The tropical stratospheric ozone response to solar UV variations associated
with the rotational cycle (
The thermal structure and the composition of the middle atmosphere are
sensitive to fluctuations in the incoming solar radiation, which in turn can
affect the Earth's surface climate variability (Gray et al., 2010). These
solar variations are dominated by the 11-year solar magnetic activity cycle
and the solar rotational cycle, also called the 27-day solar cycle. Changes in
total solar irradiance (TSI) over an 11-year solar cycle are typically lower
than 0.1 %, that correspond to 1 W m
Solar irradiance fluctuations strongly depend on the wavelength range and their relative amplitudes tend to increase sharply with decreasing wavelengths (Lean, 2000). In the UV range, the variability over the course of the 11-year solar cycle is of about 8 % at 200 nm. Several observational and modeling studies have examined the impact of 11-year UV variability on stratospheric ozone and temperature (e.g., Hood, 2004; Soukharev and Hood, 2006; Randel and Wu, 2007; Austin et al., 2008; Remsberg et al., 2008; Gray et al., 2009; Remsberg, 2014; Dhomse et al., 2016). These studies found a change associated with 11-year solar cycle in the range of 2 to 5 % in ozone mixing ratio, which maximizes near 40 km. Maycock et al. (2016) recently compared the ozone 11-year solar cycle signal of several different satellite records and found substantial differences. One inherent issue of the observational investigation of the 11-year cycle ozone response is the fact that only three complete periods of the 11-year solar cycle have been covered by satellite observations so far. Furthermore, the life span of a single satellite instrument is generally shorter than (comparable to in some cases, e.g., TIMED SABER, ENVISAT MIPAS, ENVISAT GOMOS, Aura MLS) one solar cycle and instrumental biases between different ozone profile datasets complicate statistical analysis of decadal variations (Fioletov, 2009; Dhomse et al., 2016). In this regard, a suitable alternative for understanding better the direct effect is to examine the ozone response on Sun's rotational timescale (i.e., about 27 days). Although the irradiance fluctuations during the rotational cycle are on average smaller than during the 11-year solar cycle, there are many more rotational cycles than 11-year cycles, improving the statistics considerably.
A number of observational studies have been carried out to determine the
effects of the solar rotational cycle on stratospheric ozone, generally at
low latitudes (i.e., tropical region) based on the analysis of satellite
observations (e.g., Hood, 1986; Eckman, 1986b; Keating et al., 1985, 1987;
Hood et al., 1991; Fleming et al., 1995; Hood and Zhou, 1998, 1999; Fioletov,
2009; Dikty et al., 2010). These studies have shown that the sensitivity of
tropical ozone to the solar rotational cycle maximizes at about 40 km (or
Simulations with numerical models of various complexities have been performed
to understand the influence of the rotational cycle on ozone variability.
One-dimensional photochemical–radiative model experiments (e.g., Hood, 1986;
Eckman, 1986a; Brasseur et al., 1987) allowed identifying the importance of
temperature/ozone couplings and reproducing the gross features of the
observed ozone response. In particular, they found that the negative phase
lag between the solar forcing and the ozone response in the upper
stratosphere originated from the strong influence of the temperature feedback
on ozone response through the temperature dependent chemical reactions
(Brasseur et al., 1987). However, they noticed that including the solar
induced temperature changes alone was not sufficient to adequately reproduce
the observed magnitude and phase lag of the ozone response and suggested that
atmospheric dynamical variability – which is not simulated in 1-D models –
may also have a sizeable influence (Hood, 1986; Brasseur et al., 1987). The
latter issue has later been addressed with two-dimensional models which
revealed better agreement with observations (Brasseur, 1993; Fleming et al.,
1995; Chen et al., 1997). Fleming et al. (1995) further stressed the
increasing importance with height of the solar-modulated HO
Using a large ensemble (nine 1-year long runs) of chemistry–climate model (CCM) simulations, Rozanov et al. (2006) found that the ensemble mean ozone sensitivity to the solar rotational irradiance changes was in very good agreement with observational data. However, they pointed out – despite an identical solar forcing for each experiment – a large scatter in maximum ozone sensitivities that could vary by a factor of almost 10 between the two most distant ensemble members. A large variability in ozone sensitivity was similarly found in an ensemble of three transient CCM simulations (1960–2005) (Austin et al., 2007). Bossay et al. (2015) analyzed satellite observations of two periods of 3 years during the declining phases of cycles 22 and 23 (i.e., 1991–1994 and 2004–2007) and found that the solar rotational signal in stratospheric ozone time series strongly varies from one year to another. These results suggest that the background dynamical state and variability in the atmosphere contribute to masking the solar rotational signal in ozone (Gruzdev et al., 2009).
