The parametric sensitivity of dimethylsulfide flux in the southern ocean

Abstract

A screening method proposed by Morris has been applied to a model of the oceanic production of dimethylsulfide (DMS), an important sulphur-containing gas which plays an important role in climate regulation. The model is usually refered as the GMSK (Gabric-Murray-Stone-Kohl) model and it is described by Gabric et al. (1993Gabric et al. ( ,1996.The aim of the experiment is to quantify the relative importance of some pre-selected input factors in determining the predicted value of a model state variable: the DMS annual flux.The method is based on an experimental plan, composed of individually randomised one-factor-at-a-time designs, with the purpose of collecting random samples from the distribution of "elementary effects", denoted by Fi, associated with each input parameter.A large (absolute) measure of central tendency for F i indicates an input with an overall influence on the output. A large standard deviation indicates an input whose influence is highly dependent on the values of the other inputs -i.e., one involved in interactions or whose effect is nonlinear. In particular, estimates of the means and standard deviations of these distributions will be used as indicators of which inputs should be considered important.Five of the total number of factors involved in the GMSK model have been examined. The selected factors are: the maximum nitrate uptake by phytoplankton (k23 in the model), the algal S:N (sulphur to nitrogen) ratio (gamma in the model), the parameter involved in the link phytoplankton-protozoa (k3), the initial mixed layer nitrogen concentration (DIN), and the sea surface temperature (SST). The experimental results show that none of the parameters studied has both mean and standard deviation close to zero, that is they all have a direct or indirect effect on the output, confirming that the DMS flux prediction is indeed sensitive to these parameters.

Adaptive process control for achieving consistent particles' states in atmospheric plasma spray process

“…We use Morris' [21] global one-factor-at-a-time (OFAT) screening method to rank their effects on the MPSs as: (1) negligible, (2) linear and additive, and (3) nonlinear and/ or involved in interactions with other process parameters [21]. As explained in [21,22], it requires calculating elementary effects (EEs) of a randomly chosen value of one process parameter at a time on the MPSs. We assume that each MPS is a scalar-valued function of 7 process parameters u i (i = 1, … , 7) that have been normalized to vary between 0 and 1.…”

A copula-based sensitivity analysis method and its application to a North Sea sediment transport model

“…This can be a limiting factor, since, in many cases, the physically-induced dependencies can not be overlooked. For example, in Campolongo and Gabric (1997) the authors had to eliminate certain parameters from their analysis, specifically because of this limitation. Also, in (Salacinska et al, 2010), the sensitivity of the simulated chlorophyll-a concentration to a subset of ecologically significant input factors has been carried out with the use of the Morris method and later enriched by the computation of the correlation ratios of the selected parameters on the model response at a few selected locations in the domain.…”