When atmospheric reanalysis is carried out prior to the satellite era, it is particularly challenging in the Southern Hemisphere, where conventional observations are more sparse than in the Northern Hemisphere. A dearth of observations tends to cause a locally poor analysis quality during the affected period, which in turn would lead to erroneous climate trends over longer periods.

Figure 1. Range (days) at which running 365-day mean anomaly correlations of 500 hPa height forecasts from 00 and 12 UTC reach 95%
(green), 80% (orange) and 60% (blue), from 1950-2020 for (a) Australia/New Zealand and (b) Europe. Also shown (dashed) is the skill of
ECMWF operational forecasts throughout 1981. The heaviest lines denote ERA5, the thin lines denote ERA-Interim. Shading denotes the
difference between ERA5 and ERA-Interim during the period for which both are available (1979-2019).

In order to demonstrate these problematic early years, Figure 1a shows the forecast skill, which is a measure of the quality of the analysis, over Australia and New Zealand. In the 1960s and 1970s there are small improvements in the skill, but the skill improves dramatically following the first assimilation of satellite data from the TOVS satellites at the end of 1978. For comparison purposes, Figure 1b shows the same information as Figure 1a, but for Europe. It is clear that in Europe during the years 1950-1978, the skill is much higher than that in Australia and New Zealand.

Figure 2. Twelve-month running-mean of the Australian land (110oE-160oE and 50oS-10oS), surface air
temperature anomalies (K) relative to 1981-2010, from 1950 onwards, for ERA5, JRA-55 (from 1958),
GISTEMP and ACORN-SAT, and the spread (blue shading) between all datasets other than ERA5 and JRA-55.

Figure 2 shows the time series of Australian land, surface air temperature anomalies for two reanalyses, ERA5 and JRA-55 (Kobayashi et al., 2015), and two observation based datasets, GISTEMP (Lenssen et al. (2019)) and ACORN-SAT (Trewin (2013), the country-average version-2 values). ERA5, JRA-55 and GISTEMP have been processed as described in Simmons et al. (2017). There are various differences between the reanalyses and the observation based datasets. These differences are largest before 1970, when ERA5 and JRA-55 have signicantly higher temperature anomalies than GISTEMP and ACORN-SAT. Although agreement over Australia is generally much closer after 1970, there are a few periods when the reanalyses are colder than the other datasets, relative to climatology. Clearly, surface air temperature trends over Australia for 1950-2020 will be quite different between the reanalyses and the observation based datasets.


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  • Lenssen, N., Schmidt, G., Hansen, J., Menne, M., Persin, A., Ruedy, R. and Zyss, D. (2019) Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos.,124, 6307–6326.
  • Kobayashi, S., Ota, Y., Harada, Y., Ebita, A.,Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. et al. (2015) The JRA-55 reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II, 93, 5–48.

  • Simmons, A., Berrisford, P., Dee, D., Hersbach, H., Hirahara, S. and Thépaut, J.-N. (2017) A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets. Quarterly Journal of the Royal Meteorological Society,143,101–119.
  • Trewin, B. (2013) A daily homogenized temperature dataset for Australia. International Journal of Climatology, 33,1510–1529.

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The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.

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