dataesr/scientific-paragraphs-categorization huggingface dataset:
A SOR is therefore implicitly included in the historical portion of the CMIP5 ozone database that will resemble the observations input to the MLR model. However, owing to the paucity of longterm ozone measurements at high latitudes, the SOR was only included within ±60 • latitude. This limitation led some CMIP5 modelling groups to make alterations to the CMIP5 ozone database, including extrapolation of the SOR coefficients at ±50 • latitude to the poles using a cosine latitude weighting. The CMIP5 models known to have employed this "Extended CMIP5 ozone database" include HadGEM2-CC  (Osprey et al. , 2013) , MPI-ESM  (Schmidt et al. , 2013)  and  CMCC-CC (Chiara Cagnazzo, personal communication, 2016). We note that the historical portion of the CMIP5 ozone database did not include a representation of QBO variability in ozone.
The future portion of the CMIP5 ozone database for the stratosphere was based on CCM simulations from CCMVal-2 models  (Cionni et al. , 2011). However, owing to uncertainties in how individual CMIP5 models would represent SSI variations over the 21st century, the future portion of the CMIP5 ozone database did not include a SOR. For consistency, a SOR was thus added to the future period in the Extended CMIP5 ozone database using regression coefficients for the SOR derived from the historical period  (Schmidt et al. , 2013; Osprey et al. , 2013; Chiara Cagnazzo, personal communication, 2016). The CMIP5 ozone database is described in full by  Cionni et al. (2011)  and is available at the time of writing from https://cmip. llnl. gov/cmip5/forcing. html#ozone_ forcing. A description of the CMIP5 models that employed the CMIP5 ozone database is given by  Eyring et al.
The CMIP6 ozone database The CMIP6 ozone database for the historical period (1850-2014) consists of monthly mean ozone mixing ratios on 66 pressure levels spanning 1000-0. 0001 hPa. Data are provided as a 3-D field on a regular 2. 5 • × 1. 9 • longitudelatitude grid. The database is constructed using a weighted average of simulations from two CCMs (CESM1(WACCM) and CMAM)  (Checa-Garcia et al. , 2018). The CMIP6 ozone database was downloaded for this study from https:// esgf-node. llnl. gov/projects/input4mips. The simulations from the two constituent CCMs include prescribed SSTs, sea ice, well-mixed greenhouse gas concentrations and aerosols. Surface emissions of NO x and other tropospheric ozone precursor gases are also prescribed. Both CCMs represent SSI variability in their radiation and chemi-cal schemes.
However, only CESM1(WACCM) includes the chemical effects of energetic particle precipitation. There are some differences in the set-up of the CCM simulations used to create the CMIP6 ozone database compared to the CCMI-1 versions of the same models (see Sect. 2. 1. 1), which may affect the representation of the SOR. The version of CMAM for the CMIP6 ozone database used historical stratospheric aerosols and solar variability, similar to REF-C1, extended back to 1850. However, SSTs and sea ice were prescribed from a CanESM2 historical simulation performed for CMIP5 rather than from observations. The temporal variability in SSI for CMAM was taken from the CMIP6 SSI dataset  (Matthes et al. , 2017) , but the variations were applied to the long-term background spectrum from NRLSSI-1. This is in slight contrast to the CCMI-1 version of CMAM that used both SSI variability and the background spectrum from NRLSSI-1.
However,  Matthes et al. (2017)  showed that the slightly weaker variability over the solar cycle at shorter UV wavelengths in NRLSSI-1 only reduced the amplitude of the tropical mean SOR in a CCM by ∼ 0. 3 % compared to a reference of ∼ 2 %. This difference is therefore likely to have only a small effect on the SOR in the configurations of CMAM implemented for CCMI-1 and the CMIP6 ozone database. Neither CMAM simulation includes nudging of the QBO. There are also some differences in the configuration of CESM1(WACCM) used for the CMIP6 ozone database compared to CCMI-1. The CESM1(WACCM) CCMI-1 runs prescribed the NRLSSI-1 data at daily resolution, whereas the version run for the CMIP6 ozone database used annual values as these extend back to 1850. In the lower thermosphere, values of the F10.
