Shifts in the frequency of typical meteorological patterns in an ocean basin, over interannual to decadal time scales, cause shifts in the patterns of wave generation. Therefore, ocean basin-scale climate shifts produce shifts in the wave climates affecting the coastlines of the basin. We present a hybrid methodology for downscaling observed (or predicted) climate shifts into local nearshore wave climates and then into the associated coastline responses. A series of statistical analyses translate observed (or predicted) distributions of meteorological states into the deep water wave climate affecting a coastal region and dynamical modeling combined with statistical analyses transform the deep water wave climate into the nearshore wave climate affecting a particular coastline. Finally, dynamical modeling of coastline evolution hindcasts (or predicts) how coastline shapes respond to climate shifts. As a case study, we downscale from meteorological hindcast in the North Atlantic basin since 1870 to the responses of the shape of the coast of the Carolinas, USA. We test the hindcasts using shoreline change rates calculated from historical shorelines, because shifts in coastline shape equate to changes in the alongshore pattern of shoreline change rates from one historical period to another. Although limited by the availability of historical shorelines (and complicated by historical inlet openings), the observations are consistent with the predicted signal of ocean basin-scale climate change. The hybrid downscaling methodology, applied to the output of global climate models, can be used to help forecast future patterns of shoreline change related to future climate change scenarios.@en

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.@en