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

大西洋子午线翻转环流(AMOC)是气候系统的重要组成部分，因为它在热量和盐分输送以及碳输送和储存方面发挥作用。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、异常的萨赫勒降水和欧洲天气。因此，改进对AMOC的预测将具有重要的社会效益。尽管它很重要，但AMOC的可预测性在未来一个赛季到10年的时间尺度上仍然相对未被探索，而且许多不确定性持续存在于我们对AMOC变异性的理解中。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变异性的相对重要性，也不确定它们在最先进的预测系统中的可预测性。最近的研究提供了相当多的证据表明，在这些时间尺度上，北大西洋的大气环流比以前认为的要可预测得多。然而，预测的信号太小(所谓的信噪比悖论)，需要对预测进行校准，以提供对与社会相关的变量的可信预测，例如地表温度。鉴于大气环流是AMOC的一个关键驱动因素，因此AMOC对这些时间尺度的预测可能也会受到类似的信噪比问题的影响。此外，通过将已公布的统计校准扩展到海洋环流，可以改进对AMOC及其气候影响的预测。ALPACA将使用AMOC观测(快速和OSNAP)和基于观测的AMOC重建来评估最先进的季节和十年预报系统中当前AMOC预报的质量。此外，我们将评估有助于提高技能的流程，并评估它们在模型中的一致性。我们还将使用新的模拟来更好地了解不同过程在不同时间尺度上驱动观测到的可变性的相对作用，我们将利用新的大型集合模拟来量化外部强迫在驱动AMOC可变性和变化中的作用。最后，通过利用这一新的理解，我们将确定是否可以通过物理上一致的统计校准来改进对AMOC及其气候影响的季节到十年的预测，以减少预测中的信噪比误差。羊驼是由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、美国国家大气研究中心和迈阿密大学以及西班牙巴塞罗那超级计算中心共同合作的。

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.