Open AccessSensitivity of the National Oceanic and Atmospheric Administration multilayer model to instrument error and parameterization uncertainty
Author(s) -
Cooter Ellen J.,
Schwede Donna B.
Publication year - 2000
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/1999jd901080
Subject(s) - environmental science , wind speed , leaf area index , propagation of uncertainty , uncertainty analysis , sensitivity (control systems) , atmospheric sciences , monte carlo method , meteorology , vegetation (pathology) , observational error , deposition (geology) , errors in variables models , measurement uncertainty , remote sensing , mathematics , statistics , geography , physics , structural basin , electronic engineering , engineering , medicine , ecology , paleontology , pathology , biology
The response of the National Oceanic and Atmospheric Administration multilayer inferential dry deposition velocity model (NOAA‐MLM) to error in meteorological inputs and model parameterization is reported. Monte Carlo simulations were performed to assess the uncertainty in NOAA‐MLM deposition velocity V d estimates for ozone (O 3 ), sulfur dioxide (SO 2 ), and nitric acid (HNO 3 ) associated with measurements of meteorological variables (including temperature, humidity, radiation, wind speed, wind direction, and leaf area index). Summer daylight scenarios for grass, corn, soybean, oak, and pine were considered. Model sensitivity to uncertainty in the leaf area index (LAI), minimum stomatal resistance, and soil moisture parameterizations was explored. For SO 2 and HNO 3 , instrument error associated with the measurement of wind speed and direction resulted in the greatest V d error. Depending on vegetation type, the most important source of uncertainty due to instrument error for the V d of O 3 was LAI. Of the model parameterizations studied, accurate estimation of temporal aspects of the annual LAI profile and the characterization of soil moisture supply and demand are most important to model‐estimated V d uncertainty. Considered individually, these factors can result in SO 2 and HNO 3 V d estimate uncertainty of ±25% and O 3 estimate uncertainty greater than 60%. For single plant species settings, reductions in estimate uncertainty should be possible with minor algorithmic modification, inclusion of more species‐appropriate LAI profiles, and careful application of remote sensing technology.