Abstract

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.

Highlights

  • Projections of future climate change and air quality rely on predicted changes in atmospheric composition

  • In this paper we report on the interannual variability in two idealized hindcast chemical simulations, one run with meteorology from a General Circulation Model, the Community Atmosphere Model version 3 (CAM3) (Collins et al, 2006) constrained using observed sea-surface temperatures (SSTs), the other using meteorology from the NCEP/NCAR reanalysis (Kistler et al, 2001)

  • We find the relative standard deviation of ozone over the Eastern US is less than 10% in CAM meteorology drives the chemical simulation (CAMC) and NCEP meteorology drives the chemical simulation (NCEPC); over Europe the interannual variability is less than 10% in CAMC, but reaches values of 10–20% in NCEPC

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Summary

Introduction

Projections of future climate change and air quality rely on predicted changes in atmospheric composition. As a first step this paper analyzes hindcasts of the chemical variability solely due to changes in meteorology and climate. As reanalysis products are not available for future projections, it is sensible to base hindcasts on the meteorology generated from a general circulation model (GCM). Reanalysis datasets generally capture the episodic meteorology observed during a particular field campaign but suffer from temporal changes in the observational network. In this paper we report on the interannual variability in two idealized hindcast chemical simulations, one run with meteorology from a General Circulation Model, the Community Atmosphere Model version 3 (CAM3) (Collins et al, 2006) constrained using observed SSTs, the other using meteorology from the NCEP/NCAR reanalysis (Kistler et al, 2001).

Simulations
Analysis
Regional variability
Zonal averages
Surface and mid-tropospheric interannual variability
Empirical orthogonal functions
Large scale variability
Variability in global concentration burdens
Relation to meteorological indexes of variability
Correlations between NCEP and CAMC
Trends in global concentration burdens
Correlation amongst variables
Sensitivity to changes in climate variables
Findings
Summary
Full Text
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