dataesr/scientific-paragraphs-categorization huggingface dataset:
35 K day -1 at the stratopause), cooler stratospheric temperatures (-1. 5 K in the upper stratosphere), lower ozone abundances in the lower stratosphere (-3 %), and higher ozone abundances (+1. 5 % in the upper stratosphere and lower mesosphere). Between the maximum and minimum phases of the 11-year solar cycle, there is an increase in shortwave heating rates (+0. 2 K day -1 at the stratopause), temperatures (∼ 1 K at the stratopause), and ozone (+2. 5 % in the upper stratosphere) in the tropical upper stratosphere using the CMIP6 forcing dataset. This solar-cycle response is slightly larger, but not statistically significantly different from that for the CMIP5 forcing dataset. CMIP6 models with a well-resolved shortwave radiation scheme are encouraged to prescribe SSI changes and include solar-induced stratospheric ozone variations, in order to better represent solar climate variability compared to models that only prescribe TSI and/or exclude the solarozone response.
We show that monthly-mean solar-induced ozone variations are implicitly included in the SPARC/CCMI CMIP6 Ozone Database for historical simulations, which is derived from transient chemistry-climate model simulations and has been developed for climate models that do not calculate ozone interactively. CMIP6 models without chemistry that perform a preindustrial control simulation with time-varying solar forcing will need to use a modified version of the SPARC/CCMI Ozone Database that includes solar variability. CMIP6 models with interactive chemistry are also encouraged to use the particle forcing datasets, which will allow the potential long-term effects of particles to be addressed for the first time. The consideration of particle forcing has been shown to significantly improve the representation of reactive nitrogen and ozone variability in the polar middle atmosphere, eventually resulting in further improvements in the representation of solar climate variability in global models.
, 2013; Meehl et al. , 2013). However, a systematic assessment of the regional impacts of a more realistic future solar forcing is still to be done. For example, on regional scales, a future grand solar minimum could potentially reduce Arctic amplification  (Chiodo et al. , 2016)  and reduce long-term warming trends over western Europe  (Ineson et al. , 2015). The above-mentioned uncertainty in the SSI is particularly challenging in the UV band  (Ermolli et al. , 2013). All climate model intercomparison studies relied so far on the NRLSSI1 dataset  (Lean, 2000). However, it is becoming increasingly evident that its solar-cycle variability in the UV part of the spectrum may be too low compared to updated and more recent SSI reconstructions by models such as NRLSSI2  (Coddington et al. , 2016)  and SATIRE  (Yeo et al.
, 2011) , or to the missing or inadequate representation of UV and particle-induced ozone signals  (Hood et al. , 2015). Any comparison of climate model simulations with observations could be affected by a combination of these possible sources of error. In addition, the comparison of models with observations is inhibited by the insufficient length of the observational records, and in some cases model simulations. This paper will provide the first complete overview on solar forcing (radiative, particle, and ozone forcing) recommendations for CMIP6 from preindustrial times to the future and provides in this respect an advance to earlier model intercomparison projects (CMIP5, CCMVal, CCMI) as it gives a complete and state-of-the-art overview on our current understanding of solar variability and provides the dataset in a user-friendly way.
Section 2 presents the historical to present-day solar forcing dataset with individual subsections on solar irradiance (Sect. 2. 1) and particle forcing (Sect. 2. 2). Section 3 provides a description of the future solar forcing recommendation, Sect. 4 describes the PI control forcing, and finally Sect. 5 is comprised of a description of the solar induced ozone signal. A summary with respect to differences to the CMIP5 recommendation is given in Sect. 6. 2 Historical (to present) forcing data  In this section we first describe the solar irradiance dataset (including the TSI, the F10. 7 decimetric radio index, and the SSI; see Sect. 2. 1) and subsequently address the energetic particle datasets (including solar protons, auroral electrons, medium-energy electrons, and galactic cosmic rays; see Sect.
Solar irradiance (TSI, SSI, and F10. 7) This subsection starts with a description of the available TSI and SSI datasets from two different solar irradiance models (NRLSSI and SATIRE), and one observational estimate (SOLID), before introducing the CMIP6 recommendation. Afterwards a recommendation on how to implement the solar irradiance forcing in CMIP6 models is provided. An evaluation of the comparison between different SSI forcing datasets, with a focus on CMIP5 and CMIP6 solar irradiance recommendations in a line-by-line model and two state-ofthe-art CCMs (i. e. , CESM1(WACCM) and EMAC), is performed at the end to highlight the effects of solar irradiance variability on the atmosphere and possible effects on atmospheric dynamics all the way to the ocean.
SORCE measurements at solar-minimum conditions are the basis for the adopted quiet Sun irradiance  (Kopp and Lean, 2011)  in NRLSSI2. The recently updated version of the NRL models, named NRLTSI2 (for TSI) and NRLSSI2 (for SSI), have been transitioned to the National Centers for Environmental Information (NCEI) as part of their Climate Data Record (CDR) program (see http://www. ngdc. noaa. gov), and operational updates are provided on a near-quarterly basis. Coddington et al. (2016)  describe the model algorithm, the uncertainty estimation approach, and comparisons to observations in detail. Please note that our version differs slightly from the one published by  Coddington et al. (2016)  by using a different scaling factor between the sunspot area as measured by the Royal Greenwich Observatory (from 1874 to 1976) and the NOAA/USAF Solar Observing Optical Network (SOON) since 1966.
, 2005; Ball et al. , 2012; Yeo et al. , 2014) , and that based on the sunspot number (SSN) is SATIRE-T  (Krivova et al. , 2010). Individual records are accessible at http://www2. mps. mpg. de/projects/sun-climate/data. html. We use here SATIRE-S for the satellite era  (available from 1974 to 2015). Prior to 1974 the CMIP6 SATIRE data were calculated with the SATIRE-T model  (Krivova et al. , 2010) , although this was done using annual SSN (Version 1) instead of daily Group SSN (GSSN)  (Hoyt and Schatten, 1998)  while keeping all other inputs (including sunspot area) identical to the original version. SSI variability on subannual timescales, taken from the SATIRE-T model, was added afterwards. We shall henceforth call this model SATIRE in the following. On decadal to centennial timescales, SATIRE reproduces observations such as the composite of the Lyman-α line at  121.
6 nm (since 1947 121. 6 nm (since , Woods et al. , 2000)) , the measured solar photospheric magnetic flux (since 1967), the empirically reconstructed solar open magnetic flux (since 1845,  Lockwood et al. , 2014) , and the 44 Ti activity in stony meteorites  (Krivova et al. , 2010; Vieira et al. , 2011; Yeo et al. , 2014). SATIRE and NRLSSI2 are internally consistent, in the sense that the integral of the modeled spectral irradiances equals the TSI. These are among the best model reconstructions we currently have. Note, however, that both models reconstruct the SSI prior to the satellite era by assuming the relationship between sunspot number and SSI to be timeinvariant. The model uncertainty associated with this assumption is difficult to quantify. For that reason, it is not included in the uncertainties that are provided with the model datasets, which may therefore be underestimated.
, 2017). The making of this composite involves several steps. First, the SSI datasets provided by the instrument teams (see the list of instruments in Table  1 ) are preprocessed, e. g. , corrected for outliers and aligned in time. Furthermore, the short-term and long-term uncertainties of the SSI time series are determined. These steps are detailed in  Schöll et al. (2016). Second, for each individual dataset all data gaps are filled by expectation-maximization (Dudok de  Wit, 2011). This approach makes use of observed proxies representing the SSI variation of different wavelength ranges in the solar spectrum, as listed in Table  2. We emphasize here that the gapfilling is required here to decompose the records into different timescales; at the end, the interpolated values are excluded from the composite.
Third, each individual time series is decomposed by wavelet transform into 13 timescales a = 2 j , with j being the level of the scale. These scales go from 1 day (Level 0) to 11. 2 years (Level 12). For each timescale, the uncertainty is determined by taking into account the short-term and long-term uncertainties. Fourth, the decomposed records are recombined by calculating the Our aim was to keep this composite fully independent from existing models. This means that no SSI models have been used to correct the observational data, which are taken at their face value, without any correction. One challenge of this -as with any statistical approachis its reliance on the number of independent datasets. While for the past decades several missions were dedicated to measuring the UV band of the solar spectrum, the picture becomes bleaker when considering recent observations in the visible and near-UV parts of the spectrum.
After 2003, the only remaining observations that are continuous are from SORCE/SIM, whose out-of-phase behavior  (Harder et al. , 2009)  is controversial  (Lean and DeLand, 2012; Ermolli et al. , 2013). Let us therefore stress that the SOLID composite is based on observations only, and will necessarily undergo revisions as new physical constraints are incorporated, or new versions of the datasets are released.
