Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios

Ambitious afforestation proposals in the last decade target potential flood mitigation and carbon storage benefits but without a systematic, large-scale (>1000 km2) quantitative evaluation of their impacts on streamflow. Here, we assess the impact of afforestation on streamflow across twelve diverse catchments (c.500-10,000 km2) using a high-resolution land-surface model with a large ensemble of afforestation scenarios. Afforestation consistently decreases median and low streamflow. Median modelled flow is reduced by 2.8% ± 1.0 (1 s.d.), or 10 mm yr−1 ± 2.1 (1 s.d.), for a ten-percentage point increase in catchment broadleaf woodland. We find no nationally-consistent reduction of extreme floods. In larger catchments, planting extent is a stronger control on streamflow than location. Our results suggest that despite its potential environmental and societal benefits, widespread afforestation may inadvertently reduce water availability, particularly in drier areas, whilst only providing a modest reduction in extreme flood flows.

Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations

There is a strong scientific and social interest in understanding the factors leading to extreme events in order to improve the management of risks associated with hazards like droughts. In this study, artificial neural networks are applied to predict the occurrence of a drought in two contrasting European domains, Munich and Lisbon, with a lead time of 1 month. The approach takes into account a list of 28 atmospheric and soil variables as input parameters from a single-model initial-condition large ensemble (CRCM5-LE). The data were produced in the context of the ClimEx project by Ouranos, with the Canadian Regional Climate Model (CRCM5) driven by 50 members of the Canadian Earth System Model (CanESM2). Drought occurrence is defined using the standardized precipitation index. The best-performing machine learning algorithms manage to obtain a correct classification of drought or no drought for a lead time of 1 month for around 55 %–57 % of the events of each class for both domains. Explainable AI methods like SHapley Additive exPlanations (SHAP) are applied to understand the trained algorithms better. Variables like the North Atlantic Oscillation index and air pressure 1 month before the event prove essential for the prediction. The study shows that seasonality strongly influences the performance of drought prediction, especially for the Lisbon domain.

Storm surge risk under various strengths and translation speeds of landfalling tropical cyclones

Landfalling tropical cyclones (TCs) frequently occur with strong intensity in most coastal areas, and storm surges are likely to occur in response to extreme sea level (ESL) growth. However, the level of ESL growth under various wind conditions, coastline geometries and tide-surge interactions has not been clarified. In the Pearl River Estuary and Daya Bay, observations of landfalling TCs have indicated an increasing frequency of intense and rapid landfalls in the 2010s as compared to the 2000s, accompanied by a noteworthy increase in storm surge. Based on a large ensemble (~0.5 million storm surge events with various tracks, maximum wind speeds, maximum wind radiuses, translation speeds and tidal conditions) obtained from well-validated model simulations, the ESL growth in the study area is further quantified as follows: (1) ESL growth is more sensitive to the acceleration effect of landfalling TCs than to the strengthening effect of landfalling TCs since the effect of low acceleration (+3 m/s) is comparable to that under notable strengthening (+10 m/s); (2) ESL growth is strongly modulated by coastline geometry, especially in flared or arching coastline areas. ESL growth mainly occurs along flared coastline areas when landfalling TCs strengthen into severe tropical cyclones or typhoons but can also occur along arching coastline areas for stronger landfalling TCs, such as severe typhoons or supertyphoons; and (3) ESL growth could be increased or decreased by approximately 10% under the effect of tide-surge interactions. Both the large-ensemble method and the above ESL growth characteristics are worthy of attention in risk assessment and rapid prediction of storm surges in shallow waters.

Technologies for investigating large ensemble performance

Empirical research into large ensemble performance has crossed many disciplinary boundaries from music education to management studies, and has included the investigation of musicians’ interpersonal coordination and communication, group creativity and decision-making, conductors’ gestures, group musical expression, the social organization of large groups and their leadership, audience perceptions of performances, the individual and social benefits of participation, and rehearsal practices. However, there are still relatively few empirical studies of large ensemble performance, due to the social and practical factors that make it challenging to collect research data from large numbers of people engaged in a complex musical activity. Technological developments have increasingly expanded the research methods available to include sophisticated audio capture and analysis, web-based video-stimulated recall, and motion capture. This chapter discusses the challenges faced by researchers investigating large ensembles and describes some of the technological solutions that are opening up new avenues for data collection and analysis.

Seasonal predictability of baroclinic wave activity

Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.