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Understanding south‐east Australian rainfall projection uncertainties: the influence of patterns of projected tropical warming
Author(s) -
Timbal B.,
Fiddes S.,
Brown J. R.
Publication year - 2017
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5047
Subject(s) - climatology , range (aeronautics) , climate model , climate change , environmental science , tropics , geography , geology , oceanography , ecology , materials science , composite material , biology
South‐Eastern Australia and, in particular the state of Victoria, has experienced record deficits of rainfall over the last 20 years, in which the cool part of the year from April to October has been most affected. This situation has created difficulties for water managers, farmers and fire services, with the need to provide more certainty about future climate trends becoming clear. The latest climate projections for South‐Eastern Australia project an overall drying in the cool part of the year with little change in the rest of the year. Although this is in line with current trends, very large uncertainties are associated with these projections. In this study, this range of projections has been investigated, first by assessing how the current suite of climate models simulate the regional rainfall as well as the tropical variability, known to be a key driver of south‐eastern Australian climate. The models were found to be reasonable overall, although a number overestimate Victorian summer rainfall. Model rainfall projections are found to be related to the models' projected patterns of tropical warming, where 60% of the range in cool season rainfall projections can be explained by the range of pattern of tropical changes. In addition, the projected drying tends to be more intense in the models best able to simulate summer rainfall, thus suggesting that the upper end of the uncertainty range is less likely to be realized as it may reflect inherent model biases, rather than physical changes.