Guest Post by Bob Tisdale
There’s a new post at the NOAA ENSO blog titled Climate Change and ENSO: Take 2. It’s a guest post by Matt Collins of the University of Exeter. That post probably left most people scratching their heads…not for its content, but for its purpose.
PREFACE
We’ve posted and discussed two studies over the past few years that indicate climate models are incapable of modeling ENSO. And it’s not a question of the model abilities to replicate the instrument temperature record, which they can’t. The problems run very deep. The climate models used by the IPCC for attribution studies and projections of future climate cannot simulate the basic coupled ocean-atmosphere feedback in the tropical Pacific that underlies ENSO. It’s called Bjerknes feedback. It’s the positive feedback relationship between the strength of the trade winds and the surface temperature gradient (cooler in the east, warmer in the west) of the tropical Pacific. Stronger trade winds yield a larger temperature gradient. And a larger temperature gradient yields stronger trade winds. The two are interdependent, providing positive feedback to one another.
There are a multitude of other problems, which we’ll now briefly mention.
The two papers that present model failings at simulating ENSO are:
- Guilyardi et al. (2009) Understanding El Niño in Ocean-Atmosphere General Circulation Models, and
- Bellenger et al (2012) ENSO representation in climate models: from CMIP3 to CMIP5.
You’d be hard pressed to find any component of ENSO that climate models simulate properly. One of the findings of Guilyardi et al. (2009) was that climate models do not use enough sunlight, and that’s a key finding because sunlight provides the fuel for ENSO. That is, ENSO acts as a sunlight-fueled recharge-discharge oscillator. Introducing more sunlight to the models would create an obvious problem for climate modelers: more sunlight undercuts the modelers’ reliance on infrared radiation from manmade greenhouse gases and allows sunlight through ENSO to explain more of the warming from the mid-1970s to the turn of the century, a period when ENSO was skewed to El Niño dominance.
Because of all those model failings, Guilyardi et al (2009) include the following note:
Because ENSO is the dominant mode of climate variability at interannual time scales, the lack of consistency in the model predictions of the response of ENSO to global warming currently limits our confidence in using these predictions to address adaptive societal concerns, such as regional impacts or extremes (Joseph and Nigam 2006; Power et al. 2006).
The section titled “Discussion and Perspectives” in Bellinger et al. (2012) begins:
Much development work for modeling group is still needed in order to correctly represent ENSO, its basic characteristics (amplitude, evolution, timescale, seasonal phaselock…) and fundamental processes such as the Bjerknes and surface fluxes feedbacks.
“Amplitude” refers to the strengths of ENSO events.
“Evolution” refers to the formation of El Niños and La Niñas and the processes that take place as the events are forming.
“Timescale” can refer to both the how long ENSO events last and how often they occur.
“Phaselock” refers to the fact that El Niño and La Niña events are tied to the seasonal cycle. They peak in the boreal winter. (See the post Why Do El Niño and La Niña Events Peak in Boreal Winter?)
“Bjerknes feedback,” which we discussed above, very basically, means how the tropical Pacific and the atmosphere above it are coupled; i.e., they are interdependent, a change in one causes a change in the other and they provide positive feedback to one another. The existence of this positive “Bjerknes feedback” suggests that El Niño and La Niña events will remain locked in one mode until something interrupts the positive feedback.
“Surface fluxes” refers to the variations in heat exchange between ocean and atmosphere in the tropical Pacific in response to ENSO. (See the National Oceanography Centre webpage here, for a quick overview.)
In short, according to Bellenger, et al. (2013), the current generation of climate models (CMIP5: used by the IPCC for their 5th Assessment Report) still cannot simulate basic coupled ocean-atmosphere processes associated with El Niño and La Niña events–basic processes, really basic processes.
REGARDLESS OF ALL THOSE FAILINGS
Some scientists found that some models simulate some part of precipitation in the tropical Pacific reasonably well in response to some parts of ENSO. They then go on to write papers about the future of ENSO. That means they’re overlooking the fact that the models don’t simulate other ENSO functions properly, and the precipitation portion they’re focusing on was achieved under conditions that do not relate to ENSO processes as they exist in nature. So the precipitation-based ENSO studies are basically meaningless. We discussed one of those studies in the post Will Global Warming Increase the Intensity of El Niño?
BACK TO THE NOAA ENSO BLOG POST
The post begins:
Tom previously touched on how climate change might affect ENSO, emphasizing the 2013 AR5 Intergovernmental Panel on Climate Change (IPCC) statement (footnote 1), which basically said that ENSO will continue, but we don’t know if or how its frequency or intensity might change.
That’s the answer we get when we look at the question head-on. But what about when we look at it more indirectly? Looking at other elements of the climate system, for example, we can focus on one reasonably robust finding: an intensification of mean rainfall in the central and eastern equatorial Pacific is ‘likely,’ according to the IPCC criteria. Recently, my colleagues and I have been focusing on the possibility that these overall rainfall changes may impact ENSO.
Answers at last?
There have been a number of modeling studies…
That’s as far as I originally read for reasons discussed above. I came back to the NOAA ENSO blog post later because I’d been asked to comment about it. The body of the post was about how ENSO might change in the future if the climate models were correct…which they can’t be.
THEN THERE’S THE CLOSING
The first paragraph of the closing begins:
But is that the final answer?
Just because the change is seen in models, or even a subset of models, doesn’t mean that we should believe it without question. Not only do we have to factor in errors or biases in the models, but we also have to have a convincing physical argument for the changes.
That quote’s a keeper. File that one away. It’s applicable to climate models in general.
Then there’s the final paragraph:
So, the picture of changes in ENSO, when viewed in terms rainfall response patterns, may be limited by errors and biases that have been long-term features in climate models. Research is required to test the potential impact of SST biases on the change in average precipitation in the tropics. We must improve models, but we must also to better understand the processes whereby biases in present-day simulations link to future projections. Until we get a better handle on these issues, the prediction of an overall increase in rainfall in the eastern tropical Pacific, and its year-to-year variability, remains uncertain.
One wonders why he presented possible changes to ENSO, when he knew the modeled answers were wrong.
I believe it was Richard Lindzen who once wrote that modelers should have first started with the oceans, if they wanted to understand climate on this planet…or something to that effect. That’s becoming more and more obvious to more and more people.
“Should have started with the Oceans if they wanted to understand climate on this planet”. Indeed. They should have also ignored “back radiation” entirely!
wickedwenchfan,
Is 1969 an early enough start for you? http://docs.lib.noaa.gov/rescue/mwr/097/mwr-097-11-0739.pdf
LOL, I’ll bite. Are you saying that the atmosphere doesn’t radiate back toward the surface? All those photons know which way is up and only go that direction?
Brandon: Apparently, you have definitive mathematical computations showing the net “back radiation”, after nanosecond collisions have dissipated most of the energy, leaving some energy to back radiate. Well, then, you must have a “work” gradient model to show how this back radiation heats up the surface….please provide, then, we can all go home.
All those IR photons – well the ones that happen to radiate downward and hit a water surface – have zero warming effect as they penetrate only a few microns if at all, and may liberate the higher energy water molecules on the water surface removing the latent heat of evaporation from the surface thus cooling rather than warming it.
Take that a step further and the lighter humid air rises and eventually if there is a cloud condensation nucleus available the water that evaporated may condense and liberate the latent heat of condensation – in all directions.
All those IR photons – well the ones that happen to radiate downward and hit a water surface – have zero warming effect as they penetrate only a few microns if at all, and may liberate the higher energy water molecules on the water surface removing the latent heat of evaporation from the surface thus cooling rather than warming it.
Take that a step further and……..
But we’ve been through this loop before – strange you won’t find it in the models though – or is it?
Rearrangements of heat in deep ocean currents can entirely by themselves bring about changes in climate as large as between glacial and interglacial. The whole atmosphere and “radiation” issue is a huge and pointless wild goose chase which has taken the whole climate community down the garden path (now I’m mixing metaphors).
Climate science focused on atmosphere and radiation balance dismisses the oceans as a passive puddle. This is a bigger error than to look at the oceans only and ignore the atmosphere – of the two the latter would take you closer to the truth.
” All those photons know which way is up and only go that direction?”
So the meme is … Absorbed photons are re-emitted, with half going up and half down.
Lets follow that and see what happens.
Lets say that we look at 1000 CO2-ready photons emitted by the surface. All 1000 are absorbed.
Half are re-emitted toward the surface, so 500 come back.
Now lets double the CO2 in the atmosphere. For 1000 CO2-ready photons, 500 come back.
Hmmmm Not much of a change in heat effect from that.
