New paper: climate models short on ‘physics required for realistic simulation of the Earth system’

divergence

The career path of a climate modeler – reality -vs- expectations Image: Dr Paul T. Thomas

I’m pleased to have had a chance to to review this new paper just published in the Journal of Climate:

An Evaluation of Decadal Probability Forecasts from State-of-the-Art Climate Models Suckling, Emma B., Leonard A. Smith, 2013: An Evaluation of Decadal Probability Forecasts from State-of-the-Art Climate Models*. J. Climate, 26, 9334–9347. doi: http://dx.doi.org/10.1175/JCLI-D-12-00485.1

The lead author, Emma Suckling, was kind enough to provide me with a copy for reading. This paper seeks to find the errors in the EU based ENSEMBLES project by hindcasting and evaluating the error. I was struck by the fact that in figure 2 below, there was broad disagreement between four models, with one having errors as large as 4.5 a decade out.

The conclusion rather says it all, these models just don’t have the physical processes of the dynamic and complex Earth captured yet, hence the photo I included above.

Abstract

While state-of-the-art models of Earth’s climate system have improved tremendously over the last 20 years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the skill of these models not only with each other but also with empirical models can reveal the space and time scales on which simulation models exploit their physical basis effectively and quantify their ability to add information to operational forecasts. The skill of decadal probabilistic hindcasts for annual global-mean and regional-mean temperatures from the EU Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project is contrasted with several empirical models. Both the ENSEMBLES models and a “dynamic climatology” empirical model show probabilistic skill above that of a static climatology for global-mean temperature. The dynamic climatology model, however, often outperforms the ENSEMBLES models. The fact that empirical models display skill similar to that of today’s state-of-the-art simulation models suggests that empirical forecasts can improve decadal forecasts for climate services, just as in weather, medium-range, and seasonal forecasting. It is suggested that the direct comparison of simulation models with empirical models becomes a regular component of large model forecast evaluations. Doing so would clarify the extent to which state-of-the-art simulation models provide information beyond that available from simpler empirical models and clarify current limitations in using simulation forecasting for decision support. Ultimately, the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate; their direct comparison provides information on progress toward that goal, which is not available in model–model intercomparisons.

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Introduction

State-of-the-art dynamical simulation models of Earth’s climate system1 are often used to make probabilistic pre- dictions about the future climate and related phenomena with the aim of providing useful information for decision support (Anderson et al. 1999; Met Office 2011; Weigela and Bowlerb 2009; Alessandri et al. 2011; Hagedorn et al. 2005; Hagedorn and Smith 2009; Meehl et al. 2009; Doblas-Reyes et al. 2010, 2011; Solomon et al. 2007; Reifen and Toumi 2009). Evaluating the performance of such predictions from a model or set of models is crucial not only in terms of making scientific progress but also in determining how much information may be available to decision makers via climate services. It is desirable to establish a robust and transparent approach to forecast evaluation, for the purpose of examining the extent to which today’s best available models are adequate over the spatial and temporal scales of interest for the task at hand. A useful reality check is provided by comparing the simulation models not only with other simulation models but also with empirical models that do not include direct physical simulation.

Decadal prediction brings several challenges for the design of ensemble experiments and their evaluation (Meehl et al. 2009; van Oldenborgh et al. 2012; Doblas- Reyes et al. 2010; Fildes and Kourentzes 2011; Doblas- Reyes et al. 2011); the analysis of decadal prediction

systems will form a significant focus of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assess- ment Report (AR5). Decadal forecasts are of particular interest both for information on the impacts over the next 10 years, as well as from the perspective of climate model evaluation. Hindcast experiments over an archive of historical observations allow approaches from empirical forecasting to be used for model evaluation. Such approaches can aid in the evaluation of forecasts from simulation models (Fildes and Kourentzes 2011; van Oldenborgh et al. 2012) and potentially increase the practical value of such forecasts through blending fore- casts from simulation models with forecasts from empirical models that do not include direct physical simulation (Bro€cker and Smith 2008).

