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All IPCC definitions taken from Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I, Glossary, pp. 941-954. Cambridge University Press.

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Comments 1 to 50:

  1. Seal of approval - How marine mammals provide important climate data

    WRyan:
    This figure on page 13 in Earth’s energy imbalance and implications by James Hansen et al shows the estimated energy imbalance (rate of warming or cooling) for different parts of the climate system given as watt/m² spread over the entire surface of the Earth.

    Energy imbalance

    Notice that the atmosphere has accumulated a nearly negligible fraction of the entire climate system’s heat increase and a pretty small part of the non-ocean climate system as well. The amount of heat penetrating into the ground is several times larger than the accumulation in the atmosphere and the second largest component of non-ocean, after the melting of sea ice and ice sheets.

    Regarding the heat capacity of the ocean vs. the atmosphere:
    Water has about 4 times larger heat capacity than air measured by mass, i.e. it takes 4 times more energy to heat one gram of water by 1°C than one gram of air. The total mass of the oceans is about 250 timer larger than the atmosphere, hence the 1000-fold larger heat capacity overall.

  2. Where is global warming going?

    WRyan... how does the photon know the temperature of the object it would eventually impact if it were emitted? I mean... it could have to travel hundreds of light years to get there. Say a photon emitted from a distant star is going to hit the International Space Station, but if the station hadn't been constructed (hundreds of years after the photon was emitted) then it would have just passed through empty space and continued on to hit the Sun (which in this hypothetical is hotter than the emitting star). How does the photon instantaneously 'know' something that won't happen for hundreds of years? For that matter... how is it NOT emitted just because it will eventually strike a warmer object?

    You should really publish on this. Among other things it allows faster than light communication. Aim a laser at a target location and then raise or lower the temperature of that target to higher/lower than the laser's temp and the laser will instantaneously stop emitting when the temperature is higher... regardless of the distance involved. Messages could thus be 'transmitted' instantly to the laser end by changing the temperature at the target end.

  3. Climate models accurately predicted global warming when reflecting natural ocean cycles

    What the comments on this post highlight is the difficulty in our brains coming to grips with two very distinct aspects of modeling climate (or any dynamic system):

    1)  The conceptual and quantitative understanding of mechanism

    2)  Assumptions about future states that contribute to the quantity being modeled.


    Both have to hold true in order to make skillful predictions about future conditions, especially in the short term when essentially random factors can hold sway.  Mismatch between predictions and observed conditions (assuming the observations are reliable — that's another topic) can derive from failures of 1) or 2), but 1) is the component that science is most interested in, and is most relevant to long-term prediction.  Therefore, to assess the strength of our understanding, we need to figure out how much of the mismatch can be attributed to 2).


    Here's an example:

    As I understand it, my bank balance changes according to this equation: 

    change in balance = pay + other income - expenses

    I can predict how my bank balance will change in the future if I assume some things that are pretty well understood (my monthly paycheck, typical seasonal utility bills, etc.).  However, some aspects of the future are random (unexpected car repairs, warm/cool spells affecting utility bills, etc.) — these cannot be predicted specifically but their statistical properties can be estimated (e.g., average & variance of car repair bills by year, etc.) to yield a stochastic rather than deterministic forecast.  Also, I could get an unexpected pay raise (ha!), need to help my brother out financially, etc.  All of these factors can generate mismatch between predicted changes in the balance and what actually happens.

    But (and here's the important bit):  that mismatch does not mean that my mechanistic understanding of the system is faulty, because it stems entirely from item 2).  How can I demonstrate that?  Well, if I plug the actual values of income & expenses into the equation above it yields a perfect match (hindcasting).  Alternatively, (as was done by Risbey et al), I could select those stochastic forecasts that happened to get income and expense values close to what actually occurred, and find that the forecasts of those runs are close to the actual change in my balance. 

    Examining these runs is not "cherry picking" in any sense of the word, it is a necessary step to separate out the effects of items 1) and 2) on model-data mismatch.  If these tests failed, that would imply that my understanding is faulty: some other source of gain or loss must be operating.  Perhaps a bank employee is skimming?

    Climate forecasts are necessarily much less precise than my personal economic forecasts, because the system is observed with error and because many more inputs are involved that interact in complicated, nonlinear, spatially explicit ways.  But the logic involved is the same. 

  4. Christian Moe at 00:56 AM on 25 July 2014
    New study investigates the impact of climate change on malaria

    This sentence doesn't parse:

    They downscale global climate models “to provide high-resolution temperature data for four different sites (in Kenya) and show that although outputs from the global and downscaled models predict diverse but qualitatively similar effects,” and some of the modeling approaches led to quite different findings.

    Also, there's a stray quotation mark in the Mann quote, which might indicate some text got included in the blockquote that shouldn't be.

  5. Where is global warming going?

    WRyan @94&95, that is the trap of thinking in terms of the temperature of the emitting source rather than the energy of the photon.  The Sun also emitts IR radiation, for example.  A PV cell behind such a filter would react in the same way to the IR photons from the Sun as it would to IR photons from any other source.  Light from an LED or fluorescent light is carried by high energy photons, even though the method of emission is not thermal.

