. Richard C (NZ) on July 21, 2016 at 9:22 am said:

    Gary >“Other factors” means, I presume, those factors which have caused the rise in temperature since the Little Ice Age.

    Yes those, of which Schmidt is wedded to AGW, but I think he’s alluding to other phenomena too which are the greater influence in this case I think and Schmidt seems to think too.

    When Gareth Renowden posted his February 2016 “wakeup call” post, Andy S and I both looked at the latitudinal breakdown in the Northern Hemisphere and said there’s some research needed to be done because the spike was minimal to non-existent around the NH tropics but from the NH extratropics to the NH polar latitudes the spike just got greater and greater. That’s this graph:

    GISTEMP LOTI February 2016 mean anomaly by latitude
    http://l2.yimg.com/bt/api/res/1.2/ABpZjd4AnHFp.4g2goHRfg–/YXBwaWQ9eW5ld3NfbGVnbztxPTg1/http://media.zenfs.com/en-US/homerun/mashable_science_572/bb88a1a459ed8b467290bac3540e39dd

    Sometimes that URL gives a browser error but I’ve just checked and got the graph up so shouldn’t be a problem (try ‘Open in another tab’).

    I’m sure you will agree that the graph is extraordinary. There must be much more than just an El Nino effect. I’m guessing something like AMO or Arctic Oscillation (AO a.k.a. Northern Hemisphere annular mode). But I really don’t know what the driver of that spike is except that I think the El Nino effect was the smaller factor. I’m actually inclined to agree with Gavin Schmidt’s assessment (as far as I’ve quoted him above anyway) in respect to something like 40% El Nino and 60% “other factors” e.g. NH annular mode.

    Obviously though, AGW was not a factor (see graph from 55 South to 90 South – no AGW there). Hopefully some climate scientists will present something on it eventually.

  29. Gary Kerkin on July 21, 2016 at 9:52 am said:

    Richard C, yes it does look quite extraordinary. I assume there is a strong seasonal component, i.e. the same plot for, say, July, would be something like a mirror image? Did you and Andy look at all months, or just February?

    My comment about relative attribution was based merely on eyeballing the temperature graph and looking at the size of the spike compared to where some sort of average would pass beneath it. I have to say that I try to keep it very simple. So much of the information is buried in what I can only refer to as noise that I find it hard to believe than anything useful can be extracted from the data. Even using the most sensitive type of filtering (Kalman, say) I would have difficulty in believing anything that came out of it. GIGO?

  30. Richard C (NZ) on July 21, 2016 at 9:52 am said:

    [IPCC] >“When a projection is branded ‘most likely’ it becomes a forecast or prediction”

    “Branded”?

    This is the ultimate in confirmation bias by whoever does the branding. If the forecast scenario is a “description of the future and the pathway leading to it” then it is merely speculation whatever “branding” is assigned to it.

    The forecast only becomes ‘most likely’ when it tracks reality i.e. regular checks like those of Christy and Schmidt are needed to see how the forecast is tracking. When a forecast doesn’t track reality it is useless, not fit for purpose, a reject in quality control terms. This is the scientific equivalent of industrial/commercial key performance indicators (KPIs) or budget variances.

    The only purpose 95% of the models at mid troposphere (97% at surface) have now is to demonstrate that the assumptions they are based on are false.

    We should challenge the MfE, NIWA, and Minister for Climate Change Paula Bennett to post the IPCC’s forecasts graphed against real world atmospheric temperature (also sea levels) and updated as new data comes in. All MfE and NIWA do is state forecasts, there is never any governmental check of those forecasts against reality.

  31. Gary Kerkin on July 21, 2016 at 10:11 am said:

    Richard C >”We should challenge the MfE, NIWA, and Minister for Climate Change Paula Bennett to post the IPCC’s forecasts graphed against real world atmospheric temperature (also sea levels) and updated as new data comes in”

    They might do it, but I doubt it. I rather feel they would want to restrict themselves to NZ only at best. At worst they would just ignore it. The PCE did but only succeeded in opening herself up to the criticism that the IPCC prognostications on sea level most certainly do not apply to NZ.

    The government can quite rightly claim that it is not within its purview to “check” the correctness or otherwise of agency forecasts. The forecasts are published regularly and anyone with the desire to find out for themselves can compare reality with forecast. I suppose a comparison of forecast with reality could be included in the kpi’s of an agency, the outcome of which could influence future funding. But that is another can of worms!

  32. Richard C (NZ) on July 21, 2016 at 10:17 am said:

    Gary >”Did you and Andy look at all months, or just February?”

    No unfortunately. I picked up that graph from a comment thread somewhere. I don’t where to access the original graphs. I know there are similar graphs of each month because I saw another one recently of May I think it was but I didn’t save the link. I wish I knew who produces them. It’s not GISS I don’t think. I suspect it’s someone at Columbia University because the GISTEMP Graphs page links to Columbia:

    Columbia: Global Temperature — More Figures
    http://www.columbia.edu/~mhs119/Temperature/T_moreFigs/

    About half way down there’s this latitudinal breakdown:

    Regional Changes – Zonal Means
    Zonal mean, (a) 60-month and (b-d) 12-month running mean temperature changes in five zones: Arctic (90.0 – 64.2°N), N. Mid-Latitudes (64.2 – 23.6°N), Tropical (23.6°S), S. Mid-Latitudes (23.6 – 64.2°S), and Antarctic (64.2 – 90.0°S). (Data through June 2016 used. Updated on 2016/07/19, now with GHCN version 3.3.0 & ERSST v4)
    http://www.columbia.edu/~mhs119/Temperature/T_moreFigs/ZonalT.gif

    Perfectly clear in (a) that the Arctic skews the entire global mean.

    Perfectly clear in (b) that the Antarctic is going in the opposite direction to the Arctic.

    Perfectly clear in (c) that the Northern Hemisphere is the overwhelming influence in the global mean.

    In other words, the global mean is meaningless.

  33. Gary Kerkin on July 21, 2016 at 11:18 am said:

    Richard C>”In other words, the global mean is meaningless.”

    Yes and/or no, Richard.

    My guess is that the skew on those graphs is, as I implied earlier, a seasonal factor and, as I stated, July should almost be a mirror image of February. That implies that that seasonality should be removed and the simplest way of doing that is to average over a year. The shape of the graph if all values were averaged over 12 months would show the underlying skewness applying to either pole.

    There are some (Vincent Gray, for example) that argue that a global average is meaningless. My view is that a number of some sort has to be generated if any sort of comparison is to be made. But (and it is a big “but”) having generated an average which contains both geographic and time components, we need to be extremely circumspect about how we use it and what conclusions we may draw from it. Especially if variations in time sit in what is obviously noise!

  34. Simon on July 21, 2016 at 11:27 am said:

    “The global mean is meaningless.” What do you mean? It is a useful metric.
    The important thing is that reality is panning out just as the models predicted. The Arctic would warm the fastest, whereas the Antarctic is insulated by the circumpolar winds and ozone depletion. Note that the un-insulated Antarctic peninsula, is one of the fastest warmest parts of the world. We have always known that New Zealand, because of its isolation from large land-masses, would not warm as quickly as temperate continents.
    With surface temperatures now matching closely with the models, the obfuscation has had to shift to the mid-troposphere, where there is still much to understand and measure. Natural variation may give the impression of a temporary hiatus, but the trend continues. Especially so now that the Pacific Decadal Oscillation has flipped over to a positive phase.

  35. Richard C (NZ) on July 21, 2016 at 11:29 am said:

    The Arctic is of course the Great White Warm Hope, but not going so well ………..

    ‘Global Warming Expedition Stopped In Its Tracks By Arctic Sea Ice’

    The icy blockade comes just over a month after an Oxford climate scientist, Peter Wadhams, said the Arctic would be ‘completely ice-free’ by September of this year. While it obviously isn’t September yet, he did reference the fact that there would be very little ice to contend with this summer.

    “Even if the ice doesn’t completely disappear, it is very likely that this will be a record low year,” Wadhams told The Independent in June.

    Wahdams says he expects less than one million square kilometers by summers end, but the current amount of Arctic sea ice is 10.6 million square kilometers, according to data from the National Snow and Ice Data Center (NSIDC).

    http://dailycaller.com/2016/07/20/global-warming-expedition-stopped-in-its-tracks-by-arctic-sea-ice/

    # # #

    They are not in the Arctic at this juncture, currently stuck in Murmansk.

    Pictured is another ‘Ship of Fools’, the MV Akademik Shokalskiy stranded in ice in Antarctica, December 29, 2013.

    Remember Chris Turney?

  36. Richard C (NZ) on July 21, 2016 at 12:35 pm said:

    Simon

    >“The global mean is meaningless.” What do you mean? It is a useful metric.

    For what?

    The “global mean” doesn’t exist anywhere on earth. It is a totally meaningless metric.

    >”The important thing is that reality is panning out just as the models predicted.”

    What utter rubbish Simon. Just look at Schmidt’s graph, or any other of obs vs models graphs e.g. HadCRUT4 vs models (see below). The models are TOO WARM.

    >”Note that the un-insulated Antarctic peninsula, is one of the fastest warmest parts of the world”

    ‘After warming fast, part of Antarctica gets a chill – study’
    http://www.stuff.co.nz/world/82321379/after-warming-fast-part-of-antarctica-gets-a-chill–study

    >”With surface temperatures now matching closely with the models”

    Simon, just repeating an untruth doesn’t make it true – and it makes you look ridiculous:

    “The essential English leadership secret does not depend on particular intelligence. Rather, it depends on a remarkably stupid thick-headedness. The English follow the principle that when one lies, it should be a big lie, and one should stick to it. They keep up their lies, even at the risk of looking ridiculous.” – Joseph Goebbels

    HadCRUT4 vs Models
    https://bobtisdale.files.wordpress.com/2016/07/10-model-data-time-series-hadcrut4.png

    Clearly, surface temperatures are NOT “now matching closely with the models”. Being RCP8.5 is irrelevant because the scenarios are indistinguishable at this early stage i.e. it doesn’t matter what scenario is graphed they all look the same at 2016.

    The IPCC concurs in AR5 Chapter 9 Evaluation of Climate Models:

    Box 9.2 | Climate Models and the Hiatus in Global Mean Surface Warming of the Past 15 Years

    The observed global mean surface temperature (GMST) has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years (Section 2.4.3, Figure 2.20, Table 2.7; Figure 9.8; Box 9.2 Figure 1a, c). Depending on the observational data set, the GMST trend over 1998–2012 is estimated to be around one-third to one-half of the trend over 1951–2012 (Section 2.4.3, Table 2.7; Box 9.2 Figure 1a, c). For example, in HadCRUT4 the trend is 0.04ºC per decade over 1998–2012, compared to 0.11ºC per decade over 1951–2012. The reduction in observed GMST trend is most marked in Northern Hemisphere winter (Section 2.4.3; Cohen et al., 2012). Even with this “hiatus” in GMST trend, the decade of the 2000s has been the warmest in the instrumental record of GMST (Section 2.4.3, Figure 2.19). Nevertheless, the occurrence of the hiatus in GMST trend during the past 15 years raises the two related
    questions of what has caused it and whether climate models are able to reproduce it.

    Figure 9.8 demonstrates that 15-year-long hiatus periods are common in both the observed and CMIP5 historical GMST time series (see also Section 2.4.3, Figure 2.20; Easterling and Wehner, 2009; Liebmann et al., 2010). However, an analysis of the full suite of CMIP5 historical simulations (augmented for the period 2006–2012 by RCP4.5 simulations, Section 9.3.2) reveals that 111 out of 114 realizations show a GMST trend over 1998–2012 that is higher than the entire HadCRUT4 trend ensemble (Box 9.2 Figure 1a;
    CMIP5 ensemble mean trend is 0.21ºC per decade). This difference between simulated and observed trends could be caused by some combination of (a) internal climate variability, (b) missing or incorrect radiative forcing and (c) model response error. These potential sources of the difference, which are not mutually exclusive, are assessed below, as is the cause of the observed GMST trend hiatus.

    […]

    Almost all CMIP5 historical simulations do not reproduce the observed recent warming hiatus.

    http://www.climatechange2013.org/images/report/WG1AR5_Chapter09_FINAL.pdf

    “111 out of 114 realizations show a GMST trend over 1998–2012 that is higher than the entire HadCRUT4 trend ensemble”

    “Almost all CMIP5 historical simulations do not reproduce the observed recent warming hiatus.”

    That’s unequivocal Simon. The models are TOO WARM. The IPCC even offer reasons why the models are TOO WARM, for example in Box 9.2:

    Model Response Error
    The discrepancy between simulated and observed GMST trends during 1998–2012 could be explained in part by a tendency for some CMIP5 models to simulate stronger warming in response to increases in greenhouse gas (GHG) concentration than is consistent with observations

    As time goes on this is more and more the case because GHG forcing is increasing rapidly but the earth’s energy balance isn’t (and therefore surface temperature isn’t either).

