Large Hardon Collider

[Tristam]

Sometimes an angry old man is just an angry old man. Peter Duesberg was a brilliant virologist, but doesn't believe HIV is the cause of AIDS. Anthony Watts is engaging in some pretty wild hyperbole as well. A schism among scientists isn't brewing atall wrt climate change. While they do tout his CV, he doesn't seem to have made it as a member of the NAS. Nor has he published anything recently, or in a long time. So don't be fooled, he's certainly had a great carreer, but he's no giant of american science.

How, also, does the APS benefit financially from climate change? That doesn't seem to make sense watsoever. In a limited world of science funding, one might think that focusing on a certain field derails funding into other domains. This is certainly the case for the NIH, wherein funding guidelines are very much influenced by policy decisions.

Why wasn't there any press when members of the NAS organized a statement decrying the witch hunt that is going on trying to criminalize the work done by climate scientists? I realize anti-science is politically vogue right now. But seriously, if you're going to come into this thread with a policy argument, at least try to throw in some science, as it is this is just making science into politics.

edit: And trillions of dollars? Lol on what planet? One trillion dollars is over 30x the annual NIH budget, which is much greater than the NSF budget. Where the hell do physicists get trillions of dollars to study climate science? And trips to exotic islands? BPS this year is in fucking baltimore........APS is in dallas.... wooo?

[Eliseos]

This is an important moment in science history. I would describe it as a letter on the scale of Martin Luther, nailing his 95 theses to the Wittenburg church door.

I doubt it.

[foopy]

I doubt it.

just for the record, those aren't my words. copy pasta'ed

[Tristam]

For the record, some people happen to have read his book, and there are some odd contradictions to how he is presented in this resignation, I'm copying relevant comments from a blog.

http://scienceblogs.com/stoat/2010/1...erves_some.php

For instance:

Well, isn't this interesting. I happen to have a copy of Technological Risk (actually 1990) right here. Lewis' discussion of global warming begins on p. 266, in a chapter on the risks posed by fossil fuels.

After a shaky start (he conflates global warming with greenhouse effect and gets the greenhouse analogy wrong), Lewis pretty much gets everything right. He discusses the EM spectrum and explains how greenhouse gases (mainly water and CO2) keep the earth warm. He talks about the state of climate models, writing:

"The GCMs in use nowadays do a pretty good job of calculating the effect of a potential doubling of the carbon dioxide content of the atmosphere, but more research is truly needed... The details of the impending changes of climate are still beyond our grasp, though the broad outline is clear."

He then discusses how models were mostly in agreement on a global scale but differ at local levels and says:

"All models agree that the net effect will be a general and global warming of the earth; they only disagree about how much. Non suggest that it will be a minor effect, to be ignored while we go about our business."

After some discussion of global warming effects on agriculture and SLR Lewis concludes:

"Yet, despite the complexity, the bottom line is that the earth will be substantially warmed by the accumulation of man-made gases, mainly carbon dioxide, and that warming could conceivably approximate the climate at the time of the dinosaurs. It seems likely, but not certain, that sea level will rise accordingly, conceivably by several feet or more. We are doing this to ourselves."

Lewis then goes on to discuss options to avert global warming - mainly nuclear power and greater efficiency. He is pretty bleak - "But there is nowhere in evidence any effective solution to the problem".

[That *is* interesting. I look forward to my copy with great expectation! -W]

Posted by: Joe | October 12, 2010 8:12 PM

I've read Lewis's Technological Risks book. It's a very good book. There's much I would disagree with that falls in the realm of normal scholarly disagreement, but the book is very good at presenting a sensible approach to risk with a focus on understanding probabilities and uncertainties (and distinguishing aleatory from epistemic uncertainty) and emphasizing that the risks business is about trading off one risk against another because there are precious few opportunities to reduce one risk without, at least marginally, increasing another.

One can disagree with his tendency to be dismissive of precautionary approaches to uncertain risks (i.e., he tends to say that if you can't prove it's dangerous, then it's a good bet to assume it's safe), but that's a pretty mainstream point of view, and he's in good company with it. Chapters 2-6 of his book make an excellent introduction to risk identification, assessment, and management for undergrads.

The screed that's heard 'round the blogosphere bears almost no relationship to the Technological Risks book.

