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How do Climate Models Work?

Posted on 21 January 2012 by climatesight

Guest post by Kate from Climate Sight.

This is a climate model:

T = [(1-α)S/(4εσ)]1/4

(T is temperature, α is the albedo, S is the incoming solar radiation, ε is the emissivity, and σ is the Stefan-Boltzmann constant)

An extremely simplified climate model, that is. It's one line long, and is at the heart of every computer model of global warming. Using basic thermodynamics, it calculates the temperature of the Earth based on incoming sunlight and the reflectivity of the surface. The model is zero-dimensional, treating the Earth as a point mass at a fixed time. It doesn't consider the greenhouse effect, ocean currents, nutrient cycles, volcanoes, or pollution.

If you fix these deficiencies, the model becomes more and more complex. You have to derive many variables from physical laws, and use empirical data to approximate certain values. You have to repeat the calculations over and over for different parts of the Earth. Eventually the model is too complex to solve using pencil, paper and a pocket calculator. It's necessary to program the equations into a computer, and that's what climate scientists have been doing ever since computers were invented.

A pixellated Earth

Today's most sophisticated climate models are called GCMs, which stands for General Circulation Model or Global Climate Model, depending on who you talk to. On average, they are about 500 000 lines of computer code long, and mainly written in Fortran, a scientific programming language. Despite the huge jump in complexity, GCMs have much in common with the one-line climate model above: they're just a lot of basic physics equations put together.

Computers are great for doing a lot of calculations very quickly, but they have a disadvantage: computers are discrete, while the real world is continuous. To understand the term "discrete", think about a digital photo. It's composed of a finite number of pixels, which you can see if you zoom in far enough. The existence of these indivisible pixels, with clear boundaries between them, makes digital photos discrete. But the real world doesn't work this way. If you look at the subject of your photo with your own eyes, it's not pixellated, no matter how close you get - even if you look at it through a microscope. The real world is continuous (unless you're working at the quantum level!)

Similarly, the surface of the world isn't actually split up into three-dimensional cells (you can think of them as cubes, even though they're usually wedge-shaped) where every climate variable - temperature, pressure, precipitation, clouds - is exactly the same everywhere in that cell. Unfortunately, that's how scientists have to represent the world in climate models, because that's the only way computers work. The same strategy is used for the fourth dimension, time, with discrete "timesteps" in the model, indicating how often calculations are repeated.

It would be fine if the cells could be really tiny - like a high-resolution digital photo that looks continuous even though it's discrete - but doing calculations on cells that small would take so much computer power that the model would run slower than real time. As it is, the cubes are on the order of 100 km wide in most GCMs, and timesteps are on the order of hours to minutes, depending on the calculation. That might seem huge, but it's about as good as you can get on today's supercomputers. Remember that doubling the resolution of the model won't just double the running time - instead, the running time will increase by a factor of sixteen (one doubling for each dimension).

Despite the seemingly enormous computer power available to us today, GCMs have always been limited by it. In fact, early computers were developed, in large part, to facilitate atmospheric models for weather and climate prediction.

Cracking the code

A climate model is actually a collection of models - typically an atmosphere model, an ocean model, a land model, and a sea ice model. Some GCMs split up the sub-models (let's call them components) a bit differently, but that's the most common arrangement.

Each component represents a staggering amount of complex, specialized processes. Here are just a few examples from the Community Earth System Model, developed at the National Center for Atmospheric Research in Boulder, Colorado:

• Atmosphere: sea salt suspended in the air, three-dimensional wind velocity, the wavelengths of incoming sunlight
• Ocean: phytoplankton, the iron cycle, the movement of tides
• Land: soil hydrology, forest fires, air conditioning in cities
• Sea Ice: pollution trapped within the ice, melt ponds, the age of different parts of the ice

Each component is developed independently, and as a result, they are highly encapsulated (bundled separately in the source code). However, the real world is not encapsulated - the land and ocean and air are very interconnected. Some central code is necessary to tie everything together. This piece of code is called the coupler, and it has two main purposes:

1. Pass data between the components. This can get complicated if the components don't all use the same grid (system of splitting the Earth up into cells).
2. Control the main loop, or "time stepping loop", which tells the components to perform their calculations in a certain order, once per time step.

