The roles of the Quasi-Biennial Oscillation and El Niño for entry stratospheric water vapor in observations and coupled chemistry–ocean CCMI and CMIP6 models

Shlomi Ziskin Ziv, Chaim I. Garfinkel, Sean Davis, Antara Banerjee

Atmospheric chemistry and physics · 10 June 2022 · NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)

In one sentence. Advanced machine learning and regression show the QBO matters more than ENSO for stratospheric water vapor; CMIP6 and CCMI models underrepresent the QBO link.

Abstract

Abstract. The relative importance of two processes that help control the concentrations of stratospheric water vapor, the Quasi-Biennial Oscillation (QBO) and El Nino-Southern Oscillation (ENSO), are evaluated in observations and in comprehensive coupled ocean-atmosphere-chemistry models. The possibility of nonlinear interactions between these two is evaluated both using Multiple Linear Regression (MLR) and three additional advanced machine learning techniques. The QBO is found to be more important than ENSO, however nonlinear interactions are non-negligible, and even when ENSO, the QBO, and potential nonlinearities are included the fraction of entry water vapor variability explained is still substantially less than what is accounted for by cold point temperatures. While the advanced machine learning techniques perform better than an MLR in which nonlinearities are suppressed, adding nonlinear predictors to the MLR mostly closes the gap in performance with the advanced machine learning techniques. Comprehensive models suffer from too weak a connection between entry water and the QBO, however a notable improvement is found relative to previous generations of comprehensive models. Models with a stronger QBO in the lower stratosphere systematically simulate a more realistic connection with entry water.

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