Mid-latitude Atmospheric OH Response to the Most Recent 11-year Solar Cycle
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Shuhui Wanga,1, King-Fai Lib,c, Thomas J. Pongettia, Stanley P. Sandera, Yuk L. Yungb, Mao-Chang Liangd,
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Nathaniel J. Liveseya, Michelle L. Santeea, Jerald W. Hardere, Marty Snowe, and Franklin P. Millsc, f
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a
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
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b
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
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c
Atomic and Molecular Physics Laboratories, Research School of Physics and Engineering, Australian National
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University, Canberra, ACT 0200, Australia
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d
Research Center for Environmental Changes, Academia Sinica, TaiPei, 115, Taiwan
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e
Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303, USA
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f
The Fenner School of Environment and Society, Australian National University, Canberra, ACT 0200, Australia.
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The authors declare no conflict of interest.
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This article is a PNAS Direct Submission and has a prearranged editor.
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Major Category: Physical Sciences
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Minor Category: Earth, Atmospheric, and Planetary Sciences
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Keywords: middle atmospheric chemistry, odd hydrogen, long-term variability
Corresponding author E-mail: Shuhui.Wang@jpl.nasa.gov
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The hydroxyl radical (OH) plays an important role in middle atmospheric photochemistry, in
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particular ozone (O3) chemistry. Because it is mainly produced through photolysis and has a short
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chemical lifetime, OH is expected to show rapid responses to solar forcing (e.g., the 11-year solar
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cycle), resulting in variabilities in related middle atmospheric O3 chemistry. Here we present the
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first effort to investigate such OH variability using long-term observations (from space and the
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surface) and model simulations. Ground-based measurements and data from the Microwave Limb
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Sounder (MLS) on NASA’s Aura satellite suggest a ~7-10% decrease in OH column abundance
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from solar maximum to solar minimum that is highly correlated with changes in total solar
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irradiance (TSI), solar Mg II index, and Lyman-α during Solar Cycle 23. However, model
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simulations using a commonly accepted solar UV variability parameterization give much smaller
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OH variability (~3%). While this discrepancy could result partially from the limitations in our
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current understanding of middle atmospheric chemistry, recently published solar spectral
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irradiance data from the SOlar Radiation and Climate Experiment (SORCE) suggest a solar UV
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variability that is much larger than previously believed. With a solar forcing derived from
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SORCE data, modeled OH variability (~6-7%) agrees much better with observations. Model
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simulations also reveal the detailed chemical mechanisms, suggesting that such OH variability and
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the corresponding catalytic chemistry may dominate the O3 response to SC in the upper
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stratosphere. Continuing measurements through Solar Cycle 24 are required to further
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understand this OH variability and its impacts on O3.
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\body
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Quantifying effects of the solar cycle (SC) in Earth’s atmosphere helps differentiate relative
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contributions of natural processes and anthropogenic activities to global climate change (1). From 11-
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year SC maximum (max) to minimum (min), the total solar irradiance (TSI) varies by only ~0.1%.
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However, changes in solar ultraviolet (UV) fluxes can be much larger (2). Thus, detectable SC impacts
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on Earth’s climate are more likely to be linked to changes in middle and upper atmospheric composition
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through photochemistry in the UV.
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A number of observational and modeling studies have devoted to characterize SC modulations in
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mesospheric and stratospheric chemistry, especially those in ozone (O3) (3-9). Changes in UV
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absorption by O3 at low latitudes over the SC can lead to changes in thermal structures in the upper
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atmosphere, affecting tropospheric and polar climates (10, 11) and may lead to changes in global
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circulations. Accurate simulations of the O3 response to SC is therefore required for better understanding
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the sun-climate relation (1). However, the SC signal in O3 simulated by different models show
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quantitative differences, which may be due to differences in model resolutions, model parameterizations
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related to dynamical processes, and/or photochemistry that have yet been examined in a definitive
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manner (12-14). Diagnostic studies must involve not only O3 but also its major catalytic sink species
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that are also photochemically active, such as odd-hydrogen (HOx = H + OH + HO2) (15-20).
