Project

Although tropical cyclones develop along the tropical belt, their extratropical transition phase in the Atlantic basin is of great interest for European countries due to its role in determining extreme precipitation events over the western coast of Europe. Several European countries also have economic interests in areas traditionally affected by tropical cyclones (TCs). Different indices have been developed to estimate tropical cyclone activity based on large-scale atmospheric variables. However, while such indices well represent the annual cycle of TC count, they do not properly capture the interannual variability and long term tendencies of this metric. CLINT will:

1. investigate the role of the different index components (i.e., atmospheric variables) for tropical cyclone counts, to support the ML-based formulation of new indices that better support tropical cyclone detections;
2. enhance tropical cyclone predictions by using AI to identify key aspects of the 3-dimensional atmospheric structure preceding the genesis of tropical cyclones;
3. assess the emergent European risks from extreme wind and precipitation and associated flooding from changes in extra-tropical transition of tropical cyclones induced by climate warming.

Heatwaves can severely impact ecosystems, human communities, and key socio-economic sectors. In agriculture and food production, very hot daytime temperatures can compromise yield even in presence of established irrigation plans, while recurrent episodes of elevated night temperatures (warm nights) may favour the proliferation of pests and the diffusion of diseases. Energy demand is also affected both during the day (when energy demand peaks) and at night (when, contrary to normal conditions, the demand does not drop significantly). The frequency of heatwaves and warm nights is projected to increase sharply even in a mitigated global warming scenario. Recent studies have focused on quantifying heatwave characteristics based on maximum daily temperature or daily mean temperature and warm nights based on minimum daily temperature. However, these indices do not take into account the spatial structure and time-evolving patterns of heatwaves, which are useful when assessing the affected areas and associated impacts. CLINT will:

1. improve the understanding of what processes drive heatwaves and warm nights with a focus on subseasonal to seasonal time scales, by combining information from large-scale meteo-climatic fields as well as local land surface variables via ML algorithms.
2. use the understanding of their multiple causal factors to generate improved subseasonal to seasonal forecasts of heatwaves and warm nights.
3. contribute to developing AI-enhanced climate services that will better inform local adaptation strategies and policies within the WEF Nexus for future changes in heatwaves.

A drought, defined as a temporary but significant decrease in water availability, is a slowly developing natural phenomenon that can occur in all climatic zones. Although drought management has been extensively studied, traditional drought indices often yield imprecise information on detecting critical events and their associated impacts. This is due to the difficulty of capturing the complexity of extreme drought dynamics that evolve over diverse temporal and spatial scales, including short-term meteorological droughts, medium-term agricultural droughts, and long-term hydrological droughts, as well as the non-physical aspects related to droughts (water management, irrigation, etc.). Moreover, an increase in drought severity and frequency is expected from future climate projections. Although many regions over Europe are somewhat adapted to droughts, climate change projections estimate many of these regions will be taken beyond the breaking point. CLINT will:

1. enhance the detection of extreme drought events by defining impact-based drought indices with ML algorithms that link the observed impacts of extreme droughts (e.g. reduction of electricity production or crop failures) with the candidate drivers of the event, including climatic, meteorological, and hydrological variables over different spatial and temporal scales, and
2. focus on specific Climate Change Hotspots that are particularly vulnerable to water supply: semiarid regions, river deltas, and snow-dependent river basins.

Human and natural systems are often able to cope with a single extreme event, but they might be unable to sustain compound events (i.e., a combination of events, not necessarily extremes, that lead to significant impacts) and concurrent events (i.e., different types of events occurring within a specific temporal lag, as well as events of the same type in two different locations within a specific time period). Therefore, compound and concurrent events can expose humans and ecosystems to disproportionately increased risk compared to single extreme events. From a risk perspective, the primary concern relates to events with additive or even multiplicative effects. These effects can result from mutually reinforcing cycles between individual events, such as extreme droughts and heatwaves, linked through soil moisture and evaporation. Alternatively, they may develop through induced responses at distant areas of significant impact to the global system as in the case of concurrent hot extremes during summer 2018. CLINT will:

1. prompt AI advances in the detection and characterization of the spatio-temporal evolution of compound events at the European scale and will explore global teleconnection mechanisms responsible for concurrent events, and
2. bring improved understanding of concurrent events by uncovering key drivers and exploring their interaction and interdependence.