Forecasting The Arctic Oscillation

Recently the Chief of the met office went on UK TV to say:

“OUR SHORT TERM FORECASTS ARE AMONG THE BEST IN THE WORLD.” (see video here)

Yesterday, the UK Met Office had to make a rare mea culpa, saying they had botched their own recent snow forecast, it is useful to point out that they aren’t the only one with egg on their faces.

http://wattsupwiththat.files.wordpress.com/2008/09/met_office_forecast_computer-520.jpg

In early October, the Arctic Oscillation (AO) took an unexpected dip into deeply negative territory, which led to the sixth snowiest October on record in the Northern Hemisphere and the snowiest on record in the US.  If you look at the 14 day forecast at the bottom of the graph below, you can see that the dip caught NOAA forecasters off guard.

Source: NOAA Arctic Oscillation Forecast

According to Rutgers University Snow Lab, October, 2009 was the snowiest on record in the US.

Contiguous United States
Month Rank Area Departure Mean
12-2009 1/44 4161 1292 2869
11-2009 39/44 585 -512 1097
10-2009 1/42 538 385 153
9-2009 5/41 21 13 8
8-2009 12-41/41 0 -5 5
7-2009 24-40/40 0 -17 17
6-2009 32-42/42 0 -64 64
5-2009 37/43 34 -151 185
4-2009 17/43 859 106 753
3-2009 23/43 1964 -18 1983
2-2009 17/43 3172 110 3062
1-2009 15/43 3696 185 3511

Source: Rutgers University Snow Lab

The director of NCAR captured the moment perfectly in this East Anglia Email – dated October 12.

From: Kevin Trenberth <trenbert@xxxxxxxxx.xxx>To: Michael Mann <mann@xxxxxxxxx.xxx>

Subject: Re: BBC U-turn on climate

Date: Mon, 12 Oct 2009 08:57:37 -0600

Cc: Stephen H Schneider <shs@xxxxxxxxx.xxx>, Myles Allen <allen@xxxxxxxxx.xxx>, peter stott <peter.stott@xxxxxxxxx.xxx>, “Philip D. Jones” <p.jones@xxxxxxxxx.xxx>, Benjamin Santer <santer1@xxxxxxxxx.xxx>, Tom Wigley <wigley@xxxxxxxxx.xxx>, Thomas R Karl <Thomas.R.Karl@xxxxxxxxx.xxx>, Gavin Schmidt <gschmidt@xxxxxxxxx.xxx>, James Hansen <jhansen@xxxxxxxxx.xxx>, Michael Oppenheimer <omichael@xxxxxxxxx.xxx>

Hi all

Well I have my own article on where the heck is global warming? We are asking that here in Boulder where we have broken records the past two days for the coldest days on record. We had 4 inches of snow. The high the last 2 days was below 30F and the normal is 69F, and it smashed the previous records for these days by 10F. The low was about 18F and also a record low, well below the previous record low. This is January weather (see the Rockies baseball playoff game was canceled on saturday and then played last night in below freezing weather).

Trenberth, K. E., 2009: An imperative for climate change planning: tracking Earth’s global

energy. Current Opinion in Environmental Sustainability, 1, 19-27,

doi:10.1016/j.cosust.2009.06.001. [1][PDF] (A PDF of the published version can be obtained

from the author.)

The fact is that we can’t account for the lack of warming at the moment and it is a

travesty that we can’t.

http://www.eastangliaemails.com/emails.php?eid=1048&filename=1255352257.txt

Once again, this begs the question – if the GCMs can’t forecast the AO two weeks in advance, how can they possible forecast snow and cold 70 years in advance? University of Colorado professor Mark Williams used climate models in 2008 to come up with a remarkable prediction (below) in a year when Aspen broke their snowfall record.

Study: Climate change may force skiers uphill

From the From the Associated Press

Tuesday, December 16, 2008

DENVER — A study of two Rocky Mountain ski resorts says climate change will mean shorter seasons and less snow on lower slopes.

The study by two Colorado researchers says Aspen Mountain in Colorado and Park City in Utah will see dramatic changes even with a reduction in carbon emissions, which fuel climate change.

University of Colorado-Boulder geography professor Mark Williams said Monday that the resorts should be in fairly good shape the next 25 years, but after that there will be less snowpack — or no snow at all — at the base areas, and the season will be shorter because snow will accumulate later and melt earlier.

If carbon emissions increase, the average temperature at Park City will be 10.4 degrees warmer by 2100, and there likely will be no snowpack, according to the study. Skiing at Aspen, with an average temperature 8.6 degrees higher than now, will be marginal.

Since the first of October, Colorado is averaging two to eight degrees below normal, as is most of the US:

http://www.hprcc.unl.edu/products/maps/acis/WaterTDeptUS.png

Source : NOAA High Plains Regional Climate Center

In December 2009, Colorado averaged three to fifteen degrees below normal, once again correlating with a strongly negative Arctic Oscillation

http://www.hprcc.unl.edu/products/maps/acis/hprcc/Dec09TDeptHPRCC.png

Source : NOAA High Plains Regional Climate Center

Climate models are iterative through time, which means once they go off in the weeds they can not recover.  If AO trends can not be forecast more than a few days in advance, it would seem problematic to make any sort of meaningful long-term climate projections using GCMs.

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177 thoughts on “Forecasting The Arctic Oscillation

  1. [quote]
    Climate models are iterative through time, which means once they go off in the weeds they can not recover. If AO trends can not be forecast more than a few days in advance, it would seem problematic to make any sort of meaningful long-term climate projections using GCMs.
    [/quote]
    I’m not so sure short term events like an AO send climate models off into the weeds. Or that GCMs are meant for short-term predictions.
    As we all know, weather’s not climate. And AOs and short-term predictions are both weather.

  2. Here in the North wet coast, we’ve had a relatively mild winter so far, with the Pineapple Express bringing us rain on a regular basis. I wonder how the 2010 Winter Olympics will cope with the poor conditions?
    Pineapple Express:
    http://www.komonews.com/weather/faq/4307577.html
    As for weather forecasts, anything more than a few days is hit and miss. One wonders how these climateers can keep a straight face as they give their pronouncements of guaranteed global warming 50 years into the future.

  3. Once again, this begs the question – if the GCMs can’t forecast the AO two weeks in advance
    I think this logic is faulty. Let me illustrate with some examples:
    1) Take a massive landslide. We cannot predict where each little pebble goes, but that safely predict that the whole mess will end up down in the valley.
    2) We cannot predict where next month’s sunspots will be [the astrologers expected, of course], but we can predict with some success the size of the next cycle [at least roughly]
    3) When a dam breaks and the water rushes downstream we don’t know where individual pieces of debris will end up, but can be sure that the water eventually flows to the sea.
    4) We cannot predict where an individual molecule of a hot gas will strike the wall enclosing the gas, but we can predict quite well the pressure of all the molecules in toto
    etc.

  4. Here in Northern WI they sort of got the long term outlooks right. Sort of in that they were expecting a warmer than normal winter. They were basing that mostly on ENSO, and the AO saved their bacon.
    I am not sorry to miss out on the bone chilling cold today, which is normally our coldest day of the year. Last year’s hi/lo was -2/-22F, today we are chasing 38/21F . (KRHI)

  5. And maybe there’s too much attention or focus on the impact El Niño has on weather patterns. To hear one of the local on-air TV meteorologists here in Buffalo, the El Niño has reasserted itself and, because of its effects, our weather will be closer to normal for the next couple weeks (or even a bit warmer) after a couple weeks of bitterly cold weather. No mention of any other cycles.
    Perhaps the problem is that, with a generation having learned their craft during a warm-phase PDO, other factors/cycles aren’t being adequately considered.
    I’ve said it before — how can you offer long-range predictions about the climate if you can’t forecast the cycles (such as the PDO). And how can you do that without understanding what triggers phase shifts. What’s the point in having huge supercomputers and massive numbers of data points if you can’t see the underlying drivers? In other words, how can you talk about the forest if you can’t see it for the trees?

  6. “Climate models are iterative through time, which means once they go off in the weeds they can not recover.”
    Chaotic system = cannot model

  7. These dimwits are far more concerned with election forecasting than weather. They somehow envision Meteorologists as being a ultragovernment. That is what it is all about. Frankly I prefer my weather news from the fox with big breasts on the local news, who seems more concerned about the commute or school drive rather than these left wing fanatics that have bent reality for 10 years.

  8. [Post] Once again, this begs the question – if the GCMs can’t forecast the AO two weeks in advance…
    Leif Svalgaard (22:50:02) :
    I think this logic is faulty. Let me illustrate with some examples:
    1) Take a massive landslide. We cannot predict where each little pebble goes, but that safely predict that the whole mess will end up down in the valley.

    Beg to differ. The analogy is faulty. Something as localized as a two week forecast of pressure differences in the Arctic is not AT ALL in the same league in predicting every pebble in a massive landslide….not to mention predicting that the “whole mess will necessarily end up down in the valley.”
    There have been many MANY fortnight or less teleconnection forecasts that turn out to be busts.
    The point raised is very valid: How can one trust models extrapolating into the far future, when they bust into the very near??
    Chris
    Norfolk, VA, USA

  9. We live in a biosphere called Earth, not Mars or Venus.
    Increasing C02 will feed plants.
    We are darned lucky to have enough tectonic activity to keep renewing the C02 that the biosphere wants to sequester into oblivion. Earth without C02 will snuff out life. Appreciate what we got.
    .0385 % C02 as opposed to .0250% is a pathetically small amount.
    What C02 rise are we talking about? .0135%
    – wow –
    No wonder the GCM’s are so bad. They have correlated the climate to something which doesn’t do much more than drive a drop into a swimming pool.

  10. Natural analog weather forecast based on Lunar declination, works as good, if you adjust for the increased cold and snow, due to the solar minimum being deeper and longer, than expected.
    With Polar air masses extending further South than expected from past cycles, as a result of the solar pause in heating, colder conditions will be around for most of the 5 year period forecast.
    http://www.aerology.com/national.aspx
    (retired CNC machinist, right off of the production floor.)

  11. Leif Svalgaard (22:50:02) :
    Yes, you are correct, they didn’t get the toto (big picture).
    They didn’t get the landslide that should have been rather obvious given the leapfrogging winters N to S the past couple of years.
    The point is that they have NOT been paying attention to anything going on in the real world. Look how long it took you to convince Hathaway that he was way off the mark. What Anthony’s point is that as long as they keep fiddling with thier GCM’s and not taking into account the factors that are changing in the real world, they are going to looking silly and predicting wildly.

  12. savethesharks (23:18:21) :
    Beg to differ. The analogy is faulty.
    start begging then.
    How about the other ones? My point is that it is sometimes easier to predict the behavior of an ensemble of entities than each individual one.

  13. And re-reading my post I see that it was a little faulty in and of itself. [I am sure Leif will tear through this heh heh].
    The point is that: how is one to trust long term models if (on the same scale) the short term models can go awry so easily.
    Why not ask the GFS for his piss poor performance over the past few years.
    Until his makers (we will build you stronger, faster, smarter) correct some short term problems….you definitely do not want to extrapolate him into infinity.
    Meanwhile….for a short OT diversion and entertainment….watch this video if you have never seen: 🙂 Even though they call it a cyclone…it ain’t. Just a crazy-ass thunderstorm! I know, I know….OT….but worth watching again.

    Chris
    Norfolk, VA, USA

  14. I love Lief, but his metaphors, this time, are false.
    Of course a scree slope will in time decay downhill thanks to gravity and erosion. Forecasting the most complex natural system on the planet is far less linear. No one has even delineated the strange attractors that hold global climate much less invented the mathematics necessary to forecast the evolution of such a system over time.
    A better metaphor would be describing (to the millimeter) the flux waves in a waterfall 50 years hence.

  15. savethesharks (23:38:46) :
    The point is that: how is one to trust long term models if (on the same scale) the short term models can go awry so easily.
    wes george (23:39:09) :
    I love Leif, but his metaphors, this time, are false.
    My examples are biased by what I do: I think we can predict the solar cycle [i.e. solar climate] 10 years hence, even though we have no idea how to predict next month’s sunspots. Now, there are people that think solar cycle prediction is impossible too [e.g. Tobias and Weiss] and there are people whose solar cycle prediction has failed, so perhaps those people would by more sympathetic to the post.

  16. savethesharks (23:38:46) : Meanwhile….for a short OT diversion and entertainment….watch this video if you have never seen: 🙂 Even though they call it a cyclone…it ain’t. Just a crazy-ass thunderstorm! I know, I know….OT….but worth watching again.

    Chris
    Norfolk, VA, USA
    This is a typical storm generated by the turning of the lunar declinational tides, was Maximum North on the 15 of November of 2008 and should have generated severe weather for the next four days, which it did.

  17. Simple. The Arctic is warming, ergo the Arctic Oscillation is just another positive feedback. And its worse than we expected.

  18. Leif Svalgaard (23:32:47) :
    Not begging here. But, point taken about your other examples 2 through 4.
    But sometimes even the entire ensemble is outsmarted by the observed, as pointed out in this post of the NOAA forecasts versus the observed for the AO.
    So not trusting long term models when those same ones (on a similar scale) can not get it right in less than two weeks, is not unreasonable, and not faulty logic. It is simply skepticism.
    And I know YOU appreciate that (being skeptic)!
    Chris
    Norfolk, VA, USA

  19. Leif Svalgaard (22:50:02) :
    I wouldn’t say that not being able to forecast the Arctic Oscillation is indicative of GCMs one way or the other, but I would like to say that you would assume the water would flow to the sea (picked this example for the clearest demonstration), but it may not if it were to get stopped, say in a reservoir, and drunk by a person, who would then turn it into waste product on a tree during a camping trip, which would then eventually evaporate, and eventually precipitate down into the ocean.
    So you have the right answer, but I wouldn’t call it correct. GCMs predicting with some successes so far does not mean that the issues are yet understood, or anything is even correct in the models. Yet, if you find something clearly incorrect in them, such as sunspots on the moon, then wouldn’t you at least wonder how it happened?

