A stochastic model with a low-frequency amplification feedback for the stratospheric northern annular mode

Article

Abstract

We consider three indices to measure the polar stratospheric mass and stratospheric meridional mass circulation variability: anomalies of (1) total mass in the polar stratospheric cap (60–90°N, above the isentropic surface 400 K, PSM), (2) total adiabatic mass transport across 60°N into the polar stratosphere cap (AMT), (3) and total diabetic mass transport across 400 K from the polar stratosphere into the troposphere below (DMT). It is confirmed that the negative stratospheric Northern Annular Mode (NAM) and PSM indices have a nearly indistinguishable temporal evolution and a similar red-noise-like spectrum with a de-correlation timescale of 4 weeks. This enables us to examine the low-frequency nature of the NAM in the framework of mass circulation, namely, \(\frac{d}{{dt}}{\text{PSM}}={\text{AMT}} - {\text{DMT}}\). The DMT index tends to be positively correlated with the PSM with a red-noise-like spectrum, representing slow radiative cooling processes giving rise to a de-correlation timescale of 3–4 weeks. The AMT is nearly perfectly correlated with the day-to-day tendency of PSM, reflecting a robust quasi 90° out-of-phase relation between the AMT and PSM at all frequency bands. Variations of vertically westward tilting of planetary waves contribute mainly to the high-frequency portion of AMT. It is the wave amplitude’s slow vacillation that plays the leading role in the quasi 90° out-of-phase relation between the AMT and PSM. Based on this, we put forward a linear stochastic model with a low-frequency amplification feedback from low-frequency amplitude vacillations of planetary waves to explain the amplified low-frequency response of PSM/NAM to a stochastic forcing from the westward tilting variability.

Keywords

Stratospheric NAM variability Polar stratospheric mass Poleward mass transport Linear stochastic model with a low-frequency amplification feedback 

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Nanjing University of Information Science and TechnologyNanjingChina
  2. 2.Geophysical Fluid Dynamics InstituteFlorida State UniversityTallahasseeUSA
  3. 3.Department of Earth, Ocean and Atmospheric SciencesFlorida State UniversityTallahasseeUSA
  4. 4.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  5. 5.Collaborative Innovation Center on Forecasts and Evaluation of Meteorological Disasters and KLMENanjing University of Information Science and TechnologyNanjingChina

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