It seems depending on who you talk to, climate sensitivity is either underestimated or overestimated. In this case, a model suggests forcing is underestimated. One thing is clear, science does not yet know for certain what the true climate sensitivity to CO2 forcings is.
There is a new Paper from Tanaka et al (download here PDF) that describes how forcing uncertainty may be underestimated. Like the story of Sisyphus, an atmospheric system with negative feedbacks will roll heat back down the hill. With positive feedbacks, it gets easier to heatup the further uphill you go. The question is, which is it?
Insufficient Forcing Uncertainty Underestimates the Risk of High Climate Sensitivity
Uncertainty in climate sensitivity is a fundamental problem for projections of the future climate. Equilibrium climate sensitivity is defined as the asymptotic response of global-mean surface air temperature to a doubling of the atmospheric CO2 concentration from the preindustrial level (≈ 280 ppm). In spite of various efforts to estimate its value, climate sensitivity is still not well constrained. Here we show that the probability of high climate sensitivity is higher than previously thought because uncertainty in historical radiative forcing has not been sufficiently considered. The greater the uncertainty that is considered for radiative forcing, the more difficult it is to rule out high climate sensitivity, although low climate sensitivity (< 2°C) remains unlikely. We call for further research on how best to represent forcing uncertainty.
Our ACC2 inversion approach has indicated that by including more uncertainty in
radiative forcing, the probability of high climate sensitivity becomes higher, although low climate sensitivity (< 2°C) remains very unlikely. Thus in order to quantify the uncertainty in high climate sensitivity, it is of paramount importance to represent forcing uncertainty correctly, neither as restrictive as in the forcing scaling approach (as in previous studies) nor as free as in the missing forcing approach. Estimating the autocorrelation structure of missing forcing is still an issue in the missing forcing approach. We qualitatively demonstrate the importance of forcing uncertainty in estimating climate sensitivity – however, the question is still open as to how to appropriately represent the forcing uncertainty.
h/t and thanks to Leif Svalgaard