We discuss climate models a lot, and from the comments here and in other forums it’s clear that there remains a great deal of confusion about what climate models do and how their results should be interpreted. This post is designed to be a FAQ for climate model questions – of which a few are already given. If you have comments or other questions, ask them as concisely as possible in the comment section and if they are of enough interest, we’ll add them to the post so that we can have a resource for future discussions. (We would ask that you please focus on real questions that have real answers and, as always, avoid rhetorical excesses).
Part II is here.
Quick definitions:
- GCM – General Circulation Model (sometimes Global Climate Model) which includes the physics of the atmosphere and often the ocean, sea ice and land surface as well.
- Simulation – a single experiment with a GCM
- Initial Condition Ensemble – a set of simulations using a single GCM but with slight perturbations in the initial conditions. This is an attempt to average over chaotic behaviour in the weather.
- Multi-model Ensemble – a set of simulations from multiple models. Surprisingly, an average over these simulations gives a better match to climatological observations than any single model.
- Model weather – the path that any individual simulation will take has very different individual storms and wave patterns than any other simulation. The model weather is the part of the solution (usually high frequency and small scale) that is uncorrelated with another simulation in the same ensemble.
- Model climate – the part of the simulation that is robust and is the same in different ensemble members (usually these are long-term averages, statistics, and relationships between variables).
- Forcings – anything that is imposed from the outside that causes a model’s climate to change.
- Feedbacks – changes in the model that occur in response to the initial forcing that end up adding to (for positive feedbacks) or damping (negative feedbacks) the initial response. Classic examples are the amplifying ice-albedo feedback, or the damping long-wave radiative feedback.
Questions:
- What is the difference between a physics-based model and a statistical model?
Models in statistics or in many colloquial uses of the term often imply a simple relationship that is fitted to some observations. A linear regression line through a change of temperature with time, or a sinusoidal fit to the seasonal cycle for instance. More complicated fits are also possible (neural nets for instance). These statistical models are very efficient at encapsulating existing information concisely and as long as things don’t change much, they can provide reasonable predictions of future behaviour. However, they aren’t much good for predictions if you know the underlying system is changing in ways that might possibly affect how your original variables will interact.
Physics-based models on the other hand, try to capture the real physical cause of any relationship, which hopefully are understood at a deeper level. Since those fundamentals are not likely to change in the future, the anticipation of a successful prediction is higher. A classic example is Newton’s Law of motion, F=ma, which can be used in multiple contexts to give highly accurate results completely independently of the data Newton himself had on hand.
Climate models are fundamentally physics-based, but some of the small scale physics is only known empirically (for instance, the increase of evaporation as the wind increases). Thus statistical fits to the observed data are included in the climate model formulation, but these are only used for process-level parameterisations, not for trends in time.
- Are climate models just a fit to the trend in the global temperature data?
No. Much of the confusion concerning this point comes from a misunderstanding stemming from the point above. Model development actually does not use the trend data in tuning (see below). Instead, modellers work to improve the climatology of the model (the fit to the average conditions), and it’s intrinsic variability (such as the frequency and amplitude of tropical variability). The resulting model is pretty much used ‘as is’ in hindcast experiments for the 20th Century.
- Why are there ‘wiggles’ in the output?
GCMs perform calculations with timesteps of about 20 to 30 minutes so that they can capture the daily cycle and the progression of weather systems. As with weather forecasting models, the weather in a climate model is chaotic. Starting from a very similar (but not identical) state, a different simulation will ensue – with different weather, different storms, different wind patterns – i.e different wiggles. In control simulations, there are wiggles at almost all timescales – daily, monthly, yearly, decadally and longer – and modellers need to test very carefully how much of any change that happens because of a change in forcing is really associated with that forcing and how much might simply be due to the internal wiggles.
- What is robust in a climate projection and how can I tell?
Since every wiggle is not necessarily significant, modellers need to assess how robust particular model results are. They do this by seeing whether the same result is seen in other simulations, with other models, whether it makes physical sense and whether there is some evidence of similar things in the observational or paleo record. If that result is seen in multiple models and multiple simulations, it is likely to be a robust consequence of the underlying assumptions, or in other words, it probably isn’t due to any of the relatively arbitrary choices that mark the differences between different models. If the magnitude of the effect makes theoretical sense independent of these kinds of model, then that adds to it’s credibility, and if in fact this effect matches what is seen in observations, then that adds more. Robust results are therefore those that quantitatively match in all three domains. Examples are the warming of planet as a function of increasing greenhouse gases, or the change in water vapour with temperature. All models show basically the same behaviour that is in line with basic theory and observations. Examples of non-robust results are the changes in El Niño as a result of climate forcings, or the impact on hurricanes. In both of these cases, models produce very disparate results, the theory is not yet fully developed and observations are ambiguous.
- How have models changed over the years?
Initially (ca. 1975), GCMs were based purely on atmospheric processes – the winds, radiation, and with simplified clouds. By the mid-1980s, there were simple treatments of the upper ocean and sea ice, and clouds parameterisations started to get slightly more sophisticated. In the 1990s, fully coupled ocean-atmosphere models started to become available. This is when the first Coupled Model Intercomparison Project (CMIP) was started. This has subsequently seen two further iterations, the latest (CMIP3) being the database used in support of much of the model work in the IPCC AR4. Over that time, model simulations have become demonstrably more realistic (Reichler and Kim, 2008) as resolution has increased and parameterisations have become more sophisticated. Nowadays, models also include dynamic sea ice, aerosols and atmospheric chemistry modules. Issues like excessive ‘climate drift’ (the tendency for a coupled model to move away from the a state resembling the actual climate) which were problematic in the early days are now much minimised.
