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# Why past ENSO cases aren’t the key to predicting the current case

A new El Nino blog posting from NOAA

http://www.climate.gov/news-features/blogs/enso/why-past-enso-cases-aren%E2%80%99t-key-predicting-current-case

In this post, I’ll discuss why the analog approach to forecasting often delivers disappointing results. Basically, it doesn’t work well because there are usually very few, if any, past cases on record that mimic the current situation sufficiently closely. The scarcity of analogs is important because dissimilarities between the past and the present, even if seemingly minor, amplify quickly so that the two cases end up going their separate ways.

What they are going by:

Van den Dool (1994)’s "Searching for analogs, how long must we wait?" calculates that we would have to wait about 10^30 years to find 2 observed atmospheric flow patterns that match to within observational error over the Northern Hemisphere. While the ocean is not as changeable as the atmospheric flow, it is clear that finding close matching analogs would also require a very long historical dataset.

This is counter to what I am finding. It is well known that periodic forcing when applied to a near-chaotic system can re-align the behavior to a more deterministic regime.

Osipov, Grigory V, Jürgen Kurths, and Changsong Zhou. Synchronization in Oscillatory Networks. Springer, 2007.

I think that the rationale for not finding the underlying ENSO pattern yet is that they may not have not looked hard enough. The 10^30 years is a red herring.

They do say that there is some hope, using alternative methods:

Although this uncertainty in outcomes is somewhat smaller than that what we would have if we selected years completely randomly from the history, it is larger than that from our most advanced dynamical and statistical models. This is one reason analog forecasting systems have been largely abandoned over the last two decades as more modern prediction systems have proven to provide better accuracy.

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Interesting! It sounds like their "analog forecasting" is a rather limited approach that looks for a historical pattern of data that matches something we see today, and then predicts that what we see tomorrow will match the historical pattern. A naive approach to this task can't succeed unless what we see today closely matches something that already happened. Presumably the "more modern prediction" systems are better at generalizing from past data, in part because more knowledge of climate science has been put into these more modern systems.

Comment Source:Interesting! It sounds like their "analog forecasting" is a rather limited approach that looks for a historical pattern of data that matches something we see today, and then predicts that what we see tomorrow will match the historical pattern. A naive approach to this task can't succeed unless what we see today closely matches something that already happened. Presumably the "more modern prediction" systems are better at generalizing from past data, in part because more knowledge of climate science has been put into these more modern systems.
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I think the more modern approaches they are referring to are full GCM (global circulation model) simulations. They say this:

"Interestingly, though, two of the models on this month’s IRI/CPC ENSO forecast plume (Fig. 3) do suggest the possibility of an El Niño both this year and a second year (2015-16). One of those models is NOAA/NCEP’s own CFSv2, and another is the Lamont-Doherty Earth Observatory (LDEO) intermediate model. Could they be on to something?"

I am not too sure if anyone here is interested in going the full climate physics simulation route. I certainly can't go that route because I don't think I will be able to first understand and then defend all the decisions that go into the construction of such models.

Comment Source:I think the more modern approaches they are referring to are full GCM (global circulation model) simulations. They say this: >"Interestingly, though, two of the models on this month’s IRI/CPC ENSO forecast plume (Fig. 3) do suggest the possibility of an El Niño both this year and a second year (2015-16). One of those models is NOAA/NCEP’s own CFSv2, and another is the Lamont-Doherty Earth Observatory (LDEO) intermediate model. Could they be on to something?" I am not too sure if anyone here is interested in going the full climate physics simulation route. I certainly can't go that route because I don't think I will be able to first understand and then defend all the decisions that go into the construction of such models.
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Yes, I think right now our "niche" is not full-fledged climate modelling or even the "models of intermediate complexity" mentioned in that quote. Our niche, if any, is something more like machine learning or the simple physical models you like.

Comment Source:Yes, I think right now our "niche" is not full-fledged climate modelling or even the "models of intermediate complexity" mentioned in that quote. Our niche, if any, is something more like machine learning or the simple physical models you like.
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"or the simple physical models you like."

Yup, that's what I like !

And the machine learning is there to nudge you in the right direction.

Comment Source:> "or the simple physical models you like." Yup, that's what I like ! And the machine learning is there to nudge you in the right direction.