Climate Models
This thread is for discussion of computer climate models, or General Circulation Models (GCMs).
Climate Conversation Group
Taking the heat out of global warming
For the first time in history, people shouting “the end is nigh” are somehow
For the first time in history, people shouting “the end is nigh” are somehow
the sane ones, while those of us who say it is not are now the lunatics.
Latest climate models v. reality
As the models continue to leave actual temperature readings in their dust, sizeable warming halted about 1995 — although it might resume at any time. It must hasten to have any hope of catching up with the predictions.
If you claim warming continues, we want evidence of continued warming — eminently reasonable. Making us wait for 17 years for that evidence invites us to doubt you.
Claiming that warming hasn't stopped is the same as claiming it has — and both are ridiculous, for nobody knows the future. The best you can do is describe the past.
Click graph for larger version.
Well, well, it looks like someone got the models wrong again.
How often have we heard that droughts will increase due to global warming? It’s the single most-quoted effect that alarmists use when discussing Africa, for example.
Seems they were wrong.
http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11377.html
This is why these blokes should have checked their models before shouting about the end of the world.
New paper shows negative feedback from clouds ‘may damp global warming’
A paper published today in The Journal of Climate uses a combination of two modelling techniques to find that negative feedback from clouds could result in “a 2.3-4.5% increase in [model projected] cloudiness” over the next century, and that “subtropical stratocumulus [clouds] may damp global warming in a way not captured by the [Global Climate Models] studied.” This strong negative feedback from clouds could alone negate the 3C alleged anthropogenic warming projected by the IPCC.
As Dr. Roy Spencer points out in his book,
“The most obvious way for warming to be caused naturally is for small, natural fluctuations in the circulation patterns of the atmosphere and ocean to result in a 1% or 2% decrease in global cloud cover. Clouds are the Earth’s sunshade, and if cloud cover changes for any reason, you have global warming — or global cooling.”
According to the authors of this new paper, current global climate models “predict a robust increase of 0.5-1 K in EIS over the next century, resulting in a 2.3-4.5% increase in [mixed layer model] cloudiness.”
EIS or estimated inversion strength has been shown by observations to be correlated with cloudiness, as demonstrated by the 2nd graph below from the University of Washington, indicating a 1 K increase in EIS results in an approximate 4-5% increase in low cloud cover [CF or cloud fraction]. Thus, a combination of observational data and modelling indicate clouds have a strong net negative feedback upon global warming that is “not captured” by current climate models.
CMIP3 Subtropical Stratocumulus Cloud Feedback Interpreted Through a Mixed-Layer Model
PETER M. CALDWELL,* YUNYAN ZHANG, and STEPHEN A. KLEIN
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http://hockeyschtick.blogspot.co.nz/2012/09/new-paper-shows-negative-feedback-from.html
Climate change research gets petascale supercomputer
1.5-petaflop IBM Yellowstone system runs 72,288 Intel Xeon cores
Computerworld – Scientists studying Earth system processes, including climate change, are now working with one of the largest supercomputers on the planet.
The National Center for Atmospheric Research (NCAR) has begun using a 1.5 petaflop IBM system, called Yellowstone, that is among the top 20 supercomputers in the world, at least until the global rankings are updated next month.
For NCAR researchers it is an enormous leap in compute capability — a roughly 30 times improvement over its existing 77 teraflop supercomputer. Yellowstone is a 1,500 teraflops system capable of 1.5 quadrillion calculations per second.
The NCAR-Wyoming Supercomputing Center in Cheyenne, where this system is housed, says that with Yellowstone, it now has “the world’s most powerful supercomputer dedicated to geosciences.”
Along with climate change, this supercomputer will be used on a number of geoscience research issues, including the study of severe weather, oceanography, air quality, geomagnetic storms, earthquakes and tsunamis, wildfires, subsurface water and energy resources.
[...]
Scientists will be able to use the supercomputer to model the regional impacts of climate change. A model that is 100 km (62 miles) is considered coarse because the grid covers a large distance. But this new system may be able to reduce resolution to as much as 10 km (6.2 miles), giving scientists the ability to examine climate impacts in greater detail.
[...]
Yellowstone is running in a new $70 million data center. The value of the supercomputer contract was put at $25 million to $35 million. It has 100 racks, with 72,288 compute cores from Intel Sandy Bridge processors.
