This is a continuation of a previous post including interesting questions from the comments.
More Questions
- What are parameterisations?
Some physics in the real world, that is necessary for a climate model to work, is only known empirically. Or perhaps the theory only really applies at scales much smaller than the model grid size. This physics needs to be ‘parameterised’ i.e. a formulation is used that captures the phenomenology of the process and its sensitivity to change but without going into all of the very small scale details. These parameterisations are approximations to the phenomena that we wish to model, but which work at the scales the models actually resolve. A simple example is the radiation code – instead of using a line-by-line code which would resolve the absorption at over 10,000 individual wavelengths, a GCM generally uses a broad-band approximation (with 30 to 50 bands) which gives very close to the same results as a full calculation. Another example is the formula for the evaporation from the ocean as a function of the large-scale humidity, temperature and wind-speed. This is really a highly turbulent phenomena, but there are good approximations that give the net evaporation as a function of the large scale (‘bulk’) conditions. In some parameterisations, the functional form is reasonably well known, but the values of specific coefficients might not be. In these cases, the parameterisations are ‘tuned’ to reproduce the observed processes as much as possible.
- How are the parameterisations evaluated?
In at least two ways. At the process scale, and at the emergent phenomena scale. For instance, taking one of the two examples mentioned above, the radiation code can be tested against field measurements at specific times and places where the composition of the atmosphere is known alongside a line-by-line code. It would need to capture the variations seen over time (the daily cycle, weather, cloudiness etc.). This is a test at the level of the actual process being parameterised and is a necessary component in all parameterisations. The more important tests occur when we examine how the parameterisation impacts larger-scale or emergent phenomena. Does changing the evaporation improve the patterns of precipitation? the match of the specific humidity field to observations? etc. This can be an exhaustive set of tests but again are mostly necessary. Note that most ‘tunings’ are done at the process level. Only those that can’t be constrained using direct observations of the phenomena are available for tuning to get better large scale climate features. As mentioned in the previous post, there are only a handful of such parameters that get used in practice.
- Are clouds included in models? How are they parameterised?
Models do indeed include clouds, and do allow changes in clouds as a response to forcings. There are certainly questions about how realistic those clouds are and whether they have the right sensitivity – but all models do have them! In general, models suggest that they are a positive feedback – i.e. there is a relative increase in high clouds (which warm more than they cool) compared to low clouds (which cool more than they warm) – but this is quite variable among models and not very well constrained from data.
Cloud parameterisations are amongst the most complex in the models. The large differences in mechanisms for cloud formation (tropical convection, mid-latitude storms, marine stratus decks) require multiple cases to be looked at and many sensitivities to be explored (to vertical motion, humidity, stratification etc.). Clouds also have important micro-physics that determine their properties (such as cloud particle size and phase) and interact strongly with aerosols. Standard GCMs have most of this physics included, and some are even going so far as to embed cloud resolving models in each grid box. These models are supposed to do away with much of the parameterisation (though they too need some, smaller-scale, ones), but at the cost of greatly increased complexity and computation time. Something like this is probably the way of the future.
- What is being done to address the considerable uncertainty associated with cloud and aerosol forcings?
As alluded to above, cloud parameterisations are becoming much more detailed and are being matched to an ever larger amount of observations. However, there are still problems in getting sufficient data to constrain the models. For instance, it’s only recently that separate diagnostics for cloud liquid water and cloud ice have become available. We still aren’t able to distinguish different kinds of aerosols from satellites (though maybe by this time next year).
However, none of this is to say that clouds are a done deal, they certainly aren’t. In both cloud and aerosol modelling the current approach is get as wide a spectrum of approaches as possible and to discern what is and what is not robust among those results. Hopefully soon we will start converging on the approaches that are the most realistic, but we are not there yet.
Forcings over time are a slightly different issue, and there it is likely that substantial uncertainties will remain because of the difficulty in reconstructing the true emission data for periods more than a few decades back. That involves making pretty unconstrained estimates of the efficiency of 1930s technology (for instance) and 19th Century deforestation rates. Educated guesses are possible, but independent constraints (such as particulates in ice cores) are partial at best.
- Do models assume a constant relative humidity?
No. Relative humidity is a diagnostic of the models’ temperature and water distribution and will vary according to the dynamics, convection etc. However, many processes that remove water from the atmosphere (i.e. cloud formation and rainfall) have a clear functional dependence on the relative humidity rather than the total amount of water (i.e. clouds form when air parcels are saturated at their local temperature, not when humidity reaches X g/m3). These leads to the phenomenon observed in the models and the real world that long-term mean relative humidity is pretty stable. In models it varies by a couple of percent over temperature changes that lead to specific humidity (the total amount of water) changing by much larger amounts. Thus a good estimate of the model relative humidity response is that it is roughly constant, similar to the situation seen in observations. But this is a derived result, not an assumption. You can see for yourself here (select Relative Humidty (%) from the diagnostics).
- What are boundary conditions?
These are the basic data input into the models that define the land/ocean mask, the height of the mountains, river routing and the orbit of the Earth. For standard models additional inputs are the distribution of vegetation types and their properties, soil properties, and mountain glacier, lake, and wetland distributions. In more sophisticated models some of what were boundary conditions in simpler models have now become prognostic variables. For instance, dynamic vegetation models predict the vegetation types as a function of climate. Other examples in a simple atmospheric model might be the distribution of ozone or the level of carbon dioxide. In more complex models that calculate atmospheric chemistry or the carbon cycle, the boundary conditions would instead be the emissions of ozone precursors or anthropogenic CO2. Variations in these boundary conditions (for whatever reason) will change the climate simulation and can be considered forcings in the most general sense (see the next few questions).
- Does the climate change if the boundary conditions are stable?
The answer to this question depends very much on perspective. On the longest timescales a climate model with constant boundary conditions is stable – that is, the mean properties and their statistical distribution don’t vary. However, the spectrum of variability can be wide, and so there is variation from one decade to the next, from one century to the next, that are the result of internal variations in (for instance) the ocean circulation. While the long term stability is easy to demonstrate in climate models, it can’t be unambiguously determined whether this is true in the real world since boundary conditions are always changing (albeit slowly most of the time).
- Does the climate change if boundary conditions change?
Yes. If any of the factors that influence the simulation change, there will be a response in the climate. It might be large or small, but it will always be detectable if you run the model for long enough. For example, making the Rockies smaller (as they were a few million years ago) changes the planetary wave patterns and the temperature patterns downstream. Changing the ozone distribution changes temperatures, the height of the tropopause and stratospheric winds. Changing the land-ocean mask (because of sea level rise or tectonic changes for instance) changes ocean circulation, patterns of atmospheric convection and heat transports.
- What is a forcing then?
The most straightforward definition is simply that a forcing is a change in any of the boundary conditions. Note however that this definition is not absolute with respect to any particular bit of physics. Take ozone for instance. In a standard atmospheric model, the ozone distribution is fixed and any change in that fixed distribution (because of stratospheric ozone depletion, tropospheric pollution, or changes over a solar cycle) would be a forcing causing the climate to change. In a model that calculates atmospheric chemistry, the ozone distribution is a function of the emissions of chemical precursors, the solar UV input and the climate itself. In such a model, ozone changes are a response (possibly leading to a feedback) to other imposed changes. Thus it doesn’t make sense to ask whether ozone changes are or aren’t a forcing without discussing what kind of model you are talking about.
There is however a default model setup in which many forcings are considered. This is not always stated explicitly and leads to (somewhat semantic) confusion even among specialists. This setup consists of an atmospheric model with a simple mixed-layer ocean model, but that doesn’t include chemistry, aerosol vegetation or dynamic ice sheet modules. Not coincidentally this corresponds to the state-of-the-art of climate models around 1980 when the first comparisons of different forcings started to be done. It persists in the literature all the way through to the latest IPCC report (figure xx). However, there is a good reason for this, and that is observation that different forcings that have equal ‘radiative’ impacts have very similar responses. This allows many different forcings to be compared in magnitude and added up.
The ‘radiative forcing’ is calculated (roughly) as the net change in radiative fluxes (both short wave and long wave) at the top of the atmosphere when a component of the default model set up is changed. Increased solar irradiance is an easy radiative forcing to calculate, as is the value for well-mixed greenhouse gases. The direct effect of aerosols (the change in reflectance and absorption) is also easy (though uncertain due to the distributional uncertainty), while the indirect effect of aerosols on clouds is a little trickier. However, some forcings in the general sense defined above don’t have an easy-to-caclulate ‘radiative forcing’ at all. What is the radiative impact of opening the isthmus of Panama? or the collapse of Lake Agassiz? Yet both of these examples have large impacts on the models’ climate. Some other forcings have a very small global radiative forcing and yet lead to large impacts (orbital changes for instance) through components of the climate that aren’t included in the default set-up. This isn’t a problem for actually modelling the effects, but it does make comparing them to other forcings without doing the calculations a little more tricky.
- What are the differences between climate models and weather models?
Conceptually they are very similar, but in practice they are used very differently. Weather models use as much data as there is available to start off close to the current weather situation and then use their knowledge of physics to step forward in time. This has good skill for a few days and some skill for a little longer. Because they are run for short periods of time only, they tend to have much higher resolution and more detailed physics than climate models (but note that the Hadley Centre for instance, uses the same model for climate and weather purposes). Weather models develop in ways that improve the short term predictions, though the impact for long term statistics or the climatology needs to be assessed independently. Curiously, the best weather models often have a much worse climatology than the best climate models. There are many current attempts to improve the short-term predictability in climate models in line with the best weather models, though it is unclear what impact that will have on projections.
- How are solar variations represented in the models?