This has fueled a debate (e. g. ,  Yeo et al. , 2015)  that is unlikely to settle soon. The two models have been derived independently, and as of today there is no consensus regarding their relative performance. In this context, and for the time being, the most reasonable approach (in a maximum-likelihood sense) consists of averaging their reconstructions, weighted by their uncertainty. Since, in addition, we are lacking uncertainties that can be meaningfully compared, our current recommendation is to simply take the arithmetic mean of the two model datasets: (i) the empirical model NRLTSI2 and NRLSSI2  (Coddington et al. , 2016)  and (ii) the semi-empirical model SATIRE  (Yeo et al. , 2014; Krivova et al. , 2010). Note that multimodel averaging is a widely used practice in climate modeling (e.
7 index at solar minimum has not significantly varied since 1947, when its first measurements started. The dataset, together with a technical description, and a routine for how to read and integrate the SSI data to the radiation bands used in climate models can be found at http: //solarisheppa. geomar. de/cmip6. In addition, a recommendation on how to implement the SSI changes in the models is provided in the Appendix B. A detailed description of the CMIP6 solar irradiance forcing in TSI and SSI and a comparison to the CMIP5 recommendation are presented in the following. Figure  1  presents time series of the TSI from the CMIP5, CMIP6, and the CMIP6-adapted versions of NRLTSI2 and SATIRE datasets, along with one observational composite from PMOD (version 42. 64. 1508) 2. We stress that all the data are taken at their face value, using their latest version, without any adjustments or scaling, except for NRLTSI1, whose value we uniformly reduced by 5 W m -2 to account for the new recommendation for average TSI (see below).
All TSI records agree well on daily to yearly timescales, and in some cases (e. g. , NRLTSI1 and NRLTSI2) they match as well on multidecadal timescales. The major difference arises in the long-term behavior of SATIRE and NRLTSI2 (see Sect. 2. 1. 1), which impacts the CMIP6 composite, and leads to a weaker trend compared to the CMIP5 recommendation (which was based on NRLTSI1 only). In both models, the historical reconstructions are sensitive to the assumptions made when constraining them to direct (satellite era) observations that suffer from large uncertainties. There is no consensus yet as to which one better represents long-term solar variability, and this is what motivated us to average them for making the CMIP6 composite. More subtle differences between the different TSI datasets arise in the satellite era, especially with the unusually deep solar minimum that occurred in 2008-2010: the NRLTSI2 model has a weak negative trend between successive solar minima, whereas the SATIRE reconstruction exhibits a larger one.
Solar spectral irradiance (SSI) To investigate differences and similarities between the SSI datasets, following  Ermolli et al. (2013) , we concentrate on four specific wavelength ranges: 120-200 nm (UV1), 200-400 nm (UV2), 400-700 nm (VIS), and 700-1000 nm (NIR), with special emphasis on the CMIP5 (i. e. , NRLSSI1) and the CMIP6 (average of NRLSSI2 and SATIRE) datasets. These ranges are relevant for climate studies; see for example Table 3 below. Figure  2  shows the SSI time series from 1880 through 2014. Note that we added vertical offsets by adjusting the mean values to facilitate their comparison, using CMIP6 as a reference. We note the following: -The long-term increase from 1880 to 1980 is similar in NRLSSI2, SATIRE, and CMIP6, but NRLSSI2 predicts a slightly larger increase in the VIS and NIR.
NRLSSI1 predicted a larger increase in the VIS, compensated by a smaller increase in the NIR and UV2. -As already described for the TSI behavior above (Fig. 1 ), SATIRE predicts a significant downward trend of the baseline for the last three solar cycles, as can be seen by comparing the SSI at solar minima between cycles  21-22 (1985) ,  22-23 (1995), and 23-24 (2008). NRLSSI2 does not predict significant variations and therefore the recommended CMIP6 time series has a slower downward trend than SATIRE in the recent cycles. This trend was not apparent in the dataset recommended for CMIP5. -The solar-cycle variability in CMIP6 exceeds that of CMIP5, particularly in the UV2 and NIR ranges, while it is approximately the inverse in the VIS. The change in the NRLSSI model can be explained by the use of new and higher-quality data from the SORCE mission on the rotational timescale in NRLSSI2, while NRLSSI1 was based on data from older satellite missions.
In the UV2, SATIRE predicts larger solar-cycle amplitudes, which can be explained by a larger weight of the network at these wavelengths. In Fig. 2 , the apparently less regular solar-cycle reconstruction by NRLSSI2 between 1940 and 1960 is most likely caused by the transition from one sunspot record to another in that model (see  Coddington et al. , 2016). Figure  3  compares our CMIP6 dataset with the observational SOLID composite (see description above) and some direct SSI satellite observations. Generally speaking, the observations and observation-based composite agree very well with each other, and the CMIP6 dataset up to 200 nm. Larger cycle variations than in the CMIP6 SSI occur above about 200 nm in the observations. Such discrepancies are inherent to the observation of small variations over 11 years.
On the   right panel of Fig. 3 , one can notice the different influences of the various datasets on the SOLID composite, as a consequence of their uncertainty at different scales. For example, the SORCE/SIM data have only a minor (but significant) effect on the long-term variations of the composite. In the VIS and NIR part of the spectrum, the only available measurements are from the SORCE/SIM instrument, whose solarcycle variation is controversial  (Lean and DeLand, 2012)  and hence should be considered with great caution. Figure  4  shows the contribution of the different wavelength ranges to TSI variations between solar maximum on November 1989 (solar cycle 22) and solar minimum on November 1994 (between cycles 22 and 23) for the different solar irradiance models. Both extrema are averaged over 81 days.
Not surprisingly, this reconstruction agrees well with the observations from TIMED/SEE. However, due to a lack of other long-duration EUV observations that are of sufficient radiometric quality, it is very difficult to assess the quality of our reconstruction. For the same reason, multidecadal variations are poorly constrained, and in particular, the presence of trends remains largely unknown. Note that wavelengths below 28 nm require more caution, since they rely on TIMED/XPS observations that were partly degraded  (Woods et al. , 2005). One future improvement of our dataset involves reconstructions of the EUV band that are based on more advanced models such as NRLEUV2  (Lean et al. , 2011).
Evaluation of SSI datasets in climate models Providing a first assessment of implications employing the SSI recommended for CMIP6 in comparison to CMIP5, we present results of two state-of-the-art chemistry-climate models (CCMs): the Whole Atmosphere Community Climate Model (CESM1(WACCM);  Marsh et al. , 2013)  and the ECHAM/MESSy atmospheric chemistry model (EMAC;  Jöckel et al. , 2010 Jöckel et al. , , 2016)). Additionally, we include results of single-profile radiative transfer calculations performed with the line-by-line radiative transfer code "libradtran"  (Mayer and Kylling, 2005). We use the latter to present estimates of direct shortwave (SW) radiative heating impacts neglecting the ozone chemistry feedback which is included in the CCM results.
CCM experimental design The CCM simulations with CESM1(WACCM) and EMAC are identically conducted in an atmosphere-only time-slice configuration. This means that the external forcings such as the solar and the anthropogenic forcing are fixed for the whole simulation period, i. e. , 45 model years plus spin-up (∼ 5 years for EMAC, ∼ 3 years for CESM1(WACCM)). Concentrations of greenhouse gases (GHGs) and ozonedepleting substances (ODSs) are set to constant conditions representative for the year 2000. The lower-boundary forcing is specified by the mean annual cycle of SSTs and sea ice of the decade 1995-2004 derived from the HadISST1. 1dataset (Rayner et al. , 2003). All simulations are nudged towards an observed (EMAC) or idealized 28-month varying (CESM1(WACCM)) QBO. The only difference between the simulations is in the solar forcing.
Four simulations for each of the following SSI datasets have been performed with EMAC and WACCM: CMIP6-SSI, its constituent datasets NRLSSI2  (Coddington et al. , 2016) , and SATIRE  (Krivova et al. , 2010; Yeo et al. , 2014) , as well as NRLSSI1  (Lean, 2000). The latter was recommended as solar forcing for CMIP5 including a uniform scaling of the spectrum to match TSI measurements of the Total Irradiance Monitor (TIM) instrument. As one emphasis of this study is to highlight differences to the previous phase of CMIP, we employed NRLSSI1 (including this scaling) and refer to it as NRLSSI1(CMIP5) in the following. Runs for each of the four datasets have been performed with both CCMs for a solar-minimum time slice and a solar-maximum time slice, respectively. For solarmaximum time slices, SSIs averaged over November 1989 are used (maximum of solar cycle 22) while for the solarminimum time slices averages over November 1994 are chosen.