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http://www.computerworld.com/s/article/9232382/Climate_change_research_gets_petascale_supercomputer
Rather large energy requirement too – “The facility was designed with a total capacity of 4 to 5 megawatts of electricity, but with Yellowstone now in production, usage is considerably lower. Total power for computing, cooling, office, and support functions has averaged 1.8 to 2.1 MW”
NCAR-Wyoming Supercomputing Center
Fact Sheet
https://www2.ucar.edu/atmosnews/news/nwsc-fact-sheet
I queried John Christy as to which modeling group it was that has mimiced absolute temperature and trajectory this century so far in his EPS statement Figure 2.1. This was his reply:-
Richard:
This model labeled 27 should be inmcm4 (Russia)
http://www.springerlink.com/content/x6647x575g82734j/
John C.
John R. Christy
Director, Earth System Science Center
Distinguished Professor, Atmospheric Science
University of Alabama in Huntsville
Alabama State Climatologist
What did the Russians do that everyone else didn’t in CMIP5 for AR5? Did they ramp GHG forcing down to zero I wonder? They do say there were “some changes in the formulation”
Abstract
The INMCM3.0 climate model has formed the basis for the development of a new climate-model version: the INMCM4.0. It differs from the previous version in that there is an increase in its spatial resolution and some changes in the formulation of coupled atmosphere-ocean general circulation models. A numerical experiment was conducted on the basis of this new version to simulate the present-day climate. The model data were compared with observational data and the INMCM3.0 model data. It is shown that the new model adequately reproduces the most significant features of the observed atmospheric and oceanic climate. This new model is ready to participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5), the results of which are to be used in preparing the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC).
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Good to see a modeling group validating their model against observations (GCM group that is, RTM groups do this religiously) – this is a major breakthrough.
Simulating Present-Day Climate with the INMCM4.0 Coupled Model of the Atmospheric and Oceanic General Circulations
E. M. Volodin, N. A. Dianskii, and A. V. Gusev, 2010
Institute of Numerical Mathematics, Russian Academy of Sciences, ul. Gubkina 8, Moscow, 119991 Russia
e-mail: volodin@inm.ras.ru
http://83.149.207.89/GCM_DATA_PLOTTING/documents/PhysAtm4_10VolodinLO.pdf
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‘Parametrization of radiative processes in the DNM atmospheric model’
Galin, V.Y. [Russian Academy of Sciences, Moscow (Russian Federation)]
1998
https://www.etde.org/etdeweb/details_open.jsp?osti_id=300295
Abstract:
The radiative code of the atmospheric model (DNM model) of the Institute of Numerical Mathematics (IVM), Russian Academy of Sciences is described. The code uses spectral transmission functions and the delta-Eddington approximation to take into account the absorption and scattering of radiation in the atmosphere due to atmospheric gases, aerosols, and clouds. The simplest regularization procedure in combination with the nonmonotonic factorization method is used to find a stable solution to the ill-conditioned system of delta-Eddington equations. Computation algorithms are presented, and the results obtained are compared to both the data of benchmark line-by-line calculations and the model data of ICRCCM international radiative programs. It was found that the DNM model yields a high accuracy of computing the thermal and solar radiation.
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Unfortunately I can’t access the body of the Galin paper. Unfortunate because the “absorption and scattering” characteristics of CO2 used (and any changes made in INMCM4.0) would make VERY interesting reading.
5.2 Heat emission on page 43 of Volodin, Dianskii, and Gusev gives the formulae, share of emissions across the spectrum, and references tables of coefficients.
http://83.149.207.89/GCM_DATA_PLOTTING/documents/modelen.pdf
Description of the CCM INM RAS and model experiments
Description of the atmospheric climate model inmcm4.0.(new) [hotlink]
Short description of the coupled climate model inmcm3.0 and model experiments. [hotlink]
Timetable of the model experiments.
Selected publications [hotlinked]
Volodin E.M., Diansky N.A.. “Prediction of the climate change in 19-22th centuries using coupled climate model”.
Volodin E.M., Diansky N.A. “ENSO reconstruction in the Coupled Climate Model”.
Volodin E.M.”Simulation of the modern climate. Comparison with observations and data of other climate models”.
Volodin E.M. “Reliability of the future climate change forecasts”.
Volodin E.M., Diansky N.A., Gusev A.V. “Simulating Present Day Climate with the INMCM4.0 Coupled Model of the Atmospheric and Oceanic General Circulations”.(new)
Volodin E.M. “Atmosphere-Ocean General Circulation Model with the Carbon Cycle”.(new)
http://83.149.207.89/GCM_DATA_PLOTTING/GCM_INM_DATA_en.html
“Pinatubo Climate Sensitivity and Two Dogs that didn’t bark in the night”
Interesting article on climate sensitivity over at Lucia’s
http://rankexploits.com/musings/2012/pinatubo-climate-sensitivity-and-two-dogs-that-didnt-bark-in-the-night/
Lucia’s blog analysis makes Nuccitelli et al’s DK12 Comment look somewhat ordinary.