FWIW, a bit more from Tech Risks on global warming: "[CO2] has been increasing ever since [the industrial revolution], and has just passed 350 ppm, with no end in sight. Why is it happening, is it bad, and what can be done? The answer to the last question is easy---very little. The answer to the next to last question is also easy---yes, it is bad." (p. 270)

[Thanks. I get the impression - from his papers - that he was an advocate of nuclear power. Does that show up, in the book? I might expect him, say, to downplay long-term risks -W]

Posted by: Jonathan Gilligan | October 12, 2010 11:07 PM


The general theme is that Lewis got very very mad (and rightly so) about environmentalists making nuclear power a political poison pill. His anger seems to have become misguided.

[Eliseos]

just for the record, those aren't my words. copy pasta'ed

Yeah, I know. Was just commenting on how ridiculous those words are.

[Max™]

Sometimes an angry old man is just an angry old man. Peter Duesberg was a brilliant virologist, but doesn't believe HIV is the cause of AIDS. Anthony Watts is engaging in some pretty wild hyperbole as well. A schism among scientists isn't brewing atall wrt climate change. While they do tout his CV, he doesn't seem to have made it as a member of the NAS. Nor has he published anything recently, or in a long time. So don't be fooled, he's certainly had a great carreer, but he's no giant of american science.

Sometimes an angry old man isn't just angry for no reason as well.

How, also, does the APS benefit financially from climate change? That doesn't seem to make sense watsoever. In a limited world of science funding, one might think that focusing on a certain field derails funding into other domains. This is certainly the case for the NIH, wherein funding guidelines are very much influenced by policy decisions.

Try to get scientific funding for an organization which doesn't support the anthropogenic hypothesis, or try to find money to investigate alternative explanations.

Why wasn't there any press when members of the NAS organized a statement decrying the witch hunt that is going on trying to criminalize the work done by climate scientists? I realize anti-science is politically vogue right now. But seriously, if you're going to come into this thread with a policy argument, at least try to throw in some science, as it is this is just making science into politics.

I thought Lewis resigned because of the increasing influence of political interests on scientific research?

edit: And trillions of dollars? Lol on what planet? One trillion dollars is over 30x the annual NIH budget, which is much greater than the NSF budget. Where the hell do physicists get trillions of dollars to study climate science? And trips to exotic islands? BPS this year is in fucking baltimore........APS is in dallas.... wooo?

Trillions of dollars up for grabs in the climate science arena, I think was what he meant. Though that should have been clarified better, as the only way to get that figure is to include money available in future carbon trading and such.


Amusing how often his page has been edited/viewed over the last couple days.
http://stats.grok.se/en/201010/Harold_Lewis

On the matter of global climate change, APS notes that virtually all reputable scientists agree with the following observations:

• Carbon dioxide is increasing in the atmosphere due to human activity;
• Carbon dioxide is an excellent infrared absorber, and therefore, its increasing presence in the atmosphere contributes to global warming; and
• The dwell time of carbon dioxide in the atmosphere is hundreds of years.

On these matters, APS judges the science to be quite clear. However, APS continues to recognize that climate models are far from adequate, and the extent of global warming and climatic disruptions produced by sustained increases in atmospheric carbon loading remain uncertain. In light of the significant settled aspects of the science, APS totally rejects Dr. Lewis’ claim that global warming is a “scam” and a “pseudoscientific fraud.”

How does the bolded fit with the underlined there?

It's clear that while it's uncertain, when you consider how clear it is, it's certainly not uncertain.

[Tristam]

The bolded fits with the underlined because climate models are only a part of the entire story. As in models alone do not validate AGW, nor are they expected to be perfect given our limited knowledge of the past and limited direct data. That is so consistently lost on the public that the public debate has nothing to do with 99% of the science. And if you want to find money for alternative explanations, that's easy, apply to a right wing think tank or exxon mobil. Most scientific organizations don't even have any statement regarding climate science, because it isn't under their purview. What they do NOT like is harassing scientists because their work happens to influence policy decisions. Many industrial organizations actively fund climate projects trying to throw doubt on climate science, they don't hide from it either, they're quite up front about their goals. Koch industries spends over 40 million annually on climate anti-science. (edit: Hal Lewis just joined a foundation for climate antiscience whose first goal is to start another inquiry/harassment into the climate emails, classy move, sadly they don't disclose their donors) Exxon mobil publishes these papers by their scientists

http://www.exxonmobil.com/Corporate/...tedpapers.aspx

Which their lobbyists use as citations. So yeah, there's plenty of money for whatever floats your boat.

