The single-column photochemical model is a stand-alone version of the stratosphere–troposphere chemistry mechanism used by the National Institute of Water and Atmospheric Research (NIWA) and United Kingdom Chemistry and Aerosol (UKCA) model (NIWA–UKCA), which comprises gas-phase photochemical reactions relevant to the troposphere and stratosphere . For consistency with NIWA–UKCA, the SCM uses the same chemical mechanism. Had we used a more complex mechanism (which the SCM approach lends itself to), then a direct comparison with the NIWA–UKCA output would no longer be possible, and also the results would be less relevant to other global CCMs characterized by relatively simple chemical mechanisms. The 60 vertical levels of the SCM are the same as in NIWA–UKCA, extending to 84 km. We do not use horizontal interpolation and take profiles of atmospheric properties from the grid point closest to Lauder (45∘ S, 168.75∘ E). Unlike NIWA–UKCA, the SCM excludes all non-chemistry processes, such as transport, dynamics, the boundary-layer scheme, radiation, emissions, etc. This means the model is only suitable for assessing fast photochemistry. Forcing data for the SCM are mostly interpolated from 10-daily instantaneous outputs from a NIWA–UKCA simulation (see below), except for those fields for which observational data are used.

and describe the chemistry scheme included in the SCM. The SCM includes an isoprene oxidation scheme not included in the NIWA–UKCA model version used by . In addition to CH4 and CO, the model includes a number of primary NMVOC source gases, i.e. ethane (C2H6), propane (C3H6), acetone (CH3COCH3), formaldehyde (HCHO), acetaldehyde (CH3CHO), and isoprene (C5H8). As noted above, emission and deposition of species are not considered in the SCM. The SCM includes a comprehensive formulation of stratospheric chemistry comprising bromine and chlorine chemistry and heterogeneous processes on liquid sulfate aerosols. Overall, the model represents 86 chemical species and 291 reactions including 59 photolysis and 5 heterogeneous reactions. The FAST-JX interactive photolysis scheme has been implemented in the SCM; this scheme solves a radiative transfer equation accounting for absorption by ozone. The chemical integration is organized through a self-contained atmospheric chemistry package , and the differential equations describing chemical kinetics are solved using a Newton–Raphson solver .

Time series of O3,H2O,CO, and temperature profiles are produced using mainly long-term measurements from Lauder, supplemented with NIWA–UKCA results as detailed below. Lauder is a member of several international networks, including the NDACC (http://www.ndsc.ncep.noaa.gov), the Global Reference Upper Air Network (GRUAN; http://www.gruan.org), and Global Atmosphere Watch (GAW; http://www.wmo.int/pages/prog/arep/gaw/gaw_home_en.html), where these data are archived and made available. The networks coordinate long-term observations of O3, various other constituents, and meteorological parameters. Here we briefly describe the procedure of constructing forcing data, using Lauder observations, to be used to constrain the SCM. The resulting profiles are shown in Fig. .

O3 profiles used here are a combination of ozone sonde time series from the surface to 25 km, combined with the Microwave Ozone Profiler Instrument 1 (MOPI1) time series for altitudes above 25 km , covering 1994 to 2010 (Fig. a). The ozone sondes have been launched approximately weekly; this defines the temporal coverage of the forcing data used in the SCM calculations. Microwave measurements used here come as several profiles a day at a variable spacing; we interpolate them to the ozone sonde launch times. Any missing data (during the two periods when the microwave instrument was out of service) are filled using a Fourier series gap-filling method. We compare the two datasets in the height region usefully covered by both (20 to 30 km). The differences between the two measurements range between -2 and +6%, and a mean bias that is less than 5%. O3 profiles are linearly interpolated onto the SCM's grid. Total column ozone calculated by integrating the observed O3 profiles is also compared to total-column O3 measured by the Lauder Dobson instrument; the difference is about 5 % on average . Lauder ozone measurements have been used in various World Meteorological Organization (WMO) ozone assessments e.g..