  20. Dave F (00:18:54) :
    anything is even correct in the models.
    I don’t believe the current models are correct [too many things are parameterized], but my beef was with the bland assertion that because we cannot predict two weeks ahead, all prediction further out is impossible. If the climate to a large extent is self-regulating [negative feedbacks] then a model may not necessarily ‘go off the rail’ in the far future, but may be kept within bounds by the regulator [whatever it may be].

  21. Climate models are iterative through time, which means once they go off in the weeds they can not recover.

    As butterflies in a complex, chaotic system. When one flaps its wings and flies, all havoc breaks loose! Sure more are quietly waiting in these models for Mother Nature’s queue.

  22. Not to mention the temperature in all the deep currents . Now, thats a lot of water. That is heat in the pipeline. Is it in the models?

  23. Kath (22:48:38) :
    Here in the North wet coast, we’ve had a relatively mild winter so far, with the Pineapple Express bringing us rain on a regular basis. I wonder how the 2010 Winter Olympics will cope with the poor conditions?
    Pineapple Express:
    http://www.komonews.com/weather/faq/4307577.html
    As for weather forecasts, anything more than a few days is hit and miss. One wonders how these climateers can keep a straight face as they give their pronouncements of guaranteed global warming 50 years into the future.
    If you look at the weather widget slightly up the page here on WUWT and compare the Precipitation pattern on the map with the forecast for today on my web page (I posted two years ago) you will see a very similar picture.
    There are some of us who can get things right, but only by using methods that use all of the influences on the atmosphere, that are driving the global circulation.
    If you would like to see what the winter of 2010 will look like use the calendar feature and take a look at the maps presented.

  24. I’d say Leif, that given our tiny human timeslice of the observation of processes that are active for billions of years, we can only ‘predict’ the current prevalent oscillation. Granted it doesn’t matter much that in a million years the solar cycle will be different but we have no way of knowing the solar cycles as we (assumingly) have observed are going to flip over today or in a million years.
    Same goes for any other chaotic/entropic system.

  25. A clear discussion of the skill of models.. skill in forecasting various parameters at various temporal and spatial resolutions would create a better conversation. If you did that, then you’d probably get Leif’s point in a more concrete fashion.
    Good to see you again Dr. S.

  26. ‘University of Colorado-Boulder geography professor Mark Williams said Monday that the resorts should be in fairly good shape the next 25 years, but after that there will be less snowpack — or no snow at all — at the base areas, and the season will be shorter because snow will accumulate later and melt earlier.’
    Actually, an element of that later accumulation happened in Europe from about the mid 1980s to the early 2000s – probably shifted back toward earlier snowfalls in about 2004/05. The melting comes when the melting comes, because in spring temperatures rise way up, so the melt pattern kicks in regardless. But the key thing is the cycles which compact the snow early in the season, which slows rates of thaw once the sun comes out later in the season. That’s why mountain guides say that ‘the snow which falls before the middle of January is the snow which lasts’.
    I think such things happen due to PDO cycles, although I can’t confirm that.
    Don’t know enough about Colorado climate patterns to know for them, but in europe the late autumn/early winter snows tended not to come for about 20 years, with snow really only starting properly after New Year.
    Something to monitor perhaps?

  27. Leif,
    I understand your analogy, but Earth’s energy-budget contains factors analogous to rolling all the pebbles back up the hill again. You need to add a sort of Sisyphus to your analogy, who (in rolling the rock up the steep hill,) turns energy into potential energy.
    When a La Nina’s strong trade winds push warm surface water west, and pile it up by Australia, and also drag heavy and cold water up from the deeps by Peru, the system is defying gravity, and creating potential energy.
    Also any time ice turns to water, or water turns to vapor, available energy is turned into latent energy. It doesn’t show on thermometers, but it is there.
    A positive AO creates a strong polar vortex which holds arctic air up at the poles, “against it’s will,” as the cold and heavy air wants to obey gravity and flow south.
    I’m sure there are other examples you’d know more about, involving upper atmosphere chemistry and various sorts of radiation produced by the sun.
    And of course there are the sloshings we haven’t studied much, involving thermohaline circulation and the thermocline, which may involve cycles of a thousand years.
    In effect there are all these Sisyphuses running about, screwing up our calculations……and also our analogies.

  28. Leif Svalgaard (23:49:57) : | Reply w/ Link
    My examples are biased by what I do: I think we can predict the solar cycle [i.e. solar climate] 10 years hence, even though we have no idea how to predict next month’s sunspots. Now, there are people that think solar cycle prediction is impossible too [e.g. Tobias and Weiss] and there are people whose solar cycle prediction has failed, so perhaps those people would by more sympathetic to the post.
    Well, if you had a computer program that could predict the number of sunspots for the next two months with an 80% success rate ( as the weather GCMs do for the next few days), would you trust it to predict what will happen on the sun in one hundred years, after fiddling with the parameters? That is the correct analogy between weather and climate programs.
    In your gas and pressure example it is clearer that one has to go to a “meta” state. The kinematics that describe individual molecules though known, are useless in describing the total ensemble. A new mathematical description is needed, in this case with new variables and measures like temperature and pressure. ( Bohm’s implicate and explicate orders)
    If we take this to the weather and climate setups, it means a new tool is needed and in my opinion it is only with the tools of chaos that one might be able to go into long term climate predictions.

  29. Joe D’aleo gave a great forecast about the exceptionally cold conditions the UK would experience and I seem to remember he suggested a thaw about now, which is what is happening.
    Has anyone come across a forecast from him for the UK for the rest of the winter? Should I huint the shops for salt for our drive or merely get my raincoat out as the mild wet weather reasserts itself?
    tonyb

  30. Leif Svalgaard (22:50:02)
    Here is another for your list.
    We cannot predict the average temperature for next summer. But we can predict it will be warmer than this winter.

  31. Leif Svalgaard (22:50:02) :
    Once again, this begs the question – if the GCMs can’t forecast the AO two weeks in advance
    I think this logic is faulty. Let me illustrate with some examples:
    1) Take a massive landslide. We cannot predict where each little pebble goes, but that safely predict that the whole mess will end up down in the valley.
    2) We cannot predict where next month’s sunspots will be [the astrologers expected, of course], but we can predict with some success the size of the next cycle [at least roughly]
    3) When a dam breaks and the water rushes downstream we don’t know where individual pieces of debris will end up, but can be sure that the water eventually flows to the sea.
    4) We cannot predict where an individual molecule of a hot gas will strike the wall enclosing the gas, but we can predict quite well the pressure of all the molecules in toto

    I think this logic is faulty. Climate models do work by setting off with initial conditions and summing the totals from the collection of parts (cells) going forward. So although a model might predict that the pebbles in the landslide will end up in the valley ratherthan the sky, they will probably not successfully predict how far down the valley the majority of the mess will end up.

  32. It’s the weather that’s supposedly going to hurt us (hurricanes, storms, extreme temps etc) not climate. So predicting climate but not weather is not really as usefull as we’d like.
    I’m not so sure AO is weather. I know it causes weather events, but is it weather itself?
    There is a grey area between weather and climate. Some claim (like Piers Corbyn) they can predict extreme weather months in advance by understanding the sun. I believe if the IPCC were honestly trying to understand our climate, they would not have started by gathering temperature data. They should have started with the biggest, longest cycle then worked their way down so to speak. Then they may have had a chance of some accuracy regards predicting AO ENSO and PDO

  33. anna v (01:16:38) :
    If we take this to the weather and climate setups, it means a new tool is needed and in my opinion it is only with the tools of chaos that one might be able to go into long term climate predictions.
    My reply: I am only suggesting that we study the effects of the Lunar declinational tides in the atmosphere, because if I pull out the past three patterns of 6558 days, or 240 lunar declinational cycles, and plot them side by side, day by day, they show a resultant pattern that gives a better forecast than any of the models.
    With compensation for the changes in the solar cycles, from the past three patterns of high activity, to this one of lower activity I would get the correct amount of cooling, and better representation of the depth, of the cold arctic air invasions that are arriving on time, just larger than before.

  34. Leif – They run computer models (many thousand) on football games in the US to analysis the best odds for said match. Vegas odds makers have their money on this “line”, therefore no stone is left unturned. Yet the game still must be played in reality and the outcome from time to time differs from the models/odds.
    The Earth’s climate mechanisms seem so much more complex and with events such as volcanic eruptions and external effects still to be understood I just can’t jump on any long term prediction wagon. You sure as hell wouldn’t want to commit policy and resources, which is what AGW proponents are demanding.

  35. Leif Svalgaard (22:50:02) :
    I think this logic is faulty. Let me illustrate with some examples:
    I hesitate to question Leif’s logic as I have followed his comments for quite some time. That said, it’s unclear to me how Leif’s examples apply.
    Three of the four examples are modeled by simple application of a single “law” of physics (gravity, Boyle’s Law) to predict an outcome quite precisely. One example (sunspots) requires several physical “laws” to model and (who would guess) the predictions are less rigid (“we can predict with some success the size of the next cycle [at least roughly]”). In all these examples we have a great deal of empirical evidence for the cause/effect relationships.
    Since climate modeling currently seems to require the simultaneous solution of many physical “laws”, it seems reasonable to me that the predictive certainty of such models would necessarily be a good deal less than any of the above examples. Also, from reading this blog, it appears we are still trying to derive most of the cause/effect relationships of the empirical observations that are believed to relate to global temperature changes.
    The weather=single particle vs climate=particle aggregate is an interesting way to describe this often contested relationship. However, based on the above paragraphs, I think this comparison itself actually undercuts Leif’s argument with a faulty premise that the climate models equate to a single formula or physical “law” that describe cause and effect of global temperature changes similar to the examples proposed.
    Daniel L

  36. I sometimes check (via the BBC) the Met Office weather forecast. The term is a misnomer. It is not a forecast, but a commentary. Say I look at the projection for Boscombe Down (nearest weather station) for 4.00pm, at 8.00am. I guarantee that the 4.00pm forecast will have changed when I look again at 10.00am.
    Projections for coming days are even worse. Nice blazing sun symbols at the weekend usually turn to something less amenable within the space of a few hours. So I was very pleased to see the head of the Met Office skewered by Andrew Neil – notwithstanding I can’t stand Andrew Neil.
    Our pathetic government, led by a bloke (Gordon “Prudence” Brown) who takes feedback for his speeches from his pet goldfish (just look at a clip of his speeches and you’ll know what I mean), would do better to scrap the whole thing and provide every household with a bunch of seaweed to hang outside the front door

  37. Please moderate out all this weather/climate crap.
    If you stand on a beach it’s difficult the forecast how high the next few waves will run up the strand.
    But forecasts of height of high tide reamin highly predictable.

  38. mkurbo (02:33:36) :
    snip
    The Earth’s climate mechanisms seem so much more complex and with events such as volcanic eruptions and external effects still to be understood I just can’t jump on any long term prediction wagon. You sure as hell wouldn’t want to commit policy and resources, which is what AGW proponents are demanding.
    My repy:
    The problem with what the CAGW proponents are demanding is we place all our bets on the health and performance of the kicker alone, with out looking at the usability or readiness of the rest of the team.
    Then they want us to bet the whole farm, not just our milk money.
    I would much rather take my time and evaluate all of the players till I can figure out who should even start in the game, and who to keep on the bench or in the locker room.
    My bets would be on the method that could bridge the gap, between short term forecasts that work, and connecting the dynamics that make them functional, to the longer term influences that are just the extensions of those cycles interactions with the already known factors of climate variability.
    Richard Holle

  39. For once I would partially agree with the good old doc, perhaps he has initially chosen not entirely adequate example, but his subsequent notes did put the matter right.
    Climate, sunspot cycle, stock market index etc, are all made of numerous smaller elements of apparently random (chaotic) movements, but if majority of them (not necessarily all) have the same or similar underlined tendency, than long term forecast is possible, by correlating past to one or combination of two or more plausible causing factors, even if the mechanism of it is not entirely clear. Most of the above examples are cyclical thus making longer term predictions feasible.
    Although, ‘correlation is not causation’, but correlation is necessary, so ‘no causation without correlation’.
    It was assumed that CO2 mechanism was understood, kind of misplaced ‘correlation’ was there, but now and in the past has failed, since there is no good reason put forward why it has failed, scientists have to try again and find true correlation which does hold.
    To quote A.E.
    “Occurrences in this domain are beyond the reach of exact prediction because of the variety of factors in operation, not because of any lack of order in nature.”
    Or my own “the nature is adverse to a coincidence; it is ruled by a cause and its consequence”.

  40. TFN Johnson (03:04:21) :
    Please moderate out all this weather/climate crap.
    If you stand on a beach it’s difficult the forecast how high the next few waves will run up the strand.
    But forecasts of height of high tide reamin highly predictable.
    My reply;
    To an organic gardener even a turd, is useful. The heights may very some but the timing of the peak, of the effect is the known factor that repeats well.
    The waves reaching the shore change character with the ebb and flow of the tide due to the compounding of the trends, is as true in the sand, as in the Rossby waves and jet streams.