    This is the critical discrepancy Simon:

    0.6 W.m-2 trendless – earth’s energy balance AR5 Chapter 2
    2.33+ W.m-2 trending – theoretical anthropogenic forcing AR5 Chapter 10 (CO2 1.9 W.m-2 @ 400ppm).

    Theory is 4x observations.

    “If it disagrees with experiment, it’s wrong. In that simple statement is the key to science. It doesn’t make any difference how beautiful your guess is, it doesn’t matter how smart you are who made the guess, or what his name is… If it disagrees with experiment, it’s wrong. That’s all there is to it.” – Richard Feynman on the Scientific Method

    Man-made climate change theory is wrong Simon. That’s all there is to it.

  37. Richard C (NZ) on July 21, 2016 at 1:21 pm said:

    Simon

    >”Natural variation may give the impression of a temporary hiatus, but the trend continues”

    By what trend technique does “the trend continue”?

    Extrinsic e.g. statistically inappropriate (i.e. not representative of the data) linear analysis?

    Or,

    Intrinsic e.g. Singular Spectral Analysis (SSA) or Empirical Mode Decomposition (EMD)?

    Extrinsic is an externally imposed technique. Intrinsic is the inherent data signal. A curve (e.g. polynomial) is an extrinsic technique as is linear regression but a curve represents the temperature data better by statistical criteria.

    But I actually agree with you Simon. Yes, the “the trend continues”, but trend of what?

    Upthread I’ve shown that it is necessary to subtract MDV from GMST before comparing to the model mean.

    ST = GMST – MDV

    The residual is the secular trend (ST – a curve) which looks nothing like the trajectory of the data. The trajectory of 21st century GMST data is flat but the secular trend certainly is NOT flat (see Macias et al below).

    Problem for MMCC theory is:

    A) The secular trend (ST) in GMST now has a negative inflexion i.e. increasing CO2 cannot be the driver.

    B) The CO2-forced model mean does NOT conform to the secular trend (ST) in GMST – the model mean is TOO WARM.

    The Man-Made Climate Change theory is busted Simon.

    **************************************************************************************
    ‘Application of the Singular Spectrum Analysis Technique to Study the Recent Hiatus on the Global Surface Temperature Record’
    Diego Macias, Adolf Stips, Elisa Garcia-Gorriz (2014)

    Abstract
    Global surface temperature has been increasing since the beginning of the 20th century but with a highly variable warming rate, and the alternation of rapid warming periods with ‘hiatus’ decades is a constant throughout the series. The superimposition of a secular warming trend with natural multidecadal variability is the most accepted explanation for such a pattern. Since the start of the 21st century, the surface global mean temperature has not risen at the same rate as the top-of-atmosphere radiative energy input or greenhouse gas emissions, provoking scientific and social interest in determining the causes of this apparent discrepancy.

    Multidecadal natural variability is the most commonly proposed cause for the present hiatus period. Here, we analyze the HadCRUT4 surface temperature database with spectral techniques to separate a multidecadal oscillation (MDV) from a secular trend (ST). Both signals combined account for nearly 88% of the total variability of the temperature series showing the main acceleration/deceleration periods already described elsewhere. Three stalling periods with very little warming could be found within the series, from 1878 to 1907, from 1945 to 1969 and from 2001 to the end of the series, all of them coincided with a cooling phase of the MDV. Henceforth, MDV seems to be the main cause of the different hiatus periods shown by the global surface temperature records.

    However, and contrary to the two previous events, during the current hiatus period, the ST shows a strong fluctuation on the warming rate, with a large acceleration (0.0085°C year−1 to 0.017°C year−1) during 1992–2001 and a sharp deceleration (0.017°C year−1 to 0.003°C year−1) from 2002 onwards. This is the first time in the observational record that the ST shows such variability, so determining the causes and consequences of this change of behavior needs to be addressed by the scientific community.

    Full paper
    http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107222

  38. Richard C (NZ) on July 21, 2016 at 1:36 pm said:

    [Macias et al] >”Since the start of the 21st century, the surface global mean temperature has not risen at the same rate as the top-of-atmosphere radiative energy input or greenhouse gas emissions, provoking scientific and social interest in determining the causes of this apparent discrepancy.”

    By “top-of-atmosphere radiative energy input” they are referring to radiative forcing theory i.e. the theoretical effective anthropogenic radiative forcing (ERF) was 2.33 W.m-2 at the time of AR5 and increasing.

    But neither the earth’s energy balance nor surface temperature is increasing as MMCC theory predicts as a result of a theoretical ERF of 2.33+ W.m-2:

    0.6 W.m-2 trendless – earth’s energy balance AR5 Chapter 2
    2.33+ W.m-2 trending – theoretical anthropogenic forcing AR5 Chapter 10 (CO2 1.9 W.m-2 @ 400ppm).

    This is the critical discrepancy that falsifies the MMCC conjecture.

  39. Richard Treadgold on July 21, 2016 at 1:47 pm said:

    Simon,

    The important thing is that reality is panning out just as the models predicted.

    This post describes the models overshooting observed temperatures and relates how Gavin Schmidt himself agrees with this. It falsifies the belief you express in this statement. Please either explain how you justify your belief, or retract the statement. As RC mentions, repeating an untruth doesn’t make it true, so how will you reconcile this?

  40. Richard C (NZ) on July 21, 2016 at 2:23 pm said:

    Gary

    >”That implies that that seasonality should be removed and the simplest way of doing that is to average over a year. The shape of the graph if all values were averaged over 12 months would show the underlying skewness applying to either pole.”

    Agreed. That is exactly what Columbia University did in the graphs I linked too in the comment you are replying to:

    Regional Changes – Zonal Means
    Zonal mean, (a) 60-month and (b-d) 12-month running mean temperature changes in five zones: Arctic (90.0 – 64.2°N), N. Mid-Latitudes (64.2 – 23.6°N), Tropical (23.6°S), S. Mid-Latitudes (23.6 – 64.2°S), and Antarctic (64.2 – 90.0°S). (Data through June 2016 used. Updated on 2016/07/19, now with GHCN version 3.3.0 & ERSST v4)
    http://www.columbia.edu/~mhs119/Temperature/T_moreFigs/ZonalT.gif

    After the seasonal adjustment you require there’s a massive Arctic skew to the global mean. And a Northern Hemisphere skew too.

    The global mean is irrelevant to the Southern Hemisphere excluding tropics (think Auckland, Sydney, Johannesburg, Buenos Aires) as demonstrated by GISTEMP below.

    GISTEMP: Annual Mean Temperature Change for Three Latitude Bands [Graph]
    http://data.giss.nasa.gov/gistemp/graphs_v3/Fig.B.pdf

    Here’s the anomaly data:
    Year Glob NHem SHem 24N-90N 24S-24N 90S-24S
    2000 42 51 33 71 27 34
    2001 55 64 45 80 44 44
    2002 63 71 55 81 60 48
    2003 62 72 51 80 63 41
    2004 54 68 41 75 57 32
    2005 69 84 54 98 64 46
    2006 63 79 47 95 56 40
    2007 66 83 48 108 47 48
    2008 53 65 41 86 39 40
    2009 64 70 58 73 68 50
    2010 71 88 55 98 69 48
    2011 60 71 49 92 37 58
    2012 63 77 49 97 52 44
    2013 65 76 55 87 58 53
    2014 74 91 58 104 67 54
    2015 87 113 60 125 92 41
    http://data.giss.nasa.gov/gistemp/graphs_v3/Fig.B.txt

    In 2015, 24N-90N (125) and 90S-24S (41).

    NH excluding tropics is 3x SH excluding tropics. And not once is SHem anywhere near Glob.

    “Glob” is completely meaningless. The closest to Glob in 2015 (87) is the Tropics (92) but look at 2000. Glob is 42, Tropics is 27.

  41. Richard C (NZ) on July 21, 2016 at 4:53 pm said:

    Simon

    >”Natural variation may give the impression of a temporary hiatus, but the trend continues. Especially so now that the Pacific Decadal Oscillation has flipped over to a positive phase”

    Do you really know what you are talking about Simon?

    Pacific Decadal Oscillation (PDO)

    When SSTs are anomalously cool in the interior North Pacific and warm along the Pacific Coast, and when sea level pressures are below average over the North Pacific, the PDO has a positive value.

    http://www.ncdc.noaa.gov/teleconnections/pdo/

    A positive PDO does not necessarily mean GMST warming and a positive phase of natural multidecadal variation (MDV). 2000 was max positive MDV. 2015 was MDV-neutral. From 2015 to 2030 MDV will be in negative phase (see Macias et al upthread). So to reproduce the GMST profile from 2015 to 2030 starting from ST, MDV must be subtracted from ST:

    ST – MDV = GMST

    From 2015 until ST peaks, GMST will remain flatish i.e. ST will be ABOVE the GMST profile. After ST peaks GMST will go into cooling phase unless the MMCC is proved correct (doubtful) because ST will be cooling and it has more long-term effect on GMST than the oscillatory MDV does.

    Question is: when will ST peak?

    The solar conjecture incorporates planetary thermal lag via the oceanic heat sink i.e. we have to wait for an atmospheric temperature response to solar change, it is not instantaneous. Solar change commenced in the mid 2000s (PMOD) so if we add 20 years lag say (from various studies but certainly not definitive), we get an ST peak around 2025.

    So the acid test for MMCC surface temperatures is the next 3 – 7 years. The acid test for the solar-centric conjecture doesn’t begin until the mid 2020s.

    If you really want to invoke the PDO as a climate driver Simon, you will have to accept this:

    ‘Climate Modeling: Ocean Oscillations + Solar Activity R²=.96’

    THS, January 23, 2010

    Expanding upon the last post, the “sunspot integral” (accumulated departure in sunspots v. the monthly mean of 41.2 for the observational period of sunspots 1610-2009) shows good correlation with the temperature record. Excellent correlation (R²=.96!) with temperature is obtained by adding to the sunspot integral the most significant ocean oscillations (the PDO-Pacific Decadal Oscillation + AMO- Atlantic Multidecadal Oscillation*3). Various other combinations and permutations of these factors compared to the temperature record have been posted at: 1 2 3 4 5 6, although I have not located others with a correlation coefficient of this magnitude. Contrast the R² of .96 from this simple model (near a perfect correlation coefficient (R²) of 1) vs. the poor correlation (R²=.44) of CO2 levels vs. temperature.

    More>>>>
    http://hockeyschtick.blogspot.co.nz/2010/01/climate-modeling-ocean-oscillations.html

    A near perfect correlation of natural drivers with GMST versus a poor correlation of CO2 with GMST. And if you add in a CO2 component to PDO+AMO+SSI you will overshoot, As have the IPCC climate modelers.

  42. Gary Kerkin on July 21, 2016 at 4:56 pm said:

    Richard C>”The global mean is irrelevant to the Southern Hemisphere excluding tropics (think Auckland, Sydney, Johannesburg, Buenos Aires) as demonstrated by GISTEMP below.”

    Actually, a global mean is irrelevant to any particular point somewhere on the globe! Which is to the point I was making about being circumspect about how it is to be used. I commented that it may be useful to have some sort of number for comparison purposes, but I would hesitate to give it any importance as a “metric”, which Simon considers appropriate. When noise is around ±0.5ºC differences of, say, ±0.2ºC do not convey much in the way of meaning.

    One of the obfuscation techniques commonly in use is to display the information as “anomalies”. Everyone would be well advised to plot the information in actual values of, say, ºC with the origin set at 0º. Just eyeballing the data will give even the most casual viewer some perspective. That perspective is even better appreciated when considering a typical diurnal variation.

  43. Richard C (NZ) on July 21, 2016 at 5:14 pm said:

    Should be:

    “So to reproduce the GMST profile from 2015 to [2045] starting from ST, MDV must be subtracted from ST: ST – MDV = GMST [2015 – 2045]”

    The equations over the MDV cycle are:

    2045 neutral
    2030 < MDV max negative ST – MDV = GMST
    2015 neutral
    2000 < MDV max positive ST + MDV = GMST
    1985 neutral
    1970 < MDV max negative ST – MDV = GMST
    1955 neutral
    1940 < MDV max positive ST + MDV = GMST
    1925 neutral
    1910 < MDV max negative ST – MDV = GMST
    1895 neutral

  44. Richard C (NZ) on July 21, 2016 at 7:36 pm said:

    Schmidt clarifies:

    “While the El Niño event in the tropical Pacific this winter gave a boost to global temperatures from October onwards, it is the underlying trend which is producing these record numbers,” Gavin Schmidt said.

    https://www.theguardian.com/environment/2016/jul/20/june-2016-14th-consecutive-month-of-record-breaking-heat-says-us-agencies

    He’s got a problem looming with that though, his own GISTEMP Monthly shows GMST is plummeting back to neutral.