The latter does not match the absolute minimum of solar cycle  21-22 (June 1996). However, solar activity in November 1994 was already close to the minimum. The dif-ferences in solar activity between our solar-minimum and solar-maximum time slices for the respective datasets are within a range of 0. 988 W m -2 for NRLSSI1(CMIP5) to 1. 057 W m -2 for NRLSSI2. It should be noted that these experiments will illustrate only one part of solar influence on climate. Given the atmosphere-only set-up of the runs, oceanic absorption of (mainly visible) solar irradiance and subsequent heating and feedbacks to the atmosphere -the so-called bottom-up mechanism (see  Gray et al. , 2010 , and references therein) -is not represented in our simulations. Therefore we focus only on stratospheric signals and "top-down" dynamically induced responses in the troposphere.
A second constraint of this study's experimental set-up is the choice of one solar cycle. Solar activity and hence spectral irradiance vary between different solar cycles. However, these differences are relatively small compared to a typical solar-cycle amplitude and will probably not affect the main results of this study. It should also be noted that the time-slice simulations were designed as a sensitivity study to test the impact of the different solar input datasets. They do not represent the full feedbacks of transient CMIP6 simulations.
Methods The analyses presented in the following consist of differences between climatologies derived from the various simulations. Given the time-slice configuration of the CCM runs with all external forcings equal except for the SSI dataset, we assume that statistically significant differences of two climatologies are the result of the differing solar irradiance forcings. Confidence intervals (95 %) as presented in Figs. 6 and 8, as well as statistical significance (p < 5 %) as marked in Figs. 10 and 11, are based on 1000-fold bootstrapping. Confidence intervals in Figs. 6 and 8 are only given for the CCM results related to CMIP6 SSI.
The profiles represent the tropical (averaged over 25 • S-25 • N) stratosphere and mesosphere (100-0. 01 hPa) for annual mean conditions for the CCMs and libradtran. Employing CMIP6-SSI results in significantly decreased radiative heating of large parts of the mesosphere and stratosphere (above 10 hPa) compared to NRLSSI1(CMIP5). Whereas the largest differences can be found at the stratopause with approx. -0. 35 K day -1 according to both CCMs, and even more, -0. 42 K day -1 , for libradtran (without any ozone chemistry feedback), libradtran and EMAC yield slightly increased SW heating rates below ∼ 7 and 10 hPa, respectively. This weaker SW heating in the new CMIP6 SSI dataset in the upper stratosphere and the stronger heating in the lower stratosphere are confirmed by the wavelength-dependent percentage changes between the CMIP6 and CMIP5 SSI datasets with respect to the radiation and photolysis schemes (Fig.
7 ). Regardless of the number of bands in the radiation code, both models show a smaller percentage difference of -5 % below about 300 nm and weaker or negligible differences above 300 nm (Fig. 7 ). Significant differences in radiative heating throughout the stratosphere related to the three state-of-the art SSI recon-structions are produced only with radiation codes of high spectral resolution such as in libradtran or -to a lesser degree -in EMAC (for the middle to lower stratosphere). Comparisons between CMIP6-SSI and its constituents NRLSSI2 and SATIRE in WACCM and EMAC lead to the conclusion that the choice of the CCM and its specific radiation and photolysis scheme is more important than the choice of the SSI dataset with respect to SW heating rates. In addition the ozone chemistry damps the SW heating response in the CCMs compared to libradtran, which misses the ozone feedback.
For both models the SATIRE irradiances show larger signals than NRLSSI2 irradiances, with the signal for CMIP6 in between. The ozone signals start to differ at and above the stratopause, probably due to the more detailed photolysis code and the higher model top in CESM1(WACCM) compared to EMAC. The ozone signal is much more uncertain with respect to the different SSI forcings than the SW heating rate and the temperature signals. In summary, the CMIP6-SSI irradiances lead to lower SW heating rates and lower temperatures as well as smaller ozone signals in the lower stratosphere and larger ozone signals in the upper stratosphere and lower mesosphere than the CMIP5-SSI irradiances. Differences between the three tested SSI datasets occur in the SW heating rates only with a very high spectral resolution of the radiation code (libradtran, EMAC), and the differences are more prominent for ozone in a similar way for both CCMs, i.