Abstract. We developed a coupled regional climate system model based on the CCLM regional climate model. Within this model system, using OASIS3-MCT as a coupler, CCLM can be coupled to two land surface models (the Community Land Model (CLM) and VEG3D), the NEMO-MED12 regional ocean model for the Mediterranean Sea, two ocean models for the North and Baltic seas (NEMO-NORDIC and TRIMNP+CICE) and the MPI-ESM Earth system model.We first present the different model components and the unified OASIS3-MCT interface which handles all couplings in a consistent way, minimising the model source code modifications and defining the physical and numerical aspects of the couplings. We also address specific coupling issues like the handling of different domains, multiple usage of the MCT library and exchange of 3-D fields.We analyse and compare the computational performance of the different couplings based on real-case simulations over Europe. The usage of the LUCIA tool implemented in OASIS3-MCT enables the quantification of the contributions of the coupled components to the overall coupling cost. These individual contributions are (1) cost of the model(s) coupled, (2) direct cost of coupling including horizontal interpolation and communication between the components, (3) load imbalance, (4) cost of different usage of processors by CCLM in coupled and stand-alone mode and (5) residual cost including i.a. CCLM additional computations.Finally a procedure for finding an optimum processor configuration for each of the couplings was developed considering the time to solution, computing cost and parallel efficiency of the simulation. The optimum configurations are presented for sequential, concurrent and mixed (sequential+concurrent) coupling layouts. The procedure applied can be regarded as independent of the specific coupling layout and coupling details.We found that the direct cost of coupling, i.e. communications and horizontal interpolation, in OASIS3-MCT remains below 7 % of the CCLM stand-alone cost for all couplings investigated. This is in particular true for the exchange of 450 2-D fields between CCLM and MPI-ESM. We identified remaining limitations in the coupling strategies and discuss possible future improvements of the computational efficiency.

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<p>Air Quality in Berlin is a particular problem during winter episodes. During this episodes, local emissions are only one factor contributing to the high concentrations. The other factors are the lowered height of the planetary boundary layer and the advection of pollutants, some of which are produced in Eastern Europe. To trace the share of total pollution in Berlin for 2016-18 back to its origins, the Chemistry Transport Model (CTM) LOTOS-EUROS v2.1 (LOng Term Ozone Simulation EURopean Operational Smog, invented by TNO, Netherlands) is used, which also provides a labelling approach. Some specifications were made for the emission datasets used to drive the model, including emission dependencies on temperature (e.g. cold engine starts and heating degree-days for households).</p><p>The model results are evaluated using the German AirBase monitoring sites. An incremental approach (Lenschow et al., 2001) is used for the evaluation and estimation of the urban share of Berlin. The focus is on Particulate Matter (PM): PM10, PM2.5, and the coarse-mode fraction (PM10-PM2.5). Due to the seasonal variability of PM and its composition, seasonal differentiation is investigated. The labelling approach provided in LOTOS-EUROS allows to distinguish between the sources relevant for Berlin’s PM pollution, with the focus of this work on local contributions such as households and traffic on the one hand and regional contributions from Berlin itself and Germany’s Eastern European neighbors (Poland and the Czech Republic) on the other hand.</p><p>This study is in relation to the “Berliner Luftreinhalteplan” (Berlin Clean Air Plan).</p>

The number 4.8 is added in the title of the revised version. Interactive comment on Geosci. Model Dev. Discuss.,