Low-Pass Filtering, Heat Flux, and Atlantic Multidecadal Variability

Abstract

In this model study the authors explore the possibility that the internal component of the Atlantic multidecadal oscillation (AMO) sea surface temperature (SST) signal is indistinguishable from the response to white noise forcing from the atmosphere and ocean. Here, complex models are compared without externally varying forcing with a one-dimensional noise-driven model for SST. General analytic expressions are obtained for both unfiltered and low-pass filtered lead–lag correlations. It is shown that this simple model reproduces many of the simulated lead–lag relationships among temperature, rate of change of temperature, and surface heat flux. It is concluded that the finding that at low frequencies the ocean loses heat to the atmosphere when the temperature is warm, which has been interpreted as showing that the ocean circulation drives the AMO, is a necessary consequence of the fact that at long periods the net heat flux (ocean plus atmosphere) is zero to a good approximation. It does not distinguish between the atmosphere and ocean as the source of the AMO and is consistent with the hypothesis that the AMO is driven by white noise heat fluxes. It is shown that some results in the literature are artifacts of low-pass filtering, which creates spurious low-frequency signals when the underlying data are white or red noise. It is concluded that in the absence of external forcing the AMO in most GCMs is consistent with being driven by white noise, primarily from the atmosphere.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 23, 2017
Source ID
10.1175/jcli-d-16-0810.1

Entities

People

  • Amy C. Clement
  • Katinka Bellomo
  • Lisa N. Murphy
  • Mark Cane

Organizations

  • Columbia University
  • National Aeronautics and Space Administration
  • National Science Foundation
  • Office of Naval Research
  • University of Miami

Tags

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Fluid Mechanics and Fluid Dynamics.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.