Guest essay by Roger A. Pielke Sr.
Introduction
I have posted in the past how the development of multi-decadal regional climate projections (predictions) to give to policymakers and the impact communities is a huge waste of time and resources; e.g. see
The Huge Waste Of Research Money In Providing Multi-Decadal Climate Projections For The New IPCC Report
Today, I want to discuss this issue in relation to the Working Group I Contribution to the IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis
The 2013 WG1 IPCC Report – Chapter 11 and Annex 1![cover[1]](http://wattsupwiththat.files.wordpress.com/2014/02/cover1.jpg?resize=149%2C202)
Projections are presented in Annex 1:
http://www.ipcc-wg1.unibe.ch/guidancepaper/WG1AR5_AnnexI-Atlas.pdf
and
http://www.climatechange2013.org/images/uploads/WGIAR5_WGI-12Doc2b_FinalDraft_AnnexI.pdf.
This is titled
Annex I: Atlas of Global and Regional Climate Projections
The foundation of this Atlas is based on the information provided in Chapter 11 of the IPCC WG1 report titled
“Near-term Climate Change: Projections and Predictability”
Click to access WG1AR5_Chapter11_FINAL.pdf
Multi-decadal regional climate projections, of course, can obviously not be any better than shorter term (i.e. “near-term”) projections (e.g. decadal) since decade time periods make up the longer period! The level of skill achieved for decadal time scales, must be the upper limit on what is obtainable for longer time periods.
As written in Chapter 11
Click to access WG1AR5_Chapter11_FINAL.pdf
Climate scientists distinguish between decadal predictions and decadal projections. Projections exploit only the predictive capacity arising from external forcing.
Projections then are simply model sensitivity simulations. By ignoring internal climate dynamics their presentation to the impacts communities as scenarios is a gross overstatement of what they really provide. They are only useful as improving our understanding of a subset of climate processes. To present results from them in the IPCC report without emphasizing this important limitation is not an honest communication.
The issue of how the climate model results are presented should bother everyone, regardless of one’s view on the importance of greenhouse gases in the atmosphere.
Chapter 11, fortunately, in contrast to Annex 1, is an informative chapter in the 2013 IPCC WG1 report that provides a scientific summary regarding predictability although their discussion on the uncertainties of the “external climate forcings” and skill is incomplete (e.g. see http://pielkeclimatesci.files.wordpress.com/2009/12/r-354.pdf ).
The chapter focus is described this way
This chapter describes current scientific expectations for ‘near-term’ climate. Here ‘near term’ refers to the period from the present to mid-century, during which the climate response to different emissions scenarios is generally similar. Greatest emphasis in this chapter is given to the period 2016–2035, though some information on projected changes before and after this period (up to mid-century) is also assessed.
Skilful multi-annual to decadal climate predictions (in the technical sense of ‘skilful’ as outlined in 11.2.3.2 and FAQ 11.1) are being produced although technical challenges remain that need to be overcome in order to improve skill.
Some important extracts from the chapter are [highlight added]
Near-term prediction systems have significant skill for temperature over large regions (Figure 11.4), especially over the oceans (Smith et al., 2010; Doblas-Reyes et al., 2011; Kim et al., 2012; Matei et al., 2012b; van Oldenborgh et al., 2012; Hanlon et al., 2013). It has been shown that a large part of the skill corresponds to the correct representation of the long-term trend (high confidence) as the skill decreases substantially after an estimate of the long-term trend is removed from both the predictions and the observations (e.g., Corti et al., 2012; van Oldenborgh et al., 2012; MacLeod et al., 2013).
The skill in hindcasting precipitation over land (Figure 11.6) is much lower than the skill in hindcasting temperature over land.
The skill of extreme daily temperature and precipitation in multi-annual time scales has also been assessed (Eade et al., 2012; Hanlon et al., 2013). There is little improvement in skill with the initialization beyond the first year, suggesting that skill then arises largely from the varying external forcing. The skill for extremes is generally similar to, but slightly lower than, that for the mean.
As part of Chapter 11, there is a section on Frequently Asked Questions. I have extracted excerpts from the FAQ 11.1 which is titled
If You Cannot Predict the Weather Next Month, How Can You Predict Climate for the Coming Decade?
Excerpts read highlighted text.
“Climate scientists do not attempt or claim to predict the detailed future evolution of the weather over coming seasons, years or decades.”Meteorological services and other agencies … have developed seasonal-to-interannual prediction systems that enable them to routinely predict seasonal climate anomalies with demonstrable predictive skill. The skill varies markedly from place to place and variable to variable. Skill tends to diminish the further the prediction delves into the future and in some locations there is no skill at all. ‘Skill’ is used here in its technical sense: it is a measure of how much greater the accuracy of a prediction is, compared with the accuracy of some typically simple prediction method like assuming that recent anomalies will persist during the period being predicted.Weather, seasonal-to-interannual and decadal prediction systems are similar in many ways (e.g., they all incorporate the same mathematical equations for the atmosphere, they all need to specify initial conditions to kick-start predictions, and they are all subject to limits on forecast accuracy imposed by the butterfly effect). However, decadal prediction, unlike weather and seasonal-to-interannual prediction, is still in its infancy. Decadal prediction systems nevertheless exhibit a degree of skill in hindcasting near-surface temperature over much of the globe out to at least nine years. A ‘hindcast’ is a prediction of a past event in which only observations prior to the event are fed into the prediction system used to make the prediction. The bulk of this skill is thought to arise from external forcing. ‘External forcing’ is a term used by climate scientists to refer to a forcing agent outside the climate system causing a change in the climate system. This includes increases in the concentration of long-lived greenhouse gases.Theory indicates that skill in predicting decadal precipitation should be less than the skill in predicting decadal surface temperature, and hindcast performance is consistent with this expectation.Finally, note that decadal prediction systems are designed to exploit both externally forced and internally generated sources of predictability. Climate scientists distinguish between decadal predictions and decadal projections. Projections exploit only the predictive capacity arising from external forcing. While previous IPCC Assessment Reports focussed exclusively on projections, this report also assesses decadal prediction research and its scientific basis.
What is remarkable about this Chapter is that they now recognize that at least out to a decade skillful predictions are very difficult. Only the skill in hindcasting near-surface temperature over much of the globe out to at least nine years has been emphasized. Skillful multi-decadal projections must be even more challenging.
Yet, Annex 1 provides detailed regional projections decades out into the future. It is
Annex I: Atlas of Global and Regional Climate Projections
http://www.ipcc-wg1.unibe.ch/guidancepaper/WG1AR5_AnnexI-Atlas.pdf
I have excerpted text from this Annex that explains what is provided (i.e. detailed regional multi decadal climate projections)
Annex I: Atlas of Global and Regional Climate Projections is an integral part of the Working Group I contribution to the IPCC Fifth Assessment Report, Climate Change 2013: The Physical Science Basis. It will provide comprehensive information on a selected range of variables (e.g., temperature and precipitation) for a few selected time horizons (e.g., 2020, 2050, and 2100) for all regions and, to the extent possible, for the four basic RCP scenarios.
However, there is a fundamental flaw in creating Annex 1, and, thus, any papers and studies on future climate impacts that result from it. Despite the widespread use of these model results, it is really a fundamentally flawed activity.
For this approach to be a robust approach to use for impact studies, these model results (when tested in hindcast) must show skill in not only replicating current climate (which is tested by comparison with reanalyses in which the climate model is NOT forced by the lateral boundary and nudging from the reanalyses), but must show skill at predicting CHANGES in regional climate statistics. This later requirement is a requirement to accept the models as robust projection (prediction) tools.
Necessary and Sufficient Tests of Model Prediction (Projection) Skill
To summarize
· The ability of the model to skillfully reproduce the regional climate statistics from the climate model (from the GCM or downscaled by a higher resolution regional model) is a NECESSARY first condition.
· The REQUIRED condition is that they must show, in hindcast runs, skill at predicting CHANGES in regional climate statistics.
There is a common mistake is to assume that one can use reanalyses to assess model prediction skill for the future. However, as discussed, for example, in the paper
Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum, 93, No. 5, 52-53, doi:10.1029/2012EO050008
using reanalyses to drive a model places a real world constraint on the results which does not exist when the multi-decadal climate models are run for the future decades (and indeed, lateral boundary conditions and nudging from the reanalyses must not be used in true hindcast tests of model skill). This issue is discussed in the paper
Pielke Sr., R.A. 2013: Comments on “The North American Regional Climate Change Assessment Program: Overview of Phase I Results.” Bull. Amer. Meteor. Soc., 94, 1075-1077, doi: 10.1175/BAMS-D-12-00205.1.
As discussed above, unless the global climate model (dynamically and/or statistically downscaled) can be shown to skillfully predict current climate on the regional scales [when run over multi-decadal time scales in a hindcast mode, it cannot be accepted as a faithful representation of the real world climate.
Examples of IPCC Model Shortcomings
Multi-decadal global model prediction, in hindcast runs, however, have major shortcoming even with respect to current climate! Peer reviewed examples of these shortcomings include; as summarized in the Preface to
Pielke Sr, R.A., Editor in Chief., 2013: Climate Vulnerability, Understanding and Addressing Threats to Essential Resources, 1st Edition. J. Adegoke, F. Hossain, G. Kallos, D. Niyoki, T. Seastedt, K. Suding, C. Wright, Eds., Academic Press, 1570 pp. [http://pielkeclimatesci.files.wordpress.com/2013/05/b-18preface.pdf]
are
Taylor et al, 2012: Afternoon rain more likely over drier soils. Nature. doi:10.1038/nature11377. Received 19 March 2012 Accepted 29 June 2012 Published online 12 September 2012
“…the erroneous sensitivity of convection schemes demonstrated here is likely to contribute to a tendency for large-scale models to `lock-in’ dry conditions, extending droughts unrealistically, and potentially exaggerating the role of soil moisture feedbacks in the climate system.”
Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov (2012), Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions, J. Geophys. Res., 117, D17105, doi:10.1029/2012JD017607. published 6 September 2012.
“The study confirms previous similar evaluations and raises concern for the ability of current climate models to simulate the response of a major mode of global circulation variability to external forcings.”
Fyfe, J. C., W. J. Merryfield, V. Kharin, G. J. Boer, W.-S. Lee, and K. von Salzen (2011), Skillful predictions of decadal trends in global mean surface temperature, Geophys. Res. Lett.,38, L22801, doi:10.1029/2011GL049508
”….for longer term decadal hindcasts a linear trend correction may be required if the model does not reproduce long-term trends. For this reason, we correct for systematic long-term trend biases.”
Xu, Zhongfeng and Zong-Liang Yang, 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. Journal of Climate 2012 doi: http://dx.doi.org/10.1175/JCLI-D-12-00005.1
”…the traditional dynamic downscaling (TDD) [i.e. without tuning) overestimates precipitation by 0.5-1.5 mm d-1…..The 2-year return level of summer daily maximum temperature simulated by the TDD is underestimated by 2-6°C over the central United States-Canada region”.
Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. & Mamassis, N. (2010) A comparison of local and aggregated climate model outputs with observed data. Hydrol. Sci. J. 55(7), 1094–1110
“…. local projections do not correlate well with observed measurements. Furthermore, we found that the correlation at a large spatial scale, i.e. the contiguous USA, is worse than at the local scale.”
Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P. Gabriel, and J. Haynes (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.
“…models produce precipitation approximately twice as often as that observed and make rainfall far too lightly…..The differences in the character of model precipitation are systemic and have a number of important implications for modeling the coupled Earth system …….little skill in precipitation [is] calculated at individual grid points, and thus applications involving downscaling of grid point precipitation to yet even finer‐scale resolution has little foundation and relevance to the real Earth system.”
Sun, Z., J. Liu, X. Zeng, and H. Liang (2012), Parameterization of instantaneous global horizontal irradiance at the surface. Part II: Cloudy-sky component, J. Geophys. Res., doi:10.1029/2012JD017557, in press.
“Radiation calculations in global numerical weather prediction (NWP) and climate models are usually performed in 3-hourly time intervals in order to reduce the computational cost. This treatment can lead to an incorrect Global Horizontal Irradiance (GHI) at the Earth’s surface, which could be one of the error sources in modelled convection and precipitation. …… An important application of the scheme is in global climate models….It is found that these errors are very large, exceeding 800 W m-2 at many non-radiation time steps due to ignoring the effects of clouds….”
Ronald van Haren, Geert Jan van Oldenborgh, Geert Lenderink, Matthew Collins and Wilco Hazeleger, 2012: SST and circulation trend biases cause an underestimation of European precipitation trends Climate Dynamics 2012, DOI: 10.1007/s00382-012-1401-5
“To conclude, modeled atmospheric circulation and SST trends over the past century are significantly different from the observed ones. These mismatches are responsible for a large part of the misrepresentation of precipitation trends in climate models. The causes of the large trends in atmospheric circulation and summer SST are not known.”
I could go on with more examples. However, it is clear that the climate models used in the manuscript under review are not robust tools to use to predict climate conditions in the future.
Annex 1 of the 2013 IPCC WG1 report, therefore, is fundamentally flawed as it is based on multi-decadal climate model results which have not shown skill at faithfully replicating most of the basic climate dynamics, such as major atmospheric circulation features, even in the current climate. They have also shown no skill at predicting the CHANGES of regional climate statistics to the accuracy required for impact studies.
Views by Others
Now, in closing, below I have extracted text from separate e-mails of two very major well known players in the climate area. Both accept that CO2 is the dominant climate forcing and man is responsible and we need urgent action. These quotes are in e-mails that I have. They show that despite other topics in which we disagree, they presumably would agree with me on the gross inadequacies of Annex 1 in the IPCC WG1 report.
The relevant part of the first e-mail reads
“It is also worth pointing out that neither initialised decadal predictions nor RCMs are the entirety of what can be said about regional climate change in the next few decades – and in fact, it is arguable whether either add anything very much. ;-)”
The second e-mail reads
“The climate effects are largely warming, I cannot say today’s climate models can tell us much more with any certainty. I will add that there is probably poleward and continental intensification of the warming. One further feature that appears to be robust is the movement of the storm belts in both hemispheres polewards. This has implications for the general circulation and in particular the climatology of precipitation intensity and variability (i.e., drought and flood). This poleward shift is seen in the data. There is some model evidence that over the next century ozone recovery could cancel come of this poleward shift in the Southern Hemisphere, but probably not in the NH. I think this is about as far I as we can go in forecasting climate over the next 50-100 years. Of course, sea level follows naturally from thermal expansion of the water as well as land ice melting. I think there is very little information (above noise) beyond the above in global climate models at regional level, even including downscaling.”
Since, these individuals have been silent in discussing the issue of the value of multi-decadal model regional climate predictions, I feel compelled to communicate that my flagging the failure of this approach for the impacts and policymakers communities is shared by even some in the IPCC community.
Recommendation for Responding to this IPCC Deficiency
My recommendation is that when you hear or read of climate projections on multi-decadal time periods, ask them:
What is the quantitative skill of the models used in predicting their projected CHANGES? In other words, what is the predictive skill with respect to the climate metrics that are of importance to a particular impact?
If they cannot present quantitative evidence of such skill they are inappropriately presenting their studies. Annex 1 of the 2013 IPCC WG1, therefore, still needs an honest demonstration of the skill (if any) of their projections as part of a complete assessment of the state of climate science.
I don’t believe the climate alarmists expected, or wanted, to be under such thorough and effective scrutiny. Couldn’t you just put on the blinders, and placidly agree, the way they wanted? Thank Heaven’s that there are intelligent, unbiased scientists, to refute the lies of the Leftie shrills. Keep up the great work!
Roger:
Thank you for your stellar effort here.
I would encourage you to boil down your core question* even further: so that it is fully understandable by an everyday citizen. Ultimately this is a PR matter…
[*What is the predictive skill with respect to the climate metrics that are of importance to a particular impact?]
Thank you, Dr Pielke, for this detailed critique. From a policy perspective it is regional predictions that are of most relevance – and here in the UK, we have had several: no more winter snow; drier summers with drought; loss of species with warming…..and actually the reverse is the case – colder winters, wetter summers, and species gains (especially water birds!). Despite having these deficiencies of model projections pointed out to them, the UK Climate Impacts community of scientists and planners continues to believe they can predict regional climate shifts. In fact, adaptation to impacts has become a small industry in itself – seminars and ‘capacity building’ with little to show for it; meanwhile ‘global warming’ actually gains 99% of the attention and funding for ‘mitigation’…..with UK’s contribution a tiny drop in the ocean, and deluded anyway because we have exported so much of our intensive energy demanding production overseas to the non-signatories of the Kyoto protocol that we trumpeted.
and 1957chev…
less of the ‘lefty shrills’ please – go do some homework on just who supports the scary climate story…..it commands support from both left and right….and I am left on that spectrum and have been an active critic of the IPCC’s supposed consensus for more than five years, but oddly, only the right-wing free-market press will publish my views or review my book….it is not a simple thing, this political analysis!
Let’s take a practical question that policy makers may choose to address.
1. What is the future risk of UHI to human health and life in the state of california.
Some of the work has started
http://wattsupwiththat.com/2013/03/07/california-to-quantify-uhi-statewide/
But that is just the historical view of things.
UHI is real. everyone agrees on that. And at 40 cities are the world the local officials have installed UHI warning systems. They use these warning systems to protect vulnerable citizens, the old and young who are at higher risk from dying in heat wave.
As a Policy maker I come to you Dr. Pielke and I ask.
“knowing all you all know about UHI. knowing all you know about the climate the weather and our limitations in predicting it… knowing all that.. what is your best scientific position on the following?”
A. Will we see more heat waves in the future or fewer?
B, What is your evidence for A?, show your work
C. If you think we are going to see more or fewer can you give us a range of your expectation
D. what did you base this range on, show your work
If your answer is that you have no scientific opinion on this, then fine. Many others do.
Of course people may bridle at the fact that local officials try to prepare for risks. tough. elect different people. The political reality is policy makers want answers. Some scientist somewhere will offer up his best opinion on the matter. Simple fact is you can’t merely criticize. You actually have to provide better answers.
CAGW is real, and is something for us all to be fearful of – have a look at DP’s definition in the comments here to realise the truth of that: CAGW – Computer Aided Global Warming. Dare you argue with that truth?
It commands support from monied interests; no one wants to be ‘left out’ of collecting from the gravy train, which is what attracts ‘capitalists’ willing to go along with the program, especially if you call them ‘bad names’ .. notice, too, all the big brands sporting ‘green’ products? QED.
.
While we’re here, what makes you entitled to my things (property and earnings), through government force no less?
Can’t you just ask nicely? Do you have something against charities helping the poor, and would rather have govt doing so instead?
I have always wondered these things, so, an answer would be appreciated …
pax, _Jim
.
Mosher, “UHI is real. everyone agrees on that. And at 40 cities are the world the local officials have installed UHI warning systems. They use these warning systems to protect vulnerable citizens, the old and young who are at higher risk from dying in heat wave.”
What to do about it. Simple Mosh – give the old and young higher BTU air conditioners. Don’t you agree that we be orders of magnitude cheaper than any of your foolish lukerwarmer solutions?
REPLY: the real issue is fuel poverty, green solutions have made electricity so expensive that elderly and others on meager incomes can’t afford to run A/C – Anthony
This discussion about settled science is unsettling.
Here is an experiment one could actually perform.
1. Have a vacuum chamber, dimensions several meters across.
2. Keep its walls at uniform −196°C (77 K; −321°F) by applying liquid nitrogen from the outside.
3. Put a rotating table in it, well insulated against heat conduction.
4. Set a transparent sealed container on it.
5. Fill it with a semi transparent fluid.
6. Irradiate it with narrow band short wave radiation until its effective temperature reaches several hundred degrees celsius.
7. Arrange for high spatiotemporal resolution measurements of internal motions and heat distribution of the fluid and digital data collection thereof.
8. Construct a computational model of this setup from first principles.
9. Run the experiment multiple times with several prescribed rotation speeds and spatiotemporal distributions of irradiation.
11. See how well the computational model is able to predict experimental results.
12. Change optical depth of the fluid in restricted thermal bands, repeat previous item.
Improve computational model until a reasonable match is seen between statistical behavior of it and that of the experimental setup.
I do not have high hopes regarding swift success of this project. However, it is orders of magnitude easier than to make a computational model of the climate system with any degree of skill. Another virtue of it is its full experimental verifiability, which property does not apply to terrestrial climate.
Anyway, I propose to defund computational climate modelling completely until this toy project shall be brought to success, costs being covered by a small fraction of money spared.
Simple fact is you can’t merely criticize. You actually have to provide better answers.
Tosh. If something is wrong, it is wrong.
It’s stupid to go around saying something must be used because no-one has a better version. By that logic why don’t you base your life on astrology, on the basis that no-one has a better method of predicting the future?
To paraphrase the IPCC:
“Decadal model predictions are worthless”
“Multi-decadal projections reliably predict warming; ummm, because we programmed that in”
Steven Mosher says:
> “Simple fact is you can’t merely criticize. You actually have to provide better answers.”
I understand fully your desire to have reliable answers to tough questions. But yes, you can “merely criticize”, and I would argue that it is your duty to do so when the “answers” are clearly and demonstrably wrong.
An analogy to demonstrate the point (not prove it): if an umpire calls a fast ball pitch outside, is he required to be able to throw a straight fast ball? /End of analogy.
Roger A. Pielke Sr. has made a very large contribution to the subject by pointing out problems with the IPCC’s analysis. He is NOT required to fix the problems. He is not serving here a teacher. It is up to the IPCC to fix their own mistakes. His contribution is very valuable to policy makers. It tells them to be wary of the projects.
@Steven Mosher
@CO2-The Miracle Molecule
Even cheaper, give a bucket of water each to put their feet in. In a life threatening situation radical solutions like this are justified. However, in most cases a wet T-shirt and a hat are sufficient to keep core temperatures in a safe range.
Mooloo,
You said it much better than me, AND you beat me by 15 minutes!
“Simple fact is you can’t merely criticize. You actually have to provide better answers.”
‘don’t just do something, stand there’ is often the better answer
Anthony says: “the real issue is fuel poverty, green solutions have made electricity so expensive that elderly and others on meager incomes can’t afford to run A/C”
That’s why I live in a cave. Cool in the summer and warm in the winter.
Thanks, Roger. A great post, as always.
Steven Mosher says:
> “Simple fact is you can’t merely criticize. You actually have to provide better answers.”
Roger A. Pielke Sr. makes a solid contribution to the story of the inadequacies of the IPCC and weather forecasts and immediately The ‘Mosher’ pops up like a Gopher to defend the crumbling interests of Warmism using a completely irrelevant diversion to UHI. The subject and data of UHI has been used consistently and selectively by Warmists to supply the missing warming in their manufactured temperature data records to ease the pain of having so much missing effect from the legendary CO2 emissions. Mosher’s contribution is meaningless and his ‘answers’ are valueless but Roger A. Pielke’s contribution is invaluable to the search for truth.
@Steven Mosher
Never let policy makers off the hook. They are supposed to take responsibility for their decisions, not delegate it to “experts”.
If no valid scientific opinion exists in a matter, each “scientist” offering one is a crackpot by definition. To demonstrate this fact to politicians is an undeniable contribution to their ability to handle the situation, provided there is public pressure on them to keep away from crackpots in the first place.
So yes, you can merely criticize.
BTW, a better answer starts with choosing the proper scientific question, which may be very different from the ones politicians have to deal with. If you are actually concerned about the state of art in climate science, help me convince politicians to channel funds to the toy project outlined above..
Steven Mosher – Thank you for your comment. We have a way to address threats such as from UHI. We report on this approach, in detail, in a number of our papers
Pielke Sr., R.A., R. Wilby, D. Niyogi, F. Hossain, K. Dairaku, J. Adegoke, G. Kallos, T. Seastedt, and K. Suding, 2012: Dealing with complexity and extreme events using a bottom-up, resource-based vulnerability perspective. Extreme Events and Natural Hazards: The Complexity Perspective Geophysical Monograph Series 196 © 2012. American Geophysical Union. All Rights Reserved. 10.1029/2011GM001086. http://pielkeclimatesci.files.wordpress.com/2012/10/r-3651.pdf
Pielke, R.A. Sr., 2004: Discussion Forum: A broader perspective on climate change is needed. IGBP Newsletter, 59, 16-19. http://pielkeclimatesci.files.wordpress.com/2009/09/nr-139.pdf
Pielke Sr., R.A., 2008: Global climate models – Many contributing influences. Citizen’s Guide to Colorado Climate Change, Colorado Climate Foundation for Water Education, pp. 28-29. http://pielkeclimatesci.files.wordpress.com/2009/09/nr-148.pdf
CB-37 Pielke, R.A. Sr., 2004: Introduction. Chapter E.1 In: Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. P. Kabat et al., Eds., Global Change – The IGBP Series, Springer, 483-484. http://pielkeclimatesci.files.wordpress.com/2010/01/cb-37.pdf
CB-38 Pielke, R.A. Sr., G. Petschel-Held, P. Kabat, B. Bass, M.F. Hutchinson, V. Gupta, R.A. Pielke Jr., M. Claussen, and D.S. Ojima, 2004: Predictability and uncertainty. Chapter E.2 In: Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. Global Change – The IGBP Series, P. Kabat et al., Eds., Springer, 485-490. http://pielkeclimatesci.files.wordpress.com/2010/01/cb-38.pdf
CB-39 Pielke, R.A. Sr., and T.J. Stohlgren, 2004: Contrast between predictive and vulnerability approaches. Chapter E.3 In: Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. Global Change – The IGBP Series, P. Kabat et al., Eds., Springer, 491-495.http://pielkeclimatesci.files.wordpress.com/2010/01/cb-39.pdf
CB-40 Bravo de Guenni, L., R.E. Schulze, R.A. Pielke Sr., and M.F. Hutchinson, 2004: The vulnerability approach. Chapter E.5 In: Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. Global Change – The IGBP Series, P. Kabat et al., Eds., Springer, 499-514. http://pielkeclimatesci.files.wordpress.com/2010/01/cb-40.pdf
CB-41 Pielke, R.A. Sr., C.J. Vorosmarty, J. Brunner, C. Revenga, B. Fekete, P. Green, Y. Kura, and K. Thompson, 2004: Case studies. Chapter E.6 In: Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. Global Change – The IGBP Series, P. Kabat et al., Eds., Springer, 515-536.http://pielkeclimatesci.files.wordpress.com/2010/01/cb-41.pdf
CB-42 Pielke, R.A. Sr., and L. Bravo de Guenni, 2004: Conclusions. Chapter E.7 In: Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. Global Change – The IGBP Series, P. Kabat et al., Eds., Springer, 537-538. http://pielkeclimatesci.files.wordpress.com/2010/01/cb-42.pdf
In your questions, you are not starting with the correct one. For example,instead of
“Will we see more heat waves in the future or fewer?”
the first question that must be asked is
what change in maximum and minimum daily temperatures and humidity must occur before the population of a city is threatened with increased mortality?
This will depend, on a variety of reasons, not just the actual temperatures, but the infrastructure in place to provide protection from the heat.
The IPCC models could provide forecasts of crossing key thresholds, BUT only when they show evidence of being able to skilfully predict (project) changes in these heat waves. They have not, however, so providing them to policymakers is not appropriate unless they caveat that they have no skill.
Best Regards
Roger Sr.
Its hilariously similar to people who claim they can predict the stock market.
Its always a lot easier to fit your “proven” model to past prices, than to correctly predict the future.
What can we agree on? Some amount of warming and sea level rise, consistent with that warming, is very like to occur because of increasing CO2 emissions and levels. IF THAT’S ALL we can be confident about, then that’s what policymakers should know. Mosher, isn’t that enough information for California to implement warning systems? Sea level rise similar to the last 100 years or greater is very likely. Isn’t that enough information for policymakers to implement policies for constructing barriers or limiting seashore construction and perhaps plans to return sea shores to the wetland “sponges” that many of them were before opportunistic construction.
Telling policymakers that IPCC models show regional or global futures that are quite uncertain gives them false insecurity, false hopes, and false confidence- all of which create unnecessary problems. False insecurity has resulted for hundreds of millions of coastal and island people who have felt very insecure the past two decades because false predictions about catastrophic sea level rise and allowed their politicians to use this fear for sometimes their own and not the people’s good. False hopes that the science was settled and everyone would praise the policymakers (and the scientists who instructed them) has created polarity, polemic, and ill will. It has also skewed climate science research so that we have practically lost 20 years of real research except for the courageous efforts of some like Dr. Pielke Sr. False confidence has allowed the hubris that Dr. Pielke describes, a hubris that is contagious- IPCC climate scientists to policymakers to tribal political grandstanding.
The result is a that real and potentially significant problem, global warming has become a litmus test of supposed science literacy and of good will. creating so much noise and hand waving by the extremists on all sides that both that potential problem and solutions is a casualty of climate wars and other known environmental, health, and similar problems with known solutions become neglected and trivialized. These other problems are trivial only because saving the world and humanity from catastrophe must be the overriding concern says one side, based on the settled science, climate science induced fears, and hubris of using unskillful models . The extremists on the other side attribute Machiavellian motivations of one world takeover and other conspiracies- when personal ambition, hubris, group think, and incompetence surely suffice as explanations.
Steven Mosher Feb 7 12:43pm says “Simple fact is you can’t merely criticize. You actually have to provide better answers.“. BS. Huge logical fallacy. Regrettably, we see this stupid argument used over and over again. Fact is, if an argument is wrong, it is wrong period.
Mooloo and David in Texas : you beat me to it! But I’d written it so I posted it.
” Decadal prediction systems nevertheless exhibit a degree of skill in hindcasting near-surface temperature over much of the globe out to at least nine years. ”
That’s peach. If they kinda work up to nine years , they are NOT decadal projections, are they?
Thanks Dr. Pielke. Good analysis.
One problem with this CAGW scam is it needs to be constantly fed with lies, if not, it dies.
Steven Mosher says:
February 7, 2014 at 12:43 pm
1. What is the future risk of UHI to human health and life in the state of california.
A. Will we see more heat waves in the future or fewer?
———————————–
Why no option for exactly the same number of heat waves as has always occurred, not more and not fewer. A have you stopped beating your wife question.
As Roger Pielke Sr. points out, we shouldn’t be relying on any climate model for that.
If anything, UHI is based on population projections then and the Log(of those population projections by city) since the data shows that is the important metric for UHI.
Net In-migration has basically flatlined, natural growth of births versus deaths is still growing – economic policies have flatlined job growth so there will soon be out-migration reversing the trend of 150 years.
Smaller centres are growing and not so much the larger centres. I predict a small increase in UHI-induced heat-waves over time, primarily in the smaller centres which currently have heat-waves.
http://extras.mnginteractive.com/live/media/site571/2013/0501/20130501_080125_ssjm0502calgrowth90_500.jpg
To what end Steve? Just because a bunch of whackos, yes whackos, have decided that gradual temperature increase since the last ice age are a threat?
They have no evidence, for warmer weather to be a threat, none! Yeah, there are fools reading prehistoric tea leaves and then somehow jumping to the conclusion that climate change caused a catastrophe; but again they do not have proof, just assumptions.
During the history of man, warmer temperatures have been beneficial. That word severely understates just how mankind has thrived during warmer years. Contrast that with known famines during colder years.
The blunt truth is that the models are assembled groups of assumptions, inputs and calculations. They provide rough guidance, when they work; otherwise they’re learning exercises on the path to working models.
Instead of trumpeting disaster based on prophetic readings of model innards, modelers should be keeping their mouths shut and learning!
So the world is warmer this year. That’s great!! Do you really believe mankind lacks fur coats because we look better in animal and plant fibers?
Want to blame CO2 for the warmth? That’s also great!! Plants love higher CO2 and all animal life on this planet benefits when plants benefit. Or is the next step for not harming or distressing animals and some plants a migration to direct mineral consumption? I got news for you, you’ll prefer it warmer if animals and plants get to keep their fibers and flesh.
The truth is, natural variation is apparently trumping CO2 and all of those rabid warmists. When mankind has identified and observed all of the natural cycles, then and perhaps only then will man have an inkling which direction the natural variation is taking us.