  41. Leif Svalgaard (00:26:32) :
    Dave F (00:18:54) :
    anything is even correct in the models.
    I don’t believe the current models are correct [too many things are parameterized], but my beef was with the bland assertion that because we cannot predict two weeks ahead, all prediction further out is impossible. If the climate to a large extent is self-regulating [negative feedbacks] then a model may not necessarily ‘go off the rail’ in the far future, but may be kept within bounds by the regulator [whatever it may be].
    Leif,
    Predicting the AO within two weeks and the climate over 70 years is more akin to predicting what a bunch of pebbles will do in two weeks and 70 years during a hypothetical 70 year mudslide. In short term weather models, incorrect parameter inputs will lead to short term model failure for tomorrow’s weather. Oftentimes, the weather models will have a sense of something that may happen 7 days away, but when the inputs become more real (the low pressure short wave reaches a data collection point and then fed to the computer), we all of a sudden lose the potential storm entirely.
    Getting back to your analogy, if I predict where those pebbles will be in two weeks, and they are collectively within a standard deviation of being correct, then I can say that my predictive model has merit to that point, and that my future prediction for 70 years hence has a higher likelihood of being correct than if my pebbles two weeks hence were largely in a different location than my prediction.
    As far as I can tell, not one of the GCM’s a decade ago predicted the stalling of the temp rise this past decade. If so, then this failed prediction must have long term implications on the predictions being made for 2060, 2080, 2100, etc. Again, that is how it works in weather models, and climate models for this comparison are no different. If the model predicts the storm will be over Georgia this evening, and it ends up over Cuba, I can assure you that the actual weather in NYC tomorrow will be different than what the model had predicted based on a storm in Georgia.
    Tom

  42. TFN Johnson (03:04:21) :
    Please moderate out all this weather/climate crap.
    If you stand on a beach it’s difficult the forecast how high the next few waves will run up the strand.
    But forecasts of height of high tide reamin highly predictable.
    TFN,
    Not entirely correct on your individual wave thought. One may not be able to predict the exact height of the next few waves, but wave sets do have predictive qualities as I learned in my oceanography studies in university (many, many days ago). You cannot arrive at highly predictable tide forecasts (your words) without the underlying wave propagation being predictable. No??

  43. The highest AO variability (by far) is in winter.
    Reconstructions based on PCA or factor analysis need to be conditioned seasonally.
    1899-2002 SLP-based AO reconstruction residuals show very strong seasonal bias:
    http://www.sfu.ca/~plv/AOresi.png
    Reality is consistently lower than model in January.
    The variance in the residuals can easily be reduced by a factor of 2, cutting errors in half and reducing systematic bias:
    http://www.sfu.ca/~plv/AOresi_.PNG
    If the reconstructions play a role in forecasting, there should be room for improvement.

  44. While Met Office can’t predict the weather a few months ahead, you should not doubt or question their ability to predict it 50 or 100 years ahead.
    As we all know, it’s “ settled logic” and “ consensus statistics” ”that it is much easier to predict the future the further you go out in time.
    Yes, it may be impossible to exactly predict tomorrow’s sports outcomes, but you can easily predict the outcomes of sports events decades away when you use computers and software as powerful as those used at the omniscient Met Office.
    The American great promoter, B.T. Barnum noted:
    “There’s a sucker born every minute.”
    He would have made a fortune in “carbon credits”.

  45. “University of Colorado professor Mark Williams used climate models in 2008 to come up with a remarkable prediction (below) in a year when Aspen broke their snowfall record.”
    I think the fault at the heart of this winter’s unexpected wintery-ness is climate models which predict average conditions being misinterpreted by the modellers themselves into weather conditions, and as a conseqence local authorities and national Governments not spotting this error for a variety of reasons.
    We’ve seen that in the UK to such a deleterious effect – the constant squeaks from the Met Office of ‘milder winters’ has led councils to be ill-prepared for normal wintery conditions. Even with milder winters on average you should still expect times of bitterly cold weather, icy roads and snowfall. It is winter afterall and blasts of arctic weather can hit the UK with very short notice. The modellers are seeing what they want to see in the models, things that aren’t actually there, and councils and Governments are seeing the same things.

  46. The landslide is an interesting analogy. Generally they can be predicted with simple mathematical models; however this isn’t always the case. Instances of landslides propelling themselves miles along a plain, often going up small slopes as they progressed in apparent defiance of gravity were written off as hearsay and tall tales because the modelled behaviour of landslides didn’t predict these events, and it seemed impossible that they could happen. Surely a landslide couldn’t slide up, could it?
    Turns out that the mathematics used in modelling the behaviour of landslides was faulty. Originally it was assumed that a landslide was just rock and mud moving downhill. That’s the simplest model, but it was wrong nearly all the time. This very simple model wouldn’t be able to predict long tails, or the fluid-like behaviour of certain landslides, and absolutely and completely unable to predict a sturzstrom.
    A sturzstrom is by its nature, and our current understanding, highly unpredictable. The reasons for its behaviour are not known with any real certainty. It can be modelled, but those models can’t be used for predictive purposes as the slide is chaotic. You could predict that it would flow along the valley, but that isn’t enough; Given their propensity to sweep up slopes, is the house on the south side of the valley safe from the slide or not? How far will it go? Will it revert to another sort of slide, or will it carry on flowing like a river? Will it go up that hill or down that creek? Will it even be a sturzstrom, or will it just be a “regular” landslide? A model of the dynamics of a sturstrom can’t predict this, even if based on a real potential or actual slide, because the model is necessarily chaotic.
    Just like climate models. We can model climate in a limited way but they’re useless for prediction. And beyond a few days, weather models are useless for prediction too, for the same reason. The argument over weather vs climate is moot as in both cases we are trying to use models of chaotic systems that have little predictive merit. We can model climate out to X number of years but as a prediction it is meaningless.

  47. Regarding GCM’s: Does a failure in predictive quality in the short term (ten years) require that the long term (50+ years) predictive quality must also fail due to propagation of the short term actual results??

  48. If you look at the way most GCMs are formulated numerically, they are very similar to weather models in that they start with an initial condition and integrate the governing equations (subject to user-defined unsteady, boundary conditions) using small time steps (I believe on the order of 30 minutes per time step). And like the weather models, the governing differential equations are very non-linear, which means stable solutions on any time scale are not guaranteed. Moreover, all time-dependent numerical schemes are subject to numerical errors called truncation errors, which arise because of the fact the numerical representation (discretization) of the equations is not exact. This error accumulates with each time step and can eventually swamp the desired solution over long periods of time integration. This is why, 100 year GCM simulations are, at best, should be taken with a grain of salt.
    Now, Leif and others have argued that, while we can’t predict precisely what the average surface temperatures will be in the year 2100, we can say that it is likely they will be higher than today. If that is the question we want answered, then it doesn’t take a GCM to tell you that – you can get that result from much simpler models! Unfortunately, GCMs today are being misused as predictive tools as shown in the article Anthony cited:
    “If carbon emissions increase, the average temperature at Park City will be 10.4 degrees warmer by 2100, and there likely will be no snowpack, according to the study. Skiing at Aspen, with an average temperature 8.6 degrees higher than now, will be marginal.”
    Does anyone believe that we can predict that Park City will be 10.4 degrees (I assume F) warmer by 2100 than today?? I don’t. These kinds of press releases are being done for two reasons: (1) to publicize the research and therefore justify the expenditure of thousands of research dollars on the author’s NSF or DOE research grant, and (2) to attempt to influence near-term public policy.

  49. One remark about the iterative nature of climate models and “once they’re off track they just go into the weed”. Some people here found that too much of a generalization.
    In fact, assuming a natural self-stabilizing climate (with negative feedbacks) even a faulty model will return to a normal state eventually but will be arbitrarily out of sync with events in reality.
    As the IPCC climatologists assume only positive feedbacks, tipping points and runaway greenhouse effects, (this is necessary in their models to reach doomsday efficiently and that’s necessary to keep the funding going) their models will not stabilize (they’re precariously balanced and will fall off the cliff).
    So i see two possible kinds of climate models which are both useless in the long run but for different reasons.

  50. It is with great trepidation that I weigh in on this discussion but here goes.
    I’ve often pointed out on this blog that the MET Offices uses GCM’s to make their seasonal forecasts in the UK and their poor track record over the last 3 years begs the credibility of of the GCM’s in general to make long term predictions. I also understand Leif’s point that with something as noisy (variable) as the regional weather, a model that integrates over decades might produce more accurate trends over the long term. What I don’t understand is after 3 years of missed forecasts why it is that a more reliable method is not used instead? Meterologists who look at the way the oceans are set up going into the spring or fall are much able to make better predictions, albeit not perfect, for the upcoming season.
    The second point I want to make is regarding the longer term forecasts using the GCM’s or any other models for that matter. The long term trends rely on very accurately accounting for the energy budget of the planet and integrating them over time. By their own admission, the models cannot really do clouds well and the uncertainty in the radiation budget caused by clouds is at least as great or greater than the increased heat retention from CO2 and other green house gasses. That being the case, how can anyone profess 90% certainty going forward a temperature trends? A lot more progress in climate science could had with a little humility and more team work between the climate change factions.

  51. I’m throwing the BS flag! In my area of the country, they’re showing a -2f anomaly for 1-14 Jan. Only 3 days of the 14 the HIGH temperature made it over the average of 35 for this area. Using the Min/Max temperatures from weather.com for that period, the average temperature here was 21.5f. That is 14f below normal.

  52. Richard Holle (02:17:43) :
    “if I pull out the past three patterns of 6558 days, or 240 lunar declinational cycles, and plot them side by side, day by day, they show a resultant pattern that gives a better forecast than any of the models.”
    Show us.

  53. As Baa Humbugged mentioned, Piers Corbyn claims to have a better record than, for instance, the Met. He recently took them to task in claiming that his predictions would have prevented England from running out of grit.
    See http://www.weatheraction.com/docs/WANews10No5.pdf But please don’t forget my barycenter hero, Dr. Theodor L[snip]t, who predicted years ago, before he died, Little Ice Age conditions around 2030. See
    http://www.iceagenow.com/New%20Little%20Ice%20Age.htm

  54. What are experts predicting about the future climate . I am posting what individuals said and in the second post what various organiztions are saying. i don’know if any are predicting AO levels as the key indicator.
    William M Gray, Professor Emeritus, Dept of Atmospheric sciences, Colorado State University
    “A weak global cooling began from the mid-1940’s and lasted until mid-1970’s. I predict this is what we will see in the next few decades”
    http://tropical.atmos.colostate.edu/Includes/Documents/Publications/gray2009.pdf
    Don Easterbrook, Professor Emeritus, Dept of Geology, Western Washington University.
    “Setting up of the PDO cold phase assures global cooling for next approx. 30 years.
    Global warming is over. Expect 30 years of global cooling, perhaps severe [2-5
    Degrees F]”
    He predicts several cooling scenarios
    The first is similar to 1945-1977 trends, the second is similar to 1880-1915 trends and the third is similar to 1790-1820 trends.
    http://www.heartland.org/bin/media/newyork09/PowerPoint/Don_Easterbrook.ppt#630,38,Projected global temp to 2100
    and
    http://www.heartland.org/bin/media/newyork09/PowerPoint/Don_Easterbrook.ppt#608,49,Implications
    Syun Akasofu, Professor of Geophysics, Emeritus , University of Alaska, also founding director of ARC
    He predicts the current pattern of temperature increase of 0.5C /100 years resulting from natural causes will continue with alternating cooling as well as warming phases. He shows cooling for the next cycle until about 2030/ 2040.
    http://www.heartland.org/bin/media/newyork09/PowerPoint/Syun_Akasofu.ppt#524,30,Slide%2030
    Mojib Latif, Professor, Kiel University, Germany
    He makes a prediction for one decade namely the next decade [2009-2019] and he basically shows the global average temperatures to decline to a range of about 14.18 C to 14.28 C from 14.39 C in 2008
    He also said that you may well enter a decade or two of cooling relative to the present temperature level, however he did not indicate when any two decades of cooling would happen or whether the second decade after the next decade was cooling.
    In another words he is predicting cooling for the next decade and a possibility of the second as well.
    http://www.wcc3.org/sessions.php?session_list=PS-3
    Noel Keenlyside, Dr., from the Leibniz Institute of Marine Sciences at Kiel University.
    Quote from BBC article
    The Earth’s temperature may stay roughly the same for a decade, as natural climate cycles enter a cooling phase, scientists have predicted.
    A new computer model developed by German researchers, reported in the journal Nature, suggests the cooling will counter greenhouse warming.
    http://news.bbc.co.uk/2/hi/science/nature/7376301.stm
    Anastasios, Tsonis, Professor and Head of Atmospheric Sciences Group University of Wisconsin, US
    “We have such a change now and can therefore expect 20 -30 years of cooler temperatures”
    http://www.dailymail.co.uk/sciencetech/article-1242011/DAVID-ROSE-The-mini-ice-age-starts-here.html

  55. Richard Holle (02:17:43) : | Reply w/ Link
    My reply: I am only suggesting that we study the effects of the Lunar declinational tides in the atmosphere, because if I pull out the past three patterns of 6558 days, or 240 lunar declinational cycles, and plot them side by side, day by day, they show a resultant pattern that gives a better forecast than any of the models.
    With compensation for the changes in the solar cycles, from the past three patterns of high activity, to this one of lower activity I would get the correct amount of cooling, and better representation of the depth, of the cold arctic air invasions that are arriving on time, just larger than before.

    Piers Corbyn secret weather method uses the moon and sun as he has explained in several videos, and he seems to be quite successful in long term weather predictions, so you may be right.
    On the other hand, when many dynamical inputs enter, and such is the case of earth climate, it is chaos tools that should be used.
    anecdotal: in my part of the earth, Greece, the moon phases are traditionally used by sailors and farmers to “predict the weather” as follows: If the wind/clouds/etc change with the moon phase, expect the same weather to the end of the phase. If it does not change then, it will keep the same through the next phase.

