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AGU 2010 Fall Meeting GC21B-0880 Modeling the Observed Atmospheric OH Response to the Solar Cycle Shuhui Wang, Stanley P. Sander, Nathaniel J. Livesey,

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Presentation on theme: "AGU 2010 Fall Meeting GC21B-0880 Modeling the Observed Atmospheric OH Response to the Solar Cycle Shuhui Wang, Stanley P. Sander, Nathaniel J. Livesey,"— Presentation transcript:

1 AGU 2010 Fall Meeting GC21B-0880 Modeling the Observed Atmospheric OH Response to the Solar Cycle Shuhui Wang, Stanley P. Sander, Nathaniel J. Livesey, and Michelle L. Santee Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California King-Fai Li and Yuk L. Yung Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California Mao-Chang Liang Research Center for Environmental Changes, Academia Sinica, TaiPei, Taiwan Introduction & Background Overview of Observations and Model Introduction Do we have the right solar spectral variation? MLS measures both OH vertical profiles during day and night. About 3495 profiles are generated during each day, covering a broad latitude range of 82  S – 82  N. The vertical range is hPa (see for detailed documentation). MLS OH data have been extensively validated using in-situ air-borne measurement, ground-based measurements, and model calculations [Pickett et al., 2006b, 2008; Wang et al., 2008]. 5-year record of AURA/MLS OH measurements Conclusions and Implications OH column variability Future work The Solar Cycle Signal in OH (Model vs. Observations)Conclusions 13-year record of FTUVS OH column abundances at TMF OH, together with other odd hydrogen species (i.e., H and HO 2 ), plays a key role in the middle atmospheric chemistry due to the ozone-destroying catalytic reactions involving odd hydrogen. A better understanding of the natural variability of OH and related chemistry due to solar forcing, e.g, 11-year solar cycle, will improve our ability to predict the variability of middle atmospheric ozone chemistry due to future solar forcing. Yet, there are large discrepancies among previous observations and model predictions of OH variability (including the diurnal cycle, the seasonal cycle and the solar cycles) [e.g., Canty and Minschwaner, 2002; Mills et al., 2003; Burnett and Minschwaner, 2009]. Microwave Limb Sounder (MLS) on board Aura provides the first global vertical profile measurements of OH on a daily basis since Aug 2004 [Pickett et al., 2006a, 2006b, 2008; Canty et al., 2006; Wang et al., 2008]. The diurnal variation of OH column abundance has been measured regularly by a ground-based Fourier Transform Ultraviolet Spectrometer (FTUVS) at the Table Mountain Facility (TMF) of NASA’s Jet Propulsion Laboratory (JPL) since 1997 [Cageao et al, 2001; Mills et al, 2003; Li et al, 2005; Cheung et al., 2008]. The combination of the two data sets provides a unique opportunity for studying the variability of odd hydrogen. These observations have shown that solar UV irradiance variation during the 11-year cycle is responsible for the long-term variability in atmospheric OH. Our present study use the Whole Atmospheric Community Climate Model (WACCM) to investigate such solar cycle signal in OH and the related chemistry and compare with observations. Previous discrepancies Both MLS and FTUVS OH data show clear signal of 11-year solar cycle, after removing diurnal, intra-annual, and other inter-annual variabilities. (More details about the observations will be presented at 3:10pm on Wed: GC33C-07 Observing Atmospheric OH Response to the Solar Cycle) The observed variability of OH and the related chemistry due to the 11-year solar cycle was studied using WACCM. The results, using the traditional parameterization of solar flux variation as solar forcing, show much smaller OH variability than observations. The large apparent discrepancy (at least a factor of two) between observed and modeled OH variability implies that the previously used solar forcing in models is too small or there exist possible missing pieces in the puzzle of hydrogen chemistry [Wang et al., 2009]. Observed and modeled vertical stratification of OH variability WACCM model and the solar flux inputs References WACCM runs with monthly mean outputs at TMF location are compared. Figure 5 shows the modeled long-term inter-annual OH column variabilities that are comparable to the observed OH column variability from TMF/FTUVS and MLS (see Figure 2). While the results using Lean’s solar spectral variation as input (left) show a OH column variability that is much smaller than the observations, the results using solar input based on SORCE data (right) show OH column varaibility close to observations. The diurnal pattern of modeled OH columns (using SORCE solar data) are extracted from the solar maximum year (1981) and the solar minimum year (1986). The solar cycle signal in OH column (% variability) appears to be larger when OH abundance is larger (at smaller SZA). In percentage change (Figure7 upper), the modeled OH shows a positive