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OH, in particular, is a key species in HOx reaction cycles. It is mainly produced through direct
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photolysis of water vapor (H2O) at ~120 and 170 – 205 nm and photolysis of O3 at ~200 – 330 nm
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followed by reaction of O(1D) with H2O (21). Due to its short chemical lifetime, rapid response of OH to
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SC can serve as a rapid indicator of solar-forcing-induced changes in atmospheric composition and
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chemistry. Unfortunately, very few studies have been done on HOx response to SC and little attention
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has been paid to the impacts of such changes on O3 (16). Moreover, Recent observations over the
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declining phase of Solar Cycle 23 by the Solar Stellar Irradiance Comparison Experiment (SOLSTICE)
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(22) and the Spectral Irradiance Monitor (SIM) (23) instruments aboard SORCE satellite suggest an
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unexpectedly large decrease in solar UV irradiance, which has important implications for O3 and HOx
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photochemistry (5). Such observations disagree with previous satellite observations and model
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parameterizations, adding UV variability as another dimension of uncertainty for upper atmospheric
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modeling.
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The objectives of this work include: (i) providing observational evidence of SC-related changes
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in OH column abundance (XOH) from a near-15-year ground-based measurement, augmented by 5-year
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satellite OH measurements by the Microwave Limb Sounder (MLS) aboard Aura; (ii) quantifying the
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impacts of using SORCE UV variability on XOH SC variability with a three-dimensional (3-D) whole
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atmosphere community climate model (WACCM) (2) and a one-dimensional (1-D) photochemical
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model (24, 25); and (iii) estimating sensitivity of stratospheric O3 to the SC-related OH changes
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obtained in (ii). Note that previous studies on the O3 response to SC investigate the overall O3 variability
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due to chemistry and radiation. Our objective (iii) is to illustrate the role of OH in the SC modulations of
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O3 chemistry.
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This study is the first effort of using long-term OH measurements from space and the surface to
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investigate the OH response to the SC, providing a basis for simulating long-term variability of middle
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atmospheric hydrogen chemistry.
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Observational Evidences
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Studies on SC-modulations of OH had been limited in the past by the lack of long-term
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systematic observations. The only two long-term records are XOH measurements at NASA Jet
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Propulsion Laboratory’s (JPL) Table Mountain Facility (TMF) in California (18, 26) and at NOAA Fritz
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Peak Observatory (FPO) in Colorado (27). Based on the FPO XOH data during 1977 – 2000, an XOH
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variability of ±4.2% (or 8.4%), was derived and believed to be associated with the 11-year SC (16). A
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trend suggestive of a similar SC response in TMF XOH data during 1997 – 2001 was also reported (18),
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but the robustness of such analysis was limited by the short period of the observation. In this study, we
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update TMF XOH data to 1997 – 2011, covering most of the SC 23 and the rising SC 24.
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XOH is measured by a high-resolution Fourier Transform Ultraviolet-Visible Spectrometer
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(FTUVS) at TMF at an altitude of ~2.3 km in Wrightwood, California (34.4N, 117.7W) (26). FTUVS
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makes diurnal XOH measurements during daytime. Two dominated natural variabilities of OH are the
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diurnal cycle due to the change of solar zenith angle (SZA) over the course of a day and the seasonal
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cycle which is a combined effect of varying SZA and sources of OH (16, 28). To focus on the SC signal,
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we first minimize the diurnal effect by using daily max (Figure 1a) determined by a polynomial fit of the
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diurnal pattern (see Figure S1; Supporting Information (SI)). The average time of daily max is close to
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20:00 Universal Time (UT) (local noon). To minimize the seasonal effect, we applied a Fast Fourier
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Transform (FFT) low-pass filter to the XOH daily max. The result of 2-year FFT (removing variations
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with frequencies higher than once every two years) is selected to best represent the long-term variability
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that is primarily due to the SC (Figure 1a, red). Further FFT filtering smears the SC signal, while less
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FFT filtering retains additional interannual features that are not related to SC (e.g., 1-year FFT shown in
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Figure 1a, green). We also applied a regression analysis using the long-term Lyman-α index as a proxy
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for SC. The results are consistent with FFT analysis. All results are normalized by the all-time-mean
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XOH (Figure 1c). The TMF XOH SC variability is found to be 10±3% from peak to valley, where the
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uncertainty is estimated from the regression (9). This 10% variability agrees with that observed over
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FPO (16), although the absolute values of XOH from FPO and TMF, both in mid-latitudes, have shown
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statistical differences of several tens of percent (29).
