Assessing and Understanding Oceanic Climate Forcing on Decadal Climate Variability from Surface Heat Flux

北大西洋和北太平洋都是大规模海表温度（SST）变化的发源地，这种变化持续数年甚至数十年。 一个例子是大西洋多年代际振荡（AMO），其中赤道以北的大西洋大部分地区在大约20到40年的时间内变暖和变冷。 北太平洋和北大西洋SST的低频变率必须由海洋和大气强迫的某种组合驱动，但它们的作用还没有得到很好的理解，对于哪一种是主导的仍然存在一些争论。 一个重要的考虑是，低频SST变率可以由伴随天气系统通过的蒸发和表面热交换的变化产生，即使天气系统的移动比SST变率快得多。 如果SST的缓慢变化是由快速移动系统的“天气噪音”驱动的，那么长期SST预测的前景就有些有限，而海洋动力学的强大作用，可能涉及全球翻转环流的缓慢波动，可能意味着可以提前几年预测SST异常。该奖项的工作旨在量化大气和海洋强迫对低-频率SST变率使用一个简单的随机模型。该模式的要点是，大气和海洋强迫可以通过观察SST和表面热通量异常的时间来区分，其中表面热通量是指海洋表面发生的蒸发和热交换以及由云量变化引起的表面阳光和红外辐射的变化。 如果SST变化是由大气驱动的，那么它应该先于引起SST变化的地表热通量异常。另一方面，由海洋驱动的SST变化可能会产生地表热通量的变化，从而抑制SST异常，例如，海洋驱动的暖异常可能会产生具有冷却效应的地表热通量。 在这种情况下，热通量距平与海温距平大致同步，但符号相反。 这里使用的模式包括显式表示的大气和海洋阻尼，并把海洋强迫作为一个红噪声过程。 该模式用于分析观测数据集的SST变率和使用标准和增强水平分辨率的气候模式的输出。 然后用修改后的共同体地球系统模式（CESM）进行模拟，以确定SST变率的机制。这项工作具有社会价值，因为它解决了长期SST预测的问题。SST的缓慢变化对人类有许多影响，例如，从AMO的冷阶段到暖阶段，严重的大西洋飓风的数量大约翻了一番，AMO与世纪中期的萨赫勒大干旱有关。 AMO和其他形式的低频SST的可预测性程度尚不清楚，了解驱动机制对于评估可预测性和为如何开发预测模型提供有用的指导至关重要。 此外，该项目还为两名研究生提供支持和培训，从而为该研究领域的未来劳动力提供支持。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The North Atlantic and North Pacific are both home to large-scale sea surface temperature (SST) variations that last for years and even decades. An example is the Atlantic Multidecadal Oscillation (AMO), in which a large portion of the Atlantic north of the equator warms and cools over periods of perhaps 20 to 40 years. The low-frequency variability of North Pacific and North Atlantic SST must be driven by some combination of oceanic and atmospheric forcing, but their roles are not well understood and there is still some debate as to which is dominant. An important consideration is that low-frequency SST variability can be generated by changes in evaporation and surface heat exchange accompanying the passage of weather systems even though the movement of weather systems is much faster than the SST variability. If the slow variations of SSTs are driven by the "weather noise" of fast-moving systems the prospects for long-term SST prediction are somewhat limited, while a strong role for ocean dynamics, perhaps involving slow fluctuations of the global overturning circulation, could mean that SST anomalies can be predicted years in advance.Work under this award seeks to quantify the contributions of atmospheric and oceanic forcing to low-frequency SST variability using a simple stochastic model. The gist of the model is that atmospheric and oceanic forcing can be distinguished by looking at the timing of SST and surface heat flux anomalies, where the surface heat flux refers to both the evaporation and heat exchange occurring at the ocean surface and changes in surface sunlight and infrared radiation caused by changes in cloud cover. If an SST change is driven by the atmosphere it should be preceded by the surface heat flux anomaly that caused it. On the other hand an SST change driven by the ocean is likely to produce a change in surface heat flux that acts to damp the SST anomaly, for instance an ocean-driven warm anomaly would likely produce a surface heat flux that has a cooling effect. In that case the heat flux anomaly would be roughly synchronous with the SST anomaly but with opposite sign. The model used here includes explicit representations of both atmospheric and oceanic damping and treats the oceanic forcing as a red noise process. The model is used to analyze SST variability in observational datasets and output from climate models using standard and enhanced horizontal resolution. Simulations with modified versions of the Community Earth System Model (CESM) are then used to identify mechanisms of SST variability.The work has societal value as it addresses the question of long-range SST prediction. The slow variations of SST have a number of human impacts, for instance the number of severe Atlantic hurricanes roughly doubles from the cold phase of the AMO to the warm phase, and the AMO is implicated in the great Sahel drought of the mid-20th century. The extent to which the AMO and other forms of low-frequency SST are predictable is not known, and an understanding of the driving mechanisms is essential for an assessment of predictability and to provide useful guidance as to how predictive models might be developed. In addition, the project provides support and training for two graduate students, thereby providing for the future workforce in this research area.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.