  56. Here is what various organizations are saying about the future climate.
    Met Office, UK
    “Even then, due to natural variations in climate, we expect to see 10 year periods both globally and regionally with little or no warming….”
    “We found about one in every eight decades has near or negative global temperature trends
    Met office decadal forecast predicts renewed warming in 2010 with about half the years to 2015 likely to be warmer globally than the current warmest year on record [1998?]”
    They are also predicting global temperatures to rise by 4C by 2060 which translates to about 0.08C per year or 10 times faster than the last decade or last century [0.007 C/year]
    http://www.metoffice.gov.uk/corporate/pressoffice/2009/pr20090914.html
    Hadley Center –Met Office
    The effects of global warming over the coming decades will be modified by shorter-term climate variability.
    These three possible trends of winter temperature in northern Europe from 1996 to 2050 were simulated by a climate model using three different (but plausible) initial states6. The choice of initial state crucially affects how natural climate variations evolve on a timescale of decades. But as we zoom out to longer timescales, the warming trend from greenhouse gases begins to dominate, and the initial state becomes less important. Keenlyside and colleagues2 use observations of the sea surface temperature to set the initial state of their model. Their results indicate that, over the coming decade, natural climate variability may counteract the underlying warming trend in some regions around the North Atlantic. (Figure courtesy of A. Pardaens, Met Office Hadley Centre).
    http://www.nature.com/nature/journal/v453/n7191/fig_tab/453043a_F1.html#figure-title
    CRU
    I am not aware of any published forecasts by CRU.
    Recently released e-mails from the CRU “climategate” show the IPCC scientists saying that, “we can’t account for the lack of warming at the moment and it is a travesty that we can’t”.
    IPCC
    They predicted no cooling but called for global temperatures to rise by 0.21C in each of the next two decades. They also made various climate rise projections ranging from a median of about 2-4 C to worst case option of up to 6C by 2100.
    http://www.heartland.org/bin/media/newyork09/PowerPoint/Syun_Akasofu.ppt#524,30,Slide%2030
    FORECASTS BASED ON SOLAR CYCLES
    There are at least half a dozen to a dozen climate forecasts all predicting global cooling based the next 2 solar cycles being significantly lower in terms of sun spot activity. They are not included here to keep this narrative brief.

  57. Leif Svalgaard (00:26:32) :
    I don’t believe the current models are correct [too many things are parameterized], but my beef was with the bland assertion that because we cannot predict two weeks ahead, all prediction further out is impossible.

    I think it is a weak argument to make the simple claim, so often heard here, that unpredictable short-term wiggliness means that no long-term trend can be foreseen. And remember, it isn’t the fallible models that are predicting a warming trend, but “simple physics,” supposedly. I.e., if more insulation is added to the earth, it will lose heat at a slower rate, effectively raising its thermostat, resulting in a long-term heating-up.
    The proper counter to that argument is not to say that short-term predictions can’t be made reliably, but that there are potential negative feedbacks involved that will offset this extra insulation, and/or that the amount of insulation that will be added by increasing CO2 is tiny, given a real-world absence of postulated positive feedbacks.

  58. Leif is right, but maybe a better example is that you can accurately predict the mean of a random variable (heck, you can KNOW it — in a simulation) and yet an individual sample would still be highly uncertain.
    Any time you look at a long term statistic, such as climate, many short term fluctuations average out. There are many things that flop around a lot but have a predictable mean. Quantum mechanics relies on this.
    So, bad short term predictions do not NECESSARILY undermine the long term predictions. However, they can be evidence that the forecaster misrepresents his level of confidence in predictions. This WOULD necessarily undermine any statements about his level of certainty for other predictions.

  59. jgfox
    “Yes, it may be impossible to exactly predict tomorrow’s sports outcomes, but you can easily predict the outcomes of sports events decades away when you use computers and software as powerful as those used at the omniscient Met Office. ”
    bad analogy. Sports wins are neither progressive nor cumulative. A short inter-season build-up of “Win” can be overturned by the loss of a star player or the purchase of your club by a Russian oligarch.
    The point of supposed climate change is that it piles up. More than that in fact, that positive feedback makes it accelerate. Therefore trends should be a darn sight easier to spot than whether Man Utd is going to win the cup.
    However I wonder if we switched off the Hadley supercomputer and let Exeter cool down, would we provoke a new ice age?

  60. tucker (04:46:51) : “Regarding GCM’s: Does a failure in predictive quality in the short term (ten years) require that the long term (50+ years) predictive quality must also fail due to propagation of the short term actual results??”
    No. The short term failures are due to chaotic influences on small changes in initial conditions. The long term is not a propagation of multiple short terms, but it is constrained by somewhat unknown forcings (e.g. soilar influences) and completely unpredictable events (e.g. volcanoes). The long term may also fail due to model error, for example the model may not properly simulate tropical convection, typically due to lack of resolution.
    But a useful model should converge to cycles over large scales of time and space which should include the potentialities of the recent shifts in AO. IOW, there is no reason a model run could not result in a -6 std dev like we saw in reality. But in no model would that be “predictable” in any sense of the word except one: they would occur with the frequency seen in reality.

  61. Tom Vonk once put it in perspective rather nicely:
    “In the beginning people thought of the climate as a deterministic system. The climate trajectory was supposed to be computable and predictable and inaccuracies were only due to the lack of computing power and crudeness of the parametrizations.
    After having multiplied the computing power by 100 – 150 in the last 10 years and the time spend on parametrizations by a similar factor, the models are still as inaccurate as they were and what increased is only the confidence that they indeed are inaccurate.
    This in itself is a powerful signal that there is a very fundamental error somewhere in the approach.
    If something like that had happened in a more serious scientific branch (like high energy physics f.e.), people would have dropped the wrong methodology already long ago.
    The approach begins to slightly change now.
    First as there can be no trust in any individual model, the “ensemble theory” has been invented according to which every model gets “something” right (but nobody knows what) and something wrong (everything else).
    By averaging the model results, the wrongness cancels out or at least reduces but the rightness stays.
    This theory seems to me silly and based on no serious physics.
    Second the modellers grudgingly abandoned determinismus and try to heal the problem by ergodicity.
    [Gavin] Schmidt even says that their models are “chaotic” showing hereby that he doesn’t know what chaos is.
    What they try in fact is to handle the climate with statistics – while the evolution of every individual parameter that constitutes the climate can’t be computed and predicted, the AVERAGES (time and/or space) of the said parameters are robust and significant while any difference between a realisation of the parameter and its average obeys some statistical law.
    That is the theory in which Realisation = Climate + Noise.
    It is analogous to Kolmogorov turbulence theory and that’s why I guess Schmidt is calling that “chaos”.
    Of course any analogy stops here because the assumptions taken by Kolmogorov (homogeneity and isotropy) that give sense to his theory are absent from the climate theory.
    And of course, not surprisingly, according to D. Koutsoyannis [“On the credibility of climate predictions”, 2008] and other work that begin to appear now the “healing” of the models by salvaging at least the time averages, fails too.
    What is left […] is the deterministic chaos.
    The best example and analogy is the solar system problem.
    It is clearly deterministic and even not very complex because there are only a few bodies and a few O[rdinary ]D[ifferential ]E[quation].
    Now it happens that it behaves like the Lorenz system – the trajectories of the bodies are not predictable.
    While a trajectory is always computable by numerically solving the ODE for an arbitrary time period, this computed trajectory (that Dan Hughes would call “just a series of numbers”) has little to do with the real trajectory.
    The system is not stochastic either – asking about probabilities of trajectory excentricity or time averages of distances (Body A – Body B) makes no sense.
    Making N runs (N large) with varying initial conditions and varying time periods will give some insights about what the system MIGHT do but no insight at all about what it WILL do or indeed with what probability it MIGHT do this or that.
    And the differences between trajectories are not just small numbers – it may be so dramatic as the difference between turning a circle 500 millions years and definitely leaving the solar system.
    Fortunately there is at least ONE question that can be asked of such a system and that is the one of stability.
    We may ask after having observed the system for a certain time, are the trajectories stable (e.g. will there not be a catastrophic divergence)?
    There are mathematical tools for that like f.e. the KAM theorem [Kolmogorov-Arnold-Moser theorem].
    Of course it is more than probable that the climate system is nowhere near to an integrable Hamiltonian system where KAM would apply but similar approaches could be attempted.
    As there will be more and more results like those of D. Koutsoyannis, I am convinced that people will do one day the last step to be made.
    Namely that the climate is neither deterministic nor ergodic.
    1) The trajectories unpredictably evolve between different quasi steady states. The causality is not clear cut and for instance there is an infinity of different initial conditions that lead to the same final state.
    2) There is no particular time scale at which the system is more stable or predictable (e.g. yearly averages don’t behave “better” than hourly averages).
    3) There is no statistical law describing the distances between 2 different trajectories and no probabilities of achievement of the different quasi steady states.
    4) Observation of the last 3 billions of years shows that the envelope of the possible trajectories is bounded so the system is stable.
    5) The results above are independent of the computing power and the size of integration steps.
    6) The variation of a single parameter (f.e. CO² concentration) may lead to wide range of different final states and symmetrically those states can be reached without any variation of this parameter (CO² concentration).
    Then and only then people will stop bothering about CO² because they will understand that all kind of unexpected things happen and will happen regardless of CO² concentrations and the longer we observe, the more unexpected things will happen.
    With the usual irrational resistance of people towards any change, most of these unexpected things will be perceived as bad and dangerous 🙂
    However taking action with regard to a supposed qualitative impact of some climate variable on the final state after a certain time would make sense only if the specific costs/inconvenients of the action were near to 0 or if the time horizon was very short.”

  62. Lief,
    “Once again, this begs the question – if the GCMs can’t forecast the AO two weeks in advance
    I think this logic is faulty. Let me illustrate with some examples:”
    I’ll give my own example..
    One would think they could predict flooding based on ‘inches of rain’ over a period of time. It’s a simple fluids calculation. X inches of rains = Y of gallons of water which will all end up in the river which has a maximum flow rate of Z gallons/minute without flooding.
    Unfortunately, one needs to know the level of soil saturation and the state of other watersheds when the rain begins(which requires a hydrologist). Otherwise one unnecessarily alarms the population.

  63. Just listening…
    The difference between “Micro” and “Macro” seems to be at issue here. I’m not sure of the “M.cro” word for ‘middle’ but I’ll bet it’s also a player in the game.
    Let’s say weather is “micro” and climate is “macro”, what’s in the middle? (This isn’t Abbott and Costello by the way.) Is there something in the middle that messing everthing up? Something, to date, undefined and unfactored?
    It also occurred to me that the ‘Forrest for the Trees’ problem is also applicable here. Some seem too close and some too far away. Some only see forest, some only see trees.
    Lately, on the question of Earth’s temperature.. is it 98.6F and rising? or 98.6F and falling? I keep thinking that we’re much too close to the problem. I look at all the graphs for each year at various elevations (or air pressure levels), and various lines of latitude, etc., and wonder but what’s the “Earth’s” temperature? You know, like being several million miles out in space and looking at this tiny little blue marble, and pointing some thingie wingie at the speck, pulling a trigger, and looking at a readout that say’s 98.6F?
    I’ve learned that there is something called space weather, I’ve known all my life about the weather where I stood or slept. I’ve seen, read, and heard about the weather where I wasn’t. And of course there’s the weather in history books. There must be something between Micro and Macro. Mucro? Mecro? Mocro?

  64. Lief,
    One thing that climate models and solar models share is that they both seem to be consistently wrong.
    But a big difference is that GCMs are designed to be both iterative and precise. The whole idea of a “tipping point” is completely dependent on cumulative behaviour.
    For example, Hansen believes that decreasing snow cover in Siberia will cause release of methane which will in turn cause “life as we know it to cease.” On the other hand, increasing snow cover in Siberia will clearly not have that effect.

  65. Pascvaks (07:21:27) :
    Just listening…
    The difference between “Micro” and “Macro” seems to be at issue here. I’m not sure of the “M.cro” word for ‘middle’ but I’ll bet it’s also a player in the game.
    Let’s say weather is “micro” and climate is “macro”, what’s in the middle? (This isn’t Abbott and Costello by the way.) Is there something in the middle that messing everthing up? Something, to date, undefined and unfactored?
    The problem as I see it is that the climate of the past 100 years and the future 100 years is micro climate. The real climate history of Earth extends back billions of years. Man’s recorded history is only ~10,000 years long. Even that isn’t enough to say what is “normal” in climate.
    … and isn’t that the real debate anyway. Someone out there, maybe a James Hansen, decided 25 years ago that the future climate had to equal the 1951-1980 mean, regardless of the fact that we didn’t and still do not know if that soup is too hot, too cold, or just right. The only “truth” we know is that climate is ever changing and not stable with regard to temps and precip. Why are we so arrogant to believe we have changed it in the past, and can change it in the future at our whim.

  66. Scientists Predict Big Solar Cycle (24)
    Dec. 21, 2006: Evidence is mounting: the next solar cycle is going to be a big one. Solar cycle 24, due to peak in 2010 or 2011 “looks like its going to be one of the most intense cycles since record-keeping began almost 400 years ago,” says solar physicist David Hathaway of the Marshall Space Flight Center. He and colleague Robert Wilson presented this conclusion last week at the American Geophysical Union meeting in San Francisco.
    http://science.nasa.gov/headlines/y2006/21dec_cycle24.htm

  67. I’m not sure how many people commenters here work with climate models. They break the surface of the earth up into a grid, the atmosphere up into layers, and make very precise iterative calculations of many parameters – which are dependent both temporally and spatially. They use 64 bit math to minimize cumulative errors.
    NCAR is building a $150 million supercomputer in Wyoming because of the need for precision. Success of the model in a particular time step is dependent on success of the previous time step for all neighboring grid elements and atmospheric layers.
    GCMs are not averaging models as some here have suggested. Rather they are cumulative.
    (BTW the computer is being built in Wyoming because of cheaper coal-fired electricity.)

  68. tmtisfree (07:04:28)
    Yes.
    I would add that once one has deterministic chaos in one scale, the larger scale is also chaotic. I think it is one of the theorems.
    It makes no sense to say weather is chaotic in the small time scales but climate is not.

