Models for stochastic climate prediction

Models for stochastic climate prediction.

There has been a recent burst of activity in the atmosphere/ocean sciences community in utilizing stable linear Langevin stochastic models for the unresolved degree of freedom in stochastic climate prediction. Here several idealized models for stochastic climate modeling are introduced and analyzed through unambiguous mathematical theory. This analysis demonstrates the potential need for more s...

Stochastic Climate Models

The field of climate physics is devoted to the study of the climate system as a dynamical system, that is, to the study of its spatial and temporal variability. By climate system, we refer to that tightly-coupled collection of subsystems composed of the atmosphere, the ocean, the cryosphere (icy regions), the lithosphere (land), and the biosphere (life). This system is not closed: external forc...

Normal forms for reduced stochastic climate models.

The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate ...

Mathematical Models for Prediction of Climate

Stochastic Relational Models for Discriminative Link Prediction

We introduce a Gaussian process (GP) framework, stochastic relational models (SRM), for learning social, physical, and other relational phenomena where interactions between entities are observed. The key idea is to model the stochastic structure of entity relationships (i.e., links) via a tensor interaction of multiple GPs, each defined on one type of entities. These models in fact define a set...