Appears that ignoring changes in Back Radiation might have been a good starting assumption.
Of course, the actual absorption and thermalization is much more complex than the simple half up half down meme, but NET energy flow is normally up, and back radiation into the ocean can be ignored.
I’ve heard this claim of cooling the surface before, but are you sure like sure-with-scientific-source?
Should not the IR warm all molecules randomly, of which some escape taking less heat away than was added? I find it pretty surprising effect if you could under normal conditions cool water with IR radiation.
What I believe (without actually knowing) is that most of the IR heat could be evaporated away, but quantifying that might not be easy calculation. Of course to define the question you have to define what exact wavelengths we are talking about, direction of surface with respect to IR, maybe the pressure and temperature as well.
Here is Brandon Gates proudly displaying his ignorance again concerning the absorbency of water with respect to IR.
Hugh December 11, 2014 at 6:40 am
thus cooling rather than warming it.
I’ve heard this claim of cooling the surface before, but are you sure like sure-with-scientific-source?
Don’t know about Scientific source, but here is an engineering calculation on it.
So Sublimation of water is 2,830,000 J/kg.
http://www.theweatherprediction.com/habyhints2/524/
To evaporate 1 m^3 of water with sea water specific volume @ 15 oC at 0.001009 m^3/kg for each m^3 there is an exchange of
=> 2,830,000(J/kg)/0.001009(m^3/kg) = 2,800,000,000 J/m^3
And since most of this energy is taken from the nearby sea water – it will cool
Specific Heat Sea Water = 4,009 J/kg oC 2,830,000/4,009 = 705 or 705 kg by 1- C
60 sec * 60 min * 24 hours * 365.25 days = 31,557,600 sec/yr
Fig 6- Radiative forcings for different anthropogenic and natural perturbations from 2005 relative to 1750. Re-printed from IPCC 2007. http://www.mathaware.org/mam/09/essays/Radiative_balance.pdf
Shows Radiative forcings of CO2 = 1.66 W/m^2 CH4 = 0.48 W/m^2 N2O = 0.16 W/m^2 and Halocarbons = 0.34 W/m^2. Total Long- lived greenhouse gasses = 2.64 W/m^2 for their changes from 1750 to 2005.
Joule = Watt * sec
All this leads to => 2.64 W/m^2 * 31,557,600 sec/yr = 83,312,000 J/m^2 in a year.
Compare to => 2,256,600 * 1000/ (J/KJ)/m^3 = 2,256,600,000 J/m^3 to evaporate the water
–> 83,312,000 J/m^2/2,800,000,000 J/m^3 = .03 m = or 3 cm and if open ocean/total area is 65% then 3/ .65 = 4.6 cm (2.54 to the inch) of extra ocean evaporation would eat up all the extra LW radiation from Long-lived GHG from 1750 to 2005 each year.
In comparision the total annual ocean evaporation rate = 140 CM.
from http://www.eolss.net/sample-chapters/c07/e2-02-03-02.pdf
CW,
It sounds like you’re the one with the calcs, down to the nanosecond no less. In the meantime, good old fashioned regular observation has me covered: http://www.esrl.noaa.gov/gmd/grad/surfrad/ h/t Willis Eschenbach for turning me on to that dataset. It’s a gold mine for anyone who actually wishes to understand the empirical basis for this stuff.
How about we start with this: CO2 isn’t an energy source, the Sun is. GHGs like water vapor, CO2 and methane reduce the rate at which absorbed solar energy can escape back through the atmosphere and into space.
Ian W,
Yah. But when that water vapor gets to altitude and condenses back to liquid … well, isn’t that a phase change involving latent heat as well?
Dave in Delaware,
No, that’s well established quantum physics given us by some pretty heavy hitters you may have heard of. Or not as the case may be. Here’s a hint: they weren’t climatologists.
The GHG molecules — let’s not forget the water vapor, that’s 50% of the greenhouse effect right there … tack on 25% for clouds too — in the layer just above the surface were already emitting 500 down and 500 up. Just as the molecules one layer above that. Higher altitude = less density and closer proximity to outer space = greater probability of escape. Increase the concentration of GHGs throughout the entire atmosphere, reduce the probability of escape. Reduction in rate of loss = increase in temperature until equilibrium is reestablished.
I thought the Sky Dragons had been slain on this blog.
No kidding it’s more complex, but that doesn’t mean the “meme” isn’t operative for crying out loud. What IR isn’t absorbed into the ocean, as others have mentioned, will likely cause increased evaporation. But that latent heat carried away from the surface doesn’t disappear, it’s still got miles of atmosphere to get through. Or is the 1st law of thermodynamics just a “meme” as well? And I don’t know if you’ve heard, but a good portion of the planet is covered by stuff that isn’t water and is more than happy to accept an IR photon or a zillion. You may want to factor in, like, the entire system as a whole. What happens to rainwater that hits the ground which was previously warmed by the sun prior to the storm clouds rolling in? Where does warmed rainwater eventually end up?
mpainter,
Here is mpainter again proudly displaying his forgetfulness of the latent heat of condensation. The guy who thinks land areas are inconsequential to the water cycle. The man who has never before seen a river in his life, much less realizes that river water tends to be far warmer than ocean water. The sort of bloke who considers his own willful ignorance anyone’s problem but his own.
Ian W: All those IR photons – well the ones that happen to radiate downward and hit a water surface – have zero warming effect as they penetrate only a few microns if at all, and may liberate the higher energy water molecules on the water surface removing the latent heat of evaporation from the surface thus cooling rather than warming it.
the slight increase in downwelling lwir may have no effect on surface temperature, merely speeding the rate of evaporation (hence total amount over daytimes); or, the increase may be absorbed by “just evaporated” water vapor. I am hoping to find something very good and detailed to read about the processes of evaporation from water surfaces. It seems like a gap in the knowledge, yet critical for understanding how a change in CO2 can produce a change in climate.
The rest I surmise goes as you say: the slight increase in lwir produces a slight increase in the temperature and total mass of newly evaporated water vapor, and that ascends to the upper troposphere slightly faster than before doubling the CO2, may increase cloud cover, and is the mechanism (I think) supporting the 12% increase in lightning flash rate calculated by Romps et al in a recent, widely cited, article in Science.
DD More
Thanks for your work, but really my question was not about the high latent heat of vapour but just this:
“And since most of this energy is taken from the nearby sea water – it will cool”
I still don’t follow why the water would loose temperature by incoming IR light. It could be that IR does not warm water substantially, but claiming cooling by IR absoption is not convincing at all. Could I cool my pool and make a skating rink with an IR radiator?
.
Bob, what do you make of this :
Japan weather bureau declares first El Nino in five years
http://www.reuters.com/article/2014/12/10/us-elnino-japan-idUSKBN0JO0I620141210
garymount: Here’s the quote from JMA “El Nino Outlook” webpage:
“A weak El Niño event is considered to have persisted since the Northern Hemisphere summer.”
http://ds.data.jma.go.jp/tcc/tcc/products/elnino/outlook.html
It confirms what we’ve been discussing in recent posts:
https://bobtisdale.wordpress.com/2014/10/31/the-2014-15-el-nino-part-19-is-an-el-nino-already-taking-place/
And here:
https://bobtisdale.wordpress.com/2014/12/04/did-enso-and-the-monster-kelvin-wave-contribute-to-the-record-high-global-surface-temperatures-in-2014/
Cheers
Are these recent and forecast rains in California going to put a dent in the drought?
All rain puts a dent in the drought. The size of the dent varies with the amount of rain.
“Note that Wittenberg (2009) and Stevenson et al. (2010) show that a minimum of 300 to 500 years is necessary to accurately evaluate the ENSO spectrum. As reliable observational records are still quite short, even the real ENSO spectrum remains uncertain.”
Bellenger, Hugo, et al. “ENSO representation in climate models: from CMIP3 to CMIP5.” Climate Dynamics 42.7-8 (2014): 1999-2018.
http://ncas-climate.nerc.ac.uk/~ericg/publications/Bellenger_al_CD12s.pdf
”…there is no guarantee that the 150 yr historical SST record is a fully representative target for model development. …”
“In any case, it is sobering to think that even absent any anthropogenic changes, the future of ENSO could look very different from what we have seen so far.”