    GISTEMP: Global Monthly Mean Surface Temperature Change
    http://data.giss.nasa.gov/gistemp/graphs_v3/Fig.C.pdf

    The June anomaly is unremarkable. After this a La Nina.

  45. Simon on July 21, 2016 at 8:39 pm said:

    This post describes the models overshooting observed temperatures over a period of 15 years in the tropical mid-troposphere, i.e. 10 km above us. It is unclear whether this is natural variation, model inaccuracy, measurement error, or a combination of all three. 2016 will be interesting as I suspect that will be somewhere well within the models’ envelope. Also note Gavin’s point, which you ignored, that if the models were retrospectively initialised with now known realised forcing, the models’ projections would have been lower.

  46. Richard C (NZ) on July 21, 2016 at 9:17 pm said:

    Gary

    [You] >”Did you and Andy look at all months, or just February?”

    [Me] >”No unfortunately. I picked up that graph from a comment thread somewhere. I don’t where to access the original graphs. I know there are similar graphs of each month because I saw another one recently of May I think it was but I didn’t save the link. I wish I knew who produces them. It’s not GISS I don’t think”

    I remember now. You can generate your own from a GISS page but not from GISTEMP. June is set up to be generated here:

    GISS Surface Temperature Analysis – Global Maps from GHCN v3 Data
    http://data.giss.nasa.gov/gistemp/maps/

    Just click ‘Make Map’ and scroll down to “Get the zonal means plot as PDF, PNG, or PostScript file”

    PNG graphs

    June 2016 Zonal Mean
    http://data.giss.nasa.gov/tmp/gistemp/NMAPS/tmp_GHCN_GISS_ERSSTv4_1200km_Anom6_2016_2016_1951_1980_100__180_90_0__2_/amaps_zonal.png

    February 2916 Zonal Mean
    http://data.giss.nasa.gov/tmp/gistemp/NMAPS/tmp_GHCN_GISS_ERSSTv4_1200km_Anom2_2016_2016_1951_1980_100__180_90_0__2_/amaps_zonal.png

    These graphs show the real GMST story by latitudinal zone.

  47. Richard C (NZ) on July 21, 2016 at 9:20 pm said:

    Credit to where I got to that GISS zonal anomaly generator. A warmy blogger, Robert Scribbler, has been posting the graphs at his website: robertscribbler https://robertscribbler.com/

    May 2016 Zonal Anomalies
    https://robertscribbler.com/2016/06/13/may-marks-8th-consecutive-record-hot-month-in-nasas-global-temperature-measure/nasa-zonal-anomalies-may-2016/

    June 2016 Zonal Anomalies
    https://robertscribbler.com/2016/07/19/2016-global-heat-leaves-20th-century-temps-far-behind-june-another-hottest-month-on-record/june-zonal-anomalies/

    Scribbler has this post:

    ‘Rapid Polar Warming Kicks ENSO Out of Climate Driver’s Seat, Sets off Big 2014-2016 Global Temperature Spike’
    https://robertscribbler.com/2016/06/17/rapid-polar-warming-kicks-enso-out-of-the-climate-drivers-seat-sets-off-big-2014-2016-global-temperature-spike/

    Read Arctic for”Polar”. I actually agree with this except now the “Rapid Arctic Warming” has dissipated. It turned into Rapid Arctic Cooling. Compare February Zonal Mean to June Zonal Mean side by side.

  48. Richard Treadgold on July 21, 2016 at 9:29 pm said:

    Simon,

    It is unclear whether this is natural variation, model inaccuracy, measurement error, or a combination of all three. 2016 will be interesting as I suspect that will be somewhere well within the models’ envelope. Also note Gavin’s point, which you ignored, that if the models were retrospectively initialised with now known realised forcing, the models’ projections would have been lower.

    We know the models got it wrong; the point you ignore is that Gavin agrees they got it wrong. The models are the only source for predictions of dangerous warming to come—but we need not now believe them, as they’re inaccurate. You had the same suspicion about model skill in many previous years, I would guess; but it doesn’t help us. I didn’t address Gavin’s point about rerunning the models with “now known” data because I thought it was too silly for words. It’s fairly easy to tweak a model to match past temperature; the point is to teach it to forecast the future.

  49. Richard C (NZ) on July 21, 2016 at 9:43 pm said:

    Simon

    <"This post describes the models overshooting observed temperatures over a period of 15 years in the tropical mid-troposphere, i.e. 10 km above us. It is unclear whether this is natural variation, model inaccuracy, measurement error, or a combination of all three."

    Wrong at surface too. You might read the IPCC Chapter 9 quote upthread Simon. Here's their reasoning:

    This difference between simulated and observed trends could be caused by some combination of (a) internal climate variability, (b) missing or incorrect radiative forcing and (c) model response error.

    >”2016 will be interesting as I suspect that will be somewhere well within the models’ envelope.”

    Good luck with that Simon. RSS is plummeting out of the envelope:

    RSS from 2005
    http://woodfortrees.org/plot/rss/from:2005

    >”Also note Gavin’s point, which you ignored, that if the models were retrospectively initialised with now known realised forcing, the models’ projections would have been lower.”

    Yes, EXAVTLY Simon. The IPCC agrees totally that the models are WRONG. They give 3 reasons as quoted above: (a) (b) and (c). They have neglected natural variation (a). Incorrect theoretical forcing (b) doesn’t seem to be the problem. That leaves model response error (c) in combination with neglected natural variation (a):

    (c) Model Response Error
    The discrepancy between simulated and observed GMST trends during 1998–2012 could be explained in part by a tendency for some CMIP5 models to simulate stronger warming in response to increases in greenhouse gas (GHG) concentration than is consistent with observations

    The model projections are TOO WARM because (a) natural variation has been neglected and (c) too much CO2 forcing is creating TOO MUCH HEAT i.e. CO2 forcing is at least excessive.

    By the mid 2020s we will know if CO2 forcing is superfluous.

  50. Richard C (NZ) on July 21, 2016 at 10:20 pm said:

    Simon

    [You] >”Also note Gavin’s point, which you ignored, that if the models were retrospectively initialised with now known realised forcing, the models’ projections would have been lower.”

    [Me] >”Incorrect theoretical forcing (b) doesn’t seem to be the problem”

    Here’s what the IPCC has to say about (b) theoretical Radiative Forcing:

    (b) Radiative Forcing

    On decadal to interdecadal time scales and under continually increasing effective radiative forcing (ERF), the forced component of the GMST trend responds to the ERF trend relatively rapidly and almost linearly (medium confidence, e.g., Gregory and Forster, 2008; Held et al., 2010; Forster et al., 2013). The expected forced-response GMST trend is related to the ERF trend by a factor that has been estimated for the 1% per year CO2 increases in the CMIP5 ensemble as 2.0 [1.3 to 2.7] W m–2 °C–1 (90% uncertainty range; Forster et al., 2013). Hence, an ERF trend can be approximately converted to a forced-response GMST trend, permitting an assessment of how much of the change in the GMST trends shown in Box 9.2 Figure 1 is due to a change in ERF trend.

    The AR5 best-estimate ERF trend over 1998–2011 is 0.22 [0.10 to 0.34] W m–2 per decade (90% uncertainty range), which is substantially lower than the trend over 1984–1998 (0.32 [0.22 to 0.42] W m– per decade; note that there was a strong volcanic eruption in 1982) and the trend over 1951–2011 (0.31 [0.19 to 0.40] W m–2 per decade; Box 9.2, Figure 1d–f; numbers based on Section 8.5.2, Figure 8.18; the end year 2011 is chosen because data availability is more limited than for GMST). The resulting forced- response GMST trend would approximately be 0.12 [0.05 to 0.29] °C per decade, 0.19 [0.09 to 0.39] °C per decade, and 0.18 [0.08 to 0.37] °C per decade for the periods 1998–2011, 1984–1998 and 1951–2011, respectively (the uncertainty ranges assume that the range of the conversion factor to GMST trend and the range of ERF trend itself are independent). The AR5 best-estimate ERF forcing trend difference between 1998–2011 and 1951–2011 thus might explain about one-half (0.05°C per decade) of the observed GMST trend difference between these periods (0.06 to 0.08°C per decade, depending on observational data set).

    The reduction in AR5 best-estimate ERF trend over 1998–2011 compared to both 1984–1998 and 1951–2011 is mostly due to decreasing trends in the natural forcings,–0.16 [–0.27 to – .06] W m–2 per decade over 1998–2011 compared to 0.01 [–0.00 to 0.01] W m–2 per decade ove 1951–2011 (Section 8.5.2, Figure 8.19). Solar forcing went from a relative maximum in 2000 to a relative minimum in 2009, with a peak-to-peak difference of around 0.15 W m–2 and a linear trend over 1998 2011 of around –0.10 W m–2 per decade (cf. Section 10.3.1, Box 10.2). Furthermore, a series of small volcanic eruptions has increased the observed stratospheric aerosol loading after 2000, leading to an additional negative ERF linear-trend contribution of around –0.06 W m–2 per decade over 1998–2011 (cf. Section 8.4.2.2, Section 8.5.2, Figure 8.19; Box 9.2 Figure 1d, f). By contrast, satellite-derived estimates of tropospheric aerosol optical depth (AOD) suggests little overall trend in global mean AOD over the last 10 years, implying little change in ERF due to aerosol-radiative interaction (low confidence because of low confidence in AOD trend itself, Section 2.2.3; Section 8.5.1; Murphy, 2013). Moreover, because there is only low confidence in estimates of ERF due to aerosol–cloud interaction (Section 8.5.1, Table 8.5), there is likewise low confidence in its trend over the last 15 years.

    For the periods 1984–1998 and 1951–2011, the CMIP5 ensemble-mean ERF trend deviates from the AR5 best-estimate ERF trend by only 0.01 W m–2 per decade (Box 9.2 Figure 1e, f). After 1998, however, some contributions to a decreasing ERF trend are missing in the CMIP5 models, such as the increasing stratospheric aerosol loading after 2000 and the unusually low solar minimum in 2009. Nonetheless, over 1998–2011 the CMIP5 ensemble-mean ERF trend is lower than the AR5 best-estimate ERF trend by 0.03 W m–2 per decade (Box 9.2 Figure 1d). Furthermore, global mean AOD in the CMIP5 models shows little trend over 1998–2012, similar to the observations (Figure 9.29).

    Although the forcing uncertainties are substantial, there are no apparent incorrect or missing global mean forcings in the CMIP5 models over the last 15 years that could explain the model–observations difference during the warming hiatus.

    http://www.climatechange2013.org/images/report/WG1AR5_Chapter09_FINAL.pdf

    In respect to radiative forcing, supports my statement above but not Schmidt – “there are no apparent incorrect or missing global mean forcings in the CMIP5 models over the last 15 years that could explain the model–observations difference during the warming hiatus”.

    That just leaves (a) natural variation and (b) model response error. The IPCC say their radiative forcing theory (a) is OK.

  51. Simon on July 21, 2016 at 10:21 pm said:

    June’s GISTemp measures are now out. Despite the drop in temperature, June 2016 is still by far the warmest June ever recorded.

  52. Richard C (NZ) on July 21, 2016 at 11:19 pm said:

    Simon

    >”June’s GISTemp measures are now out. Despite the drop in temperature, June 2016 is still by far the warmest June ever recorded.”

    “By far”? That Hot Whopper graph is wrong. June 2016 is unremarkable. LOTI is only 0.02 warmer than June 1998:

    Monthly Mean Surface Temperature Anomaly (C)
    ——————————————–
    Year+Month Station Land+Ocean
    2016.04 1.36 1.14 January …..[ Hot Whopper gets this right ]
    2016.13 1.64 1.33 February …..[ Hot Whopper says about 1.24 – WRONG ]
    2016.21 1.62 1.28 March …..[ Hot Whopper gets this about right ]
    2016.29 1.36 1.09 April …..[ Hot Whopper says about 1.22 – WRONG ]
    2016.38 1.18 0.93 May …..[ Hot Whopper says about 1.16 – WRONG ]
    2016.46 0.94 0.79 June …..[ Hot Whopper says about 1.1 – WRONG ]
    1998.46 1.03 0.77 June …..[ Hot Whopper says about 0.7 – WRONG ]
    http://data.giss.nasa.gov/gistemp/graphs_v3/Fig.C.txt

    By station, June 2016 is cooler than June 1998 by 0.09.

