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| Titel |
Randomness of annual precipitation and climate model projections |
| VerfasserIn |
Fubao Sun, Michael Roderick, Graham Farquhar |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250073765
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| Zusammenfassung |
| Precipitation (P), the driver of the entire hydrologic cycle, is characterised by high year-to-year variability for a given region. Because of that, the trend in P generally depends on which period one chooses. Superimposed on that are different expectations about the future possible change of regional water availability. For example, in a recent case study over the Murray-Darling Basin (MDB) in Australia, we noted that the projections of ∆P (2070-2099 less 1970-1999) has a large range (~ ±150 mm a-1 century-1) for an ensemble of 39 IPCC AR4 climate model runs using the A1B emissions scenario. When averaged across the multi-run and multi-model ensemble, the projected change (4.9 and -8.1 mm a-1 century-1) is near zero, against a background climatological P of ~500 mm a-1.
In this presentation, we describe a new approach to evaluating projections of ∆P in climate models. This approach is based on our recent finding that long-term annual P time series in both observations and each model run over the MDB were indistinguishable from that generated by a purely random process. By plotting ∆ P versus the variance of the time series, we could identify models with projections for ∆ P that were beyond the bounds expected from purely random variations. For the MDB, we anticipate that a purely random process could lead to differences of ± 57 mm a-1 (95% confidence) between successive 30-year periods. This is equivalent to ±11% of the climatological P and translates into variations in runoff of around ±29%.
This sets a baseline for gauging modelled and/or observed changes. |
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