This varies a lot because of uncertainties in the past record and complexities in the responses. But given a particular estimate of solar activity there are a number of modelled responses. First, the total amount of solar radiation (TSI) can be varied – this changes the total amount of energy coming into the system and is very easy to implement. Second, the variation over the the solar cycle at different frequencies (from the UV to the near infra-red) don’t all vary with the same amplitude – UV changes are about 10 times as large as those in the total irradiance. Since UV is mostly absorbed by ozone in the stratosphere, including these changes increases the magnitude of the solar cycle variability in the stratosphere. Furthermore, the change in UV has an impact on the production of ozone itself (even down into the troposphere). This can be calculated with chemistry-climate models, and is increasingly being used in climate model scenarios (see here for instance).
There are also other hypothesised impacts of solar activity on climate, most notably the impact of galactic cosmic rays (which are modulated by the solar magnetic activity on solar cycle timescales) on atmospheric ionisation, which in turn has been linked to aerosol formation, and in turn linked to cloud amounts. Most of these links are based on untested theories and somewhat dubious correlations, however, as was recognised many years ago (Dickinson, 1975), this is a plausible idea. Implementing it in climate models is however a challenge. It requires models to have a full model of aerosol creation, growth, accretion and cloud nucleation. There are many other processes that affect aerosols and GCR-related ionisation is only a small part of that. Additionally there is a huge amount of uncertainty in aerosol-cloud effects (the ‘aerosol indirect effect’). Preliminary work seems to indicate that the GCR-aerosol-cloud link is very small (i.e. the other effects dominate), but this is still in the early stages of research. Should this prove to be significant, climate models will likely incorporate this directly (using embedded aerosol codes), or will parameterise the effects based on calculated cloud variations from more detailed models. What models can’t do (except perhaps as a sensitivity study) is take purported global scale correlations and just ‘stick them in’ – cloud processes and effects are so tightly wound up in the model dynamics and radiation and have so much spatial and temporal structure that this couldn’t be done in a way that made physical sense. For instance, part of the observed correlation could be due to the other solar effects, and so how could they be separated out? (and that’s even assuming that the correlations actually hold up over time, which doesn’t seem to be the case).
- What do you mean when you say a model has “skill”?
‘Skill’ is a relative concept. A model is said to have skill if it gives more information than a naive heuristic. Thus for weather forecasts, a prediction is described as skillful if it works better than just assuming that each day is the same as the last (‘persistence’). It should be noted that ‘persistence’ itself is much more skillful than climatology (the historical average for that day) for about a week. For climate models, there is a much larger range of tests available and there isn’t necessarily an analogue for ‘persistence’ in all cases. For a simulation of a previous time period (say the mid-Holocene), skill is determined relative to a ‘no change from the present’. Thus if a model predicts a shift northwards of the tropical rain bands (as was observed), that would be skillful. This can be quantified and different models can exhibit more or less skill with respect to that metric. For the 20th Century, models show skill for the long-term changes in global and continental-scale temperatures – but only if natural and anthropogenic forcings are used – compared to an expectation of no change. Standard climate models don’t show skill at the interannual timescales which depend heavily on El Niño’s and other relatively unpredictable internal variations (note that initiallised climate model projections that use historical ocean conditions may show some skill, but this is still a very experimental endeavour).
- How much can we learn from paleoclimate?
Lots! The main issue is that for the modern instrumental period the changes in many aspects of climate have not been very large – either compared with what is projected for the 21st Century, or from what we see in the past climate record. Thus we can’t rely on the modern observations to properly assess the sensitivity of the climate to future changes. For instance, we don’t have any good observations of changes in the ocean’s thermohaline circulation over recent decades because a) the measurements are difficult, and b) there is a lot of noise. However, in periods in the past, say around 8,200 years ago, or during the last ice age, there is lots of evidence that this circulation was greatly reduced, possibly as a function of surface freshwater forcing from large lake collapses or from the ice sheets. If those forcings and the response can be quantified they provide good targets against which the models’ sensitivity can be tested. Periods that are of possibly the most interest for testing sensitivities associated with uncertainties in future projections are the mid-Holocene (for tropical rainfall, sea ice), the 8.2kyr event (for the ocean thermohaline circulation), the last two millennia (for decadal/multi-decadal variability), the last interglacial (for ice sheets/sea level) etc. There are plenty of other examples, and of course, there is a lot of intrinsic interest in paleoclimate that is not related to climate models at all!
As before, if there are additional questions you’d like answered, put them in the comments and we’ll collate the interesting ones for the next FAQ.