    Here’s a tip Simon: Hot Whopper is not a credible source. If you want GISS graphs and data go to GISS or someone whot knows how to plot graphs correctly.

  53. Gary Kerkin on July 21, 2016 at 11:59 pm said:

    IPCC>”This difference between simulated and observed trends could be caused by some combination of (a) internal climate variability, (b) missing or incorrect radiative forcing and (c) model response error.” (cited by Richard C)

    This is fascinating. A semantic analysis might suggest:

    (a)Internal climate variability: We haven’t been able to describe, or haven’t wished to include, all the physics.
    (b)Missing or incorrect radiative forcing: We haven’t been able to correctly describe the AGW hypothesis.
    (c)Model response error: We haven’t been able to force the assumptions and regression predictors and/or whatever to give us the answer we want.

  54. Richard C (NZ) on July 22, 2016 at 9:47 am said:

    Gary >”(b)Missing or incorrect radiative forcing: We haven’t been able to correctly describe the AGW hypothesis.”

    They looked at that but decided they’d got their forcings right. From IPCC Chapter 9 (b) Radiative Forcing quote upthread:

    [(b) Radiative Forcing] Although the forcing uncertainties are substantial, there are no apparent incorrect or missing global mean forcings in the CMIP5 models over the last 15 years that could explain the model–observations difference during the warming hiatus

    So that just leaves (a) and (c) by their reasoning. But (b) and (c) are BOTH within their theoretical radiative forcing paradigm i.e. the models are responding (c) correctly to forcings (b) that don’t exist if their theory is wrong. They haven’t considered that very real possibility.

    That their theory is wrong and the IPCC haven’t addressed the issue is laid out here:

    IPCC Ignores IPCC Climate Change Criteria – Incompetence or Coverup?
    https://dl.dropboxusercontent.com/u/52688456/IPCCIgnoresIPCCClimateChangeCriteria.pdf

    And when the internal climate variability signal (a) is added in to the model mean profile as per the MDV signal extracted by Macias et al (see upthread), it makes the model mean profile even worse. MDV was max positive at 2000 but is missing from the models. Therefore the positive MDV signal must be added to the model mean over the period 1985 to 2015.

    At 2015, MDV is neutral so the model mean stays as is at 2015 but is way too warm.

    From 2015 to 2045, the MDV signal must be subtracted from the model mean but because the model mean is already too warm the subtraction still will not reconcile models to observations.

    So it’s back to excessive anthro forcings in (b), contrary to their assessment, but that’s if their theory has any validity.

    Their theory is falsified by the IPCC’s own primary climate change criteria (earth’s energy balance at TOA) as I’ve laid out in ‘IPCC Ignores IPCC Climate Change Criteria’ so the reason for the “model–observations difference” is superfluous anthro forcing – not just excessive, but superfluous, redundant.

  55. Richard C (NZ) on July 22, 2016 at 11:21 am said:

    Hot Whopper’s whopper:

    Hottest June on record – global surface temperature with year to date

    Sou| Wednesday, July 20, 2016

    “According to GISS NASA, the average global surface temperature anomaly for June was 0.79 °C, which just pipped June 2015 (0.78 C) and June 1998 (0.77 °C).”

    http://blog.hotwhopper.com/2016/07/hottest-june-on-record-global-surface.html

    OK so far, these numbers correspond to the NASA GISS anomaly data page upthread a bit and are unremarkable in respect to June 2016 compared to June 2015 and June 1998. But then she goes on:

    “The average for the six months to the end of June is 1.09 °C, which is 0.28 °C higher than any previous January to June period.”

    This is the spin. She can’t crow about the June anomaly because it’s unremarkable compared to 2015 and 1998. NASA GISS resorts to the same spin for the same reasons:

    2016 Climate Trends Continue to Break Records

    NASA GISS Posted July 19, 2016

    Each of the first six months of 2016 set a record as the warmest respective month globally in the modern temperature record, which dates to 1880, according to scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York. The six-month period from January to June was also the planet’s warmest half-year on record, with an average temperature 1.3 degrees Celsius (2.4 degrees Fahrenheit) warmer than the late nineteenth century.

    http://www.giss.nasa.gov/research/news/20160719/

    What they don’t say of course is that the record was only broken by 0.01 (2015) and 0.02 (1998) i.e. nothing to crow about. So on with the spin – “The six-month period from January to June……….”.

    That’s all they’ve got to crow about and all Sou at Hot Whopper can agonize over. So now back to the graph Simon posted, provenance Hot Whopper:

    [Sou] Year to date average surface temperature

    The chart below tracks the year to date. Each point on the plot is the average of the year to that month. For 2016, the last point is the average of all months to date including June. This year is tracking well above 2015, largely because of the El Niño. To drop below the average for 2015, the average anomaly for the next six months would need to be less than 0.65 °C:

    https://3.bp.blogspot.com/-ONUpWjJkgME/V45PVyiiieI/AAAAAAAAOS0/BVjlQb4h_wwAVDcpYYnVpGQwu_fTRpS0wCLcB/s1600/ytd%2Bto%2BJune16.png

    This is both misleading and meaningless spin. Poor gullible serially wrong Simon swallowed it hook line and sinker.

    The last point June on Sou’s graph (1.1), which is the average of all months to date including June, means absolutely nothing if the actual anomaly for June has plummeted (0.79).

    Even worse, by station June 2016 is cooler than June 1998 by 0.09.

    Herein lies the perils of believing spin, and meaningless concocted graphs

  56. Richard C (NZ) on July 22, 2016 at 11:48 am said:

    GISS and Sou have different values for their 6 month average:

    GISS – “The six-month period from January to June was also the planet’s warmest half-year on record, with an average temperature 1.3 degrees Celsius”

    Sou – “The average for the six months to the end of June is 1.09 °C”

    They are both correct but GISS zeal trumps Sou’s. GISS have averaged the Station data – NOT the LOTI data that Sou averaged:

    GISS: Monthly Mean Surface Temperature Anomaly (C)
    Year+Month Station Land+Ocean
    2016.04 1.36 1.14 January
    2016.13 1.64 1.33 February
    2016.21 1.62 1.28 March
    2016.29 1.36 1.09 April
    2016.38 1.18 0.93 May
    2016.46 0.94 0.79 June
    2015.46 0.84 0.78 June
    1998.46 1.03 0.77 June
    http://data.giss.nasa.gov/gistemp/graphs_v3/Fig.C.txt

    The lesson being: If you are going to resort to spin, you MUST choose the data that spins best.

    Sou still has much to learn from GISS about spin apparently.

  57. Richard C (NZ) on July 22, 2016 at 12:08 pm said:

    If Sou is going to plot “the average of the year to that month” (and neglect to tell anyone in the graph title), then she would have to plot the average of January to June at March/April – not June.

    The average of January – June occurs at March/April.

    This is no different to the IPCC’s Assessment Report prediction baseline as quoted by NIWA, T&T, and whoever else. Their baseline is the average of 1980 to 1999 data nominally centred on 1990 – not 1999.

    Scurrilous Sou.

  58. Richard C (NZ) on July 22, 2016 at 1:00 pm said:

    By June, Sou has a 6 month TRAILING average which she could then plot if she stipulated as such.

    But her graph is NOT of “trailing averages”:

    ‘How do I Calculate a Trailing Average?’
    http://www.ehow.com/how_6910909_do-calculate-trailing-average_.html

    For January, the 6 month trailing average is 0.99 but Sou plots “year to date” which is the January anomaly (1.14).

    For February, Sou plots the average of January and February – NOT the 6 month trailing average.

    For March, Sou plots the average of January February and March – NOT the 6 month trailling average.

    And so on.

    Sou has 6 unrelated graph datapoints:

    1 month trialling average (January)
    2 month trailing average (February)
    3 month trailing average (March)
    4 month trailing average (April)
    5 month trailing average (May)
    6 month trailing average (June)

    What does ‘Trailing’ mean

    Trailing is the most recent time period, often used to describe the time that a particular set of data is referring to. Trailing is used to describe a past statistic, such as same-store sales, but can also be used to describe a technique, such as a trailing stop order. Most often you will hear the term “trailing 12 months,” “trailing three months” or “trailing six months.”

    http://www.investopedia.com/terms/t/trailing.asp

    Sou plots all of those 6 trailing averages on a line graph and doesn’t stipulate on the title or the x axis what the datapoints actually are. The months on the x axis MUST also have the stipulation of the respective period of trailing average. For example:

    April 4 month trailing average

    And the value should be represented by a vertical bar graph because April is unrelated to the other months i.e. all of the months are in respect to different trailing averages

    Lies, damn lies, and Sou’s graphs.

  59. Richard C (NZ) on July 22, 2016 at 3:29 pm said:

    Richard C (NZ) on July 21, 2016 at 10:20 pm said:

    >”That just leaves (a) natural variation and (b) model response error. The IPCC say their radiative forcing theory (a) is OK.”

    Got it horribly wrong to. Should be:

    “That just leaves (a) natural variation and [(c)] model response error. The IPCC say their radiative forcing theory [(b)] is OK.”

  60. Richard C (NZ) on July 22, 2016 at 5:06 pm said:

    What Schmidt actually said about incorrect forcing in regard to TMT:

    So what?

    In work we did on the surface temperatures in CMIP5 and the real world [hotlink – see below], it became apparent that the forcings used in the models, particularly the solar and volcanic trends after 2000, imparted a warm bias in the models (up to 0.1ºC or so in the ensemble by 2012), which combined with the specific sequence of ENSO variability, explained most of the model-obs discrepancy in GMST. This result is not simply transferable to the TMT record (since the forcings and ENSO have different fingerprints in TMT than at the surface), but similar results will qualitatively hold.

    http://www.realclimate.org/index.php/archives/2016/05/comparing-models-to-the-satellite-datasets/

    Qualitatively but why not quantitatively?

    Reason: “This result is not simply transferable to the TMT record” i.e. it does not necessarily follow – a non sequitur.

    The hotlink leads to this article:

    ‘Reconciling warming trends’
    Gavin A. Schmidt, Drew T. Shindell and Kostas Tsigaridis
    http://www.blc.arizona.edu/courses/schaffer/182h/Climate/Reconciling%20Warming%20Trends.pdf

    Climate models projected stronger warming over the past 15 years than has been seen in observations. Conspiring factors of errors in volcanic and solar inputs, representations of aerosols, and El Niño evolution, may explain most of the discrepancy.
    […]
    We estimate how simulated global mean surface temperature would have been different in the CMIP5 runs if two effects had been included: first, if ENSO in each model had been in phase with observations, and second, if the ensemble mean were adjusted using results from a simple impulse-response model5 with our updated information on external drivers (Fig. 1b). We find that this procedure reduces the differences between observed and simulated temperatures by a factor of 3.

    We conclude that the coincident underestimates of cooling factors in the 2000s — that is, of volcanic aerosols, solar irradiance and effects of humanmade aerosols — have combined to bias the model ensemble. According to this estimate, about a third of the difference between simulated and observed trends (in the GISTEMP analysis6) between 1997 to 2013 is a result of underestimated volcanic emissions; about one-seventh of the discrepancy comes from overestimates of solar activity and differences in ENSO
    phasing between climate models and the real world; and just under a quarter could be related to human-made aerosols.

    These estimates leave only a small residual between models and observations that can be easily accounted for by internal variability unrelated to ENSO, or to any further misspecifications of external
    influences. In comparison to an alternative temperature analysis7 the observed trend matches the adjusted simulated temperature increase even more closely (Fig. 1b).

    Lots of problems with this which I’ll revisit over the weekend but for starters:

    1) “Iinternal variability unrelated to ENSO” is MDV which is a greater factor by far than ENSO is. When an MDV signal is added in to the models (as it will eventually be conceded – give them time) that throws out all their reasoning completely.

    Upthread I’ve posted Jeff Patterson’s ‘A TSI-Driven (solar) Climate Model’:

    https://www.climateconversation.org.nz/2016/07/gavin-schmidt-confirms-model-excursions/comment-page-1/#comment-1500326

    Jeff’s model goes a long way towards modeling Multi-Decadal Variation/Oscillation (MDV/MDO) that Schmidt et al haven’t even started on.

    2) “if ENSO in each model had been in phase with observations”. Well yes, “IF”. But the models don’t do ENSO that can be compared to observations as Schmidt concedes. They have to add in known ENSO just as they would known MDV (when they get around to that). They also add in known transitory volcanic activity.

    They are not really modeling when much of their “adjusted” model is just adding in known observations. The alternative is to neglect temporary volcanics, neglect temporary ENSO, neglect MDV and compare their model to observations with MDV removed and smoothed to eliminate ENSO which is really just “noise”. The volcanic influence in observations being only temporary and washes out relatively quickly..