In addition to the dynamics, the intensity of the solar forcing naturally modulates the solar rotational signal in ozone. When the solar rotational fluctuations are well marked with large amplitudes, notably around the maxima of 11-year cycles (e.g., Rottman et al., 2004), ozone response and correlation are expected to be the largest. This has been supported by observational (e.g., Hood, 1986; Zhou et al., 2000; Fioletov, 2009; Ditky et al., 2010) as well as modeling (Kubin et al., 2011) studies which demonstrated a better identification of the ozone signal associated with enhanced rotational forcing fluctuations. This relationship has, however, been challenged by contradictory results. Hood and Zhou (1998) analyzed UARS MLS ozone data for the 1991–1994 period and found a correlation 2 times stronger during the last half of the period, i.e., when the rotational forcing fluctuations are reduced. They suggested that it might have been the result of an artifact of either instrumental or geometric (local time coverage) origin that may have affected the earliest part of the UARS MLS ozone record more than the later part. In their recent observational study which compared the declining phases of cycle 22 and cycle 23, Bossay et al. (2015) further showed that, even though the amplitude of solar rotational fluctuations of the 205 nm flux was by far the largest during the first year of both periods, the correlation with tropical ozone was found to be maximum the subsequent years.
The ozone sensitivity response to the solar rotational forcing has also been suggested to vary with the intensity of the forcing. We recall that the “sensitivity” is a quantity expressed as percent changes in ozone (or any other variable of interest) per percent change in the forcing (here specifically solar). Hence, the sensitivity is normalized by the amplitude of the forcing and may not be expected to change strongly with the amplitude of the forcing, or at least not as much as the absolute amplitude of the ozone response which directly depends on the amplitude of the forcing. Gruzdev et al. (2009) used an idealized solar rotational forcing in their model (prescribed as a sinusoidal 27-day oscillation) and found a significant reduction of the ozone sensitivity when applying an enhanced solar forcing amplitude (3 times the standard amplitude). Reciprocally, in the CCM experiments of Kubin et al. (2011), the ozone sensitivity seemed to be enhanced during periods of weak 27-day cycles. Finally, the observational study of Bossay et al. (2015) also suggests an opposite relationship between the solar rotational irradiance fluctuations and the ozone sensitivity. Given the strong influence of the dynamical background state on the variability in estimated ozone sensitivity and the rather shortness of the considered time windows of analysis, they recognized that it was not possible to conclude to a systematic effect. All these results thus highlight the uncertainty regarding the influence of the forcing intensity on ozone sensitivity and on the length of the time window required for an accurate and robust estimation of the ozone rotational signal.
In the present study, we examine the sensitivity of the tropical stratospheric ozone response to the rotational cycle by comparing satellite observations and chemistry climate model experiments to understand better the origin of the discrepancies – and sometimes contradictory results – in the estimation of the ozone response to the solar rotational cycle found in previous studies. As a first step, we follow up on the case study of Bossay et al. (2015) and make use of observations and modeling results comparison to provide a detailed picture of the ozone response to the solar rotational cycle during the declining phases of cycle 22 and cycle 23. We particularly aim to better understand the strong differences in the ozone response to solar rotational cycle found between the two periods. Two configurations of the LMDz-Reprobus chemistry climate model simulations are used, with specified dynamics (i.e., chemistry transport model, or CTM) and in its free-running mode (CCM). In the CTM configuration, temperature and wind fields calculated by the model are relaxed towards meteorological analysis; the dynamics are expected to be rather close to the reality, allowing direct comparisons with satellite observations for evaluating model chemical processes and its relevance to our study. In the CCM configuration, an ensemble of simulation is performed. Comparing the CCM ensemble results to CTM and observations during the declining phases of cycle 22 and cycle 23 allows for a better understanding of the effect of internal dynamical variability on the ozone response. As a second step, we take advantage of the ensemble of CCM simulations and its large statistics to (i) assess the influence of the solar cycle phase on the ozone sensitivity to the rotational cycle and (ii) quantify the time window required for a robust estimation of the ozone sensitivity.
Observational datasets, and model configurations and simulations are described in Sect. 2. Section 3 presents comparisons between satellite observations and model (CTM and CCM) simulations of the ozone response to the solar rotational cycle. Section 4 focuses on CCM results to examine the influence of (i) the solar activity fluctuations and (ii) the length of the time window in the estimation of the ozone sensitivity to the solar rotational cycle. The main findings are summarized in Sect. 5. Note that, for the sake of simplicity, the first period (October 1991–September 1994) during cycle 22 will be referred to thereafter as 1991–1994 period and the second period (September 2004–August 2007) during cycle 23 will be referred to as the 2004–2007 period.
The solar proxy used in regressions analyses is the UV solar irradiance at
205 nm. This wavelength is chosen because it is important for the ozone
chemical budget throughout the stratosphere. The 205 nm wavelength is
included in the Herzberg continuum region (200–242 nm) that is positioned
between two strong absorption bands: the Schumann–Runge band of molecular
oxygen and the Hartley band of ozone (Brasseur and Solomon, 2005). In the
Herzberg continuum, atmospheric absorption is relatively low and hence solar
UV radiation penetrates deeply in the atmosphere, down to the lower
stratosphere, where it photolysis molecular oxygen (O
In our study, we use the solar spectral irradiance provided by the Naval Research Laboratory Solar Spectral Irradiance (NRLSSI) model version 1 (Lean, 2000; Wang et al., 2005). NRLSSI is an empirical model which aims to reconstruct long-term SSI over the wavelength domain 120–100 000 nm. It uses historical estimates of faculae brightening and sunspot darkening to extend in time wavelength-dependent parameterizations of SSI derived from satellite measurements and model. At shorter wavelengths than 400 nm, the SSI is derived from UARS/SOLSTICE observations (Rottman et al., 2001) through a multiple regression analysis with respect to a SOLSTICE reference spectrum. The regression analysis includes a facular brightening and a sunspot darkening time-dependent term. Above 400 nm the SSI is reconstructed by adding the irradiance changes caused by the presence and the characteristics of faculae and sunspots (see Lean, 2000, for details) to a quiet Sun intensity spectrum, i.e., defined by the absence of faculae and sunspots. The intensity spectrum of the quiet Sun is a composite compiled from space-based observations made by UARS/SOLSTICE (120–401 nm) and SOLSPEC/ATLAS-1 (401–874 nm) (Thuillier et al., 1998), and a theoretical spectrum at longer wavelengths (Kurucz, 1991).
We use the stratospheric ozone measurements from the two Microwave Limb Sounder (MLS) instruments onboard UARS (cycle 22) and Aura (cycle 23).
UARS MLS was launched on 12 September 1991, into a 57
Aura MLS was launched on 15 July 2004 into a Sun-synchronous near-polar orbit
around 705 km. Detailed information on the Aura MLS instrument is given in
Waters et al. (2006). In brief, Aura MLS observes a large suite of
atmospheric parameters by measuring millimeter- and submillimeter-wavelength
thermal emission from Earth's limb with seven radiometers covering five broad
spectral regions (118, 190, 240, 640 GHz and 2.5 THz). The “standard
product” of ozone is retrieved from radiance measurement near the 240 GHz.
Here, we used version 4.2 of the Aura MLS ozone product (Livesey et al.,
2017). The Aura MLS fields of view point forward in the direction of orbital
motion and vertically scan the limb in the orbit plane, resulting in a data
coverage from 82
For our study, daily stratospheric ozone profiles averaged over the tropical
band [20
The LMDz-Reprobus model is a CCM resulting from the
coupling between the extended version of the general circulation model LMDZ5
(Sadourny and Laval, 1984; Le Treut et al., 1994, 1998; Lott et al., 2005;
Hourdin et al., 2006, 2013) and the chemistry module of the Reprobus
stratospheric chemistry-transport model (Lefèvre et al., 1994, 1998).
LMDZ was developed at the Laboratoire de Météorologie Dynamique
(LMD). The dynamical part of the code is based on a finite-difference
formulation of the primitive equations of meteorology (Sadourny and Laval,
1984). The model uses a classical hybrid
The Reprobus chemistry model (Jourdain et al., 2008; Marchand et al., 2012)
calculates the chemical evolution of 55 atmospheric species and includes a
comprehensive description of the stratospheric chemistry (O
The solar component of the radiative scheme of LMDZ5 is based on an improved
version of the two-band scheme developed by Fouquart and Bonnel (1980) and
the thermal infrared part of the radiative code is taken from Morcrette et
al. (1986). While this scheme is crude, note that the thermal component of
the solar forcing (e.g., changes in net heating from solar changes only,
keeping chemical composition unchanged) does not exhibit a dependency on
wavelength as strong as photolysis component of the solar forcing.
Nonetheless, the use of a simple two-band radiation code tends to
underestimate the temperature response when compared to other radiations
models with the same solar irradiance fluctuations (SPARC CCMVal, 2010; Forster et
al., 2011). The radiative scheme takes into account the radiative active
species H
The photolysis rates used in Reprobus are pre-calculated offline with the
Tropospheric and Ultraviolet Visible (TUV) model (Madronich and Flocke, 1999;
Sukhodolov et al., 2016) and then tabulated in a look-up table for 101
altitudes, 7 total ozone columns and 27 solar zenith angles. TUV calculates
in spherical geometry the actinic flux, scattering and absorption through the
atmosphere by the multi-stream discrete ordinate method of Stamnes et al. (1988). The spectral domain extends from 116 to 850 nm. Calculations of
photolysis rate are performed on a 1 nm wavelength grid, except in the
regions relevant for solar cycles (rotational and 11-year solar cycles). In
these spectral regions, the resolution is largely increased to accurately
describe the spectral features in the solar flux or in the absorption
cross sections: the wavelength resolution increases up to 0.01 nm in the
Schumann–Runge bands of O
LMDz-Reprobus is used in two configurations. The first one is the
free-running model configuration (i.e., CCM) that accounts for all the
interactions between chemistry, dynamics and radiation. LMDz-Reprobus is
additionally used in its nudged version (i.e., CTM), where transport and
dynamics are nudged towards temperatures and winds from the 6-hourly ECMWF
model outputs (ERA-Interim; Dee et al., 2011). As the dynamics are specified
and are close to observations, the CTM configuration allows a fair comparison
with MLS observations. The CTM configuration is used over the two 3-year
periods of MLS ozone measurements, as analyzed in Bossay et al. (2015). In
the CCM configuration, we perform an ensemble of five simulations of 17 years
each (from 1991 to 2007). As for the observations, we use the daily
stratospheric ozone profiles averaged over the tropical band [20
In this section, we analyze the ozone response to the solar rotational cycle over the declining phase of solar cycles 22 and 23 in the observations and in the CTM and CCM model simulations. The analysis presented here follows up on Bossay et al. (2015) observational study. In particular, we aim to assess the model performances, understand better the differences in the results between the two solar declining phase periods and highlight the importance of internal dynamical variability.
