CLM-Assembly 2018 Conference proceedings

From September 18 to 21, the 13th General Assembly of the CLM community (https://www.clm-community.eu/) took place at Campus South of the Karlsruhe Institute of Technology. Nearly 60 international participants learned over these four days about the latest results and developments of the COSMO-CLM and ICON model systems in 23 plenary lectures and 21 posters. The premises in building 10.81 (“altes Ingenieursgebäude”) also offered the opportunity to engage in parallel sessions in in-depth discussions in the individual working groups of the CLM community. The present conference proceedings hold all the abstracts of the oral and poster presentations during the assembly and gives a good insight in the broad work and applications of the CLM Community. Herewith, the organizing team would like to sincerely thank • the participants of the conference, • the CLM working group leaders, • the scientific advisory board, • the catering service, • the janitors of building 10.81, • the student assistants, and • all others involved in organizing the assembly

Future heat extremes and impacts in a convection permitting climate ensemble over Germany

Heat extremes and associated impacts are considered the most pressing issue for German regional governments with respect to climate adaptation. We explore the potential of an unique high-resolution convection permitting (2.8 km), multi-GCM ensemble with COSMO-CLM regional simulations (1971&ndash;2100) over Germany regarding heat extremes and related impacts. We find an improved mean temperature beyond the effect of a better representation of orography on the convection permitting scale, with reduced bias particularly during summer. The projected increase in temperature and its variance favors the development of longer and hotter heat waves, especially in late summer and early autumn. In a 2&deg; (3&deg;) warmer world, a 26 % (100 %) increase in the Heat Wave Magnitude Index is anticipated. Human heat stress (UTCI &gt; 32&deg;C) and local-specific parameters tailored to climate adaptation, revealed a dependency on the major landscapes, resulting in significant higher heat exposure in flat regions as the Rhine Valley, accompanied by the strongest absolute increase. A non-linear, exponential increase is anticipated for parameters characterizing strong heat stress (UTCI &gt; 32&deg;C, tropical nights, very hot days). Providing local-specific and tailored climate information, we demonstrate the potential of convection permitting simulations to facilitate improved impact studies and narrow the gap between climate modelling and stakeholder requirements for climate adaptation.</p

Climate change signals of extreme precipitation return levels for Germany in a transient convection‐permitting simulation ensemble

The increase in extreme precipitation with global warming (GW) and associated uncertainties are major challenges for climate adaptation. To project future extreme precipitation on different time and intensity scales (return periods [RPs] from 1 to 100 a and durations from 1 h to 3 days), we use a novel convection-permitting (CP), multi-global climate model ensemble of COSMO-CLM regional simulations with a transient projection time (1971–2100) over Germany. We find an added value of the CP scale (2.8 km) with respect to the representation of hourly extreme precipitation intensities compared to the coarser scale with parametrized deep convection (7 km). In general, the return levels (RLs) calculated from the CP simulations are in better agreement with those of the conventional observation-based risk products for the region for short event durations than for longer durations, where an overestimation by the simulation-based results was found. A maximum climate change signal of 6–8.5% increase per degree of GW is projected within the CP ensemble, with the largest changes expected for short durations and long RPs. Analysis of the uncertainty in the climate change signal shows a substantial residual standard deviation of a linear approximation, highlighting the need for transient data sets instead of time-slice experiments to increase confidence in the estimates. Furthermore, the ensemble spread is found to be smallest for intensities of short duration, where changes are expected to be based mainly on thermodynamic contributions. The ensemble spread is larger for long, multi-day durations, where a stronger dependence on the dynamical component is ascribed. In addition, an increase in spatial variance of the RLs with GW implies a more variable future climate and points to an increasing importance of accounting for uncertainties

Long-term variance of heavy precipitation across central Europe using a large ensemble of regional climate model simulations

Widespread flooding events are among the major natural hazards in central Europe. Such events are usually related to intensive, long-lasting precipitation over larger areas. Despite some prominent floods during the last three decades (e.g., 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses. This study presents and discusses a validation of different century-long data sets, decadal hindcasts, and also predictions for the upcoming decade combined to a new large ensemble. Global reanalyses for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (Consortium for Small-scale Modeling – CLimate Mode; COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. Evaluation focuses on intensive widespread precipitation events and related temporal variabilities and trends. The presented ensemble data are within the range of observations for both statistical distributions and time series. The temporal evolution during the past 60 years is captured. The results reveal some long-term variability with phases of increased and decreased precipitation rates. The overall trend varies between the investigation areas but is mostly significant. The predictions for the upcoming decade show ongoing tendencies with increased areal precipitation. The presented regional climate model (RCM) ensemble not only allows for more robust statistics in general, it is also suitable for a better estimation of extreme values

