Optimising the Design of Ensembles to Support Science and Society (ODESSS)

这个项目将建立所需的基本技术，以了解从计算机模型模拟集合中需要什么，以提供稳健可靠的知识。这些知识一方面包括对复杂的多组分系统的科学理解方面的进展，另一方面包括对社会决策的指导。重点放在气候模型上，其中集合是研究活动的核心要素。这项研究将涉及，确实需要整合一系列学科的专业知识。全球气候模型(GCM)是复杂、高维、离散的系统。他们使用最新的计算机技术来求解大量的微分方程组。不同的学科和研究人员以截然不同的方式看待它们，因此在如何探索它们的错误和不确定性方面有非常不同的观点。对于物理学家来说，他们被解释为代表了对物理的理解，所以术语“模型误差”包括他们未能有效地代表我们对物理过程的了解。对于非线性动态学家来说，它们是高维的非线性方程系统，因此人们预计，由于初始条件和模型公式(模型误差)的不确定性，即使是最小程度的不确定性，可能会产生截然不同的结果。对于风险分析师和预报员来说，他们是时间序列的生成者，可以通过与历史观测的关系来判断错误，尽管物理学家和统计学家可能担心，气候变化问题的外推性质破坏了这种评估。对于适应规划者和政策制定者等“用户”，他们提供的气候预测代表了他们自己工作的起点；科学家提供的任何不确定性都被认为是对当前最佳知识的可靠估计。这种不同的视角导致了许多不同的方法来解释误差和不确定性，并对模型本身产生了相互冲突的要求。投影不确定性通常是从各种形状和大小的模拟集合中量化的。这些集合被用来探索初始条件不确定性(ICU--试图对未来进行模拟时不知道气候系统的当前状态的后果)和模型不确定性(MU--我们的模型与现实不同的后果)的影响。今天的合奏(和模型)是在有限的计算能力的限制下建立的，所以他们的设计始于一个问题：“我们能用今天的技术做的最好的是什么？”相比之下，这个项目的一个独特和创新的方面是，它的出发点是“为了提供我们想要的信息，需要什么类型和规模的剧团？”它将开发“期望集合”的设计，即在不考虑当前计算限制的情况下回答一组特定问题所必需的集合。在此基础上，它将评估建立实用合奏所需的权衡的最佳方法，这确实考虑到了当前的计算限制。采取的方法将是双重的。首先，将使用低维非线性系统来研究在类似气候的情况下，非线性对总体设计的影响。其次，将从广泛的不同学科和国家角度建立对模型集合的需求和约束的协作、集体图景。该项目将为未来的集合设计提供坚实的基础，并将揭示不确定性探索、解决和复杂性之间的广泛辩论的计算冲突。对后两者进行了大量研究，但尚未对前者进行全面的评估。这个项目将从需要什么而不是现有什么的角度来填补这一空白。

This project will build the foundational techniques needed to understand what is necessary from an ensemble of computer model simulations to provide robust reliable knowledge. Such knowledge includes progress in scientific understanding of complex multi-component systems on the one hand, and guidance for societal decisions on the other. The focus is on climate models, where ensembles are a core element of research activities. The research will involve, indeed requires, integrating expertise across a range of disciplines.Global climate models (GCMs) are complex, high-dimensional, discretized systems. They use the latest computer technology to solve a large number of simultaneous differential equations. Different disciplines and researchers view them in radically different ways and hence have very different perspectives on how to explore their errors and uncertainties. For physicists they are interpreted as representing physical understanding so the term "model error" encompasses their failure to effectively represent what we know about physical processes. For nonlinear dynamicists they are high dimensional systems of nonlinear equations so there is an expectation that profoundly different results could potentially arise as a consequence of uncertainty in both initial conditions and model formulation (model errors) of even the smallest degree. For risk analysts and forecasters they are generators of timeseries from which errors can be judged by relation to historic observations, although physicists and statisticians might be concerned that the extrapolatory nature of the climate change problem undermines such an assessment. For "users" such as adaptation planners and policy makers they provide climate projections which represent the starting point for their own work; any uncertainties provided by the scientists are assumed to be reliable estimates of the best current knowledge. This variety of perspectives leads to many different ways of interpreting the errors and uncertainties, and creates conflicting demands on the models themselves.Projection uncertainties are typically quantified from ensembles of simulations which come in a variety of shapes and sizes. These ensembles are used to explore the impact of initial condition uncertainty (ICU - the consequence of not knowing the current state of the climate system when trying to make simulations of the future) and of model uncertainty (MU - the consequence of our models being different from reality). Today's ensembles (and models) have been built under the constraint of limited computational capacity so their designs start from the question: "what's the best we can do with today's technology?" By contrast, one of the unique and innovative aspects of this project is that its starting point is "what type and size of ensembles are necessary to provide the information we want?" It will develop designs for "aspirational ensembles" i.e. ensembles that are necessary to answer a particular set of questions without regard to current computational limitations. From this foundation it will evaluate the best way to approach the trade-offs necessary in building practical ensembles which DO allow for current computational limitations. The approach taken will be twofold. First will be to use low-dimensional nonlinear systems to study the consequences of nonlinearity for ensemble design in climate like situations. Second will be to build a collaborative, collective picture of the demands and constraints on model ensembles from a wide range of different disciplinary and national perspectives.The project will provide a solid foundation for future ensemble designs and will inform the widely debated computational conflict between uncertainty exploration, resolution and complexity. The latter two of these are much studied but there has been no comprehensive assessment of former. This project will fill that gap from the perspective of what is needed rather than what is available.