We discuss climate models a lot, and from the comments here and in other forums it’s clear that there remains a great deal of confusion about what climate models do and how their results should be interpreted. This post is designed to be a FAQ for climate model questions – of which a few are already given. If you have comments or other questions, ask them as concisely as possible in the comment section and if they are of enough interest, we’ll add them to the post so that we can have a resource for future discussions. (We would ask that you please focus on real questions that have real answers and, as always, avoid rhetorical excesses).
Part II is here.
Quick definitions:
- GCM – General Circulation Model (sometimes Global Climate Model) which includes the physics of the atmosphere and often the ocean, sea ice and land surface as well.
- Simulation – a single experiment with a GCM
- Initial Condition Ensemble – a set of simulations using a single GCM but with slight perturbations in the initial conditions. This is an attempt to average over chaotic behaviour in the weather.
- Multi-model Ensemble – a set of simulations from multiple models. Surprisingly, an average over these simulations gives a better match to climatological observations than any single model.
- Model weather – the path that any individual simulation will take has very different individual storms and wave patterns than any other simulation. The model weather is the part of the solution (usually high frequency and small scale) that is uncorrelated with another simulation in the same ensemble.
- Model climate – the part of the simulation that is robust and is the same in different ensemble members (usually these are long-term averages, statistics, and relationships between variables).
- Forcings – anything that is imposed from the outside that causes a model’s climate to change.
- Feedbacks – changes in the model that occur in response to the initial forcing that end up adding to (for positive feedbacks) or damping (negative feedbacks) the initial response. Classic examples are the amplifying ice-albedo feedback, or the damping long-wave radiative feedback.
Questions:
- What is the difference between a physics-based model and a statistical model?
Models in statistics or in many colloquial uses of the term often imply a simple relationship that is fitted to some observations. A linear regression line through a change of temperature with time, or a sinusoidal fit to the seasonal cycle for instance. More complicated fits are also possible (neural nets for instance). These statistical models are very efficient at encapsulating existing information concisely and as long as things don’t change much, they can provide reasonable predictions of future behaviour. However, they aren’t much good for predictions if you know the underlying system is changing in ways that might possibly affect how your original variables will interact.
Physics-based models on the other hand, try to capture the real physical cause of any relationship, which hopefully are understood at a deeper level. Since those fundamentals are not likely to change in the future, the anticipation of a successful prediction is higher. A classic example is Newton’s Law of motion, F=ma, which can be used in multiple contexts to give highly accurate results completely independently of the data Newton himself had on hand.
Climate models are fundamentally physics-based, but some of the small scale physics is only known empirically (for instance, the increase of evaporation as the wind increases). Thus statistical fits to the observed data are included in the climate model formulation, but these are only used for process-level parameterisations, not for trends in time.
- Are climate models just a fit to the trend in the global temperature data?
No. Much of the confusion concerning this point comes from a misunderstanding stemming from the point above. Model development actually does not use the trend data in tuning (see below). Instead, modellers work to improve the climatology of the model (the fit to the average conditions), and it’s intrinsic variability (such as the frequency and amplitude of tropical variability). The resulting model is pretty much used ‘as is’ in hindcast experiments for the 20th Century.
- Why are there ‘wiggles’ in the output?
GCMs perform calculations with timesteps of about 20 to 30 minutes so that they can capture the daily cycle and the progression of weather systems. As with weather forecasting models, the weather in a climate model is chaotic. Starting from a very similar (but not identical) state, a different simulation will ensue – with different weather, different storms, different wind patterns – i.e different wiggles. In control simulations, there are wiggles at almost all timescales – daily, monthly, yearly, decadally and longer – and modellers need to test very carefully how much of any change that happens because of a change in forcing is really associated with that forcing and how much might simply be due to the internal wiggles.
- What is robust in a climate projection and how can I tell?
Since every wiggle is not necessarily significant, modellers need to assess how robust particular model results are. They do this by seeing whether the same result is seen in other simulations, with other models, whether it makes physical sense and whether there is some evidence of similar things in the observational or paleo record. If that result is seen in multiple models and multiple simulations, it is likely to be a robust consequence of the underlying assumptions, or in other words, it probably isn’t due to any of the relatively arbitrary choices that mark the differences between different models. If the magnitude of the effect makes theoretical sense independent of these kinds of model, then that adds to it’s credibility, and if in fact this effect matches what is seen in observations, then that adds more. Robust results are therefore those that quantitatively match in all three domains. Examples are the warming of planet as a function of increasing greenhouse gases, or the change in water vapour with temperature. All models show basically the same behaviour that is in line with basic theory and observations. Examples of non-robust results are the changes in El Niño as a result of climate forcings, or the impact on hurricanes. In both of these cases, models produce very disparate results, the theory is not yet fully developed and observations are ambiguous.
- How have models changed over the years?
Initially (ca. 1975), GCMs were based purely on atmospheric processes – the winds, radiation, and with simplified clouds. By the mid-1980s, there were simple treatments of the upper ocean and sea ice, and clouds parameterisations started to get slightly more sophisticated. In the 1990s, fully coupled ocean-atmosphere models started to become available. This is when the first Coupled Model Intercomparison Project (CMIP) was started. This has subsequently seen two further iterations, the latest (CMIP3) being the database used in support of much of the model work in the IPCC AR4. Over that time, model simulations have become demonstrably more realistic (Reichler and Kim, 2008) as resolution has increased and parameterisations have become more sophisticated. Nowadays, models also include dynamic sea ice, aerosols and atmospheric chemistry modules. Issues like excessive ‘climate drift’ (the tendency for a coupled model to move away from the a state resembling the actual climate) which were problematic in the early days are now much minimised.
- What is tuning?
We are still a long way from being able to simulate the climate with a true first principles calculation. While many basic aspects of physics can be included (conservation of mass, energy etc.), many need to be approximated for reasons of efficiency or resolutions (i.e. the equations of motion need estimates of sub-gridscale turbulent effects, radiative transfer codes approximate the line-by-line calculations using band averaging), and still others are only known empirically (the formula for how fast clouds turn to rain for instance). With these approximations and empirical formulae, there is often a tunable parameter or two that can be varied in order to improve the match to whatever observations exist. Adjusting these values is described as tuning and falls into two categories. First, there is the tuning in a single formula in order for that formula to best match the observed values of that specific relationship. This happens most frequently when new parameterisations are being developed.
Secondly, there are tuning parameters that control aspects of the emergent system. Gravity wave drag parameters are not very constrained by data, and so are often tuned to improve the climatology of stratospheric zonal winds. The threshold relative humidity for making clouds is tuned often to get the most realistic cloud cover and global albedo. Surprisingly, there are very few of these (maybe a half dozen) that are used in adjusting the models to match the data. It is important to note that these exercises are done with the mean climate (including the seasonal cycle and some internal variability) – and once set they are kept fixed for any perturbation experiment.
- How are models evaluated?
The amount of data that is available for model evaluation is vast, but falls into a few clear categories. First, there is the climatological average (maybe for each month or season) of key observed fields like temperature, rainfall, winds and clouds. This is the zeroth order comparison to see whether the model is getting the basics reasonably correct. Next comes the variability in these basic fields – does the model have a realistic North Atlantic Oscillation, or ENSO, or MJO. These are harder to match (and indeed many models do not yet have realistic El Niños). More subtle are comparisons of relationships in the model and in the real world. This is useful for short data records (such as those retrieves by satellite) where there is a lot of weather noise one wouldn’t expect the model to capture. In those cases, looking at the relationship between temperatures and humidity, or cloudiness and aerosols can give insight into whether the model processes are realistic or not.
Then there are the tests of climate changes themselves: how does a model respond to the addition of aerosols in the stratosphere such as was seen in the Mt Pinatubo ‘natural experiment’? How does it respond over the whole of the 20th Century, or at the Maunder Minimum, or the mid-Holocene or the Last Glacial Maximum? In each case, there is usually sufficient data available to evaluate how well the model is doing.
- Are the models complete? That is, do they contain all the processes we know about?
No. While models contain a lot of physics, they don’t contain many small-scale processes that more specialised groups (of atmospheric chemists, or coastal oceanographers for instance) might worry about a lot. Mostly this is a question of scale (model grid boxes are too large for the details to be resolved), but sometimes it’s a matter of being uncertain how to include it (for instance, the impact of ocean eddies on tracers).
Additionally, many important bio-physical-chemical cycles (for the carbon fluxes, aerosols, ozone) are only just starting to be incorporated. Ice sheet and vegetation components are very much still under development.
- Do models have global warming built in?
No. If left to run on their own, the models will oscillate around a long-term mean that is the same regardless of what the initial conditions were. Given different drivers, volcanoes or CO2 say, they will warm or cool as a function of the basic physics of aerosols or the greenhouse effect.
- How do I write a paper that proves that models are wrong?
Much more simply than you might think since, of course, all models are indeed wrong (though some are useful – George Box). Showing a mismatch between the real world and the observational data is made much easier if you recall the signal-to-noise issue we mentioned above. As you go to smaller spatial and shorter temporal scales the amount of internal variability increases markedly and so the number of diagnostics which will be different to the expected values from the models will increase (in both directions of course). So pick a variable, restrict your analysis to a small part of the planet, and calculate some statistic over a short period of time and you’re done. If the models match through some fluke, make the space smaller, and use a shorter time period and eventually they won’t. Even if models get much better than they are now, this will always work – call it the RealClimate theory of persistence. Now, appropriate statistics can be used to see whether these mismatches are significant and not just the result of chance or cherry-picking, but a surprising number of papers don’t bother to check such things correctly. Getting people outside the, shall we say, more ‘excitable’ parts of the blogosphere to pay any attention is, unfortunately, a lot harder.
- Can GCMs predict the temperature and precipitation for my home?