H2O profiles have been constructed using the weekly radiosonde measurements of H2O vapour below 8 km (the same soundings that also measure ozone) and NIWA–UKCA model output data above. For validation, we use the monthly National Atmospheric and Oceanic Administration (NOAA) Frost Point Hygrometer (FPH) H2O vapour measurements , which start in 2003. FPHs are more accurate compared to radiosonde hygrometers, particularly for stratospheric conditions. However, due to the later start of the FPH time series and the lower measurement frequency, radiosonde measurements are used in this study. The comparison of FPH and radiosonde H2O reveals differences that are generally less than ± 5% in the lower and middle troposphere but generally increase in and above the tropopause region ∼ 11 km,. The radiosonde hygrometers have some known problems with measuring low humidity . This is reflected in the large differences observed particularly at these altitudes (up to 30 %) and, to a lesser degree, below them (Fig. a). In a comparison of NIWA–UKCA output with FPH H2O, larger discrepancies are found throughout the whole troposphere and tropopause region (Fig. b), as can be expected from a low-resolution model unconstrained by observations and subject to problems with modelling H2O. Given the consistency of FPH and radiosonde H2O below 8 km found before, here we use radiosonde data up to 8 km of altitude merged, in the absence of a more suitable dataset, with NIWA–UKCA output above that.

We use surface in situ measurements from Cape Grim, Tasmania to rescale NIWA–UKCA model profiles, producing CH4 profiles that coincide with the ozone sonde launches. The NIWA–UKCA model simulation had been constrained with historical global-mean surface CH4 values, resulting in an overestimation relative to the Cape Grim data by ∼2% (not shown), and both data show a ∼5% increase in CH4 at the surface over the period between 1994 and 2010. Cape Grim CH4 is a good surrogate for the Lauder measurements because CH4 is a long-lived, well-mixed atmospheric constituent.

The time series of CO profile over the period of 1994–2010 has been constructed using the NIWA–UKCA CO profiles, rescaled such that the total columns match those obtained from the mid-infrared Fourier Transform Spectrometer (FTS) at Lauder . Gaps in the total-column FTS series, such as the period between 1994 and 1996 when the FTS measurements had not started yet, are filled using a regression fit accounting for the mean annual cycle (modelled as a 6-term harmonic series) and the linear trend.

The time series of temperature profiles are constructed following the same procedure as used in the construction of O3 profiles, comprising the radiosonde temperature profiles (from the surface to 25 km) merged with NCEP/NCAR reanalyses temperatures used in the retrieval of MOPI1 ozone (above 25 km) for the period of 1994–2010. From near the stratopause upwards the NCEP/NCAR temperatures are merged with a mesospheric climatology based on local lidar measurements. There are some warm anomalies occurring in the data at 40–60 km during winter months (e.g. in 1996); these may reflect planetary wave breaking in the upper stratosphere.

We perform SCM simulations covering the period of 1994–2010, summarized in Table . The forcing data needed by the SCM consist of profile series of temperature, pressure, optional cloud liquid and ice mass mixing ratios, and the mixing ratios of 86 chemical compounds. With the exceptions detailed below, these fields and species are taken from a NIWA–UKCA simulation for the period of 1994–2010 interpolated to the times of the ozone sonde launches. The CCM simulation used here consists of the last 17 years of a NIWA–UKCA “REF-C1” simulation conducted for the Chemistry–Climate Model Initiative CCMI;. REF-C1 is a hindcast simulation for the period of 1960–2010, using prescribed Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) fields . The surface emissions of primary species are as described in , ozone-depleting substances (ODSs) follow the A1 scenario of the WMO Report , and surface (or bulk, in the case of CO2) abundances of GHGs follow the “historical” Intergovernmental Panel on Climate Change scenario of global-mean GHG mixing ratios .

In a “reference” simulation of the SCM all forcings are taken from this REF-C1 simulation of NIWA–UKCA. Alternatively, in sensitivity simulations O3,H2O,CH4,CO, and temperature, or all of these simultaneously, are replaced with the time series of the profiles that are constructed using long-term observational data as described above. For species other than those five fields, in all cases we use NIWA–UKCA REF-C1 forcings. We evaluate the SCM only for those times, spaced roughly weekly, for which ozone sonde data are available. With the exceptions of those simulations assessing cloud influences, simulations are conducted assuming clear-sky conditions.

Three sensitivity simulations are conducted to quantify the impact of O3 biases (defined as differences between observed O3 and NIWA–UKCA simulated O3) on OH at Lauder.