Convection-parameterized and convection-permitting modelling of heavy precipitation in decadal simulations of the greater Alpine region with COSMO-CLM

Heavy precipitation is a challenging phenomenon with high impact on human lives and infrastructure, and thus a better modelling of its characteristics can improve understanding and simulation at climate timescales. The achievement of convection-permitting modelling (CPM) resolutions (Δx<4 km) has brought relevant advancements in its representation. However, further research is needed on how the very high resolution and switching-off of the convection parameterization affects the representation of processes related to heavy precipitation. In this study, we evaluate reanalysis-driven simulations for the greater Alpine area over the period 2000–2015 and assess the differences in representing heavy precipitation and other model variables in a CPM setup with a grid size of 3 km and a regional climate model (RCM) setup at 25 km resolution using the COSMO-CLM model. We validate our simulations against high-resolution observations (E-OBS (ENSEMBLES observations), HYRAS (Hydrometeorologische Rasterdatensätze), MSWEP (Multi-Source Weighted-Ensemble Precipitation), and UWYO (University of Wyoming)). The study presents a revisited version of the precipitation severity index (PSI) for severe event detection, which is a useful method to detect severe events and is flexible for prioritizing long-lasting events and episodes affecting typically drier areas. Furthermore, we use principal component analysis (PCA) to obtain the main modes of heavy precipitation variance and the associated synoptic weather types (WTs). The PCA showed that four WTs suffice to explain the synoptic situations associated with heavy precipitation in winter, due to stationary fronts and zonal flow regimes. Whereas in summer, five WTs are needed to classify the majority of heavy precipitation events. They are associated with upper-level elongated troughs over western Europe, sometimes evolving into cutoff lows, or with winter-like situations of strong zonal circulation. The results indicate that CPM represents higher precipitation intensities, better rank correlation, better hit rates for extremes detection, and an improved representation of heavy precipitation amount and structure for selected events compared to RCM. However, CPM overestimates grid point precipitation rates, which agrees with findings in past literature. CPM systematically represents more precipitation at the mountain tops. However, the RCMs may show large intensities in other regions. Integrated water vapour and equivalent potential temperature at 850 hPa are systematically larger in RCM compared to CPM in heavy precipitation situations (up to 2 mm and 3 K, respectively) due to wetter mid-level conditions and an intensified latent heat flux over the sea. At the ground level, CPM emits more latent heat than RCM over land (15 W m−2), bringing larger specific humidity north of the Alps (1 g kg−1) and higher CAPE (convective available potential energy) values (100 J kg−1). RCM, on the contrary simulates a wetter surface level over Italy and the Mediterranean Sea. Surface temperatures in RCM are up to 2 ∘C higher in RCM than in CPM. This causes outgoing longwave radiation to be larger in RCM compared to CPM over those areas (10 W m−2). Our analysis emphasizes the improvements of CPM for heavy precipitation modelling and highlights the differences against RCM that should be considered when using COSMO-CLM climate simulations

A regional atmosphere-ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance

The frequency of extreme events has changed, having a direct impact on human lives. Regional climate models help us to predict these regional climate changes. This work presents an atmosphere-ocean coupled regional climate system model (RCSM, with the atmospheric component COSMO-CLM and the ocean component NEMO) over the European domain, including three marginal seas: the Mediterranean, the North and the Baltic Seas. To test the model, we evaluate a simulation of more than one hundred years (1900–2009) with a spatial grid resolution of about 25 km. The simulation was nested into a coupled global simulation with the model MPI-ESM, whose ocean temperature and salinity was nudged to an MPI-ESM ocean-ice component forced with the 20th Century Reanalysis (20CR). The evaluation shows the robustness of the RCSM and discusses the added value by the coupled marginal seas over an atmosphere-only simulation. The coupled system runs stable for the complete 20th century and provides a better representation of extreme temperatures compared to the atmosphere-only model. The produced long-term dataset will allow an improved study of extreme events, helping us to better understand the processes leading to meteorological and climate extremes and their prediction

