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All IPCC definitions taken from Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I, Glossary, pp. 941-954. Cambridge University Press.

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The difference between weather and climate

What the science says...

Select a level... Basic Intermediate
Weather is chaotic, making prediction difficult. However, climate takes a long term view, averaging weather out over time. This removes the chaotic element, enabling climate models to successfully predict future climate change.

Climate Myth...

Scientists can't even predict weather
...Since modern computer models cannot with any certainty predict the weather two weeks from now, how can we rely upon computer models to predict what the Earth's climate might be like a hundred years from now? They can't! Yet people like Al "Carbon-Credit" Gore want you to believe that these models can predict the future. I bet I can do at least as well with a crystal ball (source: Kowabunga)

This argument betrays a misunderstanding of the difference between weather, which is chaotic and unpredictable and climate which is weather averaged out over time. While you can't predict with certainty whether a coin will land heads or tails, you can predict the statistical results of a large number of coin tosses. Or expressing that in weather terms, you can't predict the exact route a storm will take but the average temperature and precipitation will result the same for the region over a period of time.

Climate prediction is a difficult and ever refining art. There's the problem that future behaviour of the sun is very difficult to predict. Similarly, short term perturbations like El Nino or volcanic eruptions are difficult to model. Nevertheless, climate scientists have a handle on the major drivers of climate.

James Hansen's 1988 climate predictions

Way back in 1988, James Hansen projected future temperature trends (Hansen 1988). Those initial projections show remarkable agreement with observation right to present day (Hansen 2006). Hansen even speculated on a volcanic eruption in 1995 but missed the date by a few years (we'll cut him some slack there).


Figure 1: Hansen's model projections (green, blue, purple) compared to observations (red and black).

Hansen's Scenario B (described as the most likely option and in hindsight, the one that most closely matched the level of CO2 emissions) shows close correlation with observed temperatures. In fact, Hansen overestimated future CO2 levels by 5 to 10% so if his model was given the correct forcing levels, the match would be even closer. There are deviations from year to year but this is to be expected. The chaotic nature of weather will add noise to the signal but the overall trend is predictable.

Modelling the aftermath of the Mount Pinatubo volcanic eruption

When Mount Pinatubo erupted in 1991, it provided the opportunity to test how successfully models could predict the climate response to the sulfate aersol injected into the atmosphere. The models accurately forecast the subsequent global cooling of about 0.5 °C soon after the eruption. Furthermore, the radiative, water-vapor, and dynamical feedbacks included in the models were also quantitatively verified (Hansen 2007).

Comparative plots of optical depth and observed and simulated global mean temperature
Figure 2: Observed and simulated global temperature change during Pinatubo eruption. Green is observed temperature by weather stations. Blue is land and ocean temperature. Red is mean model output (Hansen 2007).

Comparing IPCC projections to observations

Recent Climate Observations Compared to Projections (Rahmstoorf 2007) compared 2001 IPCC projections of global temperature change (coloured dotted lines) with observations from HadCRUT (blue) and NASA GISS data (red). The thin lines are the observed yearly average. The solid lines are the long term trends, which filter out short term weather fluctuations.

Figure 3: courtesy of Tamino: Solid blue and red lines are trends from GISS and HadCRU data, dashed lines are IPCC projections.

It's immediately apparent the IPCC underestimated temperature rise with observations warmer than all projections (but inside the grey uncertainty area). The paper proposes several possible reasons for the difference. One is intrinsic internal variability which is possible over such a short period. Another candidate is climate forcings other than CO2 such as aerosol cooling being smaller than expected.

A third candidate is an underestimation of climate sensitivity. The IPCC assumed a climate sensitivity of 3°C with an uncertainty range between 1.7° to 4.2°C (this is indicated in the grey area of Figure 2). However, there are a number of positive feedbacks in the climate system that are poorly understood and hence not given much influence in IPCC models. Add to this the fact that model uncertainty is inherently skewed towards greater sensitivity. My guess is higher climate sensitivity is part of the story but not all. More on IPCC's 2001 projections...

Other results successfully predicted and reconstructed by models

  • Cooling of the stratosphere
  • Warming of the lower, mid, and upper troposphere
  • Warming of ocean surface waters (Cane 1997)
  • Trends in ocean heat content (Hansen 2005)
  • An energy imbalance between incoming sunlight and outgoing infrared radiation (Hansen 2005)
  • Amplification of warming trends in the Arctic region (NASA observations)

Last updated on 26 January 2011 by John Cook.