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Since the launch of Aura in July, 2004, daily global OH distribution has been measured by MLS
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(30). Excellent quality of MLS OH data has been demonstrated through extensive validations with air-
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borne and ground-based measurements and modeling (31-33). Nearly continuous MLS OH data are
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available from 2004 (middle of the declining phase of SC 23) to the end of 2009 (start of SC 24). To
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support TMF observations, we focus on MLS OH at TMF latitude (29.5N – 39.5N). Data between
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21.5 – 0.0032 hPa are integrated to give an estimate of XOH, which covers ~90% of the total atmospheric
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OH (33). Therefore such integration is expected to include most of the SC signal. Furthermore, the
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average MLS overpass time at TMF during daytime is ~21:00 UT (33), making MLS XOH compared to
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TMF daily max XOH (~20:00 UT). Figure 1b shows the zonal mean daily MLS XOH over TMF and the
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annual average (mid-August to mid-August) in which the seasonal variation is removed. The first year
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mean (Aug 2004 to Aug 2005) is used to normalize the annual mean XOH to obtain the relative
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variability (Figure 1c, blue), which is primarily due to the SC, with small additional interannual
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variations. It is in good agreement with that of TMF XOH, although only five annual mean MLS data
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points are available and the slightly high MLS XOH during 2007 – 2008 may require further
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investigation. Between 2004 and 2009, MLS annual mean XOH decreased by over 3%. Based on the
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scale of TMF XOH variability, we estimate the total SC signal in MLS XOH to be ~7%.
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As a robustness test, the XOH SC signals obtained above are compared with observations of
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various solar parameters (Figure 2). Independent TSI measurements have been provided by a number of
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satellite instruments since 1978. Due to calibration issues, TSI data from different instruments may show
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similar trends but different absolute values. Based on these observations, various versions of TSI
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composite have been constructed, e.g., ACRIM (primarily from measurements by 3 generations of
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Active Cavity Radiometer Irradiance Monitor) (34, 35) and PMOD (from Physikalisch-
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Meteorologisches Observatorium Davos World Radiation Center) (36). These composites, as well as the
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most recent TSI measurement (2003 – 2011) by the Total Irradiance Monitor (TIM) (37) aboard SORCE,
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are plotted in Figure 2a. Despite quantitative differences between ACRIM and PMOD which may be
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due to uncorrected instrumental drifts (38), both composites clearly demonstrate a prolonged solar min
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near the end of SC 23. SORCE TSI is annually averaged and the composites are smoothed to remove the
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short-term variability. The extracted SC signals in XOH show excellent correlation with SORCE TSI
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(Figure 2b) and generally follow the TSI composites (Figure 2c), with some differences in the ascending
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phase of SC 23. While TSI is a good indicator of the integrated solar spectrum variability, short-
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wavelength UV radiation may vary differently from longer-wavelength radiation. Therefore, we also
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compare the observed XOH variability with those in solar Lyman- at 121.5 nm and the Magnesium
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(Mg)-II index near 280 nm (composites from the Laboratory for Atmospheric and Space Physics and
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NOAA, respectively), which are proxies for solar UV variations. They both well correlate with the
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observed XOH variability over SC 23 (Figure 2d).
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Model Results and Discussions
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We simulate the SC modulation in XOH with WACCM, a 3-D global atmospheric model
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extending from the surface to the thermosphere (2). The advantage of using WACCM is that chemistry,
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radiation, and dynamics are fully coupled, providing a comprehensive simulation of SC effects on XOH
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at mid-latitudes. Four 50-year-long WACCM runs with different prescriptions of solar UV variability (to
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be described below) are carried out.
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Most climate models with prescribed solar forcing use a parameterized solar spectral irradiance
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(SSI) variability developed at the Naval Research Laboratory (NRL) (39) primarily based on space-
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borne UV measurements during 1991 – 2000 (40). Figure 3a shows the simulated annual mean XOH
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from 1964 to 2010 using this NRL solar forcing. TMF and MLS XOH are represented by model OH
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integrated from the upper mesosphere down to 2.3 km and 25 km, respectively. The average SC signal
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in XOH is only ~3% from max to min, suggesting differences of a factor of ~3 between model and
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observations (Figure 3b). Note that another run with the standard WACCM SC setting (parameterized
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UV variability based on \observations in previous solar cycles (2)) shows similar results.
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While the differences could be partially caused by limitations in our current understanding of
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middle atmospheric HOx-O3 chemistry, the uncertainty in solar UV variability could be a dominate
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source. Haigh et al. (5) reported the SORCE (SOLSTICE and SIM) SSI variability during April 2004 –
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November 2007, which is significantly larger than that of NRL SSI and can better explain observed
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atmospheric O3 changes (5, 6, 8, 9). Thus we use SORCE SSI to replace NRL as the solar forcing in
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WACCM. To mimic a full SC, SORCE SSI data is extrapolated back to the max of SC 23 in January
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2002 through a linear regression with the long-term record of Mg-II index. The resultant SSI variability
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and its comparison with NRL are shown in Figure 4 (extrapolation scaling factor is shown in the inset).
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The SORCE UV variability is generally larger than that from the NRL model. The relative difference is
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~30% at Lyman- and much larger (factor of 2 – 6) at 200 – 280 nm. Considering the significant
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difference between SOLSTICE and SIM SSI variability at 210 – 240 nm, we made two WACCM runs
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using combined SSI variability from the two instruments with cutoffs at 240 nm and 210 nm,
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respectively. Figure 3c shows the annual mean model XOH using SORCE SSI variability (SOLSTICE
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below 240 nm; SIM above 240 nm). The XOH SC variability is ~6% (twice of that in Figure 3b) and
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agrees much better with observations (10% for TMF; 7% for MLS); the difference between the
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WACCM result and TMF observations is reduced to a factor of ~1.5 (Figure 3d). The other WACCM
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run using 240 nm as cutoff between SOLSTICE and SIM data gives a slightly larger XOH variability of
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~7%, reducing the difference between model and observations to a factor of ~1.3. Availability of longer
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SORCE UV data covering the recent solar min (2008 – 2009) and the rising SC 24 in the future will help
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make more robust conclusions.
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To better understand the detailed mechanism of OH response to SC, we use a 1-D photochemical
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model (24, 25) to study the vertical and spectral distribution of OH sensitivity to SSI changes. It has
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advantages including the much higher computational efficiency and flexibility than WACCM, allowing
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for a wide range of sensitivity studies to elucidate the underlying mechanisms responsible for the OH
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response to SC. The spectral OH response, defined as the ratio of the relative change in model OH to the
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relative change in solar photon flux at the top of the atmosphere (%-[OH] / %-photon flux) highlights
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the important processes for OH photochemistry (Figure 5a): (i) OH enhancements at 65 – 90 km and 50
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– 80 km occur at Lyman-α and 170 – 200 nm, where H2O photolysis is the major OH source; (ii)
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Positive OH responses at 210 – 320 nm correspond to enhanced O3 photolysis followed by enhanced OH
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production through the reaction of O(1D) (from O3 photolysis) with H2O; (iii) Negative OH responses at
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above 80 km correspond to enhanced photolysis of O2 (160 – 200 nm) and O3 (255 – 290 nm), which
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produces atomic oxygen, a sink species for OH. This effect is insignificant in XOH due to the very low
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OH abundance at these altitudes; (iv) Negative response at 190 – 220 nm below 40 km is caused by a
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shielding effect (18) resulted from enhanced UV reduction by the enhanced overhead O3 (O3 at higher
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altitudes with a positive response to SC (5) absorbs more UV and weakens photolysis rates at lower
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altitudes). It mostly cancels out with effect (ii) at these altitudes, leaving a small net negative response.
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The vertical profile of model OH response to SC (OH]) (Figure 5b) is obtained by convolving
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the spectral response in Figure 5a with SSI variability (black: using NRL; blue: using SORCE). An
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earlier modeling work by Canty and Minschwaner (16) (orange ) using a solar forcing similar to that of
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NRL is close to our model result using NRL SSI. Such OH] is the overall OH change due to changes
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in photolysis and OH sources/sinks. OH] derived using SORCE SSI is generally larger than that using
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NRL, owing to the greater solar UV variability from SORCE. It is up to 18% at 70 – 80 km, near 5% at
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40 – 60 km, and slightly negative at 30 – 40 km. In particular, by using SORCE SSI, OH SC signal
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increases by a factor of 2 at 40 – 60 km. The integrated XOH response derived using NRL SSI is 3.7%;
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when SORCE SSI is used, the XOH response increases to 6.4%. These values agree well with those from
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WACCM.