No. There are often large variation in the temperature and precipitation statistics over short distances because the local climatic characteristics are affected by the local geography. The GCMs are designed to describe the most important large-scale features of the climate, such as the energy flow, the circulation, and the temperature in a grid-box volume (through physical laws of thermodynamics, the dynamics, and the ideal gas laws). A typical grid-box may have a horizontal area of ~100×100 km2, but the size has tended to reduce over the years as computers have increased in speed. The shape of the landscape (the details of mountains, coastline etc.) used in the models reflect the spatial resolution, hence the model will not have sufficient detail to describe local climate variation associated with local geographical features (e.g. mountains, valleys, lakes, etc.). However, it is possible to use a GCM to derive some information about the local climate through downscaling, as it is affected by both the local geography (a more or less given constant) as well as the large-scale atmospheric conditions. The results derived through downscaling can then be compared with local climate variables, and can be used for further (and more severe) assessments of the combination model-downscaling technique. This is however still an experimental technique.
- Can I use a climate model myself?
Yes! There is a project called EdGCM which has a nice interface and works with Windows and lets you try out a large number of tests. ClimatePrediction.Net has a climate model that runs as a screensaver in a coordinated set of simulations. GISS ModelE is available as a download for Unix-based machines and can be run on a normal desktop. NCAR CCSM is the US community model and is well-documented and freely available.
Read:
http://www-das.uwyo.edu/~geerts/cwx/notes/chap12/nwp_gcm.html
http://www.aip.org/history/climate/GCM.htm
http://www.agu.org/pubs/crossref/2003/2001GB001841.shtml
http://sedac.ciesin.columbia.edu/mva/iamcc.tg/GCM_thematic_guide.html
“Have any of this models taken data from the 1900 and come up with the weather we are having today? …” – Ed at 296
Ed, in 1908 Arrhenius basically came up with a prediction of our weather – with just his noggin. No computer, maybe not even an adding machine.
He didn’t know we would burn a significant percentage of the earth’s extractable hydrocarbons by 2008, but I think he would have had a fairly accurate notion of what that would mean for the 2008 climate.
David Wojick, was that you named with Inhofe’s lawsuit?
# 299 That may be true… th election o politicians, but what are the advantages when these politicians are the exact same demographic that does not understand the GCM’s either? Sara Palin thought she was an ingineer and an energy expert! She thought she knew better not only in a political, sense, which we all have a right to agree or disagree, but she was telling scientists how to doi things; she complained about Drosophila reseacrh; do you realize it is because of Drosophila we understand things like neuraxins and pproperties of neurogenesis etc… I am not expecting the general public to understand forcings and trends in exquisite detail, however, if the educational system were better equipped the US would not be last in educations, specifically science and math. Politicians need atleast a broad view and some understanding above the generak public as they serve in the best interest of the public; or atleast they are supposed to.
“Unfortunately Kevin, those who *refuse* are the group I am talking about. The vast majority – those who are voting on policy issues, talking to friends, arguing with thier kids’ elementary school teachers – do not grasp the science behind GCM’s, for instance. Most have to make life decisions based on non-scientific reasons.
There is a good reason we elect politicions and not scientists.”
Not all politicians have been willing to listen to scientists for information needed for policy decisions. We have a quite clear change on that account recently in the US.
Read the new issue of Earth or go to http://www.earthmagazine.org. The October issue highlights issues of climate and agriculture issues and the need to revamp the current practices; also the October issue of Nature covers the need or the overhaul of economics as a discipline and politics as usual.
“Economics in crisis,” a scientiic solution,” ‘Nature, October 30, 2008:
‘Economics needs a scientific solution,’Financial engineers have put too much faith in untested axioms and faulty models, says Jean-Philippe Bouchard. To prevent economic havoc, that needs to change.’
“Compared with physics, it seems fair to say that the quantitative success of the economic sciences has been disappointing. Rockets fly to the moon; energy is extracted from minute changes in of atomic mass. What is the flagship achievnent of econoomics? Only its recurrent inability to predict and avert crises, including the current worldide credit crunch?’ Excerpted rom Nature Opinion, p. 1181.
Ok here is a question should we take on more of a utilitarian approach to these issues or prioritarianism approach?
Also if anyone reads the Economist on a regular basis they tend to report things down the middle and global warming is a serious issue among most of the economic experts there as well.
Well, David, investigate away. In the mean time, we’ll go ahead and explain things with the climate models we have–which despite your unsubstantiated allegation do work remarkably well. Perhaps you’d care to give us an example of physics currently missing from the models and an estimate of its impact? I do better with concrete examples that dramatic declarations.
This David Wojick?
http://www.exxonsecrets.org/html/personfactsheet.php?id=1174
Bart wrote:
Narrowing down the uncertainties might help cut through excuses for inaction, but I think you are granting those excuses far more than the present state of knowledge warrants, too.
Bart, Mark, CM, Ray, thank you for your thoughtful replies. I have asked about the spread of scenarios and a common point in your replies is that it is normal in the real world for there to be uncertainty about the risks, and nonetheless we must act.