Re: 96 JCH, your question is a good one. As greenhouse gas concentrations rise in the atmosphere, a radiative imbalance at the top of the atmosphere results due to the difference between incoming shortwave and the decreased outgoing longwave radiation (due to the extra GHG+feedbacks). This imbalance will continue until the temperature of the atmosphere rises enough to equilibriate the imbalance, so that incoming shortwave equals outgoing longwave. When I say the sides of the wash basin have been raised, it is an analogy to the extra greenhouse gas. Think of it in terms of a change in boundary conditions. As the sides of the wash basin get higher, the water level will rise until it begins running over the side, just as the temperature will rise until incoming shortwave equals outgoing longwave. The water draining out the bottom is an analogy to the heat uptake by the deep ocean.
Re: #95: Mark, Love and happiness right back at you.
Re: #99: Hank, thank you for finally figuring out what the discussion is about. but… you just told me there is no such thing as a deep ocean. Now you provide a link for this paper. Do you now think that there is in fact a deep ocean heat reservoir??
Knutti (2008) states: “For the climate change problem, in order to achieve stabilization of global temperature, the relevant response timescales are those of the deep ocean, and the short timescales found by SES are therefore irrelevant to the problem of estimating climate sensitivity”
Knutti is wrong! His wash basin is turned upside down. [edit]
[Response: No he isn’t. – gavin]
Re: #102: it is generally the upper ocean that determines the time scale for the transient warming we might expect. – gavin]
Gavin, he is wrong, and you know it! The climate change problem (how much warming we might expect) is by definition one of transient response, just as you have stated above.
Bryan
[Response: You can’t simply define the climate change problem as the ‘transient response’ (unless you extend it be any time period you can think of). Knutti is one of the clearest thinkers on this subject that there is. I would recommend you pay much more attention to his papers than you have. – gavin]
http://www.iac.ethz.ch/people/knuttir/papers/
Bryan S. says, “Mearly saying it ain’t so has no basis in either math or logic.”
and then asserts “Knutti is wrong! His wash basin is turned upside down.”
So, merely saying it ain’t so only works when YOU do it?
Much information available linked on Dr. Knutti’s web page — journal articles over the years, book chapters, an entire book, educational articles. Info but not yet links for upcoming papers sent to journals, so watch that space.
Re: 100 – Gavin, your response: “The tropopause exists because of the ozone in the stratosphere which due to its local heating effects is a barrier against convection.” – that’s the reason for the structure when your independent variable is the level of direct atmospheric absorption of sunlight (i.e. ozone). But if you kept that constant and vary infrared absorption instead, then things turn out differently – I’m thinking of interplanetary comparisons here: Mars has almost no troposphere for example, why?
The main reason I ask is that in most elementary treatments of the basic greenhouse effect (the 33 C number for Earth) the troposphere is pretty much assumed to be there for you – but it ain’t necessarily there and the two actually work together in a fashion that I’d like to see explained better than I’ve been able to up to now!
Bryan S, thanks for the clarification.
Hank, did you see this?
JCH, hit the reload button to make sure you’re seeing all the posts.
Gavin, what happens if we compare the transient response in an AOGCM with a dynamic ocean of an average depth of 150 meters and no deep ocean, to a model with a realistic fully dynamic ocean? The equilibrium climate sensitivity in the two models is known to be the same. Everything in the models is identical except the ocean. We then perform the CO2 doubling experiment that Knutti shows in his paper. I predict that we still get a long tail in the the transient response of the shallow ocean model owing not to deep ocean heat uptake, but rather to slow responding feedbacks in the model such as changing ice sheets and high clouds. The transient response will be only slightly effected (15-20% difference in time to equilibrium) by the deep ocean heat uptake.
Re: Uli (#463, Part I),
Uli, the response to all your questions is positive. The Solow and RBC models have been extensively applied to economies other than the US. There are some variations on these models that have been applied to developing countries, or to the transition from undeveloped to developed countries.
As for the long-run behavior of the economies, there are also versions of the basic models that explain the levels of output in economies over the centuries, and the endogenous transition from so-called Malthusian economies to industrial economies, as that of 18th century England. On this see the chapter:
Parente, Stephen L. & Prescott, Edward C., 2005. “A Unified Theory of the Evolution of International Income Levels,” Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 21, pages 1371-1416 Elsevier.
I reckon that your “physically based climate models” the equivalent to my “models based in sound theory”. In economics, most of these models are used for understanding the behavior of economies, and not so much for forecasts. They are the workhorse of the profession.
“Do some economic models use past correlations (of time series)?”
In short-run forecasts (one month to one year), it is very difficult to beat basic statistical models that have zero or a couple of built-in theoretical concepts, but are not structural. So we do use past correlations on the assumption that rules governing beliefs of agents do not change much in the short run.
In an argument with a relatively knowledgeable denialist elsewhere, he has claimed that GCMs show correlated errors that cannot be overcome using ensembles, and specifically, that “the errors in the clouds are in the 10s of Watts/m^2.” How would you respond?