Great post Roger A. Pielke Sr.!
Only, the more I read through your opening up the “2013 WG1 IPCC Report – Annex 1” and documented the language, the more I became convinced that the authors/lead editor does not understand the meaning of the word ‘skill’:
It seems to me that there is an attempt to imbue the weak climate models with anthropomorphic facilities. It’s bad enough that the phrase ‘computer models’ plays on popular science fiction in people’s minds, but trying to make the models sound human-like is distressing.
I’m just expressing my doubts so that others may perhaps also recognize when the IPCC is tweaking personal psychology.
Thank you for dissecting the Annex 1 and related documents so well!
With respect to Mosh – who I do actually respect. I think his comment has perfectly illustrated the blinkered view. Namely, that policy makers should be forced/coerced/cajoled/whatever into some ‘decision’ based on what amounts to general speculation. I’m sorry. but as a scientist (and engineer) I can’t support that stance. The ‘what if we are right’ , precautionary principle, etc – simply doesn’t (or more importantly it shouldn’t) wash with policymakers. Decision processes should be made on sensible facts and figures, not speculative or intuitive ideas/feelings.
As regards the UHI issue, I’m glad it is finally accepted as valid – but I have yet to see a downgrading of the surface temperature datasets to allow for it (i.e. for all ‘town’ stations, airports, etc). When we can actually look at a ‘corrected’ dataset and believe the corrections are valid (rather than some arbitrary adjustment!) then perhaps we can ‘use’ this data to ‘push’ policy makers?
The bottom line is that if you (as a scientist) cannot give reasonably scientifically supported ‘direction’ to policy makers – you should shut the feck up! – because ultimately, you are the one ‘pushing the button’ and should be held FULLY accountable. As a real simple example – Iraq was stuffed because of alleged WMD – were they found? – did the ‘specialist spies’ that said they ‘existed’ get fired? Many Billions were spent because of this ‘error’ (and yeah, I’m ignoring the ‘other’ reasons) but who paid the price? Answer – you and me…….and a sh*tload of innocent Iraqis (including the poor conscripts ‘sent’ to war by some stupid Iraqi dictator) and Allied servicemen and women….
My ultimate point being that people is such positions (i,e. climate scientists in this case) have a VERY high level of responsibility – to EVERYBODY – the fact the warmist scientific folk don’t seem to demonstrate any real CARE about their responsibility is the real bugbear, for me anyway.
rant off/ – sorry……
Steven Mosher says:
February 7, 2014 at 12:43 pm
” The political reality is policy makers want answers.”
====================
Nope, they want job security, and they’ll spend as much of the taxpayers money as it takes to keep it.
Yep, it’s reality, but why would they want an answer that makes them irrelevant.
Political opinions /
I thought the post was to point out the considerable lack of predictive skill in the IPCC reports, and some of the machinations behind this lack of skill.
Unfortunately people have let Steven Mosher distract them from thiswith:
Steven Mosher says:
February 7, 2014 at 12:43 pm
Perhaps instead of distracting, Mosher could contribute some facts (in a separate post !) about UHI.
I remember seeing urban planning documents (maybe Eurpoean?) referencing satelite measurements of surface temperature (NOT AIR TEMPERATURE) on passes from rural over city back to rural, showing 8 degrees C (up to even 11degrees C) higher surface temperatures over cities.
If Mosher is so well up with UHI he could present some of these facts with references (again in a separate post).
I doubt it will happen as it provides too much support for those saying the 0.3 deg/decade trend of Hadcrut, GISS anf Best are wrong, and the analyses of Watts and others, (the recent Chinese connection on WUWT) at 0.15 degrees /decade increase from 1979 to 2002 (after a similar drop from 1945 to 1979) are right.
I would like to add to Rodgers reply to Mosher, and that is about awareness, A few years ago a man died from heat exposure while laying out in the sun in a Vancouver park. After that incident, there was a policy implemented to check on vulnerable people, as well as the awareness that if you see someone lying out in the hot sun, you should call 911, at the very least.
Hindcasting is accurate because they have tuned in a degree of heat based on CO2/greenhouse gas forcing. However, this begs the question: Because heat is heat regardless of what forces it to rise, did the modelers consider that heat has more than one source?
I think they actually have considered that. Which has led to an early identified problem. Trial runs produced too much heat in the past.
In fact the null hypothesis is that heat is stored in the oceans and released slowly or all at once at Earth’s whim. I believe this is why aerosols and particulates are added to the hindcast atmosphere. When considering natural oceanic parameters that would predict heat release, the modelers have had to add particulates to the air to cool their over-revved inputs so that they can continue to say that added CO2 explains past temperature rise;
CO2-The Miracle Molecule says:
February 7, 2014 at 1:32 pm
Mosher, “UHI is real. everyone agrees on that. And at 40 cities are the world the local officials have installed UHI warning systems. They use these warning systems to protect vulnerable citizens, the old and young who are at higher risk from dying in heat wave.”
What to do about it. Simple Mosh – give the old and young higher BTU air conditioners. Don’t you agree that we be orders of magnitude cheaper than any of your foolish lukerwarmer solutions?
REPLY: the real issue is fuel poverty, green solutions have made electricity so expensive that elderly and others on meager incomes can’t afford to run A/C – Anthony
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
I was planning to become a lurker and stop commenting as there are so many smart people here that an aging brain can’t hope to even understand. But I just paid 90 cents a litre for a propane fill up on my farm where I usually pay between 41 and 52 cents a litre. That’s what a little bit of cold and a lack of supply infrastructure will do for you. Anthony’s comment is bang on. The only reason for a 100% jump in the price is the current high demand and while I can cut more wood on the farm, the poor and elderly will be stuck with turning down the heat and putting on a coat inside the house. Energy poverty is the big issue, whether we are taking heating or cooling.
Thanks for the comment Anthony. Hope you Californians are getting some rain now that the westerlies are blowing in.
Bob Tisdale says:
February 7, 2014 at 2:39 pm
Anthony says: “the real issue is fuel poverty, green solutions have made electricity so expensive that elderly and others on meager incomes can’t afford to run A/C”
That’s why I live in a cave. Cool in the summer and warm in the winter.
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Speaking of a cave: http://www.usatoday.com/story/news/world/2014/02/07/human-footprints-800000-years-old/5277059/
– – –
Dr. Tim B all:
http://wattsupwiththat.com/2013/01/08/wrong-prediction-wrong-science-unless-its-government-climate-science/
Dr. Pielke, your essay, while tough sledding at times for laymen such as myself (obviously my problem) was an invaluable contribution to this blog and thank you so very much. Your response to Steven Mosher’s comments that end with:
“The political reality is policy makers want answers. Some scientist somewhere will offer up his best opinion on the matter. Simple fact is you can’t merely criticize. You actually have to provide better answers.”
were priceless, on the other hand, I chuckled..
Mosher: “A. Will we see more heat waves in the future or fewer?”
They could check.
“HADCET Daily Maximum data is available from 1878 on. There were 45 days over 30C since 1878. Of the 45 days 30C and over, 5 occurred from 2003 to 2006, none after 2006.
6 occurred in the 1940s. 14 occurred in the 1970s – 9 of which occurred in 1976 alone.
Is death by heatwave imminent in the area covered by HADCET? I think not.”
http://sunshinehours.wordpress.com/2013/11/07/hadcet-maximum-temperature-days-over-30c/
Steve said: ”
Of course people may bridle at the fact that local officials try to prepare for risks. tough. elect different people. The political reality is policy makers want answers. Some scientist somewhere will offer up his best opinion on the matter. Simple fact is you can’t merely criticize. You actually have to provide better answers.”
The truth of the manner is they don’t know, there is no way to know it may not ever be possible to know. Anyone who claims to know is a charlatan.That is what the politician need to know and if they don’t they need to be told that, The real answer is be prepared for the unexpected, for that is what is going to happen. Politician must have contingencies for a warming or a colder, wetter or drier. stormier or calmer world and the best contingencies I know of for a changing world (ps Steve, its alway changing) is cheap, abundant and reliable energy. that should be their number one priority because that is what will allow all the most people adjust to what contingencies may come about. Instead the AGW crowd wants to do the reverse.
A serious waste of time and money. Sheer FANTASY.
With such level of complexity every expert will smash the other one without convincing any policy maker.
To me a simpler request for validation is: “using today’s most sophisticated models and all known historical observations up to end of 1997, please show an accurate reconstruction of the climate evolution that took place between 1998 and today!”
And to solve the quetsion of UHI, as Alphonse Allais was indicating at the end of the 19th century: if city life is getting unbearable we should build them in the country side.
Mr. Mosher, if a scientist who made a claim comes to the realization that it is wrong is he himself allowed to retract it even so he has no better answer? Or do you only deny others the right to test the claim.
The author seem to be claiming that studies based on these regional projections are not sound for policy-making. If that is true they are not sound for investment decisions.
However the World Bank, the Asian Development Bank, the European Union and development cooperation agencies such as USAID and DFID are now including climate consultants on infrastructure feasibility studies as a condition for grants and loans to developing countries..
If the author is correct it means that these institutions are wasting their resources on pseudoscience. Can it be true that all these clever and well-meaning people should be spending that money on education and health and roads instead of climate studies that will show how to regulate the climate and adapt to future climate disasters?
Berényi Péter says:
February 7, 2014 at 2:19 pm
Even cheaper, give a bucket of water each to put their feet in. In a life threatening situation radical solutions like this are justified. However, in most cases a wet T-shirt and a hat are sufficient to keep core temperatures in a safe range.
=====================
Exactly. Heating takes energy and lots of it. It is expensive and there is no low cost way to get around this.
Air conditioning also takes energy. However, there is a low cost alternative. Water. Add water to the environment and you get low cost cooling.
Thus, it is technologically simple and low cost to solve warming problems, but it remains complex and expensive to solve cooling problems.
It is a complete nonsense to worry about problems that are so cheap and easy to fix in the future if we face them. Sit naked under a palm tree on any ocean beach on the equator on the hottest day of the year. You will not be hot, you will be comfortable. If there is a breeze blowing you may even feel cold and want to move out into the sun.
In most place on earth, and in most seasons, the naked human sitting in the shade will die of exposure. They cannot eat enough food to maintain their core temperature. They must seek either clothing, shelter, or a heat source.
The domestication of fire is the only reason humans are found outside the tropics.
I have read so many peer reviewed studies that debunk the climate models. I can only assume scientists persevere with these supercomputers for fear of losing government funding. It just would not look good to admit the models are seriously flawed (which they are). So, it’s far easier to pretend the models are getting better etc etc etc etc. This way it maintains the money flood!
Hindcasting is not a valid test of any model if either the model or the model builder has seen the hindcast data. The experiment is flawed because it is not double blind.