Let me go back to the analogy of the plane low on fuel, because this is the issue. Landing on a runway is safe. Choosing an alternative improvised place to land is likely to cause injury. But an unpowered descent that crashes short of the runway could kill everyone, whilst a powered descent in a field that you have time to choose and prepare for, may result in far fewer fatalities. Or consider a medical procedure. You could cut out a small tumor and the patient will live 5 years. But if the cancer is more advanced, cutting it out could leave the patient dead on the table. In the public debate on climate change we often seem to talk about this implicit “failsafe” of action. But the Precautionary Principle, if you truly wish to abide by it, demands that you demonstrate that the cure is not worse than the disease. Skeptics and denialists resist change, sure, but they do so with some justification–change is scary, because you don’t really know what will happen. Just as changes to climate cannot be known exactly in advance, so the effects of changes to the economy cannot be known exactly and there may be unforeseen consequences, just as the climate may warm in a very unlikely but absolutely worst scenario.
We have the difficulty of trying to solve what is essentially a new problem. When a person tries to tackle a fire for the first time, using their best judgement and care, they typically make numerous and costly mistakes, and may likely die, for tacking a fire is counterintuitive. Experienced firefighters on the other hand know what to do and can make the situation safe. What I am suggesting is that there is a threshold of knowledge, and that unless we have enough understanding, it is probably safer to not try to fix things, as is the case for a small child trying to get the bread out of the live toaster. Now we may know enough already… or maybe the spread of projections is too wide and we are left wondering whether the patient will live longer if we operate or if we don’t. I just wouldn’t assume that we have a failsafe plan. That really needs to be thought about.
Mark (242), if you are referring to water vapor, I was including that in atmospheric gases. I think your and Gavin’s point is that while one could probably put all forcings into two groups (except major one-off things like volcanoes and plate tectonics…), it is academic and not particularly helpful; one is better off looking at the other individual forcings as separate entities (gives better insight), and there is no benefit (maybe even a detriment) in putting them in nice neat families. I can buy that. I was just curious, not trying to make a point.
Me, a “denialist”? Not at all, and I don’t think anything I wrote actually implied that we’re not causing dangerous warming. I liked the post of yours that I responded to, but I thought you were being a bit snippy with the “unless” … “the earth moves away”, so I poked a little fun at you.
Anyhow, I’m sorry for contributing to the noise on the forum unnecessarily. I’ll restrain myself in the future.
Stefano, It seems to me that the precuationary principle applies to the scientific side of the argument–we know human civilization, with all its complicated infrastructure functions with CO2 in the 280-350 ppmv range. If you are going to move outside of that range, you damned well better show that it will not affect infrastructure catastrophically.
What is more, we know for an absolute fact that energy infrastructure MUST change due to peak oil. We are merely saying that we need to fund sustainable energy sources rather than spewing yet more CO2 into the atmosphere.
In engineering, I cannot predict exactly what weight will cause a bridge to collapse. I can do a calculation and bound the safe range of weights. This is no different. The science of CO2 is settled. We’re now trying to come up with an engineering analysis of what is stable.
Stefano writes in #311, “Skeptics and denialists resist change, sure, but they do so with some justification–change is scary, because you don’t really know what will happen. Just as changes to climate cannot be known exactly in advance, so the effects of changes to the economy cannot be known exactly and there may be unforeseen consequences, just as the climate may warm in a very unlikely but absolutely worst scenario.”
Three points:
1) economic disaster will not negatively affect the ability of the planet to support human life; climate change can.
2) We now have several nations that have in fact met or exceeded their Kyoto targets (and had done so pre-credit crunch), notably the UK, Germany and Sweden–and did so with no major economic trauma. So significant mitigation can be accomplished, and there is now a body of experience indicating how to do it–or at least, how to begin.
3) “Climate *may* warm?” It HAS and IS.
> 215, 220, what we exhale
There’s some merit to Rod’s argument — and some numbers are available on how much of the food people are eating ccomes from energy-intensive, fossil-fuel-dependent farming.
Viz:
http://www.newscientist.com/article/dn16004-cornfed-animals-fuel-america.html
——excerpt—–
Corn-fed animals fuel America
* 22:00 10 November 2008 by Catherine Brahic
Biofuel demand is not the only market pressure being felt by US corn farmers. Much of the fast food that powers Americans – a $100 billion annual market – is indirectly made from corn as well, according to researchers in Hawaii.
Hope Jahren and Rebecca Kraft of the University of Hawaii purchased 486 servings of hamburgers, fries and chicken sandwiches from McDonald’s, Burger King and Wendy’s in Los Angeles, San Francisco, Denver, Detroit, Boston and Baltimore.
Back in the lab, they analysed the carbon isotope content of each serving. Previous research has shown that it is possible to determine whether an animal ate predominantly corn feed or grass from the ratio of C13 to C12 in its body tissue.
The pair found that 100% of the chicken in these three fast-food chains had been reared on corn alone. Some 93% of the beef came from cows that had been fed a corn-only diet. Just 12 burgers – all from west-coast Burger Kings – came from beef that had eaten something else….