As discussed above, the rate of production P of HOx via O(1D)+H2O can be expressed as P(HOx)≈2k1jO1D[O3][H2O]k2[O2]+k3[N2]+k1[H2O], where k1 is the rate coefficient for O(1D)+H2O, jO1D is the rate of O3 photolysis producing O(1D), and k2 and k3 are the rate coefficients of quenching of O(1D) with O2 and N2, respectively . Accordingly, P(HOx) is affected by ozone changes principally in two different ways: Either locally through a change in [O3] or non-locally through a change in jO1D caused by changes in the overhead total-column ozone (TCO). To separate the effects, we conduct three simulations with the SCM: The first simulation targets the local kinetics effect by applying changes in O3 concentrations but keeping all photolysis rates unchanged vs. the reference simulation. A second simulation involves applying changes in jO1D according to changes in O3 (keeping the rest of photolysis rates unchanged), but considering a fixed O3 concentration, i.e. using the O3 concentrations of the reference simulation. The jO1D calculation consistently takes into account absorption and scattering by stratospheric and tropospheric O3. A third simulation includes both effects simultaneously.

The results of these three sensitivity runs are displayed in Fig. . As expected, the pattern of O3 differences between observed O3 and modelled O3 (Fig. a) is reflected in the pattern of OH differences produced by the SCM, considering only the “kinetics” effect and assuming no changes in the photolysis rates (Fig. b), with increases of ozone in spring and decreases in autumn, relative to the reference simulation, resulting in changes of the same sign in OH. However, there is a height dependence to this relationship.

In summer and autumn, O3 biases range between -5 and -45%, meaning that the reference simulation overestimates the observations. Such a bias in O3 results in up to 12 % reductions in OH for these seasons when the bias is corrected. In spring between 2 and 6 km, observed O3 is larger than in the reference simulation by up to 10 % at 4 km in October. Consequently, this results in an increase of OH at around the same altitudes and times of up to 5 %.

Regarding the sensitivity simulation considering the photolysis effects, jO1D exhibits differences relative to the reference simulation ranging from ∼14 to ∼30%. The corrections are positive everywhere, in accordance with the overestimation of TCO in the NIWA–UKCA model with respect to observations . In accordance with eq. , such an intensification of jO1D causes OH to increase (Fig. c). The relative OH response is approximately 50% of the jO1D relative difference. However, Fig. c and d suggest that the magnitudes of the kinetics and the photolysis effects, for the O3 bias found at Lauder, are comparable, but the seasonalities differ. For example, the kinetics effect maximizes in spring at 5 % and minimizes in summer/early autumn at -15% (in the upper troposphere) whereas the photolysis effect on OH maximizes in summer at 16 to 20 % and minimizes in spring (Fig. b and d).

OH resulting from the combined kinetics and photolysis effects is displayed in Fig. e. OH responds approximately linearly to the two effects combined, compared to the sum of their individual impacts (Fig. f), despite some small differences between Fig. e and f.

Next, we examine the relationship between the slant column of O3 (SCO), jO1D, and OH. Figure a shows that there is an approximately exponential relationship between jO1D and the SCO at 6 km of altitude (this effect also exists at other altitudes). The small curvature may be the result of inaccurately diagnosing the SCO (ignoring the curvature of the Earth). Another reason could be that the cross section of O3 is wavelength-dependent, and consequently the actinic flux spectrum moves towards longer wavelengths with increasing SCO. Under Lambert–Beer's Law, a perfectly exponential relationship would be expected for a monochromatic UV light source and an isothermal atmosphere. jO1D and the OH concentration exhibit an approximately linear relationship (Eq. , Fig. b). Combining these results, we derive an approximately exponential relationship between the SCO and the OH concentration (Fig. c). The fit parameters are stated in Fig. . Due to the compact relationship between jO1D and the SCO, and to account for the curvature, we fit a quadratic relationship between the SCO and log⁡(jO1D).

To determine a simple coefficient that describes the quantitative contribution of O3 to OH, a linear regression between differences in OH and O3 relative to the reference was conducted through the following expression (note that this equation is also used to derive the linear contributions of the other key species to OH chemistry at Lauder): Δ[OH][OH]ref=AiΔXiXi,ref, where Xi is the perturbation variable (in this case [O3]), Ai is the slope of the linear regression, Δ[OH] is the absolute difference between the OH concentrations in the reference and perturbation simulations, ΔX is the absolute difference in concentrations of the perturbation variable X between the observations and the reference, [OH]ref is the OH concentration obtained from the reference simulation, and Xi,ref is the value of Xi in the reference simulation. The regression coefficients Ai represent the sensitivity of OH to changes in each individual variable for the troposphere at Lauder. The regression coefficients are depicted in Fig. . Reverting to infinitesimal notation, we note that Ai=∂ln⁡[OH]∂ln⁡Xi.