Computers are very good at memorizing data and like parrots they can spout back what they have memorized. This lets them predict the past with amazing accuracy, but provides them no skill at predicting the future.
Similarly, if the model builder has seen the hindcast, they will unconsciously select the model that best describes the past, even if they don’t mean to. This influences model performance during hindcastng, but provides no skill at forecasting the future.
For any set of data of N+1 points, you can solve mathematically a polynomial of degree N that exactly passes though all the data points. This is the training run for your model, where you adjust the coefficients (weights) of the polynomial to fit the data.
From now on, your model will perfectly hindcast the past. However, it will have no skill at predicting the future. For example, consider:
ax^2 + bX + c = y
If you now have 3 data points (x,y), the computer can solve for a,b,c such that for every value of X, you will get the correct value of Y, Now consider that X is the date, and Y is the temperature. You model will now correctly predict the past temperature (Y) for any of the 3 dates (X) provided.
However, when you ask the computer to predict the temperature for any date it has not seen, it will demonstrate no more skill than a dart board.
Kev-in-Uk says:
February 7, 2014 at 4:14 pm
Kev-in-Uk: I think you have it backwards; it is not the scientists who are pushing some bad decisions on policy-makers — it is the policy makers who are fishing for excuses to expand their authority and they have found junk science is a powerful ally. Scientists are flattered by all the attention they get from those at the seat of power and they oblige their flatterers with the answer the powerful desire. They don’t want to lose access to the powerful.
If you start out with the question:
You usually get a very different response than you do with the question:
Policy makers shop for scientists who give them the results they want, just like celebrities shop for doctors who will authorize the prescriptions they desire. The honest scientist who says “the current body of scientific knowledge doesn’t offer guidance one way or the other on this issue” will simply be passed over the next time in favor someone more helpful.
Frederick Colbourne says:
February 8, 2014 at 4:20 am
If the author is correct it means that these institutions are wasting their resources on pseudoscience. Can it be true that all these clever and well-meaning people should be spending that money on education and health and roads instead of climate studies that will show how to regulate the climate.
===============
Clever and well meaning people have routinely done great harm. Because they are well meaning they believe their motives justifies their actions. Because they are clever it is hard for other people to convince them their well meaning actions will do more harm than good.
The classic example is food aid. Send free food to a country full of hungry people. Continue this for a few years and then cut off the aid. Instead of hungry people you will now have starving people. Your well meaning actions have made the problem worse and your cleverness has blinded you to the dangers of your actions.
A great many recurring problem exist because the knee jerk solution, the obvious solution that first comes to mind has unintended consequences, and these consequences work to make the problem worse.
Frederick Colbourne – You write
“The author seem to be claiming that studies based on these regional projections are not sound for policy-making”
That is exactly what I have shown. Many millions of dollars, euros etc are being wasted on the creation of multi-decadal projections of regional climate.. Until the people who are doing these studies are required to actually document the skill of their approach, this money will continue to be wasted.
Roger Sr..
ferdberple –
You write
“Hindcasting is not a valid test of any model if either the model or the model builder has seen the hindcast data. The experiment is flawed because it is not double blind.”
I agree; an ideal test is blind. However, even when they do know the weather over the last several decades, they still fail to simulate significant aspects of the weather patterns, as I have documented in my post and papers. Thus, they would likely do even worse in a blind experiment.
Roger Sr.
Roger A. Pielke Sr.
Thankyou. Your article and your comments in the thread are excellent. Thankyou.
Richard
Climate models do tell us something interesting, which is largely overlooked. When you look at the IPCC graph of climate model result, you see a spaghetti graph. The results are all over the place.
The IPCC then tries to average these results and claim that this somehow represents the future. This is simply a nonsense, because the future is not an average of all possibilities.
What the spaghetti graph is really telling us is that the range of what may happen, with no change in external forcings, is quite large. That regardless of any actions we may take, it may get warmer or colder, and there is nothing we can do about it except adapt.
Thus, we are much wiser to spend money on adapting than on mitigating, because regardless of our actions, the climate may still change.
John Droz, jr.
Thank you for your comment. I agree with you but would modify slightly. The question that needs to be asked is
What is the predictive skill with respect to CHANGES in the climate metrics that are of importance to a particular social and/or environmental impact?
We can use reanalyses and other observations for the current climate. We do not need model predictions for that.
Roger Sr.
Roger, good article as usual. You’re indispensable in this debate. Marty
@Peter Taylor
“and 1957chev…
less of the ‘lefty shrills’ please – go do some homework on just who supports the scary climate story…..it commands support from both left and right….and I am left on that spectrum and have been an active critic of the IPCC’s supposed consensus for more than five years, but oddly, only the right-wing free-market press will publish my views or review my book….it is not a simple thing, this political analysis!”
This cannot be repeated often enough.
I’m going to buy your book.
@1957chev The truth does not benefit from turning this into a left-right thing. There is plenty of right wing support for AGW hysteria.
Roger A. Pielke Sr. says:
February 8, 2014 at 6:48 am
I agree; an ideal test is blind. However, even when they do know the weather over the last several decades, they still fail to simulate significant aspects of the weather patterns, as I have documented in my post and papers. Thus, they would likely do even worse in a blind experiment.
===========
Agreed, due to the lack of experimental controls, model performance only tells us how bad the models are performing. We cannot draw conclusions about how good they might be, because any ability to hindcast might simply be an artifact of the lack of experimental controls.
My point was directed at readers that are perhaps less familiar with computer models. These readers might naively assume, wrongly, that some ability to hindcast might indicate that the models had some ability to forecast. This assumption is wrong, because of the lack of experimental controls.
Personally, I appreciate your courage, leadership and huge contribution to science in speaking out about this abuse of the Public Trust being conducted in the name of Science. For more than a century scientists around the world have known the dangers of conducting science without experimental controls. The classic example being Clever Hans.
http://en.wikipedia.org/wiki/Clever_Hans
The only way to test a model that lacks rigorous experimental controls is to test it against the future as compiled by an independent observer. If however, as in the case of GISS for example, the model builder also collects the future data, then again there are no experimental controls and even the results against the future cannot be trusted.
We know from experiment after experiment that human beings unconsciously introduce bias into their work, no matter how honest their intentions. No matter how hard they try, their subconscious will cook the books to give them the answer they expect, and their conscious mind will remain completely unaware of what is going on behind the scenes. Thus, experimental design, the use of double blind controls to eliminate bias, must be at the heart of any scientific investigation.
Anyone that has ever proof read their own writing will have experienced this. You proof read an email over and over and it looks perfect. You send it out. The next day you re-read your email and discover their is a word missing. You can hardly believe it because when you read the email the day before, the word was there. This effect is so startling that you suspect that somehow someone must have erased the word from your email overnight.
But of course no one erased the word. It was your subconscious, trying to help. Filling in the missing information, so that the sentence made perfect sense while we were proof reading. It was only later, when you had forgotten what it was your were trying to say, that the missing word becomes visible to your conscious mind.
ferdberple:
At February 8, 2014 at 9:46 am you rightly say
Actually, there is a more fundamental reason why an ability to hindcast is not an indication of an ability to forecast.
There is only one past but there are an infinite number of ways to obtain an accurate hindcast, while there are an infinite number of possible futures but there is only one future that will evolve.
Forecast skill is demonstrated by a series of successful forecasts and nothing else. No GCM has existed for 50 years so no GCM has any demonstrated forecast skill for periods of 50 years.
Richard
richardscourtney says:
February 8, 2014 at 10:01 am
There is only one past but there are an infinite number of ways to obtain an accurate hindcast, while there are an infinite number of possible futures but there is only one future that will evolve.
==========
An interesting point and fundamentally correct. Even demonstrated ability to successfully predict the future is no guarantee, because some models may accidental get it right.
ferdberple:
Thankyou for your reply to me at February 8, 2014 at 10:44 am which says
Yes, and that is why I also wrote
emphasis added: RSC
Richard
{“Will we see more heat waves in the future or fewer?”}…..wouldn’t that be better stated as ” is there going to be a change in the frequency of heat waves?”. What are the proposed reasons for expecting such a change? Is there current evidence for more/fewer heat waves in the last decade as compared to historical records? Why is there no thought given to the possibility that heat wave frequency will not deviate from the known record?
Richard – Hindcast tests of skill is a necessary condition. I agree, however, that it is
i) not a complete assessment, but it is the best that can be done (without waiting 50 years)
and
ii) the IPCC failed to satisfactorily do that test.
Roger
Roger A. Pielke Sr. says:
February 8, 2014 at 12:41 pm
“Richard – Hindcast tests of skill is a necessary condition. I agree, however, that it is
i) not a complete assessment, but it is the best that can be done (without waiting 50 years)
and
ii) the IPCC failed to satisfactorily do that test. ”
When one builds a model and one wants to use hindcasting as a validation tool, it is necessary to not use the data on which one does the hindcasting during training (“Parametrization” in climate science) of the model.
I have never seen any mention of that in any paper about climate modeling. A serious attempt at validation by hindcasting would have to detail how “knowledge pollution” is avoided during the setup; how did they make sure that no knowledge about the hindcasting period could have influenced the training?
Have any such attempts been made? I know of none.
I think “Shut up.” is a perfectly valid answer to a stupid question.
Roger A. Pielke Sr.:
Sincere thanks for your response to my comments to ferdberple which you provide with your post at February 8, 2014 at 12:41 pm.
Please note that what I am saying is NOT a disagreement with your fine article which I applaud.
I agree your two points in your post addressed to me and I also agree with the resulting post made by DirkH at February 8, 2014 at 12:56 pm. But my concern at the use of the models is much, much more fundamental than yours.
I became concerned at what I saw as the misuse of the models when I studied development of the Hadley Center GCM in the 1990s
(ref. Courtney RS An assessment of validation experiments conducted on computer models of global climate using the general circulation model of the UK’s Hadley Centre Energy & Environment, Volume 10, Number 5, pp. 491-502, September 1999).
My concern was enhanced by reading Kiehle’s assessment in 2007. He found the same as me but his analysis was of 9 GCMs and two energy balance models.
(ref. Kiehl JT,Twentieth century climate model response and climate sensitivity. GRL vol.. 34, L22710, doi:10.1029/2007GL031383, 2007).
I had published that the Hadley GCM could not model climate and only obtained agreement between past average global temperature and the model’s indications of average global temperature by forcing the agreement with an input of assumed anthropogenic aerosol cooling. And my paper showed that the assumed aerosol cooling was not the cause – or at least not the sole cause – of the model’s failure to hindcast.
Kiehl found the same as my paper except that each model he assessed used a different aerosol ‘fix’ from every other model. This is because they all ‘run hot’ but they each ‘run hot’ to a different degree.
Simply, each climate model emulates a different climate system. Hence, at most only one of them emulates the climate system of the real Earth because there is only one Earth. And the fact that they each ‘run hot’ unless fiddled by use of a completely arbitrary ‘aerosol cooling’ strongly suggests that none of them emulates the climate system of the real Earth.
