Does soil moisture influence climate variability and predictability over Australia?

Timbal, B. and Power, S. and Colman, R. and Viviand, J. and Lirola, S. (2002) Does soil moisture influence climate variability and predictability over Australia? Journal of Climate, 15 (10). pp. 1230-1238. ISSN 0894-8755

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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.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 03 Nov 2021 05:31
Last Modified: 03 Nov 2021 05:31
Uncontrolled Keywords: strength of building materials; mechanical properties; atmospheric properties; precipitation; soils and soil mechanics; regional weather patterns; atmosphere-land surface interactions
Fields of Research (2008): 04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
Fields of Research (2020): 37 EARTH SCIENCES > 3702 Climate change science > 370202 Climatology
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9603 Climate and Climate Change > 960307 Effects of Climate Change and Variability on Australia (excl. Social Impacts)
Socio-Economic Objectives (2020): 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1905 Understanding climate change > 190502 Climate variability (excl. social impacts)
Identification Number or DOI:<1230:DSMICV>2.0.CO;2

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