Wittenberg, Andrew T. “Are historical records sufficient to constrain ENSO simulations?.” Geophysical Research Letters 36.12 (2009).
http://www.gfdl.noaa.gov/~atw/yr/2009/wittenberg_grl_2009_revised.pdf
Bellenger, Hugo et al has more…
(Please forgive the tags, if they remain… it is formatted for other types of comment-posting)
”We analyze the ability of CMIP3 and CMIP5 coupled ocean-atmosphere general circulation models (CGCMs) to simulate the El Niño Southern Oscillation (ENSO) and the tropical Pacific mean state. The large spread in ENSO amplitude is reduced by a factor of 2 in CMIP5 and the ENSO life cycle … are slightly improved. Other fundamental ENSO characteristics …remain poorly represented. … CMIP5 displays an encouraging 30% reduction of the cold bias in the west Pacific. The Bjerknes and shortwave-surface temperature feedbacks, previously identified as major sources of model errors, do not improve in CMIP5. The slightly improved ENSO amplitude , therefore, might results from error compensations. CMIP3 and CMIP5 can thus be considered as one ensemble (CMIP3+CMIP5). The ability of CMIP models to simulate the observed nonlinearity of the shortwave feedback … Only one third of CMIP3+CMIP5 models reproduce this regime shift, with the remaining models always locked in one of the two regimes. …”
A ratio of 6/10 of the new models have calculated values for an ENSO parameter that stay within 25% of the actual, observed value. The old models, used for the IPCC’s Fourth Assessment Report, were only within 50% of the observed value.
“65% of CMIP5 Niño-3 and Niño-4 ENSO amplitudes fall within 25% of the observed value …[Compare that] against 50% for CMIP3.”
“Most models tend to underestimate ENSO-related interannual anomalies of the convective activity in the central Pacific. There is no clear improvement of the average value of the metric in CMIP5 compared to CMIP3 (~40% of ENSO-related convective activity falls within 25% of the observed value for both CMIP3 and CMIP5)…
This spectral metric (Fig. 1d) hardly shows any change from CMIP3 to CMIP5.
Note that Wittenberg (2009) and Stevenson et al. (2010) show that a minimum of 300 to 500 years is necessary to accurately evaluate the ENSO spectrum. As reliable observational records are still quite short, even the real ENSO spectrum remains uncertain.
Some models simulate only 10% of El Niño events … while others reach values close to 100%.
There is a deterioration of the simulation of the east Pacific average net surface flux (Fig. 5e) with an average error exceeding 40Wm-2 for Niño-3 region.
The SST mean state in the tropical Pacific ocean exhibits errors of about 1.5°C on the average
most models [are] underestimating the amplitude of [Bjerknes] feedback by 20 to 50%.
Only 20% of CMIP3 and CMIP5 models fall within 25% of the observed value [of Bjerknes feedback]
only 10% of CMIP3 and CMIP5 models fall within 25% of the observed value [for heat flux feedback]
only half of CMIP5 models fall within 25% of the observed value [for latent heat flux feedback]
CMIP3 and CMIP5 ensembles both poorly reproduce the observed shortwave feedback value of -7 Wm-2K-1, with ensemble average values close to zero.
Only 3 of CMIP5 models display a αSW value within 25% of the observed one
CMIP5 models still struggle to represent convection and cloud processes (e.g. 332 Jiang et al 2012). Yet, these processes are critical for the simulation of the shortwave feedback as showed by Lloyd et al (2011, 2012).
This indicates that fundamental air-sea interactions responsible for ENSO amplitude are still poorly represented in CMIP5.
25% (8) of CMIP5 models, however, have a Feedback score inferior to 1…
65% of CMIP5 models ENSO amplitude falls within 25% of the 510 observed value against 50% for CMIP3.”
Bellenger, Hugo, et al. “ENSO representation in climate models: from CMIP3 to CMIP5.” Climate Dynamics 42.7-8 (2014): 1999-2018.
http://ncas-climate.nerc.ac.uk/~ericg/publications/Bellenger_al_CD12s.pdf
Thanks for catching a lot of the critical findings from the papers, Brian.
Brian: http://ncas-climate.nerc.ac.uk/~ericg/publications/Bellenger_al_CD12s.pdf
Thank you for the link.
Matthew, didn’t the link work to Bellenger et al. (2012) that I provided in the post?
http://www.euclipse.eu/Publications/Bellenger_etal_ENSO%20representation%20in%20climate%20models%20from%20CMIP3%20to%20CMIP5.pdf
If not, I’ll change it, but it works for me.
“You’d be hard pressed to find any component of ENSO that climate models simulate properly. One of the findings of Guilyardi et al. (2009) was that climate models do not use enough sunlight, and that’s a key finding because sunlight provides the fuel for ENSO. ….Introducing more sunlight to the models would create an obvious problem for climate modelers:”
not even wrong Bob.
I wish you would give more input when you post something like this. Dissent aids Debate but how can anyone debate this?
1 Are you saying that the models do use enough sunlight and Guilyardi et al. (2009) are wrong?
2 Are you saying that Guilyardi et al. (2009) does not claim the models do notuse enough sunlight?
3 Are you saying that sunlight does not provide the fuel for ENSO?
4 Are you saying that increasing the sunlight input would not be politically difficult for the modellers?
Or
5 Are you saying something else?
Please expand.
We’re seeing a private conflict here, M Courtney. Bob doesn’t need to hear what Mosher thinks, as he already understands the divide between the two. Mosher doesn’t need to explain so that us unwashed understand, because, well, we’re too stupid or brainwashed to understand what he is saying.
When non-debate tactics like this come up, you can be asssured that there is an argument with a basis in fundamental assumptions going on. The math is not at issue, but the basic equation – there are variables present in one but not the other, or constants applied differently in one from the other.
It’s all junior high. We all have to get older, but not all of us have to grow up.
Mosher absolutely refuses to admit any fault in the climate models. That is the nub of the problem. Mosher absolutely abhors discussion of this, as he knows that, fundamentally, he will be refuted.
Mosher’s problem is not the ability to mature, but rather he is one of those mathematicians who utterly lack the ability to assimilate empirical data and so modify his thinking. The GCM’s are the apex of AGW theory and Mosher is fixed to them like glue.
So Bob is right? What do you mean by “not even wrong Bob”? Seriously, I wouldn’t accept that sentence from a grade 6 student.
I don’t recall the origin but a quick translation is that the statment is such nonsense that just calling it wrong is misleading.
Hmmm…so what happened to Steven? He alludes to having some value-add thoughts on Tisdale’s comments, but goes dark when asked to actually engage in an intellectual discussion.
Besides calling BS on this behavior, I’m inventing a new term: “drive-by commenting”.
Chip, a drive by comment would have content.
Is that a synonym for a troll? To me it is, as it add zero , except for an unpleasant malodorous air of condescension.
Steven Mosher: not even wrong Bob.
Do you dispute something in the passage that you quoted? What exactly? Do you have some evidence that you could share with the rest of us readers?
Bob rarely is wrong.
Further to your inability to provide any depth of context to your comment demonstrates little more than an arrogance derived cynicism.
I think the Joni Mitchell song – Both Sides Now sums it up. We really don’t know clouds at all. Until they can model clouds, the climate models are doomed to failure.
Clouds?
Way back in 1938, Callendar observed that clouds compensate for warmth, keeping the earth in a reasonable balance… “On the earth the supply of water vapour is unlimited over the greater part of the surface, and the actual mean temperature results from a balance reached between the solar “constant” and the properties of water and air. Thus a change of water vapour, sky radiation and temperature is corrected by a change of cloudiness and atmospheric circulation, the former increasing the reflection loss and thus reducing the effective sun heat.”
Callendar, Guy Stewart. “The artificial production of carbon dioxide and its influence on temperature.” Quarterly Journal of the Royal Meteorological Society 64.275 (1938): 223-240. PDF copy is here.
http://onlinelibrary.wiley.com/store/10.1002/qj.49706427503/asset/49706427503_ftp.pdf?v=1&t=i2hp7mkq&s=5ca4636029afeea93cc59249acfa87a4df86d8f6
”Clouds have a strong impact on the radiation budget of the earth. They increase the global reflection 15–30 % (e.g., Wild et al., 2013), causing the albedo of the entire earth to be about twice of what it would be in the absence of clouds (Cess, 1976). Clouds also absorb the long-wave radiation emitted by the earth’s surface and emit energy into space at the temperature at the cloud tops (e.g., Ramanathan et al., 1989). Cloud radiative interactions also represent a large source of uncertainty, in the understanding of past and future climate changes, because of potential variations in the cloud characteristics of the earth.”
”Cloud forcing, thus, is negative, for the shortwave component, where clouds generally have a cooling effect, and positive , for the long-wave component, where clouds generally have a warming effect.”
Calisto, M., et al. “Cloud radiative forcing intercomparison between fully coupled CMIP5 models and CERES satellite data.” Annales Geophysicae. Vol. 32. No. 7. Copernicus GmbH, 2014.