Hence, the models are excellent heuristic tools. And they should be used as such.
But there is no reason to suppose that any of them is a predictive tool. And averaging model predictions (e.g. CMIP5) is an error because average wrong is wrong.
But, as you say, the models are being used as predictive tools such that their projections are influencing policy. It is less problematic to know one is ignorant when formulating policy than it is to formulate policy when influenced by false information .
Anyway, those are my views and I hope they are useful to your thought. If so, then I have provided some repayment for the interest which you have given to me by providing your excellent article.
Richard
ferdberple says:
For any set of data of N+1 points, you can solve mathematically a polynomial of degree N that exactly passes though all the data points. This is the training run for your model, where you adjust the coefficients (weights) of the polynomial to fit the data.
From now on, your model will perfectly hindcast the past. However, it will have no skill at predicting the future. For example, consider:
ax^2 + bX + c = y
If you now have 3 data points (x,y), the computer can solve for a,b,c such that for every value of X, you will get the correct value of Y, Now consider that X is the date, and Y is the temperature. You model will now correctly predict the past temperature (Y) for any of the 3 dates (X) provided.
However, when you ask the computer to predict the temperature for any date it has not seen, it will demonstrate no more skill than a dart board.
The dart board might well do a better job. Since it produces some degree of randomness. Whereas the equation will always give exactly the same results. It’s unlikely to correctly predict anything other than the original known values either. This being the case regardless of the number of known values used…
Steven Mosher says:
“As a Policy maker…”
If that is the case, then IMHO we are in deep doo-doo. ☹
Really.
Policy should be made on the basis of real world data and empirical observations, not on computer models.
Models have their place. But when the real world conflicts with models, then we should go with what the real world is telling us.
The real world is telling us that global warming stopped about seventeen years ago. So there is no urgency; ipso facto.
Besides, “policy” seems to be that the U.S. never says one word about China, Russia, India, or a hundred other countries’ ramping up of their CO2 emissions. Therefore, ‘policy’ is merely self-serving alarmist propaganda, intended to separate American taxpayers from their money. That is surely not a good thing. Who do these policy makers represent? Americans? Or other UN countries?
Wake me when ‘policy’ becomes objective again. So far, it only serves the interests of the climate alarmist clique.
dbstealey says:
February 8, 2014 at 2:01 pm
“Steven Mosher says:
“As a Policy maker…”
If that is the case, then IMHO we are in deep doo-doo.”
Only California for the moment, and they’re there already.
DirkH says:
February 8, 2014 at 12:56 pm
how did they make sure that no knowledge about the hindcasting period could have influenced the training?
===========
The evidence suggests quite the opposite. It is quite possible that 17 years ago a number of climate models predicted that temperatures would flat line. How many of those models would have survived that prediction?
Almost none, because the scientists involved would have said “this can’t be right”. So they would have adjusted the models until the models delivered a result the scientists believed was right. Thus, those models that predicted the right answer would have been thrown on the rubbish pile of history, and those models that predicted rubbish would have survived and been published by the IPCC.
So you can see, no climate model can predict the future better than the climate scientists themselves, because if the scientists don’t believe the computer model, they will change the model until they do believe it.
Hi Richard
Thank you for your follow up. We are in complete agreement, as you wrote, that
“Hence, the models are excellent heuristic tools. And they should be used as such.
But there is no reason to suppose that any of them is a predictive tool. And averaging model predictions (e.g. CMIP5) is an error because average wrong is wrong.”
The bottom line, based on our perspective of the models, is that IPCC Annex 1 results are fundamentally flawed..
Roger
Hi DirkH – I agree with you that they cannot train the models when used to objectively test predictive skill using a hindcast approach. The papers I cited with respect to hindcast runs, specifically described when they did make any adjustments to the results (e.g. see the . Zhongfeng and Yang 2012 paper), which improved them as one would expect.
Indeed showing the improvement when they do insert real data is an effective way to show the lack of skill of the original adjusted results.
Roger
I hate to pile onto Mosher. He’s really a nice guy. I mean that. But he makes it so easy:
Easy to say; impossible to do. If we could elect different people who would play fair, we would. But the system has been gamed. Even if it weren’t, votes don’t seem to matter any more. Even our Congressional Representatives no longer matter. Witness the ‘Dream Act’, which would have legalized millions of illegal aliens.
The Dream Act was thoroughly debated for months in Congress. When it was finally voted on, it lost decisively. Congress voted down the president’s Dream Act, because American citizens overwhelmingly deluged their Representatives with calls, emails and letters opposing it.
But Obama implemented it anyway. By decree! So Mosher’s “officials try to prepare for risks” disregards the plain fact that Americans Do. Not. Want. more tax money wasted on the fake “carbon” scare! But that failed Policy is being implemented anyway — witness Obama’s new ‘climate hubs’, which is yet another presidential decree — with no spending authorized, and without any Congressional approval. Therefore, I think we can disregard the failed advice to depend on elections to fix the carbon false alarm.
Mosher continues:
Yes. Self-serving policy wonks ‘want answers’. But doesn’t everyone ‘want answers’?
No. The public wants correct answers. But we aren’t getting them. We are getting wrong answers instead, based on the preconcevied assumption that there is a “carbon” crisis. But the real world makes it clear that CO2 is not a problem at all. So those trying to influence policy are doing it for job security — not because they have correct answers. They don’t, as the real world makes clear.
Mosher adds:
Physician, heal thyself! You do not have “better answers” for our country. You have answers that are better for your own wallet. But those ‘answers’ are at the expense of the rest of us.
There is no climate crisis. There never was. There is no runaway global warming. Again, there never was. The whole CO2 scare is one giant head-fake, intended to separate taxpayers from their earnings. As such, it is extremely dishonest.
Every climate parameter has been exceeded in the past, and to a much greater degree. The fact is that nothing either unusual or unprecedented is happening. The reality is that the current global climate is extremely benign. Unusually benign. The past century and a half has been wonderful for the biosphere, including humanity. Yet the climate alarmist clique constantly tries to tell the public that doomsday is upon us. That is self-serving nonsense.
I keep asking: if there is any testable, measurable scientific evidence showing runaway gloabl warming, or an approaching climate crisis, then… POST IT HERE. Show us your evidence! But there is no evidence.
I keep pointing out: computer models are not evidence. Evidence is testable, measurable raw data, and empirical [real world] observations.
The alarmist clique has no scientific evidence to support their assertions. None at all. But they keep telling the public that runaway global warming is upon us. It isn’t, as the real world makes clear. There isn’t a polite way to put it: they are lying in order to influence policy; to amass power and money for themselves. Their self-serving assertions are certainly not in the best interest of the citizens.
If the false-alarm clique decides to get its fingers out of our pockets, then we will finally be on the right track. Right now, we’re not. They can start by telling people the way things really are — not by incessantly sounding their “carbon” false alarm.
dbstealey:
In your post at February 8, 2014 at 3:16 pm you say
Indeed so.
I point out that
(a) in this thread we are discussing that the climate models are being used as predictive tools when they have no demonstrated predictive skill
and
(b) in another thread we are discussing that the statistical methods used by so-called ‘climate science’ are not fit for purpose
and
(c) in past threads we have discussed the problems with acquisition of climate data notably GASTA
and
(d) in another thread there is discussion of climate sensitivity which is a reflection of the problem of an inadequate theory of climate change.
Simply, the only thing about climate which is known with certainty is that nothing about climate behaviour is known with sufficient certainty to assist policy making. It is better have no information than to be influenced by wrong information when formulating policy.
Richard
ferdberple says:
February 8, 2014 at 2:32 pm
“So you can see, no climate model can predict the future better than the climate scientists themselves, because if the scientists don’t believe the computer model, they will change the model until they do believe it.”
Possible. But this is all so basic that they can’t possibly plead ignorance. I just wanted to hear what Dr. Pielke has to say. I might have missed attempts at validation that were actually competent, but it looks like I didn’t. Not that I’m surprised. The best way to avoid disappointments is to operate with low expectations.
February 7, 2014 at 12:43 pm | Steven Mosher says:
—-
Bumkin ! When you can provide real, and I mean REAL, observed data to support your excuse then you’ll be taken seriously. Until then politicians and activists, including you, have no right to impose your anti-human policies, based on nothing but suspect, to achieve power and control over the citizenry.
You’re all just greedily chasing down whatever shekels you can … you’ll do and say anything to get your flat snouts into the public trough. Impress me, get out there and do some manual work because intellectually you are a collection of frauds.
On hindcasting and forecasting.
Any model can be partially validated and fully invalidated by hindcasting. A bias in the validating process, if fairly described, is not difficult to debunk.
Blind or double blind experiments are no model validation but rather enable unbiased raw data discovery, as e.g. in drug efficacy testing.
And if the model fails to hindcast then there is a probability bordering certainty that it will be inapt (show no skill) to forecast. In this case validation will have failed. Aren’t the models used by IPCC of this kind?
However, if hindcasting is successful it is not certain that forecasting will be valid, but confidence on this will increase. But only time will tell if a forecast will have been accurate.
Hindcasting is a necessary but not sufficient condition to fully validate a model.
“collection of frauds”
You are much too kind. What they have done goes way beyond fraud. Start with the advocacy of the totally unnecessary spending of uncounted and fraudulently confiscated billions. Continue with the suffering and death due to fuel poverty caused by that spending and associated rules and regulations. Then finish with the totally unnecessary prohibition of cheap (fossil fuel) energy for both the developed and the undeveloped nations. It adds up to causing the suffering and deaths of uncounted millions of people who would otherwise be alive and contributing to the well being of themselves, their families, their communities, and the rest of the people of the earth.
In a word, no matter what their stated intentions they are EVIL to the core. This because of the inevitable consequences of their words and deeds. Even a cursory examination of history would have demonstrated those consequences would occur and they did what they did anyway. They are not innocent in the matter! That they willfully evaded these facts only adds to their crime against the rest of us.
Michel says:
February 9, 2014 at 1:33 am
A bias in the validating process, if fairly described, is not difficult to debunk. Blind or double blind experiments are no model validation but rather enable unbiased raw data discovery, as e.g. in drug efficacy testing.
===============
The debunker introduces their own bias. This does not cancel existing bias. Validation requires careful experimental design because it is also a form of data discovery. You are seeking to discover if the model is valid. So for example, best practice in business requires that computer software validation be done independent of the developers, with (experimental) controls to ensure the validators results match what actually happened, not what they expected to happen.
@ferdberple saying “The debunker introduces their own bias. …”
You are right!
But we shall remember that no model can be evaluated in a blind fashion by an “unexpert” having no idea about the subject matter. And it would be quite costly to have to redo the work of the modeller.
What is needed is a clear and understandable description of the validation (or invalidation) process. Impossible to get with dishonest people, I know.