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Comments

Comments 1 to 22:

  1. According to the graph and the station data Hansen uses, 2006 is apparently the warmest year on record, not 1998...? Is he therefore selecting the best data or perhaps the data that best fits his prediction?

    I would feel more confident in the accuracy of his prediction if the paper wasn't written *by* Hansen explaining to everyone how *clever* Hansen is. No bias, eh? Are there assessments of Hansen's predictive powers made by independent scientists or statisticians? I've already quoted in the other article a link to an assessment that is not favourable. Common sense dictates that his work will probably have to be reviewed at least independently before anyone should take his claims seriously.
  2. Will Nitschke, what a load of nonsense. I've already rebutted your arguments here:

    http://www.skepticalscience.com/climate-models.htm#304

    And...

    "Common sense dictates that his work will probably have to be reviewed at least independently before anyone should take his claims seriously."

    The peer review process of the Proceedings of the National Academy of Sciences of the United States of America -- in which Hansen et al.'s 2006 paper was published -- isn't "independent" enough for you?
  3. U.N. Con on Global Warming Nearly Foiled
    Tuesday, March 31, 2009 3:15 PM
    By: Philip V. Brennan Article Font Size
    LINK: http://www.newsmax.com/brennan/global_warming_UN/2009/03/31/198054.html

    The con game is about over. The attempt to portray a life-giving natural gas as a dire threat to this planet is failing rapidly, as well it should.

    It is becoming more and more obvious to the American people that carbon dioxide, the very substance that gives life to the world's plant life, is not a pollutant, as the global-warming hoaxers would have us believe, but a vital element that keeps the earth green and healthy.

    This is bad news for the would-be masters of the universe at the United Nations who have been using the supposed threat of global warming to advance their desire to turn the United States of America into a vassal state and its citizenry into its subdued subjects.

    If increased atmospheric levels of carbon dioxide are not causing the global climate to undergo a dangerous rise in temperatures, the United Nations has lost its strongest weapon in its attempt to assume world hegemony.

    Those of us who have been warning about the U.N.’s covert ambition have found an ally in Mother Nature, who has managed to cool things down despite the rapidly increasing atmospheric carbon dioxide levels during the past decade. The climate stopped warming around 1998. During the past 10 years, she's lowered the thermostat to the extent this year is moving rapidly toward the distinction as one of the coldest on record.


    In my 1997 series, Behold, The Iceman Cometh, I warned about the U.N.'S attempt to use global warming to achieve its dream of putting the United States in its hip pocket, writing that the U.N.'S Intergovernmental Panel on Climate Change was setting the stage for the international body's attempt at world domination.

    MORE: http://www.newsmax.com/brennan/global_warming_UN/2009/03/31/198054.html
  4. Please update this to use more recent RSS data, as described in http://www.topix.net/forum/news/global-warming/TBSJVGR5M9Q6A7B8H/post87
  5. An analogy that I like to make is to the seasonal cycle. For example, if I told you that I could predict with confidence the weather here in Rochester for a day three weeks hence, you would correctly laugh at me. However, if I told you that I could predict with confidence that the average temperature for next January will be roughly 40 F colder than it was in July, I don't think you would give me much of an argument.

    It is also worth noting that the chaotic behavior of the weather can be tested with the numerical weather prediction and climate models. For example, a numerical weather prediction model will give a specific weather prediction for a day 3 weeks hence, but if you run it again with just small perturbations to the initial conditions, the prediction will be very different. (Actually, such running of ensembles with perturbed initial conditions now plays an important role in weather forecasting, at least for the period out beyond a few days.)

    On the other hand, I assume that such a model will give a reasonable prediction for the climate in January relative to July and the basic features will not be sensitive to the initial conditions.

    Likewise, with a particular climate model, perturbed initial conditions result in differences in the "jiggles" of the global temperature but when run out for 100 years, the different realizations all predict roughly the same overall amount of warming. This is true because the warming that occurs is governed by the fundamental issue of radiative balance between the earth, sun, and space. (Admittedly, because of feedback effects, determining how that radiative balance plays out is not easy...but it does not seem to be sensitive to small perturbations in initial conditions.)
  6. An analogy that I like to make is to the seasonal cycle. For example, if I told you that I could predict with confidence the weather here in Rochester for a day three weeks hence, you would correctly laugh at me. However, if I told you that I could predict with confidence that the average temperature for next January will be roughly 40 F colder than it was in July, I don't think you would give me much of an argument.

