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Lightning climate change projections show large uncertainties caused by limited empirical knowledge and strong assumptions inherent to coarse-grid climate modeling. This study addresses the latter issue by implementing and applying the lightning potential index parameterization (LPI) into a fine-grid convection-permitting regional climate model (CPM). This setup takes advantage of the explicit representation of deep convection in CPMs and allows for process-oriented LPI inputs such as vertical velocity within convective cells and coexistence of microphysical hydrometeor types, which are known to contribute to charge separation mechanisms. The LPI output is compared to output from a simpler flash rate parameterization, namely the CAPE × PREC parameterization, applied in a non-CPM on a coarser grid. The LPI’s implementation into the regional climate model COSMO-CLM successfully reproduces the observed lightning climatology, including its latitudinal gradient, its daily and hourly probability distributions, and its diurnal and annual cycles. Besides, the simulated temperature dependence of lightning reflects the observed dependency. The LPI outperforms the CAPE × PREC parameterization in all applied diagnostics. Based on this satisfactory evaluation, we used the LPI to a climate change projection under the RCP8.5 scenario. For the domain under investigation centered over Germany, the LPI projects a decrease of 4.8% in flash rate by the end of the century, in opposition to a projected increase of 17.4% as projected using the CAPE × PREC parameterization. The future decrease of LPI occurs mostly during the summer afternoons and is related to (i) a change in convection occurrence and (ii) changes in the microphysical mixing. The two parameterizations differ because of different convection occurrences in the CPM and non-CPM and because of changes in the microphysical mixing, which is only represented in the LPI lightning parameterization.
The goal of limited area models (LAMs) is to downscale coarse-gridded general circulation model output to represent small-scale features of weather and climate. The LAM needs information from the driving coarse-gridded model passing through its lateral boundaries. The treatment of this information transfer causes inconsistencies between driving and nested models and, subsequently, issues in regional weather and climate simulations. This work examines errors arising from choices taken by the modeler (temporal update frequency of boundary data, spatial resolution jump, and numerical lateral boundary formulation) systematically in an idealized simulation environment. So-called Big-Brother Experiments were performed with the LAM COSMO-CLM (0.11° grid spacing). A baroclinic wave in a zonal channel was simulated over flat terrain with and without a Gaussian hill. The results reveal that the quality of the driving data, here represented by simulations only differing from the LAM simulations by reduced spatial resolution, dominates the performance of the nested model. Consequently, at the simulated mesoscale, the performance of the nested small-scale model simulations is weakly sensitive to the numerical lateral boundary formulation (Davies relaxation or the newly implemented, computationally less demanding Mesinger Eta-model formulation). The performance sensitivity to boundary update frequency and resolution jump is small when at least 6-hourly updates and a resolution jump factor of maximally six is used. Gaussian hill LAM simulations illustrated the strength of downscaling; they can represent small-scale features missing in the coarse-scale driving simulations. In the idealized simulation experiments, spectral nudging is not advisable as it imprints the driving models deficits on the nested simulation.
Extreme convective precipitation is expected to increase with global warming. However, the rate of increase and the understanding of contributing processes remain highly uncertain. We investigated characteristics of convective rain cells like area, intensity, and lifetime as simulated by a convection-permitting climate model in the area of Germany under historical (1976–2005) and future (end-of-century, RCP8.5 scenario) conditions. To this end, a tracking algorithm was applied to 5-min precipitation output. While the number of convective cells is virtually similar under historical and future conditions, there are more intense and larger cells in the future. This yields an increase in hourly precipitation extremes, although mean precipitation decreases. The relative change in the frequency distributions of area, intensity, and precipitation sum per cell is highest for the most extreme percentiles, suggesting that extreme events intensify the most. Furthermore, we investigated the temperature and moisture scaling of cell characteristics. The temperature scaling drops off at high temperatures, with a shift in drop-off towards higher temperatures in the future, allowing for higher peak values. In contrast, dew point temperature scaling shows consistent rates across the whole dew point range. Cell characteristics scale at varying rates, either below (mean intensity), at about (maximum intensity and area), or above (precipitation sum) the Clausius–Clapeyron rate. Thus, the widely investigated extreme precipitation scaling at fixed locations is a complex product of the scaling of different cell characteristics. The dew point scaling rates and absolute values of the scaling curves in historical and future conditions are closest for the highest percentiles. Therefore, near-surface humidity provides a good predictor for the upper limit of for example, maximum intensity and total precipitation of individual convective cells. However, the frequency distribution of the number of cells depending on dew point temperature changes in the future, preventing statistical inference of extreme precipitation from near-surface humidity.