This paper contrasts the performance of decadal probability forecasts from simulation models with that of empirical models constructed from the record of available observations. Empirical models are unlikely to yield realistic forecasts for the future once climate change moves the Earth system away from the conditions observed in the past. A simulation model, which aims to capture the relevant physical processes and feedbacks, is expected to be at least competitive with the empirical model. If this is not the case in the recent past, then it is reasonable to demand evidence that those particular simulation models are likely to be more in- formative than empirical models in forecasting the near future.

A set of decadal simulations from the Ensemble- Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) experiment (Hewitt and Griggs 2004; Doblas-Reyes et al. 2010), a precursor to phase 5 of the Coupled Model Intercomparison Project (CMIP5) de- cadal simulations (Taylor et al. 2009), is considered. The ENSEMBLES probability hindcasts are contrasted with forecasts from empirical models of the static climatology, persistence, and a ‘‘dynamic climatology’’ model de- veloped for evaluating other dynamical systems (Smith 1997; Binter 2012). Ensemble members are transformed into probabilistic forecasts via kernel dressing (Bro€cker and Smith 2008); their quality is quantified according to several proper scoring rules (Bro€cker and Smith 2006). The ENSEMBLES models do not demonstrate significantly greater skill than that of an empirical dynamic climatology model either for global-mean temperature or for the land-based Giorgi region2 temperatures (Giorgi 2002).

It is suggested that the direct comparison of simulation models with empirical models become a regular component of large model forecast evaluations. The methodology is easily adapted to other climate fore- casting experiments and can provide a useful guide to decision makers about whether state-of-the-art fore- casts from simulation models provide additional in- formation to that available from easily constructed empirical models.

An overview of the ENSEMBLES models used for decadal probabilistic forecasting is discussed in section 2. The appropriate choice of empirical model for probabilistic decadal predictions forms the basis of section 3, while section 4 contains details of the evaluation frame- work and the transformation of ensembles into probabilistic forecast distributions. The performance of the ENSEMBLES decadal hindcast simulations is pre- sented in section 5 and compared to that of the empirical models. Section 6 then provides a summary of conclu- sions and a discussion of their implications. The supplementary material includes graphics for models not shown in the main text, comparisons with alternative empirical models, results for regional forecasts, and the application of alternative (proper) skill scores. The basic conclusion is relatively robust: the empirical dynamic climatology (DC) model often outperforms the simulation models in terms of probability forecasting of temperature.

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suckling_fig2

FIG. 2. Mean forecast error as a function of lead time across the set of decadal hindcasts for each of the ENSEMBLES simulation models as labeled. Note that the scale on the vertical axis for the ARPEGE4/OPA model is different than for the other models, reflecting the larger bias in this model.

Conclusions

The quality of decadal probability forecasts from the ENSEMBLES simulation models has been compared with that of reference forecasts from several empirical models. In general, the stream 2 ENSEMBLES simu- lation models demonstrate less skill than the empirical DC model across the range of lead times from 1 to 10 years. The result holds for a variety of proper scoring rules including ignorance (Good 1952), the proper linear score (PL) (Jolliffe and Stephenson 2003), and the continuous ranked probability score (CRPS) (Bro€cker and Smith 2006). A similar result holds on smaller spatial scales for the Giorgi regions (see supplementary material). These new results for probability forecasts are consistent with evaluations of root-mean-square errors of decadal simulation models with other reference point forecasts (Fildes and Kourentzes 2011; van Oldenborgh et al. 2012; Weisheimer et al. 2009). The DC probability forecasts often place up to 4 bits more information (or 24 times more probability mass) on the observed outcome than the ENSEMBLES simulation models.

In the context of climate services, the comparable skill of simulation models and empirical models suggests that the empirical models will be of value for blending with simulation model ensembles; this is already done in ensemble forecasts for the medium range and on seasonal lead times. It also calls into question the extent to which current simulation models successfully capture the physics required for realistic simulation of the Earth system and can thereby be expected to provide robust, reliable predictions (and, of course, to outperform empirical models) on longer time scales.

