dataesr/scientific-paragraphs-categorization huggingface dataset:
Impacts of solar-cycle variability The second question tackled by this evaluation is the atmospheric impact of the 11-year solar cycle using different SSI irradiance reconstructions. A special focus lies on the comparison of the new CMIP6 dataset with its predecessor NRLSSI1(CMIP5). Figure  8  provides annual mean tropical (25 • S-25 • N) profiles analogous to Fig. 6  but now illustrating differences between perpetual solar-maximum and perpetual solar-minimum conditions according to simulations forced by the various SSI-datasets. All models and SSI-forcings produce the well-known solar-cycle impact of enhanced SW heating at solar maximum throughout the upper stratosphere and mesosphere. Differences to solar-minimum forcing peak at the stratopause with approx. +0. 19 to +0. 23 K day -1. Only the libradtrancalculations -that do not include any ozone feedback -yield considerably weaker responses.
However, most of these differences are not statistically significant. Comparing CMIP6-SSI-forced results with its components NRLSSI2 and SATIRE yields heterogeneous results. According to EMAC, NRLSSI2 leads to a slightly weaker solarcycle response throughout the stratosphere, while the mesospheric response is stronger than SATIRE and CMIP6-SSI. CESM1(WACCM)-results show that the stratospheric (up to approx. 2 hPa) solar-cycle response to CMIP6-SSI-forcing in temperature is slightly weaker than in both NRLSSI2-and  SATIRE-driven simulations. As opposed to that, simulations forced by SATIRE and CMIP6-SSI yield very similar warming signals in the mesosphere while NRLSSI2 produces a (significantly) weaker response in the mesosphere. The solar-cycle signal in ozone is very consistent for most parts of the stratosphere and mesosphere, with respect to the SSI datasets.
More important for the solar ozone signals seems to be the choice of the CCM (with its specific photolysis scheme; see also Fig. 9 ), especially for the lower stratosphere (10 hPa and below). In the lower mesosphere, however, the dataset-induced differences are larger than the model-induced ones. All analyzed combinations of CCMs and forcing datasets agree very well on the (relative) peak of the ozone response (+2. 3-2. 5 %) to the solar cycle at 3-5 hPa. In the lower mesosphere (0. 2-1 hPa), CMIP6-SSI (and SATIRE) leads to a significantly weaker solar-cycle ozone response (+0. 3-0. 5 % at 0. 5 hPa) than NRLSSI1(CMIP5) (and NRLSSI2; +0. 6-0. 8 % at 0. 5 hPa). For the lower stratosphere (below 7 hPa), both CCMs agree that SATIRE leads to the strongest solar-cycle ozone signals, though still within the uncertainty associated with CMIP6-SSI-forced simulations.
The comparison between CMIP6-SSI and NRLSSI1(CMIP5) yields no unequivocal result: CESM1(WACCM) exhibits a secondary maximum ozone response at approx. 70 hPa that is weaker with CMIP6-SSI than with NRLSSI1(CMIP5) while the opposite is seen in EMAC. Given the large uncertainty in the lower stratospheric solar ozone signal, we can only conclude that the signal is positive. In summary, the CMIP6-SSI irradiance forcing leads to slightly enhanced solar-cycle signals in SW heating rates, temperatures, and ozone than the CMIP5-SSI irradiance forcing. In general, differences between the different SSI datasets are not statistically significant. Note that statistically significant differences in the irradiance amplitude between CMIP5 and CMIP6-SSI irradiances are observed between 300 and 350 nm in particular, a wavelength region important for ozone destruction (below 320 nm), consistently in both CCMs (Fig.
9 ). The direct radiative effects in the tropical stratosphere from the CMIP6-SSI dataset, i. e. , enhanced solar-cycle signals in SW heating rates, temperatures, and ozone in the tropical upper stratosphere lead to the expected strengthening of the meridional temperature gradient and hence to a statistically significant stronger stratospheric polar night jet which propagates poleward and downward during winter from December through January (Fig. 10 ) and significantly affects the troposphere with a positive AO-like signal developing in late winter, i. e. , January and February (Fig. 11 ). This signal is very similar and statistically significant for both CCMs, and therefore the ensemble mean of both models is shown. Besides the radiative impact of the solar cycle, energetic particles also have an impact on the atmosphere and will be discussed in the following.
