Investigation of solar induced climate variability in chemistry-climate model simulations

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Solar induced climate variability is an important part of natural climate variability, detectable in atmospheric chemical and dynamical quantities. The physical processes driving this atmospheric solar signal involve variability in the spectral solar irradiances (SSI), the total solar irradiance (TSI) and also energetic particles precipitating (EPP) at high geomagnetic latitudes into the thermosphere and mesosphere. While the magnitude of the solar signal is large in the upper part of the atmosphere, where a direct impact of SSI variations on ozone chemistry and temperature is observed, the direct impact of TSI variations at the Earth´s surface is rather small. Due to dy-namical processes, which transfer the solar signal downward, an indirect amplification of the solar signal occurs in the troposphere. However, there still exist large uncertainties in quantifying the solar signal and in attributing physical processes to detected solar signals at the surface.The SolVarCCM project aims at further improving our understanding of solar induced climate variability by exploring the most recent model simulations. The Chemistry Climate Model Initia-tive (CCMI) provides an excellent database for the analysis of the solar signal in atmospheric chemistry and dynamics extending from the troposphere to the upper mesosphere. The first part of SolVarCCM addresses a method intercomparison to identify the most appropriate approach to detect the solar signal dependent on the location and quantity. This is of particular importance as the lower stratospheric decadal signal can still not unambiguously be attributed to solar variability. The optimized methods will then be applied to validate the solar signal in CCMI simulations of the recent five decades against observations. This objective of SolVarCCM will contribute directly to the international WCRP/SPARC-SOLARIS-HEPPA initiative. As the CCM-database has some limitations concerning solar forcing alternatives, SolVarCCM will also run a CCM coupled to an interactive ocean to address the influence of the alternative CMIP6-SSI/TSI-dataset and EPP on the solar signal under two different future prediction scenarios. These datasets together with the CCMI-database will allow for the analysis of the solar signal in a number of model subsets to identify processes responsible for the transfer of the solar signal from the middle atmosphere to the troposphere. The still uncertain role of specific solar forcings (EPP and SSI) or the role of model configuration (model top and ocean coupling), on the magnitude of the solar induced cli-mate variability at the surface will be estimated with more confidence than was possible in previ-ous studies. Another new issue to be investigated in SolVarCCM is the role of solar induced cli-mate variability in a changing climate.