And trillions of dollars is still absurd no matter how you look at it. A huge part of his letter was that the APS is caving into climate change science to get the big bucks (with alot of "them damn kids" language). He is explicit that he thinks money is driving the greed of scientists "with the (literally) trillions of dollars driving the scam". We don't need to speculate on what he meant, he was explicit;y simple. The US GDP is 14 trillion dollars. The climate science arena doesn't command a fraction of that money, Dr. Lewis is off by orders of magnitude. The more I think about it, the more pure crazy this resignation letter seems.


edit: To expand a bit on the first paragraph. We expect uncertainty in models and forecasts, and the fact that they are inadequate does not mean that the broad conclusions drawn from them and other data are insignificant. The science, as a whole, is very clear, even if the models themselves are not as clear. The mechanism of evolution was not clear in the early 1900s, but even without understanding the mechanism the amount of data from comparative embryology, physiology, and fossil evidence made for a clear story that was very compelling and ultimately correct. The same with plate tectonics and quantum mechanics. You don't need to understand every little detail about a process to make good predictions. Forest/trees and such.

[Tristam]

And now for some cool science!

Two years ago, I got to see a talk that I will never forget, it was by a postdoc from Japan who is a member of the Ando lab at Kanazawa. A number of imaging methods have broken the diffraction limit for light microscopy, we can resolve and track objects smaller than the wavelength of light. This lab, however, uses atomic force microscopy to directly measure deflection forces over a surface. It's basically like running your finger over the surface of something, feeling bumps, and making an image based on those bumps. Except there's no direct contact and the forces are infinitesimally small. Their specialty is driving the technology to provide faster scanning times, higher resolution of biological molecules, real time imaging, and lower tapping forces on the specimen (so you don't bust what you're looking at). Consequently, they are able to actually observe the motion of single proteins. A suitable system for them to visualize is the action of motor proteins like myosin, which have long been predicted and demonstrated (quasi-indirectly) to walk on actin filaments similar to the way a tightrope walker would walk suspended on a rope. One foot over the other, in what has been called the "hand over hand" model. On my home computer, the movies are not behind a paywall, so I hope you all get to see them too. They finally published it. It's absolutely beautiful, this is how proteins create directional force, and do work.

http://www.nature.com/nature/journal...ry-information

If you scroll to the bottom of the article, Supplementary movie 2 is the most intuitively easy to understand. You can see a two headed molecule attached to a track, and you can see one head remove itself, and then reattach forward of the other head. The way they formatted the movies isn't great, but just watch one panel at a time and repeat. The rope the protein is attached to is actin, you can see the helical nature of the filament clearly.

Supplementary movies 3 and 6 show "telemark" configurations that have long been predicted based on kinetic and other evidence, but to see them directly is flat out awesome.

[Max™]

Walking proteins like this?

The bolded fits with the underlined because climate models are only a part of the entire story. As in models alone do not validate AGW, nor are they expected to be perfect given our limited knowledge of the past and limited direct data.

Limited knowledge of the past/direct data? Who told you that? Hell, one of the main reasons given for supporting the GCM's is their ability to reproduce past climate variations to one extent or another, isn't it?

What bugs me about climate models though, we can't predict weather a few weeks into the future, but we can predict the overall climate behavior a hundred years into the future? I find that remarkable, particularly in a system which is apparently dominated by positive feedbacks, according to the assumptions used by the modelers.

http://img11.imageshack.us/img11/163...o2sealevel.png

[Tristam]

The movie you show is a reconstruction based on multiple electron micrographs which infer the directional movement of myosin. They actually mislabel it as kinesin in the movie, kinesin is much more constrained and takes smaller steps than myosin (8 nm vs. 36 nm). There's a big, actually an enormous difference between taking multiple snapshots of a protein adhered to a 2-d grid and imposing a sequence based on kinetic data, vs. actually watching protein motion happen in real time. Keeping a protein alive and working while touching it is not an easy task, nor is imaging something far smaller than the wavelength of light. Interestingly, kinesin has a rather different mechanism, that potentially involves docking of linker regions adjacent to the motor domains, but the bottom line is that the EM images were snapshots of an inferred mechanism. This validates what we already thought, but in an amazing way. edit: for instance, from the snapshot data, you cannot directly tell which way the protein might be moving, because each individual image is a separate molecule. It's like if you took two pictures of me walking, you can't tell if I'm walking forwards or backwards. You can only tell the direction if you have a real time movie. You actually can't even tell if I'm moving in a direction at all, those two different molecules might just be binding to actin and hanging out doing nothing. But obviously there is plenty of evidence that suggested this protein worked via this mechanism prior to these publications.