  69. Kath (22:48:38) :
    Here in the North wet coast, we’ve had a relatively mild winter so far, with the Pineapple Express bringing us rain on a regular basis. I wonder how the 2010 Winter Olympics will cope with the poor conditions?
    —————-
    The Olympics will be great because the wet weather is dumping snow on the mountains and conditions are best they have been in several years.
    247CM base at the bottom with 48Cm of new snow in the last 48 hours, base is solid at all elevations and all 200 runs are open with loose powder.
    So do not wonder anymore.

  70. “They have correlated the climate to something which doesn’t do much more than drive a drop into a swimming pool.”
    Technically speaking, when I surf on the ocean, and I take a wiz (it happens), I have just raised the oceanic levels (STRICTLY TECHNICALLY SPEAKING) for only a fraction of a moment in time… for, what if, at the same time, a wave crashed onto some rocks and a puddle formed that had not been there a moment ago? Would that not immediately offset what I have done?
    There is just NO way, using the science we have with SO MANY undetected (imo) variables, to determine what CO2 would do in a negative way. AGW focuses ONLY on the negative, NEVER on the positive… never.
    Humans can ONLY make theories based on what we know, and I say, we KNOW very little overall with much assurance… in fact, many theories that do ‘work’ may have undetectable variables that play a role–well, I’d say that is a definite.
    Leave the earth alone and focus on profiteering corporations (govs) abuse of the humans (and all species) with real pollution… mercury, aspartame, fluoride, etc., and their quest for control via agenda 21 (this would come in line with Cap/Trade as the precursor) and Codex Alimentarius.. all legitimate concerns if people ONLY knew.

  71. tallbloke (04:56:59) :
    “Yes, but that’s not how the climate models works. Which is the point at issue.”
    Ah, models, models… mostly waist of time. Explicit correlation which holds, and when it fails has a reason (e.g. volcanic eruptions) is the only one model that might produce a credible output. Here is Einstein’s much longer quote (very appropriate):
    “The fact that on the basis of such laws (laws of nature, or physics if you wish – my rem.) we are able to predict the temporal behaviour of phenomena in certain domains with great precision and certainty is deeply embedded in the consciousness of the modern man, even though he may have grasped very little of the contents of those laws. He need only consider that planetary courses within the solar system may be calculated in advance with great exactitude on the basis of a limited number of simple laws. In a similar way, though not with the same precision, it is possible to calculate in advance the mode of operation of an electric motor, a transmission system, or of a wireless apparatus, even when dealing with a novel development.
    To be sure, when the number of factors coming into play in a phenomenological complex is too large, scientific method in most cases fails us. One need only think of the weather, in which case prediction even for a few days ahead is impossible. Nevertheless no one doubts that we are confronted with a causal connection whose causal components are in the main known to us. Occurrences in this domain are beyond the reach of exact prediction because of the variety of factors in operation, not because of any lack of order in nature.”

  72. I wonder if the notion of tracking error makes sense for climate models as a way out of the “weather is not climate” problem. It is fair to say that short term variation of weather is too random to predict, and to postulate that science has a better change to predict longer term patterns. However, there needs to be some way to track the longer term prediction without waiting for the next 10-20 years. As it stands today, the climate models are not held responsible for their weather predictions. And that means they are un-falsifiable.
    It seems to make sense to track climate models based on their predictions. The tracking errors accumulated over the last 10-15 years should give us a way to determine (statistically speaking) that the models are likely to be wrong over time.
    The use of tracking errors to measure performance is not new. We use the same methodology in the financial world to measure historical portfolio performance against benchmark indices.

  73. The issue at hand is the idea of prediction. We have been at it for thousands of years. The Augurs used entrails, the Climatologists use supercomputers. We have become intoxicated with what we think we can do with faster and faster computers manipulating bigger and bigger programs. In itself this would not be harmful.
    But not having adequate physical understanding of how things work, coupled with experimental confirmation of the understanding puts us in the category of the Augurs.
    Then to push six billion people in a certain direction based on predictions not much better than those of the Augurs, that will be harmful.

  74. Steve Goddard (08:23:02) :
    The gridding is a problem, and so are the atmospheric layers.
    In a past life I worked with a number of super computing centers. At that time – 5 years ago – the best resolution that a paleo-climatic calculation could use was grids 100 Km on a side. I would hope that current resolution is better, but the parameterization of aerosols, cloud cover, etc. convinced me that they were just looking for the parameter set that gave the “right answer.”

  75. I don’t know if one can predict specific AO levels in any individual year , but on decadal level there are more winters with negative winter NAO’s during cool periods than during warm periods.[see below]. NAO is a subset of AO. Also a very high level [say -2] of a negative AO during December is a good indicator of continued negative AO into March and April and sometime even later [late cold winter in some areas?] The winter NAO level show a clear change from highest positive level in 1989 of 5 to a negative level in 2009 of -0.4. So the trend was indicating an increasing level of negative winter NAO [and AO] for the near future and colder winters. [using Jim Hurrel data]
    NUMBER OF NEGATIVE WINTER NAO YEARS DURING WARM AND COOL PERIODS
    1890- 1919[12 years] COOL PERIOD
    1920-1949 [9 years] WARM PERIOD
    1950 -1979 [17 years] COOL PERIOD
    1980-2009 [8 years] WARM PERIOD
    Note the 1960’s had the highest number of negative winter NAO years 8 out of 10

  76. anna v (08:35:50) :
    I would add that once one has deterministic chaos in one scale, the larger scale is also chaotic. I think it is one of the theorems.
    It makes no sense to say weather is chaotic in the small time scales but climate is not.

    Very true, however the timescales are different. If you look at weather on a nano-second basis it doesn’t appear all that chaotic. The same holds for climate. While it will be chaotic over millions of years, smaller time frames will not display that chaotic nature. For example, we may be heading into the next ice-age at this very moment. While we can’t see this at our timescale, it might be obvious at another scale. That still does not mean the climate will be significantly different in 2100.
    This means we have a reasonable chance of predicting changes to short timescale climate (100s of years) if we knew all the deterministic factors. The big problem is we aren’t even close to understanding those factors.

  77. wsbriggs,
    Next generation supercomputers are intended to work with finer grids and shorter time steps. But if the models can’t forecast clouds, AO, ENSO, PDO, NAO, solar cycles, asteroids, volcanoes, dust storms, etc. – what use is all that precision?
    We have seen autumn/winter snow cover increasing in recent years. That produces a very different cumulative effect than decreasing snow cover would.

  78. Steve Goddard (07:55:19) :
    Scientists Predict Big Solar Cycle (24)
    So what? That specific prediction you refer to was not based on any models or even physics [Hathaway said: “nobody knows why this works” – and sure enough it didn’t].
    A physics-based model was that of Dikpati et al. [also big SC24] and that seems to have failed. We think we know why: the difference between advection and diffusion, namely how magnetic fields are carried into the Sun’s interior. Dikpati et al. assumed diffusion was low and that hence advection [‘the conveyor belt’] was at work taking 20-40 years to play out. Choudhuri et al. [and yours truly and others] assume [and we don’t know yet] that diffusion is much more efficient [because of the short time delay (5 years) between polar fields and the ensuing solar maximum] and predict a small cycle, as it now seems we are getting.

  79. Hirst comes from big oil. His bonus comes from the headhunter that brought him 2 years ago negotiating. His models were wrong and the bonus is tied to Hirst being who he is and not based on what “they ” did.
    The Met Office is a formal soothsaying enterprise and does a splendid job of claiming credit for retrospective analysis.

  80. Steve Goddard (09:27:39) :
    We have seen autumn/winter snow cover increasing in recent years. That produces a very different cumulative effect than decreasing snow cover would.
    ***********************
    so much for runaway climate change, eh?

  81. AGW skeptic Joe Bastardi predicted the negative AO and a cold, snowy winter.
    The AGW fearmongers can’t produce accurate forcasts.
    The AGW skeptics, on the other hand, do produce accurate forcasts.
    Who you gonna trust?

  82. A kid with a pencil, a ruler, and the last 150 years of temperature data could predict that the next 50 years will be warmer than the last 50 years. Despite all the fancy jargon, expensive equipement, and advanced degrees, I haven’t seen anyone come up with a better explanation than “we’re coming out of a little ice age, so its getting warmer.”

  83. solrey (09:53:47) :
    AGW skeptic Joe Bastardi predicted the negative AO and a cold, snowy winter.
    The AGW fearmongers can’t produce accurate forcasts.
    The AGW skeptics, on the other hand, do produce accurate forcasts.
    Who you gonna trust?
    *************************
    Be careful. Bastardi always gravitates toward cold and snow. So, he is tends to be correct in a cold winter. I know dozens of long range Mets and none predicted this winter to be cold and snowy FOR THE REASONS that turned it into a cold and snowy winter in the NH. Most selected the Nino as the overriding factor this winter, when in fact we now know severe negative AO’s/NAO’s can trump a strong Nino in large part in the cold dept, and use the sub-tropical jet from the Nino to produce lots of snow. Simplistic explanation for sure, but generally correct for this post. Most Mets are honest enough to admit that they’re still in a learning process regarding weather. The climate guys should admit the same.

  84. sbarron (10:07:59) :
    A kid with a pencil, a ruler, and the last 150 years of temperature data could predict that the next 50 years will be warmer than the last 50 years. Despite all the fancy jargon, expensive equipement, and advanced degrees, I haven’t seen anyone come up with a better explanation than “we’re coming out of a little ice age, so its getting warmer.”
    *********************
    Umm, how about climate variability?? 150 years is nothing really, and if we had had this conversation in 1975, you’d be moving to the tropics to escape glacial advancement. In 2030, we may have this same conversation, and you’d be packing to escape the glaciers once again. Of course in 2060, the carbon tax will be back. See how silly and cyclical it all is and can be??

  85. lgl (05:54:36) :
    Richard Holle (02:17:43) :
    “if I pull out the past three patterns of 6558 days, or 240 lunar declinational cycles, and plot them side by side, day by day, they show a resultant pattern that gives a better forecast than any of the models.”
    Show us.
    My reply;
    The link (my name) leads to the set of forecast maps I set up tables of past data for gridding the data from in August – September of 2007. Started to make the daily maps themselves in September of 2007, and produced a set of daily maps from 2008 through 2013, which are still posted to those pages unmodified.
    http://www.aerology.com/national.aspx
    You may feel free to poke through them free of charge, comparing to the actuals from the past two years for verification or tear me a new one if it comes out that way.
    I have made many posts on the principals, in action in this method over the past 10 years, most are still on line and search able by using a Google “Richard Holle aerology” query. There is way too much volume to post here.
    I would be glad to answer specific questions about the method, and if you are interested could provide details of the programs, I had contract written to extract the data, grid the data, and make and store the maps onto the site.
    I personally have footed the bill for all expenses incurred over the past 25+ years it took me to work out this method. No tax deductions have been applied to my normal income, and no grants or outside funding have been received!

  86. Richard Holle (10:46:36) :
    I personally have footed the bill for all expenses incurred over the past 25+ years it took me to work out this method. No tax deductions have been applied to my normal income, and no grants or outside funding have been received!
    **********************
    OT, but isn’t that illegal to not ask for a handout?? 😉

  87. Richard Holle (10:46:36), “I have made many posts on the principals, in action in this method over the past 10 years, most are still on line and search able by using a Google “Richard Holle aerology” query. There is way too much volume to post here.”
    Richard, maybe you could think about updating your website with the information found via google. If you really want folks to assess it, you want to make it easy. No need to retype anything, just cut and paste from the google results.

  88. anna v (06:18:51) :
    Richard Holle (02:17:43) : | Reply w/ Link
    My reply: I am only suggesting that we study the effects of the Lunar declinational tides in the atmosphere, because if I pull out the past three patterns of 6558 days, or 240 lunar declinational cycles, and plot them side by side, day by day, they show a resultant pattern that gives a better forecast than any of the models.
    With compensation for the changes in the solar cycles, from the past three patterns of high activity, to this one of lower activity I would get the correct amount of cooling, and better representation of the depth, of the cold arctic air invasions that are arriving on time, just larger than before.
    Piers Corbyn secret weather method uses the moon and sun as he has explained in several videos, and he seems to be quite successful in long term weather predictions, so you may be right.
    On the other hand, when many dynamical inputs enter, and such is the case of earth climate, it is chaos tools that should be used.
    anecdotal: in my part of the earth, Greece, the moon phases are traditionally used by sailors and farmers to “predict the weather” as follows: If the wind/clouds/etc change with the moon phase, expect the same weather to the end of the phase. If it does not change then, it will keep the same through the next phase.

    My reply;
    When Stonehenge was built (at the end of the last Ice age) they studied the solar and lunar declination interactions and found several cycles of import.
    As the science of the understanding was reduced to a religion to present to the masses, their response was to look at the light phases, and extract wives tales, and folklore from the phase relationships applied to the time of the year, which are almost close enough to be used as a proxy for the declination angular movement, that requires equipment to measure.
    The declinational tides period of 27.32 days is not far from the 28 day light phase period. The patterns generated in the atmosphere, in the form of Rossby waves, have a four fold repeat to the patterns. Where zonal flows dominate in alternating declinational cycles, and more medial flows in the others in between. The shifting of the patterns usually occurs around the time of Maximum North lunar declinational culmination.
    Due to the topographical forcing of the terrain in the area you mention, there is sometimes little difference between the cycles, as there will be little difference in the New England area this time from the shift from the past cycle, to the next cycle shifting around the 26th to 27th of January.
    It would seem that the story you tell is a valid one. Such is the problem with global verses regional forecasting.

  89. Excellent work Anthony.
    Unfortunately right out of the gate we have the first commenter bringing us the news that
    magicjava(22:36:33) :
    “weather’s not climate”
    How is it that anyone can still possibly think that bit of information needs to be mentioned yet again?
    Yes magicjava we all know that “weather is not climate”.
    But that phrase only works as deflecting BS.
    Because climate is nothing but long term weather.
    So in order to be accurate those peddling this tired bromide should be stating “short term weather is not climate”
    making it even more obvious, elementary and unnecessary to repeat.
    IMO that “weather is not climate” phrase should be filtered by spam controls.
    It’s esentially become nothing but a middle finger from warmers to skeptics. Intentional or otherwise.

  90. Leif Svalgaard (00:26:32) :
    “Dave F (00:18:54) :
    anything is even correct in the models.
    I don’t believe the current models are correct [too many things are parameterized], but my beef was with the bland assertion that because we cannot predict two weeks ahead, all prediction further out is impossible. If the climate to a large extent is self-regulating [negative feedbacks] then a model may not necessarily ‘go off the rail’ in the far future, but may be kept within bounds by the regulator [whatever it may be].”
    This is part of my fundamental problem with GCM, especially to the extent that they are used to formulate climate policy. Who is the “regulator” who will be keeping things “within bounds?” And what are the “bounds?” The farther over time one goes in prediction, one cannot help but parameterize more and more. It is the famous “if present trends continue.” I do not trust the current “regulators,” because it is apparent they have been stacking the deck and doing precisely the parameterizing you deplore because they seek this conclusion. Nor do I believe we will ever be able to reliably predict long term trends in climate to the point where policymakers can trust the results because of the “butterfly effect.” It is that simple, Leif. Sorry, I wish I had your faith that the models in question could be improved to that degree, but we’re dealing with a pretty dicey system here, and your analogies need to be on crutches because they limp pretty badly.

  91. Steve Goddard (09:55:21) :
    Fair enough. When will cycle 24 peak, and what will that peak be?
    peak in 2014, max 6 active regions [equivalent to 72 ‘sunspot number’ – but if Livingston is correct the spots might be less visible, so the sunspot number may not an accurate measure], 123 solar flux units for the F10.7 microwave flux.
    However, for weak cycles, the maximum may be a ‘slippery; thing. Here is how cycle 14 looked [and SC24 may be like it], see page 39 of: http://www.leif.org/research/Predicting%20the%20Solar%20Cycle.pdf
    For an up-to-date version of the plot on page 40, see: http://www.leif.org/research/Livingston%20and%20Penn.png
    It is instructive to compare the two…
    Larry (11:55:54) :
    I wish I had your faith that the models in question could be improved to that degree, but we’re dealing with a pretty dicey system here
    It is likely that the current models cannot be improved, but when [if?] we learn about what causes the longer cycles that are evident, then incorporating those might lead to improved forecasting. The ‘butterfly effect’ has its limitations. In its purest form it refers to the effects of non-linearities [and calculational instability – e.g. round-off errors], but those are still limited by the energy available. The butterfly effect will not cause the Earth to emit, say, twice as much energy as it receives. So there are built-in brakes on the system.

  92. tmtisfree (07:04:28) “[…] the envelope of the possible trajectories is bounded so the system is stable.”
    We need more focus on deterministic constraints.

  93. Climate Heretic (08:37:12) “The Olympics will be great because the wet weather is dumping snow on the mountains and conditions are best they have been in several years.”
    Don’t forget that some of the events are at Cypress (lower elevation).

  94. Since the first of October, Colorado is averaging two to eight degrees below normal, as is most of the US:
    Not according to the map you showed.

  95. anna v, multivariate phase-relations of a variety of terrestrial variables show nonrandom patterns. There are scales & locations at which oscillations appear hard-bounded. My sense is that due to monitoring network limitations we have (manageable) aliasing issues in our records and that we additionally should consider the possibility that some data which appear quantitative are only ordinal (as suggested by Tsonis+ and many others before & since). There are classes of chaotic functions that conditionally allow for hard-bounded, patterned bouncing – for example the simple logistic equation. However, starting out from the assumption that everything is chaos invites more abstract computer-model fiddling that will tell us nothing about patterns awaiting discovery (via phase-conditioned analysis) in the existing empirical record. We have to invest the patience necessary to avoid misguided assumptions. Getting the assumptions wrong is the most costly error we can make.

  96. Leif Svalgaard (12:56:37) :
    It is likely that the current models cannot be improved, but when [if?] we learn about what causes the longer cycles that are evident, then incorporating those might lead to improved forecasting. The ‘butterfly effect’ has its limitations. In its purest form it refers to the effects of non-linearities [and calculational instability – e.g. round-off errors], but those are still limited by the energy available. The butterfly effect will not cause the Earth to emit, say, twice as much energy as it receives. So there are built-in brakes on the system.

    This is a far more nuanced answer than Leif gave earlier and I agree with what he says here. A better understanding of the causes of the bounding conditions will lead to better simulation of the mid term possibilities, regardless of the fact we can’t pin down the smaler scale complexities.
    However, until we understand the processes which lead to the Earth heading to one or the other attractor in it’s current bipolar shifts between ice age and interglacial quasi-stabilities, we will have to guess and hope about the longer term. I think the climate is both analogue and ‘twitchy’ within the bipolar state and not susceptible to logic gates.
    There’s plenty we can and should learn about it though.

  97. Leif Svalgaard (00:26:32) :
    An interesting point I had never thought of. Could be that the climate sensitivity is not a constant the way a GCM would treat it. Change in the climate may change the sensitivity figure.

  98. tallbloke (14:10:04) :
    This is a far more nuanced answer than Leif gave earlier and I agree with what he says here
    Really the same answer, but elaboration usually helps [not for all people though – if they don’t like what they hear]

  99. Steve Goddard (09:27:39): …We have seen autumn/winter snow cover increasing in recent years. That produces a very different cumulative effect than decreasing snow cover would.
    Steve, could you please supply me with the reference(s) that show an increasing trend in au/wi snow cover? Click on my name and use the Contact applet. This matter is of importance to forestry, especially regarding forest fires. Some are claiming the exact opposite; I need ammo for the debate. Thank you.

  100. “Dave F (14:20:17) :
    Leif Svalgaard (00:26:32) :
    An interesting point I had never thought of. Could be that the climate sensitivity is not a constant the way a GCM would treat it. Change in the climate may change the sensitivity figure.”
    I think Leif was referring not to the climate sensitivity being possibly variable, but more to things like parameterized instead of computed cloud cover.

  101. Leif Svalgaard (00:26:32) :
    An interesting point I had never thought of. Could be that the climate sensitivity is not a constant the way a GCM would treat it. Change in the climate may change the sensitivity figure.

    Climate responses may be non-linear. Who’d have thought. 🙂

  102. Good to see some recognition of LIMITS in this thread. The following was an unparameterized exercise in detecting shared boundaries (i.e. envelope):
    http://www.sfu.ca/~plv/-LOD_aa_Pr._r.._LNC.png
    Shifts in dominance amongst bounding factors present a formidable challenge – i.e. the envelope for one era may not be the right envelope for another era.

  103. DirkH (14:45:23) :
    I think Leif was referring not to the climate sensitivity being possibly variable, but more to things like parameterized instead of computed cloud cover.
    Certainly the latter, but would not exclude the former, actually.

  104. Leif Svalgaard (14:26:25) :
    tallbloke (14:10:04) :
    This is a far more nuanced answer than Leif gave earlier and I agree with what he says here
    Really the same answer, but elaboration usually helps [not for all people though – if they don’t like what they hear]

    If it’s the same answer at least it’s qualified with an if and a maybe the second time around. I’ll settle for that.
    I’ve learned to bypass the invitations to verbal combat Leif puts in square brackets. 🙂

  105. Steve Goddard (09:27:39) said “We have seen autumn/winter snow cover increasing in recent years. That produces a very different cumulative effect than decreasing snow cover would.”
    The snow cover has very little cumulative effect. To prove that and to prove that initial conditions don’t matter in climate models, simply cover Central America in 6 inches of snow and run the model. In real life, and in the model if it is accurate, the change in albedo from the snow cover would last an hour or two and resultant weather effects (including global averages) for a day or two. The change in biosphere (plants killed off by the cold) would probably last a month or two will little effect on the climate. Initial conditions don’t matter and snow doesn’t matter all that much.
    My other comment on model resolution is that it isn’t sufficient to model mesoscale weather and won’t be for a decade or two. That means that convection and clouds are essentially parameterized and as Sean and WSBriggs point out, which means the assumptions going in dictate the model results. The assumptions can come from higher resolution submodels or real world measurements, but those are difficult to integrate into the coarse model since those details are not independent of the larger scale model phenomena.

  106. Paul Vaughan (15:08:07) :
    Shifts in dominance amongst bounding factors present a formidable challenge – i.e. the envelope for one era may not be the right envelope for another era.
    What is shows is just the spectacular breakdown of a correlation. The usual reason for this was that the correlation was spurious to begin with. So, so much for that one. On the junk heap it goes 🙂 joining the hundreds already there.
    tallbloke (15:35:23) :
    I’ve learned to bypass the invitations to verbal combat Leif puts in square brackets.
    Why would you even contemplate that in the first place, unless you felt that it was applicable to you, which I don’t think it is [I could be wrong] (another set of square brackets to bypass)

  107. Leif Svalgaard (15:24:03) :
    El Nino approaches. Don’t know if you’ve had the unmistakable pleasure of experiencing one in the Golden State, so stay put, watch the show, and stay the hell away from moving water.

  108. Eric,
    A few hours of snow cover in Central America has little effect on the earth’s radiation budget. On the other hand, North America, Europe and Asia covered with snow for months – has a huge effect.

  109. anna v (06:18:51)
    I am intrigued, “chaos tools” and “deterministic chaos”. Can we tool and determine chaos or is weather best described as ‘something indeterminate going on within boundaries?’.
    The human race seems to be going through a period of extreme sensitivity to weather, we certainly know more about it than 100yrs ago in terms of placing a number to a factor but lack the skill to project weather into climate and, except for those in the IPCC, does it really matter?.
    Can chaos be defined or is it indeterminate? and just another way of saying “I don’t know” (within boundaries) ?
    I am smiling as I write this, this thread is armchair philosophy at its best.

  110. Regarding climate modelling – I had an online article published last year that goes someway in explaining the problems inherent in this approch, found here: http://www.onlineopinion.com.au/view.asp?article=9069&page=0
    Anna V
    “anecdotal: in my part of the earth, Greece, the moon phases are traditionally used by sailors and farmers to “predict the weather” as follows: If the wind/clouds/etc change with the moon phase, expect the same weather to the end of the phase. If it does not change then, it will keep the same through the next phase.”
    I live in rural south-eastern Australia and our longest termed farming resident (since deceased) used to say the exact same thing. Interesting that those people who live by the weather are usually the best observers of it – makes me think Richard Holle is on to something, too.

  111. tallbloke (17:02:14) :
    [I could be wrong] (another set of square brackets to bypass)
    Heh. If we keep this up, you’ll run out of bracket styles eventually. 🙂

    It was actually a serious question. Was I wrong?

  112. Modeled long term forecasting ability is logically failed due to it’s desire not to predict climate but extreme weather events, and more of them, based on a CO2 increase. However, there are far stronger drivers of extreme weather events that swamp CO2 affects on weather, such as the recent AO dive. It will be these parameters that cause the models to fail.
    Climate is a steady state entity unless your address changes. It does not need long-term or short term forecasting. The climate in NE Oregon will remain the same without any need to change the description of what it will be like tomorrow or in 200 years. It is the meanderings and trends of weather that are the hard part, both in terms of short range, and long range predictions. Therefore, if short term extremes are missed, most assuredly, long term extremes will be missed. Why? They arise from one and the same chaotic process. Short term weather prediction is the same as long term weather prediction. Be good at it on the one hand, you will be good at it on the other hand. Otherwise you will suck at both. End of argument.

  113. Richard Holle (23:54:22) and savethesharks (23:38:46)
    This ‘crazy-ass’ thunderstorm followed a meandering path from the border with NSW, up the Canungra valley, over Tamborine Mt to the Gold Coast then back inland to a western Brisbane suburb called The Gap. I know these areas well, I have two daughters in its path. It is a supercell that merged with other cells and dumped its energy in the form of wind, rain and hail in a ferocious ‘microburst’.
    I witnessed one from my position in Canberra, form over the city and cause a lot of roof damage to the Australian National University, mostly from hail blocking gutters. It was like a giant white, solitary cup-cake in its formation.
    For those unfamiliar with microbursts, this is interesting ………
    http://www.bom.gov.au/weather/qld/cyclone/thunderstorms/16Nov2008/qldth20081116.shtml

  114. Forecasting
    So the science is settled, has been for a few years now.
    The global temps will go up.
    GCM’s can’t predict extreme weather.
    What’s the point of keeping these GCM’s running?
    If they can’t “predict” what is their purpose now? To adjust projected temps by a few tenths of a degree here or there? What’s the point?
    Obviously it’s time to retire these GCM’s
    Any flaws in this logic?

  115. Pamela Gray (17:58:34) :
    Modeled long term forecasting ability is logically failed due to it’s desire not to predict climate but extreme weather events, and more of them, based on a CO2 increase. However, there are far stronger drivers of extreme weather events that swamp CO2 affects on weather, such as the recent AO dive. It will be these parameters that cause the models to fail.
    My reply;
    The lunar declinational tides at the time of culmination maximum North or South in the short term 27.32 day periods give rise to most of the severe weather in the whole period. As the inertia of the tidal air mass following the moon’s transit across the equator, rushes in to the mid-latitudes, at culmination as the moon hangs there for two days.
    This generates the large cyclones, that continue on with their inertia as the moon slides back toward the equator. With the (NH) wrap around effect, polar air comes from the west, and they sweep up toward the North East, creating the shear that brings the tornadoes in spring and summer.
    The operational “dry line” being the cold old polar front being sucked in from behind.
    The other times when there are surges in medial flows, seem to be caused by effects of synod conjunctions, of the outer planets that seem to have the same type of effects, as seen in the satellite image sequences.
    Knowing that correlation is not causation, I have been trying to put my finger on the causes of this obvious effect. It happens about four times a year, in sync with the outer planet synod conjunctions and is scaled to their relative size / closeness. I have been trying to find some magnetic or solar wind carried signal to relate the causation to.
    The timing is clear, the mechanism is hidden, and the strength of effect is calculable, and should be programmable into models, that could use these ideas as a base, to ground the butterflies to the flowers.