Figure 1 shows the solar UV variability represented by F205 from 1985 to 2008 with the two periods of interest highlighted in red which correspond to the declining phase of solar cycles 22 and 23. F205 is a good indicator of the NRLSSI solar forcing prescribed in CTM and CCM simulations. Thereafter, F205 is used as the UV index in the regression analysis of the solar signal in stratospheric ozone from MLS observations and model simulations.
The fast Fourier transform (FFT) power spectra of the two F205 declining
periods time series are shown in Fig. 2 (top panel). For both periods, the
high-frequency spectrum is dominated by a strong peak centered around 27 days
corresponding to the main solar rotational periodicity. The broadness of the
peaks indicates that the solar rotational cycle is not regular and covers a
rather wide frequency domain. A small secondary peak is also found at
Temporal evolution of daily F205 from NRLSSI model over solar cycles 22 (1985–1996) and 23 (1996–2008). The two 3-year periods considered here (1991–1994 and 2004–2007) are highlighted in red.
Top: F205 FFT power spectra (from NRLSSI model) for the
We first examine potential rotational periodicities in upper stratospheric
tropical ozone by carrying out a spectral analysis of daily stratospheric
ozone time series averaged over the tropical band
[20
The two periodograms of MLS ozone measurements (Fig. 3a and d) reveal no prominent peak in the range of the 20–30-day period, suggesting an absence of a solar rotational signal in ozone. More prominent peaks are found at longer periods although they are not consistent between the two periods. The large peak found at the 35-day period for 1991–1994 corresponds to the yaw-maneuver period of the MLS instrument as described previously (Froidevaux et al., 1994; Hood and Zhou, 1998). Similarly to observations, the periodograms of CTM results (Fig. 3b and e) does also not exhibit a distinctive solar rotational peak; there are some minor peaks between 20 and 30 days and their amplitudes are smaller in 2004–2007 than in 1991–1994. The analysis has been repeated at lower pressure–height levels (e.g., 10 hPa, not shown) and led to the same conclusions. Overall, the raw power spectrum analysis of observations and CTM results in the middle and upper tropical stratosphere does not allow identifying an ozone signal associated with the solar forcing fluctuations at rotational timescales for the two periods considered here.
Ozone Lomb–Scargle periodograms for
In contrast, the periodogram averaged over the five CCM simulations exhibits
a distinctive peak centered at 27 days for 1991–1994 (Fig. 3c). For
2004–2007, the peak is centered at 25 days (Fig. 3f). The peak is also less
pronounced than in 1991–1994, presumably because of the smaller amplitude of
solar rotational fluctuations and hence model forcing in 2004–2007 (see
Fig. 2). However, the 2
Mean squared coherence between ozone and F205 as a function of
period (days) and pressure level (hPa) for the
We further examine the relationship between stratospheric ozone and solar rotational cycle by performing cross-spectrum analysis between stratospheric ozone and F205. Despite the absence of a solar rotational peak in the ozone power spectrum derived from observations and CTM results, cross-spectrum analysis should help identifying coherent variability modes between the solar forcing and tropical ozone. Figure 4 presents the vertical profile of the magnitude-squared coherence (hereinafter referred to as coherence) between F205 and tropical stratospheric ozone from MLS observations (a and d), CTM model results (b and e) and CCM model results (c and f).
A strong and statistically significant coherence is found for UARS MLS
(1991–1994) between 20 and 28 days and between about 10 and 1 hPa with a
maximum of about 0.7 at the 22-day period around 6 hPa. In contrast, the
coherence for Aura MLS (2004–2007) is generally weaker with only a small
patch of significant coherence at the 90 % confidence level. The
coherence fields from the CTM results resemble those of the observations and
reproduce the main features during the two periods. The main difference
between observed and CTM signals is that the coherence patch extends farther
to lower levels in the CTM (down to 15 hPa) and covers longer periods (20 to
33 days at
The general features in the coherence fields from CCM results are also consistent with those of the observations. However, the area of statistical significant coherence around the 27-day period is wider in the CCM results. In addition, the coherence patch does not extend as low as the CTM results. The differences observed between the MLS coherence fields of the two periods are also reasonably well reproduced in the CCM coherence results. As for the CTM fields in 1991–1994, CCM results reveal a secondary area of significant signal centered at about 13.5-day period and extends almost throughout the stratosphere. For 2004–2007, there is no significant signal around 13–14 days in all the coherence fields. This is consistent with the UV forcing (Fig. 2) exhibiting a stronger 13.5-day period component in 1991–1994.
