The Impact of Rapidly-Varying Heat Fluxes on Air-Sea Interaction and Climate Variability

研究的主要重点是研究快速变化（每日和亚每日时间尺度）的海表热通量对大气-海洋耦合的影响。文献表明，海洋和大气的动力学时间尺度之间的明确分离，允许一个简单的范例，其中快速变化的表面热通量的组成部分是由一个随机项近似的海气相互作用。然而，这个热通量项不仅取决于“快”的大气，而且取决于“慢”的海洋。因此，随机噪声不应该是独立的海洋状态，相反，大多数过去的研究。值得注意的是，这种状态相关（倍增）噪声可以通过称为噪声诱导漂移的过程改变低频海洋动力学。在气候模式中，这种漂移的准确表示是必要的，但很困难，因为噪声引起的漂移不能简单地用常数项来参数化，因为其强度取决于热通量变化的方差，这是时间和空间的函数。该项目的目的是了解热通量的乘性噪声特性如何在年际到年代际时间尺度上对气候变率和可预测性做出贡献，以及它如何可能对许多耦合模式中发现的偏差做出贡献。因此，它是必要的，以研究状态依赖的快速变化的热通量（包括通过洋流，表面热通量，夹带，和水平混合的热平流）的影响，使用观测和耦合模式运行。由于偏离高斯性或异常统计可以揭示热通量的潜在状态依赖特征，因此研究的主要目标之一是全局映射非高斯性（例如，更高的时刻：偏度，峰度）的海洋和大气量相关的大气-海洋耦合从一个广泛的观测数据集和层次的耦合模式运行。与耦合模型的实验也将被用来了解如何细节的数值耦合影响模型表示这一过程。 智力优势：快速变化的热通量及其对海气相互作用的影响的细致研究是重要的，因为快速变化的热通量的变化必须准确地模拟正确的模式海表温度（SST）和空气温度（TAIR）的变化，耦合方案必须是适当的捕获热通量的变化在每日和亚每日的时间尺度。PI将（1）扩展过去十年中开发的系统随机框架，以了解气候系统中缓慢和快速变化的成分（如SST和TAIR异常）的相互作用，以及（2）开发改进的耦合方案并在天气和气候模式中实施随机参数化。 更广泛的影响：这项研究将进一步加深我们对海洋如何与大气相互作用的理解，反之亦然，并有助于气候诊断和建模社区。结果将作为各种数量的非高斯统计图在诺阿-CIRES CDC网页上传播。

The main focus of the research is to study the impact of rapidly varying (on daily and sub-daily timescales) sea surface heat fluxes on atmosphere-ocean coupling. Literature indicates that the clear separation between the dynamical timescales of the ocean and atmosphere allows a simple paradigm for much air-sea interaction in which the rapidly varying component of surface heat fluxes is approximated by a stochastic term. However, this heat flux term depends upon not only the "fast" atmosphere but also upon the "slow" ocean. Thus, the stochastic noise should not be independent of the oceanic state, contrary to most past studies. Notably, such state-dependent (multiplicative) noise can alter low-frequency ocean dynamics through a process known as noise-induced drift. Accurate representation of this drift is necessary but difficult in climate models, since noise-induced drift cannot be simply parameterized by constant terms as its strength depends on the variance of the heat flux variability, a function of time and space. The aim of this project is to understand how the multiplicative noise character of the heat flux contributes to climate variability and predictability on interannual to inter-decadal timescales, and how it might contribute to biases found in many coupled models. Thus, it is imperative to study the effect of state-dependent rapidly varying heat fluxes (including heat advection through ocean currents, surface heat fluxes, entrainment, and horizontal mixing) using observations and coupled model runs. Since deviations from Gaussianity, or anomalous statistics, can shed light on the underlying state-dependent character of the heat fluxes, one of the main objective of the research is to globally map the non-Gaussianity (e.g., higher moments: skewness, kurtosis) of oceanic and atmospheric quantities relevant for atmosphere-ocean coupling from a broad range of observational datasets and a hierarchy of coupled model runs. Experiments with coupled models will also be used to understand how details of numerical coupling impact model representation of this process. Intellectual Merit: A meticulous study of fast-varying heat fluxes and its impact on air-sea interaction is important because the fast-varying variability of heat fluxes must be accurately simulated to correctly model sea surface temperature (SST) and air temperature (TAIR) variability, and the coupling scheme must be appropriate to capture heat flux variability on daily and sub-daily time scales. The PI will (1) extend a systematic stochastic framework that has been developed over the last decade to understand the interaction of slowly and rapidly varying components of the climate system, such as SST and TAIR anomalies, and (2) develop improved coupling schemes and the implementation of stochastic parameterizations in weather and climate models. Broader Impacts: The research will further our understanding on how the ocean interacts with the atmosphere and vice versa and helps climate diagnostics and modeling communities. The results will be disseminated as maps of non-Gaussian statistics of various quantities on the NOAA-CIRES CDC web page.