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Implications
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Catalytic O3 loss above ~40 km is primarily controlled by HOx reactions (17, 19, 20). O3 in this
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region of the atmosphere is expected to show early signs of O3 layer recovery (41) and has a strong
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impact on global stratospheric temperatures, circulation, and thus climate (42). Our findings of OH
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response to SC have important implications on the O3 changes associated with HOx variability. Previous
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studies on the O3 response to SC (5, 6, 8, 9) are for the overall O3 change ([O3]) including direct
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changes through photolysis, indirect changes through O3-destroying catalysts (e.g., HOx), and possible
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indirect changes through thermal structures and circulation. It is important to quantify the impact of each
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individual process. Here we discuss the component of O3 that is solely due to [OH] (denoted by
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∂[O3]). We made another set of 1-D model runs by constraining [OH] to values from above-discussed
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runs using NRL and SORCE SSI and fixing UV flux (no other components of O3). All species other
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than OH are allowed to vary until reaching steady state. The resultant O3 change represents ∂[O3]
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(Figure 5c). Above 60 km, ∂[O3] = –[OH] holds approximately (16); The peak ∂[O3] at 75 km is –15%
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and –18% for the runs using [OH] from NRL and SORCE SSI, respectively. Below 40 km, ∂[O3] is
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negligibly small. Between 40 and 60 km, using [OH] from SORCE SSI instead of from NRL leads to
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nearly doubled ∂[O3]. Merkel et al. (6) showed that WACCM modeled [O3] at 40 – 60 km increases
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drastically from 0.5% to 1% when NRL SSI is replaced by SORCE SSI. Similar results are also obtained
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using other models (5, 8). These changes in [O3] at 40 – 60 km is close to that in ∂[O3] alone,
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suggesting that OH SC variability may be a dominant factor underlying the O3 response to SC in the
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upper stratosphere. More quantitative diagnostic studies will help confirm it.
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Concluding Remarks
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Both 1-D and WACCM models using NRL SSI produce an XOH response to SC that is much
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smaller than observed XOH at TMF. The use of SORCE SSI gives results much closer to observations.
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Thus the uncertainty in SSI variability may be a primary limitation for accurate modeling of OH
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variability and the corresponding catalytic O3 change. While the NRL model could have underestimated
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the solar forcing in SC 23, several other factors involving the trends in OH sources/sinks could have
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contributed to the larger observed OH variability.
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One candidate is the trend in atmospheric H2O (43). Satellite and ground-based measurements
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revealed a decreasing trend of a few %/year in H2O at 16 – 26 km during 2000 – 2005 (43, 44).
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Remsberg (45) reported an increasing trend in mesospheric H2O of ~1%/year at 60 – 80 km (45). We
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approximate the H2O trend at 26 – 60 km by linear interpolation and simulated the impact of these
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trends on OH using the 1D model (Figure S3 in SI). The resultant change in XOH is only –0.2%/year.
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The decreasing H2O trend (44) also stops since 2005, suggesting little impact on the observed XOH
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decrease during 2005 – 2009.
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Similarly, a non-SC O3 trend may also contribute to the observed XOH change. A recent study
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using ground-based lidar measurement over TMF showed ~2%/decade O3 trend at 35 – 45 km after
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1997. Unfortunately, there is no report of the trends at other altitudes. Assuming 2%/decade for all
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altitudes, our 1-D model suggests negligibly small change in XOH of ~0.08%/decade.
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Models using SORCE SSI variability produce an XOH response (6~7%) that agrees much better
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with observed XOH (~10% from FTUVS; ~7% from MLS). The remaining small difference is within the
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uncertainty range of TMF XOH, and could also originate from the above-mentioned small impacts of
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H2O and O3 trends. In addition, the SORCE SSI variability in this study is extrapolated from 2004 –
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2007 (5) back to the max of SC 23 in 2002, but the SSI in 2007 is expected to be slightly larger than the
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real SC min (2008 – 2009). This could also lead to a slightly underestimated OH SC signal from the
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model. Updated SORCE SSI data in the future could help to confirm this.
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While models using SORCE SSI over SC 23 as solar forcing agree better with observations than
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using NRL SSI, it is still early to conclude that climate models should switch from NRL to SORCE SSI.
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Questions remain as to why previous SSI measurements during earlier SCs did not show such large
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variability, whether SORCE SSI variability is applicable to other SCs, and whether the difference is at
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least partially due to possible shortcomings in the NRL model and/or calibration issues of SORCE
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instruments.
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In any case, continuous long-term observations of solar SSI, OH, O3 and other related chemical
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species through the SC 24 are crucial for further investigations to solve the above puzzle. While MLS
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OH observations have been temporarily suspended at the end of 2009 to reserve its remaining lifetime,
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month-long measurement in each summer in the next few years is planned to cover the peak of SC 24.
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This unique data, in combination with the continuous ground-based FTIVS measurements, will provide
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valuable information about the global and vertical distribution of the SC signal in OH. The latter, with
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an accurately measured SSI variability, can rigorously test the photochemical mechanisms in current
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models.
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Methods
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Ground-based FTUVS at TMF measures the total OH column under clear to lightly cloudy
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conditions. The major systematic error is the uncertainty in the OH line center absorption cross section
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(10%). The precision for a single data point (~15 minute interval) is generally within 10% and is
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substantially improved when using the daily max determined from the polynomial fit of the diurnal
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variation. The gaps in the time series are due to weather, instrument adjustments or upgrades, and
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measurements for other species. When deriving the SC signal using regression, an index describing the
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vertical propagation of the quasi-biennial oscillation (QBO) in the stratosphere was also included in the
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analysis, but was found to be insignificant. The effect of ENSO (El Niño/La Niña-Southern Oscillation)
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was also found to be negligible.
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MLS OH data used in this study are from v3.3 retrieval software. The systematic uncertainty is
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within 8% over 32 – 0.0032 hPa (32). We use data at 21.5 – 0.0032 hPa to avoid the increasing noise
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due to H2O absorption at higher pressures. The zonal mean around TMF [29.5N, 39.5N] are used. A
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similar analysis using data from a 10×25 grid box at TMF was also performed. The results are similar
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to those presented here.
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WACCM uses MOZART3 as the chemical mechanism (2). Chemical species are all allowed to
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vary during model runs. For each UV setting, the model is run from 1960 to 2010. The first 4 years are
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ignored to allow the model for spin-up. We use the monthly mean output to derive the OH SC signal.
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We also generated daily max outputs during solar max year and solar min year to compare with results
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using monthly mean. The diurnal effect is found to be very small.
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The 1-D model is a Caltech/JPL photochemical model that includes over 100 chemical species,
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over 460 thermal and photochemical reactions, vertical transport (eddy, molecular, and thermal
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diffusion), and coupled radiative transfer (24, 25). The chemical kinetics have been updated to JPL06-2
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(46). A more recent update of JPL10-6 (47) does not introduce significant differences on major reactions
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related to HOx chemistry. 65 layers are used to cover from the ground to 130 km. The OH fluxes at the
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surface and the top of the atmosphere are fixed as zero. During model runs, chemical species are not
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constrained unless otherwise stated. The temperature profile is fixed. The model has been applied to
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study the diurnal cycle of OH (48). It produces realistic O3 vertical profiles at 30 – 100 km (25). Typical
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model profiles of OH, O3, and related species are shown in Figure S2 (SI).
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SORCE solar spectral data: The SOLSTICE measurement (22) has a spectral resolution of 0.1
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nm and an absolute accuracy within 5%. The SIM measurement (23) has varying spectral resolution (~1
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nm in UV) and an absolute accuracy within 2%.
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Acknowledgements
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We acknowledge the support of the NASA Aura Science Team, Upper Atmosphere Research, and
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Tropospheric Chemistry programs. Work at the Jet Propulsion Laboratory, California Institute of
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Technology, was done under contract to the National Aeronautics and Space Administration. Support
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from an Australian Research Council Linkage International grant is gratefully acknowledged. We
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acknowledge R. C. Willson for providing the ACRIM TSI composite (http://www.acrim.com/), L. Puga
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and D. Bouwer for providing the latest Mg II index (http://sol.spacenvironment.net/), and the LASP
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Interactive Solar Irradiance Datacenter (LISIRD) for Lyman- record (http://lasp.colorado.edu/lisird/).