The sensitivity coefficients of OH to the kinetics and photolysis effects of O3 are shown in Fig. a. Coefficient A1, which represent the kinetics effect, ranges from 0 to 0.25 (meaning the relative response of OH is up to a quarter of the relative difference in O3). The sensitivity to photolysis (A1′′) is >0.5 throughout much of the troposphere (meaning the relative response in OH is over half the relative change in jO1D).

A perturbation simulation was performed using combined radiosonde and NIWA–UKCA H2O (Sect. ). The OH response to correcting H2O biases (Fig. ) shows an approximately linear response with respect to the relative changes in H2O, i.e. decreases in H2O generally lead to a reduction of OH concentrations (Eq. ). Note that NIWA–UKCA substantially overestimates the radiosonde-observed H2O vapour by up to 60 % between 2 and 6 km (Fig. a); this translates into an overestimation of OH by up to ∼40% in the reference simulation (Fig. c) in the same region. The sensitivity of OH to changes in H2O (Eq. ) range from 5 to 0.5 in the troposphere (Figs. e and b coefficient A2), with high sensitivity in the lower and free troposphere and reduced sensitivity in the tropopause region.

It is known that large uncertainties are associated with H2O vapour measurements. To illustrate this, we repeat the above simulation but now using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (hereafter ERAI) H2O . Irrespectively of the large differences and the opposite signs in H2O biases between Lauder radiosonde and ERAI data, the OH response to biases in H2O shows approximately the same linear relationship in both cases (Fig. ). Likewise, the sensitivity of OH to changes in H2O using ERAI data (Figs. f and b, coefficient A3) resembles the sensitivity simulation using radiosonde H2O.

The effect of CH4 changes on OH is displayed in Fig. a, c, e. The CH4 biases are generally small, up to only ∼2%, and are assumed to be vertically uniform, with some seasonal variations. Decreases in CH4 lead to increases in OH due to reduced loss of HOx by CH4+OH. The response of OH to CH4 changes maximizes at 0.6 % around 2 km, and decreases at higher altitudes. The seasonal variation of the OH response to CH4 biases maximizes in March/April (Fig. c), which coincides with the maximum absolute bias in CH4 (Fig. a) in the same months. The sensitivity coefficient describing the dependence of OH on CH4 changes (denoted as A4 in Fig. c) ranges from -0.17 at the surface to -0.32 at ∼ 2 km of altitude, and then decreases to -0.15 at 10 km.

The CO bias and the resulting differences in OH are displayed in Fig. b, d, f. The relative difference of OH with respect to the reference simulation is less than ±5% for all seasons (Fig. d), showing that decreases in CO generally lead to increases in OH through the reduced loss of OH through OH+CO. Note that during austral spring NIWA–UKCA overestimates CO, presumably due to exaggerated tropical biomass burning in the model which causes CO biases of up to 10% (Fig. b). The sensitivity of OH to changes in CO (∂ln⁡[OH]/∂ln⁡[CO]) shown in Fig. f varies from -0.3 to -0.5 and in absolute terms increases with altitude (the white band shown in October is the result of CO differences being close to zero).

The sensitivities of OH to CH4 and CO show comparable values at the surface, but the OH sensitivity to CO increases with height whereas its sensitivity to CH4 decreases. Note that the CH4+OH reaction rate is strongly temperature-dependent, which may contribute to the lower sensitivity of OH to CH4 changes at altitude than to CO. However, further investigation will need to investigate how these ratios change in different chemical regimes, and to assess whether the relative sensitivity of OH to CO and to CH4 are specific to the clean SH environment.

To assess the effects of changes in temperature on OH, we apply the same procedure as for O3, for which the effects of temperature have been decomposed into kinetics and photolysis effects. We perform three simulations: In the first simulation, we only apply temperature changes to chemical kinetics, keeping all photolysis rates fixed (noting that most uni-, bi-, and termolecular reaction rates are temperature-dependent). In the second simulation, we only consider the photolysis effect, which arises mainly because the cross section of O3, the primary UV absorber, is temperature-dependent. The impact of temperature on OH via ozone photolysis again occurs via two different mechanisms: firstly, the changes in jO1D caused by changes in the actinic flux which relates to changes in the atmospheric transmissivity in the UV (caused by a temperature dependence of the cross section of overhead ozone), and the local changes of jO1D, due to the local temperature dependence of the ozone cross section. Here, we only evaluate the combined photolysis effect in the second simulation. Finally, we perform a third simulation by applying both the kinetics and the photolysis effects simultaneously.