- What is tuning?
We are still a long way from being able to simulate the climate with a true first principles calculation. While many basic aspects of physics can be included (conservation of mass, energy etc.), many need to be approximated for reasons of efficiency or resolutions (i.e. the equations of motion need estimates of sub-gridscale turbulent effects, radiative transfer codes approximate the line-by-line calculations using band averaging), and still others are only known empirically (the formula for how fast clouds turn to rain for instance). With these approximations and empirical formulae, there is often a tunable parameter or two that can be varied in order to improve the match to whatever observations exist. Adjusting these values is described as tuning and falls into two categories. First, there is the tuning in a single formula in order for that formula to best match the observed values of that specific relationship. This happens most frequently when new parameterisations are being developed.
Secondly, there are tuning parameters that control aspects of the emergent system. Gravity wave drag parameters are not very constrained by data, and so are often tuned to improve the climatology of stratospheric zonal winds. The threshold relative humidity for making clouds is tuned often to get the most realistic cloud cover and global albedo. Surprisingly, there are very few of these (maybe a half dozen) that are used in adjusting the models to match the data. It is important to note that these exercises are done with the mean climate (including the seasonal cycle and some internal variability) – and once set they are kept fixed for any perturbation experiment.
- How are models evaluated?
The amount of data that is available for model evaluation is vast, but falls into a few clear categories. First, there is the climatological average (maybe for each month or season) of key observed fields like temperature, rainfall, winds and clouds. This is the zeroth order comparison to see whether the model is getting the basics reasonably correct. Next comes the variability in these basic fields – does the model have a realistic North Atlantic Oscillation, or ENSO, or MJO. These are harder to match (and indeed many models do not yet have realistic El Niños). More subtle are comparisons of relationships in the model and in the real world. This is useful for short data records (such as those retrieves by satellite) where there is a lot of weather noise one wouldn’t expect the model to capture. In those cases, looking at the relationship between temperatures and humidity, or cloudiness and aerosols can give insight into whether the model processes are realistic or not.
Then there are the tests of climate changes themselves: how does a model respond to the addition of aerosols in the stratosphere such as was seen in the Mt Pinatubo ‘natural experiment’? How does it respond over the whole of the 20th Century, or at the Maunder Minimum, or the mid-Holocene or the Last Glacial Maximum? In each case, there is usually sufficient data available to evaluate how well the model is doing.
- Are the models complete? That is, do they contain all the processes we know about?
No. While models contain a lot of physics, they don’t contain many small-scale processes that more specialised groups (of atmospheric chemists, or coastal oceanographers for instance) might worry about a lot. Mostly this is a question of scale (model grid boxes are too large for the details to be resolved), but sometimes it’s a matter of being uncertain how to include it (for instance, the impact of ocean eddies on tracers).
Additionally, many important bio-physical-chemical cycles (for the carbon fluxes, aerosols, ozone) are only just starting to be incorporated. Ice sheet and vegetation components are very much still under development.
- Do models have global warming built in?
No. If left to run on their own, the models will oscillate around a long-term mean that is the same regardless of what the initial conditions were. Given different drivers, volcanoes or CO2 say, they will warm or cool as a function of the basic physics of aerosols or the greenhouse effect.
- How do I write a paper that proves that models are wrong?
Much more simply than you might think since, of course, all models are indeed wrong (though some are useful – George Box). Showing a mismatch between the real world and the observational data is made much easier if you recall the signal-to-noise issue we mentioned above. As you go to smaller spatial and shorter temporal scales the amount of internal variability increases markedly and so the number of diagnostics which will be different to the expected values from the models will increase (in both directions of course). So pick a variable, restrict your analysis to a small part of the planet, and calculate some statistic over a short period of time and you’re done. If the models match through some fluke, make the space smaller, and use a shorter time period and eventually they won’t. Even if models get much better than they are now, this will always work – call it the RealClimate theory of persistence. Now, appropriate statistics can be used to see whether these mismatches are significant and not just the result of chance or cherry-picking, but a surprising number of papers don’t bother to check such things correctly. Getting people outside the, shall we say, more ‘excitable’ parts of the blogosphere to pay any attention is, unfortunately, a lot harder.
- Can GCMs predict the temperature and precipitation for my home?
No. There are often large variation in the temperature and precipitation statistics over short distances because the local climatic characteristics are affected by the local geography. The GCMs are designed to describe the most important large-scale features of the climate, such as the energy flow, the circulation, and the temperature in a grid-box volume (through physical laws of thermodynamics, the dynamics, and the ideal gas laws). A typical grid-box may have a horizontal area of ~100×100 km2, but the size has tended to reduce over the years as computers have increased in speed. The shape of the landscape (the details of mountains, coastline etc.) used in the models reflect the spatial resolution, hence the model will not have sufficient detail to describe local climate variation associated with local geographical features (e.g. mountains, valleys, lakes, etc.). However, it is possible to use a GCM to derive some information about the local climate through downscaling, as it is affected by both the local geography (a more or less given constant) as well as the large-scale atmospheric conditions. The results derived through downscaling can then be compared with local climate variables, and can be used for further (and more severe) assessments of the combination model-downscaling technique. This is however still an experimental technique.
- Can I use a climate model myself?
Yes! There is a project called EdGCM which has a nice interface and works with Windows and lets you try out a large number of tests. ClimatePrediction.Net has a climate model that runs as a screensaver in a coordinated set of simulations. GISS ModelE is available as a download for Unix-based machines and can be run on a normal desktop. NCAR CCSM is the US community model and is well-documented and freely available.
Well put Gavin.
Read and learn, find the facts, the questions asked say alot…
Nothing in science is unknowable, if one is discplined, some things are just not known yet; climatology is a branch of science that helps us gain insights into climate. With just 4 years of schooling composed of statistics, physics, calculus, chemistry, Biology, and the obvious related into courses, it should not be difficult to get one’s head around this stuff. Classical and modern physics classes teach a lot, (they are both taight in Community College!) units were stressed the first day of gen chemistry. Engineers should have no big trouble understanding forcing, earth’s wobble and tilt changes, and how math based upon averages is inputed into a complex computer system and we begin to see relevant results. Imagine what might be found based upon larger scale models with randomization. I love this site, obviously I spend time reading the articles and looking at the people’s background who run it. With a masters or phd a high level of mastery and independent thinking is acquired as part of skill sets for internships, professorships, or industry-academic reseacrh, no need to argue moot points.
Again I applaud the patience and the effort put forth by the site administrators.
Gavin, you obviously disagree. But I am not saying that I disagree with you either. I am just pointing out to your own words in response to #191: you said that “imposed change in CO2” changes “LW absorption in the atmosphere”. This extra “LW atmospheric absorption” seems to be a driver for other changes. All other changes (from feedbacks), as you say, lead to larger “total amount of LW atmospheric absorption”, which looks like the same object as the one being forced by “that initial change.” So, it is not only the units are the same, but the object is the same, the “amount of LW atmospheric absorption”. So, it looks like the distinction of which part of it comes from “initial forcing”, and which comes from “feedbacks” is lost, it is just a new “amount”. Therefore, a straight logic would dictate that the scheme should continue to iterate, the new “amount of LW absorption” should drive more changes. I am just following your explanation, formally, and the conclusion seems to be an absurd.
As I said, without additional fuzzy explanations the paragigm of “forcings” and “feedbacks” is incoherent. It does not explain nor predict anything unless you invoke results straight from the model, which, in turn, would be not an explanation but a constantation of the fact. The model just does this, as you would say, right? So, all the buzzwords “forcings” and “feedbacks” serve no purpose.
I have a short (~3000 word) “Layman’s Guide” to climate models that I am trying to refine before adapting it as a narrated slideshow. Any input welcome!
http://cce.890m.com/?page_id=22
Re: 179 Tamino. Just been looking up the sources for commercial CO2 and here is a short exerpt from google: “The most common operations from which commercially-produced carbon dioxide is recovered are industrial plants which produce hydrogen or ammonia from natural gas, coal, or other hydrocarbon feedstock, and large-volume fermentation operations in which plant products are made into ethanol for human consumption, automotive fuel or industrial use. Breweries producing beer from various grain products are a traditional source. Corn-to-ethanol plants have been the most rapidly growing source of feed gas for CO2 recovery.” So therefore fossil fuel combustion or refinemet is still the the primary source of industrial CO2 capture. C’mon there has to be another source of gas for soda drink production!! Indeed a hefty carbon tax should be levied on Coca-cola amitil, Pepsico and other such companies..after all soda drinks should be considered a luxury item in my thinking.
“Mark Says: 7 November 2008 at 3:21 AM
“1. All of it.
No, really. Just because the tropopause is getting colder
doesn’t mean it is not being affected by global warming. If
heat is being retained it isn’t heating up the tropopause.”
That’s my point, Mark – all of what?
All of the atmophere, OK, but all of the sea, right down to the bottom of the deepest ocean?
How about the temperature of the land? The top millimetre? The top kilometre? All the way down to the magma? None of it?
What about lakes and swamps? Is all the water in lakes included? Glaciers – all the way down? Just the top bit?
Ice sheets? Are they included?
What about the snow lying around – is water vapour in the air included but ignored when it turns into snow lying around in the fields?
Presumably someone, somewhere, might know the answer to these questions. It may even be in an obvious place I’ve overlooked, but I think “Which part of the planet does ‘global warming’ apply to?” would be a worthy inclusion in a climate model FAQ.
“2. Science. The main sources don’t.”
Well, yes they do, just a little bit. I’d like to know why. I’m sure other non-climate-scientist people would too. Hence my suggestion for “What are the main sources for estimates of global temperature and why do they differ?” to be included in an FAQ. You know, just some basic info about which institutions collect the data, how coverage and estimation methodology varies between them, that sort of thing.
FAQs obviously can’t go into a lot of complex detail, but to say only that the estimates come from “science” would not be particularly helpful.
(By the way Mark, your reference to the tropopause appears to argue against a proposition I didn’t, and wouldn’t, make.)
Alexi:
Feedback:
a=a*1.1
This is a feedback.
It resides nowhere in the equation as “feedback” because naturally falls out of the equation IF:
1) you have a nonzero starting a
2) you iterate
The “feedback” falls out of the iteration. Without iteration, you have a becoming 1.1a, and then stops.
Now, what do GCM’s do with their calculations…?
Iterate.
OK, Alexi. We’ll go back to your #135 and ignore the waffle since then.
1: ” What exactly is “forcing”?” Gravitational stress is a forcing. No matter what else is acting on a body in a gravitational field, gravity will consistently have its way. That is a forcing.
2: “What do you mean under “imposed from the outside”?” In what context?
3: “Do you “impose” new boundary conditions on the “physics-based” equations?” You can.
4: “Or do you change a parameter in a differential equations like a gas mix ratio?” You can.