    3) “the observed trend matches the adjusted simulated temperature increase even more closely (Fig. 1b).”

    No it doesn’t. The simulation is missing the MDV signal present in the observations. Conversely, the secular trend (ST) in the observations, which has MDV removed, is nothing like the model mean except that is is also a smooth curve but it is well below the model mean over the 21st century and coincides with MDV-neutral ENSO-neutral observations at 2015. At this date, the “adjusted” model mean is still well above the observations ST.

    4) “may explain most of the discrepancy”. Read – “We’re speculating a lot, but we still don’t know for sure what’s going on. Sorry”

  61. Richard C (NZ) on July 22, 2016 at 7:53 pm said:

    >”The simulation is missing the MDV signal present in the observations. Conversely, the secular trend (ST) in the observations, which has MDV removed, is nothing like the model mean except that is also a smooth curve but it is well below the model mean over the 21st century and coincides with MDV-neutral ENSO-neutral observations at 2015. At this date, the “adjusted” model mean is still well above the observations ST.”

    Best seen in Macias et al Figure 1:

    Figure 1. SSA reconstructed signals from HadCRUT4 global surface temperature anomalies.
    http://journals.plos.org/plosone/article/figure/image?download&size=large&id=info:doi/10.1371/journal.pone.0107222.g001

    The secular trend (ST, red line) is directly comparable to the model mean (not shown). At 2000 the ST (red line) is well below reconstructed GMST (thick black line). The model mean is above the GMST reconstruction (thick black line). Therefore the models are performing rather more badly than Schmidt, Shindell, and Tsigaridis think and even worse than most sceptics think.

    Also easy to see that the ST (red line) is the MDV-neutral “spline” about which the GMST reconstruction (thick black line) oscillates. MDV-neutral is where the GMST reconstruction (thick black line) crosses the ST (red line).

    2045 neutral
    2030 < MDV max negative ST – MDV = GMST
    2015 neutral
    2000 < MDV max positive ST + MDV = GMST
    1985 neutral
    1970 < MDV max negative ST – MDV = GMST
    1955 neutral
    1940 < MDV max positive ST + MDV = GMST
    1925 neutral
    1910 < MDV max negative ST – MDV = GMST
    1895 neutral

    The model mean tracks the ST (red line) up until MDV-neutral 1955. Not too bad either at MDV-neutral 1985 although hard to make a judgment given the retroactively added volcanic activity to the models. But after that the model mean departs from the ST (red line) and veers wildly off too warm. This is the effect of theoretical anthropogenic forcing.

    Jeff Patterson has a lot of the MDV issue figured out in his 'TSI-Driven (solar) Climate Model’ linked in previous comment. Most sceptics are oblivious to it all so there's a long way to go for them before understanding sets in (if ever).

    Question is though: How long before Schmidt, Shindell, Tsigaridis, and all the other IPCC climate modelers figure it out?

  62. Richard C (NZ) on July 22, 2016 at 8:40 pm said:

    >”Jeff Patterson has a lot of the MDV issue figured out in his ‘TSI-Driven (solar) Climate Model’ linked in previous comment.”
    >”How long before Schmidt, Shindell, Tsigaridis, and all the other IPCC climate modelers figure it out?”

    The IPCC are particularly clueless. Their “natural forcings only” simulations are laughable. Compare Jeff Patterson’s natural only model to the IPCC’s natural only models from Chapter 10 Detection and Attribution:

    Patterson Natural Only: Modeled vs Observed
    https://wattsupwiththat.files.wordpress.com/2016/02/image24.png

    IPCC Natural Only: Modeled vs Observed (b)
    http://www.ipcc.ch/report/graphics/images/Assessment%20Reports/AR5%20-%20WG1/Chapter%2010/Fig10-01.jpg

    The IPCC then go on to conclude anthropogenic attribution from their hopelessly inadequate effort.

    Patterson on the other hand, doesn’t need to invoke GHG forcing as as input parameterization. On the contrary, he models CO2 as an OUTPUT after getting temperature right.

  63. Richard C (NZ) on July 22, 2016 at 10:14 pm said:

    Patterson:

    [Re Figure 7, HadCRUT4 with wavelet denoising below]
    “The resulting denoised temperature profile is nearly identical to that derived by other means (Singular Spectrum Analysis [hotlink 1], Harmonic Decomposition [hotlink 2], Principal Component Analysis, Loess Filtering [hotlink 3], Windowed Regression [hotlink 4] etc.)”

    Figure 7
    https://wattsupwiththat.files.wordpress.com/2016/02/image_thumb21.png?w=774&h=503

    A TSI-Driven (solar) Climate Model
    https://wattsupwiththat.com/2016/02/08/a-tsi-driven-solar-climate-model/

    [1] Singular Spectrum Analysis
    Detecting the AGW Needle in the SST Haystack – Patterson

    Conclusion

    As Monk would say, “Here’s what happened”. During the global warming scare of the 1980’s and 1990’s, the quasi-periodic modes comprising the natural temperature variation were both in their phase of maximum slope (See figure 5). This naturally occurring phenomenon was mistaken for a rapid increase in the persistent warming trend and attributed to the greenhouse gas effect. When these modes reached their peaks approximately 10 years ago, their slopes abated, resulting in the so-called “pause” we are currently enjoying. This analysis shows that the real AGW effect is benign and much more likely to be less than 1 °C/century than the 3+ °C/century given as the IPCC’s best guess for the business-as-usual scenario.

    https://wattsupwiththat.com/2013/09/26/detecting-the-agw-needle-in-the-sst-haystack/

    [2] Harmonic Decomposition
    Digital Signal Processing analysis of global temperature data time series suggests global cooling ahead – Patterson

    Discussion

    With a roughly one-in-twelve chance that the model obtained above is the manifestation of a statistical fluke, these results are not definitive. They do however show that a reasonable hypothesis for the observed record can be established independent of any significant contribution from greenhouse gases or other anthropogenic effects.

    https://wattsupwiththat.com/2013/09/11/digital-signal-processing-analysis-of-global-temperature-data-suggests-global-cooling-ahead/

    [3] Loess Filtering
    Modelling Current Temperature Trends – Terence C. Mills, Loughborough University

    Discussion

    […] Thus the recent warming trend in the CET series is by no means unprecedented. While we are not suggesting that the current warming trend will necessarily be quickly reversed, this statistical exercise reveals that examining temperature records over a longer time frame may offer a different perspective on global warming than that which is commonly expressed.

    Indeed, examining much longer records of temperature reconstructions from proxy data reveals a very different picture of climate change than just focusing on the last 150 years or so of temperature observations, with several historical epochs experiencing temperatures at least as warm as those being encountered today: see, for example, Mills (2004, 2007b) for trend modelling of long-run temperature reconstructions. At the very least, proponents of continuing global warming and climate change would perhaps be wise not to make the recent warming trend in recorded temperatures a central plank in their argument.

    http://www.jds-online.com/file_download/198/JDS-436.pdf

    [4] Windowed Regression
    Changepoint analysis as applied to the surface temperature record – Patterson

    Conclusion

    One benefit of the recent discussions on the so called “pause” in global warming is a healthy re-focusing on the empirical data and on the failure of climate models to accurately reflect climate dynamics. Yet to speak of a pause infers that the rapid warming that occurred at the end of the last century reflects the true, post-industrial trend. As the analysis above shows, there is no empirical evidence to support the notion that that period was particularly unusual, much less that it was due to anthropogenic effects.

    In short it is in my view incorrect to term the nearly 20 year slowing in the rate of warming as a pause. Rather it is the natural (and perhaps cyclical) variation around a warming trend that has remained constant at ~.008 °C/decade2 since the late 1800s. There is no empirical evidence from the temperature record that mankind has had any effect one way or the other.

    https://wattsupwiththat.com/2014/12/07/changepoint-analysis-as-applied-to-the-surface-temperature-record/

    # # #

    None of these signal analysis techniques are employed by the IPCC in their Detection and Attribution.

    Easy to see why they wouldn’t want attention drawn to them either.

  64. Richard C (NZ) on July 23, 2016 at 11:16 am said:

    Schmidt, Shindell, and Tsigaridis Figure 1:

    “Adjusting for the phase of ENSO by regressing the observed temperature against the ENSO index adds interannual variability to the CMIP5 ensemble mean (dashed blue)”

    “Interannual variability” ? Whoop-de-doo.

    They adjust for a bit of noise but neglect multidecadal variabilty (MDV) with a cycle of around 60 years.

    They’re so fixated on noise that they can’t see the signals.

    They can’t see the wood for all the trees in the way.

  65. Gary Kerkin on July 23, 2016 at 1:22 pm said:

    Sorry Richard C that I haven’t been back regarding what you’ve posted since my last comment. Some medical problems got in the way yesterday which put paid to any activity!

    My comment with a “semantic analysis” was meant to be sarcastic, and much of what you found and posted reinforces my comments. Rather curiously, I think. When a document starts to over-explain why there are deficiencies in models which purport to explain an hypothesis my antennae start to quiver. Others have described it as a bullshit metre. I wouldn’t, of course—I prefer a more refined statement. Much of it, and much of the information you followed it with have all the hallmarks of self-justification and desperation.

    This discussion started a while back with the “Almost 100% of scientists …” statement from the Renwick and Naish road show and which I categorized as a fundamental need to have their stance vindicated by having others agree with them. This is, I believe, the rationale for trying to establish a “consensus”.

  66. Gary Kerkin on July 23, 2016 at 1:53 pm said:

    Richard C, the information you have posted on modelling is informative and interesting and the conclusions various authors have drawn offer an insight into what is, and what is not, possible.

    In the far distant past (about 30 years ago) I had great success with linear modelling. That is where something can be described by linear relationships such as A + Bx1 + Cx2 + … for example I modeled the control of an alumina digester where two parallel trains of 12 shell-and-tube heat exchangers heated incoming caustic liquor flowed into three digesters into which bauxite slurry was injected. The stream out of the final digester passed through 12 flash tanks which supplied steam to the heat exchangers heating the incoming liquor. Each heat exchanger had 2 liquor temperatures and a pressure, a steam temperature and pressure totaling 6x12x2 or 144 variables, incoming liquor flow rate, 3 digester pressures, 3 digester temperatures, and the flow rate out of the final digester, 12 flow rates out of each of the flash tanks, and the 12 steam flow rates out of each of the flash tanks. That’s a total of 174 variables, most of which were measured or could be determined by energy or mass balances. I used a large linear matrix from which, every 5 minutes I calculated an inverted matrix and produced new predictors. I found I could predict the temperature distribution over the whole digestion unit to with ±1ºC up to 30 minutes ahead. We later built this into the control system.

    The point about this is that a matrix of linear relationships can be employed very successfully in some circumstances without making any assumptions about physical relationships which may or may not exist between some of the parameters. If the technique is coupled with non-linear relationships such as those employed by the authors you cited above, it becomes a very useful tool, again without necessarily assuming any physical relations, let alone an hypothesis. It can also be a tool for examining potential relationships but it always has to bear remembered that such a model makes no assumptions and just because it may prove successful, as it did in my case, it does not imply any causal relationships.

  67. Gary Kerkin on July 23, 2016 at 2:02 pm said:

    Richard C > “They’re so fixated on noise that they can’t see the signals.”

    Isn’t that the point? If the signal is buried in noise it is extremely difficult to extract it. As I understand signal processing, information (such as signals from deep space explorers) can be extracted from the noise because the useful information is associated with some sort of carrier signature which can be recognised by the processing system. If, however, the carrier signature is not known how can it be recognized and how can the information be extracted. I realise that this is the basis for a huge amount of research effort in signal processing and digital filtering but none-the-less I maintain that if you don’t know what you are looking for how will you recognize it when you see it? Wasn’t this part of the problem with SETI?

  68. Richard C (NZ) on July 23, 2016 at 2:46 pm said:

    Gary

    >”f the signal is buried in noise it is extremely difficult to extract it”

    Except the 2 major signals in GMST (which is overwhelmed by the Northern Hemisphere) are neither buried nor difficult to extract. On the contrary, they are staring everyone in the face and reasonably easy to extract with EMD (I’ve done this with HadSST3) and SSA (haven’t got to grips with this unfortunately).

    The 2 major signals in GMST are the secular trend (ST) and multidecadal variation/oscillation (MDV/MDO) which has a period of about 60 years (the “60 year Climate Cycle”). Once these signals are extracted, they can then be added to reconstruct noiseless GMST.