[Response: What is this being used to claim? There are of course systematic errors in the GCMs that don’t go away when you average lots of them together (a large part of the random error does disappear though). Modellers spend a lot of time trying to reduce this of course, but no-one ever claimed models were perfect. A possible error in logic would be to claim that such a systematic bias automatically implied that the sensitivity of the model to climate forcings was grossly in error. That doesn’t follow at all. For instance, take a really simple model T4 = S*(1-a)/(4 sigma) (where S is the solar input 1365W/m2, a is the albedo). The sensitivity to a change in S, dT/dS is T_eq/(4*S). Thus for a 10% error in ‘a’ (from 0.3 to 0.33 say), the difference it makes in the sensitivity is a little more than 1% (i.e. 0.0467 to 0.0461). And a 10% error in albedo is over 40W/m2 in the absorbed solar! The bottom line is that errors in climatological values, while important, have less impact on sensitivity than you might think. – gavin]
Gavin – thanks very much. What he’s claiming is that such errors mean the models are not good enough to make predictions sufficiently good to justify taking action to limit AGW – so the error in logic you identify is indeed implicit in his argument. Effectively the same error has already been pointed out to him by John Gammon-Nielsen in an online argument he pointed me to. I’ll see what his response is to your really simple model (I already know he won’t admit he’s wrong!)
Nick, who are you referring to in your posts #111 & 112? I seem to be missing some context.
Nick, #111.
How would I respond?
With “You’re wrong”.
Why put more effort into answering than they did in getting a question?
I would like to have a better understanding of what, if any, variables that act predominantly at larger time (or space) scales, are included in GCMs but (presumably) not in weather models. Although, other that the sun spot cycle, I cannot conceive of what these would be. Seasonal albedo or evapotranspiration changes perhaps?
re 115. Nowadays? Nothing, really. Same model for weather as for climate is now common.
Gavin, Jim asks a good FAQ question — is the same model used for weather and for climate?
I’d thought not.
Mark says so.
Hank, read the 2nd comment and response in the previous post.
It was Gavin’s response there that actually spurred my question, particularly the statement: “Weather models develop in ways that improve the short term predictions – but aren’t necessarily optimal for long term statistics”. Just looking for a little more elaboration there.
BTW, the Chu confirmation hearing is now being brodcast live on C-span 3: http://www.c-span.org/Watch/C-SPAN3_wm.aspx
Thanks JCH, that’s in reference to my puzzlement over Mark’s “same model” — Gavin’s explanation, inline in that topic here: 3 novembre 2008 at 7:48 AM is, how you say, rather more nuanced (grin). I think definitely FAQworthy
I don’t claim to be the first person to ask for an explanation of the differences between a weather model and a climate model, but long ago I suggested that RC would benefit from having something to explain it better for lay people. Months later I tried again to ask something that might help me better understand. The response:
[Response: You would get a spread associated with the unforced variability in the global mean temperature – roughly a standard deviation of 0.15 deg C on top of the very small yearly increase in the long term forced trend (0.02 deg C). So the 5-95% range for next years anomaly would be something like -0.28 to 0.32 deg C (relative to some recent baseline). Note that for a proper forecast you’d need to assimilate the past changes in temperature which is something that the climate models in AR4 didn’t do, but is starting to be done more often. – gavin]
And it worked.
Gavin – I have posted a weblog which questions several of your answers [http://climatesci.org/2009/01/20/comments-on-real-climates-post-faq-on-climate-models-part-ii/]. I would be glad to post as a guest weblog your responses on Climate Science. Roger
I read a comment stating that Hadley Centre uses the same model for both climate and weather. If so, they are probably unique.
[Response: Yes – though the configurations (i.e. resolution) are different (details from anyone involved?). – gavin]
Hank 120, how can you ask of others what you will not do yourself? E.g. check up on the FAQs.
Rather than get sarcastic (and complain when I do).
To be the “good guy” you have to be BETTER than good. That’s what “role model” means. Unfair, but if you WANT to be held up as a paragon, you have to do it.
Re #122: Roger, if you haven’t figured out by now that the proper response to your extracurricular activities is to ignore you, probably you never will. The fact that you haven’t yet managed to place yourself entirely outside the scientific pale is a testament to the tolerance of the scientific community.
Steve Bloom: “Roger, if you haven’t figured out by now that the proper response to your extracurricular activities is to ignore you…”
That is a rather unhelpful attitude for those of us trying to see how criticisms of the RC point of view from a highly qualified climate scientist might be refuted.
I hope it is not shared by the professionals here.
Mark, my aspirations aren’t what you imagine. I want other people to be better, not to be better than they.
Your 2-word answer, without cite, needed help; Gavin’s answer had pointed to how to find more. That’s the idea, help people find stuff.
The below paper by Reto Knutti is an excellent supplement to this post;
Should we believe model predictions of future
climate change? Phil. Trans. R. Soc. A (2008) 366, 4647–4664
doi:10.1098/rsta.2008.0169 Published online 25 September 2008
It is entirely qualitative with clear language (so it should be readable by almost anyone) and gives an excellent summary of the processes in climate modeling and their developments.
http://www.iac.ethz.ch/people/knuttir/papers/knutti08ptrs.pdf
Steve Bloom, your post #125 is unbecoming, unprofessional, and downright silly. Sorry, I could not pass — I’m just saying…
Hank 127, the two words answered the question.