5: “Is there any “forcing” if the atmosphere mix stays constant, and the Sun shines steady?” If there are more forces that consistently act on the system, yes. If you have a very simple model that only considers those two elements, then no.
6: “What do you mean under “Feedbacks are changes in the model that occur in response…”?”. It means “feedbacks are changes in the model evolution that occur in response…”.
7: “Did you mean “changes in the state variables of the physics-based model”?” What do you mean by “state variables”?
8: “What is “initial forcing”?” Whatever you put in to start the simulation/model.
9: “The only forcing I am familiar with is a constant flux of SW solar energy.” Ignoring that the solar flux is not constant (and that inconstancy is an input to modern models), That is one. There are others.
10: “What are the other “forcings”?”. A very few are: water vapour. If your water vapour is out of equilibrium, you will have a forcing that is the systemic response to the water vapour being out of equilibrium.
11: “What do you mean under “physics-based”?” Based on physical processes simulated in the model. This was explained in the comments earlier. You never did go back and read it, did you. Or if you did, you never said “I get it now” on that point.
12: “You mentioned F=ma, but for a continuous media, the physical equivalent of the conservation laws would be Navier-Stokes Equations (NSE).”. Only for fluid flow. Heat transfer does not obey NSE. Gravitational mixing doesn’t obey NSE. Cloud formation doesn’t obey NSE…
13: “Do you mean that GCMs directly emulate NSE by numerically iterating some finite-difference (or spectral) approximation of NSE?” That and more. (See #12)
14: “What is the definition of “process-level parameterization”?” A process like cloud formation is either an intractable solution or too computationally expensive, so either smaller models modelling what conditions clouds form in and how their form changes are done and the results put into a parameter list. This parameter list then approximates what effect the clouds have under the conditions otherwise simulated in the GCM. This is where tuning comes in.
Now, notice that several of your questions didn’t get answered because you didn’t explain what your query was about.
Alexi 196.
You say you know about differentiation. This means you know some maths to some level.
Have you never heard of the Taylor series? An infinite series that sums to less than infinity. In some cases. E.g. the Sin(x) expansion is an infinite sum based on x. And it never goes above 1 or below -1. Even though you are summing an infinite feedback series. Note that Tan(x) has a Taylor expansion and that DOES sum to infinity. It depends on how the numbers are added together.
Rather like GW feedback processes on Earth.
On Venus, the feedback was of a different magnitude and there is, if not a catastrophic runaway, a feedback that has expanded beyond the ability of life as we know it to exist.
In any case, any real physical process cannot sum to infinity, else Venus may be outshining the rest of the galaxy from its infinite runaway greenhouse effect giving it infinite temperature. Which is obviously silly.
> please focus on real questions that have real answers
From the initial post.
Any thread can be derailed by anyone sufficiently persistent — if you keep typing in response.
Alternative: Google for the same question. Often it’s been asked and answered, or asked and unanswerable, elsewhere — often by the same person.
“Why?” can be asked as long as the kid can stay awake, til they outgrow it.
#180 Dietrich Hoecht: yes the anthropogenic aerosol effect is impressive isn’t it? But do place it into perspective. We can describe the increasing greenhouse forcing as an exponential growth process with a doubling time of 30 years. This means that in 1978 it was half of what it is now, and in 1948, one quarter. The difference between those two, one quarter of today’s, is what was compensated… well, partly. Volcanic aerosol variations also played a role.
I tried to find references for you on regional aerosol effects, and my hat tip is: google the INDOEX project. The Indian Ocean is perfectly located for this. One caveat I should make is, don’t expect to find any regional temperature effects. I may be too pessimistic, but the problem with regional temps is that the background noise, natural variability (“weather”), is so much larger than for the global case. And even worse for short time periods. This may be the reason why the reported results are in terms of forcing effects, not temperatures (and quite spectacular).
I didn’t search for more references but I am sure there are. I am not a climatologist so you can do this as well as I :-)
Hank’s caution is well-taken, but I am going to make a stab at this anyway. Alexi’s post, #206, had this:
“This extra “LW atmospheric absorption” seems to be a driver for other changes. All other changes (from feedbacks), as you say, lead to larger “total amount of LW atmospheric absorption”, which looks like the same object as the one being forced by “that initial change.” So, it is not only the units are the same, but the object is the same, the “amount of LW atmospheric absorption”. So, it looks like the distinction of which part of it comes from “initial forcing”, and which comes from “feedbacks” is lost, it is just a new “amount”. Therefore, a straight logic would dictate that the scheme should continue to iterate, the new “amount of LW absorption” should drive more changes.”
Alexi, I think you are conflating a brief description with the process itself. Just because one term, “LW atmospheric absorption,” is affected by multiple processes (feedbacks) does not mean that all distinction between these inputs is lost–for one thing, they will all have different constraints, response curves, etc., and will be taking effect concurrently, though interactively, over differing time frames.
Additionally, the real quantity we are trying to derive isn’t LW absorption, it is temperature.
It seems right that clouds are a positive global warming feedback, on the average. Although more clouds in summer favor cooler daytime temperatures, more clouds in summer favor warmer overnight temperatures. And, more clouds in winter in mid-high latitudes favor higher surface dewpoints with higher melt rates.
If my math and physiology is correct, breathing puts out way more, about 200 times, CO2 than Mark’s 4 cans of soda per week — and it is a net add from long sequestered carbon (though most not near as long as fossil fuel).
Marcus (184), bankrupting the coal industry and 75% of the power generation industry is bad enough. But when you blast the soda industry, that’s going too far :-P ….
reCAPTA = some protest
Samson wrote:
when I am discussing with climate skeptics, they often refer to the third report of the IPCC (page 774): “In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible.”
Gavin responded:
There are at least two aspects to this question. First, how well do we know the forcing into the future? We can’t do a very good job at estimating the future trajectory of technology for instance, or economic development, and so regardless of how well we understand climate, our ability to predict exactly what will happen is limited. Secondly, we don’t have full information about the current conditions, and so, like for weather forecasts, if there are aspects of climate change that are chaotic, we can’t predict those over the long term. However, it is worth pointing out that the statement does not imply that we can’t know anything about the climate system in the future. We know that if there is a big volcano, the climate will cool – and many aspects of the resulting changes will have been predictable. The same is true for increasing GHGs – the climate will warm. Models can’t tell you exactly what will happen where, but there is a lot they can say. – gavin
A lot which is of interest to academics, but not enough to be relevant for real world application. A prediction of the spread of possible future outcomes is far too wooly for real world application. So what if you can say that the future will be a bit warmer and maybe a lot warmer? You knew this before you ran the simulations. In the real world of growth and development, the choice of the course of action hinges on knowing the severity of the problem. Can we make it if we continue with this infrastructure, or must we change the infrastructure to something different? Will the beam carry the load? Will the fuel in the tank be enough to reach that airstrip? Well, we have somewhere between a little and a lot, but we can tell you with great confidence that the further you fly the more fuel you will use!
[edit]
Yours, a very disappointed former AGW believer.
[Response: Your mistake was thinking this is a matter of ‘belief’. – gavin]
Mark (209), a question: Aren’t solar insolation and atmospheric gas mixture the ONLY two things that can be forcing? What else might there be, at least long term (excluding volcanoes and such)? Or are you referring to the different inputs that effect either of the two forcing sources?
[Response: Forcings can be a much larger class of things that effect any aspect of the boundary conditions for a specific model. For instance, closing the Isthmus of Panama (4.5 millions years ago) was obviously a forcing that likely had profound effects on the climate – it wouldn’t be easy to characterise in radiative terms though. Similarly, orbital forcing has a very small global mean radiative effect, but dramatic consequences due to how the seasonality of the changes impacts ice sheets etc. The ones that are most relevant today though are those that affect atmospheric absorption and reflection of radiation, and surface impacts on either radiative or hydrologic fluxes. – gavin]
I mean I formerly believed the scientific community.
It is a strange thing to criticize my “mistake” for that.
Re #215
Just in case you’re not being droll!:
Breathing isn’t a net add at all, and the CO2 has only been “sequestered” for a rather short time (in plants and the poor animals that we eat).
All of the carbon in the biosphere is just continuously recycled in a relatively closed system that involves firstly the drawing of CO2 out of the atmosphere by photosynthesis:
6CO2 + 6H2O —- (CHOH)6 + 6O2
[where (CHOH)6 is generic carbohydrate]
…and then we eat the plants or the animals that eat the plants, and return the CO2 to the atmosphere by respiration (aka “breathing”):
(CHOH)6 + 6O2 —- 6CO2 + 6H20
I expect that the contribution of this specific form of the cycle “breathing” is small in fact in relation to the bacterial decomposition of dead plant matter as observed in the very large cyclic variation of atmospheric CO2 that results from the Northern hemisphere plant growth and decay cycles.
So breathing isn’t a net add. The only net adds are from tectonic activity, deforestation and other land clearance activities, and the rather humungous amounts of carbon currently being added to the short term carbon cycle through the massive burning of long-sequestered fossil fuel…
FAQs on “what don’t we know and how do researchers approach this” would be good, on a whole variety of questions.
One would be — can we get a “whole planet” measurement if we are far enough away? Can we do that for Mars or Venus for example? Could an instrument like ‘Triana’ do it for Earth?
One that keeps coming back is solar influence. “Flux tubes” were in the news recently. Any evidence of excess transfer of energy there, or is this just a better description of what happens?
Could any amount of solar energy can transfer but be stored chemically in reaction products that don’t return the energy til time elapses after they’ve spread widely through the atmosphere, for another example — part of a whole group of “what can we imagine that wouldn’t be easy to find even if we started looking” questions.
Possible example, I’m not sure: http://www.agu.org/pubs/crossref/2008/2008GL035684.shtml
Isthmus of Panama about 4 +- 1 million years old:
http://www.geologytimes.com/Research/Isthmus_of_Panama_formed_as_result_of_plate_tectonics.asp
#217. Stefano said:
“A prediction of the spread of possible future outcomes is far too wooly for real world application. (…) In the real world of growth and development, the choice of the course of action hinges on knowing the severity of the problem.”
But in the real world of growth and development, we rarely do, certainly if you are talking globally and over multi-decadal time scales. How much will the population grow by mid-century? How much oil and gas is out there waiting to be found? What technological advances will happen? The answer to any of those questions will be a spread of projections based on questionable assumptions. A “best estimate” is sometimes just a middle projection cited in a confident tone of voice.
With the partial exception of infrastructure, the “real-world” examples you cite are simple engineering problems, not global strategic issues for the 21st century. The question is not like “will the fuel in the tank be enough to reach that airstrip”. What we are talking about is more like, How much fuel will be burned by how many airplanes reaching their airstrips over the next half-century, given various projections for population growth and economic development, imponderables about changing patterns of mobility and technological breakthroughs, and market reactions to unpredictable events like terrorist hijackings? (And, lest we forget, what will the carbon from that fuel do to a complex climate system?)