At Lauder, the reference simulation is generally cold-biased (i.e. the temperature correction is positive; Fig. a). This is particularly the case in the lowest 2 km and throughout the troposphere in the autumn–winter season. The kinetics effect leads to a reduction of OH by up to 2% (Fig. b). O(1D)+H2O and the quenching reactions (Eq. ) are not, or are weakly, temperature-dependent, making CH4 + OH (which is much more sensitive to temperature) the leading factor in causing this small OH reduction. The rate coefficient for this reaction in NIWA–UKCA and the SCM is kOH+CH4=1.85×10-12exp⁡(-1690K/T); at 290 K the sensitivity of kOH+CH4 to temperature changes evaluates to about 2 % K-1. However, OH is well buffered by other reactions, so its sensitivity is considerably smaller than that. The photolysis effect is often somewhat larger than the kinetics effect but peaks in spring (Fig. c). This translates into a slight OH reduction comparable in magnitude to the kinetics effect (Fig. d). Both effects add nearly linearly in the combined simulation (Fig. e, f).

We calculate sensitivity coefficients A6 and A6′′ that define the OH responses to both effects (Fig. e, f). Coefficient A6 represents the kinetics effect and varies from 0 to -1.75 (i.e. in absolute terms, the relative OH response can be larger than the relative difference in T). The sensitivity coefficient that describes the sensitivity of OH to changes in photolysis (A6′′) ranges from 0.6 at the surface to 0 at 10 km of altitude. Figure e and f show sensitivity coefficients for both effects (A6 and A6′′). OH changes due to both effects are small (up to 2.5%) and comparable in magnitude.

Several sensitivity studies have been conducted previously to elucidate the impact of temperature on OH . None of these studies separately assessed the impacts of the kinetics and photolysis effects of temperature on OH. applied a globally uniform temperature rise of 5 K that led to a larger OH abundance and an around 10% decrease in the CH4 lifetime. showed a small impact on global OH abundances due to temperature biases; this may be because either the temperature biases in their model were both positive and negative, in different regions, leading to some cancellation of the impact on global OH, or to low OH sensitivity to temperature biases. Here, bias-correcting temperature is shown to also have only a small impact on OH abundance (Fig. e); this result broadly corroborates that of .

Here, we assess the effect of changing all forcings (O3, H2O, CH4, CO, and temperature) simultaneously on OH at Lauder. Figure a shows the responses of OH to changing all forcings. A comparison with Fig.  suggests that H2O changes dominate the total response of OH to changes in these forcings. At Lauder, NIWA–UKCA is too moist (relative to radiosonde water vapour); this translates into a large OH overestimation of up to ∼40% in the reference simulation (Fig. a). This is consistent with the underestimated CH4 lifetime by the NIWA–UKCA model , assuming that the NIWA–UKCA model is also too moist in other regions. (In the NIWA–UKCA reference simulation used here, the global CH4 lifetime, disregarding dry deposition, is 7.2 years, whereas a recent best estimate is 9.8 years, with an uncertainty range of 7.6–14 years; .) In general, in the SCM OH responds approximately linearly to the combined changes in major forcings that play an important role in OH chemistry (Fig. ).

To examine the linearity of OH responses to simultaneous changes in key forcings defined in this study, the combination of all individual contributions, i.e. O3 (kinetics and photolysis effects), H2O, CH4, CO, and temperature (kinetics and photolysis effects) to OH, was compared to the OH response to all forcings combined simulation in the SCM through Eq. (): Δ[OH][OH]ref≈A1′′Δ[O3][O3]ref+A1′′ΔjO1DO3jO1Dref+A2Δ[H2O][H2O]ref+A4Δ[CH4][CH4]ref+A5Δ[CO][CO]ref+A6ΔTTref+A6′′ΔjO1DTjO1Dref, where Δ[OH]/[OH]ref is the relative difference in the OH concentration obtained with the SCM with respect to the reference simulation, using all forcings combined. The forcings comprise the kinetics and photolysis effects of O3 (A1 and A1′′), radiosonde H2O (A2), CH4 (A4), CO (A5), and the kinetics and photolysis effects of temperature (A6 and A6′′). Equation () expresses a working hypothesis that the model responds linearly to the applied forcings; we will test this assumption in the following paragraph.

