ALPACA - Advancing the Long-range Prediction, Attribution, and forecast Calibration of Amoc and its climate impacts

APACA - 推进 Amoc 的长期预测、归因和预测校准及其气候影响

• 批准号: NE/Y005104/1
• 负责人: Jennifer Mecking
• 金额: $ 33.16万
• 依托单位: NATIONAL OCEANOGRAPHY CENTRE
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FY005104%2F1
• 关键词: ALPACA; Advancing; Long; range; Prediction

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial component of the climate system due to its role in heat and salt transports, as well as its role in transporting and storing carbon. Variability in the strength of AMOC has been linked to important climate impacts, for instance, the number of Atlantic Hurricanes, anomalous Sahel precipitation, and European weather. Therefore, improved predictions of the AMOC would have important societal benefits.Despite its importance, the predictability of the AMOC remains relatively unexplored on timescales from one season to 10 years ahead, and many uncertainties persist in our understanding of AMOC variability. For example, we are unsure of the relative importance of different processes in driving AMOC variability on different timescales and latitudes, nor how predictable they are in state-of-the-art forecasting systems. Recent studies have provided considerable evidence that the atmospheric circulation in the North Atlantic is much more predictable than previously thought on these timescales. However, the predicted signals are far too small (the so-called signal-to-noise paradox) and predictions need to be calibrated to provide credible forecasts of society relevant variables, such as surface temperature. Given that atmospheric circulation is a key driver of AMOC, then it follows that AMOC predictions on these timescales may also suffer from similar signal-to-noise issues. Furthermore, predictions of AMOC, and its climate impact, could be improved by extending the published statistical calibrations to the ocean circulation.ALPACA will utilise AMOC observations (RAPID and OSNAP) and observation-based AMOC reconstructions to assess the quality of current AMOC forecasts in state-of-the-art seasonal and decadal prediction systems. Furthermore, we will evaluate the processes that contribute to skill and assess their consistency across models. We will also use new simulations to better understand the relative roles of different processes in driving observed variability on different timescales, and we will leverage new large ensemble simulations to quantify the role of external forcing in driving AMOC variability and change. Finally, by exploiting this new understanding, we will determine whether seasonal-to-decadal predictions of AMOC and its climate impacts can be improved through physically-consistent statistical calibrations that reduce the signal-to-noise errors in predictions. ALPACA is a collaboration between the National Centre for Atmospheric Science at the University of Reading, The National Oceanography Centre Southampton, The University of Exeter, and the Met Office Hadley Centre from the U.K., and The National Center for Atmospheric Research and the University of Miami, from the U.S, and the Barcelona Supercomputing Center from Spain.

大西洋经向翻转环流（AMOC）因其在热量和盐输送以及碳输送和储存方面的作用而成为气候系统的重要组成部分。 AMOC 强度的变化与重要的气候影响有关，例如大西洋飓风的数量、萨赫勒异常降水和欧洲天气。因此，改进 AMOC 的预测将产生重要的社会效益。尽管 AMOC 很重要，但在从一个季节到未来 10 年的时间尺度上，AMOC 的可预测性仍然相对未被探索，并且我们对 AMOC 变化的理解仍然存在许多不确定性。例如，我们不确定不同过程在不同时间尺度和纬度上驱动 AMOC 变化的相对重要性，也不确定它们在最先进的预测系统中的可预测性。最近的研究提供了大量证据，表明北大西洋的大气环流在这些时间尺度上比以前认为的更容易预测。然而，预测的信号太小（所谓的信噪比悖论），需要校准预测才能提供对社会相关变量（例如地表温度）的可靠预测。鉴于大气环流是 AMOC 的关键驱动因素，因此 AMOC 在这些时间尺度上的预测也可能会遇到类似的信噪比问题。此外，通过将已发布的统计校准扩展到海洋环流，可以改进 AMOC 及其气候影响的预测。ALPACA 将利用 AMOC 观测（RAPID 和 OSNAP）和基于观测的 AMOC 重建来评估当前 AMOC 预测的质量最先进的季节性和十年预测系统。此外，我们将评估有助于技能的流程并评估其跨模型的一致性。我们还将使用新的模拟来更好地了解不同过程在驱动不同时间尺度上观测到的变异性方面的相对作用，并且我们将利用新的大型集合模拟来量化外部强迫在驱动 AMOC 变异性和变化中的作用。最后，通过利用这一新的认识，我们将确定是否可以通过物理一致的统计校准来改进 AMOC 及其气候影响的季节到十年预测，从而减少预测中的信噪比误差。 APACA 是雷丁大学国家大气科学中心、南安普顿国家海洋学中心、埃克塞特大学和英国气象局哈德利中心以及国家大气研究中心和迈阿密大学之间的合作项目，来自美国，以及来自西班牙的巴塞罗那超级计算中心。