Process-Based Emergent Constraints on Global Physical and Biogeochemical Feedbacks

我们使用气候系统的复杂计算机模型来预测在全球变暖的情况下未来气候将如何变化。尽管使用了大型超级计算机，但我们在建立气候模型时必须进行近似，以便我们能够进行长达一个世纪的气候模拟。这些近似值会导致模型中的“错误”，也就是说，我们知道气候模型不能准确地再现我们对过去平均气候和气候变化的观测。气候模型误差会导致我们在进行预测时产生不确定性。我们不断努力构建新的更好的气候模型，以减少预测中的不确定性。我们以过去的经验和直觉为指导，将资源集中在模型改进的特定领域。但是我们能更有效率吗？如果我们知道在我们模拟当今气候和气候变化的能力中存在的许多错误中，哪一个对确定未来预测的不确定性最重要，我们是否可以更好地利用有限的资源更快地改进模型？在这项提案中，我们的目标是开发一种通用技术，以更好地将资源用于改善气候模型。我们称之为基于过程的紧急约束的中心概念是将气候模型中的错误与预测中的不确定性联系起来。我们将针对三个不同的领域：水蒸气和直减率（大气温度随高度的变化率），低空云层和影响地表和植被与大气的碳交换率的过程。已知所有这些过程对全球温度预测及其区域后果的不确定性具有领先顺序的影响。我们的目标是利用最先进的模型模拟和来自新观测平台的数据或来自现有观测的数据，我们将以新的方式处理这些数据。将重点关注卫星数据。我们项目的一个主要目标是识别造成模型误差和预测不确定性的过程，并开发可用于评估模型中这些过程的措施（度量）。该项目将提供多个例子，说明如何针对对预测真正重要的过程改进气候模型。最终目标是减少预测的不确定性。然而，除此之外，该项目还将提供信息，说明如何更好地针对具体气候过程进行新的观测。我们的目标是为未来的观测计划（或现有计划的继续）提出建议，这些计划将导致气候建模和预测的更快进展。

We use complex computer models of the climate system to make predictions of how climate will change in the future under scenarios of global warming. Despite the use of large supercomputers, we have to make approximations in building climate models so that we can produce century-long climate simulations. These approximations lead to 'errors' in models i.e. we know that climate models cannot reproduce exactly the observations we have made of past average climate and climate change. Climate model errors lead to uncertainties when we make predictions.We constantly strive to build new and better climate models in order to reduce uncertainty in predictions. We are guided by our past experience and intuition in targeting resources in specific areas of model improvement. But can we be more efficient? If we knew which of the many errors that exist in our ability to simulate present day climate and climate change are most important in determining the uncertainties in future predictions, could we better use our limited resources to improve models more quickly?In this proposal we aim to develop a generic technique to better target resources on improving climate models. The central concept, which we call a process-based emergent constraint, is to link errors in climate models to uncertainties in predictions. We will target three different areas; water vapour and lapse rate (the rate of change of atmospheric temperature with altitude), low-level clouds and processes that affect the rate of exchange of carbon in the land-surface and vegetation with the atmosphere. All these processes are know to have a leading-order influence on uncertainty in global temperature predictions and their regional consequences. We aim to exploit state-of-the-art model simulations and data from new observational platforms or data from existing observations that we will process in novel ways. There will be a focus on data from satellites. A key aim of our project is to identify processes that are responsible for both errors in models and uncertainties in predictions and to develop measures (metrics) that can be used to evaluate those processes in models.The project will provide multiple examples of how to target climate model improvement on processes that really matter for projections. The ultimate aim is to reduce uncertainty in predictions. However, in addition, the project will provide information on how to better target new observations of specific climate processes. We aim to make recommendations for future observational programmes (or the continuation of existing programmes) that will lead to more rapid progress in climate modeling and prediction.