7cm flux and Kp index used to parametrize the chemical effects of energetic particle precipitation were taken from observations in CCMI-1 and from a proxy record in the simulation for the CMIP6 ozone database. Furthermore, the simulation for the CMIP6 ozone database did not include solar proton events or galactic cosmic ray effects. Both versions of CESM1(WACCM) used observed SSTs and include a nudged QBO towards observed tropical winds. In summary, there are some differences in the experimental set-ups of the two CCMs used to create the CMIP6 ozone database, in particular that they use slightly different representations of SSI variability, they do not both include QBO variability and they use different SST datasets. Bodeker et al. (2013)  describe a new observation-based ozone database for climate models covering the period 1979-2007.
Monthly and zonal mean ozone mixing ratios are provided on 70 pressure levels spanning 878-0. 05 hPa on a regular 5 • latitude grid. The ozone field is constructed from a large number of satellite and ozonesonde observations from the Binary DataBase of Profiles (BDBP;  Hassler et al. , 2008)  used to fit an MLR model that includes terms for equivalent effective stratospheric chlorine (EESC), a linear trend, the QBO, the El Niño-Southern Oscillation (ENSO), the solar cycle and the Mt Pinatubo volcanic eruption. We note that, since the BDBP contains SAGE II v6. 2 mixing ratio data, this is likely to provide a strong constraint on the SOR in the tropics and subtropics. See  Maycock et al. (2016)  and  Dhomse et al. (2016)  for a discussion of the differences in the SOR in SAGE II v6. 2 and v7.
0 data. To generate a spatially complete ozone field, a single MLR fit is performed for all points on a given pressure surface to enable regression coefficients to be derived for latitudes where the observations are relatively sparse (e. g. in polar regions). We use the Tier 1. 4 product from the Bodeker ozone database, which includes contributions from all the MLR basis functions. The Bodeker ozone database was downloaded from http://www. bodekerscientific. com/ data/monthly-mean-global-vertically-resolved-ozone.
4 index to represent ENSO. The F10. 7cm flux is used to represent solar activity because it has been shown to be well correlated with indices for UV radiation (e. g. Floyd et al. , 2005) , the key driver of the stratospheric ozone response. The results For those CCMI-1 models and ozone databases that include QBO variability (see Table  1 ), the QBO indices are calculated as the first two principal component time series of the tropical (±10 • , 5-70 hPa) zonal mean zonal winds. The ozone response to volcanic aerosols is non-linear through time owing to changing levels of inorganic chlorine in the atmosphere  (Tie and Brasseur, 1995). Thus, rather than including a term in the MLR model to represent volcanic effects on ozone, data from the 2-year periods following the three major tropical volcanic eruptions since 1960 are excluded from the analysis: Mount Agung (February 1963), El Chićhon (March 1982) and Mount Pinatubo (June 1991).
1 SFU and are therefore closely comparable to the results presented for the SOR, which assume a representative solar cycle amplitude of 130 SFU. Ozone anomalies were calculated either from the respective years of the Extended CMIP5 ozone database  (Schmidt et al. , 2013)  or using the monthly SOR coefficients from the CMIP6 ozone database shown in Sect. 3. 3. The corresponding SSI anomalies are calculated either from the CMIP5 recommended NRLSSI-1 dataset  (Wang et al. , 2005)  or from the CMIP6 recommended solar forcing dataset  (Matthes et al. , 2017).
The SOR in CCMI-1 models Figure  2  shows time series of deseasonalized tropical (30 • S-30 • N) and monthly mean percent ozone anomalies at select pressure levels (1, 3, 5, 10, 30 hPa) for the eight CCMI-1 models described in Sect. 2. 1. 1. Anomalies are defined relative to the period 1985-2009. Also plotted in Fig. 2  are time series from two satellite datasets discussed in Part 1 of this study: SBUVMOD VN8. 6  (Frith et al. , 2014 ) (black dashed) and SAGE-GOMOS 1  (Kyrölä et al. , 2015)  (black solid). For completeness, the time series of absolute ozone mixing ratios from the models are shown in the Supplement (Fig. S1 ). The CCMI-1 models show a long-term decline in stratospheric ozone abundances, particularly in the mid-and upper stratosphere. This is the result of increasing atmospheric loading of inorganic chlorine and bromine over this period and is consistent with results from earlier CCM studies (e.
However, this feature shows some sensitivity to the choice of autoregressive model in the MLR model probably because the decorrelation timescale for the regression residuals in the tropical lower stratosphere is longer than 2 months in SOCOL3 and some of the other CCMs (not shown). Further analysis of the transformed Eulerian mean residual vertical velocity does not reveal a substantial change in the rate of upwelling in the tropical lower stratosphere in any of the models (not shown). The month-by-month SORs in the individual CCMI-1 models (see Figs. S3-S10 ) show a significant positive SOR in the tropical upper stratosphere throughout the year but enhanced SOR amplitudes at high latitudes, particularly in the winter and spring seasons. This behaviour, which is also seen in some satellite ozone datasets (e.
