Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/72517
Type: Conference paper
Title: Estimating the seasonal predictability of global precipitation: an empirical approach
Author: Westra, S.
Sharma, A.
Citation: Interfacing modelling and simulation with mathematical and computational sciences: 18th IMACS World Congress, MODSIM09, Cairns, Australia 13-17 July 2009 : proceedings /​ R.S. Anderssen, R.D. Braddock and L.T.H. Newhampp (eds.): pp. 2749-2755
Publisher: The Australian National University
Publisher Place: Canberra ACT
Issue Date: 2009
ISBN: 9780975840078
Conference Name: International Congress on Modelling and Simulation (18th : 2009 : Cairns, Queensland)
Statement of
Responsibility: 
S. Westra and A. Sharma
Abstract: The asymptotic predictability of global land-surface precipitation is estimated empirically at the seasonal time scale at lead times from zero to 12 months. Predictability is defined as the maximum achievable predictive skill for a given model assuming all the relevant predictors are included and that an infinitely long training sample is available for parameter estimation, and represents an approximate upper bound to the predictive skill of statistical, and possibly dynamical, seasonal forecasting approaches. To estimate predictability, a simple linear regression model is formulated based on the assumption that land surface precipitation variability can be divided into a component forced by low-frequency variability in external boundary conditions which potentially can be predicted one or more seasons into the future, and a ‘weather noise’ component which originates from nonlinear dynamical instabilities in the atmosphere and which is not predictable beyond about 10 days. The external boundary condition is represented by an orthogonal (principal component analysis) transformation of the global sea surface temperature anomaly (SSTA) field, as this field constitutes the dominant driver of global precipitation variability.
Keywords: Seasonal forecasting; global precipitation; sea surface temperature; ENSO; predictability
Rights: © Modelling and Simulation Society of Australia and New Zealand
RMID: 0020114610
Description (link): http://www.mssanz.org.au/modsim09/
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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