In 1988, Dr. Steven Schneider said “Clouds are an important factor about which little is known. When I first started looking at this in 1972, we didn’t know much about the feedback from clouds. We don’t know any more now, than we did, then.”
Global Warming Unchecked: Signs to Watch for By Harold W. Bernard, page 80.
“…but not a single model has a statistically significant agreement with the observational datasets on yearly averaged values of [Cloud Fraction] and on the amplitude of the seasonal cycle, over all analysed areas.” What is Pamela Probst, an Atmospheric Physicist, telling you?
Probst, P., et al. “Total cloud cover from satellite observations and climate models.” Atmospheric Research 107 (2012): 161-170.
http://www.mi.uni-hamburg.de/fileadmin/files/forschung/theomet/docs/pdf_2012/2012_Probstetal_cloud_cover_AtmosRes.pdf
Apr 2014: “… a 5% increase of [Stratocumulus clouds’] coverage would be sufficient to offset the global warming induced by doubling CO2” Other scientists: Randall et al. (1984), Slingo (1990), Bretherton et al. (2004) and Wood (2012) say essentially the same thing.
“This study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific simulated by eight global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing, sea surface temperature, and large-scale atmosphere environment. The results show that the state-of-the-art global climate models still have significant difficulty in simulating the southeast Pacific stratocumulus clouds and associated cloud feedback. Comparing with observations, the models tend to simulate significantly less cloud cover, higher cloud top, and a variety of unrealistic cloud albedo. The insufficient cloud cover leads to overly weak shortwave cloud radiative forcing and net cloud radiative forcing. Only two of the eight models capture the observed positive cloud feedback at subannual to decadal time scales. The cloud and radiation biases in the models are associated with 1) model biases in large-scale temperature structure including the lack of temperature inversion, insufficient lower troposphere stability, and insufficient reduction of lower troposphere stability with local sea surface temperature warming, and 2) improper model physics, especially insufficient increase of low cloud cover associated with larger lower troposphere stability. The two models that arguably do best at simulating the stratocumulus clouds and associated cloud feedback are the only ones using cloud-top radiative cooling to drive boundary layer turbulence.”
Lin, Jia-Lin, Taotao Qian, and Toshiaki Shinoda. “Stratocumulus Clouds in Southeastern Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models.” Journal of Climate 27.8 (2014): 3000-3022.
http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00376.1
Sep 2014, Stephen E Koonin: “My training as a computational physicist—together with a 40-year career of scientific research, advising and management in academia, government and the private sector—has afforded me an extended, up-close perspective on climate science. Detailed technical discussions during the past year with leading climate scientists have given me an even better sense of what we know, and don’t know, about climate. …”
“For instance, global climate models describe the Earth on a grid that is currently limited by computer capabilities to a resolution of no finer than 60 miles. (The distance from New York City to Washington, D.C., is thus covered by only four grid cells.) But processes such as cloud formation, turbulence and rain all happen on much smaller scales. These critical processes then appear in the model only through adjustable assumptions that specify, for example, how the average cloud cover depends on a grid box’s average temperature and humidity. In a given model, dozens of such assumptions must be adjusted (‘tuned,’ in the jargon of modellers) to reproduce both current observations and imperfectly known historical records.”
http://online.wsj.com/articles/climate-science-is-not-settled-1411143565
Models can’t do clouds. What this researcher is saying, is that models chop-up the earth, in a grid, that is too large…
Nov 2014: “The global mean [Altocumulus] along-track horizontal scale is 40.2 km, with a standard deviation of 52.3 km. Approximately 93.6% of [Altocumulus] cannot be resolved by climate models with a grid resolution of 1°. The global mean mixed-phase [Altocumulus] vertical depth is 1.96 km, with a standard deviation of 1.10 km.”
“Spatial scales of altocumulus clouds observed with collocated CALIPSO and CloudSat measurements”
Atmospheric Research-Zhang et al.
DOI: 10.1016/j.atmosres.2014.05.023
http://www.sciencedirect.com/science/article/pii/S0169809514002324
But, you must know…
“Only two of 44 models produced since 2006 have a grid resolution better than 1°”
Models can’t do clouds. 93% of clouds can’t be resolved with 1° or larger grids, but only 2/44 models resolve finer than 1°… and that is what the IPCC has as their best.
“Evaluations of atmospheric downward longwave radiation from 44 coupled general circulation models of CMIP5
Journal of Geophysical Research, Atmospheres – Qian Ma et al. April 2014
DOI: 10.1002/2013JD021427
http://onlinelibrary.wiley.com/doi/10.1002/2013JD021427/full#jgrd51318-tbl-0001
2014: “Many aspects of the climate system cannot be explicitly calculated, frequently because specific phenomena develop and act on a scale smaller than used for the model domain (i.e., over distances that are much smaller than represented by a model grid cell). Clouds provide such an example. Cloud motions vary over a horizontal scale of tens to hundreds of meters. Yet, the horizontal resolution of a [General Circulation Model] is typically tens to hundreds of kilometres. In order to represent clouds in [General Circulation Models], cloud processes are approximated or parameterized.”
Clouds are fudged, faked.
Canevari, Suzanne Demars. “Comparing Proxy Versus Simulated Data Records of Past Climate Using an Energy Balance Model.” (2014).
http://www.math.hawaii.edu/home/theses/MA_2014_Canevari%20(8:7:14%201:19%20PM).pdf
Even using today’s fastest supercomputers, a single run of a high-resolution model takes three months.
“Further down the line, Wehner says scientists will be running climate models with 1 km resolution. To do that, they will have to have a better understanding of how clouds behave.” But they don’t have that understanding, now.
“Using version 5.1 of the Community Atmospheric Model, developed by the Department of Energy (DOE) and the National Science Foundation (NSF) for use by the scientific community, Wehner and his co-authors conducted an analysis for the period 1979 to 2005 at three spatial resolutions: 25 km, 100 km, and 200 km.”
“A cloud system-resolved model can reduce one of the greatest uncertainties in climate models, by improving the way we treat clouds,” Michael Wehner (a lead author of the IPCC’s Fifth Assessment Report) said. “That will be a paradigm shift in climate modelling. We’re at a shift now, but that is the next one coming.” But they don’t have that, now.
“In the low-resolution models, hurricanes were far too infrequent,” Wehner said.
“Latest supercomputers enable high-resolution climate models, truer simulation of extreme weather” http://www.sciencedaily.com/releases/2014/11/141112144825.htm
“…version 5.1 of the Community Atmospheric Model (CAM5.1) at a high horizontal resolution. Intercomparison of this global model at approximately 0.25°, 1°, and 2°”
“… a comparison to observations reveals both realistic and unrealistic model behaviour.”
In the absence of extensive model tuning, at high resolution, simulation … in this study is degraded, compared to the tuned, lower-resolution, public-released version of the model.”
Wehner, Michael F., et al. “The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5. 1.” Journal of Advances in Modeling Earth Systems (2014).
Aug 2014: This author compares the latest climate models used by the IPCC, twenty-eight of the 5th generation of the “Coupled Model Intercomparison Project” (CMIP5) models, “and compared [them] with multiple satellite observations”
The author states, “A large degree of uncertainty in global climate models [General Circulation Models] can be attributed to the representation of clouds, and how they interact with incoming solar [short-wave radiation], and outgoing longwave radiation.”
Models can’t do clouds.
“In this study, the simulated total cloud fraction (CF), cloud water path (CWP), top of the atmosphere (TOA) radiation budgets, and cloud radiative forcings (CRFs) from 28 CMIP5 AMIP models are evaluated, and compared with multiple satellite observations from [Clouds and the Earth’s Energy System, Energy Balance and Filled (CERES-EBAF)] CERES, MODIS, ISCCP, CloudSat, and CALIPSO.”
“The multi-model ensemble mean [total cloud fraction] (57.6 %) is, on average, underestimated by nearly 8% (between 65°N/S) when compared to CERES–MODIS (CM) and ISCCP results…”
“…while an even larger negative bias (17.1 %) exists compared to the CloudSat/CALIPSO results.”
“[Cloud water path] bias is similar, in comparison, to the [total cloud fraction] results, with a negative bias of 16.1 gm−2 compared to [CERES and MODIS satellite data].”
“The model-simulated, and CERES [Energy Balanced and Filled] observed [Top of the atmosphere] reflected [short-wave radiation] and [outgoing long-wave radiation] fluxes, on average, differ by 1.8 and −0.9 Wm−2, respectively.”
“The averaged [short wave radiation], [long wave radiation], and net [cloud radiative forcings] from CERES [Energy Balanced and Filled] are −50.1, 27.6, and −22.5 Wm−2, respectively, indicating a net cooling effect of clouds on the [Top of the atmosphere] radiation budget.”
“The differences in [short-wave radiation] and [long-wave radiation] [cloud radiative forcings] between observations, and the multimodel ensemble means, are only −1.3 and −1.6 Wm−2, respectively, resulting in a larger net cooling effect of 2.9 Wm−2 in the model simulations.”
“A further investigation of cloud properties and [cloud radiative forcings] reveals that the General Circulation Models biases in atmospheric upwelling (15°S–15°N) regimes are much less than in their downwelling (15°–45°N/S) counterparts over the oceans. Sensitivity studies have shown that the magnitude of [short-wave radiation] cloud radiative cooling increases significantly with increasing [total cloud fraction] at similar rates (~−1.25 Wm−2 %−1) in both regimes. The [long wave radiation] cloud radiative warming increases with increasing [total cloud fraction] but is regime dependent, suggested by the different slopes over the upwelling and downwelling regimes (0.81 and 0.22 Wm−2 %−1, respectively). Through a comprehensive error analysis, we found that [total cloud fraction] is a primary modulator of warming (or cooling) in the atmosphere…”
“Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations”
Climate Dynamics-Springer Berlin Heidelberg
DOI: 10.1007/s00382-014-2158-9
http://dx.doi.org/10.1007/s00382-014-2158-9
Aug 2014: “Significant systematic biases in the moisture fields within the tropical Pacific trade wind regions are found in the Coupled Model Intercomparison Project (CMIP3/CMIP5) against profile and and total column water vapour estimates from the Atmospheric Infrared Sounder (AIRS) and total column water vapour from the Special Sensor Microwave/Imager (SSM/I). Positive moisture biases occur in conjunction with significant biases of eastward low-level moisture convergence north of the South Pacific convergence zone (SPCZ) and south of the Inter-tropical convergence zone (ITCZ) – the V-shaped regions. The excessive moisture there, is associated with overestimates of reflected upward shortwave (RSUT), underestimates of outgoing long-wave radiation at the top of the atmosphere, and underestimates of downward shortwave flux at the surface, compared to [the satellite data gathered by] Clouds and the Earth’s Energy System, Energy Balance and Filled (CERES-EBAF) data. We characterize the impacts of falling snow and its radiation interaction, which are not included in most CMIP5 models, on the moisture fields using the National Centre for Atmospheric Research-coupled global climate model. A number of differences in the model simulation without snow-radiation interactions are consistent with biases in the CMIP5 simulations. These include effective low-level eastward/southeastward wind and surface wind stress anomalies, and an increase in total column water vapour, vertical profile of moisture, and cloud amounts in the V-shaped region. The anomalous water vapour and cloud amount might be associated with the model increase of [reflected upward shortwave] and decrease of [outgoing long-wave radiation] at [the top of the atmosphere] and [decreased downward short-wave flux at the surface] in clear and all sky in these regions. These findings hint at the importance of water vapour-radiation interactions in the CMIPS/CMIP5 model simulations that exclude the radiative effect of snow.”
“Characterizing Tropical Pacific Water Vapor and Radiative Biases in CMIP5 GCMs: Observation-Based Analyses and a Snow and Radiation Interaction Sensitivity Experiment”
Journal of Geophysical Research: Atmospheres – J.-L. F. Li et al.
DOI: 10.1002/2014JD021924
http://dx.doi.org/10.1002/2014JD021924
http://onlinelibrary.wiley.com/doi/10.1002/2014JD021924/abstract
”The representation of the marine boundary layer (BL) clouds remains a formidable challenge for state-of-the-art simulations. A recent study by Bodas-Salcedo et al., using the Met Office Unified Model, highlights that the under prediction of the low/midlevel postfrontal clouds, contributes to the largest bias of the surface downwelling shortwave radiation, over the Southern Ocean (SO). A-Train observations, and limited, in situ measurements, have been used to evaluate the Weather Research and Forecasting Model, version 3.3.1 (WRFV3.3.1), in simulating the postfrontal clouds over Tasmania and the [southern ocean]. The simulated cloud macro/microphysical properties are compared against the observations. … The simulations, however, have great difficulties in portraying the widespread marine [boundary layer] clouds, that are not immediately associated with fronts. This shortcoming is persistent to the changes of model configuration, and physical parameterization. … More comprehensive observations are necessary to fully investigate the deficiency of the simulations.”
Huang, Yi, et al. “An Evaluation of WRF Simulations of Clouds over the Southern Ocean with A-Train Observations.” Monthly Weather Review 142.2 (2014): 647-667.
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-13-00128.1
May 2014: “Uncertainties in the knowledge of atmospheric radiative processes are among the main limiting factors for the accuracy of current climate models. Being the primary greenhouse gas in the Earth’s atmosphere, water vapor is of crucial importance in atmospheric radiative transfer. However, water vapor absorption processes, especially the contribution attributed to the water vapor continuum, are currently not sufficiently well quantified.”
Like I said, models can’t do clouds. If this is what this group of scientists have to say about the computer-model process, in May of 2014… in their journal-published, peer-reviewed research, then why should you believe the IPCC?
Reichert, Andreas, Ralf Sussmann, and Markus Rettinger. “Quantification of the water vapor greenhouse effect: setup and first results of the Zugspitze radiative closure experiment.” EGU General Assembly Conference Abstracts. Vol. 16. 2014.
http://adsabs.harvard.edu/abs/2014EGUGA..16.5349R
Apr 2014: Marc Chiacchio (et al.) evaluated the RegCM4 regional climate model, studying the incoming solar radiation, and the outgoing long-wave infrared radiation. They compared the model predictions to observations at the earth’s surface, and space-satellite products, “Global Energy and Water Cycle Experiment Surface Radiation Budget, ERA-Interim, Clouds and the Earth’s Radiant Energy System (CERES), and Baseline Surface Radiation Network”.
The authors suffer from run-on-sentence syndrome, and an inability to punctuate.
“At the [earth’s] surface, the model overestimated the amount of solar radiation absorbed”
…meaning, the model has a higher “solar forcing” which leads it to assume a larger amount of warming… but that error seemed to be cancelled out by other errors: “but was compensated by a greater loss of thermal energy,”
The model also screwed up, at the top-of-the-atmosphere, underestimating both the incoming short-wave radiation (solar) and outgoing long-wave (infrared) radiation totals, “representing too little solar energy absorbed, and too little outgoing [infrared] thermal energy.”
So, the model underestimated the incoming solar, but another error -calculating too much absorption of incoming solar energy- cancelled out the incoming-solar part of the error, but left in the error of outgoing long-wave infrared radiation, leaving, what I expect is, a calculation of excess warming. That’s the goal, isn’t it?
“These biases were dependent on errors in cloud fraction, …”
Models can’t do clouds.
The whole sentence reads, “These biases were dependent on errors in cloud fraction, surface and planetary albedo, and less dependent on surface temperature associated with the surface longwave parameters, …”
“Clear-sky fluxes showed better results, when cloud cover errors had no influence.”
If ever there was an admission, that the models can’t do clouds, that was it. If this is what this group of scientists have to say about the computer-model process, in April of 2014… in their journal-published, peer-reviewed research, then why should you believe the IPCC?
“We also found a clear distinction between land versus water, with smaller biases over land at the surface, and over water at the [Top of the atmosphere], due to errors in cloud fraction and albedo.”
“…errors in cloud fraction…”
“From this result, it was discovered that planetary albedo (including cloud albedo) played a larger role than cloud fraction on errors in all-sky SW absorption at the [top-of-the-atmosphere].”
This one gets me: “Despite these biases, we found the model able to properly simulate the radiative energy budget and suitable for climate related applications.” -Go figure. If this is what this group of scientists have to say about the computer-model process, in 2014… in their journal-published, peer-reviewed research, then why should you believe the IPCC?
Chiacchio, Marc, et al. “Evaluation of the radiation budget with a regional climate model over Europe and inspection of dimming and brightening.” EGU General Assembly Conference Abstracts. Vol. 16. 2014.
http://adsabs.harvard.edu/abs/2014EGUGA..1611063C
Here, a comparison of the previous generation of models is run against the current computer models. Some SMALL improvements, applicable to SOME regions… admissions that the new models have addressed a problem of a “remarkable degree of variation among the models” … somewhat…
Jun 2013: “… intermodel differences are still large in the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations, and reveals some small improvements of particular cloud properties in some regions in the CMIP5 ensemble over CMIP3.”
“Clouds are a key component of the climate system affecting radiative balances and the hydrological cycle. Previous studies from the Coupled Model Intercomparison Project phase 3 (CMIP3) showed quite large biases in the simulated cloud climatology affecting all General Circulation Models as well as a remarkable degree of variation among the models that represented the state of the art circa 2005.”
“Simulating Clouds with Global Climate Models: A Comparison of CMIP5 Results with CMIP3 and Satellite Data”
Axel Lauer, Kevin Hamilton
J. Climate, 26, 3823–3845.
doi: http://dx.doi.org/10.1175/JCLI-D-12-00451.1
Sep 2012: “The cumulus convection schemes currently in use in General Circulation Models bypass the microphysical processes by making arbitrary moistening assumptions. We suggest that they are inadequate for climate change studies.“ If this is what this group of scientists have to say about the computer-model process, in their journal-published, peer-reviewed research, then why should you believe the IPCC?
“Radiative‐convective model with an explicit hydrologic cycle: 1. Formulation and sensitivity to model parameters”
Journal of Geophysical Research: Atmospheres -Renno, Emanuel, Stone
DOI: 10.1029/94JD00020
http://onlinelibrary.wiley.com/doi/10.1029/94JD00020/abstract
Renno, Emanuel, and Stone (1994) (PDF)
1990: “Results from the Earth Radiation Budget Experiment (14) for April 1985 show that the net effect of clouds at the top of the atmosphere (TOA) is a cooling of 13W [per meter squared]. Because the change in the [Top of the atmosphere] net radiation that is due to an instantaneous doubling of the carbon dioxide concentration is only [about] 2.5W [per meter squared] (see later), changes in cloudiness could contribute significantly to climate change.”
Slingo, A. “Sensitivity of the Earth’s radiation budget to changes in low clouds.” Nature 343.6253 (1990): 49-51.
http://www.see.ed.ac.uk/~shs/Climate%20change/Climate%20model%20results/Slingo%201990.pdf
Fantastic recopilation, but perhaps a little long for a comment. Can’t you make of this some kind of guest post?
What is it, exactly, that GCM’s do accomplish? Is it only that they support the alarmist narrative? Nothing else? Nothing actually useful?
Brian, thank you for the many links.
Brian, this link does not work for me: http://www.math.hawaii.edu/home/theses/MA_2014_Canevari%20(8:7:14%201:19%20PM).pdf
Paraphrasing Nylo, that post might make a good letter.
Matthew R Marler
http://www.math.hawaii.edu/home/theses/MA_2014_Canevari%20(8:7:14%201:19%20PM).pdf
Looks, byte for byte, identical to what I had before. I click on my link, and it works.
Another alternative, which works for most any paper, is to select the proper title of the paper, and go to Google Scholar and past it. Maybe use quotes on either end, but if that does’t work, try without. Or, parse out the last names of the authors; place those in the (advanced query field of Google Scholar), restrict the year to the year (or a range of years)… Last thing is to try the query back on plain ol’ Google, and fish through the results. I have found many papers that were behind pay-walls, published on other websites.
Crispin in Waterloo
waste billions of dollars tilting at windmills…
I have less, but quite a few notes like that, in other categories, like “temperature flaws in models” and “carbon cycle flaws in models” … I appreciate the encouragement!
Brian, I too will thank you for all of the links to cloud-related papers.
Brian,
Excellent compilation, thank you. Next time someone accuses climate scientists of not admitting their failures, I’ll refer them to this abundant evidence to the contrary.
You’re not, perchance, familiar with the term “own goal” are you?
Georgia Institute of Technology 2014: ”Scientists see a large amount of variability in the El Niño-Southern Oscillation (ENSO) when looking back at climate records from thousands of years ago. Without a clear understanding of what caused past changes in ENSO variability, predicting the climate phenomenon’s future is a difficult task.”
http://www.sciencedaily.com/releases/2014/12/141205114015.htm
Bob – maybe you should suggest to your friends at NOAA and UNISYS another change in color palette – the oceans are starting to look cool again.
http://weather.unisys.com/surface/sst_anom.gif
If he’s offered a guest post he’d be mad not to take it. That’s how careers are built.
And I doubt he’d be invited back if he just said,
“I’m a world expert on this.
I know as much as anyone.
But I happen to know nothing.”
It wouldn’t be far off though.
I can’t help but think of the work of Edward Lorenz, whenever models of chaotic systems are discussed. He showed in 1963 that models cannot work predictably over the long term, unless all model inputs are precisely known.
From Lorenz, ’63: “Two states differing by imperceptible amounts may eventually evolve into two considerably different states … If, then, there is any error whatever in observing the present state — and in any real system such errors seem inevitable — an acceptable prediction of an instantaneous state in the distant future may well be impossible….In view of the inevitable inaccuracy and incompleteness of weather observations, precise very-long-range forecasting would seem to be nonexistent.”
The work of Lorenz is not lost on modellers, but still they persist, as we pour billions into their efforts.
as we pour billions into their efforts.
well now you can work out why they persist, they got a winning formual.
Models that worked but did not support AGW would be horrendous for their future prospects and many of those working in climate ‘science ‘ because without models they got ‘butt-kiss’
Trenberth was a student of Lorenz but apparently must have missed a key lecture.
As skeptics like Dyson have long said, climatology needed decades more good observational data before presuming to model climate. But since “climate scientists” got access to supercomputers it was easier, more fun & lucrative for them all to pretend they knew enough to program the machines adequately.
Present models are worse than worthless GIGO, hence miserable, epic failures. They should scrapped. Climate starts with the oceans & the sun on this watery planet around a UV-variable star. Even the discovery in the ’90s of the PDO & AMO didn’t lead the Team to junk their GCMs, the geese which lay such golden eggs.
milodonharlani,
I am not a student of Lorenz, but before you natter on about missed lectures you may wish to read him on the concept of attractors. See also the difference between weather forecasting and climate projection. One is concerned with daily and hourly states, the other one isn’t.
milodonharlani,
Trenberth was a student of Lorenz but apparently must have missed a key lecture.
Joseph Stalin studied in an Orthodox seminary to be a priest. He must have skipped a few as well.
Brandon
On what basis would you restrict the operation of nonlinear chaotic dynamics to hours and days and not longer?
Reflect on the nature of fractality and you will realise how nonsensical such an assertion is.
phlogiston,
Practicality for one thing. I might be interested in chance of precipitation and min/max/average temps and wind speed in Toronto over the next week, and weather forecast models are getting ever better at giving us that information. We of course complain ever as much about how bad they are when our golf outing is interrupted by a surprise thunderstorm. If you told me you could predict the weather on a given day in Toronto 100 years out, I’d unflinchingly call your sanity into question.
Common real world analogs of fracticality are shorelines, trees … and leaf structure of trees. So the length of a shoreline approaches infinity as the resolution of the measurements taken of it increases. Yet for as long as cartographers have existed, they’ve provided us with useful approximations of their position and length. We’ve gotten so good at it that we can even keep track of how shorelines change and evolve over time.
Extending the analogy a bit, go down to the ocean and try to predict the position and shape of the curve each wave leaves in the sand over, say 10 meters along the (approximate) shoreline’s axis to, oh +/- 10 cm. You will fail miserably on most individual events. You might really nail the shape of a given surf runup, but more often than not that prediction will happen before or after the wave you got correct. Do this for five minutes though, and eventually you’ll be able to define a line on the beach that most surf rarely crosses, some line most surf almost always crosses, and some line representing the mean distance surf runs up the slope.
Do this for an hour and wind shifts might wreak some havoc if they come up, die down and/or shift direction quickly. But if the wind changes significantly to some new and relative steady state within five minutes you’ll have decent min/max/mean boundaries defined once again. Do that for 6 hours and you’ll notice the boundaries have moved significantly in a way that you can’t account for due to wind changes because your experiment has just revealed the tidal effects of the Sun and Moon as the Earth rotates. Keep at it for a year, and you’ll be the world’s foremost expert on the tide tables, wind conditions and surf runup on that beach. Keep at it for a decade, and you’ll know everything there is to know about sand moves around both in the surf and away from it.
Somewhere in that decade, people will probably start asking you for predictions. They will most likely be happy with you if they ask where the min/max/mean extent of the surf will be tomorrow, next week, next month, maybe even next year on a given date, so long as they’re willing to tolerate about a meter of error in the daily to weekly forecast, one or two meters in the monthly and something on the order of what, five maybe, on the annual? But if they asked you to tell them within in one meter error where the surf would be running 10 years out on a particular day, you would rightfully tell them to get lost for asking such an impossible to answer question.