The impact of magnetospheric particles on the atmosphere is strongly linked to the strength of geomagnetic activity; this has been shown both for the direct production of NO in the thermosphere  (Marsh et al. , 2004; Hendrickx et al. , 2015)  and mesosphere  (Sinnhuber et al. , 2016) , for mesospheric OH production  (Fytterer et al. , 2015b) , and for the EPP indirect effect  (Sinnhuber et al. , 2011; Funke et al. , 2014a). Geomagnetic activity can be constrained over centennial timescales by means of proxy data provided by geomagnetic indices. Since our forcing dataset for magnetospheric particle precipitation relies on these indices, their reconstruction and homogenization is discussed first.
, 2016). An atmospheric ionization dataset has been calculated based on the Ap-based precipitation model, using a computationally fast ionization parameterization  (Fang et al. , 2010)  and atmospheric composition from the NRLMSISE-00 model  (Picone et al. , 2002). This calculation considered MEE (30-1000 keV) with maximum energy deposition at altitudes between about 60 and 90 km (van de  Kamp et al. , 2016). Note that the ionization parameterization does not consider the contribution of Bremsstrahlung, which could be significant only at altitudes below 50 km  (Frahm et al. , 1997). Figure  14  shows examples of solar-cycle variability of the modeled atmospheric MEE ionization rates at ≈ 80 km altitude. At 68 • magnetic latitude (L shell 7. 25), MEE precipitation is mostly driven by magnetic substorms and the solarcycle variability is relatively weak, except in around 2009 and the mid-1960s when extended periods of very low geomagnetic activity occurred.
At 64 • (L shell 5. 25), precipitation is driven by high-speed solar wind streams. A more clear solar-cycle variability can be seen with maximum ionization lagging the sunspot maximum by 1-2 years. At 56 • (L shell 3. 25), precipitation is mainly driven by coronal mass ejections which lead to more of an event-type behavior. Relatively infrequent ionization peaks are contrasted with long periods of very low ionization. Similar behavior is seen at other altitudes as well (not shown). In the following, we demonstrate with examples the MEE impact in WACCM simulations. The purpose is to present a proof of concept, i. e. , show that the MEE ionization dataset can be used in chemistry-climate modeling and is producing the expected direct effect in the mesosphere. We simulated the 2002-2012 period, including the Ap-driven MEE ionization rates, and analyzed mesospheric OH and ozone responses at 0.
The dataset for daily average ion pair production rates at 60-90 • geomagnetic latitudes from SPEs was computed over a 52-year time period , when proton flux measurements from satellites were available. A longer-term dataset for these SPE-caused ion pair production rates was created for the 1850-1962 time period using activity levels of the measured sunspots over the solar cycles. SPEs are much more frequent during years of maximum solar activity and vice versa. This longer-term dataset was reconstructed for years 1850-1962 in a random way using solar activity levels combined with the 52-year calculated SPE-caused ion pair production. Thus, an historical record of atmospheric forcing by SPEs in the form of a daily average ion pair production rate is available over the entire period 1850-2014 for use in global models.
, 2005)  based on the GEANT package. The agreement between the two models has been verified  (Usoskin et al. , 2009)  to be within 10 %. GCR ion pair production rates are provided as a function of the barometric pressure and geomagnetic latitude and were calculated from the modulation potential values φ of the 9400-year long record by  Steinhilber et al. (2012). Since this dataset has a 22-year time resolution, it has been interpolated to interannual timescales to resolve individual solar cycles, based on the sunspot numbers (see Fig. 19 ). One can see that this agrees well with the values of φ reconstructed using data from the worldwide network of neutron monitors  (Usoskin et al. , 2011). An example of the calculated ionization rate is shown in Fig. 20. The ionization maximizes in polar regions at heights of 15-20 km, while in the equatorial region the maximum of ionization occurs at about 12 km (note that in case of using ionization per cubic centimeter, the ionization maximizes at about 10 km in the equatorial region and 12 km over the poles).
Implementation of chemical changes induced by particle-induced ionization MEE, SPE, and GCR-induced atmospheric ionization is expressed in the CMIP6 forcing dataset in terms of ion pair production rates (IPRs). Note that IPR data are provided in units of ion pairs per gram per second as a function of the barometric pressure. These units are natural for the ionization processes and are mostly independent of the atmospheric conditions. Conversion into units of ion pairs per cubic centimeters per second (cm -3 s -1 ), by multiplying with mass density, should be done on the model grid ideally at each time step, but at least once per day. Recommendations for the projection of ion pair production rates onto geographic coordinates can be found in Appendix C. Particle-induced ionization causes, along with the generation of the ion pairs, the production of NO x and HO x.
23 ). We use international sunspot number version 1. 0, because most SSI models rely on that version (see Sect. 2. 1. 1). The resulting SSN time series of both future scenarios have then been used to calculate the SSI with the SATIRE and NRLSSI2 models with annual time resolution. As for the historical CMIP6 dataset, we took for each scenario the arithmetic mean of the two model results. SSI variations on shorter timescales are taken from the corresponding past solar cycles, and are scaled to a comparable cycle-average level of activity by means of a dedicated scaling procedure, as described in Appendix H. F10. 7 radio flux data have been constructed from the resulting future SSI record as described in Sect. 2. 1. 2. A similar approach has also been chosen for the future particle forcing. Magnetospheric particle forcing (Sect.
More realistic solar forcing time series for the past 1000 years are provided within the PMIP (Paleoclimate Modeling Intercomparison Project) exercise  (Kageyama et al. , 2017) , and the solar forcing is described in more detail in  Jungclaus et al. (2016). The radiative part of the variable PI control forcing has been generated by scaling the annual and subannual components of the REF forcing dataset to a constant solar-cycle mean activity level. The scaling procedure for SSI and F10. 7 is described in Appendix H. Constant background compo-Figure  27. A preliminary estimate of the fractional (%) monthly solar-ozone response per 130 units of the F10. 7 solar flux in the CMIP6 ozone database for the period 1960-2011 diagnosed using the ozone mixing ratio files downloaded from http://esgf-node.
llnl. gov/projects/input4mips (vmro3_input4MIPs_ozone_CMIP_UReading-CCMI-1-0_gr_195001-199912. nc and vmro3_input4MIPs_ozone_CMIP_UReading-CCMI-1-0_gr_200001-201412. nc). The hatching denotes regions where the solar-ozone response, diagnosed using multiple regression analysis, is found to be not significantly different from zero at the 95 % confidence level. Please note that the CMIP6 ozone database for the future simulations was not released at the time of writing. Adapted from  Maycock et al. (2017). nents have been added. These have been adjusted such that the mean values of the resulting SSI and F10. 7 time series are consistent with the constant PI control forcing. In order to enhance the contrast for this sensitivity experiment, we scale the annual and subannual components of SSI at wavelengths greater than 115 nm to the mean activity level of solar cycles 18-22 (grand solar maximum) rather than to 1850-1873 conditions.
For the EUV channels (10-115 nm) and for F10. 7, such an enhanced mean activity level would result in unreasonably low background values (due to the adjustment to the constant PI control forcing). Therefore, the annual and subannual components of these quantities were scaled to 1850-1873 conditions. Similarly, the particle part of the variable PI control forcing has been generated by scaling the annual and subannual components of the REF forcing dataset to the 1850-1873 mean activity level. The scaling of geomagnetic Ap and Kp indices is described in Appendix I. MEE-induced ion-pair production rates for the variable PI control forcing have then been calculated from the scaled Ap data. GCRinduced ion-pair production rates have been calculated using a constant value of representative of the 1850-1873 period.
Solar-cycle variations have been added, however, scaled to the 1850-1873 mean activity level. The variable PI control proton forcing is identical to the REF forcing since it does not include any long-term trend. Note that the temporal averages of SSI, TSI, F10. 7, Ap, and Kp, as well as GCR-induced ionpair production rates are fully consistent with the values provided in the constant PI control forcing dataset. This, however, is not the case for the proton and MEE forcings, which, in the latter case, do not account for large, sporadic events. The variable PI control dataset (see Fig. 26 ) covers the time period from 1 January 1850 until 9 September 2053 (end of solar cycle 27). The dataset can be extended to cover 1000 years by multiple repetition of the solar cycle sequence 12-27. The first 450 years of the resulting forcing time se-ries are consistent in solar-cycle phase and short-term fluctuations with the REF and EXT datasets.
