Wave Energetics of the Atmosphere of Mars

A comprehensive assessment of the energetics of transient waves is presented for the atmosphere of Mars using the Mars Analysis Correction Data Assimilation (MACDA) dataset (v1.0) and the eddy kinetic energy equation. Each hemisphere is divided into four representative periods covering the summer and winter solstices, a late fall period, and an early spring period for each of the three Mars years available. Northern hemisphere fall and spring eddy energetics is similar with some inter-annual and inter-seasonal variability, but winter eddy kinetic energy and its transport are strongly reduced in intensity as a result of the winter solstitial pause in wave activity. Barotropic energy conversion acts as a sink of eddy kinetic energy throughout each year with little reduction in amplitude during the solstitial pause. Baroclinic energy conversion acts as a source in fall and spring but disappears during the winter period as a result of the stabilized vertical temperature profile around winter solstice. Traveling waves are typically triggered by geopotential flux convergence. Individual waves decay through a combination of barotropic conversion of the kinetic energy from the waves to the mean flow, geopotential flux divergence, and dissipation. The southern hemisphere energetics is similar to the northern hemisphere in timing, but wave energetics is much weaker as a result of the high and zonally asymmetric topography. The effect of dust on baroclinic instability is examined by comparing a year with a global-scale dust storm (GDS) to two years without a GDS. In the GDS year, waves develop a mixed baroclinic/barotropic growth phase before decaying barotropically. Though the total amount of eddy kinetic energy generated by baroclinic energy conversion is lower during the GDS year, the maximum eddy intensity is not diminished. Instead, the number of intense eddies is reduced by about 50%

Toward More Realistic Simulation and Prediction of Dust Storms on Mars

Global dust storms have major implications for the past and present climate, geologic history, habitability, and future exploration of Mars. Yet their mysterious origins mean we remain unable to realistically simulate or predict them. We identify four key Knowledge Gaps and make four Recommendations to make progress in the next decade

Quantitative Analysis and 3D Visualization of Nwp Data Using Quasi-Geostrophic Equations

Quasi-geostrophic (QG) analysis of the atmosphere utilizes predefined isobaric surfaces to ascertain vertical motion. One equation of the QG system is the omega equation that states that vertical forcing results from differential vorticity advection and thickness advection. Two problems arise when using the QG omega equation: the forcing terms are not independent and must be analyzed simultaneously, and vertical forcing is visually noisy. Both issues are resolved using a smoothing and quantification technique that applies the QG omega equation. The analysis fields from a selection of events were chosen from the North American Mesoscale model. Using a finite differencing methodology dependent on the wavelength of synoptic features, values of vertical forcing were calculated using the omega equation. The calculated omega field correlated well with model omega while also quantifying and visualizing large perturbations in vertical forcing. The method allows for quick diagnosis of forcing type and strength within the atmosphere

The Mars Dust Activity Database (MDAD)

The Mars Dust Activity Database (MDAD) catalogs all dust storm activity on Mars with area >10^5 km^2 between MY 24 Ls=150 and MY 32 Ls=171. The MDAD is compiled using the Mars Orbiter Camera (MOC) Mars Daily Global Maps (MDGMs) from MY 24 Ls = 150 to MY 28 Ls = 121 and the Mars Color Imager (MARCI) MDGMs from MY 28 Ls = 133 to MY 32 Ls = 171. The MDAD covers 60 N-60 S for the MOC era and 90 N-90 S for the MARCI era. The MDAD is supported by NASA MDAP Grant 80NSSC17K0475

Selection Dynamics, Asymptotic Stability, and Adaptive Behavior.

Selection dynamics are often used to distinguish stable and unstable equilibria. This is particularly useful when multiple equilibria prevent a priori comparative static analysis. This paper reports an experiment designed to compare the accuracy of the myopic best-response dynamic and an inertial selection dynamic. The inertial selection dynamic makes more accurate predictions about the observed mutual best-response outcomes. Copyright 1994 by University of Chicago Press.