To further test the robustness of the coherence signal, we perform an additional CCM simulation for the period 1991–1997, where the solar forcing is kept constant by using fixed (i.e., climatological) photolysis rates during the model simulation. Results are shown in Fig. 5. Below 15 hPa, the different experiments show no significant coherence between ozone and solar flux. Between 15 and 1 hPa, all forced experiments (black lines) reveal a similar and significant coherence signal, while for the constant solar forcing experiment (red line), the coherence is weak and within the range of randomness. The absence of significant coherence found in the constant solar experiment confirms that the coherence found between F205 and stratospheric ozone is not fortuitous and primarily originates from photolysis processes. We can also note that the reduced coherence for 2004–2007 may be expected because the solar rotational fluctuations are smaller during that period compared to 1991–1994 (Fig. 2). To summarize these first steps in our analysis, we find that, despite the weak magnitude of the signal, the upper stratosphere tropical ozone concentration fluctuates coherently with UV variability at solar rotational timescales.
Vertical profile of the mean squared coherence between ozone and F205 averaged between 22- and 30-day periods and calculated for the time period 1991–1997. The black lines correspond to the results of individual ensemble members (five in total) and the red line to the results of the experiment forced with constant solar forcing. The vertical dashed line indicates the 90 % confidence limit.
Cross-correlation between digitally filtered (see main text) ozone
and F205 as a function of time lag (in days) and pressure level (hPa) for the
To focus on periodicities relevant to the solar rotational cycle (13.5 and 27 days), all the time series are now filtered using the digital filter that has been commonly used in previous solar rotational studies (e.g., Hood, 1986; Chandra, 1986; Keating et al., 1987; Hood and Zhou, 1998; Zhou et al., 2000). The filtering procedure consists of smoothing data with a 7-day running mean which removes short-term fluctuations. Linear trend and mean value are also removed from these smoothed time series. Finally, a 35-day running mean is subtracted from the data, removing long-term fluctuations (e.g., seasonal, semi-annual, annual and quasi-biennial oscillation variations). The overall procedure is more or less equivalent to a 7–35-day band-pass filter in the frequency domain.
The vertical extent and temporal evolution of the tropical ozone response to
the solar rotational cycle are examined by calculating the cross-correlations
between filtered F205 and ozone in observations and model results. Results
are shown in Fig. 6. For 1991–1994, the observations exhibit a
cross-correlation peak at 0.28 on the 4.6 hPa level with no time lag
(Fig. 6a). This maximum value is close to the maximum of 0.35 found by Hood
and Zhou (1998) on the same pressure level. Furthermore, the overall
variation of the time lag with altitude shown in Fig. 6 is similar to that
found in previous studies (Hood, 1986; Brasseur et al., 1987; Brasseur, 1993;
Hood and Zhou, 1998) with a negative lag above 3–4 hPa (ozone “leading”
the solar flux) and a positive lag below (ozone lagging the solar flux). As
mentioned in the Introduction, the negative lag in the upper stratosphere
results of the influence of the temperature feedback on the ozone response
through the temperature dependent chemical reactions. For 2004–2007, the
cross-correlation pattern (Fig. 6d) is more distorted and weaker than for
1991–1994 (Fig. 6a). The cross-correlation maximum (0.2) is smaller than for
1991–1994 and is found at 10 hPa with a time lag of
Although the cross-correlation fields for the CTM and CCM simulations appear
smoother and with larger statistically significant (shaded) areas than for
the MLS data, most of the general features present in the MLS cross-section
fields appear consistently reproduced by the simulations in the two model
configurations. Marked differences between the CTM and the observations are
found in 1991–1994 though. The high correlation area (with a maximum of 0.4
at 7 hPa and a positive time lag of 3 days) expanding throughout the middle
stratosphere (between 30 and 10 hPa) in the CTM (Fig. 6b) is not found in
observations (Fig. 6a). Overall, the main area of significant correlation
appears also lifted upward in the observations (Fig. 6a) compared to the CTM
(Fig. 6b). The fact that the correlation signal in the middle and lower
stratosphere (below 10 hPa) is found in the CTM but not in the observations
may partly arise from the large noise present in the UARS MLS ozone dataset
at these altitudes (not shown). In contrast, the results for the period
2004–2007 reveal a particular good agreement throughout stratosphere between
the observations (Fig. 6d) and the CTM (Fig. 6e), where the maximum is found
at the same altitude (10 hPa), time lag (
Above 3 hPa (
Vertical profile of ozone sensitivity to F205 (% change in ozone
for 1 % change in F205) at lag 0 for the
In addition to correlation analysis, ozone response to solar UV flux changes can also be measured in terms of sensitivity, i.e., percentage change in ozone per 1 % change in solar UV. Considering ozone sensitivity instead of ozone absolute change allows in principle for an ozone signal to be analyzed that does not depend on the magnitude of the solar rotational forcing, assuming implicitly that the relationship between the solar forcing index (F205) and the ozone response is linear. We derive the ozone sensitivity on different pressure levels by linear regression of the filtered ozone time series on one independent variable, F205. In previous studies, ozone sensitivity profiles were either calculated at optimum lags where the correlation coefficient maximizes (e.g., Hood and Zhou, 1998) or at zero lag (e.g., Williams et al., 2001; Austin et al., 2007). Both alternatives were tried, but given the limited effect on the results and conclusions, we elected to show only ozone sensitivity profiles using a common time frame, hence at zero lag. Results are shown in Fig. 7.
