We use a sophisticated coupled land–atmosphere modelling system for a Southern Hemisphere sub–domain centred over South East Australia to evaluate differences in simulation skill from two different land surface initialisation approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, the second uses land surface states obtained from reanalyses. We find that land surface initialisation using prior offline simulations contribute to relative gains in sub–seasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10–20 % within the first 30 days of the forecast can be attributed to the land surface initialisation method using offline states. For precipitation there is no distinct preference
for the land surface initialisation method with limited gains in forecast skill irrespective of the lead time. We evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill exceeding 20 % in some regions. These results were statistically significant at the 98 % confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialise the land surface contributed to relative gains in forecast skill reaching 40 % in parts of the domain that were statistically significant at the 98 % confidence level. The contrasting impact of land surface initialisation method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms.
Therefore, land surface initialisation from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over South East Australia.
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Last updated 31st January 2013