EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Understanding intrinsic ocean variability by suppressing regional stochastic variability

Mao-Lin Shen, Noel Keenlyside, and Ping-Gin Chiu
Mao-Lin Shen et al.
  • Geophysical Institute, University of Bergen, Bergen, Norway (

Intrinsic ocean variability is essential for climate prediction because it is less sensitive to stochastic process, but it is very difficult to be identified due to internal climate variability. Here we use regional interactive ensemble applied on ocean-atmosphere interface (RIE-OA) to suppress atmosphere stochastic variability and to reveal intrinsic variability as well as to understand climate dynamic across multiple timescales. Five atmosphere general circulation models (AGCM) are coupled to an ocean general circulation model (OGCM) over the North Atlantic basin (20oN to Denmark Strait and Greenland-Scotland ridge). The OGCM interacts with fluxes from a selected AGCM globally except over the North Atlantic basin where the OGCM interacts with the ensemble averaged fluxes from the five AGCMs. The five AGCMs, on the other hand, feel the same ocean states. Hence, the atmosphere stochastic variability impacting the ocean is one-fifth weaker than stand-alone configuration (control case). This leads to reduction of the local climate variability, such as Atlantic Multidecadal Variability, but should not reduce intrinsic variability. Comparing control cases and RIE-OA case, we found the intrinsic ocean variability, a narrow-banded low-frequency (about 8 to 20 years) signal over the North Atlantic Subtropical Gyre, is not influenced by the weakened stochastic variability. More details will be discussed.

How to cite: Shen, M.-L., Keenlyside, N., and Chiu, P.-G.: Understanding intrinsic ocean variability by suppressing regional stochastic variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14346,, 2021.