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Does Soil Moisture Influence Climate Variability and Predictability over Australia?

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  • 1 Bureau of Meteorology Research Centre, Bureau of Meteorology, Melbourne, Victoria, Australia
  • | 2 National Climate Centre, Bureau of Meteorology, Melbourne, Victoria, Australia
  • | 3 Bureau of Meteorology Research Centre, Bureau of Meteorology, Melbourne, Victoria, Australia
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Abstract

Interannual variations of Australian climate are strongly linked to the El Niño–Southern Oscillation (ENSO) phenomenon. However, the impact of other mechanisms on prediction, such as atmosphere–land surface interactions, has been less frequently investigated. Here, the impact of soil moisture variability on interannual climate variability and predictability is examined using the Bureau of Meteorology Research Centre atmospheric general circulation model. Two sets of experiments are run, each with five different initial conditions. In the first set of experiments, soil moisture is free to vary in response to atmospheric forcing in each experiment according to a set of simple prognostic equations. A potential predictability index is computed as the ratio of the model's internal variability to its external forced variability. This estimates the level of predictability obtained assuming perfect knowledge of future ocean surface temperatures. A second set of five experiments with prescribed soil moisture is performed. A comparison between these two sets of experiments reveals that fluctuations of soil moisture increase the persistence, the variance, and the potential predictability of surface temperature and rainfall. The interrelationship between these two variables is also strongly dependent upon the soil water content. Results are particularly marked over Australia in this model. A novel feature of this study is the focus on the effectiveness of ENSO-based statistical seasonal forecasting over Australia. Forecasting skill is shown to be crucially dependent upon soil moisture variability over the continent. In fact, surface temperature forecasts in this manner are not possible without soil moisture variability. This result suggests that a better representation of land–surface interaction has the potential to increase the skill of seasonal prediction schemes.

Current affiliation: Météo-France, Paris, France

Corresponding author address: Dr. B. Timbal, Bureau of Meteorology Research Centre, Bureau of Meteorology, GPO Box 1289K, Melbourne, Vic 3001 Australia. Email: b.timbal@bom.gov.au

Abstract

Interannual variations of Australian climate are strongly linked to the El Niño–Southern Oscillation (ENSO) phenomenon. However, the impact of other mechanisms on prediction, such as atmosphere–land surface interactions, has been less frequently investigated. Here, the impact of soil moisture variability on interannual climate variability and predictability is examined using the Bureau of Meteorology Research Centre atmospheric general circulation model. Two sets of experiments are run, each with five different initial conditions. In the first set of experiments, soil moisture is free to vary in response to atmospheric forcing in each experiment according to a set of simple prognostic equations. A potential predictability index is computed as the ratio of the model's internal variability to its external forced variability. This estimates the level of predictability obtained assuming perfect knowledge of future ocean surface temperatures. A second set of five experiments with prescribed soil moisture is performed. A comparison between these two sets of experiments reveals that fluctuations of soil moisture increase the persistence, the variance, and the potential predictability of surface temperature and rainfall. The interrelationship between these two variables is also strongly dependent upon the soil water content. Results are particularly marked over Australia in this model. A novel feature of this study is the focus on the effectiveness of ENSO-based statistical seasonal forecasting over Australia. Forecasting skill is shown to be crucially dependent upon soil moisture variability over the continent. In fact, surface temperature forecasts in this manner are not possible without soil moisture variability. This result suggests that a better representation of land–surface interaction has the potential to increase the skill of seasonal prediction schemes.

Current affiliation: Météo-France, Paris, France

Corresponding author address: Dr. B. Timbal, Bureau of Meteorology Research Centre, Bureau of Meteorology, GPO Box 1289K, Melbourne, Vic 3001 Australia. Email: b.timbal@bom.gov.au

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