For example, take a look at the IPSL (Institut Pierre Simon Laplace) climate model architecture. In the diagram below, each bubble represents an encapsulated piece of code, and the number of lines in this code is roughly proportional to the bubble's area. Arrows represent data transfer, and the colour of each arrow shows where the data originated:

We can see that IPSL's major components are atmosphere, land, and ocean (which also contains sea ice). The atmosphere is the most complex model, and land is the least. While both the atmosphere and the ocean use the coupler for data transfer, the land model does not - it's simpler just to connect it directly to the atmosphere, since it uses the same grid, and doesn't have to share much data with any other component. Land-ocean interactions are limited to surface runoff and coastal erosion, which are passed through the atmosphere in this model.

You can see diagrams like this for seven different GCMs, as well as a comparison of their different approaches to software architecture, in this summary of my research.

Show time

When it's time to run the model, you might expect that scientists initialize the components with data collected from the real world. Actually, it's more convenient to "spin up" the model: start with a dark, stationary Earth, turn the Sun on, start the Earth spinning, and wait until the atmosphere and ocean settle down into equilibrium. The resulting data fits perfectly into the cells, and matches up really nicely with observations. It fits within the bounds of the real climate, and could easily pass for real weather.

Scientists feed input files into the model, which contain the values of certain parameters, particularly agents that can cause climate change. These include the concentration of greenhouse gases, the intensity of sunlight, the amount of deforestation, and volcanoes that should erupt during the simulation. It's also possible to give the model a different map to change the arrangement of continents. Through these input files, it's possible to recreate the climate from just about any period of the Earth's lifespan: the Jurassic Period, the last Ice Age, the present day...and even what the future might look like, depending on what we do (or don't do) about global warming.

The highest resolution GCMs, on the fastest supercomputers, can simulate about 1 year for every day of real time. If you're willing to sacrifice some complexity and go down to a lower resolution, you can speed things up considerably, and simulate millennia of climate change in a reasonable amount of time. For this reason, it's useful to have a hierarchy of climate models with varying degrees of complexity.

As the model runs, every cell outputs the values of different variables (such as atmospheric pressure, ocean salinity, or forest cover) into a file, once per time step. The model can average these variables based on space and time, and calculate changes in the data. When the model is finished running, visualization software converts the rows and columns of numbers into more digestible maps and graphs. For example, this model output shows temperature change over the next century, depending on how many greenhouse gases we emit:

Predicting the past

So how do we know the models are working? Should we trust the predictions they make for the future? It's not reasonable to wait for a hundred years to see if the predictions come true, so scientists have come up with a different test: tell the models to predict the past. For example, give the model the observed conditions of the year 1900, run it forward to 2000, and see if the climate it recreates matches up with observations from the real world.

This 20th-century run is one of many standard tests to verify that a GCM can accurately mimic the real world. It's also common to recreate the last ice age, and compare the output to data from ice cores. While GCMs can travel even further back in time - for example, to recreate the climate that dinosaurs experienced - proxy data is so sparse and uncertain that you can't really test these simulations. In fact, much of the scientific knowledge about pre-Ice Age climates actually comes from models!

Climate models aren't perfect, but they are doing remarkably well. They pass the tests of predicting the past, and go even further. For example, scientists don't know what causes El Niño, a phenomenon in the Pacific Ocean that affects weather worldwide. There are some hypotheses on what oceanic conditions can lead to an El Niño event, but nobody knows what the actual trigger is. Consequently, there's no way to program El Niños into a GCM. But they show up anyway - the models spontaneously generate their own El Niños, somehow using the basic principles of fluid dynamics to simulate a phenomenon that remains fundamentally mysterious to us.

In some areas, the models are having trouble. Certain wind currents are notoriously difficult to simulate, and calculating regional climates requires an unaffordably high resolution. Phenomena that scientists can't yet quantify, like the processes by which glaciers melt, or the self-reinforcing cycles of thawing permafrost, are also poorly represented. However, not knowing everything about the climate doesn't mean scientists know nothing. Incomplete knowledge does not imply nonexistent knowledge - you don't need to understand calculus to be able to say with confidence that 9 x 3 = 27.

