Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extended-range statistical ENSO prediction through operator-theoretic
  techniques for nonlinear dynamics   Forecasting the El Nino-Southern Oscillation (ENSO) has been a subject of
vigorous research due to the important role of the phenomenon in climate
dynamics and its worldwide socioeconomic impacts. Over the past decades,
numerous models for ENSO prediction have been developed, among which
statistical models approximating ENSO evolution by linear dynamics have
received significant attention owing to their simplicity and comparable
forecast skill to first-principles models at short lead times. Yet, due to
highly nonlinear and chaotic dynamics (particularly during ENSO initiation),
such models have limited skill for longer-term forecasts beyond half a year. To
resolve this limitation, here we employ a new nonparametric statistical
approach based on analog forecasting, called kernel analog forecasting (KAF),
which avoids assumptions on the underlying dynamics through the use of for
nonlinear kernel methods for machine learning and dimension reduction of
high-dimensional datasets. Through a rigorous connection with Koopman operator
theory for dynamical systems, KAF yields statistically optimal predictions of
future ENSO states as conditional expectations, given noisy and potentially
incomplete data at forecast initialization. Here, using industrial-era
Indo-Pacific sea surface temperature (SST) as training data, the method is
shown to successfully predict the Nino 3.4 index in a 1998-2017 verification
period out to a 10-month lead, which corresponds to an increase of 3-7 months
over a benchmark linear inverse model (LIM), while significantly improving upon
the ENSO predictability "spring barrier". An analysis of a 1300-yr control
integration of a comprehensive climate model (CCSM4) further demonstrates that
the enhanced predictability afforded by KAF holds over potentially much longer
leads, extending to 24 months versus 18 months in the benchmark LIM.

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The categories of this paper are: solv-int
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph