In this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a
variable-resolution global atmospheric model, driven by two distinct sea
surface temperature (SST) datasets: the 0.25° Optimum Interpolation Sea
Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended
Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is
assessed using a benchmarking framework, revealing good agreement
between both simulations and the climatological rainfall spatial
pattern, seasonality, and annual trends obtained from the Australian
Gridded Climate Data (AGCD). Notably, wet biases are identified in both
simulations, with CCAM_OISST displaying a more pronounced bias.
Furthermore, we have examined CCAM’s ability to capture El Niño-Southern
Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with
rainfall during Austral spring (SON) utilizing a novel hit rate metric.
Results indicate that only CCAM_OISST successfully replicates observed
SON ENSO- and IOD-rainfall correlations, achieving hit rates of 86.6%
and 87.5%, respectively, compared to 52.7% and 41.8% for
CCAM_ERSST5. Large SST differences are found surrounding the Australian
coastline between OISST and ERSST5 (termed the “Coastal Effect”).
Differences can be induced by the spatial interpolation error due to the
discrepancy between model and driving SST. An additional CCAM
experiment, employing OISST with SST masked by ERSST5 in 5° proximity to
the Australian continent, underscores the “Coastal Effect” has a
significant impact on IOD-Australian rainfall simulations. In contrast,
its influence on ENSO-Australian rainfall is limited. Therefore,
simulations of IOD-Australian rainfall teleconnection are sensitive to
local SST representation along coastlines, probably dependent on the
spatial resolution of driving SST.