Isolating a Coupled Climate Signal Using the Interactive Ensemble Modeling Approach to Study Climate Variability and Dynamic Processes in the North Pacific Ocean

Numerical modeling of the climate system includes a large range of temporal and spatial scales. Isolating the climate signal benefits from scale separation from the shorter-term weather forcing viewed as white noise. According to Hasselmann’s null hypothesis (H0), integration of this internal atmospheric noise by the upper ocean yields the longer-term variability or red spectra of the interface variables, such as the long-term sea surface temperature anomalies (SSTA). One of the methods to reduce the weather noise in pursuit of a coupled climate signal is by using an Interactive Ensemble (IE) Earth system modeling. In the IE approach, several atmospheric, land, and ice models enable the ensemble spread driven by the atmospheric variability and provide average atmospheric forcing to the ocean in the form of turbulent heat fluxes. The reduction of atmospheric noise in the IE simulations was shown to have a limited effect in reducing the overall SSTA variability to be totally explained by the H0 hypothesis, and to a varying degree in different parts of the world. This result demonstrates that a rather systematic coupled system response could drive the SSTA variability.

The current study is focused on analyzing the climate signal over the North Pacific Ocean in 500-year-long simulations, the Pacific Decadal Oscillation (PDO). The PDO signal is enhanced in IE numerical experiments, as opposed to a single-component control run of the same duration, as manifested by a greater contribution of an isolated modal signal into the total variance. The enhanced signal yields a better defined spectral peak at decadal time scales and shows greater statistical significance than in a control run. Furthermore, a maximum covariance analysis (MCA) of the several key model variables allows isolating the regions in the North Pacific where different coupled dynamical processes may contribute most into the climate system response.