Does higher temperature accentuate convective cell clustering within European MCSs?

Mesoscale Convective Systems (MCSs) are clusters of thunderstorms composed of narrow and heavy convective-type precipitation adjacent with wider and lighter stratiform-type precipitation. MCSs are the largest contributor of extreme precipitation events over Europe (Da Silva & Haerter, 2023). 

While convective and stratiform-type precipitation contributions within MCSs are each expected to increase according to the Clausius-Clapeyron law (~7%°C-1), their statistical superimposition is shown to increase at a faster rate due to increased MCS convective fraction with temperature (Da Silva & Haerter, submitted). 

 

For better prediction of floods induced by MCSs, it is also important to characterize the relationship between temperature and the spatio-temporal clustering of convective cells within MCSs. For that purpose, we use the high resolution EUropean Cooperation for LIghtning Detection (EUCLID) lightning dataset and combine it with MCS tracking data (derived from the RADOLAN radar precipitation dataset; Bartels et al., 2004) over Germany. Identifying convective cells through lightning records, we measure the degree of convection clustering using an organization index which we adapt to the MCS geometry. In this process, we use a Monte Carlo method to estimate the reference random distribution of nearest neighbor distances of convective centroids. 

 

We associate our organization index with surface dew-point temperatures from neighboring weather stations from the German Weather Service (Deutscher Wetterdienst, DWD). We select the temperature upstream of the MCS tracks, as a proxy of the moisture source involved in the formation of MCS precipitation. Idealized simulations suggest that both the mean and the spatial variability of surface temperature could be relevant for convective aggregation (Pendergrass, 2020; Shamekh et al., 2020). Our study considers both and also investigates the potential role of other triggers for convective aggregation such as convective cold pools (Haerter, 2019) or the diurnal cycle (Haerter et al., 2020).

References:

 

Bartels, H. et al. Projekt RADOLAN Routineverfahren zur Online-Aneichung der Radarniederschlagsdaten mit Hilfe von automatischen Bodenniederschlagsstationen (Ombrometer) (2004).

https://www.dwd.de/DE/leistungen/radolan/radolan_info/abschlussbericht_pdf.pdf?__blob=publicationFile&v=2

 

Da Silva, N. A., & Haerter, J. O. (2023). The precipitation characteristics of mesoscale convective systems over Europe. Journal of Geophysical Research: Atmospheres, 128, e2023JD039045. https://doi.org/10.1029/2023JD039045

 

Da Silva, N. A, & Haerter J. O.. Non super-Clausius-Clapeyron scaling of convective precipitation extremes, 08 January 2024, PREPRINT (Version 1) available at Research Square

https://doi.org/10.21203/rs.3.rs-3777860/v1

 

Haerter, J. O. (2019). Convective self-aggregation as a cold pool-driven critical phenomenon. Geophysical Research Letters, 46, 4017–4028. https://doi.org/10.1029/2018GL081817

 

Haerter, J.O., Meyer, B. & Nissen, S.B. Diurnal self-aggregation (2020). npj Clim Atmos Sci 3, 30. https://doi.org/10.1038/s41612-020-00132-z

Pendergrass, A. G. (2020). Changing degree of convective organization as a mechanism for dynamic changes in extreme precipitation. Current climate change reports, 6, 47-54.

 

Shamekh, S., C. Muller, J. Duvel, and F. D’Andrea (2020), How Do Ocean Warm Anomalies Favor the Aggregation of Deep Convective Clouds?. J. Atmos. Sci., 77, 3733–3745, https://doi.org/10.1175/JAS-D-18-0369.1.