    It is also worth noting that the chaotic behavior of the weather can be tested with the numerical weather prediction and climate models. For example, a numerical weather prediction model will give a specific weather prediction for a day 3 weeks hence, but if you run it again with just small perturbations to the initial conditions, the prediction will be very different. (Actually, such running of ensembles with perturbed initial conditions now plays an important role in weather forecasting, at least for the period out beyond a few days.)

    On the other hand, I assume that such a model will give a reasonable prediction for the climate in January relative to July and the basic features will not be sensitive to the initial conditions.

    Likewise, with a particular climate model, perturbed initial conditions result in differences in the "jiggles" of the global temperature but when run out for 100 years, the different realizations all predict roughly the same overall amount of warming. This is true because the warming that occurs is governed by the fundamental issue of radiative balance between the earth, sun, and space. (Admittedly, because of feedback effects, determining how that radiative balance plays out is not easy...but it does not seem to be sensitive to small perturbations in initial conditions.)
  7. One of the issues that many non-scientists have trouble in understanding is the concept of a small but significant change being masked by larger, more immediate, fluctuations: just like weather and climate Here’s my analogy (I think it's original but someone might know better).

    Imagine walking down to a pristine beach -- say on your first day of a holiday -- when you don't know the tide times. You stand and watch the waves crashing in on the gently sloping sandy shore. Is the tide coming in or going out? You can't tell. Carry on watching the waves come in for a while. Some are larger, some are smaller. Every so often there's a much bigger one and, between, some quite small ones. After two minutes, and let's say thirty waves, you're still not sure but you're starting to think that it looks like the tide might be going out. You have started to see a trend. You might be prepared to put a fiver on it but you still wouldn't bet your life-savings on it. But I ask you: how long before you would? Eight minutes? Ten minutes? By averaging the distance those waves travel up the beach, a pattern has emerged and the direction of the tide has been established.

    Interestingly, the analogy holds up if we extend it. Now go back to the top of the paragraph and walk along the beach as you watch the waves. Is the tide coming in or out? After half an hour we still can't tell: the waves just come in with varying strength and as you walk -- and thus your reference points change -- it's impossible to estimate the trend.

    The opposite also holds true. Start back at the top of the paragraph and come down with a bunch of friends and spread out along the beach at, say, twenty metre intervals. Now each person shouts 'A RECORD!' each time a wave appears that's bigger than any preceding wave. By sampling at intervals along the beach within a minute we can establish the pattern. Either the shouts of 'A RECORD!' diminish to zero (tide going out), or they become consistent and regular (tide coming in).

    Now think of the waves as weather and the tide as climate and you've got it. One large wave (1998) is irrelevant, there are too many other factors that dictate the height of individual waves (let's face it, it might have been the residual wave of a passing ferry). What matters is the pattern of waves as they hit the shore, which tells us that the tiny gravitational force of that distant moon underlies all earth-generated influences like the wind, the bloke on the jet ski 100 metres away, or that distant passing ferry.
  8. The wave/tide analogy is a good one. Both are cyclic.

    Long term climate is also cyclic, not linear. The formula to convert radiative flux (RF) to surface temperature change is (ΔTs): ΔTs = λRF, where λ is the climate sensitivity parameter. This assumes ALL GHG forcing factors are equivalent despite that clouds produce negative feedback. Models that use a linear equation can only produce linear predictions based on recent trends.

    Even IPCC scientists say that GCMSs should be exercised on weather.

    http://environmentalresearchweb.org/cws/article/opinion/35820

    The global energy budget (Kiehl/Trenberth) features high in the computations but the net positive radiative forcing is derived from Hansen's computer model.

    "GH theory (IPCC: Myhre et al.) tells us that the CO2 climate forcing from this increase equals 5.35 times the ln(ratio) = 0.035 W/m^2

    Even if we escalate this by a factor of 3.2 to account for net positive feedbacks, as estimated by the IPCC model simulations, we arrive at 0.112 W/m^2

    How do K+T arrive at 0.9 W/m^2 or eight times this value?"
    See
    http://chriscolose.wordpress.com/2008/12/10/an-update-to-kiehl-and-trenberth-1997/#comment-1493
  9. John Russell: great analogy, thanks a lot!
  10. The link above to Hansen 2007 is broken. I believe the correct link is http://pubs.giss.nasa.gov/docs/2007/2007_Hansen_etal_1.pdf
    Response: Thanks, that's the problem with linking to external websites - they change. I've updated the link and many thanks for pointing this out.
  11. I'm not totally convinced about the accuracy of climate prediction, for the following reasons. Weather predictions are based on physics, which we understand reasonably well from a theoretical point of view. It seems to me that the main limiting factor when we predict the weather is not a question about limitations of the model used. The problem is that the model itself depends very sensitively on the initial conditions.

