PREVENT

It is well-established that the Mediterranean is a climate change hot spot that warms faster than the global mean rates and the frequency of extremes increases. Moreover, climate models often fail to provide sufficient skill in predicting extremes. Extremes are of outmost importance for many socio-economic sectors and activities, including human health, agriculture and water resource management, ecology, and tourism. Most of these activities are dominant drivers of the Mediterranean macro- and micro-economy. The overall objective of PREVENT is to improve the predictability of impact-relevant extremes in the Mediterranean region on timescales from seasonal to decadal using state-of-the-art dynamical, statistical, and machine learning methods. Additionally, PREVENT brings together experts in different disciplines and geographical regions for a comprehensive study of impact-relevant climate extremes in the Mediterranean with the goal to improve their seasonal and decadal predictions in a changing climate. PREVENT intends to 1) Define for local climate extreme hotspot regions, including major urban centers in the Mediterranean. 2) Provide new management tools that can be used in many domains, to guide and direct processes, support monitoring activities, and increase organizational efficiency 3) Develop awareness and competencies by enabling policymakers, industry, farmers, and other producers to understand, promote and practice the inclusion of seasonal and decadal data in their project management. PREVENT has a small flexible consortium consisting of colleagues with great experience in the Mediterranean climate and especially the analysis of extreme climate events and the use of impact models. One of the main PREVENT ambitions is to enhance gender balance in the coordination of EU-funded research programs and give young researchers the opportunity to participate as leaders in the work packages by providing fresh new ideas for research.

Task 4.1 Identification of causal drivers of hot and dry events in reanalysis data (Leader: PIK, ¿1-¿24) Task 4.3 Future changes in causal drivers (Leader: PIK, ¿12-¿30) Task 5.1 Estimation of fire risk and atmospheric causal drivers of fire weather (Leader PIK; M12-33)