    The best SSA example of this that I know of is Macias et al linked upthread. Here’s their paper, their Figure 1, and my commentary again:

    ‘Application of the Singular Spectrum Analysis Technique to Study the Recent Hiatus on the Global Surface Temperature Record’
    Diego Macias, Adolf Stips, Elisa Garcia-Gorriz (2014)
    Full paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107222

    Figure 1. SSA reconstructed signals from HadCRUT4 global surface temperature anomalies.
    http://journals.plos.org/plosone/article/figure/image?download&size=large&id=info:doi/10.1371/journal.pone.0107222.g001

    The secular trend (ST, red line) is directly comparable to the model mean (not shown). At 2000 the ST (red line) is well below reconstructed GMST (thick black line). The model mean is above the GMST reconstruction (thick black line). Therefore the models are performing rather more badly than Schmidt, Shindell, and Tsigaridis think and even worse than most sceptics think.

    Also easy to see that the ST (red line) is the MDV-neutral “spline” about which the GMST reconstruction (thick black line) oscillates. MDV-neutral is where the GMST reconstruction (thick black line) crosses the ST (red line).

    2045 neutral
    2030 < MDV max negative ST – MDV = GMST
    2015 neutral
    2000 < MDV max positive ST + MDV = GMST
    1985 neutral
    1970 < MDV max negative ST – MDV = GMST
    1955 neutral
    1940 < MDV max positive ST + MDV = GMST
    1925 neutral
    1910 < MDV max negative ST – MDV = GMST
    1895 neutral

    The model mean tracks the ST (red line) up until MDV-neutral 1955. Not too bad either at MDV-neutral 1985 although hard to make a judgment given the retroactively added volcanic activity to the models. But after that the model mean departs from the ST (red line) and veers wildly off too warm. This is the effect of theoretical anthropogenic forcing.

    # # #

    Note these are nominal dates. It breaks down a bit around 1985 due to volcanic activity.

    BTW #1. That was an impressive control model – out of my league by about a light year or two.

    BTW #2. I had gut inflammation for years, lost a lot of weight and couldn't put it on because I couldn't digest food properly. Exacerbated by night shift work. Cured with anti-inflammatory foods. A persimmon a day had an immediate effect and I also drink a glass of grape juice a day. Note there is a difference between anti-inflammatory and anti-oxidant foods. Just at the end of persimmon season right now (I think) so you will only see end of season fruit in the shops – if any. But worth every cent at any price.

    You have GOT to cure inflammation if you have it. Inflammation is the root of many diseases which in turn can lead to cancer.

  69. Richard C (NZ) on July 23, 2016 at 3:07 pm said:

    >”BTW #2. I had gut inflammation for years, lost a lot of weight and couldn’t put it on because I couldn’t digest food properly. Exacerbated by night shift work.”

    I’ve been wondering lately whether it was started by glyphosate (think Roundup), possibly in bread. This is the big controversy in Europe and North America, wheat growers in USA spray Roundup on the crop just before harvest as a desiccant. NZ gets wheat from Australia but I don’t know if there is the same practice in Australia or not.

    Not sure where the issue has got to but here’s an article from April 15, 2014 for example:

    ‘Gut-Wrenching New Studies Reveal the Insidious Effects of Glyphosate’

    Glyphosate, the active ingredient in Monsanto’s Roundup herbicide, is possibly “the most important factor in the development of multiple chronic diseases and conditions that have become prevalent in Westernized societies”

    http://articles.mercola.com/sites/articles/archive/2014/04/15/glyphosate-health-effects.aspx

  70. Gary Kerkin on July 23, 2016 at 3:26 pm said:

    Richard C thanks for your concern and advice. Yesterday the pool at Porirua had to be closed because of 5 people infected with cryptosporidiosis. It appears to be going round the Wellington region and the symptoms exactly match what I have encountered over the last 10 days: stomach cramps & etc! The symptoms can last for up to 2 weeks so hopefully that is about over for me.

  71. Richard C (NZ) on July 23, 2016 at 3:28 pm said:

    ‘U.N. experts find weed killer glyphosate unlikely to cause cancer’ – May 16, 2016

    The pesticide glyphosate, sold by Monsanto in its Roundup weed killer product and widely used in agriculture and by gardeners, is unlikely to cause cancer in people, according to a new safety review by United Nations health, agriculture and food experts.

    In a statement likely to intensify a row over its potential health impact, experts from the U.N.’s Food and Agriculture Organization (FAO) and World Health Organization (WHO) said glyphosate is “unlikely to pose a carcinogenic risk to humans” exposed to it through food. It is mostly used on crops.

    Having reviewed the scientific evidence, the joint WHO/FAO committee also said glyphosate is unlikely to be genotoxic in humans. In other words, it is not likely to have a destructive effect on cells’ genetic material.

    http://www.reuters.com/article/us-health-who-glyphosate-idUSKCN0Y71HR

    # # #

    “Unlikely” is not very reassuring.

    I think the danger, if any, is more insidious than as a direct cause of cancer.

  72. Richard C (NZ) on July 23, 2016 at 3:48 pm said:

    Gary >”cryptosporidiosis. …….. symptoms ………..stomach cramps & etc!…….2 weeks”

    Inflammatory bowel disease much the same but months and years not weeks. I had stomach cramps for 12 hours straight one day, everything cramped up and I couldn’t straighten up. I wouldn’t wish it on anyone.

    I thought it was just night shifts but when Psa struck kiwifruit certain people in the industry had to have medical tests. That’s how I found out my condition eventually but I had it before night shifts when I thought about it. People were finding out conditions they never knew about from the Psa medical tests. I talked to a guy who was found to have prostate cancer he didn’t know about. He was in hospital the week after the test result. Saved his life.

  73. Gary Kerkin on July 23, 2016 at 4:35 pm said:

    Richard C we’re kinda straying away from the topic. There is a strong message coming through though. If you have a problem, don’t delay doing something about it. I lost a good friend a couple of weeks ago with some form of stomach cancer. He ignored it for years until the pain became too much. By then it was too late to do anything. Peter Williams, the QC and activist ignored prostate cancer until it was too late: probably cost him 10 years of life. My prostate was removed a couple of years ago after 10 years of close monitoring. PSA markers are now undetectable. My recent condition is exacerbated by a bout of anæmia which landed me in hospital for a week or so. Still don’t know what the problem was but I’m awaiting the results of a camera capsule examination. The wonders of modern technology!

  74. Gary Kerkin on July 23, 2016 at 4:53 pm said:

    Richard C, thanks for the reference on singular spectrum analysis. I’m working through it and trying to reconcile it with some things I know. What I don’t know is how they allocated certain meanings to the Eigenvalues. And I am still puzzling over their use of the term “secular”—although it may be appropriate in that proponents of AGW seem to have a “belief” or “faith” in the hypothesis that physics doesn’t seem to yield! I have found that UCLA has a program (an App) which will calculate the Eigenvalues and is implementable on a UNIX based system (various flavours of Linux, or Mac OS X version 10) which I am looking at with a view to installing it on my MacBook Pro. If I can successfully implement it I might try running the NIWA data on it (Raw and adjusted 7SS series) to see what we can get out of it. Might be worth a post, Richard T?

    For what it is worth, although I have only just done some preliminary reading, it has a familiarity about it. 40 years ago I developed a method for endeavouring to analyse very noisy pressure measurements at the wall of a tube carrying a two-phase mixture of air and water. The technique I developed was based on a method of Hamming for analyzing discrete (i.e. digital) data in which autocorrelations were calculated after successive decimations of the data. In this case, decimation means removing every 10th value. I went a step further and applied a Fourier Analysis (using a Fast Fourier Transform) at each stage. In this way I managed to find the fundamental and some other frequencies in the mess of information. I suspect that the SSA is very similar. In those days we didn’t have the online search facilities we now have so I was unable to dig out some of the references cited in the paper you found, and I didn’t have the computational facilities to be able to play with the matrices in the way we now can. I look at my Masters Thesis from time to time and gawp at the maths I used then. I wouldn’t want to do it again! I couldn’t now—it is beyond me.

    I’ll let you know how I get on, but don’t hold your breath.

  75. Richard C (NZ) on July 23, 2016 at 7:41 pm said:

    Gary

    I was hoping to pique your interest in SSA because you have the background and skills to go further with SSA than I can. I’ve looked at some of those packages including coding in R but have not gone any further. I saw that UCLA App listed too.

    >And I am still puzzling over their use of the term “secular”

    Just the more appropriate term for the inherent trend after all the noise and fluctuations are eliminated. In EMD it is the residual after all of the intermediate frequencies have ceased to be extracted e.g. rough example, IMFs 1, 2, 3 just noise, IMF 5 a minor oscillation, IMF 7 a major oscillation, last a residual. When new data is added to the series, eventually the residual becomes the last IMF (IMF 8 in example) and a new residual emerges. In other words, you cannot really “project” an EMD residual because after a period of time elapses you will have only projected an IMF.

    I think there’s a similar situation with SSA i.e. you may have subjectively selected a spectral “window” in which you think the secular trend lies but new data may prove you wrong. Not sure at all about this though.

    SSA is much different to EMD as I understand e.g. the SSA-M technique is a moving “window” of data the length of which the analyst selects from which a “single” spectrum is identified i.e. some human subjectivity is involved (EMD is completely objective). I may not have this quite right though.

    In a climate time series the “secular trend” (ST) is just the “long-term trend” in the data sans any oscillatory components (e.g. MDV), fluctuations or noise. This is very important, the ST in GMST is certainly not linear as climate scientists seem to think and it is only the ST that has an apples-to-apples comparability to the CMIP model mean. Climate scientists (and most sceptics) can’t grasp this though.

    There is also a very important fundamental difference between trend techniques – intrinsic vs extrinsic.

    Intrinsic: Is extraction of the inherent data signal, In EMD there is complete analyst objectivity. In SSA there is an element of analyst subjectivity (I think) but an intrinsic exercise nonetheless.

    Extrinsic: Is the external imposition of a trend on the data by an analyst assumption e.g. assuming the trend in fluctuating data is linear and determining the trend by linear regression. Similarly, assuming the trend in the data is a polynomial or is exponential an imposing the respective technique on the data.

    I think the extrinsic assumption is one of the worst aspects prevalent in climate science, now that I’ve delved into it.

    >”If I can successfully implement it I might try running the NIWA data on it (Raw and adjusted 7SS series) to see what we can get out of it”

    May not be a long enough series to be of much use. I suggest BEST NZ monthly data. Pre 1970 it is a load of rubbish but at least you have a much longer series and it’s easy to get hold of the data at the BEST website.

    I did an EMD exercise using the 7SS that Richard T posted (unsophisticated by your standards I’m guessing Gary):

    NZ vs S. Hemisphere temperatures [EMD analysis vs Linear trend]
    https://www.climateconversation.org.nz/2011/01/nz-vs-s-hemisphere-temperatures/

    With a longer series the EMD residual I found would just be an IMF. It is VERY sensitive, note the Update.

    >”Might be worth a post, Richard T?”

    Gary, what I would really REALLY like to see is the ST in Southern Hemisphere HadCRUT4 versus the ST in Northern Hemisphere HadCRUT4. Or a combination of say NH vs SH CRUTEM4 and HadSST3

    Take a look at these graphs:

    HadSST3 NH vs CRUTEM4 NH vs HadSST3 SH vs CRUTEM4 SH (5 yr smoothing)
    http://woodfortrees.org/plot/hadsst3nh/mean:60/plot/hadsst3sh/mean:60/plot/crutem4vnh/mean:60/plot/crutem4vsh/mean:60

    Separately,

    HadSST3 NH vs CRUTEM4 NH
    http://woodfortrees.org/plot/hadsst3nh/mean:60/plot/crutem4vnh/mean:60

    HadSST3 SH vs CRUTEM4 SH
    http://woodfortrees.org/plot/hadsst3sh/mean:60/plot/crutem4vsh/mean:60

    HadCRUT4 Land+Ocean is HadSST3+CRUTEM4 (assuming you weren’t quite clear about this maybe)

    Obviously the secular trends in each respective series (4 of them) are all different. We’ve been used to looking at a globally averaged GMST but that is illusory. The Southern Hemisphere is primarily overwhelmed by Land in the Northern Hemisphere. Then it is overwhelmed by NH Ocean too. So the NH completely skews GMST into a meaningless metric.

    Ultimately the comparison is with the ST in the CMIP model mean – THAT would be most revealing.

  76. Richard C (NZ) on July 23, 2016 at 8:04 pm said:

    Two more interesting comparisons:

    HadSST3 NH vs HadSST3 SH
    http://woodfortrees.org/plot/hadsst3nh/mean:60/plot/hadsst3sh/mean:60

    CRUTEM4 NH vs CRUTEM4 SH
    http://woodfortrees.org/plot/crutem4vnh/mean:60/plot/crutem4vsh/mean:60

    Ocean is similar but Land is radically different in each hemisphere.

