Stochastic parameterization and El Niño–Southern Oscillation

Journal article

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical Pacific. However, the models in the ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have large deficiencies in ENSO amplitude, spatial structure, and temporal variability. The use of stochastic parameterizations as a technique to address these pervasive errors is considered. The multiplicative stochastically perturbed parameterization tendencies (SPPT) scheme is included in coupled integrations of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4). The SPPT scheme results in a significant improvement to the representation of ENSO in CAM4, improving the power spectrum and reducing the magnitude of ENSO toward that observed. To understand the observed impact, additive and multiplicative noise in a simple delayed oscillator (DO) model of ENSO is considered. Additive noise results in an increase in ENSO amplitude, but multiplicative noise can reduce the magnitude of ENSO, as was observed for SPPT in CAM4. In light of these results, two complementary mechanisms are proposed by which the improvement occurs in CAM. Comparison of the coupled runs with a set of atmosphere-only runs indicates that SPPT first improve the variability in the zonal winds through perturbing the convective heating tendencies, which improves the variability of ENSO. In addition, SPPT improve the distribution of westerly wind bursts (WWBs), important for initiation of El Niño events, by increasing the stochastic component of WWB and reducing the overly strong dependency on SST compared to the control integration.

Authors: Christensen, H; Palmer, T; Berner, J; Coleman, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Meteorological Society
• Journal: Journal of Climate
• Volume: 30
• Issue: 1
• Pages: 17–38
• Publication date: 2016-12-16
• Acceptance date: 2016-08-22
• DOI: 10.1175/JCLI-D-16-0122.1
• EISSN: 1520-0442
• ISSN: 0894-8755
• Keywords: climate variability; model errors; coupled models; stochastic models; nonlinear models; ENSO