Those with a “real-world” need to know if the infrastructure will hold (will there be enough of those air strips?) presumably have to model travel demand. And they don’t even have the luxury of being able to base their models on well-known physics.
Stefano, did you become disappointed with AGW because it turned out to be more complex than a textbook engineering problem? Surely you knew that before they ran the simulations.
OT I know. But regarding the saturation I was previously asking about.
http://bhanwara.blogspot.com/
Re 215 & 220:
You know, breathing *is* a net add, systematically speaking, because our food production involves massive amounts of fossil fuel consumption.
The following question might be useful for the FAQ, though I dunno if it’s frequently asked: what are the “main types” of GCMs? I.e. I’m asking for taxonomy(s). Since my contact to date with GCMs has been limited, I can only guess at some candidate classifications, but some things that come to me are
* algorithmic, e.g. Lagrangian vs Eulerian.
* lineage. Given how models are created by research groups which propagate over time, I’m guessing that some models begat other models in Biblical fashion.
Re 157
Mark, your land-ocean heat capacity argument appears to suggest that the SH would be warmer than the NH, because of more efficient energy capture by the oceans.
Is that what you intended ?
It just that(NH=14.6,SH=13.4)
http://cdiac.ornl.gov/trends/temp/jonescru/jones.html
Now that I think about it, prior to the previous question should be one that probably *is* frequently asked: what are some of the major/leading GCMs? Apologies if this was discussed in the voluminous comments, but I searched for “major” and “leading” and didn’t find a match for this question.
On the CO2 from a can of soda: Marks says:
“Lawrence, look it up. From a quick google:
CO2 per can of soda 6g CO2.
22 cans a week is a lot, so say 4 cans average.
4Billion people.
4×10^9×4×50×6g = 5×10^12g = 5×10^9kg = 5×106T = 5Million tons.”
That’s the local frame – and we are concerned with the global frame. Imagine you are measuring the flow rate of a river – but where does the water come from? Is it from rainfall, from snowmelt, from glacier melt, or is someone mining hundred thousand year old groundwater up in the mountains?
With the can of soda, you could measure the ratio of 14-C to 13-C in the CO2 coming out of the soda, and if it was identical to the atmospheric ratio, you would know that the CO2 last saw the atmosphere very recently. If there was no 14-C, you would know the opposite. If you measure the 14C content of CO2 coming out of a gasoline engine, you’ll see none – unless the engine is running on biofuels.
So, let’s take this approach. 100 g of pure water can hold 0.34 g of CO2 at O Celsius. So, where does your soda company get their CO2 from? They pressurize the soda using pure CO2, from atmospheric sources. Thus, the actual answer is zero, if we look only at the carbonated water – but if we look at the entire process of producing and drinking a can of soda, we get a different number.
The actual atmospheric cost of drinking a soda is not the release of dissolved gases, but rather the fossil fuels used in mining and processing the aluminum needed to make the can, to power the factory that converted the corn syrup, benzoic acid and other ingredients into a tasty beverage, and to grow and process the corn in the first place. Then there’s transportation, distribution, etc. This could all be done with renewable solar and wind, or with coal power – so that number is highly variable – there is no one right answer.
The conclusion is that the solution there is not to feel guilty about drinking a soda, but rather to push for a large-scale solution that will make our agricultural and industrial systems free of the need for fossil fuel inputs. Wind, solar, biofuels, geothermal and nuclear are all low-carbon energy solutions that directly address the problem.
Most of the cap-and-trade solutions, however, don’t take the carbon cycle into account. Under those solutions, the acceleration of CO2 emissions (seen clearly in the 50-year record from Hawaii, linked below), will continue. Take a look at the graph, and note that if CO2 emissions had stayed constant at 1960-1965 levels, we’d now be at around 340 ppm, not 380 ppm. Look for yourself:
http://www.aip.org/history/climate/images/mlo_record_2007.jpg
Thus, we are on track for the BAU scenarios in the IPCC. Petroleum supplies are not increasing, but there is a lot of coal. Since dirtier source of oil (Canadian tar sands) are now being promoted, the accelerating trend is expected to continue.
So far, the only thing that has lead any country to reduce fossil fuel use is an economic collapse (Russia, 1990s). The problem is that economics relies, eventually, on ecological and climatic stability, and fossil fuel use tends to undermine both. This is something that most economists have not been taking into account.
That’s part of the reason that “econometric models” routinely produce nonsense – their predictions are no better than blind guesses, and are probably worse due to their biases. Electricity demand forecasting is one particularly notorious area, as it was widely used to justify very unrealistic programs. Another is NAFTA – the econometric models claimed the accord would result in wage increases for both U.S. and Mexican workers, which was not at all true. Econometric modeling is mostly nonsense, and no sane government should ever use it as the basis of policy decisions.
Climate models, on the other hand, have a successful track record – look at the melting Arctic, warming around Antarctica, the surface temperature, the water feedback effect, the reduction in mountain glaciers… etc.
Found great stuff through unlimited world.com a cutting edge internet information site:
http://www.climatescience.gov/Library/sap/sap3-1/final-report/sap3-1-final-all.pdf
http://www.gcrio.org/orders/product_info.php?products_id=220&osCsid=sk16ebef3gqqchqn83isbb5it2
The issues with the IPCC report are discusses, reanalyzed and the applications of the models are re explored.
Just a thought for the FAQ, specifically on difference between physics-based models and statistics-based models. In an old post here I summarized some different kinds of models.
Some people are used to models that either give *exact* answers that must be *right*, or are simply useless, and if so, are inclined to be cynical about results of climate models. Some are actually physics models, i.e., like some of the protein-folding mentioned in the above post, and which has the awful property that an error early in a sequential chain causes wild divergence in the final result.
