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

该项目将建立必要的基础技术，以了解从计算机模型模拟的集合中需要什么，以提供强大可靠的知识。这些知识一方面包括对复杂多组分系统的科学理解的进步，另一方面包括对社会决策的指导。重点是气候模型，其中集合是研究活动的核心要素。这项研究将涉及，实际上也需要，整合一系列学科的专业知识。全球气候模式（GCMs）是复杂的、高维的离散系统。他们使用最新的计算机技术来解大量的联立微分方程。不同的学科和研究人员以完全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，它们被解释为代表了对物理的理解，所以“模型错误”一词包含了它们未能有效地代表我们对物理过程的了解。对于非线性动力学家来说，它们是非线性方程的高维系统，因此有一种期望，即使是最小程度的初始条件和模型公式（模型误差）的不确定性，也可能潜在地产生截然不同的结果。对于风险分析师和预测者来说，它们是时间序列的产生者，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会破坏这种评估。对于适应规划人员和决策者等“用户”，它们提供气候预测，作为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。这种观点的多样性导致了解释错误和不确定性的许多不同方式，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通过各种形状和大小的模拟集合来量化的。这些集合用于探索初始条件不确定性（ICU -在试图模拟未来时不知道气候系统当前状态的后果）和模式不确定性（MU -我们的模型与现实不同的后果）的影响。今天的整体（和模型）是在有限的计算能力的约束下建立的，所以他们的设计从这个问题开始：“我们能用今天的技术做得最好的是什么？”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“什么类型和规模的集合是必要的，以提供我们想要的信息？”它将开发设计的“理想合奏”，即合奏是必要的，以回答一组特定的问题，而不考虑当前的计算限制。在此基础上，它将评估构建实际集成所需的权衡的最佳方法，这些集成允许当前的计算限制。采取的方法将是双重的。首先将是使用低维非线性系统来研究非线性在类似气候的情况下对集成设计的影响。其次，从不同学科和国家的角度，建立一个协作的、集体的关于模型集成的需求和约束的图景。该项目将为未来的集成设计提供坚实的基础，并将为不确定性探索、分辨率和复杂性之间广泛争论的计算冲突提供信息。后两者的研究很多，但没有对前者进行全面的评估。这个项目将从需要而不是可用的角度填补这一空白。

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.