Rather like “What’s the difference between floor cleaner and worktop cleaner”. Well, none, really, depends on the application. However, it used to be that the floor cleaners were more caustic …
and so on for four pages.
Or none, really.
Two words answering the question.
I mean, why do you think the Met Office has a Unified Model? What would it be unified with?
re 123 and gavin’s answer. There’s the problem right there, Gavin and Hank. Davd asked question with “is it a or b” and gavin answered with yes.
Now, if you know that, yes they are the same, then you can work out what yes gavin is saying yes to.
The Met Office do a unified model. The diagnostics output and the physics put in can change, but this is usually done from trying to work out what works best with a new model configuration and whether it works well enough to replace the current model.
And it is called the unified model because it is used in weather and in climate forecasting.
So, David, the answer to YOUR question is, they use the same model.
NOTE: I don’t do this, but I know a man who does…
PS to that last reply. David, why do you wish to know? I believe (though this is old information, I have no real need to know, so don’t ask) that the US use a separate model for the climate from their weather forcasting, but several countries have bought the Met Office UM and France has changed to a unified model.
The reason? Computers are now fast enough to manage to process the complex physics for climatological simulation in a usable timeframe whereas before the inclusion of these physics would mean the answers would not be forthcoming in a timely manner and so the more physically accurate weather forcasting model was not usable for climate work.
Climate models then use parameterisations for many of these calculations.
But, unless I’m VERY mistaken in my undestanding, this is all described in the text of this thread right at the top, which I would have assumed people would have, y’know, READ.
re #129: I agree. RP Sr deserves respect. Which means an intelligent and reasoned response. Lets see what RC can do!!
Kevin McKinney@113, Mark@114,
This concerns a commenter at Pharyngula, calling himself “africangenesis”. I tried to post more but got told my comment was spam.
Thanks for clarifying that, Nick.
20 janvier 2009 at 1:24 PM
Thanks David; do you recall where you read that? Given that, we’ve heard _one_ group is now using a single model — in different ways — for both weather and climate. Proof of concept should be there, if anyone from there is reading and can say more on that. Let’s hope.
Knutti’s paper Chris points to is definitely helpful on why so many different climate models are in current use and development, being used and useful, and some of them used in different ways for different aspects of modeling (thank you Chris for, I’d guess, starting to read through that large online archive). Lots of information there.
Re #s 129/133: If RP Sr. wants to be treated with respect, he needs to learn to work and play well with others on a level more advanced than that of a petulant junior high student.
His blog is riddled with examples of how not to behave; see e.g. this recent post. I don’t know who thought it was desirable to invite Roger to such an event, but it’s a mistake that seems unlikely to be repeated. BTW, this is not a criticism of Roger’s scientific views (to which he is entitled), but of his behavior and of the paranoid ideation expressed in his closing paragraph:
“The agency representatives at the NRC planning meeting on December 8 2008, either are inadvertantly neglecting the need for independent oversight, or they are deliberately ignoring this lack of an independent assessment because the IPCC findings fit their agenda on the climate issue. In either case, the policymakers and the public are being misled on the degree of understanding of the climate system, including the human role within in it.”
Note also that the meeting presenters seem to have figured out that Roger requires special treatment:
“Most of the several powerpoint talks that were given at the planning meeting, however, are not being made available by the presenters [a very unusual arrangement; as contrasted with other meetings such as the 2006 SORCE meeting – see]. The talks were each very informative, so it is unfortunate that they have chosen not to share.”
See also this previous episode involving Roger (noting that his blog has many subsequent posts on the subject of this resignation). In the comments, a scientist working in an unrelated field comments:
“Honestly, Professor, if I had a collegue who, upon a disagreement about writing up a section of a project, loudly and publically stormed away, then insisted that I was politicizing the work, and telling me that I was ‘inappropriately, vigorously discourag[ing] the inclusion of diversity of perspectives’ in the paper, I would find it very tiresome.
“Yes, working with other people is difficult, particularly in committees where it’s possible the majority of people might well disagree with you, but we are scientists, and that is the lot we have chosen in life. Also, I too have had bad articles written in the NYT about my line of work. Somehow I managed to carry on. Their science writing isn’t very good, you know.
“At any rate, please be sure and release your version of the chapter; I think you owe it to the world to Reveal the Truth that the Authorities have Suppressed.”
So there’s a long history to these problems.
Mark (131, 132) — You misread my comment #123. Also, I did not particularly need to know, but some earlier commenter asked.
Hank Roberts (136) — I don’t recall, nor does it seem to matter. Gavin afirms it is so.