GCMs would not be expected to predict local weather, because local weather is dependent on short term and local effects, climate is defined over the long term. Cell motility is a perfectly valid analogy. We know how fast cells can respond and move towards a chemoattractant depending on the concentration of the chemoattractant. We can predict speeds and persistence based on what/how much is used. We do not understand in detail how it does this. We can model leading edge cytoskeleton polymerization, asymetric adhesion forces, polarization based on local lipid concentration etc etc etc, but we do not have an accurate short term model for the phenomenon that mathematically predicts the long term behavior. But the disconnect does not mean one or the other method is invalid, it means there are "coarse grained" and "fine grained" models, which predict behaviors over different time windows.

For climate, it's the same thing. We don't need to know what's going to happen tomorrow exactly to predict what is roughly going to happen over 50-100 years from now on average. If you don't understand the power of averaging, you really cannot do much in science.

edit: also, a model does not reproduce data necessarily, a model PREDICTS data based on known parameters (edit: and then tests agreement with direct temperature measurements for when they are availabl). The climate system is not necessarily dominated by positive feedbacks, but we know from milankovitch theory that both positive and negative feedbacks have large effects. The current warming appears to be dominated by a positive feedback, which is completely out of phase with milankovith cycles, thus requiring a new explanation. In 1880, Svente Ahrennius gave us a new explanation, and it has since been validated by more, and more, and more, and more data. But still not enough for some it seems.

[Max™]

I had someone give an excellent example.

Imagine if you will that we've got a rocket full of fuel, but nobody's told us how much the rocket weighs or how much fuel in it. Most scientists will tell you that igniting it'll make it go really really really high but that they can't tell you exactly how high because we don't have enough data to model it perfectly.

To which I responded:
"I'd be able to tell you it could go really high, I could even estimate roughly from the visual size a range of altitudes that I think it might reach, based on certain assumptions.

I'd also point out that it could fly off at an angle, on a parabolic trajectory, it could be designed to reach a certain altitude and hover, it could begin spinning wildly out of control, heck, it might even explode in mid-air, or even before it ever gets off the ground.

Rockets are pretty simple though, weight, exhaust velocity, reaction material mass, whatnot, not really a huge number of variables in the basic concept. Ever seen a bottle rocket? Rolled up cardboard, a nosecone-stopper in one end, some powder, and a stick.

The climate isn't quite so simple, it is a vastly more complicated system, and it is an open system.

Know that saying, "well, it isn't rocket science"?

They should probably say "well, it isn't climate science" instead."


They aren't just averaging the future climate behavior though, they're giving a fairly precise prediction across 100 years which varies by a couple degrees celsius.

If I shot at a target from 100 yards and was a few inches off either way, I'd be pretty fucking impressed if I was only a few inches off when shooting at a target 50 miles away.

Even though long term climate prediction is a bit different from being 30 times better than the best sniper ever, there are still a huge number of variables influencing the climate, and the long term variability in the past doesn't inspire quite as much confidence that it will play nice in the future just because GCM's say it will. Making a 50 mile sniper shot in Halo is not quite the same as making one in real life, and I'm skeptical that we're so much more advanced in our climate models than, well... we are at making realistic video games.

[Tristam]

But that's why the GCMs have confidence intervals associated with them, they give a huge range of probabilities centered around the most likely based on the model and data. Scientists are only giving estimates based on the likelihood, if you've heard otherwise, it's probably because it's been simplified. Parts of the climate system are actually very simple. It's basically just an energy budget, by which the climate is closed in an earth-sun system, there are no other sources of energy. And because there is one outside source of energy (the sun, and if you want to get very technical, there's a minimal addition from the spinning core of the earth) which is very stable over periods greater than 10 years. GCMs do not faithfully predict temperatures the further you go from the present, there are huge errors associated with them, but the general trends are the same. That's the power of averaging though, the more complicated a system, the more you must average consecutive data points to show a trend. (edit: And that's also a limitation, because we have so little direct data from satellite and ground based temperature measurement, the statistical confidence decreases rapidly with future prediction editx2 paleo data is indirect). That's why climate is defined over long windows as opposed to weather, to average out the noise. By averaging over long windows, you make the general trends apparent from complicated data. That's what climate is, averaged weather over 30 years.