And not just impossible, but totally useless for any practical consideration normally taken into account by people who have a lot of stake in how far water runs up a given beach. Like real estate developers. They’ll take your max 10 year estimate, bump your 2-sigma error bounds out to 5 and tack on at least another meter or 10, space permitting, for good measure. And because you’ve told them that every x number of days or years a really monster storm blows off the ocean and inundates the entire area your quite accurate and useful predictions be damned, a good engineer for a conscientious developer would design the foundation to more than withstand that onslaught. Maybe put in a sea wall or two, artfully disguised as landscaping.
Even the Mandelbrot set is bounded. So is a double pendulum. Or a quintuple one. Or an n-tuple one.
Likewise bounded is risk assesment. The higher the uncertainty in the projections, the greater the assumed risk. This kind of thinking has been around ever since insurance was invented.
Brandon Gates
Bounded by what? By an attractor. The message of Lorenz – the original topic of this sub-thread – is that a climate system with no change in parameters can “spontaneously” oscillate and jump to different plateaus, showing an ongoing variation that is only really bounded by the log-log fractal distributions – i.e. small changes often, big changes more occasionally.
Deterministic Nonperiodic Flow 1963 by Lorenz is the foundation of climate science and while the majority know of it only a tiny minority are willing to take on board its message. Anyone seeing a change in global temperature and experiencing a need to find a “forcing” to explain that change, is ignorant of Lorenz’ discovery. Climate can change all by itself. Forcing is not a useful term.
phlogiston,
Bounded by physical properties which resolve over time to patterns which can be described mathematically as an attractor.
No obvious change in parameters. Spontaneous really does belong in scare quotes.
It’s an eminently quotable paper. I’m not sure you’ve read all of it.
Maybe you’ve read all of it, but only remember the bits which conform to your argument. First graf of the Conclusion (section 8) contains some goodies for you:
Certain mechanically or thermally forced nonconservative hydrodynamical systems may exhibit either periodic or irregular behavior when there is no obviously related periodicity or irregularity in the forcing process. Both periodic and nonperiodic flow are observed in some experimental models when the forcing process is held constant, within the limits of the experimental control. Some finite systems of ordinary differential equations designed to represent these hydrodynamical systems possess periodic analytic solutions when the forcing is strictly constant. Other such systems have yielded nonperiodic numerical solutions.
Models?! He based his conclusions on models? This paper is junk! But out of morbid curiosity let’s see what other garbage he has to offer:
A finite system of ordinary differential equations representing forced dissipative flow often has the property that all of its solutions are ultimately confined within the same bounds. We have studied in detail the properties of this sort. Our principal results concern the instability of nonperiodic solutions. A nonperiodic solution with no transient component must be unstable, in the sense that solutions temporarily approximating it do not continue to do so. A nonperiodic solution with a transient component is sometimes stable, but in this case its stability is one of its transient properties, which tends to die out.
I’m tempted to say “duh” here, but I must remember that these concepts, being novel at the time, were not part of the conventional wisdom of subsequent generations of scientists and mathematicians whose work has informed my view of things. Onward:
There remains a question as to whether our results really apply to the atmosphere. One does not usually regard the atmosphere as either deterministic or finite, and the lack of periodicity is not a mathematical certainty, since the atmosphere has not been observed forever.
I’ll add, not observed forever in its entirety at nanometer resolution. To say nothing of the oceans. Once more into the breech:
The foundation of our principal result is the eventual necessity for any bounded system of finite dimensionality to come arbitrarily close to acquiring a state which it has previously assumed. If the system is stable, its future development will then remain arbitrarily close to its past history, and will be quasi-periodic.
Hmm, where have we heard the term quasi-periodic in the context of climatology? And what’s this “arbitrarily close” nonsense, we want accurate predictions at annual or better resolution dangit all, not a bunch of armwaving about the inherent unpredictability of internal variabilites which revert to a mean over long periods of time! Utter rubbish!! I almost can’t read this tripe any further, but like a slow motion trainwreck I simply can’t look away:
In the case of the atmosphere, the crucial point is then whether analogues must have occurred since the state of the atmosphere was first observed. By analogues, we mean specifically two or more states of the atmosphere, together with its environment, which resemble each other so closely that the differences may be ascribed to errors in observation. Thus, to be analogues, two states must be closely alike in regions where observations are accurate and plentiful, while they need not be at all alike in regions where there are no observations at all, whether these be regions of the atmosphere or the environment. If, however, some unobserved features are implicit in a succession of observed states, two successions of states must be nearly alike in order to be analogues.
What kind of gibberish is that? Implicit unobserved features my butt! We’re doing science here, not assuming stuff because some made-up equations in a computer code tell us what must be so! How can you validate something which is unobserved and/or unobservable? Jumping ahead a bit:
There remains the very important question to how long is “very-long-range.” Our results do not give an answer for the atmosphere; conceivably it could be a few days or a few centuries. In an idealized system, whether it be the simple convective model described here, or a complicated system designed to represent the atmosphere as closely as possible, the answer may be obtained by comparing pairs of numerical solutions having nearly identical initial conditions.
Was he drunk when he wrote this? Comparing two wrong models to each other is less useful than trying to ask Schrödinger’s cat for investment advice.
In the case of the real atmosphere, if all other methods fail, we can wait for an analogue.
Thanks Dr. Lorenz. I just wasted an hour of my life reading your so-called “research” only for you to tell me that I’ve got to wait some unknown number of possible centuries for you to prove any of your hokum. Enough of this crap, I’ve got better things to waste my time doing.
And there it is. It’s logically possible for unexpected y thing to happen so that’s what I’m going to believe — evidence to the contrary be damned. Not one place in Lorenz’s paper did I see him invoke such magical thinking. What he did go through in admirably rigorous detail is that while some systems may appear stable over some arbitrary period of observation, and therefore may be reliably approximated and bounded by numerical solutions there always exists the possibility that some non-linear and/or previously unobserved analogue will emerge from even a deterministic chain of prior events.
His paper provides a framework for knowing what to look for when current observation begins to diverge from prior prediction, and the strong admonition to bear in mind that all approximations are just that — they’re limited by the finite quantity and fidelity of previous observation (not to mention our ability to interpret them properly) and it’s all but certain there’s something you don’t know about when you build your first, second, third or nth model of a non-trivial physical system.
NONE of this has been forgotten since he first wrote it. Every IPCC AR discusses it in depth because practically every paper written on the subject notes in passing at the very least if not being wholly dedicated to possible or observed non-linearities, previously unseen and/or unanticipated state changes plus the inherent uncertainties those abundantly evident realities attest.
What is the discussion of things like tipping points all about if not, “Hey there’s been a perturbation of the system here we’ve not explicitly observed before and we’re … ah … not entirely sure but our modelled simulations suggest the following possible non-reversible outcomes”?
Oh good grief. I can’t count how many times he used the word, and nowhere did I see him state that chaotic fluid systems will not respond to obvious, observed parameter changes. Lorenz did not throw Newton out the window in favor of spooky interaction at a distance, though he did note his conclusions also work if system in question is non-deterministic.
Alan Robertson, thanks for the Lorenz quote.
post above should read: “…models can not predict accurately over the long term…”
Alan
Current climate models can’t even make accurate short term predictions.
As you’ve commented, chaos is certainly a factor (and can never be eliminated). However, the proximate cause is evangelical warmest simply refuse to accept that something other than CO2 drives the process.
As long as modelers defend gross ignorance of the physical process by claiming “settled science” and refusing to engage in reasonable scientific debate, they’re tightly locked into the dark ages.
If you have not already done so, now is a good time to get that 1st cup of coffee & spend 5 minutes reading Feynman’s “Cargo Cult”.
A friend of mine, here in town, was a friend of Feynman… he shares Feynman’s attitude and we’ve had some very interesting discussions. My friend’s take on “climate scientists” is beyond caustic.
However, it would be a reliable prediction to say that I will make errors when posting before 1st cup of coffee.
Thanks, Bob. A clear explanation of the Bjerknes feedback and how ENSO events remain locked in one mode until something interrupts the positive feedback.
Let the sunshine in, let the sunshine in!
I wonder how a Markow chain analysis of the thousands of years of El Nino’s might have a predictive value.
Good idea. Maybe a hack of the old Bellcore canned program, but if that didn’t fly, I don’t imagine doing something from the ground up would be insurmountable.
Does LW radiation only occur from the Suns rays hitting land surface and ice. If so, how does ~30% of earths surface which is land and ice cause back radiation over ~70% of the earths surface which is ocean?