Also, history has shown us that when climate models make mistakes, they tend to be too stable, and underestimate the potential for abrupt changes. Take the Arctic sea ice: just a few years ago, GCMs were predicting it would completely melt around 2100. Now, the estimate has been revised to 2030, as the ice melts faster than anyone anticipated:

At the end of the day, GCMs are the best prediction tools we have. If they all agree on an outcome, it would be silly to bet against them. However, the big questions, like "Is human activity warming the planet?", don't even require a model. The only things you need to answer those questions are a few fundamental physics and chemistry equations that we've known for over a century.

You could take climate models right out of the picture, and the answer wouldn't change. Scientists would still be telling us that the Earth is warming, humans are causing it, and the consequences will likely be severe - unless we take action to stop it.

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1. Very accessible explanation of climate models. Great job. I'll be sharing it. Personal nit - the advocacy in the last sentence jars with the rest.
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2. Excellent article! I have in the past used the zero dimensional model in discussions and for my own evaluations - use one of the many radiative transfer programs such as MODTRAN to see how changes in GHG's affect total emissivity, and directly calculate resulting global temperatures from direct forcings. Or look at paleo evidence such as Milankovich insolation changes, calculate the direct forcing changes, compare to recorded total temperature changes, and see what that tells you about feedbacks and climate sensitivity. High resolution? Regional forcasts? No. But even the simplest model can provide useful, indicative results.
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3. Excellent work, Kate. It's nice to see the fruit of all your work in preparation for the AGU meeting. Your final paragraph says it all though: Palaeoclimatology tells us all we really need to know (as in James Hansen's Storms of my Grandchildren). Unfortunately, so-called "skeptics" refuse to accept the evidence of either models or palaeoclimatology. May be record-breaking numbers of tornados over places like the University of Alabama in Huntsville (Roy W Spencer's home turf?) and the spiralling insurance costs of fixing what were once rare events will change their minds? We can but hope...
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4. Do any models use adaptive grids or adaptive timestep ? sidd
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5. I'd have preferred "projection" instead of "prediction," as I think that's more accurate, but that's a nit compared to the overall post. Thanks!
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6. Oh, another interesting point - you can apply Shannon's Sampling Theorem to determine how small a surface grid you need to accurately represent the climate in a region, or to say how much accuracy you get from a given grid. 5x5 degree grids are pretty inaccurate (they'd miss pretty much all of the climate variation in my home state of Colorado), but a 1x1 degree grid gets the bulk of it. Sampling theory indicates that horizontal resolution isn't nearly as important as vertical resolution, too. Fun stuff. :)
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7. Thank you for a clear description.
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8. #1 barry - I see what you mean about the last sentence, but I would disagree that it's "advocacy". It's just cause and effect. If we stop emitting so many greenhouse gases, the problem will diminish; if not, it will continue to be a problem. I tried to go for the "policy relevant but not policy prescriptive" angle. Thanks for reading! Kate
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9. Kate : very clear and informative, but I don't understand your conclusion, "You could take climate models right out of the picture, and the answer wouldn't change. Scientists would still be telling us that the Earth is warming, humans are causing it, and the consequences will be severe - unless we take action to stop it." The sole radiative effect of a doubling CO2 on surface temperature is not so severe, approx 1,2 K. So, don't we need a model to evaluate the coming feedbacks from 2xCO2 and the real equilibrium temperature after relaxation? After all, if models were concluding that negative feedbacks equilibrate with positive and that 540 ppmv CO2atm would produce something like 1 K of surface warming, we would probably be quite indifferent to AGW. It is just because models do not conclude in this way that we have some reasons for concern. So, I would say we do need climate models and rely on their quality to inform our collective decisions.
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10. skept.fr @9, I agree, but only to a point. We both know that there are other ways of estimating equilibrium climate sensitivity. We also know about, for example, the Clausius-Claperyon relation and how that relates to the WV feedback. We also know that way back when Arrhenius and Tyndall estimated what might happen if we doubled CO2 without the aid of complex AOGCMs (coupled atmosphere ocean general circulation models). Additionally Annan and Hargreaves (2006) wrote a neat paper. This is how RealClimate describe what they (A+H) did and found: "A+H combine three independently determined constraints using Bayes Theorem and come up with a new distribution that is the most likely given the different pieces of information. Specifically they take constraints from the 20th Century (1 to 10ºC), the constraints from responses to volcanic eruptions (1.5 to 6ºC) and the LGM data (-0.6 to 6.1ºC – a widened range to account for extra paleo-climatic uncertainties) to come to a formal Bayesian conclusion that is much tighter than each of the individual estimates. They find that the mean value is close to 3ºC, and with 95% limits at 1.7ºC and 4.9ºC, and a high probability that sensitivity is less than 4.5ºC. Unsurprisingly, it is the LGM [Last Glacial Maximum] data that makes very large sensitivities extremely unlikely. The paper is very clearly written and well worth reading for more of the details. The mathematics therefore demonstrates what the scientists basically thought all along." We also know from paleo data, that rapid injections of CO2 (and other GHGs) into the atmosphere changed the climate dramatically and that those changes brought about significant changes to the biosphere. This is what respected scientist Dr. Matt Huber (Purdue University) has to say on lessons we can learn from the past: "Climate scientists don’t often talk about such grim long-term forecasts, Huber says, in part because skeptics, exaggerating scientific uncertainties, are always accusing them of alarmism. “We’ve basically been trying to edit ourselves”, Huber says. “Whenever we we see something really bad, we tend to hold off. The middle ground is actually worse than people think. “If we continue down this road, there are really is no uncertainty. We’re headed for the Eocence. And we know what that’s like." Dr. Matt Huber, October 2011. RealClimate also provide a neat back-of the-envelope calculation in six easy steps.