A couple degrees celsius, for what it's worth, is a pretty huge window. That's far more than the warming we've seen over the past couple hundred years. In the 1700s, snow was common in virginia. Most paleo data including the graphs you show predict 1 degree over five thousand years on average.

edit: And even so, just because a system is complicated (like cell motility) that doesn't mean we can't say how fast a cell will move towards a chemoattractant. That's the power of averaging. I really can't simplify it any more than that.

[Max™]

http://www.ipcc.ch/publications_and_...n/faq-8-1.html

How Reliable Are the Models Used to Make Projections of Future Climate Change?

There is considerable confidence that climate models provide credible quantitative estimates of future climate change, particularly at continental scales and above. This confidence comes from the foundation of the models in accepted physical principles and from their ability to reproduce observed features of current climate and past climate changes. Confidence in model estimates is higher for some climate variables (e.g., temperature) than for others (e.g., precipitation). Over several decades of development, models have consistently provided a robust and unambiguous picture of significant climate warming in response to increasing greenhouse gases.

Climate models are mathematical representations of the climate system, expressed as computer codes and run on powerful computers. One source of confidence in models comes from the fact that model fundamentals are based on established physical laws, such as conservation of mass, energy and momentum, along with a wealth of observations.

Spoiler: show

A second source of confidence comes from the ability of models to simulate important aspects of the current climate. Models are routinely and extensively assessed by comparing their simulations with observations of the atmosphere, ocean, cryosphere and land surface. Unprecedented levels of evaluation have taken place over the last decade in the form of organised multi-model ‘intercomparisons’. Models show significant and increasing skill in representing many important mean climate features, such as the large-scale distributions of atmospheric temperature, precipitation, radiation and wind, and of oceanic temperatures, currents and sea ice cover. Models can also simulate essential aspects of many of the patterns of climate variability observed across a range of time scales. Examples include the advance and retreat of the major monsoon systems, the seasonal shifts of temperatures, storm tracks and rain belts, and the hemispheric-scale seesawing of extratropical surface pressures (the Northern and Southern ‘annular modes’). Some climate models, or closely related variants, have also been tested by using them to predict weather and make seasonal forecasts. These models demonstrate skill in such forecasts, showing they can represent important features of the general circulation across shorter time scales, as well as aspects of seasonal and interannual variability. Models’ ability to represent these and other important climate features increases our confidence that they represent the essential physical processes important for the simulation of future climate change. (Note that the limitations in climate models’ ability to forecast weather beyond a few days do not limit their ability to predict long-term climate changes, as these are very different types of prediction – see FAQ 1.2.)

A third source of confidence comes from the ability of models to reproduce features of past climates and climate changes. Models have been used to simulate ancient climates, such as the warm mid-Holocene of 6,000 years ago or the last glacial maximum of 21,000 years ago (see Chapter 6). They can reproduce many features (allowing for uncertainties in reconstructing past climates) such as the magnitude and broad-scale pattern of oceanic cooling during the last ice age. Models can also simulate many observed aspects of climate change over the instrumental record. One example is that the global temperature trend over the past century (shown in Figure 1) can be modelled with high skill when both human and natural factors that influence climate are included. Models also reproduce other observed changes, such as the faster increase in nighttime than in daytime temperatures, the larger degree of warming in the Arctic and the small, short-term global cooling (and subsequent recovery) which has followed major volcanic eruptions, such as that of Mt. Pinatubo in 1991 (see FAQ 8.1, Figure 1). Model global temperature projections made over the last two decades have also been in overall agreement with subsequent observations over that period (Chapter 1).

http://www.ipcc.ch/publications_and_...n/faq-1-2.html

Climate is generally defined as average weather, and as such, climate change and weather are intertwined. Observations can show that there have been changes in weather, and it is the statistics of changes in weather over time that identify climate change. While weather and climate are closely related, there are important differences. A common confusion between weather and climate arises when scientists are asked how they can predict climate 50 years from now when they cannot predict the weather a few weeks from now. The chaotic nature of weather makes it unpredictable beyond a few days. Projecting changes in climate (i.e., long-term average weather) due to changes in atmospheric composition or other factors is a very different and much more manageable issue.

...

Likewise, projections of future climate are shaped by fundamental changes in heat energy in the Earth system, in particular the increasing intensity of the greenhouse effect that traps heat near Earth’s surface, determined by the amount of carbon dioxide and other greenhouse gases in the atmosphere. Projecting changes in climate due to changes in greenhouse gases 50 years from now is a very different and much more easily solved problem than forecasting weather patterns just weeks from now. To put it another way, long-term variations brought about by changes in the composition of the atmosphere are much more predictable than individual weather events. As an example, while we cannot predict the outcome of a single coin toss or roll of the dice, we can predict the statistical behaviour of a large number of such trials.