    I don’t how anyone can even consider “trying to tease out the signal of human-caused climate change” from all of this (as Micheal Mann puts it).

  77. Gary Kerkin on July 23, 2016 at 10:23 pm said:

    Richard C > “I was hoping to pique your interest in SSA ”

    You’ll keep!

    I have downloaded the package from UCLA: requires a Unix command to start it. I’ve also downloaded about 150 pages of notes to read. As I said, don’t hold your breath, this is going to take some time.

    As to what I look at I think I will look at the 7SS series first.

  78. Gary Kerkin on July 24, 2016 at 2:53 pm said:

    Richard C, just to let you know what you have let me in for!

    “SSA allows one to unravel the information embedded in the delay-coordinate phase space by decomposing the sequence of augmented vectors thus obtained into elementary patterns of behaviour. It does so by providing data-adaptive filters that help separate the time series into components that are statistically independent, at zero lag, in the augmented vector space of interest. These components can be classified essentially into trends, oscillatory patterns, and noise. As we shall see, it is an important feature of SSA that the trends need not be linear and that the oscillations can be amplitude and phase modulated.”

    http://research.atmos.ucla.edu/tcd//PREPRINTS/2000RG.pdf

    Whew!

  79. Richard C (NZ) on July 24, 2016 at 11:29 pm said:

    Gary >”Richard C, just to let you know what you have let me in for!”

    Heh. How many PhDs’ were involved in producing that first sentence? Nuts. Now I will have to get some familiarity with that paper I suppose.

    I see M-SSA in there beginning page 27:

    “In M-SSA, on the other hand, on the basis of the single channel experience reviewed in sections 2 and 3.3, one usually chooses L [smaller than or equal to] M (see also Appendix A). Often
    M-SSA is applied to a few leading PCA components of the spatial data, with M chosen large enough to extract detailed temporal and spectral information from the multivariate time series.”

    More on M-SSA below because in the author list I see Michael E. Mann:

    ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES
    M. Ghil, M. R. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, M. E. Mann, A. W. Robertson, A. Saunders, Y. Tian, F. Varadi, and P. Yiou (2001)

    Ok, so Mann's been on to this since before 2001 so why has he not been applying SSA appropriately since then in climate series analysis where it matters?

    Wyatt and Curry 2014 (online 2013) employed M-SSA to examine aspects of their hypothesized "Stadium Wave". The hypothesis underpinning the stadium-wave is that on long time scales, climate variability organizes into network behavior executed through coupled dynamics among ocean, ice, and atmospheric circulation patterns.

    Mann et al (2014) took a different approach to the same study and say in effect that Wyatt and Curry's analysis and hypothesis is incorrect. Marcia Wyatt writes about the differences in approach here:

    'Disentangling forced from intrinsic climate variability'
    Posted on July 9, 2014 | by Marcia Wyatt
    https://judithcurry.com/2014/07/09/disentangling-forced-from-intrinsic-climate-variability/

    In response to Mann et al (2014), Sergey Kravtsov, Marcia Wyatt, Judith Curry, and Anastasios Tsonis produced a paper, Kravtsov et al (2014), which further investigates the stadium wave hypothesis again employing M-SSA. Curry and Wyatt write about it here:

    'Two contrasting views of multidecadal climate variability in the 20th century'
    Posted on September 28, 2014 | by Judith Curry
    https://judithcurry.com/2014/09/28/two-contrasting-views-of-multidecadal-climate-variability-in-the-20th-century/

    In that post Wyatt writes:

    “Merging this view of M-SSA generated phase-shifted signals plus noise with the strategy of Mann et al. in constructing surrogate networks, Kravtsov et al. show that the phase uncertainties of each index are significantly smaller than the actual phase lags (lag time in years between propagating indices) among those indices in the “real” stadium wave. This finding supports the Kravtsov et al. counterargument to Mann et al’s contention that artificial propagation is a product of sampling associated with climate noise. According to Kravtsov et al., such sampling variations are unlikely to explain the propagation observed in the “real” stadium wave; thus weakening Mann et al.’s challenge.

    Thing is: Mann was at the forefront of the use of M-SSA but did not employ it in Mann et al (2014). But Wyatt and Curry (2014) and subsequently Kravtsov et al (2014) did employ M-SSA. The Mann et al challenge to Wyatt and Curry makes no recourse to M-SSA.

    So why did Mann pioneer ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES but then not employ them?

    Given Wyatt’s account of Mann et al (2014), it seems to me that Mann does not like the results of M-SSA because he has discovered those results disallow his belief that the variability in question is radiatively forced and thence an opportunity for anthropogenic attribution. Marcia Wyatt puts it this way in ‘Disentangling forced from intrinsic climate variability’

    “The hypothesis underpinning the Mann et al. work is that the low-frequency component of variability in Northern Hemisphere surface average temperatures (NHT) is dominantly a product of a radiatively forced signal.”

    You have to wonder why Mann first embraced the M-SSA technique but then dropped it like a hot potato. I think I know why.

  80. Richard C (NZ) on July 25, 2016 at 12:03 am said:

    Gary.

    Following on from the comment above involving Mann and how he will do anything to get the “right” answer. I’m reproducing a comment from a post earlier this year:
    *********************************************************************
    Richard C (NZ) on March 20, 2016 at 6:06 pm said:

    Steve Sherwood and Stefan Rahmstorf at Hot Topic:

    “In the longer run the global warming trend agrees very well [hotlink] with longstanding predictions”

    http://hot-topic.co.nz/februarys-global-temperature-spike-is-a-wake-up-call/#more-14425

    This is dead wrong. The hotlink is to this Rahmstorf Tweet:

    https://twitter.com/rahmstorf/status/698380997222510592

    Which contains this graph:

    https://pbs.twimg.com/media/CbElthfUAAA1DVS.jpg

    Graph looks dodgy. Reason becomes clear by following the link in the Tweet to this paper:

    ‘The Likelihood of Recent Record Warmth’
    Michael E. Mann, Stefan Rahmstorf, Byron A. Steinman, Martin Tingley & Sonya K. Miller (2015)
    http://www.nature.com/articles/srep19831

    Yes you are looking at GISTEMP. No you are not looking at CMIP5 model output. You are looking at “adjusted” model output i.e. a residual.

    The giveaway is this:

    “It is critical to take into account these contributions in estimating the likelihood of record temperature values. One body of past work5,6,7 has employed model-based fingerprint detection methods to study temperature extremes in a generic sense, though without any focus on the types of questions posed here (i.e. the likelihoods of specific observed runs of record warmth). In this approach, natural variability is estimated from the climate models themselves, which means that assessments of the likelihood of extremes is dependent on the models producing realistic natural variability. Another past study8 estimated the parameters of statistical noise models directly from the instrumental temperature record, without the use of information from climate models.

    Not accounting for the impact of anthropogenic climate change on surface temperatures, however, could yield biased estimates of the noise parameters (e.g. by overestimating the apparent degree of natural persistence and, hence, the likelihoods of sequences of rare events). Moreover, such an approach cannot distinguish between the influence of forced and purely internal natural variability. Here, we instead use a semi-empirical method that combines the most recent (CMIP5)9 multimodel suite of climate model simulations with observational temperature data to estimate the noise characteristics of global and hemispheric temperature variabilty.

    It is totally unnecessary to make recourse to climate models in order to eliminate “noise” from GMST e.g. minor fluctuations, ENSO activity, MDV. But Mann et al left GMST as-is, instead “adjusting” the model mean. This is bogus.

    The model mean is ENSO-neutral and MDV-neutral. Therefore the model mean should have been left as-is and GMST “adjusted” i.e. a GMST residual, NOT a model mean residual. The model mean already IS a residual.

    More>>>>>>>
    https://www.climateconversation.org.nz/2016/02/met-office-shock-forecast-warming-to-continue/#comment-1448020

    # # #

    Mann et al (2015) could have used SSA to eliminate MDV form GMST in order to compare to the CMIP5 model mean. Instead, they have adjusted the model mean in order to reconcile models vs observations i.e. the “right” answer.

    I hope the sheer bogosity of Mann et al (2015) does not escape you Gary.

  81. Richard C (NZ) on July 25, 2016 at 12:08 am said:

    Note that Rahmstorf Tweeting his bogus graph is not much different to Simon upthread posting the shonky Hot Whopper graph.

    It’s the Warmy way apparently.

  82. Richard C (NZ) on July 25, 2016 at 11:41 am said:

    Latest reason the models are wrong (the models are right, the observations are wrong):

    ‘Historical Records Miss a Fifth of Global Warming: NASA’ – Jet Propulsion Laboratory July 21, 2016

    A new NASA-led study [Richardson et al (2016)] finds that almost one-fifth of the global warming that has occurred in the past 150 years has been missed by historical records due to quirks in how global temperatures were recorded. The study explains why projections of future climate based solely on historical records estimate lower rates of warming than predictions from climate models.

    The study applied the quirks in the historical records to climate model output and then performed the same calculations on both the models and the observations to make the first true apples-to-apples comparison of warming rates. With this modification, the models and observations largely agree on expected near-term global warming. The results were published in the journal Nature Climate Change. Mark Richardson of NASA’s Jet Propulsion Laboratory, Pasadena, California, is the lead author.

    More>>>>
    http://www.jpl.nasa.gov/news/news.php?feature=6576

    # # #

    What “largely agree” means remains to be seen (paywalled). The “Fifth of Global Warming” the observations “miss” (apparently), is Arctic warming only the models produce and some other minor warming that MUST have been there because the observations need to be adjusted to “account” for it.

    Here’s the kicker:

    The Arctic is warming faster than the rest of Earth, but there are fewer historic temperature readings from there than from lower latitudes because it is so inaccessible. A data set with fewer Arctic temperature measurements naturally shows less warming than a climate model that fully represents the Arctic.

    Because it isn’t possible to add more measurements from the past, the researchers instead set up the climate models to mimic the limited coverage in the historical records.

    The new study also accounted for two other issues. First, the historical data mix air and water temperatures, whereas model results refer to air temperatures only. This quirk also skews the historical record toward the cool side, because water warms less than air. The final issue is that there was considerably more Arctic sea ice when temperature records began in the 1860s, and early observers recorded air temperatures over nearby land areas for the sea-ice-covered regions. As the ice melted, later observers switched to water temperatures instead. That also pushed down the reported temperature change.

    Scientists have known about these quirks for some time, but this is the first study to calculate their impact. “They’re quite small on their own, but they add up in the same direction,” Richardson said. “We were surprised that they added up to such a big effect.”

    These quirks hide around 19 percent of global air-temperature warming since the 1860s. That’s enough that calculations generated from historical records alone were cooler than about 90 percent of the results from the climate models that the Intergovernmental Panel on Climate Change (IPCC) uses for its authoritative assessment reports. In the apples-to-apples comparison, the historical temperature calculation was close to the middle of the range of calculations from the IPCC’s suite of models.

    The assumption, by circular reasoning, is this : The warming was there all along (we now “know” from our models – all the extra 19% of it) but it is only mainly in the Arctic, and we have to “tweak” the observations a bit too. If we neglect the Arctic and the Arctic warming in the models and “tweak” the observations, the models and observations will reconcile.

    Question is though, what difference was there between the “limited coverage” models and the CMIP5 models? Again, this remains to be seen (paywalled)

    Still problematic because the satellites have the coverage that surface measurements don’t including much of the Arctic region e.g. the Schmidt and Christy graphs in the post. The surface disparity that JPL’s Richardson et al is addressing began way back in the 1860s (apparently), but the major disparity between temperature derived from NASA’s own satellites and the models only began in the mid 1990s. Same at the surface although the divergence began after ab out 1985.

    The CMIP5 models are spot on surface observations up until 1955. And still good at 1985 i.e. there was no reason to “adjust” either models or observations prior to 1985. If Richardson et al have “adjusted” both models and observations prior to 1985 then they have turned a very satisfactory situation into a problematic situation.

    NASA’s JPL are digging themselves into a very big hole.

  83. Richard C (NZ) on July 25, 2016 at 2:22 pm said:

    Climategate email:

    “What if climate change appears to be just mainly a multidecadal natural fluctuation? They’ll kill us probably “

    — Tommy Willis, Swansea University

  84. Gary Kerkin on July 25, 2016 at 11:54 pm said:

    Mann has figured in several references in the papers I have been reading. But I note that he does not appear to be a lead author in many of them. Which suggests to me that he has been lending his imprimatur to the papers without necessarily contributing much in a technical way. That is not to say that he isn’t well versed in the methods, just that he has not chosen to use them. That’s not an unusual situation. Anyway, I would have though that his tree ring analyses would not have lent themselves to this sort of analysis, and it wouldn’t be appropriate to attempt it on the time series he generated because that would have been subjected to assumed statistical processes.

