Abstract
Abstract. The statistical framework of Part 1 (Sundberg et al., 2012), for comparing ensemble simulation surface temperature output with temperature proxy and instrumental records, is implemented in a pseudo-proxy experiment. A set of previously published millennial forced simulations (Max Planck Institute – COSMOS), including both "low" and "high" solar radiative forcing histories together with other important forcings, was used to define "true" target temperatures as well as pseudo-proxy and pseudo-instrumental series. In a global land-only experiment, using annual mean temperatures at a 30-yr time resolution with realistic proxy noise levels, it was found that the low and high solar full-forcing simulations could be distinguished. In an additional experiment, where pseudo-proxies were created to reflect a current set of proxy locations and noise levels, the low and high solar forcing simulations could only be distinguished when the latter served as targets. To improve detectability of the low solar simulations, increasing the signal-to-noise ratio in local temperature proxies was more efficient than increasing the spatial coverage of the proxy network. The experiences gained here will be of guidance when these methods are applied to real proxy and instrumental data, for example when the aim is to distinguish which of the alternative solar forcing histories is most compatible with the observed/reconstructed climate.
Highlights
Variations of solar irradiance on long time scales have a potential influence on global climate
The high solar simulation is significantly correlated even for the lowest coverages when E2 serves as target (Fig. 6b), whilst achieving significant unified correlation-based statistic (UR) values for coverages upwards of 1 % when E1 serves as target
We apply a new statistical framework (Sundberg et al, 2012) designed for comparing ensemble model simulation surface temperature from one or more locations with proxy and instrumental data. This framework derives a unified correlation-based statistic (UR) that provides an initial test of whether a set of simulation time series from different locations correlates with a set of target series for the corresponding real locations, and a distance-based measure (UT ) that can be used to assess the goodness-of-fit of a given forced simulation in comparison with those that are unforced
Summary
Variations of solar irradiance on long time scales have a potential influence on global climate. One way to attempt constraining the long-term amplitude of solar forcing is to use alternative TSI histories to drive climate model simulations, and see which forcing history provides simulated temperatures that are most compatible with the observed past temperatures and reconstructed past temperatures derived from proxy data (Ammann et al, 2007; Jungclaus et al, 2010; Feulner, 2011; Schmidt et al, 2011) This approach, is associated with difficulties because of the always present noise in the climate proxy data (Jones et al, 2009) in combination with the stochasticity of the internal (unforced) variability of the climate system (Yoshimori et al, 2005).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