It might be worth a few words to note the difference between models that must be accurate/perfect at every step to be useful, and those that are constrained approximations that are still quite useful without needing to be perfect. MCAD fluid dynamic codes and petroleum reservoir models, as well as climate codes, seem to fit the latter category.
Chris 220 keep in mind, carrying capacity. With over six billion people on the planet and the bacteria and deforestation you mentioned, all the CO2 from us does not offer zero net effect.
Hank 221 all kind of technology do exist which could potentially hold and harvest solar energy….I do believe that bacteria in properly engineered system could also capture and convert CO2.
Re:220 Chris, you forgot to include in your calcualtions that the number of cattle, sheep, pigs and poultry etc have been growing close to exponentially with the rate of human expansion- their waste products mean a nett increase in the amount of CO2 being puffed into the air over time but also the increase in other gasses such as methane and nitrous oxide. All this is happening when tropical rainforests are being wiped out wholesale across the world that are designed to take in the CO2 in the air and sequester it as wood- typically hardwood long term. The CO2 taken in by rainforests per hectare is massively greater than a hectare field of sorgom for pigfood. This is a grossely unsustainable system whichever way you want to cut it.
Re: Ike Solem, in that case I would guess there is very little 14C in soda cans/bottles due to the fact that the majority of industial CO2 is still captured from fossil fuel sources. I would still still feel guilty about drinking it becasue a sizable percentage is not immediately tranformed and recycled in the biospherical carbon cycle but makes it’s way over the decades into the upper atmosphere. I agree that the entire production process from the manufacture of the aluminuim or the moulding of the glass or the fabrication of the plastic bottles uses huge amounts of CO2 and produces plenty of fine particulate carbon emissions. This must also be tackled vigorously by governments and the appropriate carbontax applied on those factories. As I mentioned to Chris the ‘biospherical’ carbon cycle is out of balance. Not enough and rapidly vanishing carbon sinks and too much livestock and people pumping CO2 into the air which a part will make it’s way into the upper atmosphere over the coming decades.
Mark (#209 Ò). I praise your effort. It is quite telling. You got 1, 5, 10, 11, 12 and 13 wrong. To learn what a “state variable” is, read carefully section 2.1 of this assessment report (thanks to jcbmack for link):
http://www.climatescience.gov/Library/sap/sap3-1/final-report/sap3-1-final-all.pdf
first paragraph. About NSE, see the second paragraph, and please remember that “fluid dynamics of an ideal gas” is not NSE. Also, be aware that the water vapor is canonized here as a classic “feedback”, although I could concur with your heretic view that it is a forcing as well, just as any other “state variable” under appropriate time scale.
Rod B (#218): It looks like you can call anything as “forcing”. For a normal people, closing of Panama isthmus constitutes a new BOUNDARY CONDITION while starting from initial conditions that resulted from old boundary. The orbital variations are astronomically parameterized as part of insolation, which is again the main boundary condition imposed on the system. Atmospheric absorption and reflection is mostly determined by the instant state of cloud cover, which is usually presented as “feedback”. The more I read this thread, the more conflicting messages appear.
Very arresting article in ScienceDaily…http://www.sciencedaily.com/releases/2008/11/081108155834.htm That shows that the present level of CO2 @ 386ppm is already in the dangerzone that will cause catastrophic climate change if not reduced to well below 350ppm in the coming few decades. The group of 10 scientists include Prof.James Hansen, 2 other leading academics from Nasa Goddard, a scientist from Yale, the UK and France. They propose replacing coal as a fuel source by 2030. They say geo engineering is a waste of time since to reduce CO2 by even 50ppm would require a global expenditure of US$20 trillion. What do you think?
The article is a good example of global warming. I noticed another similar one that talks about reaching the danger zone due to global warming
http://www.kanbal.com/index.php?/Latest/danger-zone-carbon-dioxide-to-blame.html
Ike,
Now add that you have to pressurise the CO2.
Using a machine.
Which runs of HC fuels.
In any case, read a later response I gave to Tamino. The answer to your point is contained within.
Ta.
Cumfy 227.
How to you get that?
The small surface penetration means that there is little thermal inertia. Land heats up quicker in response.
So if there’s WARMING, you would see a lag in the SH because the water has a lot of thermal inertia whereas the NH land has responded quickly.
If there’s cooling, the inertial would hold the SH warmer for longer.
As Gavin said, RodB (#218), they are not the only forcings. I even gave one that WASN’T one of those two in the message. Pity you didn’t read that far.
ANYTHING that feeds back its output into its input is a feedback. If it feeds more, then it is a positive feedback, if it feeds it as a reduction, it’s a negative feedback.
How come when it comes to “feedback in climate” no bugger knows what it means, but when it comes to “feedback in the rabits/foxes cycle” or “feedback as in my mic is next to the loudspeaker for the mic”, everyone knows it?
How about this: temperature changes ice cover. Ice cover changes albedo. Albedo changes temperature. Temperature chances ice cover. Ice cover changes albedo. Albedo chances temperature. Temperature…
If the models didn’t have ice cover feedback, you’d be complaining that this proves them useless. When it does, you complain that it shouldn’t be considering them because they aren’t feedbacks (because that is the direct meaning of your listing of the “ONLY TWO feedbacks”).
Stefano, #217. Gravity (based on a model showing it as a curve in spacetime) will continue to make you fall even if I believe it is right.
So calling it a belief (which is incorrect in the first place) doesn’t make AGW wrong.