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11. Kate @ 8 One of the best things Pielke Snr has said is that we should take action not because we're certain (which we're not), but because we DON'T know what's going to happen. There's a chance the disruption could be less than we fear, but it's also possible that the consequences could be dire. If you had written that there could potentially be severe consequences, then I'd have no nit to pick. No, you're not prescribing policy, you're prescribing a generalised future. It may be that I'm overly sensitive to unqualified language from many rounds with argumentative contrarians, but OTOH, I'm sure sci profs would also raise an eyebrow at unqualified predictions. When the rest of the post is a straightforward explanation of the science, the last sentence seems to come from a different place. ------------- Well, I've beaten that nit to death. Your post is the clearest, most accessible description of climate models for lay people I've yet read (reading on climate sci voraciously since March 2007). Good on you.
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12. Very worthwhile read. I would join in a small concern about the use of prediction instead of projection. Prediction is so closely associated with weather that using it in place of projection invites problems. Weathermen are about 50-50 in prediction success.
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13. #3 Martin Lack - at the AGU meeting, I was struck by some of the work by James Hansen and Ken Caldeira that showed climate sensitivity in the PETM to be around 6 C. That may or may not be a good analogue for today, though, because climate sensitivity can change depending on factors such as the position of continents.
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Moderator Response: [muoncounter] A quick look through the archives shows that such 'advocacy' posts have shown up from time to time. Posts regarding communication of climate science, whether intended to persuade or inform, are always relevant.
15. Hmm, good point about prediction vs projection. Although the correct scientific term is projection, in light of Susan Joy Hassol's and Richard Sommerville's paper in Physics Today, I thought prediction would be easier for a general audience to understand. Worth reconsidering, though - it's a grey area.
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16. barry @ 14... I think it's tough because so many of the SkS authors are also people who frequently comment of various online forums where there is an element of "doing battle" with severe attempts to undermine climate science. I believe the intent is to persuade people to listen to the science, warts, uncertainties and all. Honestly, I still continually deal with people who are absolutely emphatic that CO2 is too small a part of the composition of the atmosphere to have any effect whatsoever. And there is everything from that extreme up to even Dr Pielke who recently stated to high school students that "Arctic ice is melting but Antarctic ice is growing" without any hint of how completely misleading such a statement is with regards to global ice mass loss. I think the intent is to persuade people to better understand climate science in the face of considerable and organized efforts to the contrary. Sometimes that may translate into over reactions. If there is any advocacy, it should be the advocacy and communication of science.
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17. barry @14, I think there is a little in what you say, but it is also a little cock eyed. Consider, for example, the post supporting Katherine Hayhoe. It is not advocacy. On the contrary, it is merely standing up to say that death threats and threats of violence against children are not acceptable means of public discourse. I happily grant that that is not a scientific point, but nor need it be. It is perfectly acceptable, regardless of the particular objectives of a website, to take a stand against such torrents of hate. I am certain that should a prominent denier receive similar threats against them, SkS would take a stand against that as well, and would not thereby be advocating their political position. Consider also the Debunking Handbook. That is an exposition of the tools and pitfalls of rational exposition. As such it is useful (in principle) to any side of politics, and to any side of a scientific argument. One of the reasons for its great success is its very applicability outside of the climate debate. Its publication, therefore, does not take sides, and does not advocate. Indeed, where the various opponents of climate science as science orientated as they purport to be, the debunking handbook would be as useful a tool to them as it is to us. Or, again, consider the Stephen Schneider video. Stephen Schneider's message was straightforwardly scientific. That the science has policy implications does not alter that fact, for Schneider did not advocate any particular policy response. That the comments focused on issues of communication rather than the scientific issues canvassed by Schneider is hardly the fault (and definitely opposite to the intention) of the author of the article. However, there have been some examples of more political blog posts on Skeptical Science, some of which undoubtedly qualify as advocacy. Two examples of more political posts have been posts by Daniel Bailey simply asking the question as to what climate responses would be considered consistent with a conservative political philosophy given the fact that a response does seem necessary based on the science. While definitely political, that is not advocacy. A recent blog by Sphaerica could reasonably be characterized as advocacy. Although based on science, it went beyond science to reflect a personal view of why Sphaerica has concerns for the future. The key feature here, however, is that it is a blog post. The articles which form the back bone and reason for existence of Skeptical Science are the myth rebutals. All 173 of them. They are strictly science only. So are most of the blog posts, but as blog posts there is room for occasionally greater latitude. I don't see a problem with that.