The evaluation and comparison of decadal forecasts will always be hindered by the relatively small samples involved when contrasted with the case of weather forecasts; the decadal forecast–outcome archive currently considered is only half a century in duration. Advances both in modeling and in observation, as well as changes in Earth’s climate, are likely to mean the relevant forecast–outcome archive will remain small. One improvement that could be made to clarify the skill of the simulation models is to improve the experimental design of hindcasts: in particular, to increase the ensemble size used. For the ENSEMBLES models, each simulation ensemble consisted of only three members launched at 5 years intervals. Larger ensembles and more frequent forecast launch dates can ease the evaluation of skill without waiting for the forecast–outcome archive to grow larger.9

The analysis of hindcasts can never be interpreted as an out-of-sample evaluation. The mathematical structure of simulation models, as well as parameterizations and parameter values, has been developed with knowledge of the historical data. Empirical models with a simple mathematical structure suffer less from this effect. Prelaunch empirical models based on the DC structure and using only observations before the fore- cast launch date also outperform the ENSEMBLES simulation models. This result is robust over a range of ensemble interpretation parameters (i.e., variations in the kernel width used). Both prelaunch trend models and persistence models are less skillful than the DC models considered.

The comparison of near-term climate probability forecasts from Earth simulation models with those from dynamic climatology empirical models provides a useful benchmark as the simulation models improve in the future. The blending (Bro€cker and Smith 2008) of simulation models and empirical models is likely to provide more skillful probability forecasts in climate services, for both policy and adaptation decisions. In addition, clear communication of the (limited) expectations for skillful decadal forecasts can avoid casting doubt on well-founded physical understanding of the radiative response to increasing carbon dioxide concentration in Earth’s atmosphere. Finally, these comparisons cast a sharp light on distinguishing whether current limitations in estimating the skill of a model arise from external factors like the size of the forecast–outcome archive or from the experimental design. Such insights are a valuable product of ENSEMBLES and will contribute to the experimental design of future ensemble decadal prediction systems.

 

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46 thoughts on “New paper: climate models short on ‘physics required for realistic simulation of the Earth system’

  1. Computational climate models’ failure to exhibit observed interhemispheric symmetry in reflected shortwave radiation is a much more serious issue, than deviation from climate projections for a few decades. It indicates directly some missing physics in theory underlying all models, not just implementation bugs, therefore it is not even correctable until a general theory of irreproducible quasy stationary non equilibrium thermodynamic systems emerges, backed by actual experiments performed on members of said class.

    Journal of Climate, Volume 26, Issue 2 (January 2013)
    doi: 10.1175/JCLI-D-12-00132.1
    The Observed Hemispheric Symmetry in Reflected Shortwave Irradiance
    Aiko Voigt, Bjorn Stevens, Jürgen Bader and Thorsten Mauritsen

  2. And further…

    According to Raymond W. Schmitt, a senior scientist of the Department of Physical Oceanography at Woods Hole Oceanographic Institution, it will take about 162 years to get adequate resolution in computer models of the ocean. Of course, that was in 2000, so less than 150 years to go now…

    ” It will take a factor of 108 improvement in 2 horizontal dimensions (100 km to 1 mm, the salt dissipation scale), a factor of 106 in the vertical dimension (~10 levels to 107) and ~105 in time (fraction of a day to fraction of a second); an overall need for an increase in computational power of ~1027. With an order of magnitude increase in computer speed every 6 years, it will take 162 years to get adequate resolution in computer models of the ocean. ”

    http://www.whoi.edu/page.do?pid=8916&tid=282&cid=24777

  3. hey you don’t understand the point, in fact we have to ‘trust’ the model, and to test a model is a proof of lack of trust.

  4. Ah Ha! Same is true for ENSO forecasts. The mean of the statistical models has been consistently outperforming the dynamical models so much so that the “consensus” prediction has been nearly identical to the statistical forecasts over the past year.

  5. A scientific ‘no change in temperature’ model outperforms IPCC climate models by factor of 7

    http://hockeyschtick.blogspot.com/2013/10/a-scientific-no-change-in-temperature.html

    New paper finds simple laptop computer program reproduces the flawed climate projections of supercomputer climate models

    http://hockeyschtick.blogspot.com/2013/11/new-paper-finds-simple-laptop-computer.html

    New paper shows the ‘simple basic physics’ of greenhouse theory exaggerate global warming by a factor of 8 times

    http://hockeyschtick.blogspot.com/2013/11/new-paper-shows-simple-basic-physics-of.html

  6. Here is a copy of the creed for the project she is currently involved in:

    The Truth About Global Warming

    The following statement from the UK Science Community, dated December 10th 2009 was signed by over 1700 UK scientists.

