2 results
Forecasting commodity returns by exploiting climate model forecasts of the El Niño Southern Oscillation
- Vassili Kitsios, Lurion De Mello, Richard Matear
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- Journal:
- Environmental Data Science / Volume 1 / 2022
- Published online by Cambridge University Press:
- 13 April 2022, e7
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- Article
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The physical and socioeconomic environments in which we live are intrinsically linked over a wide range of time and space scales. On monthly intervals, the price of many commodities produced predominantly in tropical regions covary with the dominant mode of climate variability in this region, namely the El Niño Southern Oscillation (ENSO). Here, for the spot prices returns of vegetable oils produced in Asia, we develop autoregressive (AR) models with exogenous ENSO indices, where for the first time these indices are generated by a purpose-built state-of-the-art general circulation model (GCM) climate forecasting system. The GCM is a numerical simulation which couples together the atmosphere, ocean, and sea ice, with the initial conditions tailored to maximize the climate forecast skill at multiyear timescales in the tropics. To serve as additional benchmarks, we also test commodity forecasts using: (a) no ENSO information as a lower bound; (b) perfect future ENSO knowledge as a reference upper bound; and (c) an econometric AR model of ENSO. All models adopting ENSO factors outperform those that do not, indicating the value here of incorporating climate knowledge into investment decision-making. Commodity forecasts adopting perfect ENSO factors have statistically significant skill out to 2 years. When adopting the GCM-ENSO factors, there is predictive power of the commodity beyond 1 year in the best case, which consistently outperforms commodity forecasts adopting an AR econometric model of ENSO.
9 - Stochastic Subgrid Modelling for Geophysical and Three-Dimensional Turbulence
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- By Jorgen S. Frederiksen, CSIRO Oceans and Atmosphere, Vassili Kitsios, CSIRO Oceans and Atmosphere, Terence J. O'kane, CSIRO Oceans and Atmosphere, Meelis J. Zidikheri, Australian Bureau of Meteorology
- Edited by Christian L. E. Franzke, Universität Hamburg, Terence J. O'Kane
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- Book:
- Nonlinear and Stochastic Climate Dynamics
- Published online:
- 26 January 2017
- Print publication:
- 19 January 2017, pp 241-275
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Summary
Abstract
Stochastic modelling and closure-based approaches to the representation of the effects of subgrid turbulence in large eddy simulations (LES) of turbulent fluids are reviewed. The focus is on methods in which the subgrid model is calculated self-consistently from higher resolution benchmark simulations or closures. Eddy viscosity and stochastic backscatter parametrisations are presented for two-dimensional turbulence of barotropic flows, for baroclinic quasi-geostrophic turbulence of the atmosphere and oceans, for atmospheric flows in multi-level primitive equation models and for three-dimensional boundary layer turbulence in channels. The performance of LES with these parametrisations is examined. Subgrid scale parametrisations for the complex problem of inhomogeneous flows over topography are also analysed.
Introduction
Recent progress in the development of parametrisations of subgrid scale turbulence for large eddy simulations (LES) of geophysical and three-dimensional flows is reviewed. The classes of subgrid interactions with the resolved scales for turbulent flows over topography were detailed by Frederiksen (1999, 2012a,b) and include eddy-eddy, eddy-meanfield, meanfield-meanfield, eddy-topographic and meanfield-topographic. Our main focus is on approaches where the subgrid terms are determined self-consistently from high-resolution benchmark closures or the statistics of direct numerical simulations (DNS). Unlike traditional methods of subgrid scale parametrisation no tuning parameters are employed in the LES. A brief historical introduction to the different approaches for developing subgrid models is also presented.
Deterministic Parametrisations for Atmospheric Flows
It has been clear since the very first atmospheric climate simulations that the accuracy of the large-scale flows and energy spectra is dependent upon the modelling of the subgrid processes (Smagorinsky, 1963). In its most basic form, subgrid modelling prescribes the relationship between the resolved field and the subgrid tendency, which is the contribution of the subgrid interactions to the evolution of the resolved field. One of the most widely adopted and celebrated models is the empirical Smagorinsky model (Smagorinsky, 1963), in which the subgrid stress tensor is related to the local strain rate (symmetric part of the velocity gradient tensor) via a single specified parameter. This model is more appropriate for three-dimensional turbulence than for quasi-geostrophic (QG) turbulence, where the subgrid dissipation operator typically takes the form of the Laplacian raised to a specified power.