Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green’s Function Experiments. Part I: Linear Response Function

Abstract

Abstract This paper explores the use of the linear response function (LRF) to relate the mean sea surface temperature (SST) response to prescribed ocean heat convergence (q flux) forcings. Two methods for constructing the LRF based on the fluctuation–dissipation theorem (FDT) and Green’s function (GRF) are examined. A 900-yr preindustrial simulation by the Community Earth System Model coupled with a slab ocean model (CESM–SOM) is used to estimate the LRF using FDT. For GRF, 106 pairs of CESM–SOM simulations with warm and cold q-flux patches are performed. FDT is found to have some skill in estimating the SST response to a q-flux forcing when the local SST response is strong, but it fails in inverse estimation of the q-flux forcing for a given SST pattern. In contrast, GRF is shown to be reasonably accurate in estimating both SST response and q-flux forcing. Possible degradation in FDT may be attributed to insufficient data sampling, significant departure of the SST distribution from Gaussianity, and the nonnormality of the constructed operator. The GRF-based LRF is then used to (i) generate a global surface temperature sensitivity map that shows the q-flux forcing in higher latitudes to be 3–4 times more effective than low latitudes in producing global surface warming, and (ii) identify the most excitable SST mode (neutral vector) that shows marked resemblance to the interdecadal Pacific oscillation (IPO). The latter discovery suggests that the IPO-like fluctuation exists in the absence of the coupling to the ocean dynamics. Coupling to the ocean dynamics in CESM, on the other hand, only enhances the spectral power of the IPO at interannual time scales.