In a changing climate, a key role may be played by the response of convective-type cloud and precipitation to temperature changes1,2,3. Yet, it is unclear if convective precipitation intensities will increase mainly due to thermodynamic or dynamical processes4. Here we perform large eddy simulations of convection by imposing a realistic diurnal cycle of surface temperature. We find convective events to gradually self-organize into larger cloud clusters and those events occurring late in the day to produce the highest precipitation intensities. Tracking rain cells throughout their life cycles, we show that events which result from collisions respond strongly to changes in boundary conditions, such as temperature changes. Conversely, events not resulting from collisions remain largely unaffected by the boundary conditions. Increased surface temperature indeed leads to more interaction between events and stronger precipitation extremes. However, comparable intensification occurs when leaving temperature unchanged but simply granting more time for self-organization. These findings imply that the convective field as a whole acquires a memory of past precipitation and inter-cloud dynamics, driving extremes. For global climate model projections, our results suggest that the interaction between convective clouds must be incorporated to simulate convective extremes and the diurnal cycle more realistically.
Subscribe to Journal
Get full journal access for 1 year
only $15.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
O’Gorman, P. A. Sensitivity of tropical precipitation extremes to climate change. Nat. Geosci. 5, 697–700 (2012).
Kendon, E. J. et al. Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Clim. Change 4, 570–576 (2014).
Tan, J., Jakob, C., Rossow, W. B. & Tselioudis, G. Increases in tropical rainfall driven by changes in frequency of organized deep convection. Nature 519, 451–454 (2015).
Westra, S. et al. Future changes to the intensity and frequency of short-duration extreme rainfall. Rev. Geophys. 52, 522–555 (2014).
Berg, P., Moseley, C. & Haerter, J. O. Strong increase in convective precipitation in response to higher temperatures. Nat. Geosci. 6, 181–185 (2013).
Lenderink, G. & van Meijgaard, E. Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci. 1, 511–514 (2008).
Loriaux, J. M., Lenderink, G., De Roode, S. R. & Siebesma, A. P. Understanding convective extreme precipitation scaling using observations and an entraining plume model. J. Atmos. Sci. 70, 3641–3655 (2013).
Singleton, A. & Toumi, R. Super-Clausius–Clapeyron scaling of rainfall in a model squall line. Q. J. R. Meteorol. Soc. 139, 334–339 (2013).
Meredith, E. P., Semenov, V. A., Maraun, D., Park, W. & Chernokulsky, A. V. Crucial role of Black Sea warming in amplifying the 2012 Krymsk precipitation extreme. Nat. Geosci. 8, 615–619 (2015).
Attema, J. J., Loriaux, J. M. & Lenderink, G. Extreme precipitation response to climate perturbations in an atmospheric mesoscale model. Environ. Res. Lett. 9, 014003 (2014).
Singh, M. S. & O’Gorman, P. A. Influence of microphysics on the scaling of precipitation extremes with temperature. Geophys. Res. Lett. 41, 6037–6077 (2014).
Muller, C. J., O’Gorman, P. A. & Back, L. E. Intensification of precipitation extremes with warming in a cloud-resolving model. J. Clim. 24, 2784–2800 (2011).
Ban, N., Schmidli, J. & Schär, C. Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster? Geophys. Res. Lett. 42, 1165–1172 (2015).
Tompkins, A. M. Organization of tropical convection in low vertical wind shears: The role of cold pools. J. Atmos. Sci. 58, 1650–1672 (2001).
Böing, S. J., Jonker, H. J., Siebesma, A. P. & Grabowski, W. W. Influence of the subcloud layer on the development of a deep convective ensemble. J. Atmos. Sci. 69, 2682–2698 (2012).
Schlemmer, L. & Hohenegger, C. The formation of wider and deeper clouds as a results of cold-pool dynamics. J. Atmos. Sci. 71, 2842–2858 (2014).