For the 1991–1994 period, the observational (UARS MLS) sensitivity peaks at 0.4 (0.4 % of ozone change for 1 % change in F205) near 4–5 hPa (35 km), consistent with the results of Hood and Zhou (1998) (Fig. 7a). For the 2004–2007 period, the shape of the observational (Aura MLS) sensitivity profile is distorted and the sensitivity peaks at only 0.2 around 5 hPa (Fig. 7d); it is consistent with a peak value of 0.15 derived at the same level shown in Dikty et al. (2010) for a similar period (2006–2007) but with a different instrument (ENVISAT SCIAMACHY). In the middle stratosphere, the sensitivity profile calculated from the CTM results for the period 1991–1994 (Fig. 7b) is consistent with the MLS sensitivity profile (Fig. 7a); the CTM sensitivity profile peaks at 4–5 hPa with a value slightly lower (0.3) than that derived from the MLS observations. Discrepancies between CTM and observational sensitivities are more pronounced in the upper stratosphere. In the CTM, above the peak, the sensitivity suddenly drops around 3 hPa to values close to 0 (Fig. 7b), while in the observation the sensitivity gradually decreases from 3 to 4 hPa to the stratopause region (around 1 hPa) (Fig. 7a). Below 10 hPa, we also note that the uncertainties of the sensitivity profile estimates are larger in the observations than in the CTM. This is consistent with the absence of solar–ozone correlation signal at these altitudes in the observations (Fig. 6a) and, inversely, the clear solar–ozone correlation signal in the CTM (Fig. 6b). For 2004–2007, the CTM sensitivity profile appears to be highly consistent with observations throughout the stratosphere, in accordance with the previous coherence and correlation analyses (Figs. 4 and 6).
We now analyze the CCM ensemble results. The ensemble mean ozone sensitivity
profiles (Fig. 7c and f) markedly differ with ozone sensitivity profiles
derived from observations (Fig. 7a and d) and CTM (Fig. 7b and e) at the
corresponding periods. These differences are particularly pronounced in the
upper stratosphere (above
As mentioned in Sect. 2.2, the results based on UARS MLS measurements may be
affected by the imbalance between night and daytime sampling due to the ozone
diurnal cycle becoming significant in the upper stratosphere. To test the
influence of the ozone diurnal cycle, we repeated all the analysis performed
in this section by mimicking an irregular sampling over the period covered by
Aura MLS (i.e., 2004–2007). Each day,
Overall, our results demonstrate that the LMDz-REPROBUS model produces an ozone response to the solar rotational cycle that is consistent with observations, especially when the dynamical variability is accounted for in the analysis. The results of our ensemble of transient CCM simulations further support the importance of atmospheric internal variability in modulating or masking the solar signal in ozone at solar rotational timescales. In the following, we exploit the ensemble simulation to examine thoroughly the temporal variability in the ozone sensitivity to the rotational cycle.
Results from CCM studies of Gruzdev et al. (2009) and Kubin et al. (2011)
suggested that ozone sensitivity seems to decrease with increasing amplitude
of the rotational cycle. The amplitude of the rotational cycle depends on the
inhomogeneous brightness structure of the solar disc (i.e., distribution of
sunspots and faculae). Given that the amount of sunspots and faculae
increases with increasing solar activity, inhomogeneity in the brightness is
likely to increase during solar maximum phases. One may thus expect minimum
and maximum sensitivity during 11-year solar maximum and minimum phases,
respectively. Next, we test this hypothesis by dividing 15 years (1991–2005)
of the CCM simulations into five 3-year windows corresponding to the four
different phases of the 11-year solar cycle (i.e., maximum, minimum,
descending, ascending phases). These time windows are highlighted with
different colors in the insert panel of Fig. 8a. Figure 8b–f show, for each
3-year time window, the ensemble mean sensitivity profiles and the associated
2
Whatever the solar cycle phase considered (Fig. 8a), all the mean sensitivity profiles have similar shapes with a maximum at around 3 hPa, consistent with observed and modeled sensitivity profiles during solar declining phase (Fig. 7). The most pronounced difference is the maximum sensitivity which varies between 0.3 (green) and 0.5 (red). Overall, the ensemble mean sensitivity profiles appear to vary little from a 3-year window to another. Thus, the model ensemble mean ozone sensitivity seems to be rather independent of the level of solar activity (Fig. 8a), at least when 15 years of model data are considered in total. In comparison, the model ensemble spread is clearly more sensitive to the 11-year solar cycle phase than the ensemble mean. The ensemble spread is found to be generally smaller during periods of high solar activity. It is not surprising. The estimation of the ozone sensitivity is expected to be less affected by the noise and more robust when the solar rotational fluctuations are stronger: the amplitude of the ozone response is much greater, improving the signal-to-noise ratio. We also notice that the ensemble spread is smaller during the maximum phase of cycle 22 (black) than that of cycle 23 (green). It is consistent with the results of Fioletov (2009) observational study that also shows a stronger rotational periodicity in the upper stratosphere tropical ozone during the maximum phase of the solar cycle 22 than the maximum phase of the cycle 23.