response to the solar 11-year cycle from hPa down to 3 – 6 hPa (The model using SORCE solar data shows solar cycle signals that penetrate deeper than in the model using Lean’s solar flux). The peak variability occurs at 0.02 – 0.06hPa. A negative response occurs at above ~0.01hPa (~80km), which is unimportant for the total column variability due to the extremely low OH density there. These patterns are consistent with the analysis by Li et al., [2006]. In density change, both models (Figure 7, lower middle and right) generate vertical profiles of OH variability that are similar to MLS observations (Figure7, left). But the magnitudes of OH variability in the model using SORCE solar data are much closer to observations than those in the model using Lean’s. Both modeled (SORCE) and observed OH variabilities show two peaks (where OH density peaks). The results from the other model (Lean’s) miss the second variability peak around 2 hPa. The 5-year MLS global observations of OH on a daily basis and the 13-year measurements of OH column abundances with FTUVS at TMF show an OH column variability due to the 11-year solar cycle of near 10%. We use WACCM model with various solar spectral variations as solar forcing input to investigate such variability. The solar cycle effect extracted from WACCM putputs shows that the OH variability from the model using SORCE solar data as input, near 8%, is very close to observations, while the model using Lean’s solar input gives much smaller solar cycle signal in OH column. Detailed analysis of the vertical stratification of OH variability shows that the model using Lean’s solar input generally underestimates the OH response to the solar cycle. The solar cycle signal in this model stops at a higher altitude than observed. In particular, one of the two peaks of OH variability observed by MLS, near the OH density peak at ~2hPa, is missing in this model. The model using SORCE solar data as solar input shows results much closer to observations. The diurnal effect of such solar cycle signal in OH is also investigated. The largest OH variability due to solar cycle is shown to occur in mid-day when OH abundance is high. These findings are consistent with the conclusions from Haigh et al., [2010], preferring SORCE measurements of solar spectral variation over Lean’s model. The solar cycle variability of ozone and H 2 O (important source species of OH) will also be investigated based on these model simulations and satellite observations. The latitudinal effects of these atmospheric responses to the 11- year solar cycle with be investigated as well. WACCM HO 2 data show similar variability due to the solar 11- year cycle. The MLS HO 2 observations will also be investigated after applying required averaging techniques. Austin, J. et al. (2008), Coupled chemistry climate model simulations of the solar cycle in ozone and temperature, J. Geophys. Res., 113, D11306, doi: /2007JD Burnett, C.R. and Minschwaner, K. (2009), OH column abundance apparent response to Solar Cycle 23, AGU Fall Meeting, San Francisco, CA, Dec 2009 Cageao, R.P., J.F. Blavier, J.P. McGuire, et al. (2001), High-resolution Fourier-transform ultraviolet-visible spectrometer for the measurement of atmospheric trace species: Application to OH, Appl. Opt., 40, 2024 – 2030 Canty, T., and K. Minschwaner (2002), Seasonal and solar cycle variability of OH in the middle atmosphere, J. Geophys. Res., 107 (D24), 4737, doi: /2002JD Cheung, R., K.F. Li, S. Wang, et al. (2008), Atmospheric hydroxyl radical (OH) abundances from ground- based ultraviolet solar spectra: an improved retrieval method, Appl. Opt., 47(33), Canty, T., H.M. Pickett, R.J. Salawitch et al. (2006), Stratospheric and mesospheric HOx: Results from Aura MLS and FIRS-2, Geophys. Res. Lett., 33, L12802 Haigh, J.D., et al. (2010), An influence of solar spectral variations on radiative forcing of climate, Nature, 696 – 699 Li, K.F., R.P. Cageao, E.M. Karpilovsky, et al. (2005), OH column abundance over Table Mountain Facility, California: AM-PM diurnal asymmetry, Geophys. Res. Lett., 32, L13813 Li, K.F., X. Jiang, R. Shia, et al. (2006), Periodicities of solar activity from atmospheric hydroxyl radicals, AGU Fall Meeting Marsh, D.R., et al. (2007), Modeling the whole atmosphere response to solar cycle changes in radiative and geomagnetic forcing, J. Geophys. Res., 112, D23306, doi: /2006JD Mills, F.P., R.P. Cageao, S.P. Sander, et al. (), OH column abundance over Table Mountain Facility, California: Intraannual variations and comparisons to model predictions for 1997 – 2001, J. Geophys. Res., 108(D24), 4785 Pickett, H.M. (2006a), Microwave Limb Sounder THz module on Aura, IEEE Trans. Geosci. Remote Sens., 40(5), 1122 – 1130 Pickett, H.M., R.J. Drouin, T. Canty, et al. (2006b), Validation of Aura MLS HOx measurements with remote-sensing balloon instruments, Geophys. Res. Lett., 33, L01808 Pickett, H.M., R.J. Drouin, T. Canty, et al. (2008), Validation of Aura Microwave Limb Sounder OH and HO2 measurements, J. Geophys. Res., 113, D16S30 