g. , NO x ). Several CMIP5 models included a stratospheric chemical scheme  (Hood et al. , 2015) , and it seems likely that more models will have this capability in CMIP6. However, there will be CMIP6 models that do not include chemistry but which resolve the stratosphere and specify SSI, and thus have some of the major ingredients for simulating a topdown pathway for solar-climate coupling  (Mitchell et al. , 2015b). For these models, the simulated climate response to solar variability will partly depend on the representation of the solar-ozone response in their prescribed ozone field. CMIP5 models without chemistry were recommended to use the SPARC/AC&C ozone database  (Cionni et al. , 2011). The historical part of this dataset for the stratosphere provided monthly and zonal mean ozone concentrations based on a multiple regression analysis of measurements from the Stratospheric Aerosol and Gas Experiment (SAGE) satellite instruments.
The regression coefficients for various key drivers (e. g. , ODS, GHG, solar forcing) were used to reconstruct stratospheric ozone values back to 1850 as a function of latitude and pressure. The historical part of the CMIP5 ozone dataset therefore implicitly included a solar-ozone response derived from satellite observations. However, uncertainties in the amplitude of the solar-ozone response in SAGE II measurements, which cover only around two solar cycles, have been recently documented by  Maycock et al. (2016). It is therefore important to document the representation of the solar-ozone response in the WCRP/SPARC Chemistry Climate Model Initiative (CCMI) ozone database for CMIP6, which has been constructed using existing CCM simulations. At the time of writing, the paper describing the CMIP6 ozone database and its construction has not been published; however, for illustrative purposes, Fig.
Since the peak amplitude of the solar-ozone response in Fig. 27  is considerably smaller, and exhibits a different vertical structure compared to the CMIP5 ozone database  (Maycock et al. , 2017) , we anticipate that the peak magnitude of the stratospheric temperature response over the solar cycle may also be smaller in models using the CMIP6 ozone database  (Maycock et al. , 2017). We recommend that CMIP6 models without interactive chemistry use the SSI forcing dataset described above and the recommended CMIP6 ozone database to ensure consistency in the representation of the solar-cycle forcing across models. If CMIP6 models opt to use an alternative ozone dataset containing a different representation of the solar-ozone response, it would be very valuable for this to be documented by modeling groups, so that differences in simulated responses to solar forcing might be better understood  (Mitchell et al.
Conclusions This paper provides a comprehensive description of the recommended solar forcing dataset for CMIP6. The dataset con-sists of time series covering 1850-2300 of solar radiative (TSI, SSI, F10. 7) and particle (Ap, Kp, ionization rates due to SPEs, MEEs, and GCRs) forcings. This is the first time that solar-driven particle forcing has been included as part of the CMIP recommendation and represents a new capability for CMIP6. TSI and SSI time series for the historical period are defined as averages of two solar irradiance models: the empirical NRLTSI2-NRLSSI2 model and the semi-empirical SATIRE model, which have been adapted to CMIP6 needs as described above. Since this represents a change from the CMIP5 recommended NRLTSI1 and NRLSSI1 dataset, this paper places special emphasis on the comparison between the radiative properties of the CMIP5 and CMIP6 solar forcing recommendations.
The solar forcing components are provided separately at daily and monthly resolutions for the historical simulations, i. e. , 1850-2014; for the future period, i. e. , 2015-2300, including an additional extreme Maunder Minimum-like sensitivity scenario; and as constant and timedependent variants for the preindustrial control simulation. The particle forcing is only included in the daily resolution files. The dataset as well as a metadata description and a number of tools to convert and implement the solar forcing data can be found here: http://solarisheppa. geomar. de/cmip6. In the following we summarize the key features of the CMIP6 solar forcing dataset in comparison to CMIP5 that provide the reader with an overview without reading the paper in detail.
Radiative forcing -A new and lower TSI value is recommended: the contemporary solar-cycle average is now 1361. 0 ± 0. 5 W m -2  (Prša et al. , 2016). -Over the last three solar cycles in the satellite era there is a slight negative TSI trend in the CMIP6 dataset. A recent reconstruction of the TSI, with a proper estimation of its uncertainties, suggests that this downward trend between the solar minima of 1986 and 2009 is not statistically significant (Dudok de  Wit et al. , 2017). The TSI trend leads to an estimated radiative forcing on a global scale of -0. 04 W m -2 , which is small in comparison with other forcings over this period. -The new CMIP6 SSI dataset is the arithmetic mean of the empirical NRLSSI2/TSI2 and the semi-empirical SATIRE irradiance models and covers wavelengths from 10 to 10 000 nm.
