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Identifying Martian atmospheric instabilities and their physical origins using bred vectors
Author(s) -
Greybush Steven J.,
Kalnay Eugenia,
Hoffman Matthew J.,
Wilson R. John
Publication year - 2012
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.1990
Subject(s) - baroclinity , barotropic fluid , middle latitudes , climatology , northern hemisphere , martian , atmospheric sciences , atmospheric circulation , geophysical fluid dynamics , environmental science , geology , meteorology , physics , mars exploration program , astrobiology
The bred vector (BV) technique applied to the Geophysical Fluid Dynamics Laboratory (GFDL) Mars General Circulation Model (MGCM) identifies regions and seasons of instability of the Martian atmosphere, and a kinetic energy equation applied to the control and perturbed states elucidates their physical origins. Instabilities prominent in the late autumn to early spring seasons of each hemisphere along the polar temperature front result from baroclinic conversions from BV potential to BV kinetic energy near the surface, whereas both baroclinic and barotropic conversions play a role for the westerly jets aloft. The low‐level tropics and the northern hemisphere summer are relatively stable, with negative bred vector growth rates. Bred vector growth precedes initiation of travelling wave activity in the midlatitudes during the transition seasons, and their structure relates to the eddy field. Topography plays a role in determining favoured locations for near‐surface instabilities. Bred vectors are also linked to forecast ensemble spread in data assimilation and help explain the growth of forecast errors. We finally note that the ability to use breeding to identify instabilities as well as their physical origin depends on the fact that both the control and the perturbed solutions that give rise to bred vectors satisfy exactly the model's governing equations. As a result, this approach can be used with any dynamical system represented by a model. Copyright © 2012 Royal Meteorological Society