    Richard C > “The assumption, by circular reasoning, is this : The warming was there all along (we now “know” from our models – all the extra 19% of it) but it is only mainly in the Arctic, and we have to “tweak” the observations a bit too. If we neglect the Arctic and the Arctic warming in the models and “tweak” the observations, the models and observations will reconcile.”

    This is an example of a logical fallacy, a direct analogy to the CO2/temperature relationship. post hoc ergo propter hoc: CO2 rises and temperature rises therefore CO2 rising caused the temperature to rise. But, of course, as we know, temperature rose first because man didn’t really start adding significant CO2 to the atmosphere until the 1950’s, or so we are told. Actually I rather like the idea that temperature leads CO2 by 800 years, so we can blame the MWP for the current increase in CO2.

    By the way, Richard, I still dislike you! 100 pages of reading, a package that doesn’t work well because the graphics side is hard (at least for my feeble brain) to implement, and I’m likely to spend some considerable funds on another package I know will work (because I have purchased a much cheaper limited implementation). I guess you get what you pay for! But … it does look promising. What do you know about eigenvectors? 😉

  85. Richard C (NZ) on July 26, 2016 at 10:08 am said:

    Gary

    >”I would have though that his tree ring analyses would not have lent themselves to this sort of analysis”

    His recent opposition to Wyatt and Curry and subsequently Kravtsov, Wyatt, Curry, and Tsonis isn’t about tree rings though. His 2014 paper in response to Wyatt and curry is meteorological, climate, and oceanographic data as I understand without checking (but no tree rings I’m sure), and the signals they present. SSA more than lends itself to this sort of analysis as his opposition authors demonstrate (as do Macias et al). But Mann didn’t take that route because, I’m inclined to think, he would have to throw out his radiative forcing approach if he did. He can’t give that approach up because then he can’t make an anthro attribution because that’s the paradigm that gives him the opportunity – if he can work it in his favour. SSA (and EMD) extracts so many natural variation signals that there is no room left for an anthro signal, he can’t “tease” it out as he puts it.

    And then following that, the Mann et al (2015) models vs observations of temperature paper is exactly the application that SSA is particularly suited in order to get to the respective comparative signals. My ending comment on that was this:

    “Mann et al (2015) could have used SSA to eliminate MDV from GMST in order to compare to the CMIP5 model mean. Instead, they have adjusted the model mean in order to reconcile models vs observations i.e. the “right” answer.”

    It is impossible to compare models to observations on an apples-to-apples basis when the MDV is left in the observations because there is no MDV signal in the models. The alternative is to identify the MDV signal in the observations, for which SSA is ideal, and then ADD that signal to the model mean. Then both sets have the MDV signal resident in them. Jeff Patterson did similar to this in his TSI-driven climate model but he didn’t use SSA to identify the MDV signal.

    But it might be as you imply Gary, that Mann is not a technical exponent of SSA so he can’t use it. But he used principal components analysis (PCA) in his tree ring study so surely he could make the step to SSA?

    Keep in mind in all of this that the 60-yr (nominal) MDV signal is a Northern Hemisphere only phenomenon that overwhelms the global metric. There certainly isn’t that same 60-yr MDV signal in either the Australian or New Zealand data coinciding with the NH signal. There does appear to be something cyclic going on in NZ temperature when you look back into the mid to late 1800s but it’s not clear and the data isn’t great and it’s a much longer cycle if there actually is one. Australia do have good data back then but BOM aren’t using it. Jo Nova has written about this. Australia was as warm as it is now back in the late 1800s in the unused data.

    >”What do you know about eigenvectors?”

    Nothing that would be useful to you Gary, I assure you. The Overview in Wikipedia is about my limit:

    Eigenvalues and eigenvectors – Overview
    https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors#Overview

    I found the Mona Lisa example very helpful visually.

  86. O/T
    Dr Jarrod Gilbert: Why climate denial should be a criminal offence

    http://www.nzherald.co.nz/nz/news/article.cfm?c_id=1&objectid=11681154
    Dr Jarrod Gilbert is a sociologist at the University of Canterbury and the lead researcher at Independent Research Solutions. He is an award-winning writer who specialises in research with practical applications.

    There is no greater crime being perpetuated on future generations than that committed by those who deny climate change. The scientific consensus is so overwhelming that to argue against it is to perpetuate a dangerous fraud. Denial has become a yardstick by which intelligence can be tested. The term climate sceptic is now interchangeable with the term mindless fool.

    Perhaps this “sociologist” could suggest a suitable punishment for the criminal offense of denying the climate.

    Execution by firing squad? 25 years in the clinker? Hanging? Electric chair? Lethal injection?

    All offers are open.

  87. Interesting that this gent has written a book on gangs in NZ
    http://www.jarrodgilbert.com/

    He’ll be well familiar with the gang at the University of Victoria then

  88. Gary Kerkin on July 26, 2016 at 1:47 pm said:

    Andy, this is a topic Richard T should explore in another post, and I have no doubt he probably will. He should be told that the RICO bandwagon in the US looks like it is losing its wheels. “Rats deserting sin king ships” comes to mind!

  89. Gary Kerkin on July 26, 2016 at 1:50 pm said:

    Richard C > “But it might be as you imply Gary, that Mann is not a technical exponent of SSA so he can’t use it. But he used principal components analysis (PCA) in his tree ring study so surely he could make the step to SSA?”

    He is one of the authors of the seminal paper for the UCLA-developed paper I cited above http://research.atmos.ucla.edu/tcd//PREPRINTS/2000RG.pdf.

  90. Richard C (NZ) on July 26, 2016 at 3:01 pm said:

    Gary

    >”He [Mann] is one of the authors of the seminal paper for the UCLA-developed paper I cited above”

    Yes I saw that immediately and noted it upthread here:

    Richard C (NZ) on July 24, 2016 at 11:29 pm said:
    https://www.climateconversation.org.nz/2016/07/gavin-schmidt-confirms-model-excursions/comment-page-1/#comment-1501514

    That was what got me going on the Mann vs Wyatt & Curry/Kravtsov et al saga.

  91. Richard C (NZ) on July 26, 2016 at 3:31 pm said:

    Re Dr Jarrod Gilbert: Why climate denial should be a criminal offence

    Given Gilbert’s deference to the 97% consensus, this topic would have been better in the Naish and Renwick thread – but good to know Andy. I’ll link and reply to the Gilbert article in that thread.

  92. Richard C (NZ) on July 27, 2016 at 10:12 am said:

    Willis Eschenbach:

    Precipitable Water

    Figure 4. Decomposition of the total precipitable water data (upper panel) into the seasonal (middle panel) and residual (bottom panel) components.
    https://wattsupwiththat.files.wordpress.com/2016/07/plotdecomp-total-precipitable-water-tpw.png?w=720&h=674

    Some things of interest. First, in the bottom panel you can see the effect on TPW of the El Nino episodes in 1997/98, 2010/11, and 2015/16. You can also see that we haven’t quite recovered from the most recent episode.

    Next, there is a clear trend in the TPW data. The total change over the period is ~ 1.5 kg/m^2, centered around the long-term mean of 28.7 kg/m^2.

    And utilizing the relationship between water content and atmospheric absorption derived above, this indicates an increase in downwelling radiation of 3.3 W/m2 over the period.

    Now, please note that this 3.3 W/m2 increased forcing from the long-term increase in water vapor since 1988 is in addition to the IPCC-claimed 2.3 W/m2 increase since 1750 in all other forcings (see Figure SPM-5, IPCC AR5 SPM). The IPCC counts as forcings the long-term changes in the following: CO2, CH4, Halocarbons, N2O, CO, NMVOC, NOx, mineral dust, SO2, NH3, organic carbon, black carbon, land use, and changes in solar irradiance … but not the long-term changes in water vapor.

    This leads us to a curious position where we have had a larger change in forcing from water vapor since 1988 than from all the other IPCC-listed forcings since 1750 … so where is the corresponding warming?

    https://wattsupwiththat.com/2016/07/25/precipitable-water/

    # # #

    Yes. Where?

    Total of 5.6 W.m-2 but earth’s energy imbalance remains at 0.6 W.m-2.

  93. Richard C (NZ) on July 28, 2016 at 2:33 pm said:

    Two papers that together falsify the Man-Made Climate Change Theory.

    1) ‘An observationally based energy balance for the Earth since 1950’
    D. M. Murphy, S. Solomon, R. W. Portmann, K. H. Rosenlof, P. M. Forster, T. Wong (2009)
    http://onlinelibrary.wiley.com/doi/10.1029/2009JD012105/full

    There is obviously a massive discrepancy between theoretical forcing and actual earth heating. Murphy et al describe the discrepancy as “striking”:

    [38] A striking result of the Earth energy budget analysis presented here [Figure 6 below] is the small fraction of greenhouse gas forcing that has gone into heating the Earth. Since 1950, only about 10 ± 7% of the forcing by greenhouse gases and solar radiation has gone into heating the Earth, primarily the oceans.

    Murphy et al (2009) Figure 6
    http://onlinelibrary.wiley.com/store/10.1029/2009JD012105/asset/image_n/jgrd15636-fig-0006.png?v=1&s=1d48ee59aed4b059a12eea9575028e88a7b134ce

    Total cumulative theoretical forcing is 1700 x 10^21 Joules 1950 – 2004 of which 10% is 170 x 10^21 Joules.

    In Figure 6, solar forcing is already 100 x 10^21 Joules leaving a residual of 70 x 10^21 Joules. Nordell and Gervet (2009) below estimate only 27.3 x 10^21 Joules (say 30 at 2004) actual energy accumulation total over the entire extended period 1880–2000 (say 2004):

    2) ‘Global energy accumulation and net heat emission’ [1880–2000 ]
    Bo Nordell and Bruno Gervet (2009)
    http://www.ltu.se/cms_fs/1.5035!/nordell-gervet%20ijgw.pdf

    “The global heat accumulation in the air, ground and water during 1880–2000 is thus 75.8.1014 kWh (27.3.10^21 J). This heat is distributed in the air (6.6%), ground (31.5%), water (28.5%) and melting of land and sea ice (33.3%) according to Figure 3.

    It is noticeable that the heat content in air only corresponds to 6.6% of global warming.

    So Murphy et al’s 1950 – 2004 earth heating is still 140 x 10^21 Joules MORE than Nordell and Gervet’s estimate for earth heating over the entire period 1880-2004 (170 vs 30 x 10^21 Joules) even after Murphy et al throw out almost all GhG forcing and a bit of solar forcing (90% total discard).

    In other words, Murphy et al would have to discard ALL GhG forcing (and a bit more solar forcing) to reconcile with Nordell and Gervet.

    It gets worse for the Man-Made Theory after the GhG discard:

    [39] “…………Small changes in heat transfer into the ocean, cloudiness, or other terms can create significant changes in surface temperature………..”

    Not specified but obviously Surface Solar Radiation (SSR) i.e. solar radiation reaching the surface through clouds, aerosols, etc, would drive “small changes in heat transfer into the ocean” along with windiness or changes in wind patterns.

    Murphy et al (2009) together with Nordell and Gervet (2009) effectively falsify the Man-Made Climate Change Theory.

  94. Richard C (NZ) on July 29, 2016 at 1:29 am said:

    From my Richardson et al (2016) comment

    >”Still problematic because the satellites have the coverage that surface measurements don’t including much of the Arctic region e.g. the Schmidt and Christy graphs in the post.”

    Not quite right here. Only the Christy graph is “Global”. Schmidt’s is only Tropics.

  95. Richard C (NZ) on July 29, 2016 at 10:17 am said:

    Is Climate Too Complex to Model or Predict? Scientists say no

    By Seth B. Darling & Douglas L. Sisterson, posted Jul 27th, 2016

    Seth B. Darling is a scientist at the Argonne National Laboratory, specializing in energy and water. Douglas L. Sisterson is a senior manager at the Argonne National Laboratory.
    http://www.popsci.com/is-climate-too-complex-to-model-or-predict

    Hidden away in the very long screed (no graphs) is this:

    “What’s the good news? Well, perhaps this sort of thing will make the skeptic stop saying that the IPCC is exaggerating the problem. The models don’t get some things right, but where they get it wrong, it is almost always in the direction of underestimating the scale and pace of the problem. (A rare counterexample would be the surface-air-temperature trends in the past decade or so, which have risen more slowly than the vast majority of models had projected. Climate scientists are beginning to understand the reasons for this error, but this reminds us that climate models are not necessarily accurate over short timescales.)”

    The “rare counterexample” just happens to be at the time of the highest fossil fuel emissions in the entire industrial era.

    So no, “this sort of thing will [NOT] make the skeptic stop saying that the IPCC is exaggerating the problem”.

    On the contrary.

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