Note that the SATIRE and the NRLSSI2/TSI2 datasets are CMIP6-adapted and not the original datasets. While SATIRE uses the annually averaged international sunspot number (v1) and applies daily variability from the SATIRE-T model, NRLSSI2/TSI2 uses the daily group sunspot number. This leads to different long-term trends in the presatellite era (see Sect. 2. 1. 1). -The CMIP6 SSI dataset agrees very well with available satellite measurements (i. e. , those which are used for building the SOLID observational composite) in the contribution of solar-cycle variability to TSI in the 120-200 nm wavelength range. In the 200-400 nm range, which is also important for ozone photochemistry, the CMIP6 dataset shows a larger contribution to solarcycle TSI variability than in CMIP5 (50 % compared to 35 %).
However, there is a lack of accurate satellite measurements to validate variations in this spectral region. In the VIS part of the spectrum, the CMIP6 dataset shows smaller solar-cycle variability than in CMIP5 (25 % compared to 40 %). In the NIR, the CMIP6 dataset shows slightly larger variability than in CMIP5. The implications of the differences in the spectral characteristics of SSI between CMIP5 and CMIP6 for climatological and solar-cycle variability in atmospheric heating rates and ozone photochemistry have been tested using two stateof-the art CCMs, EMAC and CESM1(WACCM), and a lineby-line radiative transfer model, libradtran. -When comparing differences in annual mean climatologies under perpetual solar-minimum conditions, the CMIP6-SSI irradiances lead to lower SW heating rates (-0.
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, compared to the CMIP5-SSI irradiances. These radiative effects lead to a weakening of the meridional temperature gradient between the tropics and high latitudes and hence to a statistically significant weakening of the stratospheric polar night jet in early winter. -The differences in irradiances between 11-year solarcycle maximum and minimum in the CMIP6-SSI dataset result in increases in SW 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.
These direct radiative effects lead to a strengthening of the meridional temperature gradient between the tropics and high latitudes and a statistically significant strengthening of the stratospheric polar night jet in early winter, which propagates poleward and downward during mid-winter and affects tropospheric weather, with a positive Arctic Oscillation signal in late winter. This regional surface climate response is similar and statistically significant in both CCMs. The CMIP6-SSI irradiances lead to slightly enhanced solar-cycle signals in SW heating rates, temperatures, and ozone, compared to the CMIP5-SSI. However, the differences in the 11year solar-cycle signals between the two SSI datasets are generally not statistically significant and are smaller than the differences in climatological conditions between CMIP6 and CMIP5 described above.
Particle forcing -The reconstruction of geomagnetic Ap and Kp indices backwards in time (starting in 1850) enabled a consistent historical dataset of geomagnetic particle forcing to be created in order to capture the atmospheric impact of precipitating auroral and radiation belt electrons. Regarding the latter, we employed a novel precipitation model for mid-energy electrons, based on the Ap index. Computed MEE ionization rates have been successfully tested in the (CESM1)WACCM model. To capture the effects of polar winter descent of EPPgenerated NO x in climate models that have an upper lid in the mesosphere (i. e. , below the EPP source region), recommendations for the implementation of an odd nitrogen upper-boundary condition are provided. The UBC has been successfully tested in the EMAC model by comparison to observations.
Inclusion of the CMIP6-recommended magnetospheric particle forcing in climate model simulations significantly improves the agreement with observed NO x , HO x , and ozone distributions in the polar stratosphere and mesosphere. -Solar proton and galactic cosmic ray forcings have been built from well-established datasets that have been used in many atmospheric model studies. However, observed proton fluxes are only available since 1963. Therefore, prior to this date the proton forcing included in our dataset is fictitious, although it broadly captures the expected variation in overall strength and distribution throughout the 11-year solar cycle. -In most cases, particle forcing can be expressed in terms of ion pair production rates. We have provided detailed recommendations for their implementation into atmospheric chemistry schemes.