  6. Where is global warming going?

    p.s. With regard to my previous comment. The temperature difference requirement applies only to thermal radiation.

    A PV panel can absorb light from a light source like an LED or a fluorescent light without having ot be cooler than the light source. This is because the light from those sources is not being produced by a thermal process.

  7. Where is global warming going?

    just read this article and comments. With regard to the hypothetical photovoltaic IR panel, it would work if it was cooled below the temperature of the emitting body (the earth's surface, in this case.) PV panels work because they are cooler than the Sun's surface, which is where the light originates.

  8. Climate models accurately predicted global warming when reflecting natural ocean cycles

    The text you quoted about spatial trends is from the abstract and it is stated without any context. Perhaps someone who has read the paper can provide that context by giving a description of how the authors support that statement in the main body of the paper.

  9. Seal of approval - How marine mammals provide important climate data

    I'm guessing that the heat absorbed to produce the net loss of ice is also included in the land-ice-air value.

    There is also some heat involved in raising the land's temperature. It would be interesting to see that calculated. I can't imagine that heat would penetrate far below the land's surface, but it would have to gain some heat to keep the surface temperature in a rough equilibrium with the increased air temperature over land.

  10. Rob Painting at 18:25 PM on 24 July 2014
    Climate data from air, land, sea and ice in 2013 reflect trends of a warming planet

    Ashton - Yes, perhaps it was in the original text and later omitted. Rightly so, there are other data which suggest a robust increase in the Antarctic sea ice even though the Earth is very obviously warming.

  11. Seal of approval - How marine mammals provide important climate data

    That graph is showing the change in heat content. So the relative values probably reflect the fact that that the temperature of the tropopause has increased more than the temperature of the ocean, combined with the much larger volume of the tropopause compared to the volume of ocean water that has undergone a measurable increase in temperature. That's just a guess though.

  12. Climate models accurately predicted global warming when reflecting natural ocean cycles

    The words used in the context provided definitely seem to signify the spatial trend for the entire Pacific. So therefore the line above is either poorly worded or taken out of context (I don't have access to the paper and so I can't verify, but going off history I'd put my money on the latter). 

    Indeed I agree that it's not an important point in the context of the paper's goals, but most deniers will be happy to focus on the one incidental discrepency and ignoring the point made by the paper as a whole. This helps them ignore the fact that this paper completely decimates just about the only argument they were hanging onto - that climate models failed to predict the current period of slower warming. This unambiguously shows that the models did in fact predict the current slowdown in warming - within the bounds of what they attempt to predict.

  13. Climate models accurately predicted global warming when reflecting natural ocean cycles

    I'd go with 1/ more or less. The spatial pattern of interest is the cooling eastern pacific cf warming central-western. This pattern is visible in both the selected models and observation but missing in the anti-phased model. I would definitely say "good" means something different to the authors than it does to Russ. I think it is accurate for the 15 year trend, but somewhat dependent on your expectation to apply it to the spatial trend. However, I think it is a very small point blown right of proportion when it comes to evaluation of the paper as a whole. The main text barely mentions it.

    It is easier to make the comparison looking at the figs at HotWhopper than in the Russ gif, if you dont have access to the paper.

  14. Chris Crawford at 14:13 PM on 24 July 2014
    Seal of approval - How marine mammals provide important climate data

    Thanks for explaining that for me, Mr. Painting. I had not taken into account the differences due to distribution between land and ocean as a function of latitude. I suppose that there are further differences between the heat capacity in ideal conditions and the heat capacity in practice.

    Thanks again.

  15. Climate models accurately predicted global warming when reflecting natural ocean cycles

    We present a more appropriate test of models where only those models with natural variability (represented by El Niño/Southern Oscillation) largely in phase with observations are selected from multi-model ensembles for comparison with observations. These tests show that climate models have provided good estimates of 15-year trends, including for recent periods and for Pacific spatial trend patterns.

    I'm a bit confused by this as well. I must admit looking at the maps of the regional trends around the Pacific look inaccurate based on the graphs shown by Russ. This seems to conflict with the bolded text above. I'm not convinced anyone has really provided a reasonable answer to this. Either;

    1) The authors actually mean a different thing when they talk about "Pacific spatial trend patterns" than what Russ believes, and that phrase does not refer to the regional distribution of warming in the Pacific region but rather something else. In this case, what exactly are the authors referring to here?

    2) The maps are misleading in some way, making similar trends actually look completely different.

    3) The models are in fact inaccurate, and the authors are incorrect in the bolded statement.

    It's confusing because the paper's goal seems to be to test whether models can provide the correct global temperature scales if the ENSO input is modelled correctly, and it shows that the models are actually accurate globally. But this almost throwaway line seems to suggest that the spatial distribution of the warming was also predicted correctly, when it really looks like it wasn't.

    Some commentators have pointed out that the model's aren't expected to get the spatial distribution of warming accurate, and that's fine, I don't think anyone (excluding Watts, Monckton, et al) can reasonably expect accuracy where the models are not designed to provide it, but if that's the case, why is the bolded phrase even included in the paper? 

