An applied mathematics perspective on stochastic modelling for climate

Area of Science:

• Climate Science
• Applied Mathematics
• Atmospheric Physics

Background:

• Stochastic modeling is crucial for understanding climate variability and phenomena not resolved by deterministic models.
• Mid-latitude low-frequency variability and tropical convection exhibit complex, intermittent features requiring advanced modeling approaches.

Purpose of the Study:

• To review systematic strategies from applied mathematics for stochastic climate modeling.
• To develop and test a new low-dimensional stochastic model for mid-latitude variability.
• To illustrate a design principle for stochastic column models capturing tropical convection intermittency.

Main Methods:

• Review of applied mathematics strategies for stochastic climate modeling.
• Development of a low-dimensional stochastic model mimicking atmospheric general circulation models.
• Application of a stochastic column modeling design principle to idealized deep tropical convection.

Main Results:

• A new stochastic model was developed to test stochastic mode reduction procedures.
• The study demonstrates the physical mechanisms behind multiplicative noise in teleconnection patterns.
• Stochastic column modeling effectively captures irregular and intermittent features in tropical convection, slowing waves and increasing fluctuations.

Conclusions:

• Systematic application of mathematical strategies enhances stochastic climate modeling capabilities.
• The developed models provide insights into mid-latitude variability and tropical convection dynamics.
• Stochastic modeling is essential for accurately representing complex climate phenomena.