PNNL

Abstract

Monotonicity constraints and gradient preserving flux corrections employed by many advection algorithms used in atmospheric models make these algorithms non-linear. Consequently, any relations among model variables transported separately are not necessarily preserved in such models. These errors cannot be revealed by traditional algorithm testing based on advection of a single tracer. New type of tests are developed and conducted to evaluate the preservation of a sum of several number mixing ratios advected independently of each other, as is the case, for example, in models using bin or sectional representation of aerosol or cloud particle size distribution. The tests show that when three tracers are advected in 1D uniform constant velocity flow, local errors in the sum can be on the order of 10%. When cloud-like interactions are allowed among the tracers, errors in total sum of three mixing ratios can reach up to 30%. Several approaches to eliminate the error are suggested, all based on advecting the sum as a separate variable and then normalizing mixing ratios for individual tracers to match the total sum. A simple scalar normalization preserves the total number mixing ratio and positive definiteness of the variables but the monotonicity constraint for individual tracers is no longer maintained. More involved flux normalization procedures are developed for the flux based advection algorithms to maintain the monotonicity for individual scalars and their sum.