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18. This is from the viewpoint of a non-scientist aka one of the general public. From the general public’s point of view, most people would make a prediction about who will win the super bowl. Projection is viewed as a potential range. There are two reasons why using prediction can be a problem – especially when discussing models. First, it is more than a simple understanding for the public. People that understand, and communicate the information to the public frame the discussion for the rest of us. With a prediction, ‘we’ will have a mindset of high expectations for accuracy. Anything less will encourage doubt amongst those that are not yet familiar with models if/when they are off by very little. Projection sets the frame of a range of possibilities, and allows for a broader view of what we may expect. Second, when this does not come to pass with the “exact” prediction it gives ammo to create new arguments as well as sidetrack and confuse the important issues. Particularly, the arguments models are inaccurate, scientists do not understand climate well enough etc. Neither word is wrong, but coming from experts they carry more weight than may be realized. This does not take away from the post, however I feel it is important that science communicators understand how they can affect viewpoints and expectations. Regardless, this is the best information I have read about climate models. Great post!
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19. Rob@16 - empathise completely. I've slogged it out in the trenches to the limits of my meagre ken and become raw with frustration. Carried the effects around for a while, too. muon inline @14 How does the article on a Christian scientist's 'values', the abusive responses to her video, and the suggestion at the end to 'like' a facebook page against scientific intimidation fit within the context of removing "the politics from the debate by concentrating solely on the science"? That's the clearest recent example, but it's not the only one. It looks like a subtle shift is under way here, and if so it should be flagged one way or another, embraced or warded against. (Don't mean to drag the thread off-topic. Let me know if there's a better place to discuss this - if the comments are worth pursuing)
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Moderator Response: [muoncounter] My observation regarding prior 'advocacy' articles was to suggest that there is no evidence of a change in trend. If you would like to continue discussing support for Dr. Hayhoe, here is the appropriate thread.
20. barry @19, "liking" the facebook page is not an endorsement of Hayhoes science, let alone he Christianity or her politics. It is an endorsement of the fact that engaging in politics by intimidating opponents with threats of violence to their children, and death threats to them is not acceptable. How can it be inappropriate in any context to make that stand? In fact, of any critics of climate change are reading this, I heartily recommend that you "like" the "We support Katherine Hayhoe" page as a clear endorsement of your opposition to politics by intimidation.
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21. Excellent an informing post. For those of us who do not already know, what is Kate's research on? It must be interesting.
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22. Kate, I have some experience in using modeling software (CORMIX for water and Beeline for air). As anything else accurate output depends on accurate input. Your article is very interesting and informative. I have a few questions: 1. Is the software available to the general public? 2. Is there a standard database for the input? 3. Are you able to remove/add anthropogenic factors? Also, I'm curious about what "actions" you realistically propose.
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23. Thanks to Kate for this accessible explanation. The "skeptical" sites tend to trivialize the computer models as if they were an iPad app that "alarmists" whip up in their spare time.
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24. #21 michael sweet: I am an undergraduate math student just starting to get into climate modelling research. Eventually I want to develop and run experiments on climate models (not necessarily GCMs, I am also interested in lower-resolution, faster EMICs). Last summer I did a project with Steve Easterbrook, a computer science professor at the University of Toronto, to examine the software architecture of climate models: how they are built and organized. I give a summary of our research and findings here. #22 apiratelooksat50: 1) In general, it's really easy to get access to GCM code. I summarize my experiences here: the process ranges from an open download to an automated registration process to an informal email requesting access. Some of the models developed at governmental labs outside the United States require that a more formal license agreement be signed. 2) Not really. Input format depends hugely on the architecture of the model, which changes so drastically between GCMs. 3) Yes, absolutely. You can give the models any combination of forcings, whether natural or anthropogenic, that you want. You can even turn the Sun off and see what happens. What actions I realistically propose - this is straying away from my original post, so I must stress that I am taking off my scientist hat and putting on my public hat. That said, I am a fan of the revenue neutral "fee and dividend" system that James Hansen advocates.
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25. Fascinating article, many thanks for writing and posting it. I thought it was particularly interesting that the models are run from scratch rather than trying to initialise them with real world data. The fact that they then accurately simulate observations is good evidence that they have 'skill' and aren't just being tweaked by making them adjust to observations, as some would have you believe. Here's something I'm curious about: In the example of Arctic sea ice extent falling substantially faster than projected, is that because the projections got the Arctic climate wrong (perhaps polar amplification is greater than projected), or is it because they got the climate right but the ice has turned out to respond faster than projected? Or is it neither of these things, or both? Cheers...