  16. Climate models accurately predicted global warming when reflecting natural ocean cycles

    Charlie A @32 shows the following image, and comments:

    "Figure 2 of this paper show the "small amount" by which forecasted trends have diverged from reality in the sort period of true forecast vs. hindcast. Look closely at the trends from recent observations vs the models. Note it is nearly outside the 2.5 percentile line."

    From 97 fifteen year trends, 5 instances of observations being at the 2.5% limit, and two of them being at the 97.5% limit.  (Because trends overlap, clusters of trends at the limit are treated as single instances.)  That is enough to suggest the models do not capture the range of natural variability, but not enough to data to suggest a bias towards warm or cool result.

    Of the two warm episodes, both are associated with strong positive 15 year trends in the inverted, lagged SOI.  Of the 5 cool episodes, four are associated with strong negative trends in the lagged SOI.  That is, 6 out of seven strongly tending temperature excursions in observed temperatures relative to modelled temperatures are associated with same sign excursions in lagged inverted SOI, and therefore are probably the results of large La Nina trends.  The one low escursion not related to ENSO trends occurs in the twenty year period from 1880 to 1899 in which there were twelve major volcanic erruptions (VEI 4 +), leading of with Krakatoa.

    When comparing the lagged, inverted SOI trends to GISS LOTI, the match in the early half of the century is quite good (with the exception of the first 20 years).  In the latter half of the twentieth, and the early thirtyieth century two discrepancies stand out.  One is the major positive trend excursion around 1980 associated with the 1982/83 El Nino.  That event coincided with the 1982 El Chichon erruption, the effects of the two events on global temperatures more or less cancelling out.  The other is the large disparity in the early twentieth century, where GMST trends are far more positive than would be expected from the SOI trends.  Something, in other words, has been warming the Earth far more strongly than would be expected from looking at natural variation alone.

  17. Climate models accurately predicted global warming when reflecting natural ocean cycles

    Yes, thank you Russ for withdrawing your claim of cherrypicking.

    You still misunderstand the main purpose of the paper, as revealed by your comment "The higher the correlation, the more the method would treat luck as skill."  The authors of the paper did not treat luck as skill.  Indeed, they conceived their project on the basis of their and everyone else's explicit and repeated acknowledgment that the GCMs get the timing of ENSO events correct entirely by chance!  Their main conclusion was, as scaddenp noted, that the GCMs could be improved substantially (not completely!) in their projections of 15 year periods if the GCMS' timing of ENSO events was improved substantially.  The authors did not claim any method for accomplishing that improvement of ENSO timing, and did not even claim that it is possible for anyone, ever, to accomplish that improvement.  Their paper leaves unchallenged the suspicion that GCMs forever will lack the skill to accurately project the timing of ENSO events.  That means their paper leaves unchallenged the suspicion that GCMs forever will lack the skill to much more accurately project global mean surface temperature for 15 year periods.

    What the authors did claim (I think; somebody please correct me if I'm wrong) is that:


    • The consequences of ENSO events for global mean surface temperature are responsible for a large portion (not all!) of the GCM's poor projection of global mean surface temperature in 15 year timescales.

    • GCMs fairly accurately project the spatial pattern of ENSO events within (only) the Nino 3.4 geographic area (see Steve Metzler's comment of 22:41 PM on 23 July, 2014, on Lewandowsky's post), when by sheer chance the GCMs happen to project the timing correctly.  It is fair for you to use your own judgment of what qualifies as "fairly accurate," but my judgment is that the smeared-out temperature of the bad-timing-GCM runs is sufficiently different from the concentrated temperature of the good-timing GCM runs.  (See HotWhopper's reproduction of Figure 5's pieces, for easy visual comparison.)

  18. Climate models accurately predicted global warming when reflecting natural ocean cycles

    Charlie A @37:

    "[If] the only model problem is phasing of Enso, and the current 15 year GMST trends are below all model runs (or perhaps only below 97.5% of all model runs). then I would expect that either 1) that La Nina in the real world over the last 15 years is at or above the 97 percentile point, or 2) that the distribution of Enso in the entire CMIP5 ensemble of model runs is overwhelmingly biased towards El Nino."

    1)  The authors of the paper, SFAIK, make no claim that ENSO is the only factor supressing recent observed trends in GMST.  Therefore you are not entitled to assume that because the observed 1998-2012 trend in GMST is at the 2.5% limit that the ENSO trend will also be at or near that limit.  Indeed, the 4 best modelled trends are unlikely to be within the 2.5% limit of ENSO trends as they are selected only for having the same phase out of far fewer than 100 realizations.  Yet they match the observed trend fairly closely (see first figure in OP), therefore falsifying your assumption. (Note, the lower limit is the 2.5% limit, not the 97.5% limit.)

    2)  Even if a ENSO trend approaching the 2.5% limit was required to explain the depressed observed trend in GMST, it is the trend that needs to be statistically unlikely, not the individual ENSO states in any period.  An unusual trend can be formed by a couple of stronger than normal El Nino events at the start of the trend period and a couple of stronger than usual La Nina events at the end of the trend period without any of those events being 97.5% (for El NIno) or 2.5% (for La Nina) events.

    3)  In the so obvious it is unbelievable that you missed it category, an unusually strong El Nino at the start of the trend period is just as capable of generating a very strong trend as an unusually strong La Nina at the end.  Your restricting the test to the later condition only is uncalled for, and very puzzling given that it is known the 97/98 El Nino was unusually strong:

    4)  Your claim that the observed ENSO trends were not unusual (based solely on claims regarding the strength of recent La Ninas) is not backed up by the data.  For temperature based indices (plotted above), the observed percentile rank of the 1998-2012 ENSO trends are:

    NINO1+2_|_ NINO3_|_ NINO4_|_ NINO3.4
    _10%_____|_ 7.1%___|_ 38.1%__|_ 25.7%

    That is, two out of four such indices do show very low percentile ranks.  That they do not show lower percentile ranks is probably due to two unusualy strong El Ninos appearing in the short record.  (Note, the ONI is just the three month running mean of NINO 3.4, and so will differ little from that record.)

    5)  Single region temperatre indices for ENSO are fatally flawed (IMO) in that they will incorporate the general warming trend due to global warming as a trend to more, and stronger El Ninos.  Far better are multiple region indices (such as ENSO 1+2) where the common global warming signal can be cancelled out, or non temperature indices such as the SOI:

    The inverted five month lagged SOI trend for 1998-2012 has a percentile rank of 2.52%, compared the GISS LOTI trend of 42.9%.  For what it is worth, the inverted, lagged 1998 ranks at the 95.6th percentile in the SOI, and 2011 ranks at the 0th percentile.  The inverted, lagged SOI index for 2011 was  -17.3, which is less than the strongest shown on the graph above (which was not lagged).  The five month lagged 2011 La Nina has a percentile rank of 0.8% among all 12 month averages of the SOI index

    So, when you say the 2011 La Nina was not unusually strong, that only indicates over reliance on one ENSO index, and an unsuitable one in a warming world.

    In summary, nearly every claim you make @37 is wrong.  To be so comprehensively wrong should be a matter of embarrassment for you.  You should certainly pause and reconsider your position.

  19. Climate models accurately predicted global warming when reflecting natural ocean cycles

    Thank you Russ. That is appreciated.

  20. Climate models accurately predicted global warming when reflecting natural ocean cycles

    scaddenp @47,

    I'll withdraw my "cherry-picking" comment for two reasons: 

    1.  It was never intended as an allegation of fraud, rather of selection bias, in that their selection criterion for the "best" models (agreement by chance with ex-post ENSO trends) was likely correlated with the model output being used to assess of predictive skill (global temperature trends).   The higher the correlation, the more the method would treat luck as skill.

    2.  On further review it turns out that these two items (NINO3.4 trend and global temperature trend) are not nearly as correlated as I had imagined.   When looking at 15-year trends, the correlation coefficient is only 0.13 between them.

    So... withdrawn.

     

  21. Joel_Huberman at 09:58 AM on 24 July 2014
    Seal of approval - How marine mammals provide important climate data

    Thanks, BaerbelW and Anne-Marie Blackburn, for a very interesting post!

  22. Climate models accurately predicted global warming when reflecting natural ocean cycles

    This is my personal view on this paper. The paper takes a novel way to test the hypothesis that poor match between ensemble mean and observations is due fact the model mean includes many different states of ENSO whereas observations a "one member of the ensemble". The paper does demonstrate that a mean created from runs which are in phase with actual state are a closer match to observed global temperature. This does underline the importance of ENSO on short term global temperatures. I am sure everyone is very surprized by that result (not!).

    I do not think the paper can preclude (and the authors make no such claim) that there are other problems with the modelling. Beyond well-known problems with models, the question about accuracy of aerosol forcing seems to need more data (at least another year) from the Argo network. There could obviously be other errors and inaccuracies still hidden in modelling of feedbacks.

    However, what you can conclude is that there is not as yet conclusive evidence of some unknown failure in the models on the basis of a mismatch between ensemble mean and observations: It would appear that issue of ENSO is quite sufficient to explain the mismatch in global surface temperature for such a short term trend.

  23. Climate models accurately predicted global warming when reflecting natural ocean cycles

    " A minor concern is that the selection criteria (NINO3.4 Index trend) is correlated with the outcome (global trends), meaning that to some extent the study will suffer from retrospective selection bias (which I called "cherry-picking")."

    This comment makes no sense. Cherry-picking is a very serious allegation to make about a published paper since it implies fallacious argument with the underlying suspicion of scientific fraud. Unsubstantiated accusations like this are not tolerated by the comments policy. I ask again, how could you test their hypothesis with this data in a way that doesnt use that selection method?

    To your points:

    1/ The "good" is being used to describe the pattern (not cell-by-cell estimate) compared to the anti-phased ensemble.

    2/ It is no news to climate science that models are poor at regional level. Eg see Kerr (2013) (which was discussed at Realclimate here.)

    However it does not follow that "if they cant do regional, then global is wrong". This is discussed in detail in papers referenced in the RealClimate article.