—–end excerpt——-
So, there’s actually a useful, educational response to the claim that “exhaling CO2” affects climate. To the extent you’re exhaling carbon identifiably from fossil fuel, higher than the atmospheric background, anyhow.
You are what you eat. So is the atmosphere.
“Rewards less” says ReCaptcha
315, Kevin, Contrarians will always have fun with temperature variations. It’s time for switching tactics
and use Density Weighted Temperatures of the entire atmosphere.
So do we value the current generation more, the future or try to be equal in light o environmental and economic concerns?
# 315 Kevin; I get your point, but an economic collapse would most certainly negatively impact the globe, and the ability of millions, if not hundreds of millions of people to support themselves; eat, have a roof over their head, compete in a non-existent or greatly shattered market.
The environment is certainly something to care about and for and the long term effects of rising fossil fuels emissions will both increase cancer cases, inflation, and economic despair in general. The two are related. Big business is everywhere; those supercomputers are not cheap and these computers we chat on come from old military advancements, as do HD screens and so forth, and yet as the carbon footprints go up in the places like China, well, people are also eating better and living more comortable.
As climatologists attempt to create and improve upon exisitng models,this does not mean that the 3 and six box, STELLA etc… are to be discarded, rather the sensitivity of models, better if not perect modeling o clouds and ocean atmosphere interfaces, there always seems to be something that is lost in translation which is improved upon by input of former models. The boundary conditions, the variance and variables get more easily modeled as more time is utilized and of course various scenarios from empirical observations are inputed; the sites I pasted and the book I recommended explain a lot about the weather and climate models, similarities and differences.
Stefano # 311, excellent points, here is where I question and have asked Gavin and Realclimate on their thoughts, both here and in email. SO2 in the stratosphere is not a good idea, neither is playing around with the system without better predictive qualities of how certain drastic efforts might present with their effects. Many innovative ideas that would work and would ar safer are being ignored at this time. We do need to reduce CO2 emissions, but we also do not want high levels of aerosol sulfates or low altitude ozone everywhere either. Here we must use some caution and discretion. When we speak of hotter periods in the paleoclimate record or more absorption of radiation and other historical-pre-historical climate conditions, keep in mind that 6.6 billion people were not here and that the conditions needed to sustain such a global population (with advent of agriculture, and urbanization) both in way of climate and economics has changed dramatically. I honestly cannot believe any scientist, let alone a climatologist would seriously consider extreme measures that could be even worse to the state of the global climare system. It is true that what to do precisely and in what regards and to what degree in light of reality and human condition is difficult to say the least.
Gavin, I am sorry, I have to ask you again. You said between the lines in #270: “as you equilibrate”, “by the time you get there”, all sounds like now you are talking about a transient process, while in #269 you said “that equilibrium state … happens to absorb more LW than you started with” (which I read as an absorption process related to the state of equilibrium). One might think that if a thermally insulated physical object continues to absorb more than it emits, it would eventually overheat with no limit … A really accurate formulations would be very helpful here.
[Response: The planet is not thermally insulated. If you want equations, this was all worked out in a simple toy model a while back. – gavin]
Then again there is no question I cannot answer for myself… getting through all their published work I can find…any publications you can suggest?
More esoteric and involved?
David B. Benson #118, forgot to get back to you sooner; you are on the right track with SO2 emissions. Clouds do not always form in the manner we might think of them as forming, in relation to our own regional bias. Precipitation does decrease soil moisture goes up; winds change, so depending upon the location, wind effect will influence shading…partially.
Alexi 322, 4W in Feedback isn’t *instantaneous* so the equilibrium state of the system is delayed until you get to whatever the feedback makes that 4W change when the feedbacks are in a stable state.
RodB #312. Water vapour is one. So is ice albedo. So is circulatory change if the change lasts long enough. Lots. You say you included it in your description but you didn’t, did you. You just said “atmosphere”. As David asked, what did you leave out? State specifically. What did you include. State specifically.
They are feedbacks not forcings because they will not change without a force to keep them out of the previous state. Once that happens, they feed back into the system to change the original change either more (positive) or less (negative) feedback processes.
Now how come you don’t understand this when it comes to climate but are absolutely a-ok with it in other places?
Greg, no need to constrain yourself if I can explain why I call you a denialist.
You come across as one because, without ANY working out to see if it’s enough, you just say “The sun is getting hotter, so that could be it” and then use the standard denialist mantra that has been incorrect for the last two years: “The earth is actually cooling now”. Which is a denialist rather than skeptic statement because when it was moving up, ten years wasn’t long enough. When it is going down, it seems 10 years is abolutely fine and definitive.
It isn’t skepticism because a skeptic would be thinking of your statements, Greg, “Has the sun gotten warmer? How much, or is it just in the noise? And has the earth gotten cooler or is it a measurement error?”.
You see, they’d be skeptical of the “no AGW” side too. That is what a skeptic DOES.
