HR: 0800h
AN: A31A-0044    [Abstracts]
TI: Stratospheric Ozone Variation Induced by the 11-Year Solar Cycle: Recent 40-Year Simulation Using 3-D Chemical Transport Model with Reanalysis Data
AU: * Sekiyama, T T
EM: tsekiyam@mri-jma.go.jp
AF: Meteorological Research Institute, 1-1 Nagamine, Tsukuba, 3050052 Japan
AU: Shibata, K
EM: kshibata@mri-jma.go.jp
AF: Meteorological Research Institute, 1-1 Nagamine, Tsukuba, 3050052 Japan
AU: Deushi, M
EM: mdeushi@mri-jma.go.jp
AF: Meteorological Research Institute, 1-1 Nagamine, Tsukuba, 3050052 Japan
AU: Kodera, K
EM: kodera@mri-jma.go.jp
AF: Meteorological Research Institute, 1-1 Nagamine, Tsukuba, 3050052 Japan
AU: Lean, J L
EM: lean@demeter.nrl.navy.mil
AF: E. O. Hulburt Center for Space Research, Naval Research Laboratory, 4555 Overlook Ave. S.W., Washington, DC 20375 United States
AB: The chemical and dynamical impacts of the 11-year solar cycle are investigated with the focus on stratospheric ozone variation and its correlations with meteorological variations, e.g. temperature and zonal wind, using a three dimensional chemical transport model (CTM) of the Meteorological Research Institute (MRI). When we investigate the influence of the solar activity on climate, an abundance of meteorological observations, including objective analyses, are usable. In contrast, there are few datasets of spatially and temporally wide-raging observations of atmospheric chemical constituents. Models have been then used for investigation for correlations between the chemical and dynamical impacts of the 11-year solar cycle. However, the meteorological and chemical fields generated by these models are hypothetical cases in virtual worlds; they balance each other physically in the models, but it is not known exactly that these cases occur in the actual atmosphere. We have then compared meteorological fields of re-analyses with the ozone distributions simulated by a CTM using the same meteorological fields and variable solar flux, without ozone-temperature feedback processes. The meteorological fields of re-analyses are supposed to include the ozone-feedback effect as well as the QBO and volcanic effects occurred in the actual atmosphere. Photodissociation coefficients generated in the CTM are derived from the actual solar flux which varies quasi-decadally. The simulated ozone, therefore, includes the actual solar cycle variation which synchronizes with the meteorological variations. Consequently, solar cycle components of the simulated ozone are supposed to be physically consistent with those of the meteorological fields of re-analyses; this case must have occurred in the actual atmosphere. [Model description] The chemical module includes 122 chemical reactions with 49 chemical species. The chemical species are transported with semi-Lagrangian scheme. The dynamical module is based on a general circulation model, which has been developed by MRI. The vertical resolution is set to 45 layers (surface to 0.01 hPa), while the horizontal resolution is set to 64 by 32 in longitude and latitude (5.6 by 5.6 degrees). Meteorological fields in the dynamical module are data-assimilated into ECMWF re-analysis (ERA-40). ERA-40 covers the years 1957-2002 and the altitude range from the surface to 1 hPa. The solar flux used in the chemical module was compiled by Lean et al.; the flux dataset contains time series of ultraviolet spectrum (120-735 nm) based on solar observations. A 40-year simulation (1960-2000) was made under the condition of the ERA-40 meteorological fields and the transient solar flux. In order to extract solar cycle components from the model result, a multiple linear regression analysis was adopted using F10.7 observations as an explanatory variable. The distribution of the amount of the solar cycle component of ozone varies not only altitudinally and latitudinally but also longitudinally. Peak differences of ozone concentration between solar maximum and minimum are calculated as approximately 4% in the stratosphere. Solar cycle changes of temperature also show altitudinal, latitudinal, and longitudinal variations from -1.0K to +0.8K in the stratosphere. There is a high correlation (~0.8) between the two horizontal distributions in the lower stratosphere and a strong but negative correlation (~-0.7) in the upper stratosphere.
DE: 3334 Middle atmosphere dynamics (0341, 0342)
DE: 3337 Numerical modeling and data assimilation
DE: 1600 GLOBAL CHANGE (New category)
DE: 1650 Solar variability
DE: 0340 Middle atmosphere--composition and chemistry
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting