Premium
Forecasting climate variables using a mixed‐effect state‐space model
Author(s) -
Kokic Philip,
Crimp Steve,
Howden Mark
Publication year - 2011
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.1074
Subject(s) - downscaling , linear regression , projection (relational algebra) , multivariate statistics , climate change , climatology , linear model , state space representation , bayesian multivariate linear regression , climate model , temperate climate , econometrics , statistics , state space , mathematics , meteorology , environmental science , geography , algorithm , geology , oceanography , botany , biology
This paper demonstrates the potential advantage of using a linear, mixed‐effect state‐space model for statistical downscaling of climate variables compared to the frequently used approach of linear regression. This comparison leads to the development of a method for estimation of model parameters using the EM algorithm approach. The model is applied to the prediction of temperature and rainfall statistics at both a sub‐tropical and temperate location in Australia. The results indicate that for lead times of 1–10 years this state‐space approach is able to predict observed seasonal temperature and rainfall means with substantially greater precision than climatology, multivariate linear regression (MLR) or a standard linear state‐space (LSS) approach. The model is seen as a first step in the development of a short‐term climate change projection system that will utilise both historical climate data as well as dynamically derived mean climate change projection information obtained from global climate models (GCMs). Copyright © 2010 John Wiley & Sons, Ltd.