The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi-model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA-2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.

Weather extremes have gained great attention to the general public and policy makers recently. Extratropical cyclones, frontal systems and atmospheric rivers are central components of weather over mid latitudes. These phenomena are associated with compound weather conditions, including dramatic changes in temperature, wind and extreme precipitation. In fact, wind extremes and heavy precipitation events occurring in the winter over land in the mid latitudes are mostly associated with extratropical cyclones. It is well known that the Iberian Peninsula, due to its location, is prone to the occurrence of these compound extreme events and associated hazards (Liberato et al., 2013; 2014). In this project our aim is to explore the usage of expert crowdsourcing for annotating weather systems associated to compound hydrometeorological extreme events over the Euro-Atlantic region, so automated methods and computational resources can be optimized in a future hybrid approach. This approach allows a sharing of lessons learned and a common design ground. Atmospheric phenomena annotation aims at bringing new dimensions to current big data problems in climate and atmospheric sciences. Today big data full potential in weather and climate science domain is still restricted by the poor semantic knowledge of data gathered and the inability to correlate data with other domains. Acknowledgements: This work is supported by the Portuguese Science and Technology Foundation (Fundação para a Ciência e Tecnologia – FCT), under the projects UID/GEO/50019/2013 – Instituto Dom Luiz and CMU/CS/0012/2017 – “eCSAAP - expert Crowdsourcing for Semantic Annotation of Atmospheric Phenomena”. Liberato et al. 2013 Nat. Hazards Earth Syst. Sci., 13:2239-2251 doi: 10.5194/nhess-13-2239-2013 Liberato 2014 Weather and Climate Extremes, 5-6: 16-28 doi: 10.1016/j.wace.2014.06.002

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (1) preconditioned, (2) multivariate, (3) temporally compounding, and (4) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, based on a bottom-up approach, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (1) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (2) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (3) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (4) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors.