Aimee Merkel, Jerald Harder, Thomas Woods, Anne Smith, David Rusch, Mar<n Mlynczak

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1 Further evidence of solar cycle variability in middle atmospheric ozone and the importance of incorporating solar spectral irradiance in atmospheric modeling Aimee Merkel, Jerald Harder, Thomas Woods, Anne Smith, David Rusch, Mar<n Mlynczak Laboratory for Atmospheric and Space Physics (LASP) University of Colorado Na<onal Center for Atmospheric Research (NCAR ACD ) NASA Langley Research Center, Hampton, VA Jan. 28, 2014 SORCE Science Mee5ng 1

2 nse of the the specen 0.2 mm ) instru- (SORCE) declining er decline our prensated in ble wavear to have tospheric bove this chemical ments of me period lso show, climate is sufficient observed r findings n tempero current d as num- December e between M. This is al models, d area, as SIM data a factor of radiation, empirical violet than ent in the, are indence Com- The data Supplementary Information for further details.) This type of model produces realistic simulations of the upper stratosphere (above about 25 km) but is less reliable at lower altitudes where photochemical time constants are longer and a more accurate representation of transport processes is required. The results below come from four model runs using solar spectra derived from the SIM measurements (with SOLSTICE data for wavelengths less than 200 nm) and those It all started with Haigh et al. Nature produced by the Lean model, each for both 2004 and In Fig. 2 we present latitude height maps of the difference between 2004 and 2007 in December ozone concentrations. The Lean spectral data produce a broad structure of ozone concentrations greater in 2004 than in 2007, with maximum values of around 0.8% near 40 km, whereas the SIM data produce a peak enhancement of over 2% in low latitudes around 35 km, along with significant reductions above 45 km. The predicted temperature differences (Supplementary Difference in spectral irradiance at >242 nm (mw m 2 nm 1 ) SSI influence study using a 2- D (la5tude- height) radia5ve- chemical- transport model of the atmosphere (IC2- D). 0.5 Modeled ozone response 30 (max- min) SOLSTICE nm important for mesospheric ozone boratory for Atmospheric and Space Physics, University of Colorado, 1234 Innovation Drive, Boulder, 400 Wavelength (nm) 500 NRLSSI Lean model Figure 1 Difference in solar spectrum between April 2004 and November The difference ( ) in solar spectral irradiance (W m 22 nm 21 ) derived from SIM data 4 (in blue), SOLSTICE data 8 (in red) and from the Lean model 5 (in black). Different scales are used for values at wavelengths less and more than 242 nm (see left and right axes respectively). SIM Difference in spectral irradiance at >242 nm (mw m 2 nm 1 ) 1 a Lean model 0.3 Pressure (hpa) MLS ozone regression Mesosphere out- of- phase Latitude ( N) b SIM/SOLSTICE data Decreased ozone at solar max. NRLSSI model run a Lean model 0.3 Pressure (hpa) Pressure (hpa) Approximate altitude (km) Latitude ( N) Latitude ( N) Fig. 1) are also very different, with the Lean data set showing temperatures K greater in 2004 than in 2007 at the top of the model domain, whereas the SIM data set produces a peak warming of 1.8 K at the summer polar stratopause. These temperature differences are qualitatively similar to, but about 50% larger than, those estimated by ref. 12 with an idealized forcing in a full climate model, possibly owing to the broader spectral resolution imposed and the lack of ozone temperature feedback in that model version. The very different scenarios produced by the two spectral data sets suggest they might be distinguishable in observational records. A multiple regression analysis has been carried out of deseasonalized monthly mean ozone data from the Microwave Limb Sounder (MLS) instrument on the Earth Observing System (EOS) Aura satellite. Four regression indices were used: a constant, two orthogonal indices representing the quasi-biennial oscillation (which dominates ozone variability in the tropical stratosphere) 13 and a solar index constructed from SIM data integrated over nm. Motivated by the model results (Fig. 2), we chose two spatial regions, both span- Jan. 28, 2014 SORCE Science Mee5ng ning the tropics, one at altitude hpa, where the model predicts SORCE model run b SIM/SOLSTICE data the largest difference , and one at hpa, where the model shows largest negative values. Figure 3 shows the raw data and the fits reconstructed from the four regression components; it also shows (in red) the derived solar component, which is statistically significant at.95% at the upper levels and.99% at the lower levels Figure 2 Modelled difference in ozone between December 2004 and December Estimates of the percentage difference ( ) in zonal mean ozone concentration (labels on contour lines in per cent) produced by the model using solar spectra from the Lean model (a) and SIM/ SOLSTICE data (b). Stratosphere in- phase Approximate altitude (km) Approximate altitude (km) a hp O 3 anomalies (%) O 3 anomalies (%) b hpa Figure 3 Tim (solid black lin deseasonalized values reconst dashed lines. T components a Fig. 2. Data we 6.8 hpa (b). Over the p at altitudes 3 increasing co 2000, howev stopped decl probably also the chemical the present s factors from seems likely ozone below is also more tral variation solar signal i (1979 to 200 that the decl previous sola behaviours d To unders the modelled The sharp d (Fig. 2b) is c with the dom These losses through pho nates the loss a self-healing to lower leve (See Supplem To assess t sured irradia another set o SOLSTICE t There are dif fields but the stratosphere tosphere. Th response but the impact o the radiative The respon

WACCM 3- D fully coupled chemistry, radia5on and dynamics global model. Merkel et al. 2011 GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L13802, doi:10.

3 WACCM 3- D fully coupled chemistry, radia5on and dynamics global model. Merkel et al GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L13802, doi: /2011gl047561, 2011 Simulate Irradiance Changes in WACCM Compare Simula<ons with SABER Observa<ons 25S 25N TIMED/SABER O 3 emission measurements from the 9.6μm channel Version 1.07 SABER 02/03-08/ Model w/nrlssi Model w/sorce NRLSSI model d) Δ O 3 (%) SABER O 3 average vmr SABER ozone: 2- yr running annual zonal mean trends 0.2 to 0.08 mb 60-66km Day 25 S 25 N Night 2 to 4 mb 38-44km Descending phase of SC23 Jan. 28, 2014 SORCE Science Mee5ng 3

4 SSI Solar Forcing and Earth Atmospheric Response Understandably, this discovery revitalized the irradiance and atmospheric modeling communi5es. Lots of recent modeling ac5vity! Author Reference Model/Topic Haigh et al. Nature, 2010 IC2D model/sc Ozone Cahalan et al. GRL, 2010 GISS ModelE/Trop. Temp. Merkel et al. GRL, 2011 WACCM/SC ozone & TIMED SABER Ineson et al. Oberländer et al. Nature Geosci., 2011 GRL, 2012 HadGEM3/NAO EMAC- FUB/Strat. temp Swartz et al. ACP, 2012 GEOS CCM/ Strat. Ozone & temp Wang et al. PNAS, 2013 WACCM/MLS & grnd based hydroxyl Shapiro et al. JGR, 2013 SOCOL/SC response Wen et al. JGR, 2013 GISS ModelE/Temp. response Ineson et al. (in prepara5on) HadGEM3/NAO/CMIP5 study, Maunder Minimum response Modeling studies focusing on SSI implica5ons: Photochemistry Radia5ve response Circula5on - NAO Top down vs Bojom up Jan. 28, 2014 SORCE Science Mee5ng 4

5 Pressure (hpa) Compiled Modeling Results In the Ermolli et al ACP paper, Katja Majhes (GEOMAR, Germany) compiled the results of these modeling studies. Ozone response (max- min) to SORCE and NRLSSI GEOSCCM NRLSSI GEOSCCM SORCE IC2D NRLSSI IC2D SORCE SOCOL NRLSSI SOCOL SIM SOCOL SOLSTICE WACCM NRLSSI WACCM SORCE WACCM* NRLSSI MMM NRLSSI MMM SORCE GEOS CCM IC2D SOCOL WACCM MMM Percent ozone variability due to solar Height (km) Jan. 28, 2014 SORCE Science Mee5ng 5

6 Ques5ons and Debate Now there is sugges5ve evidence of this surprising SC signal in mesospheric ozone measurements that you can only get if more UV variability is incorporated into atmospheric models. Lots of ques5ons and debate. Solar ques5ons: - Integrity of the SORCE dataset (Degrada5on correc5ons?) - Why do previous measurements at these wavelengths disagree with SORCE? - Can we use a solar spectrum scaled by a variability proxy (TSI, MgII, Lyman α, F10.7) as a standard to characterize the sun in atmospheric models? - Do all wavelengths vary the same way as TSI? - How good do the solar measurements need to be? Gaps in 5meseries? - Does this variability only pertain to SC23-24 or has it been there all along? Atmospheric ques5ons: - Integrity of the ozone measurements. Why haven t we seen this signal before? - Why is the mesospheric signal out of phase with solar cycle and different than stratospheric ozone? - Is this a special solar cycle? Is it in previous measurements? - What does this mean for the modeling community? Is a SSI proxy good enough for atmospheric modeling studies? Are we missing important SC variability? Jan. 28, 2014 SORCE Science Mee5ng 6