The SOR in ozone databases for climate models Figure  4  shows time series from 1960 to 2011 of deseasonalized tropical and monthly mean percent ozone anomalies at select stratospheric levels (1 to 30 hPa) from the Bodeker (orange line), CMIP5 (red) and CMIP6 (blue) ozone databases. Also plotted in black are the same satellite datasets shown in Fig. 2. Anomalies are defined relative to the period 1985-2007. The Extended CMIP5 ozone database is not shown because it is identical to the original CMIP5 database in the tropics. Although the time series have been deseasonalized, the CMIP5 and Bodeker ozone databases show a residual annual cycle particularly in the upper stratosphere. This is because in these databases the amplitude of the ozone annual cycle is larger in the early part of the record, when the background levels are higher, and smaller in the latter part of the record following the long-term decline in ozone.
Since the ozone anomalies in Fig. 4  are shown as anomalies from the 1985-2007 mean, there is therefore a residual annual cycle particularly in the period before 1985. Conversely, the CMIP6 database, which is constructed from CCM simulations, does not show a significant change in the amplitude of the ozone annual cycle over time. At 1 hPa, the CMIP5 and Bodeker databases show a larger linear trend in ozone over 1979-2007 (diagnosed using linear regression) of around -3. 5 % decade -1 compared to -1 % decade -1 in the CMIP6 database. The latter is, as expected, similar to the long-term ozone trends in the CCMI-1 models shown in Fig. 2. At 3 hPa, the CMIP5 database also shows a larger long-term decrease in ozone by around a factor of 2 compared to the Bodeker and CMIP6 databases. Thus, a model that uses the recommended CMIP6 ozone database might be expected to show weaker upperstratospheric cooling over recent decades than an equivalent simulation using the CMIP5 database, owing to the smaller negative trend in upper-stratospheric ozone.
At 10 and 30 hPa, the Bodeker and CMIP6 databases show a QBO signal in ozone, whereas the CMIP5 database does not include QBO variability. This is an important distinction because a model that employs the CMIP6 ozone database, but which does not simulate a dynamical QBO, will impose a QBO-ozone signal that may alter the model's behaviour. Alternatively, a model that internally generates a dynamical QBO that is not in phase with the prescribed QBOozone signal in the CMIP6 ozone database will be subject to a diabatic heating anomaly from ozone that is inconsistent with the model's dynamical evolution. Both of these cases would be physically unrealistic. However, a model that nudges a QBO towards observations and uses the CMIP6 ozone database should have a more consistent representation of temporal variability in winds and ozone associated with the QBO.
Modelling groups may therefore choose to postprocess the CMIP6 ozone database in order to treat the QBOozone signal in a consistent manner for their model. Note that since the CMIP6 ozone database is produced by averaging two CCMs, one that does include QBO-ozone variability (CESM1(WACCM)) and one that does not (CMAM), the QBO-ozone signal is weaker in the CMIP6 ozone database than in the CESM1(WACCM) model alone (compare blue line in Fig. 2  with dark pink line in Fig. 4 ). The absence of a QBO-ozone signal in the CMIP5 ozone database means that CMIP5 models that simulated a QBO would have neglected any radiative feedback from ozone on the QBO. Figure  5  shows latitude-pressure cross sections of the annual mean SOR in the three ozone databases shown in Fig. 4  and the Extended CMIP5 ozone database.
In the tropics, the Bodeker ozone database, Fig. 5a , shows a positive SOR of up to 4 % peaking at around 2-3 hPa with a distinct minimum at ∼ 10 hPa. The latitudinal structure of the SOR is smoother than in the SAGE II v6. 2 mixing ratio data (cf. Fig. 4d  of Part 1) probably because the construction of the Bodeker database fits an MLR model to all data points along pressure surfaces rather than to individual latitude bands. At high latitudes, the magnitude of the SOR in the Bodeker database is small and the spatial structure is noisy likely because of the small number of observations there. In the lower stratosphere, the Bodeker database indicates a positive SOR at most latitudes, as was found in a number of satellite ozone datasets in Part 1. However, the uncertainty in the magnitude of the SOR at these levels is comparatively large (see below).