However, I've read most of this AR4 report, and I know about stuff like this:

http://www.ipcc.ch/publications_and_...8-8.html#8-8-1

8.8.1 Why Lower Complexity?

An important concept in climate system modelling is that of a spectrum of models of differing levels of complexity, each being optimum for answering specific questions. It is not meaningful to judge one level as being better or worse than another independently of the context of analysis. What is important is that each model be asked questions appropriate for its level of complexity and quality of its simulation.
The most comprehensive models available are AOGCMs. These models, which include more and more components of the climate system (see Section 8.2), are designed to provide the best representation of the system and its dynamics, thereby serving as the most realistic laboratory of nature. Their major limitation is their high computational cost. To date, unless modest-resolution models are executed on an exceptionally large-scale distributed computed system, as in the climateprediction.net project (http://climateprediction.net; Stainforth et al., 2005), only a limited number of multi-decadal experiments can be performed with AOGCMs, which hinders a systematic exploration of uncertainties in climate change projections and prevents studies of the long-term evolution of climate.

At the other end of the spectrum of climate system model complexity are the so-called simple climate models (see Harvey et al., 1997 for a review of these models). The most advanced simple climate models contain modules that calculate in a highly parametrized way (1) the abundances of atmospheric greenhouse gases for given future emissions, (2) the radiative forcing resulting from the modelled greenhouse gas concentrations and aerosol precursor emissions, (3) the global mean surface temperature response to the computed radiative forcing and (4) the global mean sea level rise due to thermal expansion of sea water and the response of glaciers and ice sheets. These models are much more computationally efficient than AOGCMs and thus can be utilised to investigate future climate change in response to a large number of different scenarios of greenhouse gas emissions. Uncertainties from the modules can also be concatenated, potentially allowing the climate and sea level results to be expressed as probabilistic distributions, which is harder to do with AOGCMs because of their computational expense. A characteristic of simple climate models is that climate sensitivity and other subsystem properties must be specified based on the results of AOGCMs or observations. Therefore, simple climate models can be tuned to individual AOGCMs and employed as a tool to emulate and extend their results (e.g., Cubasch et al., 2001; Raper et al., 2001). They are useful mainly for examining global-scale questions.

To bridge the gap between AOGCMs and simple climate models, EMICs have been developed. Given that this gap is quite large, there is a wide range of EMICs (see the reviews of Saltzman, 1978 and Claussen et al., 2002). Typically, EMICs use a simplified atmospheric component coupled to an OGCM or simplified atmospheric and oceanic components. The degree of simplification of the component models varies among EMICs.

Spoiler: show

Earth System Models of Intermediate Complexity are reduced-resolution models that incorporate most of the processes represented by AOGCMs, albeit in a more parametrized form. They explicitly simulate the interactions between various components of the climate system. Similar to AOGCMs, but in contrast to simple climate models, the number of degrees of freedom of an EMIC exceeds the number of adjustable parameters by several orders of magnitude. However, these models are simple enough to permit climate simulations over several thousand of years or even glacial cycles (with a period of some 100 kyr), although not all are suitable for this purpose. Moreover, like simple climate models, EMICs can explore the parameter space with some completeness and are thus appropriate for assessing uncertainty. They can also be utilised to screen the phase space of climate or the history of climate in order to identify interesting time slices, thereby providing guidance for more detailed studies to be undertaken with AOGCMs. In addition, EMICs are invaluable tools for understanding large-scale processes and feedbacks acting within the climate system. Certainly, it would not be sensible to apply an EMIC to studies that require high spatial and temporal resolution. Furthermore, model assumptions and restrictions, hence the limit of applicability of individual EMICs, must be carefully studied. Some EMICs include a zonally averaged atmosphere or zonally averaged oceanic basins. In a number of EMICs, cloudiness and/or wind fields are prescribed and do not evolve with changing climate. In still other EMICs, the atmospheric synoptic variability is not resolved explicitly, but diagnosed by using a statistical-dynamical approach. A priori, it is not obvious how the reduction in resolution or dynamics/physics affects the simulated climate. As shown in Section 8.8.3 and in Chapters 6, 9 and 10, at large scales most EMIC results compare well with observational or proxy data and AOGCM results. Therefore, it is argued that there is a clear advantage in having available a spectrum of climate system models.