    My impression is that the limiting factor of climate prediction is different. They is not based on a theoretical understanding of the long term average of the models used to describe weather. It would probably be far too difficult to justify on theoretical grounds that the long term climate are NOT chaotic. That is, I don't believe that the statement above of what "the science says" can be justified by theoretical computations. Correct me if I'm wrong (but I don't think so).

    However, it might be possible to justify the statements by observations.

    My impression is that climate prediction is to a large extent based on observations. It is an attempt to systematize the data we have collected. That is of course an excellent method, but it makes the field more similar to biology or geology and less similar to physics.

    If we had a long series of observations of the global weather, we could inspect the data and check that up to some small uncertainty, climate shows no signs of being "chaotic".

    What makes me unsure is that we don't have that much assembled observational material. We don't have reliable global observational data for more than a century or so, and if we go back more than a thousand years, its almost all in the grey. The time scale is small compared to the time scale of climate changes. It is not at all clear to me that the series of observations is long enough to produce a reliable theory from available data, in particular to state that climate is "not chaotic".

    Also, the idea of systematizing a theory from observations must be based on some version of "all situations we encounter are similar to a situation we have seen before, so we can base our predictions on that similarity, and assume that history will repeat itself". We can predict climate change that is similar to climate change we have already seen. But we are (most likely) changing the climate into something we have not seen before, something that is not covered by previous observations - so how can we be sure that our model works in this new situation? If the model were based on theory, that would be an argument in itself, but if the model is based on observations, we should be less confident.

    The argument cuts two ways of course. If we are discussing the effects of human activities, uncertainties in current climate predictions could as easily underestimate them as overestimate.
  12. Marcel Bökstedt,
    what you say is quite strange given that the physics behind climate models is very similar to that of weather predictions models.
  13. Marcel Bökstedt at 09:54 AM on 11 March, 2010

    Be sure to read Weart's retrospectives on the evolution of climate models. Getting the whole story will help clear up a lot of misunderstandings about how climate models function, what it is they're skilled at predicting, how skilled they are:

    Simple Models of Climate Change

    General Circulation Models of Climate

    Pretty fascinating just for the history if nothing else.
  14. Marcel Bokstedt, regarding chaos, please also see the Skeptical Science post Climate is chaotic and cannot be predicted.
  15. I'm delighted - three comments within a day to a post made at a comparatively obscure place! This site is impressive.

    Riccardo > Actually, the physics is not similar. Allow me to be a bit longwinded.

    There is an explicit mathematical model for weather. This model is of course only a simplified model. It will not give completely correct answers, but it is quite sufficent for predicting the weather for the next few days. There is a catch though. The model depends is very sensible to small variations in the initial conditions, so that a small cause today will lead to a big effect a month from now. That's the main reason we cannot make long term predictions of weather.

    "Climate" is a kind of averaged weather. In physics, it is sometimes true that if you form the average over a large number of systems, the averaged system will behave in a deterministic way. This is how thermodynamics works. The thermodynamical laws used to predict weather are actually in themselves
    "averages" over a overwhelmingly huge number of systems consisting of individual molecules. But there is no a priori guarantee that an "averaged" system must behave in a deterministic way. Especially if
    the averaging is done over a small number of systems.

    So lets look carefully at how we do the averaging. We can try to say "We take average weather over a year, and call that climate". Now, this procedure will fail to give a deterministic system. We know that there are systematic variations of the time scale of a year. These variations do not seem to be easy to model - I don't think that we can make reliable predictions of when the next el Nino will happen.

    Next, we can try to average over a larger time intervall. We could hope that if we average over a decade, then we get a deterministic system, or maybe even that the "climate" is completely determined by various forcing. That is, it could be that at this time scale there is no dynamics of climate. Maybe this is so, but now we are getting unconfortably close to the time scale of the entire history of reliable measurments of global climate.

    So how can we know that there are not "chaotic"
    variations of the climate on larger time scales? (There are definitions of various level of precision of the word "chaos" - I assume that what we are talking about here is "a huge sensitivity to initial conditions").

    Doug_bostrom> Yes, the history is quite interesting, thanks for giving the link! But I am not sure exactly which misunderstandings you expect the link to clear up. Maybe you could be more specific?