Schlemmer, L. & Hohenegger, C. Modifications of the atmospheric moisture field as a result of cold-pool dynamics. Q. J. R. Meteorol. Soc. 142, 30–42 (2015).
Rio, C., Hourdin, F., Grandpeix, J.-Y. & Lafore, J.-P. Shifting the diurnal cycle of parameterized deep convection over land. Geophys. Res. Lett. 36 (2009).
Torri, G., Kuang, Z. & Tian, Y. Mechanisms for convection triggering by cold pools. Geophys. Res. Lett. 42, 1943–1950 (2015).
Arakawa, A. & Schubert, W. H. Interaction of a cumulus cloud ensemble with the large-scale environment, part i. J. Atmos. Sci. 31, 674–701 (1974).
Xu, K.-M., Arakawa, A. & Krueger, S. K. The macroscopic behavior of cumulus ensembles simulated by a cumulus ensemble model. J. Atmos. Sci. 49, 2402–2420 (1992).
Stevens, B. et al. Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Mon. Weather Rev. 133, 1443–1462 (2005).
Arakawa, A. The cumulus parameterization problem: Past, present, and future. J. Clim. 17, 2493–2525 (2004).
Moseley, C., Berg, P. & Haerter, J. O. Probing the convection life-cycle by iterative rain cell tracking. J. Geophys. Res. 118, 13361–13370 (2013).
Molnar, P., Fatichi, S., Gaál, L., Szolgay, J. & Burlando, P. Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature. Hydrol. Earth Syst. Sci. 19, 1753–1766 (2015).
Wasko, C. & Sharma, A. Steeper temporal distribution of rain intensity at higher temperatures within Australian storms. Nat. Geosci. 8, 527–529 (2015).
Bony, S. et al. Clouds, circulation and climate sensitivity. Nat. Geosci. 8, 261–268 (2015).
Tobin, I., Bony, S. & Roca, R. Observational evidence for relationships between the degree of aggregation of deep convection, water vapor, surface fluxes, and radiation. J. Clim. 25, 6885–6904 (2012).
Moncrieff, M. W. The multiscale organization of moist convection and the intersection of weather and climate. Clim. Dyn. 3–26 (2010).
Yano, J.-I., Liu, C. & Moncrieff, M. W. Self-organized criticality and homeostasis in atmospheric convective organization. J. Atmos. Sci. 69, 3449–3462 (2012).
Pincus, R. & Stevens, B. Monte Carlo spectral integration: a consistent approximation for radiative transfer in large eddy simulations. J. Adv. Model. Earth Syst. 1, 1 (2009).
Seifert, A. & Beheng, K. A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: model description. Meteorol. Atmos. Phys. 92, 45–66 (2006).
Weisman, M. L. & Klemp, J. B. The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Weather Rev. 110, 504–520 (1982).
Randall, D. A. & Cripe, D. G. Alternative methods for specification of observed forcing in single-column models and cloud system models. J. Geophys. Res. 104, 24527–24545 (1999).
We thank M. Sakradzija and S. Bühler for helpful comments and fruitful discussions. The authors acknowledge the German Weather Service, Meteorological Observatory Lindenberg, and F. Beyrich, for providing the Lindenberg observational data, as well as the University of Wyoming for the sounding data. C.M. acknowledges financial support from the project DH(CP)2, funded by the German Federal Ministry of Education and Research. J.O.H. acknowledges financial support by the Danish National Research Foundation through the Center for Models of Life.
The authors declare no competing financial interests.
About this article
Cite this article
Moseley, C., Hohenegger, C., Berg, P. et al. Intensification of convective extremes driven by cloud–cloud interaction. Nature Geosci 9, 748–752 (2016). https://doi.org/10.1038/ngeo2789
Geoscientific Model Development (2019)
Journal of Geophysical Research: Atmospheres (2019)
Geophysical Research Letters (2019)
Geophysical Research Letters (2019)
Weather and Climate Extremes (2019)