This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A high-quality monthly pan evaporation dataset of 60 stations has been developed for monitoring long-term pan evaporation trends over Australia. The quality control process involved examination of historical station metadata together with an objective test comparing candidate series with neighboring stations. Identified points of discontinuity were located, including installations of bird guards, site relocations and changes in exposure. Appropriate inhomogeneity adjustments have been applied using established methods to produce the first homogeneous pan evaporation dataset for Australia. Analysis of these data reveals that Australian annual mean pan-evaporation shows large interannual variability with no trend over the 1970-2005 period. Previous studies using unadjusted data have shown a decline in pan evaporation, highlighting the importance of checking data for homogeneity before drawing conclusions about long-term trends. A strong inverse correlation is evident between all-Australian means of pan evaporation and rainfall, while a moderate positive correlation is found between pan evaporation and mean temperature. The positive correlations between mean temperature and pan evaporation that exist on the interannual time scales are not reflected in the long-term trends, highlighting that the mechanisms that are responsible for variations on the short and longer time scales are different. This result cautions against the expectation that large changes in potential evaporation are a natural consequence of global warming.

A high-quality monthly total cloud amount dataset for 165 stations has been developed for monitoring and assessing long-term trends in cloud cover over Australia. The dataset is based on visual 9 a.m. and 3 p.m. observations of total cloud amount, with most records starting around 1957. The quality control process involved examination of historical station metadata, together with an objective statistical test comparing candidate and reference cloud series. Individual cloud series were also compared against rainfall and diurnal temperature range series from the same site, and individual cloud series from neighboring sites. Adjustments for inhomogeneities caused by relocations and changes in observers were applied, as well as adjustments for biases caused by the shift to daylight saving time in the summer months. Analysis of these data reveals that the Australian mean annual total cloud amount is characterised by high year-to-year variability and shows a weak, statistically non-significant increase over the 1957-2007 period. A more pronounced, but also non-significant, decrease from 1977 to 2007 is evident. A strong positive correlation is found between all-Australian averages of cloud amount and rainfall, while a strong negative correlation is found between mean cloud amount and diurnal temperature range. Patterns of annual and seasonal trends in cloud amount are in general agreement with rainfall changes across Australia, however the high-quality cloud network is too coarse to fully capture topographic influences. Nevertheless, the broadscale consistency between patterns of cloud and rainfall variations indicates that the new total cloud amount dataset is able to adequately describe the broadscale patterns of change over Australia. Favourable simple comparisons between surface and satellite measures of cloudiness suggest that satellites may ultimately provide the means for monitoring long-term changes in cloud over Australia. However, due to the relative shortness and homogeneity problems of the satellite record, a robust network of surface cloud observations will be required for many years to come.

No abstract

This study compares observations of 22 climate variables [rainfall, minimum and maximum temperature, mean sea level pressure (MSLP), and air temperature, geopotential heights, relative and specific humidity, and u-wind and v-winds at 500, 700 and 850 hPa] over Australia with NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) and ERA-Interim, two most often used reanalysis products. The results indicate that both NCEP-NCAR and ERA-Interim generally reproduce the observed spatial patterns of long-term mean annual rainfall, daily maximum/minimum temperature and MSLP, the monthly distribution (annual cycle) of rainfall and temperature, although temperature is generally better simulated than rainfall and ERA-Interim shows an overall better performance than NCEP-NCAR in term of continental scale. In term of linear trends, both NCEP-NCAR and ERA-Interim simulate observed trend signs in some regions, but not in others, and the spatial distributions of temperature trends are generally not as well simulated as that of annual rainfall for both NCEP-NCAR and ERA-Interim. In addition, NCEP-NCAR and ERA-Interim show similar spatial patterns of annual mean air temperature, geopotential heights, humidity and winds. This implies that studies using NCEP-NCAR data could also make use of ERA-Interim to explore the range of uncertainties for the regions and climate variables where NCEP-NCAR and ERA-Interim show differences. This conclusion may also apply to regions other than Australia. For example, the different trend signs of MSLP between NCEP-NCAR and ERA-Interim could present a scientific challenging for climatic change studies using MSLP trends, and the split between large-scale and convective rainfall would also have merits globally to understand if the differences between the two reanalysis data sets are global or if we see convergence on key areas, such as tropices and frontal systems.