This is just one of dozens of responses to common climate change denial arguments, which can all be found at How to Talk to a Climate Sceptic.
Objection:
Scientists can't even predict the weather next week, so why should we believe what some climate model tells us about 100 years from now?
Answer:
Climate and weather are really very different things and the level of predictability is comparably different.
Climate is defined as weather averaged over a period of time, generally around 30 years. This averaging over time removes the random and unpredictable behaviour of weather. Think of it as the difference between trying to predict the height of the fifth wave from now that will come splashing up the beach versus predicting the height of tomorrow's high tide. The former is clearly quite a challenge, as your salty, wet sneakers will bear witness to, but the latter is routine and reliable.
This by no means says that it is necessarily easy to predict climate changes, but clearly seizing on the weather man's one week failure to cast doubt on a climate model's 100 year projection is an argument of ignorance.
This is just one of dozens of responses to common climate change denial arguments, which can all be found at How to Talk to a Climate Sceptic.
"We Can't Even Predict the Weather Next Week" was first published here, where you can still find the original comment thread. This updated version is also posted on the Grist website, where additional comments can be found, though the author, Coby Beck, does not monitor or respond there.
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Where'd you come up with that number 30 years? Why not 500 years? Because you won't live that long? Hm?
Posted by: Dave | February 9, 2009 12:11 AM
Hi Dave,
Check this article for a discussion of that number, "30". The short answer is that it is how climate is defined in the textbooks, I did not make it up. It was not made up for the AGW debate either.
Posted by: coby | February 9, 2009 11:45 AM
Regarding the short term prediction vs. long-term prediction, isn't short-term prediction more focused on specifics on a particular day (which is difficult, as you say) and long-term focused on averages?
Or did I basically just re-phrase your post?
Posted by: Adam | February 9, 2009 1:40 PM
Coby,
I believe what Dave is getting at isn't that you yourself chose such a 30 year interval, but rather it seems arbitrarily labeled by the scientists responsible for it.
In the article you linked, Robert Grubin talked about air movement, and determining relevant data depending on the hypothesis or variable being tested. One can start with one air molecule to determine velocity, yet the one individual does not hold much sway on the grander picture of total wind velocity. So, we can consider another molecule, and another, and another, and so on. However, our scope should not cover too much area, or we risk losing the data in the noise. I thought this was a fitting example, as it reflects the great philosophical conundrum of identity. Since Grubin uses air, I thought it proper to invoke David and Stephanie Lewis' dialogue regarding how one defines what a "hole" is. (http://pwp.netcabo.pt/0154943702/Holes.pdf) The main point of the discussion is regarding the defining power of human observation and the limitations of language. How does one define the boundaries of a hole? An area where one substance ceases and another starts, amidst the original medium? The problem here is that, depending on the scale from which one observes the hole, the difference between materials could appear stark as day (macro), or blurred and congealed (magnified). When dealing with an example as inherently dramatic and forcefully tangible as a hurricane, this question seems arbitrary. However, when one applies such thinking to the intangible notion of a 30 year span for climate change, it is not clear from which perspective one is making this seemingly arbitrary temporal distinction.
While Grubin did in fact address (with a large degree of satisfaction) why we ignore the lower denominations of time (simply because the data is too chaotic and subject to cherry picking), he was not all that clear on why we should ignore larger denominations. It would appear that the limit only comes from the lack of additional information regarding some of the other plotted data on the graph (as denoted by the fact that entire plots are eliminated for simplicity in his example). Such a limit is still arbitrarily placed. So, why not 100 years? Grubin would most likely argue that it would cause the trends of the graph to blend into the background, which I would by and large agree with. However, this would not prevent alarming DATA from still being alarming, so if the case can be made that the average fluctuation in temperature over time is vastly detrimental, then it won't matter the climate time window, or the upward bounds of the graph he uses.
Regardless of what Grubin says, this sort of timeline is still cherrypicking, however that doesn't have to be negatively associated. There must be some way to generate useful statistics from useless noise, however limitations inherent with the process of choosing which data to ignore causes warranted skepticism in your numbers. The beautiful thing about life, according to Heisenberg, is that there are just too many variables to grapple with to make a perfect theory about natural laws and processes. While you may think your particular cherry picking is useful, the true answer could lie in a region of thought you did not consider to begin with; eliminating what you perceive to be noise could blind you from the actual truth at hand.
However, I don't want to pretend that I am necessarily right on the matter. It is a possibility that my analysis is mistaken, since I am not at all clear on what the individual plots mean (120, 240, 360, etc.). I just wanted to clear up what Dave's comment was; not where you got the number from, but rather, how the number came to be canon.
Posted by: Rob | March 27, 2009 2:05 PM
Oh, and I want to appologize to Mr. GRUMBINE, not Grubin, for mistyping his name, and thus misappropriating credit for his thorough and useful article.
Posted by: Rob | March 27, 2009 2:08 PM
This is more than a bit misleading - your suggestion is that it is impossible to predict outcomes in the short term, but on the long term the "averaging" effects make prediction easier, is not coherent. Weather is not random and unpredictable, neither is climate - both are part of the same chaotic system. Chaotic systems are characterised by multi-variate relationships (that is, simple two factor cause and effect assessments can not be made). To imply the two tasks are so separate as to be unrelated is not rational. A chaotic system can not be described with statements like 'more CO2 means higher temperature'.
The same challenge exists with predicting weather and projecting climate. The ability to predict weather in the short term involves modelling the atmosphere - and so does modelling climate. So you can not easily separate the two challenges. The difference is the time scale and the size of 'cells' used to model the atmosphere.
I'm wasting electrons, I know, because I've already run into many of the disciples of climate change who bow before the altar of junk science. I've done modelling of chaotic systems and published in international, referred journals - and I have a bit of an understanding about numeric modeling. It isn't that good, and certainly not good enough to justify dramatic political revolution (unless of course that has been your goal all along, and "global warming" forms a nice hat rack on which to hang your cultural change agenda).
What amazes me is the willingness of so many to cling to irrational argument - which is ultimately just another hallmark of post-modernism.
Posted by: M Oliver | April 19, 2009 8:00 PM
You imply that it is more reliable to predict climate change than the weather. What track record do we have in predicting climate change that you can make such an implication?
Posted by: Mark Wisedale | July 13, 2009 12:44 PM
Mark Wisedale -
You imply that it is more reliable to predict climate change than the weather.
This isn't a particularly controversial claim. Scientists can predict climate better than weather for the same reason meteorologists can predict the average temperature of August more accurately than the temperature on August 12 at noon.
Or, for a common analogy, on any given die roll, you cannot accurately predict which number you will roll, but if you average many die rolls, you can predict the average with a high degree of confidence. The same holds true if you are using a loaded die, except that you will get a different average.
If you're concerned about whether climate models bear legitimate results, check these discussions:
http://scienceblogs.com/illconsidered/2006/03/models-are-unproven.php
http://www.skepticalscience.com/climate-models.htm
Posted by: Adam | July 13, 2009 3:32 PM