The fact that Real Climate allows Steve Bloom’s comments through its moderation suggests a willing proxy attack on Prof. Pielke. Why not just disallow the ad homs and respond to the substance of the post? You guys are all adults I believe.
Steve Bloom, who in the world are you? And why should anyone care about your views?
Real Climate, don’t let this become a nasty site, what about a focus on comments on science?
I have always wondered whether plant life is sufficiently accounted for in climate models. More specifically, as ice sheets recede, and plant life expands, do they absorb CO2 and start to reverse the process? Also, since photosynthesis is a endothermic process, does that also help?
Re #139: It’s a very well-known factor, noting that an effect opposite to (and IIRC larger than) CO2 absorption is albedo change. I strongly suspect the endothermic effect would be a very small term.
Why doesn’t Pielke Sr. open his blog to comments, so people can point out his “mistakes”? He’s an adult, i believe, and if he’s honestly interested in seeking truth, why does he make it impossible for other people to correct his errors posted on his blog?
Steve Tirrell, 140, yes, plant life is included in the climate models.
Remember, as the plants move polewards, they move away from the current dessert areas (because the dessert is expanding). And there’s more equatorial land per degree than there is at the pole.
David B 138, I don’t see the question from anyone but me, but I also don’t see an answer to it.
If you can supply one.
Education doesn’t have to be one-way you know.
#140 (Steve):
Vegetation analysis adds another layer of complexity. I don’t believe that most climate models specifically integrate terrestrial and oceanic carbon cycling directly, though some might now. Rather, there is a series of models known as DGVMs (Dynamic Global Vegetation Models), which are coupled to GCMs to estimate carbon cycle feedbacks in response to atmospheric and climatic changes. There was a comparison of several of these coupled model runs a few years ago, in a project called C4MIP, which I don’t know whether is ongoing or has been superseded by something else. The overall finding was that the non-atmospheric part of the carbon cycle has an upper limit (tends toward saturation), which reduces the responsiveness to atmospheric CO2 over time (over next century and beyond). It is also important to remember that terrestrial carbon is labile–sensitive to heat and drought, via increased respiration, plant mortality, and fire, and land use changes etc. DGVMs attempt to account for some or all of these things.
Vegetation feedbacks are not as simple as just carbon accounting though. Changes in albedo and evapotranspiration have to be accounted for too, and can, either locally or globally, counteract the carbon sequestration effect. For example, boreal tree expansion in the tundra may store carbon, but may do so at the expense not only of increased respiration of vast soil carbon stocks (and also possible methane releases), but also a decreased albedo, since trees are darker than tundra vegetation and much darker than open snowcover in the spring. Water budgets and evaporation are also involved, so you now have numerous complex interactions between temperature, precip, CO2 and vegetation, operating at all sorts of time and space scales. The effects at one scale can oppose those at another.
Interesting concept re the endothermic photosynthesis process. Have not heard that before and have no idea how that might factor in, if at all. My guess is very little, because only a small fraction of light energy goes into ps.
Check out:
http://gaim.unh.edu/Structure/Future/MIPs/C4MIP.html
http://c4mip.lsce.ipsl.fr/protocol_final.txt
http://c4mip.lsce.ipsl.fr/C4MIP_protocol.doc
and
Bonan, G. 2008. Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. Science 320:1444-1449.
Friedlingstein et al. 2006 Climate–Carbon Cycle Feedback Analysis: Results from the C4MIP Model Intercomparison. J Climate 19:3337-
Steves (Bloom and Tirrell): From the point of view of science, the problem with Roger’s approach is not his point of view or even his behavior [edit]. Rather, the problem is that Roger takes a scattershot approach, launching bold attacks where he perceives a weakness, but then never following through. As a result, I find little in his work that advances understanding, and if you don’t advance understanding, you don’t get far in science. The only unifying theme in Roger’s work is a hostility to the consensus science–and ironically, all he accomplishes in his scattershot opposition is highlight how well the science has done at advancing our understanding of climate.
> I don’t see the question
Jim asked it:
16 janvier 2009 at 10:00 PM
I seconded it:
19 janvier 2009 at 9:06 PM
Jim mentioned an earlier answer he’d hoped someone would elaborate on:
20 janvier 2009 at 8:42 AM
David confirmed one known example, Hadley, a model does double duty:
20 janvier 2009 at 1:24 PM
Gavin, inline, added some detail and invited someone knowledgeable about how Hadley’s model does this to contribute.
Nice work on everybody’s part winkling out the detail requested, perhaps someone who knows more will eventually add to this.
Above, I submitted a comment stating: “that’s what happens when computer models replace brains”. Gavin properly edited my comment since it might have been construed (incorrectly) that I was personally insulting a respected scientist with whom Gavin happened to agree. If given the opportunity to clarify, I would have made it clear that the comment was not meant to refer to the author, only to make a pointed opinion that computer modeling may at times act to obscure certain physical processes that need to be more fully researched, but are tacitly *assumed* by the scientist to already be well understood.