Wang, S., H.M. Pickett, T.J. Pongetti, et al. (2008), Validation of Aura MLS OH measurements with FTUVS total OH column measurements at Table Mountain, California, J. Geophys. Res., 113, D22301 Wang, S., et al., (2009), Observing odd hydrogen species from space- and ground-based instruments, AGU Fall Meeting, San Francisco, CA, Dec 2009 Woods, T.N. and Rottman, G.J. (2002), Solar Ultraviolet variability over time periods of aeronomic interest, Atmospheres in the Solar System: Comparative Aeronomy, Geophysical Monograph, /130GM14, National Aeronautics and Space Administration FTUVS measures OH total column by taking solar spectra at the west and east limbs of the rotating Sun. The Doppler shift of the spectra from one limb to the other is used to remove the strong solar Fraunhofer features, leaving the terrestrial OH lines. Detailed description of the instrument is given in [Cageao et al., 2001; Cheung et al., 2008]. TMF (34.4  N,  W) is located at ~2.3 km altitudes in the mountains northeast of Los Angeles. The daily max OH total column shows a variability of ~10% due to the 11-year solar cycle. The 5-year MLS OH column data show a similar trend. Since MLS OH column covers near 90% of the total column, the variabilities of TMF and MLS OH columns are very similar. The solar spectral variation is a key input of models simulating the ob- served atmospheric response to solar cycle. The solar spectral data from Solar Irradiance Monitor (SIM) and Solar Stellar Irradiance Comparison Experi- ment (SOLSTICE) on board SORCE show much larger solar irradiance variability than previously used by models [e.g., Haigh et al., 2010, see above figure from the paper]. The modeled ozone variability due to 11-year solar cycle, using combined solar spectral variation from SIM and SOLSTICE as solar input, shows much better agreement with the trend extracted from MLS observations. In this study, we use such combined observations of solar spectral variation as WACCM input to simulate the observed atmospheric OH variability and compare results with previous models. The implications are discussed. For a better comparison with the ground-based FTUVS data, we focus on MLS data around TMF location [29.5N, 39.5N]. Figure 1. MLS OH data at TMF [29.5N, 39.5N]. (Left: Daily OH; Middle: Annual mean OH; Right: Variability of annual mean from 5-year mean). Figure 2. FTUVS OH column variability due to the 11-year solar cycle. Lower: Daily max OH column (black) and time of max (gray bars and red lines). Middle: Monthly mean of daily max OH (black dots) and the long- term trend after FFT smoothing (green and red curves). Upper: The long- term OH column variability normalized to the all-time mean (green and red curves) and the comparable variability derived from MLS observations (blue). The WACCM model in this study is based on version 3 of the Community Atmosphere Model (CAM3). It has 66 levels from the surface to the mesosphere. The spatial resolution is 5º longitude by 4º latitude. The chemistry module is MOZART3 (the Model for Ozone and Related Chemical Tracers version 3) with 57 species and 211 photochemical reactions, updated to the JPL 2003 standard chemistry. To describe the solar cycle variations, in particular from Lyman-α to 350 nm, the standard WACCM3 uses modeled solar flux based on UARS/SOLSTICE measurements [Woods and Rottman, 2002; Marsh et al., 2007; Austin et al., 2008]. The UARS/SOLSTICE solar flux variation is very close to Lean’s model, which is widely accepted in models, but very different from the observations from SORCE [e.g., Haigh et al., 2010]. We use all three sets of solar flux variation as inputs in our WACCM simulations. The difference between using Lean’s and using UARS-based solar flux is insignificant. Our discussions focus on comparing results using Lean’s and SORCE data (based on SOLSTICE data below 200nm and SIM data above 200nm). Figure 3. Estimated solar irradiance variation from solar min to solar max (%) based on SORCE data in Haigh et al., [2010]. (A scaling factor is estimated by comparing the lyman-α changes during a full solar cycle and during the considered SORCE period ( ); see lyman-α composite record at LASP.) Figure 4. The comparison of solar irradiance ratio (solar max / solar min) in the previous WACCM (UARS, similar to Lean’s; in black) and the new WACCM run (based on SORCE observations; in red). Modeled OH Column Solar Cycle Signal (SORCE) Modeled OH Column Solar Cycle Signal (Lean’s) Diurnal pattern of OH column variability Figure 6. Bi-hourly model results at TMF in 1981 (solar max) and 1986 (solar min) and the diurnal pattern of solar cycle signal in OH column. Figure 5. Modeled long-term OH column variability associated with the solar cycle (left: Using Lean’s solar flux; Right: Using SORCE solar flux) Figure 7. Vertical profile of OH variability associated with the solar cycle ― variability of annual mean from all-time mean (Upper: %; Lower: Density ) MLS observationsWACCM using Lean’s solar input WACCM using SORCE solar data


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