Long-term patterns of solar irradiance forcing in model experiments and proxy based surface temperature reconstructions

Comparisons are made of long-term empirical and model-estimated patterns of solar irradiance forcing during a 200-year period (1650-1850), which precedes any apparent anthropogenic influence on climate. This interval encompasses a considerable range (approximately 4 W/m) of estimated variation in solar output, including the ‘‘Maunder’’ and ‘‘Dalton’’ Minima of solar irradiance, and an intervening interval of relatively high values of irradiance, but does not encroach into the industrial era wherein it is difficult to separate solar and anthropogenic influences. Particular emphasis is placed on comparing empirical and modeled patterns of forced surface temperature variation. The empirical patterns bear a greater similarity to the pattern of forced response of a coupled ocean-atmosphere general circulation model (AOGCM) than with an independent model simulation result using an ocean with specified heat transport, both in terms of the spatial pattern of response and implied global mean sensitivity to forcing. Heightened sensitivity in the western Pacific warm pool apparent in the empirical response pattern, is not observed in the forced response of the coupled model. It is possible that this pattern is the result of feedback processes not currently reproduced in course-resolution coupled models. The greatest empirical response is found at the multidecadal-to-century (> 40 year period) time scale, for which the forcing is dominated by the roughly 90-year Gleissberg Cycle of irradiance. This indicates a global-mean sensitivity (approximately 0.3 K/W/m), which is close to the coupled model result (approximately 0.4 K/W/m). At decadal time scales (825 year period), for which the forcing is dominated by the 11-year and 22-year period solar cycles), the temperature sensitivity is moderately reduced, and its spatial pattern of response is dominated by an apparent resonance with known decadal modes of climate variability.