ASPECT: Adaptation-oriented Seamless Predictions of European ClimaTe

Aspects的目标是建立和展示一个时间跨度长达30年的无缝气候信息(SCI)系统，同时支持研究和利用气候信息用于部门应用(“中层”1)。其目标是改进现有的气候预测系统，并将其在时间尺度上的产出与气候预测合并，以统一SCI作为部门决策的标准。重点将放在欧洲气候信息上，但我们也将更广泛地关注与政策相关的领域(例如，备灾)和欧洲感兴趣的地区。我们将保持与从WCRP灯塔活动中学习解释和预测地球系统变化的开发的强有力的联系。为了提供带宽多样化的信息，SCI系统将以多模式气候预测为基础，并将借鉴EUCP等项目的经验。它将与欧洲数字双胞胎的新活动保持一致，包括Destine。SCI将把自然科学方面与其他学科的方面结合起来，以确保信息是稳健、可靠的，并与一系列用户驱动的决策案例相关。一揽子信息将纳入基线预测和预测(加上不确定性)，但也将探索新的领域(例如，具有社会经济高层兴趣的极端情况)。为了取得成功，这项研究将包括：对时间尺度上各种过程的理解和归属(例如探索信噪比)及其对可预测性的影响、预报系统初始化的新方法、预报与预测的合并、通过降低尺度(统计方法，AI)和HighRes模式(包括允许对流的模式)为欧洲提供区域SCI，以及加强气候预报的技能和稳健性的创新后处理方法。

ASPECT aims for the setup and demonstration of a seamless climate information (SCI) system with a time horizon up to 30yr, accompanied by underpinning research and utilisation of climate information for sectoral applications (‘middle-ground level’1). The goal is to improve existing climate prediction systems and merge their outputs across timescales together with climate projections to unify a SCI as a standard for sectoral decision-making. The focus will be on European climate information but we will also look more widely where there is a policy interest (e.g., disaster preparedness) and in regions of European interest. We will maintain a strong link into an exploit learning from the WCRP lighthouse activities on explaining and predicting earth system change. To provide a bandwidth diversity of information the SCI system will be based on multi-model climate forecasts, and will build on learning from projects such as EUCP. It will align with new activities on Digital Twins within Europe, including DestinE. The SCI will combine physical science aspects with those from other disciplines to ensure the information is robust, reliable and relevant for a range of user driven decision cases. The information package will incorporate baseline forecasts and projections (plus uncertainty), but also new frontiers will be explored (e.g., extremes which are of socioeconomic high-level interest). To be successful the research will encompass: Understanding and attribution of various processes along the timescales (such as exploring signal-to-noise ratio) and their impact on predictability, new ways of initialisation of the prediction systems, merging predictions with projections, provision of regional SCI for Europe by downscaling (statistical methods, AI) and HighRes models (including convection-permitting models) and innovative post-processing method enhancing the skill and robustness of the climate forecasts.