    We, members of the UK science community, have the utmost confidence in the observational evidence for global warming and the scientific basis for concluding that it is due primarily to human activities. The evidence and the science are deep and extensive. They come from decades of painstaking and meticulous research, by many thousands of scientists across the world who adhere to the highest levels of professional integrity. That research has been subject to peer review and publication, providing traceability of the evidence and support for the scientific method. The science of climate change draws on fundamental research from an increasing number of disciplines, many of which are represented here. As professional scientists, from students to senior professors, we uphold the findings of the IPCC Fourth Assessment Report, which concludes that “Warming of the climate system is unequivocal” and that “Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations”.

    http://www.equip.leeds.ac.uk/the-truth-about-global-warming/

    They forgot the ‘Amen’ at the end.

  7. Love the spin in the abstract and introduction of the similar ENSO paper not matching the conclusions at the end. Why does this not matter to AGWers? Because the abstract is what gets published on open access form, never the conclusion section. I imagine there must be some PR expert now employed in each Ivory Tower that helps “dress” the abstract for meet and greet sessions.

    http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-11-00111.1

  8. Scroll down to page 26 for a peek at the consensus forecast. Notice how close it is to the statistical forecast and how far away it is from the dynamical forecast (didn’t used to be that way). In the paper I linked to above, it was mentioned that statistical models improve with time because of increased amount of data available (we have lots of holes in the historical data that are now being filled in because of better coverage across the globe). At that time, the dynamical models were slightly better than the statistical models (BUT worse than simpler older dynamical models from the 80’s and 90’s).

    Dynamical models continue to suck because of the static nature of their biased input (set the dials and forget about it). Eventually statistical models will prove to be superior in every aspect to dynamical models because of ever increasing amounts of observational data input, unless someone finds the holy grail of general circulation model dynamics that can be set to mathematical equation.

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

  9. A model of a chaotic system must be chaotic.
    Chaos represents randomness.
    Randomness is lack of order.
    Lack of order is lack of knowledge.
    Lack of knowledge is ignorance.
    As the models improve, they must come closer to describing chaos.
    Improved models will incorporate more and more ignorance.
    An ensemble of ignorance is still ignorance.

  10. Refreshing to see a paper that acknowledges serious problems with climate models. Even with massive improvements, one issue that can’t be resolved is sample size.

    Hind cast simulations using better physics will lessen disparities between the models and the real world with time. A 2013 model for instance would be able to replicate the past 20 years much better than any 1993 models did projecting 20 years out from that time. However, judging the new model’s performance with a similar 20 year period……….takes 20 years.

    Consider weather models predicting 2 weeks out for instance. The GFS, being updated every 4 hours, has 365- 14 days X 4 =1,404 separate model runs/predictions out to 2 weeks that can be compared with the real world for each entire 2 week period.

    A climate model going out 20 years and updated every year would need 1,404 +20 = 1,424 years to have the same sample size.

    A climate modeler, building his first 50 year global climate model at 30 years old in the year 1990, will be 80 when it’s performance can be completely evaluated in 2040. Even today’s slow to respond climate modelers will have long since trashed those models by then. The long term nature of climate models/lack of timely replacements and extreme bias of those using them has caused denial of their complete failure for over a decade………a significant span, which greatly diminishes their value at this point in time.

  11. A key question is whether the empirical models are projecting solar-magnetic effects in accordance with their past statistical explanatory power. This is where the IPCC’s reliance on its particular physical models is positively anti-scientific. The IPCC acknowledges (barely) that correlations between solar activity and climate indicate a more powerful influence on climate than be accounted for by the very slight variation in TSI, but TSI variation is the only influence they include in their models. They are not satisfied with the available theories of how a more powerful solar driver of climate could be operating and so they ignore the evidence that such a driver is at work, which is an exact inversion of the very definition of science: that evidence trumps theory, not vice versa.

    Now that the sun has gone quiet, scientific reasoning predicts that the unscientific physical-model predictions of temperature should be too high. It would be interesting to see whether this is indeed where the better performance of the referenced empirical models is coming from: are they incorporating a stronger solar effect than the physical models, or are they also weighing solar effects below their historical explanatory power but are deviating from the physical models elsewhere?