7 Interes5ng Conundrum The modelers - want the solar physicist to give them something to put in their models. Most models have been upgraded to included SSI on a daily cadence. The solar physicist - want the modelers to tell them how good they need to measure the Sun. Good enough is different depending on the model, atmospheric region studied, type of model (photochemistry, radia5ve). Work in progress: We are having a workshop next month with the NCAR folks to discuss this very thing. Jan. 28, 2014 SORCE Science Mee5ng 7

Understanding Mesospheric Ozone Variability Top of the atmosphere ozone~ 1% of total column Dominated by

Local 5me is important for solar cycle analysis. More UV causes more loss of ozone at solar maximum.

Mesospheric ozone influenced by the solar radia5on in the 200-300nm band.

8 Understanding Mesospheric Ozone Variability Top of the atmosphere ozone~ 1% of total column Dominated by photochemistry. Photochemical life5me is hours. Strong diurnal component. Local 5me is important for solar cycle analysis. More UV causes more loss of ozone at solar maximum. Loss due to photolysis and cataly5c cycles with OH and H. Mesospheric ozone influenced by the solar radia5on in the nm band. (produc5on and loss) Herzberg Con5nuum ( nm) J2 rate: O 2 Photolysis O 3 produc5on Herzberg Con5nuum Hartley bands Hartley Bands ( nm) J3 rate - O 3 Photolysis O 3 loss Jan. 28, 2014 SORCE Science Mee5ng 8

9 TIMED/SABER Ozone 12 years of ozone data We can now look at the ascending phase of solar cycle 24. Did the mesosphere respond? Data now spans from (12 years of data) Recently updated to Version 2 Reprocess all results 9.6μm channel O 3 emission measurements Version- 2 data validated by A. Smith (NCAR) Known systema5c bias compared to other ozone measurements. Bias is constant over 5me, so does not influence differences. Jan. 28, 2014 SORCE Science Mee5ng 9

10 SABER Ozone Time Series Yearly average Equatorial average 5me series SABER O 3 Average vmr O 3 VMR Mesosphere Out- of- phase 0.2 to 0.08 mb 60-66km ~4% Stratopause Licle varia5on 1 mb 48 km O 3 VMR O 3 VMR Stratosphere in- phase 9.6 day +/-25deg Strat. yearly average 2-4mb 2 to 4 mb 38-44km ~5% Jan. 28, 2014 SORCE Science Mee5ng 10

11 SABER Ozone Time Series SSI Proxy based on the Active Network Area, SIM nm Fractional Irradiance Change Irradiance Variability Ac5ve Area Network - PSPT SIM nm Year Courtesy of Jerry Harder O 3 VMR O 3 VMR day +/-25deg Meso. yearly average.2-.08mb Mesosphere Out of phase Stratosphere in phase 0.2 to 0.08 mb 60-66km 9.6 day +/-25deg Strat. yearly average 2-4mb 2 to 4 mb 38-44km Jan. 28, 2014 SORCE Science Mee5ng 11

12 Residual ozone variation in percent Residual ozone variation in percent Year Mesosphere out- of- phase Mesosphere ozone variation, 0.08 mb 66 km Stratosphere in- phase SABER Regression Analysis 4- component regression, deseasonalized 5me series at each pressure level. Solar, Annual, 2- QBO Solar Coefficient Residual ozone variation regression fit solar component Polynomial fit Stratosphere ozone variation, 5.5 mb 36 km Residual ozone variation regression fit solar component Polynomial fit Year Pressure levels (mb) Percent ozone variation due to solar Solar coefficient Jan. 28, 2014 SORCE Science Mee5ng approximate altitude (km)

13 Ozone measurements in previous solar cycles Ques5on: Why have we not seen this mesospheric ozone behavior in previous solar cycles? Reason Solar cycle variation of stratospheric ozone: Multiple regression analysis of long-term satellite data sets and comparisons with models Regression analysis results Historical ozone data comprised of occulta5on SAGE HALOE data or only measure up to 50km. B. E. Soukharev 1 and L. L. Hood 1 SBUV Received 22 January 2006; revised 28 June 2006; accepted 24 July 2006; published 31 October Remsberg et al., The solar signal is washed out due to the mixing of source and loss mechanisms in a photochemical dominate region of the atmosphere. (sunrise and sunset) Jan. 28, 2014 SORCE Science Mee5ng 13

Solar Mesosphere Explorer 1982-1989 UV ozone channel (Rusch et al.