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26. Icarus @ 25 the graph is taken from The Copenhagen Diagnosis, page 32 of the first edition. They say:
This dramatic retreat has been much faster than that simulated by any of the climate models assessed in the IPCC AR4 (Figure 13). This is likely due to a combination of several model deficiencies, including: 1) incomplete representation of ice albedo physics, including the treatment of melt ponds (e.g., Pedersen et al. 2009) and the deposition of black carbon (e.g. Flanner et al. 2007; Ramanathan and Carmichael 2008); and 2) incomplete representation of the physics of vertical and horizontal mixing in the ocean (e.g. Arzel et al. 2006).
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27. Question: Why should I trust GCM's future projections when they can't even mimic seasonal ocean temperature variability? The CMIP3 ensemble was lacking in this regard, but perhaps CMIP5 will be better.
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28. @climateadj: I suspect that the GCMs were not designed to "mimic seasonal ocean temperature variablilty."
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29. climateadj how can I tell that next summer will be warmer than now if I can't predict the weather 10 days from now? Short term variability is one thing, climate another.
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30. @John/Riccardo: Seasonal ocean temperature variability is a somewhat constant feature. I'm not talking about trends which fluctuate. It's simply measuring the annual variance of temps for each gridbox, averaging out the longitudinal dimension, and plotting the results. The ARGO derived pattern is consistent from year to year or any 12 month period, regardless of ENSO. There's a clear pattern that the CMIP3 ensemble doesn't replicate very well. Now the CMIP3 models were generated circa 2004 when the ARGO network was in it's infancy. Perhaps this test is asking too much. It'll be interesting to see if the CMIP5 ensemble does better.
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31. @John: I suspect that GCM's model radiative forcings, conduction, and convection. Annual variability is a function of all three. It is predictable and testable against observations.
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32. Quote However, the big questions, like "Is human activity warming the planet?", don't even require a model. The only things you need to answer those questions are a few fundamental physics and chemistry equations that we've known for over a century. You could take climate models right out of the picture, and the answer wouldn't change. Scientists would still be telling us that the Earth is warming, humans are causing it, and the consequences will likely be severe Exactly my point - For the Public Understanding of the issue the science is clear and uncomplicated. By surrendering that clarity of message and allowing the skeptics to muddy the waters a great dis-service has been allowed. Within Science itself of course debate, experiment and challenge must continue to tease out the nuances, feed-back loops and the simple understanding of process and detail but how Science works shouldn't have obscured the basic science and the clarity of its message to the General Public. Johnb.
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33. climateadj: What is the source of your assertion that GCMs "can't even mimic seasonal ocean temperature variability"? BTW, climate models cannot "replicate" the real world, they can only "simulate" it.
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34. @John: The source of my assertions are my own calculations. The link below is my analysis. Since I'm just a hack with marginal programming skills, there is a high likelihood of error. I am not a scientist of any sort. Caveat Emptor: https://sites.google.com/site/climateadj/ocean_variance
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35. climateadj; Thanks for the clarification and the link to your calculations. Alas, I am not qualified to assess the reasonableness of your computations and of your tentative conclusion.
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36. climateadj @35, I do not see where you performed a mathematical step in order to examine seasonal variance rather than variance simpliciter. I'm not up on the maths involved, but it seems to me that if you just plot the variance of the data, it may well be dominated by quasi-periodic oscillations such as ENSO, or indeed possibly even by long term trends if seasonal variation is low compared to long term changes in temperature.
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37. barry @ 14 What do you recommend then; just carry on as we are watching the world go to hell in a handcart? We see Copenhagen go by in a flurry of missed opportunities and then Durban do the same. We see the sort of tricks that the likes of Monckton, Lawson, Lindzen, Christy, Spencer and Michaels, to name but a few of the denialati, get up to. We see the enormous support given by the fossil fuel industry towards a b.a.u. scenario and nothing much happens to raise that publicly as an issue. We have America, one of the world’s most polluting nations, with a mainstream political party that has climate change denial as a rite of passage to membership. We have articles published here repeatedly reporting how things are just moving from bad to worse and we do nothing, but simply get the science right! Anything else is “Just not cricket, old boy!” Well, to hell with that. I come here to understand the science and this site is excellent at meeting that need. But the reason behind my desire to understand the science is that I am very concerned about the future that my children and grandchildren face. I assume that many others have the same concerns as me and come to the same trough of knowledge for the same purpose. So from time to time I might write something that could be considered advocacy. I make no apologies for that. I will do whatever I can to protect my family and if that is frowned upon, well go on then, frown. My family come before all other considerations. I am not alone in getting exasperated at the lack of action on combating climate change. It was obvious in her excellent article on Methane outgassing in the Arctic that Dr Natalia Shakhova is fed up with the lack of action on the issue. Perhaps she, more than most, can see just how urgent things are, especially if the methane outgassing follows a worst case scenario. She too can see just how much of a hindrance all this arguing about whether the warming is anthropogenic or not is. If it were not climate science, but the science surrounding that of a particular model of aircraft, say, we would have grounded the whole fleet long before now because of the danger posed by continued operation. Yet planet earth can carry on with business as usual. We can always catch another aircraft. We cannot catch another earth.