    Is anyone here familiar with the empirical models that are being used, and how much weight they give to solar activity?

  12. “It also calls into question the extent to which current simulation models successfully capture the physics required for realistic simulation of the Earth system and can thereby be expected to provide robust, reliable predictions (and, of course, to outperform empirical models) on longer time scales.”

    How can a model capture the physics? I know how a model can reproduce a curve on a graph, but that has nothing to do with physics or physical theory. My point is that the concepts at the foundation of IPCC-speak are just hopelessly ambiguous. Someone serious who has the time should come up with a description of what can be done with models regarding the physics of climate.

  13. The main problem with all of the ‘physics’ based programs is that they are all constrained by a radiative imbalance of 1.5 W/M^2. Over time the accumulated energy has to create higher temperatures. That is why they all have logrithmic temperature increases if the projections go out far enough. I don’t believe the statistic based programs have the 1.5 W/M^2 problem and that is why they are more accurate.

    I personally think that we are close to creating a ‘physics’ based model based on first principles that has a shot at being able to make accurate predictions and it would be a low resolution fairly simple program. We just have to clear out some bad assumptions and data and correctly identify the relevant drivers (primarily water).

  14. ‘physics required for realistic simulation of the Earth system’

    Physics and biology are required for realistic simulation of the Earth system.

  15. With regard to weather models, you may find these links interesting.

    http://www.emc.ncep.noaa.gov/GFS/perf.php

    A good summary of how far weather models have come is found by going to the 2009 review of GFS forecast skill.
    “Skill of 5-day forecasts has doubled in 20 years in the Southern Hemisphere and 25 years in the Northern Hemisphere”

    Another link:

    http://www.hpc.ncep.noaa.gov/html/model2.shtml#diagnostics

    Meteorologists analyze and evaluate all aspects of these models constantly/daily, use experimental models and update the physics of models from time to time(every few years?), increasing skill greatly the last 2 decades.

    With climate models, on the other hand, reality and output of expected global temperature has been going the other/opposite way the last decade.

    http://www.drroyspencer.com/2013/06/still-epic-fail-73-climate-models-vs-measurements-running-5-year-means/

  16. Modeling so far out will never work successfully. The simple reason being you do not have sufficient accuracy and coverage of the system being modeled to be able to stop long term divergence. Combine that with random actions (or non observable inputs, i.e. what happening underground) and you don’t have a bats chance in hell of being accurate over the longer term. Then add in the impacts weather events have the system (cyclones leading to land coverage changes, pollution events, etc) and again your model is well off track due to an ‘error’ you cannot predict.

    The modellers seem to have forgotten that only the error rate accumulates over time, accuracy never does. Plus you can only reliably model a system if you can properly ‘box’ it (account for all inputs and states) – at this rate one would have to box the whole solar system and find a way of modelling hidden currently unobservable states – good luck!

  17. phlogiston says:
    November 28, 2013 at 11:17 am
    ‘physics required for realistic simulation of the Earth system’

    Physics and biology are required for realistic simulation of the Earth system.
    ……………………………………..

    You are being way too optimistic. The first step should be to correctly model the energy flux generally through the system starting with Ocean currents, winds and clouds. Have to nail down the main drivers first. Biological effect is going to be relatively small on a global scale (huge locally though). Got to start with the basics and a stable climate model first.

  18. ecoGuy says:
    November 28, 2013 at 11:37 am

    The modellers seem to have forgotten that only the error rate accumulates over time, accuracy never does. Plus you can only reliably model a system if you can properly ‘box’ it (account for all inputs and states) – at this rate one would have to box the whole solar system and find a way of modelling hidden currently unobservable states – good luck!

    The way Models are currently written you are correct. But programs can be taught to learn and make guesses about future data. Data Compression models do it all the time, as do Chess and Go Programs. Maybe it is time for the gamers to show the newbies how it is done.

  19. phlogiston says:
    November 28, 2013 at 11:17 am

    “Physics and biology are required for realistic simulation of the Earth system”

    Great point. The accelerated rate of photosynthesis, greening of the planet has profound effects that are a game changer.