RodB, #215.
So you now agree 100% that human actions can have a MASSIVE effect on the climate?
Please educate those people and correct them when they say “But the planet is huge, there’s no way man can mange that. If you think so, it merely shows your arrogance that we can have an effect on this world”.
ta.
Gary, #207 All of the globe. Since the query was “what part of the globe is affected by GW” I didn’t see the need of specifying it.
If you are unable to remember your questions, how will you remember the answers?
All of the globe is affected by GW. Tautologically. But in reality too.
All of the atmosphere is affected by GW.
All of the ocean. All the way down, all the way up.
All of it. “It” being defined by your question.
Your “issue” seems to be you didn’t mean “affected” but “warmed”. But in that case, why do you want to know? Are you going to move house to where the various effects cancel out and no net change found? And then move again when the variables change (because they could change, for example, by people moving all to the same area, creating a heat island effect, thereby being a self-defeating prediction).
What do you mean by “the main sources of estimates”? The estimates are in the IPCC report. The sources are noted in there. The differ because they aren’t measuring temperature but something that varies with temperature (and, one would hope, nothing else). However, that doesn’t work too well. Try measuring a temperature of 400C with a mercury thermometer. Even if you measure a temperature it COULD manage, you need a different instrument to measure temperatures in a range. Read up on metrology.
But even if you have a thermometer that is effectively 100% accurate, you haven’t measured the global temperature, you’ve just read one very small segment of the global atmosphere. The bit that has brushed past the thermometer.
So that’s one error. Sampling error.
Radiosondes have a similar problem, since they hold a thermometer in them.
Radar measurements sum up the radiation over a beam whose apeture and sampling are settled (under ideal conditions) by the diffraction limit of the sensor. However, two problems:
1) That isn’t a nice simple square, but working with the diffraction pattern is computationally expensive and cannot be used in a satellite where it can’t plug into the mains.
2) This is most definitely NOT temperature.
a) You have the effect of all other “visible” layers doing its thing to your data. E.g. if you shine an IR torch up at it, it can’t tell it’s a torch, it just looks “too hot”
b) The effect you see from one layer is affected by all the other layers above it (absorbtion, dispersion, etc)
c) The layer itself is messing with your data (pressure broadening, mixed atmosphere, etc)
And with satellites, you have to work out where all these measurements are for. If you have the satellite 5 seconds of arc out, you are pointing at a different place than when you last calibrated where it was pointing.
I don’t think that question really helps anyone decide what’s going on. You have to get REALLY deep into a lot of other things to understand WHY there are uncertainties and they require knowledge of many different disciplines of physics, maths and engineering.
None of which you can do anything other than “OK, the error in temperature is X they say and I will believe them”. Or, if you’re a denialist “I won’t believe them” or “This PROVES they are wrong!!!”. Then again, a denialist won’t bother to do any work if they can just jump STRAIGHT to a conclusion, so adding that info doesn’t actually help.
Errors are stated.
Believe them or don’t.
Anything else requires more work than just an FAQ.
CM wrote:
The answer to any of those questions will be a spread of projections based on questionable assumptions. A “best estimate” is sometimes just a middle projection cited in a confident tone of voice.
……
Stefano, did you become disappointed with AGW because it turned out to be more complex than a textbook engineering problem? Surely you knew that before they ran the simulations.
CM, recall Samson’s question, what do you say to skeptics who quote the IPCC and say the future climate state is “not predictable” ?
If we take your point in that context, part of the answer becomes, “A ‘best estimate’ is sometimes just a middle projection cited in a confident tone of voice.”
I think you just made a skeptic’s day.
I’m not trying to be facetious. Bear in mind that the public are hearing very confident assertions from spokespeople on the climate change issue who appear to all intents and purposes to be representing the scientific community.
So a problem arises, how do you answer members of the public who subsequently read a little and found that, “AGW turned out to be more complex” ?
That might even be one for the FAQ.
Re 106: can the remaining wiggles be compared to the Gibbs phenomenon observed in generating for instance a square wave by a Fourier series… The amplitude of the initial wiggles does not decrease using more and more Fourier terms… Or is discontinuity not a feature occurring in climate models?
Stefano: “what do you say to skeptics who quote the IPCC and say the future climate state is “not predictable” ?”
I would say it is as predictable as anything else can be in this uncertain world.
“So a problem arises, how do you answer members of the public who subsequently read a little and found that, “AGW turned out to be more complex” ?”
I would quote: “The best laid schemes of mice and men go oft astray”. I would say that it is a know issue that the best tactical position never survives contact with the enemy. I would say that if they have ever tried to fix stuff themselves, they have found out that there’s a lot more to their solution than they expected.
And in none of these cases have we then turned around and said “OK, we can’t be certain, so let’s not do it”. We’ve still schemed, still assume our generals make tactical decisions in war and people STILL try to fix their plumbing when it seems a simple problem.
So why not here?
So Alexi, #236. In order to know your question we must read other threads and other elements.
Please do the same here.
Read up elsewhere and leave.
I notice you didn’t even say why some were “wrong”.
You seem to require, nay DEMAND, more rigour from others than you deign to supply yourself.
On #237. Yes, you can call anything a forcing. Sun forces air to rise. Work forces you out of bed. So you must ask what you want to know. Do you want to know what climatological forcings are? They are forces that last climatological timescales.
Also, have you considered that your question seems to you to be “anything” is because it was a stupid question?
PS re #237. How can I answer a question “wrong” when you say that you don’t know the answers? Either you know the answer and so know it wrong or you don’t know it is wrong.