Climate change impact on thunderstorms: Using high-resolution COSMO-CLM simulations to determine changes in thunderstorm occurrences

KlimawandelIt is generally assumed that temperature increase associated with global climate change will lead to increased thunderstorm intensity and associated heavy precipitation events. In the present study it is investigated whether the frequency of thunderstorm occurrences will in- or decrease and how the spatial distribution will change for the A1B scenario for mid-Europe. Hourly model data of the COSMO-CLM is used with a horizontal resolution of 0.04°(~4.5km) for mid-Europe. The simulations were carried out for two different periods: 1971-2000 (C20) and 2071-2100 (A1B). The two-step nesting chain starts with a CCLM run with 18km resolution covering whole Europe nested in ECHAM5 runs, then a run with a resolution of 4.4km has been performed for mid-Europe. Thunderstorm indices are applied to detect potential thunderstorms and differences in their frequency of occurrence in this periods. The indices used are CAPE (Convective Available Potential Energy), SLI (Surface Lifted Index), and TSP (Thunderstorm Severity Potential), which combines deep-layer-shear and the maximum vertical velocity. The significance of a potential climate signal was tested with a t-test and a power analysis was performed to quantify the uncertainty of the signal. The focus of this study is the Saar-Lor-Lux region (Saarland, Lorraine, Luxembourg). The investigation of the present and future thunderstorms shows that the regional averaged frequencies will decrease in general, but several regions like the Saarland and especially elevated areas will have a potential increase in thunderstorm occurrences and intensity. Statistically, regions of the Alps, the Netherlands and Belgium show significant climate signals. The power analysis yields low power to detect changes of severe thunderstorms but high power for classes with no to light thunderstorms. In conclusion, our study shows that the frequency of severe thunderstorm is not likely to increase during the next century

A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS

Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.</jats:p

Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM

Impact of the horizontal resolution on the simulation of extremes

The simulation of extremes using climate models is still a challenging task. Currently, the model grid horizontal resolution of state-of-the art regional climate models (RCMs) is about 11–25 km, which may still be too coarse to represent local extremes realistically. In this study we use dynamically downscaled ERA-40 reanalysis data of the RCM COSMO-CLM at 18 km resolution, downscale it dynamically further to 4.5 km and finally to 1.3 km to investigate the impact of the horizontal resolution on extremes. Extremes are estimated as return levels for the 2, 5 and 10‑year return periods using ‘peaks-over-threshold’ (POT) models. Daily return levels are calculated for precipitation and maximum 2 m temperature in summer as well as precipitation and 2 m minimum temperature in winter. The results show that CCLM is able to capture the spatial and temporal structure of the observed extremes, except for summer precipitation extremes. Furthermore, the spatial variability of the return levels increases with resolution. This effect is more distinct in case of temperature extremes due to a higher correlation with the better resolved orography. This dependency increases with increasing horizontal resolution. In comparison to observations, the spatial variability of temperature extremes is better simulated at a resolution of 1.3 km, but the return levels are cold-biased in summer and warm-biased in winter. Regarding precipitation, the spatial variability improves as well, although the return levels were slightly overestimated in summer by all CCLM simulations. In summary, the results indicate that an increase of the horizontal resolution of CCLM does have a significant effect on the simulation of extremes and that impact models and assessment studies may benefit from such high-resolution model output

Climate change impact on thunderstorms: Analysis of thunderstorm indices using high-resolution regional climate simulations

It is generally assumed that the temperature increase associated with global climate change will lead to increased thunderstorm intensity and associated heavy precipitation events. In the present study it is investigated whether the frequency of thunderstorm occurrences will in- or decrease and how the spatial distribution will change for the A1B scenario. The region of interest is Central Europe with a special focus on the Saar-Lor-Lux region (Saarland, Lorraine, Luxembourg) and Rhineland-Palatinate.Daily model data of the COSMO-CLM with a horizontal resolution of 4.5 km is used. The simulations were carried out for two different time slices: 1971–2000 (C20), and 2071–2100 (A1B). Thunderstorm indices are applied to detect thunderstorm-prone conditions and differences in their frequency of occurrence in the two thirty years timespans. The indices used are CAPE (Convective Available Potential Energy), SLI (Surface Lifted Index), and TSP (Thunderstorm Severity Potential).The investigation of the present and future thunderstorm conducive conditions show a significant increase of non-thunderstorm conditions. The regional averaged thunderstorm frequencies will decrease in general, but only in the Alps a potential increase in thunderstorm occurrences and intensity is found. The comparison between time slices of 10 and 30 years length show that the number of gridpoints with significant signals increases only slightly. In order to get a robust signal for severe thunderstorm, an extension to more than 75 years would be necessary

Improvement of MODIS-based winter sea-ice production estimates in Arctic polynyas by means of a model-based temperature adjustment scheme

Knowledge of winter sea ice production in Arctic polynyas is an important prerequisite for estimating the dense water formation that drives vertical mixing in the upper ocean. Satellite techniques using relatively high-resolution thermal infrared data from MODIS in combination with atmospheric reanalysis data have proved to be a powerful tool for monitoring large and regularly forming polynyas and for resolving narrow thin ice areas (i.e. leads) along shelf breaks and across the Arctic Ocean. However, the selection of atmospheric data sets has a strong influence on the derived polynya characteristics, as it affects the calculation of heat loss to the atmosphere, which is determined by the local thin-ice thickness. To overcome this methodological ambiguity, we present a temperature adjustment algorithm that provides corrections to the 2-m air temperature through MODIS ice surface temperatures. It thus reduces the differences in calculated surface heat fluxes that can result from the use of varying atmospheric input data sets. The adjustment algorithm itself is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies of the developed algorithm and present time series of polynya characteristics in the winter season of 2019/20, which in general was characterized by a particularly strong polar vortex and inherent effects on sea ice dynamics. It becomes apparent that the application of the empirically derived correction significantly reduces the difference between the different atmospheric products used from 49% to 23%. We apply additional filtering strategies that aim to increase the ability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites

Bias correction of ENSEMBLES precipitation data with focus on the effect of the length of the calibration period

Bias correction (BC) has become a standard procedure in climate change impact studies, since climate model output often shows a bias when compared to observed data. Especially for daily precipitation, we expect the performance of the BC to depend on the length of the period used for the BC calibration. In this study we analyzed how the length of the calibration period affects the BC performance of quantile mapping (QM). We subsequently reduced the length of the calibration period, starting with a calibration period length of 30 years, and analyzed the effect on the BC performance based on three skill scores.The results show that already a small reduction in the length of the calibration period can result in a significant decrease of the BC performance. However, the critical calibration period length at which this decrease occurs, varies strongly. Nevertheless, it is larger than ten years in all experiments for all skill scores. Furthermore, the critical calibration period length is found to depend on the choice of the control period and especially on the choice of the QM method. But it has to be noted that these results are slightly different for the three skill scores. Overall, the results indicate that QM methods with many degrees of freedom, especially the empirical QM, are more vulnerable to a reduction of the calibration period length. Based on our results, we recommend to use a calibration period as long as possible and to apply QM methods with few degrees of freedom, when using QM for the BC of data that was not used in the calibration