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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. [ABSTRACT FROM AUTHOR]

To account for model error on multiple scales in convective‐scale data assimilation, we incorporate the small‐scale additive noise based on random samples of model truncation error and combine it with the large‐scale additive noise based on random samples from global climatological atmospheric background error covariance. A series of experiments have been executed in the framework of the operational Kilometre‐scale ENsemble Data Assimilation system of the Deutscher Wetterdienst for a 2‐week period with different types of synoptic forcing of convection (i.e., strong or weak forcing). It is shown that the combination of large‐ and small‐scale additive noise is better than the application of large‐scale noise only. The specific increase in the background ensemble spread during data assimilation enhances the quality of short‐term 6‐hr precipitation forecasts. The improvement is especially significant during the weak forcing period, since the small‐scale additive noise increases the small‐scale variability which may favor occurrence of convection. It is also shown that additional perturbation of vertical velocity can further advance the performance of combination. Key Points: Additive noise based on model truncation error is used to represent small‐scale model errorThe combination of large‐ and small‐scale noise outperforms the application of large‐scale noise only in short‐term forecastsThe outperformance is especially significant in weak forcing situations [ABSTRACT FROM AUTHOR]

For ensemble data assimilation, background error covariance should account for sampling and model errors. There are a number of approaches that have been developed that try to consider these errors; among them, additive noise and relaxation methods (relaxation to prior perturbation and relaxation to prior spread) are often used. In this work, we compare additive noise, based on random samples from global climatological atmospheric background error covariance, to relaxation methods as well as combinations. Our experiments have been conducted in framework of convective‐scale data assimilation with conventional and radar reflectivity observations hourly assimilated for a 2‐week convective period over Germany. In the first week under weather conditions characterized by strong large‐scale forcing of convection, additive noise performs equally or even better than relaxation methods and combinations during both assimilation and short‐range forecasts. In addition, it is shown that the relaxation to prior perturbation may be associated with smoothing of background errors that negatively affect small‐scale structures and that the relaxation to prior spread yields more unbalanced model states. For the second week in absence of strong forcing, the performance of additive noise relative to combinations has been degraded a bit but results are still comparable. Overall, additive noise provides a good benchmark for further developments in representation of model error for convective‐scale data assimilation. Key Points: Additive noise based on climatological background error covariance partially accounts for model error in convective‐scale data assimilationUnder strong forcing conditions, additive noise performs better than relaxation methodsUnder weak forcing conditions, performance of additive noise relative to combinations degrades a bit but results are still comparable [ABSTRACT FROM AUTHOR]

Convection-permitting simulations for a limited area of the hibernal Arctic were performed with the atmospheric modeling system ICON-ART.We aim at clarifying the microphysical processes triggered by introducing artificial aerosol particles into the upper troposphere with the aim of modifying cirrus clouds in the framework of climate engineering.Former modeling studies investigating the climate effect of this method were performed with simplifying assumptions and much coarser resolution, reaching partly contradicting conclusions concerning the methods effectiveness. The primary effect of seeding is found to be a strong reduction of ice crystal number concentrations in cirrus clouds, leading to increased outgoing longwave radiative fluxes at the top of the atmosphere, thereby creating a cooling effect.Furthermore, a secondary effect is found, as ice crystals formed from the injected seeding aerosol particles lead to enhanced riming of cloud droplets within the planetary boundary layer, hence effectively reducing the coverage of mixed-phase clouds, thus generating additional cooling by increased upward longwave radiative fluxes at the surface.The efficacy of seeding cirrus clouds proves to be relatively independent from the atmospheric background conditions, scales with the seeding aerosol number concentrations and is highest for large aerosol particles. [ABSTRACT FROM AUTHOR]

Lightning climate predictions show large uncertainties caused by limited empirical knowledge and strong assumptions inherent to coarse-grid climate modelling. This study addresses this issue by implementing and applying the lightning potential index parameterisation (LPI) into a fine-grid (with a horizontal grid spacing of 2.8 km) convection-permitting regional climate model (CPM). This configuration takes advantage of the explicit representation of deep convection in CPM and allows for process motivated LPI inputs such as buoyancy, vertical velocity, and microphysical mixing. Our implementation of the LPI in the regional climate model COSMO-CLM successfully represents the lightning climatology (in central Germany) including its daily distribution, diurnal and annual cycles, and latitudinal gradient. In addition, the modelled temperature dependence of lightning reflects the observed dependence. With the CPM nested into a global climate projection simulated with the model EC-Earth assuming the RCP8.5 scenario, lightning flash rates are projected to decrease by the end of the 21st century. Even though the projected change favours large-scale circulation patterns with high lightning probability, changes in local processes (esp. less favourable microphysical mixing within convective clouds) reduce the number of lightning flashes. Coarse-grid models cannot represent the local processes sufficiently, and thus lightning prediction with CPMs is recommended. [ABSTRACT FROM AUTHOR]