  24. Climate models accurately predicted global warming when reflecting natural ocean cycles

    scaddenp@45,

    "I am not totally sure about what question Russ R refers to, but I believe the question may @8. This I answered directly in here..."

    Apologies, I didn't realize that the first paragraph of your comment @10 was actually a response to my oft-repeated question (@1, @3, @8, and again @21)... which is why I kept on asking, with increasing frustration.

    Since you've been kind enough to answer me, I'll be happy to answer your question @30.  (I'll put that response its own dedicated comment next.)

    "I think Russ is interested in pushing the point that even the selected scenarios do not match observations particularly well in absolute terms"

    The point I've been pushing is this... the paper claims that the "in-phase" models accurately predicted global warming over the 15-year time period AND that the models "provided good estimates of 15-year trends, including for recent periods and for Pacific spatial trend patterns".

    I don't take major issue with the first part... the average trends.  A minor concern is that the selection criteria (NINO3.4 Index trend) is correlated with the outcome (global trends), meaning that to some extent the study will suffer from retrospective selection bias (which I called "cherry-picking").  

    But claiming that the models made even "good" spatial trend pattern predictions appears absolutely wrong, as shown by the authors themselves (Figure 5.) where they show a regional comparison of model predictions (best and worst) vs. observations.  The relationship between even the "best" model predictions and the data is backwards in every ocean region except for the one region used for selection of the "best" models.

    So, two points to make here.

    1. The claim that the "in-phase" models made "good" predictions of recent spatial trend patterns appears to be invalidated by Figure 5.
    2. Even if they "in-phase" models got the global trend right on average, that feat looks more like luck than skill when they got every regional trend wrong.
  25. Climate models accurately predicted global warming when reflecting natural ocean cycles

    I am not totally sure about what question Russ R refers to, but I believe the question may @8. This I answered directly in here though Russ obviously understand spatial pattern differently from me and the authors.

    I think Russ is interested in pushing the point that even the selected scenarios do not match observations particularly well in absolute terms (does he really expect any ensemble to do that??) but seems much interested in this rhetoric point than in the more interesting questions as to why and the relative matching skill of phased and anti-phased scenarios.

  26. Climate models accurately predicted global warming when reflecting natural ocean cycles

    The abstract is only 5 sentences long.  The last two sentences (with my bolding) are:

            "We present a more appropriate test of models where only those models with natural variability (represented by El Niño/Southern Oscillation) largely in phase with observations are selected from multi-model ensembles for comparison with observations. These tests show that climate models have provided good estimates of 15-year trends, including for recent periods and for Pacific spatial trend patterns."

    So the authors selected a subset of the model runs and compared them to observations.   They found that this subset of model runs provided good estimates for Pacific spatial trend patterns.

    Can someone clarify what is meant by "Pacific spatial trend patterns" in this context.   Do the authors mean basin-wide trend patterns; trend patterns in the same Enso 3.4 area used to select the subset of models; something else entirely?

  27. Rob Painting at 06:07 AM on 24 July 2014
    Seal of approval - How marine mammals provide important climate data

    Chris - They are two different things. The ocean has the capacity to hold a thousand times more heat than the atmosphere, but we live on a rapidly rotating sphere where the bulk of incoming energy from the sun is received in the tropics.

    In practice, because of Earth's geometry, the shape of the ocean basins, the percentage of land vs ocean in the tropics, and a host of other considerations, we're never going to actually see a thousand times more heat in the ocean than the atmosphere.

    The 93.4% figure is the percentage of heat going into the ocean, whereas the specfic heat capacity is an idealized scenario. 

  28. Chris Crawford at 05:32 AM on 24 July 2014
    Seal of approval - How marine mammals provide important climate data

    Actually, I have a serious question, concerning the graph of ocean heat versus atmospheric heat. Long ago, I did the calculation of the relative heat capacities of the oceans and the atmosphere and came up with a result of 99% of the net heat capacity of the two was held in the oceans. I have seen other calculations yielding 99.9%, 99%, and even 90%. Figure 1 suggests 90%. I realize that there are complexities arising from freshwater, and the amount of water vapor in the air, especially involving the latent heat in water vapor. Can somebody please explain the basis for the different answers?

  29. Chris Crawford at 05:22 AM on 24 July 2014
    Seal of approval - How marine mammals provide important climate data

    I'm sorry, but that seal looks very much like she's saying "Does this hat make me look fat?"

  30. Why we care about the 97% expert consensus on human-caused global warming

    troyvit @6.

    Your linked item "The Control Group Is Out Of Control" is mainly about parapsychology and the examples it looks at in wider science are still pretty soft psychology (eg social priming) plus the odd reference to pharma research. The argument offered by your linked item is that a subject that is so obviously crackpot like parapsychology must then be subject to much more suspicion, and thus much higher levels of checking, if parapsychology can fail to spot so much unreplicable work, what of other sciences where suspicion and checking would be less? Is it valid to conclude as the item quotes "It just means that the standard statistical methods of science are so weak and flawed as to permit a field of study (parapsychology) to sustain itself in the complete absence of any subject matter."?