Models are no experiments. They are deterministic constructs that generate computed data that can be then compared with actual observations. If validated and within a limited scope they can be used, with much caution, for forecasting or for engineering designs.
This kind of critical model evaluation and use has not been presented or discussed by IPCC although, as the ultimate reviewer having the blessing of the international community, this institution is expected to have done this rather than to speculate on the meaning of various model outputs.
I wonder how many people died because of the combination of extreme winter cold and fuel poverty in Great Briton during the last 10 years and contrast that with how many died because of extreme heat in summer. The question: will there be more heat waves may be the wrong question.
Michel says:
February 9, 2014 at 1:33 am
And if the model fails to hindcast then there is a probability bordering certainty that it will be inapt (show no skill) to forecast. In this case validation will have failed. Aren’t the models used by IPCC of this kind?
=============
What if the models were indirectly trained using the hindcast data? This is easily done, even if the developers are not aware of it. For example:
I build a model, call it M1 and validate it against the hindcast. It performs poorly, so I as a develoepr manually adjust the response to aerosols and rename this model M2. I validate M2 against the hindcast and it now performs well, so I publish my M2 model.
This is how I understand all the IPCC climate models were built, and it is a complete rubbish. It violates the experimental controls required to maintain the independence of the hindcast for validation. As soon as the developer uses the hindcast in any fashion to improve the performance of the model against the hindcast, the hindcast cannot be used for validation.
This is one of many reasons why the IPCC models have no skill at prediction. Software validation requires that neither the developers nor the software be permitted to view the validation test. These sort of blind controls are essential to software validation, otherwise the computer many simply spew out the correct answer, unintentionally copied from the answer sheet in the validation test.
Lionell Griffith says:
February 9, 2014 at 1:58 am
Then finish with the totally unnecessary prohibition of cheap (fossil fuel) energy for both the developed and the undeveloped nations.
=============
This is the ultimate goal of carbon legislation. To pay the third world leaders to keep their populations from realizing the benefits of low cost fossil fuel energy. The worry being that if everyone in the world had a living standard equal to the US, then the US standard of living would suffer.
@ferdberple saying : “What if the models were indirectly trained using the hindcast data?…”
Obviously models that would do that would just be correlation attempts, no models.
People trying to do this (as for example with ENSO oscillations) are basically out of modelling but only in cherry picking possible correlations.
The reason that the “pause” since 1997 was not predicted by such no-models is exactly because of this conceptual error.
To be fair with such “modellers” this task is so huge that sometimes some sets of parameters must be taken as an “output-to-input black box” without understanding its inner parts. Honesty requires then to explicit these limitations.
But if a mathematical model about aerosol is developed with equations describing the underlying physical-chemical phenomena, then it may improve previous models that did not take such things into account.
Michel says:
February 9, 2014 at 8:42 am
Models are no experiments.
==========
http://en.wikipedia.org/wiki/Computer_experiment
“For example, climate models are often used because experimentation on an earth sized object is impossible.”
Michel says:
February 9, 2014 at 9:01 am
Obviously models that would do that would just be correlation attempts, no models.
===========
My understanding is that all climate models used by the IPCC fall into this category, yet the model builder and the IPCC all refer to them as models. It is my understanding that the IPCC climate model relies on tunable parameters to improve its fit to the hindcast. And that the values of these tunable parameters vary widely from model to model, and thus cannot be based on known physics.
The IPCC recognized in their second report that predicting climate from first principles was impossible. So they changed the name to projections. But still they present the results as though they were predictions. They claim that there will be warming because the model show warming, and cite this as evidence.
More importantly, the scientists involved continue to promote the myth that climate models show us where climate is headed in the future. If the model cannot predict the future climate, how can their result be used as evidence of future climate, unless it is via scientific fraud?
OK ferdberple, failed validation, we agree!
Who else in the IPCC fan club?
Ferdberple
At February 9, 2014 at 9:28 am you say
I agree that claims of such “predictions” are fr@ud, but the IPCC does claim the climate models do make predictions as well as projections.
The IPCC AR5 Glossray is here. It provides these IPCC definitions
Richard
Michel says:
February 9, 2014 at 9:01 am
People trying to do this (as for example with ENSO oscillations) are basically out of modelling but only in cherry picking possible correlations.
=============
On the contrary, pattern recognition is the only currently know method to provide a partially reliable forecast of the future state of chaotic systems.
The future state of chaotic systems cannot be calculated reliably from first principles using existing mathematics. The results diverge rather than converge. Thus the Climate modelling exercise using GCM’s as a starting point is a dead end for predicting future climate, unless and until new mathematical theories and methods are developed.
However, this does not mean climate prediction is hopeless. When we look at climate data it is not random. The human eye can see events repeating. Like intersecting waves trains on the ocean, the pattern is complex, but it has the elements of predictability that requires no understanding of the underlying process.
Nature operates in cycles because all linear trends must ultimately lead to extinction if they persist. These cycles leads to patterns and these patterns lead to successful predictions.
Hi ferdberple – You wrote
“It is my understanding that the IPCC climate model relies on tunable parameters to improve its fit to the hindcast. And that the values of these tunable parameters vary widely from model to model, and thus cannot be based on known physics”
This is not a correct interpretation as to what they do.
The parameterizations (e.g. for long- and short-wave radiation; deep cumulus convection, etc) are individually tuned against observations, theory and/or higher resolution models, but the total model is not tuned when run in hindcast. They may make multiple model runs but but this is not an approach to adjust their results. Indeed they have systematic biases as I gave examples on in my post.
I discuss how models are created in my book
Pielke Sr, R.A., 2013: Mesoscale meteorological modeling. 3rd Edition, Academic Press, 760 pp..http://store.elsevier.com/Mesoscale-Meteorological-Modeling/Roger-A-Pielke-Sr/isbn-9780123852373/
with respect to mesoscale models, but the same issues apply to climate models.
Roger
richardscourtney says:
February 9, 2014 at 9:51 am
Because the future evolution of the climate system may be highly sensitive to initial conditions, such predictions are usually probabilistic in nature.
==============
Thanks Richard, is there value in a probabilistic climate forecast?
For example, a forecast that in 30 years there is a 30% chance future climate will be hotter, a 30% chance it will be cooler, and a 40% chance it will be the same seems to me a reasonable forecast that no one can disprove. On that basis we divide 30% of our budget preparing for hotter temps, 30% preparing for cooler, and 40% preparing for things to stay the same.
Now we might say, OK take 10% from our budget and spent it to change the future climate (numbers chose for reasons of easy math). And the climate model now say there is a 20% chance future climate will be hotter, a 30% chance it will be cooler, and a 50% chance it will be the same. So now we allocate our now reduced budget according to these new probabilities. We spend 18% of our budget preparing for hotter temps, 27% preparing for cooler, and 45% preparing for things to stay the same.
What is interesting is that if we divide our budget according to a probabilistic climate forecast of climate change, we end up spending less in preparation for change, and more on the assumption climate will not change. It looks like the end result of preparing for climate change will be to leave us less prepared to climate change.
Which in many ways seems to fit with observations. Rather than spending money to make sea walls higher, we are instead spending money to raise fuel costs which make everything more expensive. We end up spending more money assuming things will stay the same, rather than spending the money on assuming things will change.
ferdberple:
The conclusion of your post at February 9, 2014 at 10:28 am is
Yes. And we in the British West Country are suffering because precisely that policy has been adopted and imposed on us. Many of us not flooded have lost our essential rail link. And at this moment the army has been called in and is trying to stop the floods spreading into Bridgewater tonight.
You may have noticed my outrage at Gareth Phillips who has been promoting such harmful policies on several WUWT threads. I thought he was an eco-loon but on the ‘Black Swans’ thread it has been revealed he is a shill employed by the ‘Carbon Trading’ industry to spread disinformation and propaganda. This is what science has been perverted to provide.
The ‘chickens are coming home to roost’ and I fear the damage to the reputation of science will take generations to repair.
Richard
The debunker introduces their own bias. This does not cancel existing bias. Validation requires careful experimental design because it is also a form of data discovery. You are seeking to discover if the model is valid. So for example, best practice in business requires that computer software validation be done independent of the developers, with (experimental) controls to ensure the validators results match what actually happened, not what they expected to happen.
Oh yeah. So absolutely on the money true. You also cannot validate your model using the training set which is precisely what climate models do, especially if when ithey are applied to hindcast e.g. HADCRUT4 back to 1860 as they are in figure 9.8a of AR5, they spend the entire first half of the 20th century completely missing the initial cooling and subsequent rapid warming, a warming that causes HADCRUT4 from 1900 to 1950 to almost perfectly match HADCRUT4 from 1950 to 2000. But the CMIP5 Multiple-Model Ensemble mean does nothing of the kind — the individual models all spend thirty odd years significantly warmer than past reality and failing to correctly exhibit the natural variability of the actual climate on the entire interval outside of the training set (marked in brown on this diagram).
In actual fact, one doesn’t really need expensive stats consultants to reject most of the models in CMIP5 on a criterion of matching what has actually happened — CMIP5 models individually and collectively fail the pure eyeball test of acceptable agreement with the data outside of the training interval. There are only two intervals in which it is in decent agreement — before 1900 (where the uncertainty in the data itself is so large that it is meaningless to be in reasonable agreement) and in the single stretch from maybe 1940 to 1960 where temperatures where nearly flat. Indeed, if one eyeballs only the red MME mean and black HADCRUT4 across all 150+ years of figure 9.8a, the black curve lies above the red curve only a grand total of (being generous) 25 years out of over 150, and is never significantly above it outside of a single, meaningless spike back in circa 1875. Even the 1997-1998 super-ENSO that created a record high temperature spike barely managed to shove HADCRUT4 back to the running CMIP5 MME mean prediciton and barely, transiently, past it. Even with this spike and consequent jump in mean surface temperature, box 9.2 of AR5 carefully explains that 111 out of 114 CMIP5 models are currently in significant disagreement with nature and offers three distinct equally unprovable hypotheses to explain this “hiatus” while preserving the illusion that we should pay attention to the CMIP5 MME mean as if it has some meaning as the result of “validated” models.
What I just don’t understand is how anybody can buy this as “success” of the CMIP5 models, forget about their systematic divergence from reality almost from the minute they were loosed on the wild after being initialized on the 1961-1990 reference period. They simply do not work to explain the principle features of HADCRUT4 over the thermometric era. They also badly underestimate the role of natural variability, largely because success on the training set is touted as “validation of the model” instead of “being able to successfully build a model” and then extending that concept of “success” back in time to span the 40 year “hiatus” in the early 20th century that was all purely natural variability as greenhouse and aerosol forcing at the time were basically neutral.
rgb
Roger A. Pielke Sr. says:
February 9, 2014 at 9:59 am
This is not a correct interpretation as to what they do.
