Evaluation and Expected Changes of Summer Precipitation at Convection Permitting Scale with COSMO-CLM over Alpine Space

There is an ongoing debate in the climate community about the benefits of convection-permitting models that explicitly resolve convection and other thermodynamical processes. An increasing number of studies show improvements in Regional Climate Model (RCM) performances when the grid spacing is increased to 1-km scale. Up until now, such studies have revealed that convection-permitting models confer significant advantages in representing orographic regions, producing high-order statistics, predicting events with small temporal and spatial scales, and representing convective organization. The focus of this work is on the analysis of summer precipitation over the Alpine space. More specifically, the driving data are downscaled using the RCM COSMO-CLM first at an intermediate resolution (12 km) over the European Domain of Coordinated Downscaling Experiment (EURO-CORDEX domain). Then, a further downscaling at 3 km, nested into the previous one, is performed over the Alpine domain to exploit the results over a complex orography context. Experiments of evaluation, historical and far future under the Intergovernmental Panel on Climate Change (IPCC) RCP8.5 scenario have been considered. Indices as mean precipitation, frequency, intensity, and heavy precipitation are employed in daily and hourly analyses. The results, observed from the analysis of 10 year-long simulations, provide preliminary indications, highlighting significant differences of the convection permitting simulations with respect to the driving one, especially at an hourly time scale. Moreover, future projections suggest that the convection permitting simulation refines and enhances the projected patterns, compared with the coarser resolution.

A New Method to Assess Fine-Scale Outdoor Thermal Comfort for Urban Agglomerations

In urban areas, high air temperatures and heat stress levels greatly affect human thermal comfort and public health, with climate change further increasing the mortality risks. This study presents a high resolution (100 m) modelling method, including detailed offline radiation calculations, that is able to efficiently calculate outdoor heat stress for entire urban agglomerations for a time period spanning several months. A dedicated measurement campaign was set up to evaluate model performance, yielding satisfactory results. As an example, the modelling tool was used to assess the effectiveness of green areas and water surfaces to cool air temperatures and wet bulb globe temperatures during a typical hot day in the city of Ghent (Belgium), since the use of vegetation and water bodies are shown to be promising in mitigating the adverse effects of urban heat islands and improving thermal comfort. The results show that air temperature reduction is most profound over water surfaces during the afternoon, while open rural areas are coolest during the night. Radiation shading from trees, and to a lesser extent, from buildings, is found to be most effective in reducing wet bulb globe temperatures and improving thermal comfort during the warmest moments of the day.

Spatial Optimization of Future Urban Development with Regards to Climate Risk and Sustainability Objectives

Future development in cities needs to manage increasing populations, climate-related risks, and sustainable development objectives such as reducing greenhouse gas emissions. Planners therefore face a challenge of multidimensional, spatial optimization in order to balance potential tradeoffs and maximize synergies between risks and other objectives. To address this, a spatial optimization framework has been developed. This uses a spatially implemented genetic algorithm to generate a set of Pareto-optimal results that provide planners with the best set of trade-off spatial plans for six risk and sustainability objectives: (i) minimize heat risks, (ii) minimize flooding risks, (iii) minimize transport travel costs to minimize associated emissions, (iv) maximize brownfield development, (v) minimize urban sprawl, and (vi) prevent development of greenspace. The framework is applied to Greater London (U.K.) and shown to generate spatial development strategies that are optimal for specific objectives and differ significantly from the existing development strategies. In addition, the analysis reveals tradeoffs between different risks as well as between risk and sustainability objectives. While increases in heat or flood risk can be avoided, there are no strategies that do not increase at least one of these. Tradeoffs between risk and other sustainability objectives can be more severe, for example, minimizing heat risk is only possible if future development is allowed to sprawl significantly. The results highlight the importance of spatial structure in modulating risks and other sustainability objectives. However, not all planning objectives are suited to quantified optimization and so the results should form part of an evidence base to improve the delivery of risk and sustainability management in future urban development.