  116. Pamela Gray (17:58:34) :
    “Climate is a steady state entity unless your address changes.”
    Well put! I see the same view. Over years the Earth is basically the same year after year after year except for displacements of pressure (air masses), energy (heat) and moisture, ignoring the sun’s variance.
    See if you agree along the lines of your statement. The long term change in global temperature is throttled by overall albedo primarily. A lighter colored earth is cooler, a darker earth is warmer, over time. Albedo determined by amount of clouds, ice, snow, plant cover, new parking lots, roads, roofs, their specific colors, roughness of seas, etc. And weather has a large input into that, hence the chaos in the Earth weather / climate system. This is rarely even mentioned but to me seems prime.

  117. Paul Vaughan (20:03:32) :
    Re: Leif Svalgaard (15:44:25)
    You might read up on Ben Chao’s ideas about NAO.

    I why would I like to do that? Your own graph clearly invalidates the correlation.

  118. Leif Svalgaard (15:44:25) “[…] spectacular breakdown of a correlation […] On the junk heap it goes […]” / Leif Svalgaard (20:24:18) “[…] graph clearly invalidates the correlation.”
    Zhou, Y; Zheng, D.; Zhao, M.; & Chao, B.F. (1998). Interannual polar motion with relation to the North Atlantic Oscillation. Global and Planetary Change 18, 79-84.
    Chao & associates point out that the main north-south NAO axis is near the Greenwich meridian.
    Note that the 88-98 phase-concordance here…
    http://www.sfu.ca/~plv/GLAAM_LOD_AO_NAO.png
    …corresponds with the sharp jog here:
    http://www.sfu.ca/~plv/CumuSumAO70.png
    The commencement of coupling (& related sudden drop in Arctic ice) coincides with the crossing here…
    http://www.sfu.ca/~plv/NutationLongitude.png
    The discrepancy here …
    http://www.sfu.ca/~plv/-LOD_aa_Pr._r.._LNC.png
    …also corresponds with the pattern here:
    http://www.sfu.ca/~plv/NutationObliquity_.png
    …so I gained appreciation for what the experts say: EOP are affected by celestial bodies and it takes a minimum of 3 EOPs to complete the picture …but it seems 2 can account for most of the variance and that even 1 can do well over long intervals ….but gamblers should be aware of serious risk when placing bets on forecasts based on a single EOP, since such forecasts eventually break down (due to missing conditioning) after long runs of success.

  119. Paul Vaughan (21:25:36) :
    since such forecasts eventually break down (due to missing conditioning) after long runs of success.
    Due to spurious correlation in the first place.

  120. tucker (10:29:23) : “Be careful. Bastardi always gravitates toward cold and snow. So, he is tends to be correct in a cold winter.”
    That is a simplistic statement. He was predicting this winter WAY back in July.
    “I know dozens of long range mets and none predicted this winter to be cold and snowy FOR THE REASONS that turned it into a cold and snowy winter in the NH.”
    That’s a nice copout, EH? Hmm….maybe JB, for all his faults, is on to something.
    “Most selected the Nino as the overriding factor this winter, when in fact we now know severe negative AO’s/NAO’s can trump a strong Nino in large part in the cold dept, and use the sub-tropical jet from the Nino to produce lots of snow.”
    Maybe they were assuming Hansen’s Super El Nino prediction?? Hmm. What happened to that? Notice one doesn’t hear that prediction in the news anymore.
    Point is is that even the best scientists today are swayed by the “appeals to authority” in the form of goons like Jim Hansen, who truly is going down the wrong path, even though he thinks he is on the right, and is dragging much of the forecast world down with him with his NASA stature.
    HE NEEDS TO BE FIRED.
    “Simplistic explanation for sure, but generally correct for this post.”
    Glad to see you fess up to the word “simplistic”, because many of us will not disagree there.
    “Most mets are honest enough to admit that they’re still in a learning process regarding weather.”
    But I can most agree with you on this point. There are plenty of damn good met minds out there.
    And Bastardi is one of them….and he predicted the general outcome of this winter…..long long ago.
    Chris
    Norfolk, VA, USA

  121. “Leif Svalgaard (17:37:31) :
    tallbloke (17:02:14) :
    [I could be wrong] (another set of square brackets to bypass)
    Heh. If we keep this up, you’ll run out of bracket styles eventually. 🙂
    It was actually a serious question. Was I wrong?”

    Even though you guys don’t typically get along (haha sounds like us right?) I think he was playing along with your sense of humor, Leif. If you go back to the posts….you will see.
    You offered some humor/seriousness at the same time…and he did the same.
    I mean….your [ ] bracket system I gotta confess I find myself using it too. And we all appreciate your sense of humor.
    Then sometimes I use the ( ).
    So the next one is…..TAH DAH! { }
    At this point I just have one thing to say, parenthetically, that is.
    ( [ { } ] ) but also
    { [ ( ) ] ) !!!
    🙂
    Chris
    Norfolk, VA, USA

  122. Paul Vaughan (21:25:36) :
    Hey Paul I posted this question on another thread and I can’t remember where I posted it.
    Can you give me some links to good papers on the GLAAM and the QBO?
    Thank you.
    Chris
    Norfolk, VA, USA

  123. Keith Minto (16:24:14) :
    anna v (06:18:51)
    I am intrigued, “chaos tools” and “deterministic chaos”. Can we tool and determine chaos or is weather best described as ’something indeterminate going on within boundaries?’.

    …..
    Can chaos be defined or is it indeterminate? and just another way of saying “I don’t know” (within boundaries) ?
    Chaos as a mathematical theory exists and is well defined.
    Have a look at http://en.wikipedia.org/wiki/Chaos_theory
    For tools in handling chaotic conditions have a look at the thread here
    http://wattsupwiththat.com/2009/03/16/synchronized-chaos-and-climate-change/ .

  124. Richard M (09:15:46) : | Reply w/ Link
    anna v (08:35:50) :
    “I would add that once one has deterministic chaos in one scale, the larger scale is also chaotic. I think it is one of the theorems.
    It makes no sense to say weather is chaotic in the small time scales but climate is not.”
    Very true, however the timescales are different. If you look at weather on a nano-second basis it doesn’t appear all that chaotic. The same holds for climate. While it will be chaotic over millions of years, smaller time frames will not display that chaotic nature. For example, we may be heading into the next ice-age at this very moment. While we can’t see this at our timescale, it might be obvious at another scale. That still does not mean the climate will be significantly different in 2100.
    This means we have a reasonable chance of predicting changes to short timescale climate (100s of years) if we knew all the deterministic factors. The big problem is we aren’t even close to understanding those factors.

    Hmm. I have the impression that once chaos sets in in one scale, all larger scales are chaotic. I will have to read up to make sure it is a theorem. If true, it means that what your are saying does not hold.

  125. anna v (00:40:11) :
    Richard M (09:15:46) :
    And chaotic systems can approach long term stability if they also have internal balancing or limiting feedbacks to keep from wandering far off-track (into the weeds as stated above).

  126. Richard M,
    “If you look at weather on a nano-second basis it doesn’t appear all that chaotic. The same holds for climate. While it will be chaotic over millions of years, smaller time frames will not display that chaotic nature.”
    I’m not sure of the point of this statement. It may be true that at sufficiently small time scales, climate or weather will not APPEAR to be chaotic, but that does not mean it is not chaotic. If something is chaotic when viewed over a long time scale, it must be chaotic in all smaller time scales, because by definition, chaos exhibits a fractal structure.

  127. savethesharks (23:51:02) :
    Paul Vaughan (21:25:36) :
    Hey Paul I posted this question on another thread and I can’t remember where I posted it.
    Can you give me some links to good papers on the GLAAM and the QBO?

    Chris, mosey over and have a look at Paul’s more recent posts on various threads here:
    http://tallbloke.wordpress.com

  128. Steve Goddard (16:22:25) “On the other hand, North America, Europe and Asia covered with snow for months – has a huge effect.”
    Hi Steve, not really. Most of the albedo is gone around here (Northern VA) in a few sunny days unless there is cold air flowing down from Canada or in the case of Europe from Siberia. The global radiation budget change from the snow reflecting sunlight is dwarfed by other variables like El Nino. Look at Roy’s satellite temperatures for example. They dropped in December in response to negative NAO and other blocking factors before there was much snow. They having been rising in January despite all the snow on the ground.
    I am not denying that snow and ice albedo has an effect, it does. But its effect is mostly as added feedback from much larger forcings, like icing on the cake.

  129. Eric,
    The earth’s radiation budget is almost completely determined by the amount of short wave radiation which is absorbed by the earth’s surface and atmosphere. If a lot of short wave radiation is being reflected by snow, much less is absorbed. As a result, temperatures drop and outgoing long wave radiation is reduced.
    You are confusing temperature with the radiation budget.

  130. Ref – Pascvaks (07:21:27) :
    “Just listening…
    “There must be something between Micro and Macro. Mucro? Mecro? Mocro?”
    _______________________
    OK! I believe we’re all agreed then, Mecro it is. (No sense overusing meso.)

  131. Re Leif Svalsgard’s thread:
    As often, Leif S’s comments expose a key issue—-the key issue for this website. But his conclusion confuses the issue:
    Of course, water ultimately runs downhill, and pebbles fall from the mountain into the valley. There is gravity.
    But to accept the GCM”S, one is accepting that CO2 forcing of the Earth’s climate is as established as gravity—-but CO2 forcing is not established.
    Therefore, every test of the GCM’s against measured weather data represent more data points for or against the “truth” of the models. Over time the weather data will either confirm or deny the truth value of the models. The recent relative cold over the last decade, compared with the universal predictions of the Warmest models for steady warming in the due to rising CO2, indicates those models are broken. Minor tinkering with the models is unlikely to fix them.
    And the nature of many of the models is that periods of weather set the stage for future weather—-eg, warming to some extent, through positive feedback will exacerbate more extreme warming in the future. (This is the “iterative” process referenced in the initial comments.) So the failure of warming in the last decade doubly destroys the Warmists’ extreme predictions: no warming for the last decade, plugged into their models, will show less extreme warming in the future—-Important for the political/economic decisions that need to be made.
    KW

  132. keith winterkorn (08:39:33) :
    But to accept the GCM”S, one is accepting that CO2 forcing of the Earth’s climate is as established as gravity—-but CO2 forcing is not established.
    I don’t think even the models assume that. As far as I know, the GCMs try to calculate the climate from ‘first principles’ as far as they can. They parameterize what they can’t compute [clouds, for example], but I don’t think they put in CO2 forcing as a given. That is supposed to become an output of the model. If someone out there can show that CO2 is assumed in the Model, I would like be corrected. Mosh? you have looked at the code IIRC.

  133. GCMs include CO2 as one of a dozen or so gases which are used in calculating the radiation budget. CO2 absorbs something like 20% of LW radiation emitted from the surface, so it is a very important part of the calculations.
    You can download one of the most commonly used radiative transfer models here:
    http://rtweb.aer.com/rrtm_frame.html

  134. Steve Goddard (09:48:43) :

    CO2 absorbs something like 20% of LW radiation emitted from the surface …

    What? Impossible I think. You (or the GCM you are quoting) speak as if a CO2 molecule can keep absorbing and absorbing and absorbing heat over and over again. A CO2 molecule, or conglomerate of molecules, can only absorb once per band. Once excited in a band it must re-radiate at some point. If prompted by another photon of that same frequency both photons will leave with the same direction as the later photon, always toward space. If you disagree please show to me the science behind this while conserving the momentum of this LW radiation. Seems you can only heat that way ONCE (may be 20% from cold state) not 20% of the earth’s heat output!

  135. Steve Goddard (09:48:43) :
    GCMs include CO2 as one of a dozen or so gases which are used in calculating the radiation budget. CO2 absorbs something like 20% of LW radiation emitted from the surface, so it is a very important part of the calculations.
    So you are saying [as I thought] that the models do not assume the ‘forcing’ effect of CO2, but rather calculate the flow of radiation in the atmosphere from underlying well-understood physics.

  136. Steve, I see your point. Yes, I was referring to temperature, specifically the difference in a given location like mine between having snow on the ground and not having snow. Other than extra radiational cooling and heat absorbed by melting snow during the day, there’s not much difference in weather, especially after a few days.
    But the albedo effects (reflected shortwave) mostly go away after a day or two of sun as well. I see it on the south facing surfaces here with the exception of large storms like we had on December 19th. I also see it in the visible satellite photos, which have a lot less whiteness from snow after a few days. I guess a lot of that has to do with how far south I am (Virginia). Our snow doesn’t last long in direct sun (shaded snow lasts longer, but shade doesn’t matter).

  137. Steve Goddard (09:48:43) :
    GCMs include CO2 as one of a dozen or so gases which are used in calculating the radiation budget. CO2 absorbs something like 20% of LW radiation emitted from the surface, so it is a very important part of the calculations.
    From the spectra, 20% for the CO2 share seems too high.
    http://wattsupwiththat.files.wordpress.com/2008/06/atmospheric_spectral_absorption.png?w=509&h=411
    wayne (10:51:21) :
    I think we have been through this before. The CO2 molecule when it absorbs a photon in the range it can as seen in the plot referenced above, goes into a higher rotational or vibrational level and deexcites promptly since it is not a ground state and it can decay with usually two or more photons to reach the ground state, or can transfer the energy through collisions to other molecules, N2 and O2 which are most probably its neighbors.
    If prompted by another photon of that same frequency both photons will leave with the same direction as the later photon, always toward space.”
    This might happen with very low probability, because CO2 will have returned to the ground state before meeting another appropriate photon. It is not a laser situation, too few CO2 and no currents, see
    http://en.wikipedia.org/wiki/Carbon_dioxide_laser.
    Momentum is conserved by the bulk of the atmosphere, that is why the temperature will rise a bit.

