Interannual variability in hindcasts of atmospheric chemistry: the role of meteorology

Abstract. Two 40-year meteorological datasets are used to drive the Model of Ozone and Related Tracers chemical transport model, version 2 (MOZART2) in hindcast simulations. One dataset is from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, the second dataset uses meteorology from the Community Atmosphere Model (CAM3) forced with observed interannually varying sea surface temperatures. All emissions, except those from lightning are annually constant. Analysis of these simulations focuses on the period between 1979–1999, due to meteorological discontinuities in the NCEP reanalysis during the 1970s. The meteorology using CAM3 captures observed trends in temperature and water vapor; the simulation using NCEP meteorology does not. This paper examines the regional and global interannual variability of various chemical and meteorological fields: CO, OH, O₃ and HNO₃, the surface photolysis rate of NO₂ (as a proxy for overhead cloudiness), lightning NO emissions, water vapor, planetary boundary layer height, and temperature. The variability due to changes in emissions is not considered in this analysis. In both the NCEP and CAM3 simulations the relative variability of CO, OH, O₃ and HNO₃ are qualitatively similar, with variability maxima both in the tropics and the high latitudes. Locally, relative variability generally ranges between 3 and 10%; globally the tropospheric variability generally ranges from half to one percent, but can be higher. For most fields the leading global Empirical Orthogonal Function explains approximately 10% of the variability and correlates significantly with El Niño. In both simulations the first principal component of a multiple tracer, globally averaged analysis shows a strong coupling between surface temperature, measures of the hydrological cycle, CO and OH, but is not correlated with El Niño. In both simulations we examine the global response of the selected variables to changes in global surface temperature, and compare with a climate simulation over the 21st century.