El Niño Wears Five Masks

Original: "Contributions of El Niño Southern Oscillation (ENSO) Diversity to Low-Frequency Changes in ENSO Variance"
arXiv:2307.11552 · 2023-07-21 · CC BY 4.0 · 1 min
Scientists have identified five types of El Niño and La Niña and figured out why the climate has become more variable since 1970.

The Pacific Ocean is like a big pot of water. Sometimes warm water gathers near the coasts of South America, and that's called El Niño. Previously, it was thought that El Niño comes in only two flavors: a strong one near the coast and a weak one in the middle of the ocean. But new research shows that El Niño actually wears five masks, that is, five distinct types.

Scientists took ocean temperature data spanning 122 years and applied a clever method that doesn't force each event to be just one type. For instance, the 2015 El Niño is 80% extreme, 15% strong, and 5% weak. It's like looking at someone wearing a half-mask: you can't say exactly who they are, but you can estimate the odds. It turns out that since 1970, extreme El Niño and strong La Niña (the opposite phenomenon, when waters are cooler than usual) have become more frequent. As a result, the climate has become more jumpy.

The CESM2 climate model, used for forecasts, cannot currently simulate the extreme type of El Niño. This means it may underestimate future droughts and floods.

Now that we know all five masks, we can test how well models predict the weather. If a model misses the extreme type, it makes mistakes. And that matters for everyone living in regions affected by El Niño: it brings drought to Australia, rain to Peru, and warm winters to Siberia.

The 2015-16 El Niño, one of the strongest on record, did not trigger the expected California drought nor lead to a strong La Niña, hinting at its mixed nature. This underscores the limitations of binary classifications.

p(z) = \sum_{k=1}^{K} \pi_k \mathcal{N}(z | \mu_k, \Sigma_k)
The data distribution in principal component space is described by a mixture of K Gaussian bells, each with its own mean and covariance; the weights π_k are the probabilities of belonging to a cluster.
y(t) = \sum_{k} p(c_k | z(t)) \cdot y(t) := \sum_{k} y_k(t)
Any quantity (e.g., the Niño3.4 index) can be expressed as the sum of its parts, weighted by the probability that each time point belongs to each category.
\langle y^2 \rangle = \sum_{k} \langle y_k^2 \rangle + \sum_{l \neq m} \langle y_l, y_m \rangle
Total variance is the sum of each category's variance plus the sum of covariances between different categories, allowing their relative contributions to be assessed.
Concepts
ENSO El Niño index Southern Oscillation El Niño-Southern Oscillation Multivariate ENSO Index La Niña spring predictability barrier ERSST Jin-Timmermann model recharge oscillator model Westerly wind burst NOAA CPC TAO/TRITON moorings equatorial wave propagation natural variability relative ONI North Equatorial Countercurrent Warm water volume interannual variability mixed mode oscillations