Sample sizes of observed climate extremes are typically too small to reliably constrain return period estimates when there is non-stationary behaviour. To increase the historical record 100-fold, we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead times from the ECMWF seasonal prediction system SEAS5. We fit the GEV distribution to the UNSEEN ensemble with a time covariate to facilitate detection of changes in 100-year precipitation values over a period of 35 years (1981–2015). Applying UNSEEN trends to 3-day precipitation extremes over Western Norway substantially reduces uncertainties compared to estimates based on the observed record and returns no significant linear trend over time. For Svalbard, UNSEEN trends suggests there is a significant rise in precipitation extremes, such that the 100-year event estimated in 1981 occurs with a return period of around 40 years in 2015. We propose a suite of methods to evaluate UNSEEN and highlight paths for further developing UNSEEN trends to investigate non-stationarities in climate extremes.

Large-ensemble climate model simulations can provide deeper understanding of the characteristics and causes of extreme events than historical observations, due to their larger sample size. However, adequate evaluation of simulated ‘unseen’ events that are more extreme than those seen in historical records is complicated by observational uncertainties and natural variability. Consequently, conventional evaluation and correction methods cannot determine whether simulations outside observed variability are correct for the right physical reasons. Here, we introduce a three-step procedure to assess the realism of simulated extreme events based on the model properties (step 1), statistical features (step 2), and physical credibility of the extreme events (step 3). We illustrate these steps for a 2000 year Amazon monthly flood ensemble simulated by the global climate model EC-Earth and global hydrological model PCR-GLOBWB. EC-Earth and PCR-GLOBWB are adequate for large-scale catchments like the Amazon, and have simulated ‘unseen’ monthly floods far outside observed variability. We find that the realism of these simulations cannot be statistically explained. For example, there could be legitimate discrepancies between simulations and observations resulting from infrequent temporal compounding of multiple flood peaks, rarely seen in observations. Physical credibility checks are crucial to assessing their realism and show that the unseen Amazon monthly floods were generated by an unrealistic bias correction of precipitation. We conclude that there is high sensitivity of simulations outside observed variability to the bias correction method, and that physical credibility checks are crucial to understanding what is driving the simulated extreme events. Understanding the driving mechanisms of unseen events may guide future research by uncovering key climate model deficiencies. They may also play a vital role in helping decision makers to anticipate unseen impacts by detecting plausible drivers.

In this paper, we present and apply a new three-dimensional model for the prediction of canopy-flow and turbulence dynamics in open-channel flow. The approach uses a dynamic immersed boundary technique that is coupled in a sequentially staggered manner to a large eddy simulation. Two different biomechanical models are developed depending on whether the vegetation is dominated by bending or tensile forces. For bending plants, a model structured on the Euler–Bernoulli beam equation has been developed, whilst for tensile plants, an N-pendula model has been developed. Validation against flume data shows good agreement and demonstrates that for a given stem density, the models are able to simulate the extraction of energy from the mean flow at the stem-scale which leads to the drag discontinuity and associated mixing layer

Low‐likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high‐impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low‐likelihood high‐impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium‐Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record‐shattering August 2020 California‐Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low‐likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low‐likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science‐policy interface to manage high impacts.

Vegetation is a characteristic feature of shallow aquatic flows such as rivers, lakes and coastal waters. Flow through and above aquatic vegetation canopies is commonly described using a canopy mixing layer analogy which provides a canonical framework for assessing key hydraulic characteristics such as velocity profiles, large-scale coherent turbulent structures and mixing and transport processes for solutes and sediments. This theory is well developed for the case of semi-rigid terrestrial vegetation and has more recently been applied to the case of aquatic vegetation. However, aquatic vegetation often displays key differences in morphology and biomechanics to terrestrial vegetation due to the different environment it inhabits. Here we investigate the effect of plant morphology and biomechanical properties on flow–vegetation interactions through the application of a coupled LES-biomechanical model. We present results from two simulations of aquatic vegetated flows: one assuming a semi-rigid canopy and the other a highly flexible canopy and provide a comparison of the associated flow regimes. Our results show that while both cases display canopy mixing layers, there are also clear differences in the shear layer characteristics and turbulent processes between the two, suggesting that the semi-rigid approximation may not provide a complete representation of flow–vegetation interactions.