The SOR in the CMIP5 ozone database, Fig. 5b , shows a very similar structure to that found in SAGE v6. 2 mixing ratios (cf. Fig. 4d  in Part 1), consistent with those data forming the backbone for the historical portion of the dataset  (Cionni et al. , 2011). Note that the MLR fits were applied separately at each latitude band in the construction of the CMIP5 database, and this likely explains why the horizontal structure of the SOR is more heterogeneous than in the Bodeker ozone database. In particular the three-peaked structure of the SOR found in the tropical upper stratosphere in the SAGE II v6. 2 mixing ratio dataset is evident in the CMIP5 ozone database. The sharp cut-offs in the SOR at ±60 • latitude are spurious and result from a lack of data points to define a SOR at high latitudes.
As described in Sect. 2. 1. 2, the Extended CMIP5 ozone database, Fig. 5c , applied an extrapolation to introduce a SOR in the extratropics. The details of this structure, which shows a positive SOR extending into the northern extratropics and a negative SOR at pressures greater than ∼ 5 hPa in the Southern Hemisphere poleward of 60 • S, is likely to be subject to considerable uncertainties owing to the simple spatial filling method employed. In the CMIP6 ozone database, Fig. 5d , the amplitude of the SOR is around 1-2 % in the upper stratosphere, which is, as expected, broadly consistent with the CCMI-1 results shown in Fig. 3. The peak amplitude of the SOR is therefore 2-3 times smaller, and is considerably smoother in latitude, than in the CMIP5 ozone database. In the lowermost tropical stratosphere, the CMIP6 database shows a positive SOR of up to ∼ 3 % in the southern tropics.
This is slightly larger than the SOR in the tropical lower stratosphere simulated by the CCMI-1 versions of the two CCMs used to produce the CMIP6 ozone database (CESM1(WACCM) and CMAM) (see Fig. 3b  and c ). To further investigate the verti- cal structure of the tropical SOR and its uncertainties, Fig. 6  shows the best-estimate tropical (30 • S-30 • N) mean SOR along with the 2. 5-97. 5 % confidence intervals for the climate model ozone databases and the two satellite datasets from Fig. 2  (see Part 1). Also shown in grey shading is the range of the best-estimate SORs from the eight CCMI-1 models. The best-estimate SOR in the tropical lower stratosphere ranges from a small negative signal in the CMIP5 ozone database to +6 % in the Bodeker ozone database. In the CMIP6 ozone database, the best-estimate tropical SOR is 2 % at 80 hPa, which is, as expected, within the range of the signals in the CCMI-1 models.
The substantial spread amongst the estimates along with the large uncertainties reinforces the challenge of constraining the SOR in the tropical lower stratosphere (e. g. Marsh and Garcia, 2007). Despite the relatively large uncertainties, the SOR in the tropical lower stratosphere is larger in the CMIP6 database than in CMIP5; this may be important for the modelled atmospheric response to solar variability in CMIP5 and CMIP6 models (see Sect. 3. 4). Figure  6  further confirms that the two climate model ozone databases that include SAGE II v6. 2 mixing ratio data (CMIP5 and Bodeker) show a significantly stronger SOR in the tropical upper stratosphere. This is likely to be unrealistically large since the updated SAGE II v7. 0 mixing ratio data, which show a smaller SOR in the tropical upper stratosphere  (Maycock et al.
Comparison of SOR annual cycle in CMIP5 and CMIP6 ozone databases Earlier studies have shown evidence for an annual cycle in the structure and amplitude of the SOR in satellite observations (e. g. Soukharev and Hood, 2006; Maycock et al. , 2016). Figure  7  shows the monthly mean SOR in the Extended CMIP5 ozone database, and Fig. 8  shows the same for the CMIP6 ozone database. The SOR in the CMIP5 database has a fixed structure and constant amplitude in all months; the small annual cycle in the fractional SOR amplitude arises purely from the annual cycle in background ozone concentrations. There are well-understood photochemical arguments for why the structure of the SOR is expected to track the position of the Sun through the year  (Haigh, 1994). Furthermore, the coupling between ozone and stratospheric dynamics may lead to variations in the SOR at high latitudes in some months due to the formation in winter of the polar vortices and their subsequent break-up in spring  (Hood et al.
