Process-Based Emergent Constraints on Global Physical and Biogeochemical Feedbacks

对全球物理和生物地球化学反馈的基于过程的紧急约束

• 批准号: NE/K016016/1
• 负责人: Matthew Collins
• 金额: $ 50.66万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FK016016%2F1
• 关键词: Process; Based; Emergent; Constraints; Global

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.

我们使用复杂的气候系统计算机模型来预测未来在全球变暖的情况下气候将如何变化。尽管使用了大型超级计算机，我们仍然必须在构建气候模型时进行近似，以便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”，即我们知道气候模型无法准确再现我们对过去平均气候和气候变化的观测结果。气候模型的错误会导致我们做出预测时的不确定性。我们不断努力建立新的、更好的气候模型，以减少预测的不确定性。我们以过去的经验和直觉为指导，将资源用于模型改进的特定领域。但我们能提高效率吗？如果我们知道我们模拟当前气候和气候变化的能力中存在的众多错误中，哪一个对于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我们有限的资源更快地改进模型？在本提案中，我们的目标是开发通用技术，更好地将资源用于改进气候模型。我们称之为基于过程的紧急约束的核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；水蒸气和递减率（大气温度随高度的变化率）、低层云以及影响地表和植被与大气的碳交换率的过程。众所周知，所有这些过程都对全球气温预测的不确定性及其区域后果产生先导影响。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，我们将以新颖的方式处理这些数据。重点将放在卫星数据上。我们项目的一个关键目标是识别导致模型错误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（指标）。该项目将提供多个示例来说明如何瞄准气候模型对对预测真正重要的过程进行改进。最终目标是减少预测的不确定性。然而，除此之外，该项目还将提供有关如何更好地针对特定气候过程进行新观测的信息。我们的目标是为未来的观测计划（或现有计划的延续）提出建议，从而在气候建模和预测方面取得更快的进展。

项目成果

Are strong fire-vegetation feedbacks needed to explain the spatial distribution of tropical tree cover?

是否需要强烈的火灾植被反馈来解释热带树木覆盖的空间分布？

• DOI: 10.1111/geb.12380
• 发表时间: 2015
• 期刊: Global Ecology and Biogeography
• 影响因子: 6.4
• 作者: Good P

Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types

• DOI: 10.5194/gmd-11-2857-2018
• 发表时间: 2018-07-13
• 期刊: GEOSCIENTIFIC MODEL DEVELOPMENT
• 影响因子: 5.1
• 作者: Harper, Anna B.;Wiltshire, Andrew J.;Duran-Rojas, Carolina
• 通讯作者: Duran-Rojas, Carolina

Spatiotemporal patterns of terrestrial gross primary production: A review

• DOI: 10.1002/2015rg000483
• 发表时间: 2015-09-01
• 期刊: REVIEWS OF GEOPHYSICS
• 影响因子: 25.2
• 作者: Anav, Alessandro;Friedlingstein, Pierre;Zhao, Maosheng
• 通讯作者: Zhao, Maosheng

Multi vegetation model evaluation of the Green Sahara climate regime

• DOI: 10.1002/2017gl073740
• 发表时间: 2017-07-16
• 期刊: GEOPHYSICAL RESEARCH LETTERS
• 影响因子: 5.2
• 作者: Hopcroft, Peter O.;Valdes, Paul J.;Beerling, David J.
• 通讯作者: Beerling, David J.

Quantifying the temperature-independent effect of stratospheric aerosol geoengineering on global-mean precipitation in a multi-model ensemble

量化多模式集合中平流层气溶胶地球工程对全球平均降水的与温度无关的影响

• DOI: 10.1088/1748-9326/11/3/034012
• 发表时间: 2016
• 期刊: Environmental Research Letters
• 影响因子: 6.7
• 作者: Ferraro A