    Actually, Weart is quite careful about the uncertainty of climate models. He states for instance
    "However, experience shows that scientists tend to be too optimistic about their level of certainty." and
    "For all the millions of hours the modelers had devoted to their computations, in the end they could not say exactly how serious future global warming would be."


    Tom Dayton> I'm aware of that post. Actually I posted a comment to it along similar lines as here - asking : how do we know that climate is not chaotic? I don't think that I got a response, and it seems that the comment has now been deleted.
  16. Marcel Bökstedt writes: Tom Dayton> I'm aware of that post. Actually I posted a comment to it along similar lines as here - asking : how do we know that climate is not chaotic? I don't think that I got a response, and it seems that the comment has now been deleted.

    Is it possible that the comment you're remembering was in the other thread about chaos?

    It's a bit confusing -- there are often pairs of posts on similar topics at the same time, one a "blog post" (where there's usually more discussion among commenters) and one a "response to skeptical argument".

    For example, on Friday, 22 January, 2010, there was a blog post titled The chaos of confusing the concepts. I see that you do have a comment in that thread (here), which hasn't been deleted, and there was in fact a reply by the guest author of the original post.

    The link Tom Dayton provided was to a "skeptical argument" by the same guest author at about the same time: Chaos theory and global warming: can climate be predicted? which only got one comment.
  17. Marcel Bökstedt,
    the physics is the same for both weather and climate by definition. They both describe the movement of air and water masses and the chemical/physical interactions between them and with the land subject to some input of energy. The actual calculations might differ somewhat due to computational restrictions as, for example, when climate models use parametrizations to save computing time.
    Climate models have their own variability, although somewhat different between models, that reflect what is actually seen. Obviously, you should not expect to have a temporal match between, say, calculated and actual El Nino events; but theese so called oscillations are indeed more or less reproduced by models.
    As for the over-emphasis on chaos, it looks like invoking chaotic behaviour in physical systems may be used to inflate uncertainties much beyond any reasonable expectation. Do we have any reason to believe that our sun will suddenly collapse or expand disproportionally beyond what we already know it does? There's a lot of chaos inside our star but that's not the whole story. The same is true for climate, we have no reason to think that our climate will suddenly go weird (chaotic). For weather it is different in what models try to forecast phenomena confined both in time and space, but we can still predict warmer air temperatures next summer or the seasonal arrival of the monsoon.
    The only thing that might "go weird" with climate is the passing of a so called tipping point, quite hard to predict. Someone may be tempeted to call its effect chaotic behaviour, but sure it's not.
  18. Ned> Thanks! You are absolutely right, I was confusing the two blog posts. That's a relief ...:)

    It's true as you say that I did get an answer from the guest poster in the sense that my name was mentioned in a post. I must have forgotten about it because I did not really understand how it was an answer to my post. That is probably, my own fault, I'll give it a new try now.

    Ricardo>
    The point I tried to make about the ENSO is different. I think that we agree that we cannot accurately predict an el Nino events. So we cannot predict climate on a scale of one year (typical timescale of el Nino events), or to put it more formally: If we tentatively define climate as "average of weather over 1 year", we cannot predict climate.

    Of course we can lengthen the time scale, and define climate as "average of weather over twenty years". That would more or less even out the el Nino peaks, but how do we know that there are not longer term fluctuations which we still cannot predict? It seems to me that the only sensible answer would be "because we have studied the climate for a time much longer than 20 years, and we did not see any long term variations". However, we haven't yet observed weather on a global scale for timescales significantly bigger than 20 years, so how do we know for sure?
  19. Marcel Bökstedt,
    definitely we will never know for sure untill it will happen, or not happen for that matter. But we know a lot more things of the past climate than you are admitting. They tell us that at least to a first order aproximation if forcing increases so does temperature. (ok, it's clearly an over-simplification, but i hope it gives the idea).
    I can not see any room for chaotic behaviour of climate, it can just be an abstract hypothesis; to become a real one there have to be something pointing in this direction. Maybe in the future, who knows; but as far as actual knowledge is concerned it's still abstract.
  20. Editing problem in the Intermediate version.  

    I can't parse this sentence.  Can you?

    "Or expressing that in weather terms, you can't predict the exact route a storm will take but the average temperature and precipitation will result the same for the region over a period of time."

  21. BillWalker, it makes sense if you substitute "be" for "result".

  22. Steve Easterbrook has a great balloon analogy of weather versus climate.

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