, 2015). For these reasons a complete absence of seasonal variation in the SOR as found in the CMIP5 ozone database is unrealistic. In contrast, the SOR in the CMIP6 ozone database, Fig. 8 , shows greater seasonal variation. Locally enhanced signals in the SOR are found in the high latitudes in winter and spring, which may be linked to variations in the strength of the polar vortex  (Kuroda and Kodera, 2002). Thus, including some semblance of an annual cycle in the SOR, as seen in Fig. 8 , is likely to be a truer reflection of the behaviour of the real atmosphere than the complete absence of an SOR annual cy- cle as in Fig. 7. However, the associated uncertainties in the monthly SORs are larger than the annual mean results presented in the previous section, and there are quantitative differences between the SOR annual cycle in the CMIP6 ozone database and that estimated from satellite observations (cf.
e. g. Fig. 13  of  Maycock et al. , 2016). Such differences may result from uncertainties in estimating the SOR from relatively short observational records, from errors in the representation of the SOR in the models used to construct the CMIP6 ozone database or a combination of factors. Thus we should not consider the evolution of the monthly SOR in the CMIP6 ozone database as a precise representation of the true SOR, but it is likely an improvement compared to the representation in the CMIP5 ozone database.
Atmospheric impact of change in SOR between CMIP5 and CMIP6 ozone databases We now explore the atmospheric impacts of the differences between the SOR in the CMIP5 and CMIP6 ozone databases using the ECHAM6. 3 model sensitivity experiments described in Sect. 2. 3. Figure  9  shows the tropical average annual mean temperature differences in the four perturbation experiments representing 11-year solar cycle maximum conditions with respect to the control solar minimum simulation. Note that the tropospheric temperature responses in all simulations are small because the model includes fixed SSTs, and therefore the troposphere does not fully adjust to the imposed solar forcing (e. g. Misios et al. , 2016). The experiments performed to capture the total (i. e. SSI + SOR) solar cycle impact (dashed lines) show considerable differences in the tropical mean stratospheric temperature response between the recommended CMIP5 (red line) and CMIP6 (blue line) solar forcings.
In the CMIP5 case, the maximum temperature response is around 1. 25 K near the stratopause, which can be compared to a much smaller response to the CMIP6 solar forcing inputs of 0. 8 K. The SORonly sensitivity experiments (solid lines) reveal that much of the difference in the total temperature response can be attributed to the differences in the SOR between the CMIP5 and CMIP6 ozone databases. The SOR in the Extended CMIP5 ozone database induces a peak tropical temperature response of 0. 85 K (solid red), which is nearly 3 times larger than the maximum response to the SOR in the CMIP6 ozone database with an amplitude of 0. 3 K (solid blue). In addition to the marked differences in the maximum temperature response, there are also distinct differences in vertical structure. In the CMIP5 case, there is a stronger vertical gradient in the temperature response to the imposed solar forcing, which can be attributed to the highly peaked structure of the SOR in the CMIP5 ozone database at the stratopause compared to the smoother vertical structure of the SOR in the CMIP6 database (cf.
Fig. 5c  and d ). The simulation forced with the SOR from the CMIP6 ozone database also shows a small secondary peak in tropical lower-stratospheric temperature of ∼ 0. 3 K due to the presence of a locally enhanced SOR of ∼ 3 %, which is not present in the CMIP5 ozone database. The results show that the change in the representation of the SOR between the recommended CMIP5 and CMIP6 ozone databases induces a much larger difference in the stratospheric temperature response between solar cycle minimum and maximum than do changes in the recommended SSI forcing (see also Fig. 8  in  Matthes et al. , 2017). The ECHAM6. 3 model results help to elucidate the findings of  Mitchell et al. (2015) , which show a clear difference in the annual mean stratospheric temperature response to the solar cycle between CMIP5 models that used the CMIP5 ozone database (HadGEM2-CC, MPI-ESM, CMCC) and those with interactive chemistry that simulated their own internally consistent SOR (CESM1(WACCM), GFDL-CM3,.
GISS-E2-H, MIROC-ESM-CHEM, MRI-ESM1 ). Specifically, models that used the CMIP5 ozone database exhibit a markedly larger temperature response near the tropical stratopause, with a stronger vertical gradient, than the models with interactive chemistry (see Fig. 5  in  Mitchell et al. , 2015). One might therefore anticipate that the difference in the stratospheric temperature response to the solar cycle between models with and without interactive chemistry will be smaller in CMIP6 than was found in CMIP5 owing to the fact that the SOR in the CMIP6 ozone database is derived from CCM simulations, albeit without full consistency with the other CMIP6 external forcings such as SSI.