Stochastic climate theory and modeling

@article{Franzke2014StochasticCT,
  title={Stochastic climate theory and modeling},
  author={Christian L. E. Franzke and Terence J. O'Kane and Judith Berner and Paul D. Williams and Valerio Lucarini},
  journal={Wiley Interdisciplinary Reviews: Climate Change},
  year={2014},
  volume={6}
}
Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid‐scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all… 

Figures from this paper

Advances and challenges in climate modeling

In spite of the chaotic nature of the atmosphere and involvement of complex nonlinear dynamics, forecasting climate fluctuations over different timescales is feasible due to the interaction between

Stochastic weather and climate models

  • T. Palmer
  • Environmental Science
    Nature Reviews Physics
  • 2019
The ways in which introducing stochasticity into the parameterized representations of subgrid processes in comprehensive weather and climate models has improved the skill of forecasts and has reduced systematic model error are surveyed.

Stochastic Parameterization: Towards a new view of Weather and Climate Models

AbstractThe last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic

Stochastic Climate Theory

This chapter provides a conceptual framework for stochastic modelling of deterministic dynamical systems based on the Mori-Zwanzig formalism and expresses standard model reduction methods such as averaging and homogenization which eliminate the memory term.

Global Climate Models Exploring the Reliability, Consistency, Limitations, Deficiencies, Uncertainties, and Methods of Global Climate Models in a Nonlinear and Chaotic Climate System

The current climate change dialogue revolves around many different climate elements and their processes but central to the anthropogenic warming hypothesis (some call it a theory but I’m not going to

Predicting Climate Change Using Response Theory: Global Averages and Spatial Patterns

The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source

UvA-DARE (Digital Academic Repository) Stochastic climate theory

This chapter provides a conceptual framework for stochastic modelling of deterministic dynamical systems based on the Mori-Zwanzig formalism and expresses standard model reduction methods such as averaging and homogenization which eliminate the memory term.

Spatial Covariance Modeling for Stochastic Subgrid‐Scale Parameterizations Using Dynamic Mode Decomposition

This paper presents a meta-modelling procedure that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of modeling subgrid scale processes through stochastic parameterizations.

Data Assimilation in a Multi-Scale Model

This work examines the role of unresolved scales and model error in data assimilation using the Ensemble Kalman filter with the two-level Lorenz-96 model as a conceptual prototype model of the multi-scale climate system and uses stochastic parameterization schemes to mitigate the model errors from the unresolved scales.

Using regional scaling for temperature forecasts with the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

Over time scales between 10 days and 10–20 years—the macroweather regime—atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that
...

References

SHOWING 1-10 OF 188 REFERENCES

A nonlinear dynamical perspective on model error : A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models

Conventional parametrization schemes in weather and climate prediction models describe the effects of subgrid‐scale processes by deterministic bulk formulae which depend on local resolved‐scale

Simulating regime structures in weather and climate prediction models

It is shown that a global atmospheric model with horizontal resolution typical of that used in operational numerical weather prediction is able to simulate non‐gaussian probability distributions

Models for stochastic climate prediction.

The strategy for stochastic climate modeling that emerges from this analysis is illustrated on an idealized example involving truncated barotropic flow on a beta-plane with topography and a mean flow.

Stochastic climate models Part I. Theory

A stochastic model of climate variability is considered in which slow changes of climate are explained as the integral response to continuous random excitation by short period “weather” disturbances.

Introduction. Stochastic physics and climate modelling

  • T. PalmerP. Williams
  • Environmental Science
    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
  • 2008
The application of stochastic physics to climate modelling is a rapidly advancing, important and innovative topic and the latest research findings are gathered together in the Theme Issue for which this paper serves as the introduction.

Stochastic Averaging of Idealized Climate Models

AbstractVariability in the climate system involves interactions across a broad range of scales in space and time. While models of slow “climate” variability may not explicitly account for fast

Stochastic Parameterization Schemes for Use in Realistic Climate Models

A systematic method—“Hasselmann’s method”—of stochastic parameterization is developed through the direct application of rigorously justified limit theorems that predict the effective slow dynamics in systems with coupled slow and fast variables.

Stochastic models for convective momentum transport

  • A. MajdaS. Stechmann
  • Environmental Science, Physics
    Proceedings of the National Academy of Sciences
  • 2008
A combination of mathematical and physical reasoning is utilized to build simple stochastic models that capture the significant intermittent upscale transports of CMT on the large scales due to organized unresolved convection from squall lines.

Stochastic Models of Climate Extremes: Theory and Observations

This chapter discusses the theoretical framework, observational evidence, and related developments in stochastic modeling of weather and climate extremes, and defines an extreme event in terms of the non-Gaussian tail of the data’s probability density function (PDF).

Uncertainty in predictions of the climate response to rising levels of greenhouse gases

Results from the ‘climateprediction.net’ experiment are presented, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details, finding model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11’K.
...