  138. Leif Svalgaard (22:46:58) “[…] spurious correlation […]”
    Conditioning as explained & snipping off wavelet edge-effects:
    http://www.sfu.ca/~plv/-LOD_aa_Pr._r.._LNC_NL.png
    This is why it is important to analyze relationships between residuals and other variables. For example, I provided an example upthread [Paul Vaughan (04:11:59)] where something as basic as the terrestrial year appears to have been overlooked. In that case there is no denying the seasonal bias (see the systematic January spikes). The errors were cut in half simply by adjusting for the difference between winter & summer – something to which everyone can relate, even if they don’t know what partial phase-residuals are.

  139. Paul Vaughan (13:34:47) :
    This is why it is important to analyze relationships between residuals and other variables.
    Still does not detract from the correlation being spurious to begin with. Any spurious correlation can be ‘dressed up’ to look more significant by adding suitable other extraneous variables. And when the dressed-up version falters, just add some more conditioning variables, etc, ad infinitum.

  140. anna v (12:31:24) :
    Momentum is never conserved just because of bulk. And why do you keep bringing up a laser, as synchronized polarization. No such thing in the process I’m speaking of. Your right, this is not a one photon for one photon process, split between multiple lower energy states, but the original momentum will be preserved in the closed system. Referencing mostly from “Physics of the atom, Wehr & Richards”, rather old but these core physics processes have never changed.

  141. Leif Svalgaard (14:24:31) “[…] spurious correlation […]”
    It is well-known & accepted that celestial factors affect EOP.

  142. Anthony,
    I think we should send dart boards to: Jones, Mann, Briffa, etc. –
    It should help them take some of the load off of Harry –
    While improving the forecasting –
    We may also consider sending some trained monkeys along to manage the boards.

  143. Leif Svalgaard (17:41:04) “The spurious correlation is with aa-index….”
    It’s not aa index.
    I acknowledge that I need to explain f(aa) better – I’ve drafted some notes.
    My instinct (in part based on what I’ve learned from you) is that I’ve isolated a decadal variation in the rate of change of the magnitude of year-to-year variations after removing solar variation at Schwabe & Hale timescales (which leaves modulation occurring at the Earth end).
    There was a paper to which you linked some months ago that gave me ideas about how to isolate the signal. The authors were using techniques I’d never seen before. I’ll see if I can dig out the link.

  144. Steve Goddard (17:50:07)
    with a peak value of 15 microns, and shoulders at 13.6- 16.2 – that is around 6%-8% of atmospheric energy. C02 doesn’t absorb normal temperature radiation

  145. of course, c02 absorbs some SW radiation, so that doesn’t reach the surface. 20% of the entire incoming-outgoing radiation sum is an ambitious figure

  146. wayne (10:51:21) :
    Steve Goddard (09:48:43) :
    CO2 absorbs something like 20% of LW radiation emitted from the surface

    Here’s an atmospheric absorption spectrum, the large notch at ~700 wavenumber is due to CO2. …
    http://i302.photobucket.com/albums/nn107/Sprintstar400/Modtran-dry.gif
    What? Impossible I think. You (or the GCM you are quoting) speak as if a CO2 molecule can keep absorbing and absorbing and absorbing heat over and over again. A CO2 molecule, or conglomerate of molecules, can only absorb once per band.
    Not true, if it absorbs a 15μm photon it gets promoted from the ground state v=0 to the first excited state, that state can absorb a second photon to v=2. However collisions (~10 times/nsec) with other molecules deactivate the excited states so fast that this is exceptionally unlikely.
    Once excited in a band it must re-radiate at some point. If prompted by another photon of that same frequency both photons will leave with the same direction as the later photon, always toward space.
    This is stimulated emission, the principle of a laser, it requires a population inversion which you wouldn’t have in the atmosphere.
    If you disagree please show to me the science behind this while conserving the momentum of this LW radiation. Seems you can only heat that way ONCE (may be 20% from cold state) not 20% of the earth’s heat output!
    Your understanding of how photons interact with molecules is confused, after absorbing a photon the molecule will undergo many vibrations and rotations before emitting a photon, so there will be no relationship between the direction of the incoming and outgoing photons.

  147. Steve Goddard (17:50:07) :
    Have you considered the ratio of H2O to Co2 ? Co2 maybe from 5% to 25%, so that would make the contribution much smaller than the graph 20% ?

  148. wayne (15:03:50) :
    anna v (12:31:24) :
    Momentum is never conserved just because of bulk.

    When sunshine hits you, that is lots of photons with momentum, what happens to their momentum? If we followed each individual molecule of your skin that absorbed a photon, it will take up its momentum and change its original direction according to where it was when it was hit. This happens statistically to all hit molecules and finally ends up in increasing temperature even before the decay of the absorbed photons. Statistically, the photons hitting you from the sun will have the same direction and all those little momenta will add to giving you an impulse so small that you do not stumble when you walk, unlike the wind. Friction with the ground dissipates it 🙂 back into photons. That is what is meant by bulk.
    And why do you keep bringing up a laser, as synchronized polarization. No such thing in the process I’m speaking of.
    Well, you were imagining a coherent propagation of the momentum coming from the molecule that had absorbed a photon when hit by a second photon, that is induced emission and in bulk it is a laser and would have a direction. As it is not a laser situation, as you agree, the bulk behavior is random statistically and the radiation pressure ( seeing all those IR photons as a front, that is what the momentum of the individual ones is) of these infrared photons is too small to have an observable effect.

  149. anna,
    Whatever the exact amount of absorption is, I was just trying to make the point that CO2 is a very important greenhouse gas – particularly the first few ten parts per million.

  150. Steve Goddard (20:08:26)
    I know what IR radiation is. Its long and shortwave radiation – for the earth, that is incoming and outgoing radiation. c02 is 2.7 and 4.3 microns (incoming shortwave) and 15 microns (longwave – outgoing). For the climate, longwave radiation is the issue, which is 6-8% of available radiation at the 15 micron peak

  151. Phil. (21:14:09) :
    anna v (12:31:24) :
    Taken me too light but I’m not going further. Phil knows not of the tie to my comments weeks ago. No big deal. Thanks for your help.

  152. (Pamela Gray 17:58:43)
    Therefore, if short term extremes are missed, most assuredly, long term extremes will be missed. Why? They arise from one and the same chaotic process. Short term weather prediction is the same as long term weather prediction. Be good at it on the one hand, you will be good at it on the other hand. Otherwise you will suck at both. End of argument.
    This just isn’t true and anyone who has long term forecast for more decade would know this. There are bigger players in pattern trends that gobble up the smaller areal regional extremes. Which then allows the semi permanent teleconnections to end up coming back to their preferred – accepted pattern state.
    And the state of the AO this winter has not been as big a surprise as most like to suggest. Not when you considered the strong likelihood of a western based negative NAO state. Which tends to go hand and hand with a – AO.
    And for the record. The reason I mentioned more than decade above deals with how one was taught early on. The CPC did not even give out official long rage forecasts when I started doing this in 1994. They were called experimental long range forecasts.
    So you had to research and understand the atmospheric feedback waves by themself (which today is called the GWO), since models were unable to do this accurately from far out. So the early pioneers of this craft were not raised on model outlooks. Hence not model huggers….sort of like relying upon a calculator to do simple math. Bad habits equal bad forecasts.

  153. Steve Goddard (06:39:33)
    Shortwave is between 0.1 and 5 microns – infrared between 0.7 and 300 microns – so there is no exact cut off between IR and shortwave – they do overlap.

  154. Leif Svalgaard (22:05:06) :
    Phil. (21:14:09) :
    Here’s an atmospheric absorption spectrum, the large notch at ~700 wavenumber is due to CO2. …
    That doesn’t look right. You could just be imprecise in your wording. Here is what the solar spectrum looks like at the Earth’s surface: http://en.wikipedia.org/wiki/File:Solar_Spectrum.png
    The percentages of incoming radiation absorbed by various gases look like this: http://www.leif.org/research/Erl71.png

    It’s the outgoing radiation Leif not the incoming.

  155. Phil. (12:00:50) :
    Phil. (21:14:09) :
    “Here’s an atmospheric absorption spectrum, the large notch at ~700 wavenumber is due to CO2. …”
    Me: That doesn’t look right. You could just be imprecise in your wording.

    It’s the outgoing radiation Leif not the incoming.
    So the emission spectrum, not absorption spectrum …

  156. Further to Paul Vaughan (18:28:27) & Re: Leif Svalgaard (17:41:04)
    Found the paper:
    Le Mouel, J.-L.; Courtillot, V; Blanter, E.; & Shnirman, M. (2008). Evidence for a solar signature in 20th-century temperature data from the USA and Europe. C. R. Geoscience xxx.
    http://www.pensee-unique.fr/courtillot3.pdf
    I applied the simple method to monthly aa, then found the decadal-timescale rate of change of the resulting curve.
    My instinct is that something as simple as a decadal-timescale antipodal-contrast oscillation could produce a curve like the one I’ve isolated, but you’re the expert on secular variations (at the Earth end) that might survive removal efforts. Where can I find the revised aa index series you recommend? (I’m using NOAA data.) Repeating the analysis on a better series might produce valuable insight into your rationale for the adjustments.

  157. Paul Vaughan (00:24:34) :
    Le Mouel, J.-L.; Courtillot, etc
    Those folks are regarded by the community as being somewhat ‘on the fringe’. Constantly looking to correlate anything with anything, with little regard for the physics or for the data quality.
    The paper cited uses the high-frequency part of data from Eskdalemuir. That particular station has problems with its data, as explained in http://www.leif.org/research/2007JA012437.pdf (see section A4.1 para 53 ff).
    Where can I find the revised aa index series you recommend? (I’m using NOAA data.) Repeating the analysis on a better series might produce valuable insight into your rationale for the adjustments.
    My criticism of the aa-index relates to it long-term [secular] change [ibid section 5.3 para 34] and not to its variability. My proposed correction would make little or no difference for your type of analysis. To first order, the correction is simply to add 3 nT to all values before 1957. [ibid, Figure 14].

  158. Paul Vaughan (00:24:34) :
    Le Mouel, J.-L.; Courtillot, etc
    I have to confess that I have often served as peer-reviewer on their papers [and the one you cited was rejected both by me and by the other reviewer – but then the authors just go down the list of journals to one with less stringent quality control – eventually you find one that accepts you paper].

  159. Re: Leif Svalgaard (06:43:33) & Leif Svalgaard (05:49:42)
    I’m not endorsing the authors’ assumptions & conclusions. I recall the WUWT thread on the paper – I share your concerns. Their interpretations are a mess.
    Clarification: I’m referring to the statistical method outlined in the paper – that is why I am citing the paper — that was the first time I’d seen that method, but I’ve since noticed that it is standard statistical methodology.
    aa index has a very skewed distribution. Monthly averages are thus pulled high in some decades. A statistician would cringe at this. (This corrupts analyses.)
    The signals I’ve isolated are challenging to interpret because there are layers of complexity in the index. I share your concerns about the secular variation — from what I know about stats-fundamentals, antipodal-contrast drift and skewed-averaging could be part of the problem with the series, but it will take time (particularly since my attention is divided over competing obligations) to run more analyses and learn more about secular-variation-removal challenges. It won’t be anytime soon (if ever) that I reach your level of understanding of what goes into the index. For now my interpretation is: “Inconclusive, but definitely not random”. I might further speculate: “probably largely nonsolar”.
    Your various notes help rule out possibilities.

  160. Richard M (11:08:54) :
    Richard Holle (10:46:36), “I have made many posts on the principals, in action in this method over the past 10 years, most are still on line and search able by using a Google “Richard Holle aerology” query. There is way too much volume to post here.”
    Richard, maybe you could think about updating your website with the information found via Google. If you really want folks to assess it, you want to make it easy. No need to retype anything, just cut and paste from the Google results.
    My reply;
    Your suggestion taken to heart and as soon as I get my income tax refund, to have money for gas I need to travel to Phoenix I’ll have my daughter who does my web site work, update it to include most of the past text narrative with graphics, needed to present it all in one space.
    I might have to add a couple more pages, to encompass the data and details, of surges producing tornadoes, and a detailed forecast for dates of production for the rest of 2010, and another for hurricane forecast as to dates of probable occurrence, and resultant strengths.
    We already had a spate of 14 (preliminary data total) on the 20th January 2010 [today], just as the Moon crossed the equator headed North. Which brought in warmer moist air from the gulf, and a nice tight body of more polar, dry line air mass, mid afternoon to start things off.
    If you overlay the locations of the tornadoes produced today, onto my forecast map [that goes from 6am the 20th to 6am the 21st] you will see that they fall into the area, where the precipitation is lighter due to shear and speed of transit, then quit around midnight or later to produce the heaver solid, less turbulent and less severe rain that is the brighter yellows and light reds in my forecast map.
    This trend is usual for the location of the tornado production fit onto the maps I generate with my method. The concentration of tornadoes falls into the netted or filamentary looking precipitation patterns from the last three cycles where the tornadoes were also produced.
    If you were to post the long/lat locations of the tornadoes produced the last three cycles, onto the maps they would give a fair representation of this years tornado expected production, years in advance.
    I am going to try to incorporate this method into a separate page for severe weather forecast that may be capable of tornado production for the rest of this year at least and maybe for all four remaining years of the maps posted, if I get the time to do it in the next month or so.
    There is only so much I can do on a budget of less than $2,000.00 a year.

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