For about 2 yrs data and “a single ocean heat capacity model” (one-heat-sink), Lucia’s model “is “seeing” an ocean capacity of 53 watt-months/deg C/m2 – equivalent to about 30 to 40m water depth”. Further down page, the model is “(still) “seeing” a total ocean heat capacity corresponding to about the top 30-40m of ocean”. This for 60S to 60N only.
According to Nuccitelli et al, that’s all “noise” and 5 yr smoothed data should be used down to 2000m.
Can’t say I’m convinced by globally averaged approximations for these calculations. I think the 0-GCM approach using observed ocean heat climatology (which one?) corresponding to TOA satellite observations cell-by-cell are about the only way to arrive at anything anywhere near meaningful. Not that I know what it is about at Lucia-level.
AR5 Chapter 11; Hiding the Decline (Part II)
http://wattsupwiththat.com/2012/12/30/ar5-chapter-11-hiding-the-decline-part-ii/#more-76591
Figure 11.33: Synthesis of near-term projections of global mean surface air temperature. a), b) and c):-
http://wattsupwiththat.files.wordpress.com/2012/12/image_thumb1.png?w=936&h=1143
They hid the decline! In the first graph, observational data ends about 2011 or 12. In the second graph though, it ends about 2007 or 8. There are four or five years of observational data missing from the second graph. Fortunately the two graphs are scaled identically which makes it very easy to use a highly sophisticated tool called “cut and paste” to move the observational data from the first graph to the second graph and see what it should have looked like:
http://wattsupwiththat.files.wordpress.com/2012/12/image_thumb2.png?w=939&h=414
Well oops. Once one brings the observational data up to date, it turns out that we are currently below the entire range of models in the 5% to 95% confidence range across all emission scenarios. The light gray shading is for RCP 4.5, the most likely emission scenario. But we’re also below the dark gray which is all emission scenarios for all models, including the ones where we strangle the global economy.
+ + +
Also John Christy’s preliminary plot (incomplete) of CMIP5 RCP4.5 vs observations (UAH/RSS):-
http://curryja.files.wordpress.com/2012/07/christy-fig.jpg?w=808&h=622
The controversy
by Anastassia Makarieva, Victor Gorshkov, Douglas Sheil, Antonio Nobre, Larry Li
Thanks to help from blog readers, those who visited the ACPD site and many others who we have communicated with, our paper has received considerable feedback. Some were supportive and many were critical. Some have accepted that the physical mechanism is valid, though some (such as JC) question its magnitude and some are certain it is incorrect (but cannot find the error). Setting aside these specific issues, most of the more general critical comments can be classified as variations on, and combinations of, three basic statements:
1. Current weather and climate models (a) are already based on physical laws and (b) satisfactorily reproduce observed patterns and behaviour. By inference, it is unlikely that they miss any major processes.
2. You should produce a working model more effective than current models.
3. Current models are comprehensive: your effect is already there.
Let’s consider these claims one by one.
Models and physical laws
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Thus, while there are physical laws in existing models, their outputs (including apparent circulation power) reflect an empirical process of calibration and fitting. In this sense models are not based on physical laws. This is the reason why no theoretical estimate of the power of the global atmospheric circulation system has been available until now.
The models reproduce the observations satisfactorily
As we have discussed in our paper (p. 1046) current models fail when it comes to describing many water-related phenomena. But perhaps a more important point to make here is that even where behaviours are satisfactorily reproduced it would not mean that the physical basis of the model are correct. Indeed, any phenomenon that repeats itself can be formally described or “predicted” completely without understanding its physical nature
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For example, a climate model empirically fitted for a forest-covered continent cannot inform us about the climatic consequences of deforestation if we do not correctly understand the underlying physical mechanisms.
You should produce a better model than the existing ones
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To expect a few theorists, however keen, can achieve that is neither reasonable nor realistic. We have invested our efforts to show, using suitable physical estimates, that the effect we describe is sufficient to justify a wider and deeper scrutiny. (At the same time we are also developing a number of texts to show how current models in fact contain erroneous physical relationships (see, e.g., here)).
Your effect is already present in existing models
Many commentators believe that the physics we are talking about is already included in models. There is no omission. This argument assumes that if the processes of condensation and precipitation are reproduced in models, then the models account for all the related phenomena, including pressure gradients and dynamics. This is, however, not so. Indeed this is not merely an oversight but an impossibility. The explanation is interesting and deserves recognition – so we shall use this opportunity to explain.
