Global mean sea level rise and its acceleration are projected to aggravate coastal erosion over the 21st century, which constitutes a major challenge for coastal adaptation. Projections of shoreline retreat are highly uncertain, however, namely due to deeply uncertain mean sea level projections and the absence of consensus on a coastal impact model. An improved understanding and a better quantification of these sources of deep uncertainty are hence required to improve coastal risk management and inform adaptation decisions. In this work we present and apply a new extra-probabilistic framework to develop shoreline change projections of sandy coasts that allows consideration of intrinsic (or aleatory) and knowledge-based (or epistemic) uncertainties exhaustively and transparently. This framework builds upon an empirical shoreline change model to which we ascribe possibility functions to represent deeply uncertain variables. The model is applied to two local sites in Aquitaine (France) and Castellón (Spain). First, we validate the framework against historical shoreline observations and then develop shoreline change projections that account for possible (although unlikely) low-end and high-end mean sea level scenarios. Our high-end projections show for instance that shoreline retreats of up to 200 m in Aquitaine and 130 m in Castellón are plausible by 2100, while low-end projections revealed that 58 and 37 m modest shoreline retreats, respectively, are also plausible. Such extended intervals of possible future shoreline changes reflect an ambiguity in the probabilistic description of shoreline change projections, which could be substantially reduced by better constraining sea level rise (SLR) projections and improving coastal impact models. We found for instance that if mean sea level by 2100 does not exceed 1 m, the ambiguity can be reduced by more than 50 %. This could be achieved through an ambitious climate mitigation policy and improved knowledge on ice sheets.

中文翻译：

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海岸线变化预测的深度不确定性：应用于沙滩的超概率方法

预计全球平均海平面上升及其加速将在 21 世纪加剧海岸侵蚀，这对海岸适应构成了重大挑战。然而，海岸线后退的预测高度不确定，即由于平均海平面预测非常不确定，并且对海岸影响模型缺乏共识。因此，需要更好地理解和更好地量化这些深度不确定性的来源，以改善沿海风险管理并为适应决策提供信息。在这项工作中，我们提出并应用了一个新的超概率框架来开发沙质海岸的海岸线变化预测，该框架允许彻底和透明地考虑内在（或偶然）和基于知识（或认知）的不确定性。该框架建立在经验海岸线变化模型之上，我们将可能性函数归因于该模型来表示深度不确定的变量。该模型应用于阿基坦（法国）和卡斯特利翁（西班牙）的两个当地站点。首先，我们根据历史海岸线观测验证该框架，然后制定海岸线变化预测，以考虑可能（尽管不太可能）的低端和高端平均海平面情景。例如，我们的高端预测显示，到 2100 年，阿基坦和卡斯特利翁的海岸线后退高度分别为 200 m 和 130 m 是合理的，而低端预测显示，分别为 58 米和 37 米的适度海岸线后退也是合理的。未来可能发生的海岸线变化的这种延长间隔反映了对海岸线变化预测的概率描述的模糊性，可以通过更好地约束海平面上升 (SLR) 预测和改进沿海影响模型来大幅减少。例如，我们发现如果 2100 年的平均海平面不超过 1 m，则模糊度可以降低 50% 以上。这可以通过雄心勃勃的气候减缓政策和提高对冰盖的了解来实现。