Anja Westermayer Tomas Pucik Pieter Groenemeijer Eberhard Faust Robert Sausen Statistical modelling of thunderstorms in the present and future climates 2nd Workshop on Severe Convection and Climate March 9-10, 2016, Columbia University, New York, NY

Motivation Goals:  How well can we model thunderstorm climatology in Europe using reanalysis data?  What changes can we expect in future climates?

Lightning- and Reanalysis Data Lighting cases between

Dry midlevel air strongly suppresses CI Probability of lightning in CAPE – RH parameter space 4 observations linear logistic regression

5 observations additive logistic regression (2D) Probability of lightning in CAPE – RH parameter space

Lightning observations (EUCLID) Annual cycle of thunderstorms July is peak season Hotspot in northeast Italy south north all south north

Additive logistic regression (3D: LI, RH and DLS) ERA-Interim Annual cycle fairly well reproduced Overestimation of thunderstorms in winter Hotspot in northeast Italy north south north

Additive logistic regression (3D: LI, RH and DLS) ERA-Interim Discontinuity around 2002 for south domain Hotspot in northeast Italy south north south

EuroCORDEX regional climate simulations

Underestimation of instability on North Italy Change in thunderstorm probability for rcp8.5 Additive logistic regression (3D: LI, RH and DLS) EuroCORDEX smhi MPI-ESM-LR 2071 – 2100 Differences between to Distribution of historical thunderstorms modelled average of thundery 6h periods per year

EuroCORDEX SMHI MPI-ESM-LR differences between to rcp 8.5rcp 4.5 (K) Change in 1st percentile of LI

EuroCORDEX SMHI MPI-ESM-LR differences between to rcp 4.5rcp 8.5 (%) Change in 1st percentile of RH

EuroCORDEX SMHI MPI-ESM-LR differences between to rcp 4.5rcp 8.5 (m/s) Change in 1st percentile of DLS

How well can we model thunderstorm climatology in Europe using reanalysis data? Fairly well with an additive logistic regression (3D: LI, RH and DLS) with some limitations in winter What changes can we expect in future climates? small increases of the number of thunderstorms in most of Central Europe small decrease in the number of thunderstorms in the South of Europe Conclusions

Increase in instability dominating Decrease in relative humidity dominating Changes in DLS small throughout the domain Thank you for your attention! change in percent for rcp – 2100

EuroCORDEX ensemble (14 members) differences between to rcp 4.5rcp 8.5 (m/s) Change in 1st percentile of LI

EuroCORDEX ensemble (14 members) differences between to rcp 4.5rcp 8.5 (%) Change in 1st percentile of RH

EuroCORDEX ensemble (14 members) differences between to rcp 4.5rcp 8.5 (m/s) Change in 1st percentile of DLS

Underestimation of instability on North Italy Change in thunderstorm probability for rcp4.5 Additive logistic regression (3D: LI, RH and DLS) EuroCORDEX smhi MPI-ESM-LR 2071 – 2100 Differences between to Distribution of historical thunderstorms modelled areal average of thundery 6h periods per year modelled average of thundery 6h periods per year

Generalized linear models (GLM) Generalized additive models (GAM) smoothing function response variable explanatory or predictor variables error coefficient constant

Atmospheric parameter: CAPE, CIN, relative humidity and deep-layer bulk shear deep-layer bulk shear DLS relative humitity