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

研究的重点是研究海面热通量快速变化(在日和次日时间尺度上)对海-气耦合的影响。文献表明，海洋和大气的动态时间尺度之间的明显分离为许多海-气相互作用提供了一个简单的范例，其中表面热通量的快速变化分量被随机项近似。然而，这个热通量项不仅取决于“快”大气，而且还取决于“慢”海洋。因此，与过去的大多数研究相反，随机噪声不应该独立于海洋状态。值得注意的是，这种与状态相关的(乘性)噪声可以通过一种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，准确表示这种漂移是必要的，但也很困难，因为噪声引起的漂移不能简单地用常量项来参数化，因为它的强度取决于热通量可变性的变化，热通量可变性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有助于年际到年代际时间尺度上的气候可变性和可预测性，以及它如何有助于在许多耦合模式中发现的偏差。因此，利用观测和耦合模式运行来研究与状态相关的快速变化的热通量(包括通过洋流的热平流、表面热通量、夹带和水平混合)的影响是非常必要的。由于偏离高斯性或异常统计可以揭示热通量与状态有关的潜在特征，这项研究的主要目标之一是从广泛的观测数据集和耦合模式运行的层次中全局绘制与大气-海洋耦合有关的海洋和大气数量的非高斯性(例如，高阶矩：偏度、峰度)。耦合模型的实验也将被用来理解数值耦合的细节如何影响模型对这一过程的描述。知识价值：仔细研究快速变化的热通量及其对海-气相互作用的影响是重要的，因为必须准确地模拟热通量的快速变化，以正确地模拟海洋表面温度(SST)和气温(TIR)的可变性，并且耦合方案必须适当地捕捉每日和次日时间尺度上的热通量可变性。PI将(1)扩展过去十年发展起来的系统随机框架，以了解气候系统中缓慢和快速变化的组成部分之间的相互作用，例如海温和塔尔异常，以及(2)制定改进的耦合方案，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与大气如何相互作用的理解，并有助于气候诊断和模型界。结果将在NOAA-CIRES疾控中心网页上以各种数量的非高斯统计地图的形式传播。

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.