    Well, the level of defense in psychology/psychiatry literature appears to be the problem and one that doesn't extend to geosciences & enviro-eco science, as this graphic from a subsequently-linked Nature article illustrates. (The position of Material Sciences second from bottom may be to do with initial results being far more strongly based in such a physical area of study.)

    Nature - accentuate the positive

     

  31. Bob Lacatena at 04:34 AM on 24 July 2014
    Climate models accurately predicted global warming when reflecting natural ocean cycles

    RussR does not appear to understand the most basic aspects of the paper, or of climate modeling.  His tone and anger suggest that his misreading / misinterpretation of the subject and paper is either consciously or unconsciously willful.

    I would suggest that arguing with him is a complete waste of time. Certainly, correct his mis-statements, for the sake of lurkers and other readers, but engaging him directly is pointless.

  32. Climate models accurately predicted global warming when reflecting natural ocean cycles

    "ENSO-like variations in the models differ in phase based upon the individual runs."

    I think a couple of people posting here probably haven't figured out that a finite number of runs (I think a few dozen are typical) which exhibit internal variability means that it's largely chance as to which model runs (and therefore which models) will be in phase for the historical recent ENSO history for each 15 year period.

    Run each model a few more dozen times, select according to their algorithm in the same way, and for each 15 year period the model's whose runs match the historical 15 year period will differ ...

    I think it's a rather ingeneous approach.  Some model teams can (and have) performed hindcast runs plugging in known values for natural variability but that's expensive, much more expensive than this approach, and can't be done external to the team (unless you happen to have a massive supercomputer sitting in your basement).

  33. Dikran Marsupial at 02:19 AM on 24 July 2014
    Why we care about the 97% expert consensus on human-caused global warming

    troyvit academics have very little to gain by following the crowd, we are generally a rather disagreeable lot that spend our time finding fault with the work of others (and more importantly trying to do better).  To get highly cited papers, you need to find out something that people don't already know, and the best way to do that is to be different.

    The reason that we have a consensus is not because we are all following the crowd, but because reality places constraints on which hypotheses are plausible.  The reason that there is a consensus on, for example that the rise in CO2 is anthropogenic, is not because we all agree with eachother, but because the alternative hypothesis is inconsistent with the observations.  Of course that doesn't mean there are not still some scientists that disagree, and even the occasional one that manages to get something published in a journal, despite it being incorrect.  That is why it is 97% not 100%.

  34. Rob Honeycutt at 01:51 AM on 24 July 2014
    Why we care about the 97% expert consensus on human-caused global warming

    troyvit...  Don't you think it would be hard to get to a 97% consensus if scientific results were biases along political lines?

    I think what you do find is, when scientists express their opinion outside their specific area of expertise, that opinion is going to be far more influenced by their political views over research.

  35. Why we care about the 97% expert consensus on human-caused global warming

    Maybe I'm wrong, but my conservative friends aren't distrustful of science, but rather of scientists, and they have a point. If an intelligent, analytical conservative is more likely to go with his or her crowd, then who is to say that (regardless of political affiliation) scientists wouldn't be victims of their own cultural biases?

    Before you say that scientific method is designed to short-circuit that kind of behavior I recommend reading this article:

    http://slatestarcodex.com/2014/04/28/the-control-group-is-out-of-control/

    which shows just how easy it is to taint the scientific method with preconceptions — even unconsciously.

    By the way I believe global warming is human-created.

  36. Climate models accurately predicted global warming when reflecting natural ocean cycles

    Russ R. - From the paper:

    To select this subset of models for any 15-year period, we calculate the 15-year trend in Niño3.4 index in observations and in CMIP5 models and select only those models with a Niño 3.4 trend within a tolerance window of 0.01K y-1 of the observed Niño 3.4 trend. This approach ensures that we select only models with a phasing of ENSO regime and ocean heat uptake largely in line with observations.

    In the 1998-2012 period 4 models met that criteria, for 1997-2011 a different subset, and so on for each 15-year window. As noted above, "The sizes of the dots are proportional to the number of models selected" for each window.

    Your false statement is "excluding other models" - all of them were considered for each window, and subsets were selected for each window based on the stated similarity criteria to see how they differed. 

    ENSO-like variations in the models differ in phase based upon the individual runs. Since the entire purpose of the paper was to see if those models in phase with observational ENSO matched better or worse than those not in phase, the criteria used is entirely reasonable. 

  37. Climate models accurately predicted global warming when reflecting natural ocean cycles

    Perhaps CBDunkerson @38 is taking exception to your use of terms model and model run interchangeably.

    I note that the authors also do this.  For an example, see the caption to first figure in this blog post where "The sizes of the dots are proportional to the number of models selected. "

    It is unclear to me whether the dot size is truly the number of models selected or the number of model   runs.   

  38. Climate models accurately predicted global warming when reflecting natural ocean cycles

    scaddenp @30,

    I'll happily answer your question, as soon as someone answers mine.  

    I asked first (two days ago).  You can kindly wait your turn.

     

    CBDunkerson @38


    Russ wrote: "The authors took an ensemble of 38 models, and selected a narrow subset (~4) for analysis, excluding the other models."


    "Russ, you do get that people are objecting because this is straight up false, right?"


    Perhaps you might like to try this simple quiz...


    1. How many models were available for study in the CMIP5 archive?

    2. How many models were excluded because they lacked outputs of sea surface temperatures for the NINO3.4 region?

    3. Of the remaining models with NINO3.4 outputs, how many were selected for analysis in each 15-year period as being "in-phase" with ENSO?


    Answers:


    1. 38.

    2. 20.

    3. The number of selected "in-phase" models varied for each 15 year period, but only 4 models were selected for the most recent period from 1998-2012.


    What part of my statement was "straight up false"?

    Moderator Response:

    [Dikran Marsupial] Please can both sides of this discussion dial back the tone.  RussR, answer scaddenp's question and address CBDunkerson's question directly, without sarcasm.  If you want scaddenp to answer your question, please restate it politely. I will be monitoring this discussion and will summarily delete any post that violates the comments policy.  Note especially:

    No profanity or inflammatory toneAgain, constructive discussion is difficult when overheated rhetoric or profanity is flying around.

    Please can everybody resist responding to RussR's post until he has first answered these two questions in a reasonable manner.

  39. One Planet Only Forever at 23:29 PM on 23 July 2014
    Deep Decarbonization Pathways Project (DDPP) Presents Interim Report to UN Secretary-General Ban Ki-Moon

    This is a fabulous discussion.

    I would add that economic activity that the entire human population can be allowed to choose to develop to benefit from and continue doing forever will allow constant growth by the creative development of even better 'sustainable activity'.

    Any other kind of activity may be incredibly popular and profitable among a portion of the global population for a moment of human future, but damaging activity that cannot be benefited from by everyone, and cannot be continued to be benefited from indefinitely, is a threat to the sustainability of the economy and of humanity. That type of activity is the root cause of most of the viciuous and damaging conflicts that have ever occurred and continue to occur.

    This planet is finite and should be habitable by humanity for several hundred million years. Humanity really needs to figure out how to collectively make the best of this good thing, meaning humanity's future depends on competing to develop the best ways of keeping people who don't care about the develoment of a sustainable better future for all life from 'succeeding'.

  40. greenhousegaseous at 22:37 PM on 23 July 2014
    Deep Decarbonization Pathways Project (DDPP) Presents Interim Report to UN Secretary-General Ban Ki-Moon

    Larry, my twisting of the Pareto Principle was a joke, one I have used in managing projects during a very long career. I start with the traditional 80/20 idea and then focus the team down to the *really* important things to help them find the critical path.

    And the 5% I mention isn’t quite the same thing as what Anderson talks about. I was referring rather to the 5% of countries that are doing almost all the damage, in terms of emissions. He’s talking about the societal segment that represent the really serious burners.

    Nor did I mean to suggest that the solution implemented should only include the energy gluttons. I completely concur that all need to be actively involved. But the vast majority of countries burn very little FFs per capita. So the main difference is that for most countries, the solution needs to focus on the females: get them educated and empowered to get the birthrates down faster than the UN projects, and get them to financial independence as small business owners ASAP. This goal also confronts the religious and cultural barriers that have driven social economy for centuries.

    As Rob repeats, putting a price on carbon via taxation is the critical path step. I add regulation and alternative energy funding. And reaching critical political mass is the prerequisite to both.

    The cold truth is that in the taxation/regulatory steps, the poor and developing countries will not play a significant role, in the next 2 or 3 decades. That doesn’t mean they should be ignored.

    I agree that the affluent minority is the barrier to seriously meaningful decarbonization. I do not think that class will soon or willingly surrender their privileged lifestyle. So our real challenge is to stage a (hopefully bloodless) revolution, one that restructures consumption globally without imposing some dogmatic sociopolitical agenda.

  41. Climate data from air, land, sea and ice in 2013 reflect trends of a warming planet

    Barry - Over the years a variety of different algorithms (not just bootstrap) have been applied to data from a variety of different satellites. By way of example see:

    https://climatedataguide.ucar.edu/climate-data/sea-ice-concentration-data-overview-comparison-table-and-graphs

    I don't recall a similar debate over an Arctic sea ice "step change", but changes have certainly happened from time to time. See for example "old" DMI versus "new" DMI extent.

  42. Climate data from air, land, sea and ice in 2013 reflect trends of a warming planet

    It was in the piece from Tamino for which I gave the URL in 7 above.  It is http://tinyurl.com/qxt7jts.  I checked the abstract at Cryosphere (http://www.the-cryosphere.net/8/1289/2014/tc-8-1289-2014.html) and agree with you.  As the paper critiqued by Tamino was a pre-release obviously the final words were thought not appropriate. Also I note that the words "raise the possibility that this expansion may be a spurious artifact of an error in the satellite observations"  are modified to read "much of this expansion".  I should have but didn't, check the final version and apologise for that.  However the conclusion from Tamino is that the "increase in Antarctic sea ice cover is robust"

  43. Rob Painting at 21:46 PM on 23 July 2014
    Climate data from air, land, sea and ice in 2013 reflect trends of a warming planet

    Ashton - where does your quoted text come from? The last part of the sentence, after the comma, doesn't appear in the abstract. 