Long-term Variances of Heavy Precipitation across Central Europe using a Large Ensemble of Regional Climate Model Simulations

Widespread flooding events are among the major natural hazards in Central Europe. Such events are usually related to intensive, long-lasting precipitation. Despite some prominent floods during the last three decades (e.g. 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses. This study presents and discusses a validation of different century-long data sets, a large ensemble of decadal hindcasts, and also projections for the upcoming decade. Global reanalysis for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. The simulations show a good agreement with observations for both statistical distributions and time series. Differences mainly appear in areas with sparse observation data. The temporal evolution during the past 60 years is well captured. The results reveal some long-term variability with phases of increased and decreased heavy precipitation. The overall trend varies between the investigation areas but is significant. The projections for the upcoming decade show ongoing tendencies with increased precipitation for upper percentiles. The presented RCM ensemble not only allows for more robust statistics in general, in particular it is suitable for a better estimation of extreme values

Skill and added value of the MiKlip regional decadal prediction system for temperature over Europe

In recent years, several decadal prediction systems have been developed to provide multi-year predictions of the climate for the next 5–10 years. On the global scale, high decadal predictability has been identified for the North Atlantic sector, often extending over Europe. The first full regional hindcast ensemble, derived from dynamical downscaling, was produced within the German MiKlip project (‘decadal predictions’). The ensemble features annual starting dates from 1960 to 2017, with 10 decadal hindcasts per starting year. The global component of the prediction system uses the MPI-ESM-LR and the downscaling is performed with the regional climate model COSMO-CLM (CCLM). The present study focusses on a range of aspects dealing with the skill and added value of regional decadal temperature predictions over Europe. The results substantiate the added value of the regional hindcasts compared to the forcing global model as well as to un-initialized simulations. The results show that the hindcasts are skilful both for annual and seasonal means, and that the scores are comparable for different observational reference data sets. The predictive skill increases from earlier to more recent start-years. A recalibration of the simulation data generally improves the skill further, which can also be transferred to more user-relevant variables and extreme values like daily maximum temperatures and heating degree-days. These results provide evidence of the potential for the regional climate predictions to provide valuable climate information on the Abstract Formulae display:MathJax Logo? In recent years, several decadal prediction systems have been developed to provide multi-year predictions of the climate for the next 5–10 years. On the global scale, high decadal predictability has been identified for the North Atlantic sector, often extending over Europe. The first full regional hindcast ensemble, derived from dynamical downscaling, was produced within the German MiKlip project (‘decadal predictions’). The ensemble features annual starting dates from 1960 to 2017, with 10 decadal hindcasts per starting year. The global component of the prediction system uses the MPI-ESM-LR and the downscaling is performed with the regional climate model COSMO-CLM (CCLM). The present study focusses on a range of aspects dealing with the skill and added value of regional decadal temperature predictions over Europe. The results substantiate the added value of the regional hindcasts compared to the forcing global model as well as to un-initialized simulations. The results show that the hindcasts are skilful both for annual and seasonal means, and that the scores are comparable for different observational reference data sets. The predictive skill increases from earlier to more recent start-years. A recalibration of the simulation data generally improves the skill further, which can also be transferred to more user-relevant variables and extreme values like daily maximum temperatures and heating degree-days. These results provide evidence of the potential for the regional climate predictions to provide valuable climate information on the decadal time-scale to users

Adaptation and application of the large LAERTES-EU regional climate model ensemble for modeling hydrological extremes: a pilot study for the Rhine basin

Enduring and extensive heavy precipitation events associated with widespread river floods are among the main natural hazards affecting central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to running hydrological models (HMs) with respect to extremes. To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing over 12 000 simulated years. LAERTES-EU is adapted for use in an HM to calculate discharges for large river basins by applying quantile mapping with a parameterized gamma distribution to correct the mainly positive bias in model precipitation. The Rhine basin serves as a pilot area for calibration and validation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU also improves the statistical representativeness for high return values above 100 years of discharges. We conclude that the bias-corrected LAERTES-EU data set is generally suitable for hydrological applications and posterior risk analyses. The results of this pilot study will soon be applied to several large river basins in central Europe