    The increase in evapotranspiration in the United States Midwest during the growing season, from tightly packed rows of corn, increases dew points by up to 5 degrees at times and creates a micro climate over the size of half a dozen states. The added moisture from these crops causes numerous weather changes that include heavier/more rains, lower LCL(lifting condensation levels) and formation of cumulus earlier in the day(cooling effect during the day).
    Warmer/muggier nights and positive feedback effects on the water cycle.

    This is a clear example of a micro climate created by doubling the concentration of corn plants over the last 3 decades.

    Increasing CO2 has caused the planets biosphere and vegetation to experience explosive growth. Just the increase in evapotranspiration alone from the explosive plant growth is causing significant changes to our planets climate from biology.

    Interesting article:

    https://www2.ucar.edu/atmosnews/opinion/4997/corn-and-climate-sweaty-topic

    “Computer models also take vegetation into account. As used by the National Weather Service, the Weather Research and Forecasting model—which divides the United States land area into rectangles roughly 7.5 miles (12 kilometers) on each side—incorporates daily satellite data on the greenness of the landscape within each rectangle (though not on specific plant types). The model then assesses how much water will enter the atmosphere via the vegetation in each grid box. Forecasters can adjust the resulting model guidance based on their knowledge of local planting patterns and crop behavior.”

  20. Translation: Climate models are unfit for any purpose beyond acting as a job creation scheme for geeks.

    But on a lighter note – can anybody give a quick summary of something that climate models *are* useful for? There seems to be a huge dearth of actual examples where they are any good at anything at all.

  21. We have much to be thankful for this Thanksgiving Day!

    Watching “The Team” squirm is one thing I’m very thankful for.

  22. I cannot think of any scientific endeavor that has gone on for such a long period of time, at such great expense and employed so many people while producing so little practical results as climate modeling. Couch it any way you wish, but the bottom line is PATHETIC!

  23. Also Prof Michael Beenstock (Hebrew University) has a team starting to test all 35 or so GCMs. He explained yesterday, in a lecture at the IEA, that getting the programme data for each is like drawing teeth. So far tests have been done on six of them:
    Results:
    “Errors don’t mean revert”
    HEGY (-4.5) KPSS (0.25)
    ACCESS -1.6 1.36
    CCSM5 -1.67 7.48
    CSIRO -1.63 3.76
    GISS-R -1.66 0.524
    GISS-H -1.70 3.25
    HADLEY -1.58 8.48

    Which, I understand, means they fail dismally. Full results will not be available (because it takes so much time getting what should be available to anyone but isn’t) for another year.

  24. thallstd says:
    November 28, 2013 at 9:03 am
    And further…

    According to Raymond W. Schmitt, a senior scientist of the Department of Physical Oceanography at Woods Hole Oceanographic Institution, it will take about 162 years to get adequate resolution in computer models of the ocean. Of course, that was in 2000, so less than 150 years to go now…

    ”It will take a factor of 108 improvement in 2 horizontal dimensions (100 km to 1 mm, the salt dissipation scale), a factor of 106 in the vertical dimension (~10 levels to 107) and ~105 in time (fraction of a day to fraction of a second); an overall need for an increase in computational power of ~1027. With an order of magnitude increase in computer speed every 6 years, it will take 162 years to get adequate resolution in computer models of the ocean.”

    http://www.whoi.edu/page.do?pid=8916&tid=282&cid=24777

    Excellent reference (link) to Raymond W. Schmitt’s Testimony to the Senate Committee on Commerce, Science and Transportation on The Ocean’s Role in Climate.

    Thank you.

  25. Model simulations cannot be accurate because they miss five macrodrivers
    in their input. Small change input can be left out, but not five major
    macrodrivers: http://www.knowledgeminer.eu/eoo_paper.html
    No wonder, of 112 models, only 3 remain within the confidence range! In a few years,
    all of them are, plainly speaking: “wrong”. No wonder, typical professoral ineptitude.

  26. No model will work long term apart from the earth itself. That’s because the behaviour is chaotic. You cannot model chaos long term not even using probabalistic models, dynamic models, ensemble models. Get used to it. You can’t model it. Anyone who suggests they can predict Europe’s, Asia’s, America’s, or Polar future climate change is a charlatan.

