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Project details
| Title: | Learning to us GCMs and hybrid methods for seasonal forecasts |
| Id: | 1950 |
| Acronym: | Learning to use GCMs & hybrid methods |
| Investigator(s): | Peter McIntosh
CSIRO Oceans & Atmosphere - Hobart [details] |
| Description: | |
| Description (full): | The main goal is to learn how skilful Global Circulation Models (GCMs) are at making seasonal predictions, particularly targeted at the Upper Burdekin region of NE Queensland (which is a key region of WfHC). Where the models are not skilful enough on their own, investigate hybrid stats/model techniques for improving skill. The basis of most current seasonal prediction schemes is the fact that El Nino Southern Oscillation (ENSO) events represent the greatest source of interannual climate variability beyond the seasonal cycle. There are numerous schemes of varying complexity providing predictions of ENSO events at long lead times (i.e. 3 to 12 months ahead in time). There is some debate about whether the relatively complex dynamical schemes (usually coupled ocean-atmosphere models) provide more skill than do relatively simple statistical schemes. Evaluations of skill have tended to focus on the ability of schemes to correctly predict sea surface temperature indices (such as NINO3 or NINO34). There have been few studies dealing with methods for converting seasonal predictions into useful information for the benefit of end-users. In this study we consider the skill of dynamical seasonal prediction models and their potential application to Australian end-users. We consider which model outputs are most useful for the Upper Burdekin region where agriculture and water resource management decisions could be assisted. |
| Years: | 2005 to 2007 |
| Hierachy: | Australian Climate Variability and Change |
There are no surveys directly linked to this project.