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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...

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El Niño physics and El Niño predictability.

Allan J Clarke¹

¹Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida 32306-4320;

Annual Review of Marine Science

|January 11, 2014

Summary

El Niño and the Southern Oscillation (ENSO) drives Earth's climate variability. Understanding ENSO's seasonal cycle and predicting its transitions are key for climate forecasting.

Area of Science:

Climate Science
Oceanography
Atmospheric Science

Background:

Year-to-year climate variability is largely influenced by El Niño and the Southern Oscillation (ENSO).
ENSO originates from ocean-atmosphere instability in the western equatorial Pacific warm pool.
The phenomenon exhibits phase locking to the seasonal cycle.

Purpose of the Study:

To elucidate the physics governing ENSO variability.
To explain the seasonal cycle and phase locking of ENSO events.
To discuss ENSO prediction challenges and precursors for transitions.

Main Methods:

Review of the physical mechanisms driving ENSO.
Analysis of ENSO's seasonal cycle and growth patterns.
Discussion of dynamical and statistical ENSO forecasting models.

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Main Results:

ENSO develops between April/May and November, with peak influence extending into the next year.
Predicting ENSO is straightforward from July to February.
Forecasting is more challenging during the April/May transition to a new ENSO event.

Conclusions:

The seasonal cycle significantly impacts ENSO predictability.
Understanding ENSO precursors is crucial for improving climate forecasts.
Dynamical and statistical models offer valuable tools for ENSO prediction.