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We
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(http://www.pmodwrc.ch/pmod.php?topic=tsi/composite/SolarConstant), and acknowledge unpublished
also
acknowledge
receipt
of
dataset
from
PMOD/WRC,
Davos,
Switzerland
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data from VIRGO (the Variability of solar IRradiance and Gravity Oscillations on board the SOlar and
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Heliospheric Observatory). Some early years FTUVS OH data were collected by R. P. Cageao. We
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thank H. M. Pickett, the PI (retired) for the MLS OH measurements and a NASA Aura Science Team
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project. We also thank R.-L. Shia and S. Newman for help with the models and insightful discussions.
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Author contributions
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S. Wang, K.-F. Li, S. P. Sander, Y. L. Yung, and F. P. Mills designed the research. S. Wang carried out
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the observational data analysis and investigations. K.-F. Li carried out the 1-D model runs and
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contributed to the investigations under the guidance of Y. L. Yung and F. P. Mills. Under the guidance
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of S. P. Sander, T. J. Pongetti made most TMF measurements. M.-C. Liang carried out the WACCM
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model runs. N. J. Livesey and M. L. Santee contributed to MLS data quality analysis. J. W. Harder and
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M. Snow provided SORCE SSI data. The manuscript was mainly written by S. Wang, with the 1-D
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model section contributed from K.-F. Li. All authors contributed comments on the manuscript.
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Additional information
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The authors declare no competing financial interests. Correspondence and requests for materials should
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be addressed to S. Wang.
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Figure Legends
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Figure 1. Daily max OH column at TMF and its SC variability.
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(a) Daily max FTUVS XOH (black) and its long-term variability based on FFT (red and green). (b) Daily
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zonal mean MLS XOH around TMF latitude (black) and its annual mean (blue). (c) Long-term FTUVS
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XOH variability normalized to all-time-mean (red and green: from FFT; gray: from regression) and the
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comparable variability in MLS XOH (blue).
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Figure 2. OH SC variability correlates well with solar parameters.
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(a) Daily mean TSI from SORCE (black), PMOD composite (purple), and ACRIM composite (green).
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(b) FTUVS (red) and MLS (blue) XOH variability (see Figure 1) in comparison with variability in annual
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mean SORCE TSI (black). (c) Same as (b), but replacing SORCE TSI with smoothed ACRIM and
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PMOD composites. The PMOD TSI was adjusted by 4.7 W/m2. (d) XOH variability in comparison with
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variabilities in Lyman-α (gray) and Mg II (cyan).
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Figure 3. Comparison of OH SC variabilities from WACCM and observations.
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(a) Modeled variability of annual mean XOH (using NRL solar forcing) integrated over the altitude
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ranges for FTUVS (red) and MLS (blue) XOH. (b) Model XOH variability is increased by a factor of 3
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(gray) to compare with observed XOH SC signal (red and blue). The gray band indicates the scatter range
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of model XOH variability over the simulated SCs. (c) and (d) are equivalent to (a) and (b) but for model
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results using SORCE SSI.
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Figure 4. Solar UV spectral variability derived from SORCE SSI.
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The blue and orange lines correspond to SSI data from SOLSTICE and SIM, respectively. The purple
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line shows the mean of the two at 210 – 240 nm. The black line is the NRL SSI variation. All spectra
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have been convolved to the model grid. The inset shows the spectral scaling factors for extrapolating the
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observed SSI (April 2004 – November 2007) to the solar max in January 2002. For above 340 nm (not
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important for OH chemistry), an arbitrary factor of 3.5 is applied (dashed).
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Figure 5. Vertical profile of OH SC signal and its implications for O3.
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(a) The spectral response of OH to changes in wavelength-resolved solar irradiance (the relative change
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in OH divided by the relative change in the photon flux) from the 1-D model. The reference UV
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spectrum for perturbation is constructed with SOLSTICE data below 210 nm, SIM data above 240 nm,
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and the mean of SOLSTICE and SIM at 210 – 240 nm where they disagree (see Figure 4). (b) Vertical
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profiles of OH SC signal from the model run using NRL SSI (black), using SORCE SSI (blue; shade
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representing the upper and lower limits of results derived by using SOLSTICE or SIM data at 210 – 240
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nm.), and from Canty and Minschwaner (16) (orange). (c) The corresponding O3 variability (solely due
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to the changes in OH in panel (b)).
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