How good have models been at predicting ENSO in the 21st century?
In the, roughly, 250 years of the ENSO Blog and
our 3.4 million posts, we’ve discussed ad nauseum how complicated El
Niño and La Niña are, and how difficult it is to forecast all of the ENSO
nuances. Heck, I even wrote a three-part series 75 years ago
that evaluated all of our seasonal forecasts (ok, it was 2014-2015 but it still
feels that long ago). In a new paper, Azhar Ehsan, friend of the blog
and a member of NOAA’s ENSO forecast team, and colleagues analyzed over 20
years’ worth of climate model forecasts of ENSO and found some interesting
results.
Why is this paper unique? Well, most seasonal forecasting
evaluations focus on model hindcasts, which are forecasts run using past
observational data as the start (or initial) condition. For example, if the
models are provided the initial conditions on July 1st, 1983, what
forecast would it have made? The nice thing about running on past data is that
you already know what occurred and can immediately see how well the forecast
did. The downside is that sometimes the model development itself can be
influenced by this past data. The purest test for models is how well they do in
the future, on data that the model has never ever seen. This type of evaluation
on “real-time forecasts” is much rarer, and is exactly what Azhar and his
co-authors did.
ENSO Terms and conditions
La Niña and El Niño make up the El Niño/Southern
Oscillation, or ENSO. La Niña is characterized by cooler-than-average sea
surface temperatures across the central and eastern tropical Pacific Ocean. El
Niño is the opposite phase, with warmer-than-average water present across the
tropical Pacific Ocean. These changes in sea surface temperature across the
Pacific jumble up the atmosphere above which can lead to global impacts on
climate patterns. Seems pretty important, right? And unlike most other climate
phenomena, the state of ENSO can be forecast months in advance, giving
communities time to prepare.
Tell me about that sweet, sweet data
Let me paint you a picture. It’s February 2002. Crossroads starring
Britney Spears has just come out, while Ja Rule and Nickelback are burning up
the music charts. At Columbia University’s International Research
Institute for Climate and Society (IRI), a plucky group of scientists has
begun issuing ENSO forecasts. That effort has now become the world’s longest
archive of real-time monthly ENSO forecasts from modeling groups across the
globe. The list of forecast contributors has continued to grow since 2002, and
the tally of the treasure trove of climate model data currently stands at 28
different climate models.
Why so many? No single model forecast is ever going to be
exactly correct all the time. To get a sense of the range of potential
outcomes, it’s important to not only have a bunch of forecasts using the same
climate model due to the chaos of climate but also forecasts from a
bunch of different models due to the idiosyncrasies of each individual (we call
these combined bunches multi-model ensembles). A well-constructed
forecast ensemble won’t tell you precisely what outcome to expect, but it will
tell you how much the odds are tilted toward one outcome or the other
(i.e., probabilities).
The model forecasts can be split into two types, dynamical
and statistical. Dynamical models refer to models which take observational data
to simulate earth’s future climate by using equations that represent our best
understanding of the laws of physics (e.g., like the computer climate models
that make up the North American Multi-Model Ensemble, or NMME, that are
frequently featured in this blog). Statistical models, on the other hand, use
the historical relationships between ENSO and other climate variables from the
observational record and then use these relationships to make predictions for
the current situation.
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