Now comes Mr. Bloom’s comments mounting a direct personal attack on Roger Pielke Sr. For the record, Dr. Pielke has over 300 peer-reviewed published papers to his credit, 50 chapters in books, co-edited 9 texbooks, has been elected a fellow of the AMS, and in 2004* to the American Geophysical Union (after his views on climate change were well known). He is an *ISI Highly Cited Researcher* (hard to accomplished if one’s work is deemed “scattershot” or unimportant by the community), was a tenured faculty member in the Department of Atmospheric Science at Colorado State University, is the former Colorado State Climatologist, and is presently a senior researcher at the University of Colorado at Boulder. His resume doesn’t guarantee that all of his science arguments will be compelling, but he has obviously earned the right to speak with some authority on the issue of climate change.
Also for the record, it should be known that for about two years, Dr. Pielke did indeed accept open comments on his Climate Science weblog. During that time, Mr. Bloom was a regular commentor, often taking the opportunity to hurl personal insults toward Dr. Pielke. During this time, Dr. Pielke was remarkably magnanimous and gracious toward Mr. Bloom despite the personal attacks, while continually re-directing the discussion to the peer-reviewed science literature. One might wonder why Mr. Bloom crudades against Dr. Pielke? One might also wonder whether or not Mr. Bloom possesses a degree of any kind in a natural science, whether he has ever published a peer-reviewed science paper, whether he possesses a genuine curiosity for gaining understanding of the earth system, or rather whether he is instead driven more by a certain political agenda in the social sciences. Only he knows the answers.
Finally, in allowing Bloom’s comments to stand, Real Climate has by proxy slandered Dr. Pielke’s reputation. Even several of the regular Real Climate bloggers (who are usually sympathetic to your science claims have openly expressed regret at these comments. It therefore seems that there is a strong suggestion by a number of your readers that Mr. Bloom’s comments be removed.
[Response: The balance between justifiable criticism and unjustifiable comments is a fine line (and assessing it is a full time job). Given this is only a part time gig, there will be times when judgment calls go different ways at different times. On balance, I’m going to allow Bloom’s comments to stand (along with your critique) because he alludes to a valid point – not that behaviour or attitude determines the correctness of ones argument (it doesn’t), but that the way one behaves towards colleagues is a big determinant of how much time people will devote to addressing your concerns. Roger’s post on the NRC meeting was very odd, full of unverifiable and untrue suppositions of motives of the people there, and which did not reflect the substantive conversations that actually took place there (which concerned solar impacts on climate, not evaluating the IPCC). It is valid to point this out, as it is valid to note that people need to choose who to interact with (given the limited time everyone has). Respect is very much a two way street. – gavin]
Hank #147.
You’re welcome (see #116, #131 & #132)
But I guess you’re shy around me, eh?
Climate Science has posted a response to Gavin Schmidt’s comment in #148
http://climatesci.org/2009/01/22/real-climate-gavin-schmidt-response-to-the-climate-science-post-comments-on-real-climate%e2%80%99s-post-%e2%80%9cfaq-on-climate-models-part-ii%e2%80%9d/
[edit – to save time, here is the comment:]
Gavin Schmidt has responded to the Climate Science request for further information on the Q&A he completed on climate modeling. His comment is below.
Quite frankly, I am very disappointed by his lack of professional courtesy. He also clearly wants to avoid answering the scientific questions posed in response to his Q&A. He has also decided to misrepresent the NRC meeting.
I have tried repeatedly to constructively interact with Real Climate and Gavin Schmidt, in particular, but my interactions with him at the NRC meeting and through his blog comments clearly demonstrate that he is not interested in scientific discourse. He only supports discussing viewpoints that agree with his, and both undertakes himself, and supports ad hominin attacks by others, on those who differ with him in their scientific perspective.
[Response: Roger, If you think that accusing people of being in a conspiracy to defend the IPCC and imply that disagreeing with you means that people are actively trying to “mislead” the public, is ‘constructive interaction’, you might want to buy a new dictionary. The NRC meeting I attended was a discussion of whether a new report on solar-climate interactions would be helpful for the NRC to do. Most presentations focussed very specifically on solar-climate links. It remains a puzzle to me why you thought the proposed report would be something related to the IPCC report at all. Thus your characterisation of that meeting was, frankly, extremely partial and unfair to the participants and agency reps who attended. Your accusation that I misrepresented that meeting in the comment above is ludicrous.
As for your comments on the above FAQ, I have not yet responded to your comments through lack of time. Hopefully, I will be able to in the future, perhaps when I don’t have to respond to juvenile accusations about my integrity. To be clear, I have the utmost respect for your body of work over your career, but your current style of engagement and constant accusatory tone online is not conducive to ‘constructive interaction’. If that is your aim, you might want to rethink your tactics. – gavin]