ASPECT: Adaptation-oriented Seamless Predictions of European ClimaTe

ASPECT的目标是建立和示范一个时间跨度长达30年的无缝气候信息（SCI）系统，同时对气候信息进行基础研究和利用，用于部门应用（“中级水平”1）。目标是改进现有的气候预测系统，并将其跨时间尺度的产出与气候预测合并在一起，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有政策利益的领域（例如，备灾）和欧洲感兴趣的地区。我们将与WCRP灯塔活动在解释和预测地球系统变化方面的应用保持紧密的联系。为了提供带宽多样化的信息，SCI系统将以多模式气候预报为基础，并将借鉴EUCP等项目的经验。它将与包括destiny在内的欧洲Digital Twins的新活动保持一致。SCI将物理科学方面与其他学科相结合，以确保信息健壮、可靠，并与一系列用户驱动的决策案例相关。整套资料将包括基线预测和预测（加上不确定性），但也将探索新的领域（例如，具有高社会经济意义的极端情况）。为了取得成功，研究将包括：了解和归因时间尺度上的各种过程（如探索信噪比）及其对可预测性的影响，预测系统初始化的新方法，将预测与预估合并，通过缩小尺度（统计方法，人工智能）和HighRes模型（包括对流允许模型）为欧洲提供区域SCI，以及创新的后处理方法，提高气候预测的技能和稳健性。

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.