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38. climateadj use a dice as an analogy. You can hardly predict next result but in the long run you know the probability.
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39. Tom... how astute of you! I simply (mis)described climatoloy for annual variability. My experience is that one can run any 12mth perod thru my program and end up with the same result. Then again, I'd like to see a professional examination of this subject.
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40. funglestrumpet@38, what you say is exactly what I imagine is happening. We have for years tried to unlace politics and ideology from the discussion of the science, demanded of ourselves that we maintain a purely scientific argument, and pointed out to the contrarians where they err in this respect. Their perversion of reason is the most frustrating aspect of the general debate, I reckon, and I think the reason-based community has become weary of being primly objective with little apparent effect. At its heart, the debate over climate science is an activist pursuit. It may be we've become tired of covering it with a mask of neutrality, and that it's not enough to "carry on as we are." Tom Curtis@17, agree with all of that. However, the posts you cite are not representative of occasional forays at SkS into areas that are not purely scientific. These are all recent. I believe this is a trend, and the extra latitude in the articles here lately has resulted in some disconnects with the mission statement, which is explicit about devotion to pure science. http://www.skepticalscience.com/about.shtml Technically, the mission statement might be amended. But I just wanted to bring this adjustment in priorities (if such is occurring) to light so that we're aware of it and more effective for being so.
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Moderator Response: [muoncounter] Please take continued discussion of what you perceive to be some sort of 'sea change' at SkS to The Big Picture. You will note that particular thread, originally written in 2010, has a section on advocacy and policy.
41. Does your model predict the slowdown in temperature increase since 1998? Does it have the property of even 'accurate' regression models of increasing forecasting error as fufure forecasting distance increases? By what criteria do you judge the forecasting accuracy of your model?
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42. As this is a basic article on how climate models work, I'm not sure if my question is on the right place. The last figure shows the observed ice loss vs. the IPCC projection, do you know how much energy this extra melt has taken? I'd imagine it's out of the other parts of the earth system, so a slight overestimate of temperatures could be seen elsewhere in the models. I guess what I mean to ask is, has the heat transport from the tropics to polar areas speeded up, and how could this be measured?
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43. Kate, I've been a bit overwhelmed with work, so I didn't get to see this post before you put it up, but when you mention that grid cells are often 100 km wide, which seems large, you might also point out that the surface of the earth is 510,072,000 km2. A 100 km cube/wedge will have a contact surface area of of 10,000 km2. This means (not accounting exactly for the spherical shape of the earth, and how the wedges change in dimensions as one approaches the poles) that you have somewhere around 51,000 cubes in your surface grid alone!!! And then you need to multiply that by the number of levels in your atmospheric representation! So while 100 km may seem "huge" to a small, lonely, self-centered person (or any other sort of person), when one is talking about a global climate model... not so much.
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44. barry @ 13:08pm (comment numbers all at '1' at time of writing) I would support your point if the articles showed any sign whatsoever of becoming typical of GreenPeace output. However, I see no harm in tub thumping in the comments section, if only because it is likely to be read by persons such as myself, who come here for accurate information on the topic and quite possibly feel as I do that there are overwhelming forces with a vested interest in keeping b.a.u. At least it sure feels that way. I for one will feel it all worthwhile if, say, my suggestion that the opening ceremony of the Olympics by subject to protest by the younger generation who are going to be subject to major effects of climate change, gets taken up by some activist group or other. Heaven knows, can it really be argued that simply proving people like Monckton wrong is a success? Lets face it, he doesn't seem to have changed his script, does he? He even gets introduced to Congress as one of the world's leading climate science experts! Perhaps SKS needs a 'paramilitary' wing! (I joke - with Shakespeare's 'many a true word ...' in mind.)
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45. Very good, informative and easy-to-understand article. Thanks.
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46. There is considerable confidence that climate models provide credible quantitative estimates of future climate change, particularly at continental scales and above. This confidence comes from the foundation of the models in accepted physical principles and from their ability to reproduce observed features of current climate and past climate changes. Confidence in model estimates is higher for some climate variables (e.g., temperature) than for others (e.g., precipitation). Over several decades of development, models have consistently provided a robust and unambiguous picture of significant climate warming in response to increasing greenhouse gases. Source: “Frequently Asked Question 8.1: How Reliable Are the Models Used to Make Projections of Future Climate Change?”, Working Group 1: The Physical Science Basis, UN IPPC Click here to access this document.
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47. For the technically minded Global climate model on wikipedia.org.

sidd
I found some mention of adaptive meshing in some of the models named in the graphic. You probably need to read through the model documentation.
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48. Moderator (Muoncounter) @ my last post (numbers still all being ‘1’ at the time of writing) Thanks for redirecting me to a thread that appears to have died in September of 2010. May it rest in peace. Goes to show just how big the picture is considered to be. While I can accept that ‘barry’ @ 19 has a point, it is difficult to agree that there is not a political bias at an institutional level in this site when all those in the section that includes ‘Monckton Myths’ are members of the denialati. If there really were no political bias, it would also include, say, ‘GreenPeace Goolies’ for they and others like them have in their time produced misinformation as outrageous as any myths promulgated by his lordship and his mates. Having said that, I will try to adhere to the house rules, but would appreciate something a bit more relevant to the urgency of the situation than a long dead forum venue.
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Response:

[DB] "a long dead forum venue"

There are no "dead" or closed comment threads at SkS.  All 4,700+ threads are open and available for placing comments on.  The vast majority may be currently dormant, but since regular participants in the forum closely follow the Recent Comments thread, it is a rare posting that fails to get a response by someone.

As for the other bits you raise, playing "whack-a-myth" is a time-consuming and labor-intensive process.  Feel free to develop your own examination of Greenpeace "and others like them" and their supposed misinformation and submit it for consideration as a guest-posting here (provided it conforms with the Comments Policy and falls within the aegis of the mission statement of this site).

49. Thank you TOP for the pointer. Here is another, and the thesis work by Jablonowoski linked on the page has a good lit. review. http://www-personal.umich.edu/~cjablono/amr.html sidd
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