About the global temperature trend maps
Analyses available
Global and hemispheric surface temperature trend maps are available for annual seasonal mean temperatures. Seasonal means are December-February (DJF), March-May (MAM), June-August (JJA) and September-November (SON). Gridpoint trends are calculated from the beginning of each decade from 1900 to 1980 until present. Analysis periods starting after 1980 are considered too short to calculate meaningful trend values.
Interpreting the analyses
Some differences exist between the Bureau's high quality surface temperature data and the global data set over the Australian region. These are due to differences in the observation networks used and the more regular updating of the Bureau's data set in comparison with globally compiled data. Nevertheless such differences are small and the global data set used here has been judged to adequately represent global and regional temperature variability and change (see Jones et al, 1999; Folland et al, 2001). For the Australian region, much better confidence can be placed in the Bureau's high quality temperature data.
The trend maps are a useful way to compare how the temperature has changed in different regions of the globe over time. However, they need to be interpreted with caution. Trend values have been determined from a linear (straight line) fit to the data, but the change indicated may not have been gradual. For example, a calculated trend could be due to a relatively rapid "step" change, with the remainder of the series being fairly flat (see some of the timeseries graphs). Also, for regions and seasons in which the year-to-year changes are large, the value of the calculated trend will depend on the start and end values of the data series, potentially misrepresenting the background trend.
In addition, the above factors are compounded by missing data in certain regions. In order for a trend value to be shown, gridpoints were required to have no more than one third of the relevant time period as missing data. The presented trends are not sensitive to changes in the missing data requirement. In the scientific literature more stringent missing data requirements are often used in climate change studies. However, many of these studies (eg. Braganza et al, 2003) show that the effect of missing data on global scale trends is small. Users are advised to keep in mind the period over which trend values have been calculated and interpret them alongside the timeseries of spatially averaged values. In addition, users should also consider the spatial consistency of trends within sub-regions to infer confidence in individual grid point values.
The trend values calculated here using past observations should not be used to imply future rates of change. Due to the complex interactions between the natural and human drivers of climate change and variability, the climate of any location is always changing. Future rates of change will depend on how these drivers interact in future, which will not necessarily be the same as in the past.
Data used
The trend analyses use the Climatic Research Unit HadCRUT5 global gridded (5x5 degree resolution) temperature data set (see Morice et al, 2021).
These data are the blended, near land surface temperature and sea surface temperature anomalies from the 1961-90 reference period, that have recently developed by the Climatic Research Unit (CRU) at the University of East Anglia, in conjunction with the Hadley Centre of the UK Met Office. This data set replaces the older HadCRUT4 (see Morice et al, 2012) which is no longer being updated. The data are publically available from the Climatic Research Unit (CRU) website. A list of relevant scientific papers relating to the HadCRUT5 dataset is also provided below.
Please note that any use of these analyses should be acknowledged to the Bureau of Meteorology and the institutions listed above. Apart from the purposes of study, research, criticism and review, no part of these data may be reproduced, or redistributed for any commercial purposes, or distributed to a third party for such purpose, without written permission from the Director of Meteorology.
Further information
Braganza K., Karoly D.J., Hirst A.C., Mann M.E., Stott P.A., Stouffer R.J., Tett, S.F.B. 2003. Simple indices of global climate variability and change: Part I, variability and correlation structure. Climate Dynamics 20 491-502. DOI: 10.1007/s00382- 002-0286-0.
Brohan, P., J.J. Kennedy, I. Haris, S.F.B. Tett and P.D. Jones, 2006. Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J. Geophysical Research 111, D12106, doi:10.1029/2005JD006548.
Christy, J.R., Parker, D.E., Stendel. M. and Norris, W.B., 2001. Differential trends in tropical sea surface temperature and atmospheric temperatures since 1979. Geophysical Research Letters 28, 183-186.
Folland, C.K., Rayner, N.A., Brown, S.J., Smith, T.M., Shen, S.S.P., Parker, D.E., Macadam, I., Jones, P.D., Jones, R.N., Nicholls, N. and Sexton, D.M.H., 2001a. Global temperature change and its uncertainties since 1861. Geophysical Research Letters 28, 2621-2624.
Folland, C.K., Karl, T.R., Christy, J.R., Clarke, R.A., Gruza, G.V., Jouzel, J., Mann, M.E., Oerlemans, J., Salinger, M.J. and Wang, S.-W., 2001b: Observed climate variability and change. pp. 99-181 In: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K. and Johnson, C.A. Eds.). Cambridge University Press, Cambridge, UK, 881pp.
Jones, P.D., Osborn, T.J. and Briffa, K.R., 1997. Estimating sampling errors in large-scale temperature averages. Journal of Climate 10, 2548-2568.
Jones, P.D., New, M., Parker, D.E., Martin, S. and Rigor, I.G., 1999. Surface air temperature and its variations over the last 150 years. Reviews of Geophysics 37, 173-199.
Jones, P.D., Osborn, T.J., Briffa, K.R., Folland, C.K., Horton, B., Alexander, L.V., Parker, D.E. and Rayner, N.A., 2001. Adjusting for sampling density in grid-box land and ocean surface temperature timeseries. Journal of Geophysical Research 106, 3371-3380.
Jones, P.D. and Moberg, A., 2003. Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001. Journal of Climate 16, 206-223.
Morice, C.P., J.J. Kennedy, N.A. Rayner, J.P. Winn, E. Hogan, R.E. Killick, R.J.H. Dunn, T.J. Osborn, P.D. Jones and I.R. Simpson, 2021, An updated assessment of near-surface temperature change from 1850: the HadCRUT5 dataset. Journal of Geophysical Research (Atmospheres) doi:10.1029/2019JD032361.
Morice, C.P., J.J. Kennedy, N.A. Rayner, and P.D. Jones., 2012, Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset Journal of Geophysical Research 117, D08101, doi 10.1029/2011JD017187.
Parker, D.E., Alexander, L.V. and Kennedy, J., 2004. Global and regional climate in 2003. Weather, 59, 145-152.
Rayner, N.A., Parker, D.E., Horton, E.B., Folland, C.K., Alexander, L.V, Rowell, D.P., Kent, E.C. and Kaplan, A., 2003. Globally complete analyses of sea surface temperature, sea ice and night marine air temperature, 1871-2000. Journal of Geophysical Research 108, 4407, doi 10.1029/2002JD002670.
