Climatic Change

, Volume 118, Issue 3, pp 771–782

Global increase in record-breaking monthly-mean temperatures


    • Earth System AnalysisPotsdam Institute for Climate Impact Research
  • Alexander Robinson
    • Earth System AnalysisPotsdam Institute for Climate Impact Research
    • Astrofísica y CC de la AtmósferaUniversidad Complutense de Madrid
  • Stefan Rahmstorf
    • Earth System AnalysisPotsdam Institute for Climate Impact Research

DOI: 10.1007/s10584-012-0668-1

Cite this article as:
Coumou, D., Robinson, A. & Rahmstorf, S. Climatic Change (2013) 118: 771. doi:10.1007/s10584-012-0668-1


The last decade has produced record-breaking heat waves in many parts of the world. At the same time, it was globally the warmest since sufficient measurements started in the 19th century. Here we show that, worldwide, the number of local record-breaking monthly temperature extremes is now on average five times larger than expected in a climate with no long-term warming. This implies that on average there is an 80 % chance that a new monthly heat record is due to climatic change. Large regional differences exist in the number of observed records. Summertime records, which are associated with prolonged heat waves, increased by more than a factor of ten in some continental regions including parts of Europe, Africa, southern Asia and Amazonia. Overall, these high record numbers are quantitatively consistent with those expected for the observed climatic warming trend with added stationary white noise. In addition, we find that the observed records cluster both in space and in time. Strong El Niño years see additional records superimposed on the expected long-term rise. Under a medium global warming scenario, by the 2040s we predict the number of monthly heat records globally to be more than 12 times as high as in a climate with no long-term warming.

Supplementary material

10584_2012_668_MOESM1_ESM.pdf (197 kb)
SOM Fig. 1 Global-mean surface temperatures from NASA-GISS2 over the last 131 years for each month. Latitudinal distributions of zonal mean (b) trend and (c) standard deviation for periods 1880–2010, 1911–1940 and 1971–2010. (PDF 196 kb)
10584_2012_668_MOESM2_ESM.png (488 kb)
SOM Fig. 2 Global maps of serial correlation in non-linearly detrended monthly time-series for boreal summer (top) and austral summer (bottom). Over all continents and almost all ocean regions serial correlation is between −0.2 and 0.2. (PNG 488 kb)
10584_2012_668_MOESM3_ESM.png (107 kb)
SOM Fig. 3 Results of Monte Carlo experiments showing the deviation of the number of records compared to the iid-solution (1/n) for different levels of autocorrelation. The red dots indicate the mean and the red lines the 5–95 % confidence interval of each Monte Carlo experiment. For autocorrelations from −0.9 to +0.4 the number of records is statistically not significantly different from the iid solution. (PNG 106 kb)
10584_2012_668_MOESM4_ESM.png (618 kb)
SOM Fig. 4 Number of records in the non-linearly detrended time series, globally averaged (top) and averaged over continents (middle) and oceans (bottom). It shows that the iid-expected 1/n produces accurate results for the number of continental records over a 10 year period. Peaks in the detrended time series for individual years (i.e. 1983 & 1998) are discussed in the main text. (PNG 617 kb)
10584_2012_668_MOESM5_ESM.pdf (85 kb)
SOM Fig. 5 Latitudinal distributions of the zonal-mean record ratio X as observed (dashed) and estimated by Eq. 2 (solid), for boreal (a) winter, (b) spring, (c) summer, (d) autumn and (e) annual mean, using the 1971–2010 dataset. (PDF 84 kb)

Copyright information

© Springer Science+Business Media Dordrecht 2013