  27. Well you can encounter chaos and once in the grip you may end up in a chaotic condition yourself where you can not even measure yourself.

    The earths climate is a chaotic combat zone.

  28. Genghis writes “The first step should be to correctly model the energy flux generally through the system starting with Ocean currents, winds and clouds. Have to nail down the main drivers first.”

    Thats fine for weather forecasts but for climate where you’re modelling how things fundamentally change due to subtle influences over long durations then you need everything modelled perfectly. That will never happen. Not in my lifetime anyway.

  29. Perhaps this was lost in the selective bold high-lighting:

    In addition, clear communication of the (limited) expectations for skillful decadal forecasts can avoid casting doubt on well-founded physical understanding of the radiative response to increasing carbon dioxide concentration in Earth’s atmosphere.

    One can only hope. We don’t have to know everything to know enough.

  30. Would someone like to translate “ENSEMBLES models”, “dynamic climatology empirical model”, and “static climatology” into plain English for me? TIA.

  31. Here’s one I want to see modeled: a sphere suspended in a vacuum is illuminated by a light, and covered with sensors.

    The vacuum temp is stabilized and recorded.

    an bath of reflective gas insulation is injected around the sphere. The reflective gas stops 20% of the energy from ever reaching any of the spheres’ sensors.

    The climate model had better show
    the sphere being cooler than when the additional 20% light was illuminating it.
    Instead of the current paradigm that accompanies this dumpster sized bucket of feces called current Climateur Inversion Fraud.

    The second thing I want to see modeled is the concept of that identical bath, being quite cold in relation to the sphere; indeed I want to bath to be a lot colder than the sphere and I want the sphere subsequently suspended in the cold bath,
    and then
    I want to see every sensor on that sphere rise, yet again;
    in spite of being immersed in an ice cold thermally conductive gas bath.

    You know: the way the CURRENT RESEARCH SHOWS IT GOING in MAGIC GAiS WERLD.

    UNTIL THAT TIME,

    Climateur Pseudo-science is not representing the earth’s system. The atmosphere is a cold thermally conductive bath, augmented by convection.

    Not maybe, not Tuesday, not when the Prime Minister jiggles the yen so he can buy himself a new estate in the mountains.

    That’s how it is climate kids. The earth’s atmosphere’s a thermally conductive, cold bath, that starts out reflecting 20ish percent f the sun’s total energy away from ever even being near a thermal sensor on earth.

    But “Evurbody arownd heeyur nos the atmusfear warms yew up. Speshlie at night.”

    Yeah: and when I close the door of my refrigerator and it gets dark in there, the cold air conductively and convectively removing heat from my two liters of soda, suddenly starts backerdistically warming my soda pop instead of cooling it the way I built the refrigeration unit to do it.

    The entire conceptual framework of Backerdistical Magic Gaisism is an
    i.n.v.e.r.s.i.o.n.
    of
    r.e.a.l.i.t.y.

    and when you don’t see any ‘reality’ come out of “Backerdistical Back-&-Forthisms what caint no insturmunt mayzur”

    and when you see it’s most staunch defenders denying quantized energy in matter so they can claim radiant energy enters solids that are already filled to that frequency emissions,

    you know you’ve got your self a fraud so large people deny you can quantize how much energy matter holds at any given temperature.

    That’s somewhere past insane and a long way into willing fraud.

    Along the way one of these pinhead twits needs to just jot on a dinner napkin how he figures that adding more,

    of the class gas removing that original 20ish percent of energy in, to the sphere in his
    ‘Magic Melter Model’

    is going to block 21, then 22, then 23, 4, 5 % energy in total
    and make the temp rise even more.

    Now mind yas I recognize perfectly well the last question’s just an extension of the first one,

    but I’m here to tell each and every one of you that if you don’t realize the atmosphere is actually a cold, thermally conductive bath,

    a bunch of government employees told you immersion into which,
    adding it’s additional conduction, and convection, to the radiation which would be solely available without it –

    then you’re so far behind the real life scientific curve, you believed it when somebody told you, that when you turn off a light in a refrigerator (at night for earth)
    the frigid, conductive, and convective gas mixture in your refrigerator,
    starts ‘warming’ your soda.
    =======
    Now: you are listening to the brainless drivel of men who would tell you immersion into a thermally conductive cold bath
    removes heat slower than no bath at all.

    You’re listening to the criminopathological scammings of men who have insisted the whole world believe adding more of the gas that blocks a fifth of the sun’s energy in total,

    by blocking more sunlight in,
    will of course make it yet hotter, on every heat sensor on earth or so;
    because of course,
    when you added the reflective CO2 and Water the first time,

    blocking that initial 20% total energy in,
    you “made all the sensors on the globe register that it got warmer on earth”
    than when more energy was arriving, at the energy sensors.

    It’s a scam from the first word to the last, and if you think what I’m saying’s an inversion itself, I suggest you sit down and give the matter a big long think: and ask yourself why every time one of these Magic Gais Mavens speaks, he winds up sounding like he’s tripping on peyote.

    S.R.V.

  32. Ultimately, the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate

    The menu of available “physical principles” will have to expand and be revised considerably before any such thing happens, I suspect.

  33. Oops sorry “a bunch of government employees told you immersion into which,
    adding it’s additional conduction, and convection, to the radiation which would be solely available without it –

    should have been

    “to the radiation which would be solely available without it – *made everything washed in it get hotter”*

    I fumbled that & missed it.

  34. This paper is too generous to their dynamic modeling colleagues. A major refinement in model performance is possible right now. The IPCC ensembles consistently over estimate forecast temperatures (I know, I know, ‘projections’) over the long term. This is the definition of a bias.

    Why is this? Simply because of the agenda to cling onto a central meaningful place for anthropo CO2. Take the ensemble mean, reduce the slope T/time by 1/2 to better fit the long term real trend. Then go through the tedious trial and error process of reducing CO2’s effect and adjusting other factors (positive and negative feedbacks).

    From what we’ve seen over the last several years in failures, new research and extension of the empirical record, it surely means reducing CO2s effect even more than that required to fit the empirical record, increase negative feedbacks and decrease positive feedbacks. Work on the mega-fluctuations of natural variability to use as a base for hanging the rest of it on (there should be less resistance to this wise course these days). This will take some decades. Please let me hear a compelling argument AGAINST reducing CO2’s role. Even if the radiative physics were to support double what the IPCC says the climate sensitivity is, logic tells us with 100% certainty that there therefore has to be some very large negative feedbacks in the system. Clearly in the very long term, CO2’s effect must be largely neutralized by such feedbacks (cycles into ice ages and back).

    Meanwhile, carry on experimentation and observation and, over time, break down the agencies producing the positive and negative feedbacks, add new findings. Had we not frozen the theory in 1988, we would not have wasted 25 yrs and would be further along with this task. As it is, even the consensus is grudgingly having to admit that we have to start over from scratch despite a generation of self congratulatory hoopla, awarding of thousands of PhDs for worthless studies, spending of trillions and presentation of prestigious medals and awards.

  35. Mike Jonas comments: “Would someone like to translate “ENSEMBLES models”, “dynamic climatology empirical model”, and “static climatology” into plain English for me?”

    That depends on who you talk to and what paper you read. The coinage of new labels is faster than the pace of the widening gap between projections and reality. It is a breathless race to keep up. Dynamical models are generally those that try to model the process the modeler thinks is driving a climate trend. Empirical (or statistical) models are those that have decades of actual pre-conditions and resultant climate affects that are then used to create a suite of model runs from a number of different pre-conditions that have actually happened in the past. There is generally a final component of CO2 increased temperature WAG that is tagged onto the calculations before the runs commence.

    As for ensembles, there is this from AR4:

    “Ensembles of models represent a new resource for studying the range of plausible climate responses to a given forcing. Such ensembles can be generated either by collecting results from a range of models from different modelling centres (‘multi-model ensembles’ as described above), or by generating multiple model versions within a particular model structure, by varying internal model parameters within plausible ranges (‘perturbed physics ensembles’).”

  36. Cozy way to study climate isn’t it. Can you imagine this kind of work in agriculture? Model how a new grape variety will survive [hot/cold/wet/dry climate change]. Don’t bother with plots at all. Decide its worth the expense of marketing it based on your computerized results. Spend millions in advertising campaigns. Convince entire farming communities to plant such a crop with confidence. Wait for your ship to come in.

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