Although the rotational cycle amplitude varies with the phase of the 11-year
solar cycle, the relationship is not systematic as revealed by the wavelet
analysis of Fig. 2. In the following, the ensemble mean ozone sensitivity and
its spread are examined as a function of the amplitude of the solar
rotational cycle fluctuations using sliding time windows. The analysis
focuses on the 3 hPa level, where the maximum sensitivity is found (Fig. 8).
Figure 9 compares the temporal evolution (from 1 January 1991 to
31 December 2005) of the variance of the filtered F205 time series (Fig. 9b)
with the ensemble mean (Fig. 9c) and variance (Fig. 9d) of the ozone
sensitivity derived from the five CCM simulations. Each point of the time
series is obtained by first calculating the ozone sensitivity for each
ensemble member over a 1-year time window and then computing the ensemble mean
and its variance over the five simulations. The time window is then shifted
by 1 month and the same procedure is repeated. This gives a total of 168
1-year time slices (14 years
Digitally filtered
The mean ozone sensitivity time series (Fig. 9c) on 1-year time window
strongly fluctuates from 0 to 0.6 around an average value of
Figure 10 shows the regression analysis of the ensemble mean (Fig. 10a) and
spread (Fig. 10b) of ozone sensitivity (i.e., dependent variables) on the
solar rotational variance (i.e., explanatory variable). We assess the
statistical significance of the regression slope using a block bootstrapping
technique to account for the autocorrelation in the residuals that can lead
to an underestimation of the standard error (Mudelsee, 2014). The bootstrap
procedure is carried out as follows. The original residuals are first
obtained by subtracting the original fitted model (i.e., derived from the
linear regression) to the dependent variable. The original residual time
series is then segregated into moving blocks of length
Scatter plots of the CCM ensemble
Figure 10a reveals no significant negative trend between the mean ozone
sensitivity and the F205 variance. Although the linear regression hints at
increasing mean ozone sensitivity for decreasing F205 variance, the
likelihood for the slope to be positive or equal to zero cannot be excluded
statistically (
Finally, the robustness of the estimated ozone sensitivity is examined with
respect to the size of the time window. The procedure is as follows. For each
ensemble simulation (of maximum size
Figure 11a shows the ozone sensitivity profiles when a 1-year time window is
considered. In agreement with the previous ensemble mean ozone sensitivity
profiles calculated for 3-year time windows and at different solar cycle
phases (Figs. 7 and 8), a maximum mean sensitivity of 0.4 is found near
3 hPa. The ozone sensitivity spread (dashed envelope) is larger though and
even expands towards negative values, demonstrating that a 1-year window is
not at all long enough to robustly estimate the ozone sensitivity. Figure 11b
focuses on the 3 hPa pressure level, where the sensitivity peaks, and
reveals that, as expected, the longer the time window is, the smaller the
spread is. Figure 11c shows the coefficient of variation of the ozone
sensitivity (1
In this paper, we examined the tropical stratosphere ozone response to the
solar rotational cycle in satellite observations and simulations of the
chemistry–climate model LMDz-Reprobus. We first focused our analysis on the
case study of two 3-year periods associated with the declining phases of
solar cycles 22 and 23. The solar rotational fluctuations are stronger during
the first period than the second period. We found that, although the solar
rotational signature in the UV forcing is reasonably well marked during both
periods, the amplitude of ozone variations at the corresponding timescales
(i.e.,
Lag correlations and linear regressions have then been used to characterize the vertical profile of the ozone response to the solar rotational cycle in the observations and the model during the same periods. Although these results are consistent with estimates of previous studies (Hood, 1986; Brasseur et al., 1987; Brasseur, 1993; Hood and Zhou, 1998) and a reasonable agreement is found between the MLS observations and the CTM experiments, significant differences are found between the two periods. This may be attributed to differences in solar UV forcing or in dynamical variability between the two periods. Analysis of the CCM ensemble simulations suggest that the differences mostly originate from the dynamical variability. The large spread in the ensemble mean sensitivity profile calculated for 3-year intervals reflects the “masking” effect of non-solar dynamical variability in the estimation of the solar rotational signal in ozone and may certainly explain some inconsistencies found in previous studies.