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In current models in the absence of a theoretical stipulation on the circulation power, a reverse logic is followed. The horizontal pressure gradients are determined from the continuity equation, with the condensation rate calculated from the Clausius-Clapeyron law using temperature derived from the first law of thermodynamics with empirically fitted turbulence. However, as we have seen, to correctly reproduce condensation-induced dynamics, condensation rate requires an accuracy much greater than γ << 1. Meanwhile the imprecision of the first law of thermodynamics as applied to describe the non-equilibrium atmospheric dynamics is precisely of the same order of γ. The kinetic energy of the gas is not accounted for in equilibrium thermodynamics.
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Summary and outlook
The Editor’s comment on our paper ends with a call to further evaluate our proposals. We second this call. The reason we wrote this paper was to ensure it entered the main-stream and gained recognition. For us the key implication of our theory is the major importance of vegetation cover in sustaining regional climates. If condensation drives atmospheric circulation as we claim, then forests determine much of the Earth’s hydrological cycle (see here for details). Forest cover is crucial for the terrestrial biosphere and the well-being of many millions of people. If you acknowledge, as the editors of ACP have, any chance – however large or small – that our proposals are correct, then we hope you concede that there is some urgency that these ideas gain clear objective assessment from those best placed to assess them.
http://judithcurry.com/2013/01/31/condensation-driven-winds-an-update-new-version/
New paper finds IPCC climate models unable to reproduce solar radiation at Earth’s surface
A new paper published in the Journal of Geophysical Research – Atmospheres finds the latest generation of IPCC climate models were unable to reproduce the global dimming of sunshine from the ~ 1950s-1980s, followed by global brightening of sunshine during the 1990′s. These global dimming and brightening periods explain the observed changes in global temperature over the past 50-60 years far better than the slow steady rise in CO2 levels. The authors find the models underestimated dimming by 80-85% in comparison to observations, underestimated brightening in China and Japan as well, and that “no individual model performs particularly well for all four regions” studied. Dimming was underestimated in some regions by up to 7 Wm-2 per decade, which by way of comparison is 25 times greater than the alleged CO2 forcing of about 0.28 Wm-2 per decade. The paper demonstrates climate models are unable to reproduce the known climate change of the past, much less the future, that the forcing from changes in solar radiation at the Earth surface is still far from being understood and dwarfs any alleged effect of increased CO2.
‘Evaluation of multidecadal variability in CMIP5 surface solar radiation and inferred underestimation of aerosol direct effects over Europe, China, Japan and India’
R. J. Allen 1, J. R. Norris 2, M. Wild 3
DOI: 10.1002/jgrd.50426
http://hockeyschtick.blogspot.co.nz/2013/04/new-paper-finds-ipcc-climate-models.html
‘Global warming slowdown retrospectively “predicted” ‘
By Ashutosh Jogalekar
When I was in graduate school I once came across a computer program that’s used to predict the activities of as yet unsynthesized drug molecules. The program is “trained” on a set of existing drug molecules with known activities (the “training set”) and is then used to predict those of an unknown set (the “test set”). In order to make learning the ropes of the program more interesting, my graduate advisor set up a friendly contest between me and a friend in the lab. We were each given a week to train the program on an existing set and find out how well we could do on the unknowns.
After a week we turned in our results. I actually did better than my friend on the existing set, but my friend did better on the test set. From a practical perspective his model had predictive value, a key property of any successful model. On the other hand my model was one that still needed some work. Being able to “predict” already existing data is not prediction, it’s explanation. Explanation is important, but a model such as mine that merely explained what was already known is an incomplete model since the value and purpose of a truly robust model is prediction. In addition, a model that merely explains can be made to fit the data by tweaking its parameters with the known experimental numbers.
These are the thoughts that went through my mind as I read a recent paper from Nature Climate Change in which climate change modelers “predicted” the last ten years of global temperature stagnation.
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This kind of retrospective calculation is a standard part of model building. But let’s not call it a “prediction”, it’s actually a “postdiction”. The present study indicates that models used for predicting temperature changes need some more work, especially when dealing with tightly coupled complex systems such as ocean sinks. In addition you cannot simply make these models work by tweaking the parameters; the problem with this approach is that it risks condemning the models to a narrow window of applicability beyond which they will lack the flexibility to take sudden changes into account. A robust model is one with a minimal number of parameters which does not need to be constantly tweaked to explain what has already happened and which is as general as possible. Current climate models are not useless, but in my opinion the fact that they could not prospectively predict the temperature stagnation implies that they lack robustness. They should really be seen as “work in progress”.
I can also see how such a study will negatively affect the public image of global warming. People are usually not happy with prediction after the fact……..
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http://blogs.scientificamerican.com/the-curious-wavefunction/2013/05/15/global-warming-slowdown-retrospectively-predicted/