=========
Thank you Dr. Pielke,
Doesn’t model validation against the hindcast require that the model have no knowledge about the hidncast? Thus, if one can show that the model had opportunity to gain knowledge about the hindcast, doesn’t this in itself invalidate using the hindcast to validate the model?
The reason I ask is because it seems there are a number of avenues for models to gain knowlege of the hindcast, which calls into question the notion that hindcast can be used to validate climate models. For example.
1. Information about the hindcast passed from model to model by parameter transfer.
If Model A that has been validated against the hindcast, and a parameter value from Model A is used to set a paramter in Model B, then even if Model B has never seen the hindcast, it still has received information about the hindcast. This invalidates using the hindcast to validate models that have received parameter values from other models.
2. Information about the hindcast passed from validator to model by selection bias:
Computer code goes through revision after revision. Poor performing code dies off and code that performs well is retained. If the criteria used to make the selection is performance against the hindcast, this passes information about the hindcast on to succeeding generations of models. This invalidates using the hindcast to validate succeeding generations of model.
3. Information about the hindcast passed from developer to model by unconscious bias:
Nothing says a model that performs well against a hindcast is correct. It may well have offsetting errors. However, when a model performs well against the hindcast, there is resistance on the part of the developers to change. They want to believe the model is correct. This also provides the model with information about the hindcast, allowing errors to survive and potentially pass on to other models.
richardscourtney says:
February 9, 2014 at 11:00 am
You may have noticed my outrage at Gareth Phillips who has been promoting such harmful policies on several WUWT threads. I thought he was an eco-loon but on the ‘Black Swans’ thread it has been revealed he is a shill employed by the ‘Carbon Trading’ industry to spread disinformation and propaganda. This is what science has been perverted to provide.
===========
Ah, that explains it. His writing appeared knowledgeable yet goofy at the same time. I couldn’t decide if he was an avid reader new to the subject, or a modelling shill hoping to spread FOD.
The future state of chaotic systems cannot be calculated reliably from first principles using existing mathematics. The results diverge rather than converge. Thus the Climate modelling exercise using GCM’s as a starting point is a dead end for predicting future climate, unless and until new mathematical theories and methods are developed.
Not quite. They are a dead end at the currently accessible spatiotemporal resolution, which is almost certainly too coarse grained to come close to spanning the relevant quasiparticles (as it were, nonlinear large scale structures if you prefer a different terminolology) that represent dissipative processes that might contribute and be assessable via the fluctuation-dissipation theorem and a study of spatiotemporal autocorrelation.
Also, chaotic system trajectories diverge in phase space but that doesn’t mean that they are like random walks and diverge to cover the space or wander without bound away from some neighborhood. The overall climate system is de facto limited by the constraint that it is an open system poised between the sun and outer space and manifestly has sufficient negative feedbacks to remain bounded in its thermal behavior across geological time, although the range of the bounds is large especially in glacier eras like the present one.
However, you are quite correct that the statistical analysis of model results in AR5 is abysmal and inexcusable. In section 9.2.2 AR5 openly admits in 9.2.2.2 and 9.2.2.3 that problems with any sort of collective statistical analysis of the incorrectly named “ensemble” of models “creates challenges for how best
to make quantitative inferences of future climate”. What an understatement. And where is this “challenge” expressed in a degradation of confidence in MME-mean model predictions anywhere in the entire document? It’s like: “Sorry, we know perfectly well that averaging over a bunch of models with equal weight when the models aren’t independent, when they contain unequal numbers of model runs that are all going to be averaged in as if they have equal weight, and where lots of the individual models suck when they are compared to actual data, but we’re going to do it anyway and then treat the result as if it is an average of independent, identically distributed samples drawn from a compact distribution whenever we want to talk about “confidence” in the MME mean predictions”.
It truly is shameful. A critical reading of Chapter 9 in AR5 basically leads one to the conclusion that CMIP5 models are crap, we know it, and we’re going to use them anyway and pray that another super-ENSO comes along to save our asses or that we get to retire and draw our pensions before the hiatus stretches out to 20, 30, 40 years and the deviation is so great that we cannot even pretend that it isn’t there when making up the SPM.
Right.
rgb
“The worry being that if everyone in the world had a living standard equal to the US, then the US standard of living would suffer.”
That would not be the case. What would happen is that the producers in the US would finally have to compete with the rest of the world on quality, quantity, performance, and price. If they can’t, they don’t deserve to improve their standard of living. As far as I am concerned, that is the way it should be. If you can’t compete, go to the bottom of the heap and start working to improve yourself. If you can’t or won’t, you have no one to blame but yourself.
No one owes you a livelihood or standard of living. You must earn it. If you can’t, you will have to depend upon voluntary charity from those who have.
rgbatduke says:
February 9, 2014 at 11:56 am
the fluctuation-dissipation theorem
============
Dr Brown, thank you. I was not aware of this theorem. The progress made in science in the early 20th century still continues to astound me, with the same names appearing over and over. I wonder if modern science has lost some of this with our reliance on computers and numerical methods.
The fluctuation-dissipation theorem is (from what I can tell) one of the most underappreciated theorems of open systems in climate science:
http://en.wikipedia.org/wiki/Fluctuation-dissipation_theorem
and its still more general variations in the generalized theory of:
http://en.wikipedia.org/wiki/Non-equilibrium_thermodynamics
Fluctuation dissipation basically says that you can learn a lot about linear/exponential processes contributing to the internal dynamics driving a stable dynamical equilibrium by watching how the system responds to a “sudden” perturbation — a delta-correlated event such as Mount Pinatubo or the 1997-1998 ENSO — and then relaxes back to equilibrium. Sadly, it has limited reach because of its assumptions of steady state behavior in the mean. However, it is the subject of much interesting work including some reasonably contemporary work even in the field of climate science in the context of self-organized criticality, a hypothesis by Prigogene about how open systems self-organize (both pro and con — to maximize entropy production and minimize energy transfer or maximize energy transfer and minimize energy production or permutations thereof). Crude examples are the appearance and disappearance of convective rolls (self-organized structures) when heating a fluid from below and cooling it at the top. A lot of the complexity of the Navier-Stokes equation derives from the fact that these self-organized structures often have long lifetimes — to the extent where fluctuation dissipation can reasonably apply to the meso-scale — but can also “suddenly” transition to a completely different structure that is also locally “stable” in the same driving regime but that has very different e.g. energy/entropy transfer rates (one large convective roll to two, to ten, to turbulence). It’s this sort of thing that makes the mathematics difficult — mathematicians cannot currently even prove that general solutions to the Navier-Stokes equation always exist.
I’m not claiming to be able to put this to use at this instant in climate science, but it is very definitely one of the areas that “should” be explored, because it is intimately related to things like how large natural variability is compared to e.g. external forcing due to CO_2 increases in the climate system. If the climate system self-organizes to more efficiently lose heat when overdriven (negative feedback) that produces a significantly different future climate than what would result from self-organization to less efficiently lose heat when overdriven (positive feedback). Right now the assumption is overwhelmingly positive feedback, a multiplier of 2 to 5 over the CO_2-only warming one might expect. But this estimate isn’t really based on analysis of the variability of the self-organized quasiparticles or structures of the climate system, it is based on assuming asymmetric linear covariance between CO_2 and water vapor as an amplifying greenhouse gas. But as many people have suggested, water vapor feedback could easily be overall negative. Fluctuation-dissipation analysis of cloud cover might actually be able to tell us at the very least the sign.
rgb
Hi ferdberple – You wrote
“Doesn’t model validation against the hindcast require that the model have no knowledge about the hindcast?”
Of course, that is ideal. But even with short-term weather forecast models, they assimilate real world observed data as it becomes available (e.g. 4-D data assimilation). This does give them improved results.
With respect to multi-decadal predictions in hindcast, even when they know how the weather patterns evolved over that time period, they still have major problems, as I exemplified in my post. Fully coupled climate models (ocean-atmosphere-land) do not assimilate data in those studies as the model is run, nor do they have any overarching tuning.
Roger Sr.
Dr.Pielke, thanks for your posting.
IMHO, the problem with climate models is fundamental: that which is not understood cannot be modeled. The claim that we understand climate processes well enough to devise competent models is simply pretense and this pretense is easily exploded.
Some scientist somewhere will offer up his best opinion on the matter. Simple fact is you can’t merely criticize. You actually have to provide better answers.
<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>
Here is a better answer: “Do not believe the climate models nor those who devise them. These are merely contrivances designed to project a warming trend indefinitely into the future. The fact is that we do not understand climate well enough to make valid predictions. AGW is mere theory which has been refuted by observations. Global warming is beneficial, it is cooling that brings harm to life on this planet.”
richardscourtney says:
February 9, 2014 at 11:00 am
You may have noticed my outrage at Gareth Phillips who has been promoting such harmful policies on several WUWT threads.
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Over at the Telegraph.com, there have been several articles of late on the Somerset flooding. I have noticed two bloggers who have posted multiple comments that in effect state, ‘there are not enough families/voters in the flooded area to warrant the expenditure of funds to maintain the dredging or to maintain the broken rail lines. All of the flooded victims should move or be forced to move. At the very least no help should be given to any of the properties damaged by flooding’.
What a sick perverted view to look at some of your distressed fellow countrymen and say ‘ There isn’t enough of you to worry about, so you need to move into a larger community. Your land stands lost to the floods. Get over it’.
goldminor:
re your post at February 10, 2014 at 1:49 pm.
Yes. Thankyou.
Richard
Steven Mosher you were correct- someone will provide information to policy makers in regards to heat waves in CA-
From: http://www.climatechange.ca.gov/
• “The Office of Health Hazard assessment has released Indicators of Climate Change in California, a report presenting 36 indicators tracking trends in atmospheric gases that influence climate, changes in the state’s climate, and the impacts of climate change on California’s environment and people. In addition, OEHHA has compiled Recent Research on Climate Change: An annotated bibliography with an emphasis on California, which covers publications from mid-2009 to 2012, as a means of organizing and presenting data and information from research organizations, governmental entities and academia.
• In a new report, Preparing California for Extreme Heat, a working group representing multiple California state agencies has developed recommendations to help the state become better prepared and more resilient to increasing temperature and extreme heat events”
…….
Specifically: from page 45 of the annotated bibliography noted above-
“Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling. Pierce DW, Das T, Cayan DR, Maurer EP, Miller NL, Bao Y, et al. (2012). Climate Dynamics, online publication. http://dx.doi.org/10.1007/s00382-012-1337-9 CALIFORNIA
….Results: Looking at monthly averages, July temperatures shift so that the hottest July found in any simulation over the historical period (1985-1994) becomes a modestly cool July in the future period (2060-2069). Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Winters show modestly wetter conditions in Northern California, while spring and autumn show less precipitation. Increasing precipitation is projected in the Southeastern part of the state.
Conclusions: These results have wide application to the needs of resource managers and other decision makers when adapting to forthcoming climate change in California.”