Forecasting The Arctic Oscillation

BRILLIANT POST ANTHONY.
Damn irrefutable.
Chris
Norfolk, VA, USA

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?

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.

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.

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.

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

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

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

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?

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.

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.

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.

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

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.

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

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.

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

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.

“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.

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??

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.

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.

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.

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

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.

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.

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.

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.

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

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.

“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.

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.

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.”

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?

Lief,
Fair enough. When will cycle 24 peak, and what will that peak be?

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.

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!

Is yesterday’s weather forecast today’s climate?

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.

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.

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

“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.

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.

Stormy weather for the Met Office:
“BUFFETED by complaints about its inaccurate weather forecasts, the Met Office now faces being dumped by the BBC after almost 90 years.
The Met Office contract with the BBC expires in April and the broadcaster has begun talks with Metra, the national forecaster for New Zealand, as a possible alternative. ”
http://entertainment.timesonline.co.uk/tol/arts_and_entertainment/tv_and_radio/article6991064.ece

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.

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.

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.

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.

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

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.

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.

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.

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

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

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.

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.

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.

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.)

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!

It is albedo that is supposed to come out of the models and is not parametrized.

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.

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.

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.

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.

Anna,
From the graph you linked, it looks to me like at 20% of absorbed IR is reasonable for CO2.
http://wattsupwiththat.files.wordpress.com/2008/06/atmospheric_spectral_absorption.png?w=509&h=411
Note that a lot of IR frequencies are not absorbed by any atmospheric gases, but as far as absorbed IR goes, CO2 takes up a fairly large percentage.

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

P Wilson,
IR stands for infrared, not “incoming radiation.”

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% ?

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.

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

(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.

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.

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.