Experiences from seven living labs in the use of local knowledge and local data for tailored climate services

Climate services (CS) play a relevant role in providing tools for establishing societies resilient to global change considering its complex variability at multiple temporal and spatial scales. The involvement of end users in the processes of co-creation, co-development, and co-evaluation of CS, combined with the integration of local data (LD) and knowledge (LK) in forecast modelling and enables the development of user-tailored CS, improving the local impact of climate predictions.

The present work explains the lessons learnt, in terms of CS usability, in the co-creation process developed in seven living labs (LL) within the I-CISK project (https://icisk.eu/). Six LL are located in Europe (Andalucía-Spain, Alazani-Georgia, Budapest-Hungary, Rijnland-The Netherlands, Emilia Romagna-Italy, Crete-Greece) and one in Africa (Lesotho). All of them are identified as climate change hotspots focusing on different climate vulnerabilities affecting different sectors: drought, urban heat waves, water scarcity, landslide susceptibility. 

To respond to the different needs and challenges of the 7 LL, we implemented tailored methods for the CS, using LK and LD. These methods include downscaling for seasonal hydrological forecasting, downscaling for meteorological seasonal forecasts and climate projections, seasonal landslide susceptibility forecasts, drought vulnerability assessment and urban heat distribution. In those implementations, LD has a crucial role in all downscaling methods. LK is essential to advisor the selection of explanatory variables into the models, to define alert thresholds in risk events, in the design of adaptation strategies and in supporting vulnerability assessments. On top of these tailored methods, we developed the front-end interfaces of the CS, incorporating user feedback through constant interaction with stakeholders.

From the usability perspective, the main lessons learnt from the experience of the 7 LL in the co-creation of these CS are:

• Most tailored CS aim to produce outputs with higher spatial resolution than those available from existing global, regional, or national services.
• Downscaling techniques are widely applied as tailored methods across many LL, with local data playing a crucial role in these efforts.
• Local data falls short of meeting FAIR data principles.
• Despite the extensive information collected during co-creation processes, only a few LL fully benefit from local knowledge contributions. This knowledge is primarily used to enhance the understanding of climate information, but not to build comprehensive climate knowledge.
• In some LL, using the local language is a requirement for a complete understanding of the climate information, while in others, the scientific/technical terminology poses a barrier.
• Interpretation of the provided climate information (particularly, the uncertainty) is key for developing actions for water resources planning, climate adaptation and vulnerability reduction in different sectors at different LL.
• Sector-tailored information and/or sector-specific indicators are repeatedly demanded in some LL.