Calibration, bridging and merging to improve GCM seasonal temperature forecasts in Australia

Schepen, Andrew, Wang , Q.J., and Everingham, Yvette (2016) Calibration, bridging and merging to improve GCM seasonal temperature forecasts in Australia. Monthly Weather Review, 144. pp. 2421-2441.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: http://dx.doi.org/10.1175/MWR-D-15-0384....
 
28
2


Abstract

There are a number of challenges that must be overcome if GCM forecasts are to be widely adopted in climate-sensitive industries such as agriculture and water management. GCM outputs are frequently biased relative to observations and their ensembles are unreliable in conveying uncertainty through appropriate spread. The calibration, bridging, and merging (CBaM) method has been shown to be an effective tool for postprocessing GCM rainfall forecasts to improve ensemble forecast attributes. In this study, CBaM is modified and extended to postprocess seasonal minimum and maximum temperature forecasts from the POAMA GCM in Australia. Calibration is postprocessing GCM forecasts using a statistical model. Bridging is producing additional forecasts using statistical models that have other GCM output variables (e.g., SST) as predictors. It is demonstrated that merging calibration and bridging forecasts through CBaM effectively improves the skill of POAMA seasonal minimum and maximum temperature forecasts for Australia. It is demonstrated that CBaM produces bias-corrected forecasts that are reliable in ensemble spread and reduces forecasts to climatology when there is no evidence of forecasting skill. This work will help enable the adoption of GCM forecasts by climate-sensitive industries for quantitative modeling and decision-making.

Item ID: 46709
Item Type: Article (Research - C1)
ISSN: 1520-0493
Keywords: physical meteorology and climatology; surface temperature; forecasting; seasonal forecasting; statistical forecasting; models and modeling; general circulation models
Date Deposited: 20 Dec 2016 22:11
FoR Codes: 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics @ 100%
SEO Codes: 96 ENVIRONMENT > 9609 Land and Water Management > 960913 Water Allocation and Quantification @ 50%
82 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 8203 Industrial Crops > 820304 Sugar @ 50%
Downloads: Total: 2
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page