Climate change is accelerating and affects livelihoods already now, while society needs to prepare for a future, even hotter climate. Among many other impacts, climate change affects human health and leads to excess mortality due to heat. In cities, the Urban Heat Island (UHI) effect exacerbates the problem, all the more as health effects increase nonlinearly with the exceedance of certain thresholds. Furthermore, the combination of heat and air pollutants increases the risk of respiratory and cardiovascular diseases, while the urban population increases.

The urgency of tackling climate change in urban areas is now well recognized, as evidenced by the plans of the IPCC to produce a Special Report on Cities and Climate Change. The topic has also become a matter of urgency in Swiss cities. Tropical nights were rare in Switzerland until recently but are now more common and will occur frequently in the future. Cities are aware of the situation and the challenges they will face in the future. Climate change adaptation has become an important aspect of city planning, legislation, and administration. Locally relevant climate information is however required to support decisions.

Planning of green spaces, unsealing of surfaces, or installation of blue infrastructure requires high-resolution temperature maps as well as simple systems to assess the effects of such measures. This proposal, which is related to COST Action FAIRNESS, will produce, enhance, and intercompare local climate information based on measurements from ca. 16 European cities. Using geostatistical approaches, the project will generate maps of temperature indicators and statistically analyse the effect of urban green space on UHI mitigation. Temperature products for many cities are available from numerical-statistical approaches (e.g., downscaling of reanalyses) and remote sensing through various portals.

However, in-situ observations of air temperature are crucial to provide local information about its spatiotemporal variability. For instance, land surface temperature (LST) data from satellites show a qualitatively different UHI than air temperature networks, with often stronger UHI during the day whereas air temperature often show a stronger UHI during the night. Particularly at night, local measurements are required to capture, e.g., local cold air production and cold air drainage flows. In Switzerland, several cities such as Basel, Bern, and Zurich have initiated dense urban climate networks. The same is true for other European cities.

The objective of COST Action FAIRNESS is to provide a knowledge share platform that collects this information and facilitates exchange of both, data and expertise, between the cities, scientific communities, and other stakeholders. The proposed project will make use of the emerging network of the FAIRNESS consortium for science and, at the same time, will contribute to the COST Action. Using urban climate data from ca. 16 urban networks of the FAIRNESS community, we will (1) use statistical approaches to characterise the UHI at high spatiotemporal resolution based on the air temperature measurements, (2) apply geostatistical approaches (land use regression, machine learning) based on land cover data for European cities to provide local temperature maps and compare them to satellite-based LST data and downscaling, (3) use the geostatistical models to assess the effect of urban green space and of unsealing of surfaces. The close link with COST Action FAIRNESS and its stakeholder network is important. FAIRNESS builds a knowledge share platform which brings together a wide range of climate monitoring networks from many European cities. Building a data base from such heterogeneous sources requires metadata and data standards, which are not yet established. The proposed project therefore contributes to the COST Action by serving as trail blazer and demonstration project. It will also provide important information for future projects (e.g., network design). ZHAW will support this GIUB/UniBE SNF Project by means of technical/measurement/data analysis know-how transfer.