Figure a and b indicate that the model responds approximately linearly to the combinations of all forcings, with OH responses in the all-forcings simulation correlating at 0.9 with the sum of the OH responses in the individual-forcing simulations. Figure c however also suggests that there is some notable non-linearity in the chemistry of the troposphere at Lauder. Chemical feedbacks between the impacts of correcting water vapour and ozone may contribute to this non-linearity; for example, a change in the water vapour abundance may impact the sensitivity of OH to changing O3.

We examine variability and trends in OH using the SCM simulation including all key forcings separately for different altitude bins. The results (Fig. ) indicate that there are no significant long-term trends in OH throughout the troposphere for the period of the simulation (1994–2010) We find trends of -2.1±4.8% at 0–2.5 km, 0.9±2.3% at 2.5–5 km, 2.6±3.5% at 5–7.5 km, and 3.6±4.1% at 7.5–10 km over the period of 1994–2010), but there is evidence of interannual variations at all altitudes e.g..

In addition, we explore variability and trends in the OH column at Lauder to be compared with other estimates of global OH. As expected from the results of OH trends at different altitude bins, we find no significant long-term trend in the OH column (0.5±1.3%) (Fig. ). However, there is evidence of short-term variations (5–10%), in agreement with other studies that used observations to infer global OH concentrations e.g..

We have assessed the OH sensitivity to correcting biases in key forcings assuming clear skies. Here we explore the impact of simulated clouds on OH, recognizing that this process is associated with large uncertainties due to difficulties with representing clouds in models. Measurements of cloud profiles do not exist at Lauder, hence a bias correction like that performed with the composition and temperature fields is not possible. Therefore, here we only examine the impact of clouds simulated by NIWA–UKCA on jO1D and OH at Lauder, relative to the clear-sky reference simulation used before. The impacts of liquid water clouds (LWCs) and ice clouds (ICs) were assessed separately and in combination. Three simulations are defined here, i.e. (1) including only ICs, (2) including only LWCs, and (3) considering both combined (LICs).

Figure a, c and e show the response of jO1D and OH to the presence of the ICs. jO1D and OH are generally reduced below the ICs, relative to the cloud-free situation. The maximum reduction in OH is 10 to 15% in winter below 2 km, coinciding with the maximum reduction in jO1D. There are increases in both fields (up to ∼8%) above the ICs in austral spring, associated with the seasonal peak in IC occurrence at the same time. In general, jO1D and OH impacts vary strongly with season, with the maximum reduction occurring in winter close to the surface, and the maximum increase in spring above the ICs.

LWCs are mostly present between 1 and 4 km with the seasonal peak in austral spring (Fig. b). Similarly to ICs, jO1D and OH are enhanced above and throughout much of the cloud layer, and reduced in the lowest 1 km above the surface (Fig. e, g). The enhancement in jO1D and OH peaks at 12% between 2 and 4 km of altitude, coinciding with the spring maximum in liquid water content at 1–2 km. Conversely, the reduction in jO1D and OH with respect to the clear-sky condition is ∼10% and is produced below the clouds.

The simulation with the combined effect of ICs and LWCs (LICs) produces a reduction in jO1D and OH that ranges between 0 and 20% below the transition of ICs to LWCs at around 2 km, since LWCs are as much as twice as optically dense as ICs (Fig. g). An enhancement is produced above this altitude of up to 18%. The magnitudes of changes in jO1D and OH are similar when either ICs or LWCs are considered in the SCM. Furthermore, their effects add up slightly less than linearly when both are present in the simulations (Fig. h).

The results shown here indicate that lower clouds generally produce an enhancement in jO1D (Fig. d), but higher clouds generally produce a reduction in jO1D in the free troposphere Fig. b;. Furthermore, the vertically and seasonally averaged enhancement and reduction in jO1D are about 2 and 6% respectively for the LWCs, similar to the response for the ICs' condition; this suggests that the cloud vertical distribution has a bigger effect on photolysis than the change in cloud water content .