In our CCM experimental design, the direct radiative effect of UV on heating rates has been neglected, leading to an underestimated temperature response to the 27-day cycle. As a consequence, this may affect the ozone response significantly by reducing the temperature feedback on chemical reaction rates, notably ozone destruction through the Chapman cycle. Recently, Sukhodolov et al. (2016) examined the separate effects of heating rates and photolysis rates in solar-driven ozone changes using a 1-D radiative–convective–photochemical model and different SSI datasets. Using the NRLSSI solar forcing dataset, they showed that, over the course of the 11-year solar cycle, the direct heating rate anomaly leads to a decrease in ozone of 1 % in the middle and upper stratosphere (above 30 hPa), while the photolysis induces an ozone increase of 2 to 4 %. Since, the direct radiative effect of UV on heating rates is neglected in our CCM experiments, the ozone response to solar variability may hence be overestimated. Nevertheless, a comparison of the ozone response in our analysis with results from previous independent CCM studies (Rozanov et al., 2006; Sukhodolov et al., 2017) revealed a very good correspondence, despite the fact that their experimental design included the direct radiative heating effect. This comparison must be considered with caution as Sukhodolov et al. (2016) found substantial differences in calculated photolysis rates between LMDz-Reprobus and SOCOL photolysis codes. Therefore, accounting for the direct heating rate effect in SOCOL may compensate for differences between the two models in ozone response controlled by photochemical processes only. In addition, the results of Sukhodolov et al. (2016) are based on 1-D model calculations and may also change when accounting for dynamical variability (i.e., using 3-D CCM), particularly at 27-day timescales where the atmospheric internal variability largely dominates stratospheric temperature variability (Sukhodolov et al., 2017). To quantify the impact of neglecting solar-induced temperature feedback on our results, the spectral resolution of the LMDz-Reprobus radiative scheme should also be increased and new experiments including the direct radiative effect of UV on heating rate should be performed. We further notice that these improvements are necessary to simulate the “top-down” mechanism, which is based on dynamical consequences of the upper stratospheric thermal response.
Next, we take advantage of the ensemble of five CCM simulations to test whether the ozone sensitivity depends on the phase of the 11-year solar cycle. Considering an ensemble of simulations allows in particular to reduce the masking effect induced by the dynamical random variability. Our results suggest that the level of solar activity does not have an impact on the expected value (i.e., ensemble mean) of the ozone sensitivity. However, the ensemble spread decreases during periods of high solar activity, making the ozone sensitivity retrieval easier and more robust, e.g., during the maximum phase of the 11-year solar cycle.
The ensemble mean ozone sensitivity and its spread have been additionally examined as a function of the amplitude of (i) the solar rotational cycle fluctuations (shown) and (ii) the phase of the 11-year solar cycle (not shown). Here again, no robust dependence of the ensemble mean ozone sensitivity against each of the two variable is found when the results of the five 15-year simulations are averaged. Although the results hint at a slightly negative trend, i.e., increasing ensemble mean ozone sensitivity for decreasing rotational fluctuations (or 11-year solar cycle activity), neither the slopes nor the correlation coefficients are statistically significant. Hence, our results could not confirm previous findings of Gruzdev et al. (2009) or Kubin et al. (2011), who, using model experiments, suggested an increased ozone sensitivity with decreasing solar rotational fluctuations. Nevertheless, it must be noted that the conclusions of Gruzdev et al. (2009) were reached by carrying out experiments with a solar rotational forcing that had an amplitude 3 times larger than a realistic one. Further model experiments, considering for instance longer simulations and/or stronger forcing, would help to address this issue more thoroughly.
In contrast with the ensemble mean ozone sensitivity, as expected, the ensemble spread ozone sensitivity shows a clear increase with decreasing solar rotational cycle fluctuations. The negative trend further intensifies during the period with very low solar rotational fluctuations, corresponding here to the period of minimum solar activity between the end of the solar cycle 22 and the beginning of the solar cycle 23 (i.e., 1994–1997). These findings are consistent with the results of Fioletov (2009), who showed a noticeable difference in the estimate of the ozone sensitivity profile in 1994–1998 by comparison with other periods. Hence, when the solar rotational fluctuations are small, the “masking” effect of dynamical variability becomes more prominent and makes the estimate of the ozone sensitivity less accurate.
Finally, we demonstrate that, while the mean ozone sensitivity (e.g.,
Overall, it is likely that the discrepancies in the estimated value of ozone sensitivity found in previous studies originate from differences in the length of time windows that were used for analysis and in the level of solar activity associated with these periods. Both parameters significantly influence the accuracy of solar rotational signal estimates. In this regard, it is likely that similar issues have also affected the accuracy in the estimation of ozone response to the 11-year solar signal. The estimation is expected to be even more difficult because observational time series cover a very limited number of 11-year cycles and there are other well-known sources of decadal variability in the atmosphere and climate system. Maycock et al. (2016) recently found very large discrepancies in the estimation of the ozone response to the 11-year cycle using various satellite datasets which cover different time periods of different length.
UARS MLS and Aura MLS satellite data are publicly available
at
The authors declare that they have no conflict of interest.
This project was supported by the European Project StratoClim (7th Framework Programme, grant agreement 603557) and the grant “SOLSPEC” from the Centre d'Etude Spatiale (CNES). Rémi Thiéblemont was supported by a grant from the LABEX L-IPSL, funded by the French Agence Nationale de la Recherche under the “Programme d'Investissements d'Avenir”. The authors thank the three anonymous reviewers for their detailed review comments, which improved the manuscript. Edited by: Gabriele Stiller Reviewed by: three anonymous referees