1982) SME covers parts of solar cycle 21-22 - Good ozone measurements between 1982-1986 Day5me

14 Solar Mesosphere Explorer UV ozone channel (Rusch et al. 1984) Solar irradiance measurements. (Rojman et al. 1982) SME covers parts of solar cycle Good ozone measurements between Day5me (3pm) limb profiles of ozone with good global coverage. SME ozone measurements are analyzed consistent with SABER analysis. (Woods and Rojman 1997) Jan. 28, 2014 SORCE Science Mee5ng 14

15 Solar Irradiance variability 2 decades apart LISIRD site Courtesy of Jerry Harder Jan. 28, 2014 SORCE Science Mee5ng 15

16 SME Ozone Time Series Pressure level (mb) SME yearly average vmr approximate altitude (km) O 3 VMR SME yearly mean 5me series 0.2 to 0.08 mb 60-66km SME yearly average latitude Delta Irradiance (W/m^2) SME irradiance variability nm Year Jan. 28, 2014 SORCE Science Mee5ng 16

17 SME Regression Residual ozone variation in percent Residual ozone variation in percent Mesosphere 60 km Mesosphere ozone variation, 0.2 mb 60km Residual ozone variation regression fit solar component Polynomial fit Year Stratopause 48 km Mesosphere ozone variation, 1 mb 48km Residual ozone variation regression fit solar component Polynomial fit Year Pressure levels (mb) Solar Coefficient Percent ozone variation due to solar Solar coefficient approximate altitude (km) Jan. 28, 2014 SORCE Science Mee5ng 17

18 SME compared to SABER Fractional Irradiance Change Normalized ozone variability Years from solar minimum Irradiance Variability nm SME UV - SC 21 SORCE - SC Ozone yearly 5me series SME UV - SC 21 SABER SC Pressure levels (mb) Regression solar component Day Solar Variation SABER 0.1 SME UV Percent Solar variation approximate altitude (km) Jan. 28, 2014 SORCE Science Mee5ng 18

19 Summary Sugges5ve evidence that UV variability in the nm range and mesospheric ozone from SC 21 are consistent with SC Has it been there all along? Further evidence that this signal is real. Mul5ple modeling studies show that increased UV variability as observed by SORCE (both SIM and SOLSTICE) helps to resolve differences between modeled ozone and observa5ons in the mesosphere. The UV variability is probably somewhere in between NRLSSI and SORCE, however it is apparent that the atmosphere is sensi5ve to this difference. When compiling SSI proxy model please consider that this type of variability in the UV majers in the mesospheric photochemistry. Need to approach the issue from both direc5ons. Atmospheric modelers and solar physicists/modelers need to work together to constrain this variability. The atmospheric modelers can perform case studies to fine tune the response to different solar variability but this can t be used to validate the solar, can only be used as a guideline. Wavelength dependent. Importance of the con5nua5on of mesospheric ozone measurements in the future. Jan. 28, 2014 SORCE Science Mee5ng 19

20 Thank You! Jan. 28, 2014 SORCE Science Mee5ng 20

21 9.6 day +/-25deg Meso. yearly average.3-.08mb Version 1.07 Version 2 O 3 VMR Jan. 28, 2014 SORCE Science Mee5ng 21

Analyze SABER as if Occulta5on Experiment Day

Solar signal is washed out and response is more

22 Analyze SABER as if Occulta5on Experiment Day Night Par5al Day- Night Annual Mean Difference 25S- 25N - When SABER is analyzed with only measurements taken at occulta5on local 5mes: Solar signal is washed out and response is more similar to night results Jan. 28, 2014 SORCE Science Mee5ng 22

Analyze WACCM at Occulta5on local 5mes Day Modeled

analyzed with only measurements taken at occulta5on

23 Analyze WACCM at Occulta5on local 5mes Day Modeled ozone results Night Par5al Day- Night When WACCM is analyzed with only measurements taken at occulta5on local 5mes: Solar signal is washed out and response is more similar to night results. Confirms results from SABER. Jan. 28, 2014 SORCE Science Mee5ng 23

Solar Cycle Variations in Mesospheric Carbon Monoxide (CO)

Solar Cycle Variations in Mesospheric Carbon Monoxide (CO) Jae N. Lee 1, Dong L. Wu 2, Alexander Ruzmaikin 3, And Juan Fontenla* * Las Estrellas, Juan. 1. Joint Center for Earth Systems Technology, University