Jiansong Zhou and Ka Kit Tung, Department of Applied Mathematics, University of Washington, Seattle, Washington, USA
The purpose of the present work is to demonstrate that a solar-cycle response exists in surface temperature using the longest global dataset available, which is in the form of Sea-Surface Temperature (SST) 1854–2007, with emphasis on methods and procedures, data quality and statistical tests, and the removal of deterministic signals such as volcano aerosol forcing and greenhouse gas warming. It is found, using the method of Composite-Mean Difference (CMD) Projection, a signal of warming during solar max and cooling during solar min years in the global SST over the 14 cycles, dispelling previous claims that the solar-cycle response is opposite before 1920 as compared to the modern era. The magnitude of the solar cycle response averaged over the oceans between 60S and 60N is about 0.1 C per Wm-2 of variation of the solar constant (but is slightly lower, at ~0.085 C, when periods of suspected bad data are averaged in, consistent with the previous results of White et al. ). The signal is robust provided that we exclude the years near the Second World War; during which transitions from British ships to US ships introduced warm bias in the SST, as discovered by Thompson et al. . Monte-Carlo tests show that the extracted signal has less than 0.02% chance of being a random occurrence. This establishes the existence of a solar-cycle response at the earth’s surface at high statistical confidence. Contamination of the signal by volcano aerosols is estimated using the Multiple CMD Inversion method and found to be small over this long record, although ENSO contamination varies depending on the period chosen but is also small.
The multi-decadal trend of response to solar forcing is found to account for no more than a quarter of the observed warming in SST during the past 150 years, under a reasonable but unproven assumption that the climate response to secular solar forcing and to solar cycle forcing has the same spatial pattern.