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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.
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.
Convective shower characteristics simulated with the convection-permitting climate model COSMO-CLM
(2019)
This paper evaluates convective precipitation as simulated by the convection-permitting climate model (CPM) Consortium for Small-Scale Modeling in climate mode (COSMO-CLM) (with 2.8 km grid-spacing) over Germany in the period 2001–2015. Characteristics of simulated convective precipitation objects like lifetime, area, mean intensity, and total precipitation are compared to characteristics observed by weather radar. For this purpose, a tracking algorithm was applied to simulated and observed precipitation with 5-min temporal resolution. The total amount of convective precipitation is well simulated, with a small overestimation of 2%. However, the simulation underestimates convective activity, represented by the number of convective objects, by 33%. This underestimation is especially pronounced in the lowlands of Northern Germany, whereas the simulation matches observations well in the mountainous areas of Southern Germany. The underestimation of activity is compensated by an overestimation of the simulated lifetime of convective objects. The observed mean intensity, maximum intensity, and area of precipitation objects increase with their lifetime showing the spectrum of convective storms ranging from short-living single-cell storms to long-living organized convection like supercells or squall lines. The CPM is capable of reproducing the lifetime dependence of these characteristics but shows a weaker increase in mean intensity with lifetime resulting in an especially pronounced underestimation (up to 25%) of mean precipitation intensity of long-living, extreme events. This limitation of the CPM is not identifiable by classical evaluation techniques using rain gauges. The simulation can reproduce the general increase of the highest percentiles of cell area, total precipitation, and mean intensity with temperature but fails to reproduce the increase of lifetime. The scaling rates of mean intensity and total precipitation resemble observed rates only in parts of the temperature range. The results suggest that the evaluation of coarse-grained (e.g., hourly) precipitation fields is insufficient for revealing challenges in convection-permitting simulations.
Convection-permitting models (CPMs) have proven their usefulness in representing precipitation on a sub-daily scale. However, investigations on sub-hourly scales are still lacking, even though these are the scales for which showers exhibit the most variability. A Lagrangian approach is implemented here to evaluate the representation of showers in a CPM, using the limited-area climate model COSMO-CLM. This approach consists of tracking 5‑min precipitation fields to retrieve different features of showers (e.g., temporal pattern, horizontal speed, lifetime). In total, 312 cases are simulated at a resolution of 0.01 ° over Central Germany, and among these cases, 78 are evaluated against a radar dataset. The model is able to represent most observed features for different types of convective cells. In addition, the CPM reproduced well the observed relationship between the precipitation characteristics and temperature indicating that the COSMO-CLM model is sophisticated enough to represent the climatological features of showers.
Convection-permitting climate model are promising tools for improved representation of extremes, but the number of regions for which these models have been evaluated are still rather limited to make robust conclusions. In addition, an integrated interpretation of near-surface characteristics (typically temperature and precipitation) together with cloud properties is limited. The objective of this paper is to comprehensively evaluate the performance of a ‘state-of-the-art’ regional convection-permitting climate model for a mid-latitude coastal region with little orographic forcing. For this purpose, an 11-year integration with the COSMO-CLM model at Convection-Permitting Scale (CPS) using a grid spacing of 2.8 km was compared with in-situ and satellite-based observations of precipitation, temperature, cloud properties and radiation (both at the surface and the top of the atmosphere). CPS clearly improves the representation of precipitation, in especially the diurnal cycle, intensity and spatial distribution of hourly precipitation. Improvements in the representation of temperature are less obvious. In fact the CPS integration overestimates both low and high temperature extremes. The underlying cause for the overestimation of high temperature extremes was attributed to deficiencies in the cloud properties: The modelled cloud fraction is only 46 % whereas a cloud fraction of 65 % was observed. Surprisingly, the effect of this deficiency was less pronounced at the radiation balance at the top of the atmosphere due to a compensating error, in particular an overestimation of the reflectivity of clouds when they are present. Overall, a better representation of convective precipitation and a very good representation of the daily cycle in different cloud types were demonstrated. However, to overcome remaining deficiencies, additional efforts are necessary to improve cloud characteristics in CPS. This will be a challenging task due to compensating deficiencies that currently exist in ‘state-of-the-art’ models, yielding a good representation of average climate conditions. In the light of using the CPS models to study climate change it is necessary that these deficiencies are addressed in future research.