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Mesoscale weather systems cause spatiotemporal variability in offshore wind power, and insight into their fluctuations can support grid operations. In this study, a 10-year model integration with the kilometre-scale atmospheric model COnsortium for Small-scale MOdelling – CLimate Mode (COSMO-CLM) provided a wind and potential power fluctuation analysis in the Kattegat, a midlatitude sea strait with a width of 130 km and an irregular coastline. The model agrees well with scatterometer data away from coasts and small islands, with a spatiotemporal root-mean square difference of 1.35 m s -1. A comparison of 10 min wind speed at about 100 m with lidar data for a 2-year period reveals very good performance, with a slight model overestimation of 0.08 m s -1 and a high value for the Perkins skill score (0.97). From periodograms made using the Welch's method, it was found that the wind speed variability on a sub-hourly timescale is higher in winter compared to summer. In contrast, the wind power varies more in summer when winds often drop below the rated power threshold. During winter, variability is largest in the northeastern part of the Kattegat due to a spatial spin-up of convective systems over the sea during the predominant southwesterly winds. Summer convective systems are found to develop over land, driving spatial variability in offshore winds during this season. On average over the 10 summers, the mesoscale wind speeds are up to 20 % larger than the synoptic background at 17:00 UTC with a clear diurnal cycle. The winter-averaged mesoscale wind component is up to 10 % larger, with negligible daily variation. Products with a lower resolution like ERA5 substantially underestimate this ratio between the mesoscale and synoptic wind speed. Moreover, taking into account mesoscale spatial variability is important for correctly representing temporal variability in power production. The root-mean square difference between two power output time series, one ignoring and one accounting for mesoscale spatial variability, is 14 % of the total power generation. [ABSTRACT FROM AUTHOR]

As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km -2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses. [ABSTRACT FROM AUTHOR]

In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44 ∘ (∼ 50 km), 0.22 ∘ (∼ 25 km), and 0.11 ∘ (∼ 12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM–RCM modeling chain. [ABSTRACT FROM AUTHOR]

In the last decade, the Climate Limited-area Modeling (CLM) Community has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM community model, ERA-Interim reanalysis and eight Global Climate Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44° (~50 km), 0.22° (~25 km) and 0.11° (~12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modelling communities is needed to increase the reliability of the GCM-RCM modelling chain. [ABSTRACT FROM AUTHOR]

With more than 1.5 million inhabitants, the capital city of Kampala, Uganda, is one of the most populated areas in Sub-Saharan Africa. Moreover, with a growth rate of more than 4 percent, it is considered as one of the fastest growing cities on the planet. As current and future impacts of this urbanization on the regional climate is still unknown, some recent studies developed frameworks in order to apply urban climate models over this city. Brousse et al. (2019) uses the simple urban canopy scheme TERRA URB embedded in the COSMO-CLM model. Although this scheme has already proven its validity in several cases (Wouters et al., 2016; Demuzere et al., 2017), obtaining input parameters for the urban canopy model valid for Kampala is difficult. In fact, as urban climate models requires thermal and geometrical canopy input variables, Local Climate Zones are often considered as powerful tools for parameterization of the model following the WUDAPT framework (Ching et al., 2018). Yet the values extracted out of the ranges proposed by Stewart and Oke that were applied until now, are mostly derived out of Western cities. Therefore in order to improve the parametrization of the urban climate model and to validate the applicability of the LCZ scheme over East African cities, this study sets up a methodology for obtaining a novel range of parameters per LCZ class within the city of Kampala.In situ data were obtained during a field exercise in the summer months of 2018. A representative sample of measuring points was selected along transects throughout the city of Kampala. This selection required a point coverage that is proportional to the spatial occurrence of each LCZ class. This sampling procedure permitted the acquisition of about 1300 measurement points. Measured variables consist of quantitative (road width, distance between houses, heights of buildings) and qualitatively estimated (vegetation fraction, road-wall-roof material) variables needed (in)directly by the urban parametrization scheme. Combining these measurements with the LCZ map created by Brousse et al. (2018) allow for the definition of a more trustworthy representation of the city of Kampala. The new ranges obtained from the field campaign are thoroughly examined for each of the abovementioned parameters and compared with the defaults, focusing on physical explanations for statistically significant differences between LCZ classes. First, based on deviations from default settings, the potential effect of the tropical urban structure on the local climate is estimated. In a next step, the results of this study will serve as input for the urban parametrization scheme. Online coupled with a high resolution climate model, the effects of the newly defined urban presence on the regional climate in larger Kampala will be explored. [ABSTRACT FROM AUTHOR]

As one of the stormiest places in the world, the Lake Victoria region (East-Africa) is prone to hazardous climate extremes. More than 5000 fishermen die every year due to heavy and hardly predictable thunderstorms happening over the lake (Semazzi et al., 2011), emphasizing the need to thoroughly study the area. Thiery et al. (2015) were first to model the climate of this region at high resolution (~7 km) and gain insights on the regional climatology of the African Great Lakes. As a follow-up, this study therefore aims at unraveling the interaction of Lake Victoria on the initiation, development and behavior of mesoscale convective systems.To assess the influences of Lake Victoria on the mesoscale convective systems, two regional climate simulations are performed with COSMO-CLM and compared over a time period of 8 years (2010-2017). One reference simulation is run with the presence of Lake Victoria while the second excludes the lake. The simulation techniques are identical and consist of a direct dynamical downscaling procedure from ERA-5 reanalysis to a domain at a convection permitting resolution of 2.8 km. The model set-up follows the CORDEX-Africa tropical set-up proposed by Panitz et al. (2014), and supplements the original microphysics scheme by the two-moment scheme. To accurately represent the lake structure in the first simulation, the model is coupled to the Freshwater Lake model (FLake).Analysis of the model output will improve our understanding of the impact of unstable boundary layer air over Lake Victoria on the development and progress of lake-land interaction induced convective systems. The accurate model output will also serve as boundary conditions for a 1 km resolution domain centered over Kampala (the capital city of Uganda) to study the urbanization impact on the regional climate as proposed by Brousse et al. (2018, 2019). [ABSTRACT FROM AUTHOR]