  44. Climate models accurately predicted global warming when reflecting natural ocean cycles

    Russ wrote: "The authors took an ensemble of 38 models, and selected a narrow subset (~4) for analysis, excluding the other models."

    Russ, you do get that people are objecting because this is straight up false, right? You keep plowing ahead as if you've got great points, and you would... if the basic foundations of your arguments weren't fictional.

    You might as well be arguing that 'the Risbey paper is bad because it advocates killing puppies'. I understand your outrage at the heinous things you imagine them to have done... but given that these offenses exist in your own mind, its an exercise in self-delusion which is painful to watch. Either the Risbey paper picked ~4 climate models out of 38 or it didn't. Until you can connect with reality enough to see the truth on that point (and various others) you are arguing from a set of 'facts' different from the rest of us, and thus naturally reaching different conclusions.

  45. Climate data from air, land, sea and ice in 2013 reflect trends of a warming planet

    Rob Painting @8 The authors wrote this in their Abstract:

      “The results of this analysis raise the possibility that this expansion may be a spurious artifact of an error in the satellite observations, and that the actual Antarctic sea ice cover may not be expanding at all".

    Agreed they don't say categorically Antarctic sea ice hasn't expanded but they certainly intimate it may well not have done.

  46. Climate models accurately predicted global warming when reflecting natural ocean cycles

    The second area of confusion is related to what is the expected performance of the models not in phase with actual real-world Enso.

    While the 15 years period 1998-2012has had more La Nina years than El Nino, the ratio hasn't been spectacularly, abnormally lopsided compared to past history.  See NOAA ONI table.   So, if the random Enso phases of the model runsis the cause of mismatch in GMST trends between models and observations over the last 15 years, then quite a few model runs should have 15 year trends that lie above observations.   Correct?

    To put it another way, if the only model problem is phasing of Enso, and the current 15 year GMST trends are below all model runs (or perhaps only below 97.5% of all model runs). then I would expect that either 1) that La Nina in the real world over the last 15 years is at or above the 97 percentile point, or 2) that the distribution of Enso in the entire CMIP5 ensemble of model runs is overwhelmingly biased towards El Nino.

    #1 is not true.  I have not inspected the model oututs, but I doubt that #2 is true.

  47. Climate models accurately predicted global warming when reflecting natural ocean cycles

    I'll list a few of the key points this blog post made, and then explain more clearly my concern.  Please tell me if you disagree with any of these statements made in the blog post

    1. " ...because over the long-term, temperature influences from El Niño and La Niña events cancel each other out. However, when we examine how climate model projections have performed over the past 15 years or so, those natural cycles make a big difference."

    2.  "They looked at each 15-year period since the 1950s, and compared how accurately each model simulation had represented El Niño and La Niña conditions during those 15 years, using the trends in what's known as the Niño3.4 index."

    3.  "Each individual climate model run has a random representation of these natural ocean cycles, so for every 15-year period, some of those simulations will have accurately represented the actual El Niño conditions just by chance."

    -------------------

    Everything sounds fine to me up to this point.   For each 15 year period, the authors of the paper select that subset of model runs where trends in the Enso 3.4 region best match observations.   If they pick the models where enso 3.4 trend best matches observations, I would expect a good match in that area, as shown in fig 5 cell a.    However, the authors make a more general claim.  They claim the selected "climate models have provided good estimates of 15-year trends, including for recent periods and for Pacific spatial trend patterns."

    Surely, the authors and the people posting here at SkS mean some other Pacific spatial trend pattern other other than the Enso 3.4 trend for which those specific models were selected.

    Correct?

  48. Is global warming causing extreme weather via jet stream waves?

    @Edward - My apologies for my belated response, but given the UK context you may also wish to take a look at another paper by James Screen, which I discuss on my own blog:

    Does the Arctic Sea Ice Influence Weather in the South West?

    @Ashton - I arrive here fresh from the Arctic Sea Ice Blog, where one of my comments precedes your own! I note you have yet to reply to Neven's comment in response to the question you posed over there. Are you now content that the "remark" you highlight above is not in fact "extraordinary" at all?

    @scaddenp - Neven's link doesn't actually cover "all" observations of the Arctic. Particularly if you're interested in Arctic sea ice thickness and/or volume you may wish to also take a look at this collection of my own devising:

    Arctic Sea Ice Graphs

  49. Rob Painting at 18:58 PM on 23 July 2014
    Climate data from air, land, sea and ice in 2013 reflect trends of a warming planet

    Ashton - The paper indicates that the magnitude of the increase may be exaggerated, not that Antarctic sea ice hasn't increased.  

  50. Climate data from air, land, sea and ice in 2013 reflect trends of a warming planet

    John Hartz Looking at the figure in the article you have mentioned the step change is about 200,000 sqare kilometers.  Tamino at Open MInd has critiqued the paper to which you refer and concludes that the increase in Antarctic sea ice is statistically significant (http://tinyurl.com/qxt7jts)

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