But when you pick up threads and only show them as proving no AGW and have not done anything like as much checking of those facts as you demand from the pro AGW proofs, you aren’t being skeptical, you’re denying AGW.
Now if you really are skeptical, use it on both sides of the argument.
If you are a denialist, then trying to hide it will require you to moderate yourself here where the inference proof is written down here in this post.
Stefano, 311: Not as costly a mistake as standing there saying “Well, there’s a 10% chance the wind will blow it away from me”.
Michael, the reason why we vote for politicians rather than scientists is twofold:
1) Only politicians go into politics
2) If scientists go into politics they become politicians
It isn’t their career but the lobbying that causes them not to listen. Either cupidity or the fact that they keep being told a lie and as Goering(?) said, if you repeat a big lie often enough, it will be believed.
And lobbying is done in private.
FOIA on all meetings with lobbyists and no meetings outside of work may solve it, but they employ expensive solicitors and so will find a way around any reasonable law and no unreasonable one should be passed.
David #294. OK, so how do we model when these abrupt events happen? Could the Black Death be modelled? No. There was no way to say WHEN it would spread, even if the contagion and its virulence could be.
So you don’t put them in models because you can’t model WHEN they happen and so can’t model their happening in your model.
Now, when you go and try to scrag Ray, reread your past posts. You’ve been WRONG on more than two occasions. Yet you have never said “oops” or changed your tune.
Now, what events are large enough to change the CLIMATE? Go on, tell us some.
Five. Five that have a reasonable chance of happening in the next 50 years.
And then wait as we tell you how you’re wrong FIVE TIMES. And we’ll not be making it up, you really WILL be wrong five times. Because the only things that can change are already included in the IPCC reports as risk. And you can’t reuse them because you are claiming things not reported on. And you’ll probably not like ones that make GW worse.
Go on.
Five.
Alexi, I’m not sure I understand the difficulty you are having. Of course the system absorbs more IR when it returns to equilibrium–because it is at a higher temperature, it is also emitting more IR (and with a slightly shifted spectrum). Yes feedbacks amplify the effect, but the series is convergent, and therefore the warming finite.
Apart from Co2 positive forcing I hear that other other positive forcings are cancalled out by the negative forcings to about 2.7 w/m^2 and hence the models do not represent them due to this cancellation and hence only Co2 forcings is represented. Is this the case ?
David Wojick writes:
The sun hasn’t changed its luminosity noticeably in 50 years. We’ve been measuring it from satellites since the 1960s and have good proxies for long before that. And what are “abrupt changes?” What kind of abrupt changes? Changes in what?
Mark #326. The 4W forcing isn’t instantaneous either, so the feedback is happening while the forcing is still in progress, so how do you distinguish which is which?
[Response: The forcing is caused by the change in an external parameter – something that isn’t a prognostic variable in the model. It’s very easy to define. That forcing will cause the prognostic variables to change (like the surface temperature) and which in turn will change other aspects (like water vapour) which add to the surface temperature changes. The mechanisms of feedbacks are built into the model, the forcing comes from outside. -gavin]
Mark – Go back and read what Greg originally wrote, particularly the timeframe to which he was referring. His whole point was that it is not as simple as “Make the sun hotter. What happens on Earth? Temperature goes up. Permanently (unless the sun gets cooler, earth moves away, etc).” All temperature reconstructions suggest that the earth is considerably cooler than it was 55 Million years ago despite the current understanding that the sun is hotter and the earth isn’t any farther from the sun. Therefore, the change must be in the “etc.” Your jumping all over him for no good reason.
wayne davidson, #317:
A major source of the variance in GMST is ENSO. ENSO’s redistribution of heat involves the ocean. In the West Pacific and Indian Ocean there is a huge pool of very warm water known as the West Pacific (or Indo-Pacific) warm pool. It’s not just warmer in temperature – it also has a higher sea level. Large, persistent high-pressure cells in the North East Pacific and South East Pacific drive winds which push warm surface waters west, and pile them up in the West Pacific. From time to time – these high pressure cells weaken, which allows the West Pacific Warm pool to expand. The immense heat content of the West Pacific Warm pool spreads out over a much larger area. This is an El Nino event. It causes GMST to rise – but all the heat comes from the ocean. There is also an opposite event – La Nina, which casues GMST to drop. Together the oscillation is referred to as ENSO. One of the strongest El Ninos on record occurred in 1997-1998. It is a major reason why 1998 – tied with 2005 – is the warmest year on record. The claim that the earth hasn’t warmed since 1998 relies on the El Nino driven warmth of 1998 (as well as other misunderstandings). So density weighted temperatures of the entire atmosphere would be a step in the right direction (provided good long-term records for the rest of the atmosphere – which only go back to the 1970s), but strong El Ninos would continue to drive substantial variations in global density weighted atmospheric temperatures. 1998 would very likely still be tied with 2005.
Mark, when you make a flat statement of fact, like this:
> Could the Black Death be modelled? No.
Would you please back it up with a cite or some basis for your belief?
You are being very certain about what you believe. Cites would help those of us who’d rather rely on sources than on trust.
You can look up the work done on that question in Medline; you will also find the public health work done by the military during both WWI and WWII helpful, as well as much recent work since computers came along. Seriously.
about your response 270.
Maybe is it possible to consider that a 4 W/m2 forcing with a 16W/m2 feedback is equivalent, to get an idea of resultant surface temperature, to a 20 W/m2 forcing without feedback?
Gavin, I’d welcome a sanity check on
https://www.realclimate.org/index.php/archives/2008/11/faq-on-climate-models#comment-102620
Seems to me David’s mistake is not noticing that the rapid events are internal to the climate system, not external; they may cause fast changes in albedo for example for a while; and they are modeled, see Dr. Bitz’s work on Arctic sea ice, or any model including volcanos or Atlantic deep water currents etc.
[Response: David is one of the people who won’t accept any attribution study because they can’t include the ‘unknown unknowns’ – he’d be great jury member for the defence; no matter how strong the evidence there is always some doubt. He won’t even accept the attribution of climate changes 1991-1993 to Pinatubo – because, how do you know something else didn’t come along and give the same response at the same time canceling out the effects of the aerosols? Conversations with him on this topic tend to be short and unproductive. – gavin]
337 llewelly, I cant agree more. But I dont know what El-Nino does to DWT measurements.
I have an idea what La-Nina does to a DWT at a distant from the South Pacific Arctic station….
Nothing significant! DWT of Feb Mar 07 vs Feb Mar 08 were very similar, in fact 08 Was warmer by 1 K by radiosonde DWT’s as per graph on my website. La-NIna also likely triggers a cooling effect
over Arctic surface temperatures, as has happened in February March 08, especially has happenned in the 70’s… Even if Arctic surface got cooler, the whole Upper Air remained just as warm or warmer. I think its because during La-Nina
there is less Anvil seeds in the stratosphere, effectively rendering a greater part of the Arctic cloud free, cooling its surface significantly, but not the entire Arctic atmosphere. I would estimate EL-Nino events do the opposite, triggering more thunderstorms world wide, as per evidence in this nice piece of lightning research for North America… :
http://ams.allenpress.com/perlserv/?request=get-document&doi=10.1175%2F1520-0493(2002)130%3C2098%3ATNALDN%3E2.0.CO%3B2&ct=1&SESSID=711e9735fb0a96f10fa517245676ff70
31.1 million lightning strikes for North America in 1998 significantly less for 1999 and 2000….
DWT’s vary a whole lot less than surface temperature averages, reflecting the true nature
of the state of heat in the entire atmosphere. Hence the reason to switch to a stronger
more compelling proof of Global Warming would suck away the last breaths of doubts
spun by mischievous contrarians knowing full well that surface temperature trends are long term,
but count on the ignorance of the lay, and jump up and down very excited by any short term surface temperature drop.
David: What do you mean by “indirect effects”?
If you mean the hypothetical modulation of cosmic radiation by the sun, there is a new study out:
http://www.atmos-chem-phys-discuss.net/8/13265/2008/acpd-8-13265-2008.pdf
Based on this experimental result, it appears that the impacts are very marginal, if anything at all. Seems to settle the case pretty well.
#337, llewelly
“From time to time – these high pressure cells weaken, which allows the West Pacific Warm pool to expand”
I’ll bet the weakening of these cells around 1977, causing the great climate shift and most of the global warming (and all the warming of Siberia and Alaska) the following 30 years is not found in the model runs.
http://virakkraft.com/Low-trop-ENSO.jpg
http://virakkraft.com/SI.ppt
gavin #335 The mechanisms of feedbacks are built into the model, the forcing comes from outside. -gavin
What system is the model modelling? What does “outside” mean?
[Response: It depends on the model. Whatever it is modelling, there are internal prognostic variables and then fixed elements that provide external boundary conditions. For a standard AGCM, the amount of CO2 is a fixed input, as is the sea surface temperature, the shape of the mountains etc. Changes in those external parameters are a forcing. For a coupled ocean-atmosphere model, the sea surface temperature is a prognostic variable and so no longer acts as a forcing. In climate-speak, people often talk about ‘forcings’ as a shorthand for the forcings in a standard coupled ocean-atmosphere model and refer mainly to their TOA radiative effect which is useful for comparing their effects. – gavin]
Wow, right back at you. I was pointing out that your statement was known to be false, not addressing AGW theory. If you had said “A warmer Sun means the Earth will be warmer than it otherwise would be” you would have been close to correct. Perhaps that’s what you meant, but it’s not what you said.
I didn’t see any need to justify my points, because I think they are reasonably in accord with scientific consensus, but let’s look at them now.
Were you unaware of this? Google and some interpolation gives me a figure for the solar constant of about 6 watts per square meter less than today. That’s not a huge amount, but it’s not trivial, either.
Since I’m comparing today with the late Paleocene, when Antarctica was largely ice free, I don’t think this is controversial.
