Probabilistic Estimates of Transient Climate Sensitivity Subject to Uncertainty in Forcing and Natural Variability

In this paper, the authors address the impact of uncertainty on estimates of transient climate sensitivity (TCS) of the globally averaged surface temperature, including both uncertainty in past forcing and internal variability in the climate record. This study provides a range of probabilistic estimates of the TCS that combine these two sources of uncertainty for various underlying assumptions about the nature of the uncertainty. The authors also provide estimates of how quickly the uncertainty in the TCS may be expected to diminish in the future as additional observations become available. These estimates are made using a nonlinear Kalman filter coupled to a stochastic, global energy balance model, using the filter and observations to constrain the model parameters. This study verifies that model and filter are able to emulate the evolution of a comprehensive, state-of-the-art atmosphere ocean general circulation model and to accurately predict the TCS of the model, and then apply the methodology to observed temperature and forcing records of the twentieth century. For uncertainty assumptions best supported by global surface temperature data up to the present time, this paper finds a most likely present-day estimate of the transient climate sensitivity to be 1.6 K, with 90% confidence the response will fall between 1.3 and 2.6 K, and it is estimated that this interval may be 45% smaller by the year 2030. The authors calculate that emissions levels equivalent to forcing of less than 475 ppmv CO2 concentration are needed to ensure that the transient temperature response will not exceed 2 K with 95% confidence. This is an assessment for the short-to-medium term and not a recommendation for long-term stabilization forcing; the equilibrium temperature response to this level of CO2 may be much greater. The flat temperature trend of the last decade has a detectable but small influence on TCS. This study describes how the results vary if different uncertainty assumptions are made and shows they are robust to variations in the initial prior probability assumptions.