The ocean also radiates IR based on the temperature of the top couple of micrometers of the surface.
Yes, and to take that further by incorporating the arguements earlier and later in this post about the increased evaporation , and surface cooling by Latent Heat losses, leads to some interesting surmises.
IF back radiation causes surface cooling , then the upward return IR will be slightly reduced , as will its spectrum (very slightly) .Meanwhile if CO2 concentrations are increasing, down radiation increases, but also increases the cooling , reducing up radiation . One can see that a steady increase in CO2 could mean a stable surface temperature , whilst an increase in CO2 generation rate above the current 2ppm/year could overwhelm the negative feedback of cooling and IR intensity shift.
Lots of “coulds” and “might” but capable of being put into a mathematical form to determine what rate of CO2 is genuinely alarming and whether just reducing the rate of increase would produce overall cooling.
Brandon Gates
December 11, 2014 at 10:01 am
milodonharlani,
Trenberth was a student of Lorenz but apparently must have missed a key lecture.
___________
“I am not a student of Lorenz, but before you natter on about missed lectures you may wish to read him on the concept of attractors. See also the difference between weather forecasting and climate projection. One is concerned with daily and hourly states, the other one isn’t.”
———————————-
Brandon,
Words almost fail me… it would not be a stretch to call that statement a ridiculous rationalization.
Weather is chaotic meaning its governing equations are sensitive to arbitrarily small changes back in time. Climate defined as mean of measurables of these very same equations obviously is not.
The trouble is, the equations and their parameterization are approximations of a larger equation system which incorporates seas, glaciers, land use, sunspots, volcanism, litosphere and other difficult-to-model, nonlinear stuff, which can hardly be proven to be nonchaotic, when even approximating let alone expressing it mathematically is beyond difficult.
Worse than the complexity: slight round off errors in the computer code can drive two different model runs off in different directions. Chaos based on initial conditions variations is only part of it. I once simulated a process that had an analytical solution on a computer that resulted in a bifurcation graph. The output agreed with the analytical solution for the first twenty or thirty iterations, but then different runs would diverge radically from each other. Averaging these diverging results came nowhere near the analytical solution at any point beyond where the divergence became significant, yet that is what we do with climate models? It makes no sense!
Hugh,
I agree with oceans, glaciers and land use. Tack onto that cloud, albedo in general and aerosols. I don’t think sunspots are a big issue unless we’re due for another Maurander minimum, which might well be good news. Volcanism and the lithosphere are non-factors in the timeframe of the planning horizion being discussed.
That said, chaotic does not imply non-constrained. Where constraints exist, useful projections may be reasonably possible. Predictions in the sense of forecasting exact timing not so much. Of course dead nuts on predictions wouldn’t be necessary if we could arguably count on things remaining more or less the same. Approaching a blind curve at night on even a familiar road, a prudent driver at leasts covers the brake pedal.
Brandon Gates, @ 9:51 am
####
Once again Brandon asserts that latent heat is returned with precipitation. He says:
“mpainter has never seen a river” meaning, I suppose, that all of that water represents latent heat returned to the surface.
Brandon has no clue regarding the physics of the problem.
mpainter,
Last I checked (last night), land areas are warming faster than the oceans. Pop quiz: is river water warmer or cooler on average than ocean water? You shouldn’t have to look this one up.
Bob says below which is so correct and can be applied to any aspect of climate. I liken the models and the people that believe in them to the blind leading the blind.
The models will ALWAYS have incomplete, inaccurate data, and missing data therefore anything they put out will be WRONG.
The models can not even forecast a season ahead. Weatherbell has had forecast a season ahead that have left the models in the dust. Weatherbell has used past weather analogs which gives a much superior forecast as to what to expect then the forecast models. It has been proven time and time again. Results do not lie.
You’d be hard pressed to find any component of ENSO that climate models simulate properly. One of the findings of Guilyardi et al. (2009) was that climate models do not
It has occurred to me that some here may not understand the fundamentals of the role of latent heat regarding cooling of the earth’s surface.
Latent heat (evaporation of water) is transported aloft by convection where phase change (condensation) releases the heat to the atmosphere and thence to space via radiation. This process is the primary means of cooling of the earth’s surface.
A long time ago, that was the subject of my first comment, here on WUWT. I once used a steam enthalpy diagram, when in the steam-turbine-driven electric generator business.
“This process is the primary means of cooling of the earth’s surface.”
No, the primary means of cooling the earth’s surface is a clear, calm, dry night time sky. You get maximum LWIR escaping to space & thus, cooling of the earth’s surface. The only thing latent heating does is enhance the condensation process by adding a little heat to the rising parcel so it can reach cooler levels & condense more water vapor.
mpainter has it correct.
Conduction drives convection http://www.trifield.com/content/about-air-ions/
Well, in any case, the great Kelvin wave blob is gifting us here on the Left Coast handsomely, thus far, in water year 2014 – 2015. Let it continue to at least 200% of normal.
@ Bob
Do you think this will help clear things up ? http://www.fondriest.com/environmental-measurements/parameters/water-quality/water-temperature/
This affects the oceans which then affects the atmosphere http://physicsworld.com/cws/article/news/2011/jul/19/radioactive-decay-accounts-for-half-of-earths-heat
jmorpuss – the radioactive decay idea rang a bell… somebody accounted for that in a model…
“A spatially varying geothermal heat flux is applied though the ocean floor (Emile-Geay and Madec 2009), which global mean value is 86.4 mW m−2”
Suzanne Demars Canevari July 25, 2014
Canevari, Suzanne Demars. “Comparing Proxy Versus Simulated Data Records of Past Climate Using an Energy Balance Model.” (2014).
http://download.springer.com/static/pdf/306/art%253A10.1007%252Fs00382-011-1259-y.pdf?auth66=1418330748_d6a2db4810c543ca831cd897d4e31f59&ext=.pdf
Steve Goddard has noticed something [odd] about the Unisys sea surface temperature map.
http://stevengoddard.wordpress.com/2014/12/10/something-is-very-rotten-in-denmark/
The Unisys map says that the strait between Greenland and Iceland is 6 degrees hotter than normal (red).
But that strait is currently almost full of sea ice.
Hot ice? WUWT anyone?
phlogiston, as far as I can tell, the sea surface temperature anomalies are from open waters, not sea ice.
? Here is the sea surface anomaly chart
?w=1088&h=816
?w=640
Here is the ice extent graphic…
There does appear to be inconsistencies in several areas For instance the Great lakes are about 10 F cooler then normal, but 1/2 are shown as warmer then average.
David A, thanks. My mistake. Scratch my earlier comment. Yes it does look odd. I don’t know enough about the sea surface temperature dataset that Unisys now uses to provide a comment.
Plus the Hudson bay and inlets and straits northward toward Greenland, all completely ice-filled, are shown as warm anomalies.
Its like I said – must be hot ice.
The reason is simple – so many people in India that curry powder is actually depositing on Arctic ice.
“Introducing more sunlight to the models would create an obvious problem for climate modelers: more sunlight undercuts the modelers’ reliance on infrared radiation from manmade greenhouse gases and allows sunlight through ENSO to explain more of the warming from the mid-1970s to the turn of the century”
My instant reaction was that this was the objective of the model.
Because of the little processes that big processes can grow http://www.physicsclassroom.com/class/estatics/Lesson-3/Coulomb-s-Law
You ever heard the catchy tune ” From little things big things grow”
Agood starting point to create understanding starts here.https://www.youtube.com/watch?v=bht9AJ1eNYc
Re Power et al (2013),
Increased forcing of the climate increases positive NAO states, which is directly associated with La Nina and not El Nino.
As a retired computer modeller I would suggest that since CO2 related gases are the supposed main variable a complete model of its natural behaviour outside man’s influence, especially biological and geological sources and sinks should be the starting point. Without that there is actually zero proof that any changes are not purely coincidental with the theoretical one.
At most perfect understanding of that can prove that CO2 is a possible driver but to be beyond question as claimed requires modelling of any variable known to affect weather. Remember that by definition climate is merely the average of weather over time and area when people wrongly say weather is not climate. Every single measurement has to be provably fully factored in with independently and regularly certified data of the accuracy required for the conclusions and the model predictions regularly tested against actual data.. These are just the basics of a good computer model.
I put the computer models through the same procedure used for design of an electronic product sold to Poundland and found it would not have even got close to passing first review let alone final stage to put the climate models standards into an everyday perspective.
What did you expect? We are in the longest El Niño drought since record keeping began. All the predictions from the climate scientists and so called “experts” concluded that El Niño’s would not only happen more often, but also be stronger. Let the excuses for failed predictions continue.