I’m on shakier ground here, so I qualified it. Water vapor is the most important greenhouse gas, but it’s an educated guess that the change in water vapor has been more important than the change in carbon dioxide, but since, as I wrote, “That’s a feedback, and I can’t count it”, it doesn’t really matter.
As the Earth warms carbon dioxide is driven from the ocean, when it cools it goes back in. That makes it a feedback, but I didn’t say it was only a feedback. Our current burning of fossil fuels is clearly a forcing.
One suggestion for the long term cooling is that the arctic ocean has become partially land locked, leading to an ice cap and increasing the Earth’s albedo. Another is that the rise of the Himalayas has sucked carbon dioxide out of the air. A related possibility is that there was increased vulcanism as India approaches Asia, and a reduction since then, has allowed the level to decrease. All of these are related to “continental drift”, to use the old term for it.
The only thing I’m really skeptical about is how soon some say the dire consequences of global warming will manifest, but I don’t see that as a reason not to drastically lower our greenhouse gas emissions now. We should have started twenty years ago.
Bob 336 read what Greg was “replying” to. In the context of “what is forcing” how does that relate? The heat of the sun was still a forcing.
Greg 345. Same thing.
Either of you want to answer?
And yes, I’m aware (as are climatologists who do this for a living. They are aware of this “Daystar” the hot burny thing up in the sky). Now do you think that isn’t taken into account?
So either of you, how does your return change the sun being a forcing?
Hopefully Greg you will understand why I called you a denialist.
Hank, #338. How would it have been modelled? The *effects* could be now, because they are based on measurements we can make and predict. E.g. human population density, virulence, contagiousness and incubation of the plague. Mobility of the people. But it then depends on multiple factors that are not up for measurement (in the same way as we can say “If we had another pinatubo, over here, it would have $this effect” but cannot say WHEN we have it). Such as the vector (we thought it was satan so killed cats. Then rats. Now it is thought something else). Where the pool was and whether it was mutating into something worse. Etc.
Or, alternatively, you could see if you can find a model of the Black Death that would have predicted its occurrence on the date/time it did. I couldn’t.
Could find lots of models on how such a plague WOULD spread. One was used in the Bird Flu scare to see what effect it would have. Another use was for the Foot and Mouth in the UK to see what needed to be done to stop its spread.
Which, incidentally, would have been a lot easier if farmers hadn’t lied about it or moved stock illegally so they could make short term profit. Sound familiar?
gavin #344 “the shape of the mountains etc. Changes in those external parameters are a forcing”
Please explain how changes in the shape of the mountains are an issue.
[Response: They are an external parameter that could change, leading to a response in the climate. Bit of a longer timescale than the 20th C, but the principle is the same. I think you are overthinking this – it really isn’t that difficult. – gavin]
Further to Gavin’s #335 reply. It’s something that isn’t modelled in the model itself.
The Sun is the prime example because no earth atmosphere model includes the Sun simulation within.
Another would be anthropological CO2. Because that can’t be predicted. E.g. the crash recently has caused oil demand to drop dramatically. A climate model doesn’t include economic models too. And if it had, it would have got it as wrong as the economists did.
Although a guess could be used to see what would happen if… Which is what the IPCC did. “if we double 1980 CO2 what would happen?” etc.
IMO though the easiest way to tell the difference is if the process causes a change to remain. Water squirted in the air won’t. It rains too quickly for the trapped heat to keep the air that moist.
A feedback causes more change based on the change in *forcing* levels but won’t cause a change in itself with no forcing changes.
So a change in geography could do that. If Antartica weren’t stuck over the south pole, it would not be so cold there. Or so dry. And continents move on a scale that makes climatology seem quick.
If you’re looking for a list of what is a forcing and what is not, you’ll have to answer the question “for what model?”. Either that or accept the ones that are in all of the IPCC models included. David won’t like it because they don’t include models with the unknowns in it, but it’s either that or answer “which model do you mean”. And in that case, you’re better off talking to whoever wrote the model.
Whichever the latest Met Office (UK) model is, ask the Met Office.
Meteo France’s model? Ask Meteo France.
Someone here may know the answer, but there are a lot of models out there, so you may get a lot of different answers. This doesn’t mean the models are wrong, it means they work differently when you look to the detail.
And that, really, is what “what is forcing” that seems to be the current meme is. A detail. The “not a detail” answer is “read a book” (Apologies to One Ton Tony/Handy). A dictionary will tell you.
> Hank, #338. How would it have been modelled?
Of course at the time of the Black Death, modeling wasn’t being done.
Abrupt events do occur, and are modeled. FAQ.
See Gavin’s inline comment earlier. We aren’t likely to convince David; we’re here to suggest questions for the FAQ. Let’s dance.
349. The result IF one happens is modeled. When they happen is not effective at doing so. See earthquake predictions (if you haven’t seeded it LIBERALLY with sensors, and even with them you only have a few weeks if that of advance warning).
PS They were $700 glasses you s.o.b.!