Then:
http://ipcc.ch/publications_and_data.../en/spms3.html

http://ipcc.ch/publications_and_data...gurespm-5.jpeg

Notes:
a) Temperatures are assessed best estimates and likely uncertainty ranges from a hierarchy of models of varying complexity as well as observational constraints.
b) Year 2000 constant composition is derived from Atmosphere-Ocean General Circulation Models (AOGCMs) only.
c) All scenarios above are six SRES marker scenarios. Approximate CO2-eq concentrations corresponding to the computed radiative forcing due to anthropogenic GHGs and aerosols in 2100 (see p. 823 of the Working Group I TAR) for the SRES B1, AIT, B2, A1B, A2 and A1FI illustrative marker scenarios are about 600, 700, 800, 850, 1250 and 1550ppm, respectively.
d) Temperature changes are expressed as the difference from the period 1980-1999. To express the change relative to the period 1850-1899 add 0.5°C.

[Psion]

eh, speaking of global warming, an article I found:

http://www.sciencedaily.com/releases...1014171146.htm

ScienceDaily (Oct. 15, 2010) — Water vapor and clouds are the major contributors to Earth's greenhouse effect, but a new atmosphere-ocean climate modeling study shows that the planet's temperature ultimately depends on the atmospheric level of carbon dioxide.

The study, conducted by Andrew Lacis and colleagues at NASA's Goddard Institute for Space Studies (GISS) in New York, examined the nature of Earth's greenhouse effect and clarified the role that greenhouse gases and clouds play in absorbing outgoing infrared radiation. Notably, the team identified non-condensing greenhouse gases -- such as carbon dioxide, methane, nitrous oxide, ozone, and chlorofluorocarbons -- as providing the core support for the terrestrial greenhouse effect.

Without non-condensing greenhouse gases, water vapor and clouds would be unable to provide the feedback mechanisms that amplify the greenhouse effect. The study's results are published Oct. 15 in Science.

A companion study led by GISS co-author Gavin Schmidt that has been accepted for publication in the Journal of Geophysical Research shows that carbon dioxide accounts for about 20 percent of the greenhouse effect, water vapor and clouds together account for 75 percent, and minor gases and aerosols make up the remaining five percent. However, it is the 25 percent non-condensing greenhouse gas component, which includes carbon dioxide, that is the key factor in sustaining Earth's greenhouse effect. By this accounting, carbon dioxide is responsible for 80 percent of the radiative forcing that sustains the Earth's greenhouse effect.

The climate forcing experiment described in Science was simple in design and concept -- all of the non-condensing greenhouse gases and aerosols were zeroed out, and the global climate model was run forward in time to see what would happen to the greenhouse effect.

Without the sustaining support by the non-condensing greenhouse gases, Earth's greenhouse effect collapsed as water vapor quickly precipitated from the atmosphere, plunging the model Earth into an icebound state -- a clear demonstration that water vapor, although contributing 50 percent of the total greenhouse warming, acts as a feedback process, and as such, cannot by itself uphold the Earth's greenhouse effect.

"Our climate modeling simulation should be viewed as an experiment in atmospheric physics, illustrating a cause and effect problem which allowed us to gain a better understanding of the working mechanics of Earth's greenhouse effect, and enabled us to demonstrate the direct relationship that exists between rising atmospheric carbon dioxide and rising global temperature," Lacis said.

The study ties in to the geologic record in which carbon dioxide levels have oscillated between approximately 180 parts per million during ice ages, and about 280 parts per million during warmer interglacial periods. To provide perspective to the nearly 1 C (1.8 F) increase in global temperature over the past century, it is estimated that the global mean temperature difference between the extremes of the ice age and interglacial periods is only about 5 C (9 F).

"When carbon dioxide increases, more water vapor returns to the atmosphere. This is what helped to melt the glaciers that once covered New York City," said co-author David Rind, of NASA's Goddard Institute for Space Studies. "Today we are in uncharted territory as carbon dioxide approaches 390 parts per million in what has been referred to as the 'superinterglacial.'"

"The bottom line is that atmospheric carbon dioxide acts as a thermostat in regulating the temperature of Earth," Lacis said. "The Intergovernmental Panel on Climate Change has fully documented the fact that industrial activity is responsible for the rapidly increasing levels of atmospheric carbon dioxide and other greenhouse gases. It is not surprising then that global warming can be linked directly to the observed increase in atmospheric carbon dioxide and to human industrial activity in general."

[Eliseos]

What bugs me about climate models though, we can't predict weather a few weeks into the future, but we can predict the overall climate behavior a hundred years into the future? I find that remarkable, particularly in a system which is apparently dominated by positive feedbacks, according to the assumptions used by the modelers.

I had someone give an excellent example.



To which I responded:
"I'd be able to tell you it could go really high, I could even estimate roughly from the visual size a range of altitudes that I think it might reach, based on certain assumptions.

I'd also point out that it could fly off at an angle, on a parabolic trajectory, it could be designed to reach a certain altitude and hover, it could begin spinning wildly out of control, heck, it might even explode in mid-air, or even before it ever gets off the ground.

Rockets are pretty simple though, weight, exhaust velocity, reaction material mass, whatnot, not really a huge number of variables in the basic concept. Ever seen a bottle rocket? Rolled up cardboard, a nosecone-stopper in one end, some powder, and a stick.

The climate isn't quite so simple, it is a vastly more complicated system, and it is an open system.

Know that saying, "well, it isn't rocket science"?

They should probably say "well, it isn't climate science" instead."


They aren't just averaging the future climate behavior though, they're giving a fairly precise prediction across 100 years which varies by a couple degrees celsius.

If I shot at a target from 100 yards and was a few inches off either way, I'd be pretty fucking impressed if I was only a few inches off when shooting at a target 50 miles away.

Even though long term climate prediction is a bit different from being 30 times better than the best sniper ever, there are still a huge number of variables influencing the climate, and the long term variability in the past doesn't inspire quite as much confidence that it will play nice in the future just because GCM's say it will. Making a 50 mile sniper shot in Halo is not quite the same as making one in real life, and I'm skeptical that we're so much more advanced in our climate models than, well... we are at making realistic video games.

If you don't understand the power of averaging, you really cannot do much in science.

That's just how max always shows off his lack of science skills.

edit: I can't see those videos Tristam, says I have to pay/log-in

[Tristam]

edit: I can't see those videos Tristam, says I have to pay/log-in

Shucks, I just noticed that for some reason Nature thinks I'm at Towson University Library, even though I'm at my apartment which is quite far away from Towson lol, I've never even been affiliated with Towson, it's a bit weird. So that's probably how I'm getting behind paywalls. Lucky me!

edit: The ando lab updated their webpage, let me know if these work.

http://www.s.kanazawa-u.ac.jp/phys/b.../M5_movies.htm

edit: And as a last note with the climate models, I didn't want to suggest that all the models are is averaged historical data and the projections, they're certainly more complicated than that. My point was just that high frequency noise in any system can be averaged out over sufficiently long windows, and that's an important thing when we talk about climate vs. weather. This isn't directed at you, I'm just not sure I'm being clear in my posts.

[Ferion]

I don't know how many of you guys are undergrad/grad students, so I'm sorry if this is off topic. I'm just kind of curious how you guys keep yourself motivated to keep up with everything. The latest research, your research, classes, papers, etc. Had an exam the other day in a math class that I'm pretty sure I bombed it since my professor told us to study one way, then made the test completely different which really threw me off. Also probably didn't help that she's from MIT haha. Just kind of curious how you guys bounce back from something like this. Hopefully I'm not the only one that has hit some serious road blocks a long the way in their studies haha.

[Shiroikage]

You mostly keep motivated by the fact that you won't graduate/get fellowships or funding/get into where you want to go without continuing on.
This becomes even more true in graduate work than undergraduate work.

[Tristam]

In my experience, I've always found a way to convince myself that even though my last set of experiments was a total failure, the NEXT one will give me results. Then when that set of experiments fail, I try like crazy to find a reason it didn't work and find a rational way to make a slight adjustment, convince myself that adjustment will be the difference, try again, repeat, etc forever.

Once in a great while, something works. When that happens, I don't go home, I collect data until my eyes bleed or something breaks. Then I analyze the data to make a graph that isn't nearly as cool as I think it is and run around the lab showing everyone my shiny new data like a little kid who lost his first tooth.

Also, it helps to have something to do outside of science, work out, play video games, read books. Find SOMETHING to get your head outta the lab for at least an hour a night or you will go crazy.

edit: The one thing that I think gets easier is reading papers, I'm getting better at digesting them faster and deciding which ones are most important for me and which ones I'm only casually or tangentially interested in. This took me a long long time, even in grad school I had to read most papers lots of times before I felt really comfortable with them.

[Shiroikage]

Procrastination is also a very important tool to avoid getting burned out.