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Increases in tropical rainfall driven by changes in frequency of organized deep convection


Increasing global precipitation has been associated with a warming climate resulting from a strengthening of the hydrological cycle1. This increase, however, is not spatially uniform. Observations and models have found that changes in rainfall show patterns characterized as ‘wet-gets-wetter’1,2,3,4,5,6,7 and ‘warmer-gets-wetter’5,8,9. These changes in precipitation are largely located in the tropics and hence are probably associated with convection. However, the underlying physical processes for the observed changes are not entirely clear. Here we show from observations that most of the regional increase in tropical precipitation is associated with changes in the frequency of organized deep convection. By assessing the contributions of various convective regimes to precipitation, we find that the spatial patterns of change in the frequency of organized deep convection are strongly correlated with observed change in rainfall, both positive and negative (correlation of 0.69), and can explain most of the patterns of increase in rainfall. In contrast, changes in less organized forms of deep convection or changes in precipitation within organized deep convection contribute less to changes in precipitation. Our results identify organized deep convection as the link between changes in rainfall and in the dynamics of the tropical atmosphere, thus providing a framework for obtaining a better understanding of changes in rainfall. Given the lack of a distinction between the different degrees of organization of convection in climate models10, our results highlight an area of priority for future climate model development in order to achieve accurate rainfall projections in a warming climate.

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Figure 1: Joint-histograms of the centroids of the convective cloud regimes.
Figure 2: Precipitation distributions of the convective CRs.
Figure 3: The spatial distribution of the changes in precipitation from 1998 to 2009.
Figure 4: The spatial distribution of the changes in precipitation from July 1983 to December 2009.


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. We thank S. Sherwood and B. Stevens for comments on the study. The GPCP combined precipitation data were developed and computed by the NASA/Goddard Space Flight Centre’s Mesoscale Atmospheric Processes Laboratory as a contribution to the GEWEX Global Precipitation Climatology Project, and provided by National Oceanic and Atmospheric Administration (NOAA) Office of Oceanic and Atmospheric Research and Earth System Research Laboratory Physical Sciences Division (PSD) at The TRMM 3B42 and 3A25 data were provided by the NASA/Goddard Space Flight Center’s Mesoscale Atmospheric Processes Laboratory and Precipitation Processing System as a contribution to TRMM, and archived at the NASA Goddard Earth Sciences Data and Information Services Center. J.T. and C.J. are funded under the Australian Research Council Centre of Excellence for Climate System Science (CE110001028). W.B.R. is supported by NASA grant NNX13AO39G. G.T. acknowledges the support of the NASA Modeling Analysis and Prediction (MAP) programme managed by D. Considine. J.T. acknowledges support from the Monash University Postgraduate Publication Award.

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Authors and Affiliations



J.T. and C.J. designed the study. J.T. conducted the analysis and obtained the results. C.J., W.B.R. and G.T. advised on the approach. J.T., W.B.R. and G.T. checked regime time series for satellite artefacts. All authors discussed the results and contributed to the preparation of the manuscript.

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Correspondence to Jackson Tan.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Geographical distribution of the convective cloud regimes.

The frequency averaged over the entire period (July 1983 to December 2009) in each grid box for CR1 (a), CR2 (b), and CR3 (c).

Source data

Extended Data Figure 2 Time series of the frequencies of the convective regimes.

Monthly-mean frequencies of CR1 (a), CR2 (b) and CR3 (c), as well as the sum of all convective regimes CR1–CR3 (d) in the entire domain between ±30° latitudes (solid lines). The linear least-squares regression slopes are also shown (dashed lines). The differences in means between the two halves at two standard deviations (95%) are 0.0043 ± 0.0008 (a), −0.014 ± 0.002 (b), 0.003 ± 0.003 (c) and −0.006 ± 0.004 (d).

Source data

Extended Data Figure 3 Correlations and root mean squared errors of the spatial changes to change in total precipitation.

Correlations and root mean squared errors with the first panel of the other panels in Fig. 3 (a), Fig. 4 (b) and Extended Data Fig. 4 (c).

Source data

Extended Data Figure 4 The spatial distribution of the changes in precipitation from 1997 to 2009 using GPCP 1DD.

a, Change in monthly-mean precipitation from GPCP 1DD. b, Contribution from the change in CR1 frequency. c, Contribution from the change in within-CR1 rainfall. d, Sum of the contributions from the terms of CR2 and CR3. e, Sum of the contributions from the terms of all three convective regimes. See the legend to Fig. 3 on stippling, and Extended Data Fig. 3c for correlations and root mean squared errors.

Source data

Extended Data Figure 5 Comparison between changes in organized deep convection and changes in dynamics from 1998 to 2009.

a, Changes in grid-mean vertical motion at 500 hPa from ERA-Interim (negative is ascending motion). b, Changes in frequency of CR1.

Source data

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Tan, J., Jakob, C., Rossow, W. et al. Increases in tropical rainfall driven by changes in frequency of organized deep convection. Nature 519, 451–454 (2015).

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