Highlighting the key parts of the Abstract of this very important paper below:
The global temperature response to increasing atmospheric CO2 is often quantified by metrics such as equilibrium climate sensitivity
and transient climate response1. These approaches, however, do not account for carbon cycle feedbacks and therefore do not fully represent the net response of the Earth system to anthropogenic CO2 emissions … Here we … show that the carbon–climate response (CCR), defined as the ratio of temperature change to cumulative carbon emissions, is approximately independent of both the atmospheric CO2 concentration and its rate of change on these timescales. From observational constraints, we estimate CCR to be in the range per trillion tonnes of
carbon (Tt C) emitted (5th to 95th percentiles), consistent with twenty-first-century CCR values simulated by climate–carbon models. Uncertainty in land-use CO2 emissions and aerosol forcing, however, means that higher observationally constrained values cannot be excluded. The CCR, when evaluated from climate–carbon models under idealized conditions, represents a simple yet robust metric for comparing models, which aggregates both climate feedbacks and carbon cycle feedbacks. CCR is also likely to be a useful concept for climate change mitigation and policy; by combining the uncertainties associated with climate sensitivity, carbon sinks and climate–carbon feedbacks into a single quantity, the CCR allows CO2-induced global mean temperature change to be inferred directly from cumulative carbon emissions.
People are forgetting what was calculated in 2009.
Update. 27th February 2015
The news from Nature Geoscience and Journal of Climate today is that, even if climate science does not improve, the uncertainty in estimates of transient climate sensitivity (“TCS”) will decrease significantly soon, simply because we’re continuing to dump more fossil fuel carbon dioxide in atmosphere, and so the effect will be more pronounced. This comes from two papers, one by Myhre, et al, the other by Padilla, Vallis, and Rowley. They use different methods but come up with about the same estimate: By 2030, the uncertainty in TCS will be about halved. I’m interested in the Padilla, Vallis, and Rowley paper because thy use a nonlinear Kalman filter in their work, and I’ve applied a modified Kalman filter to a problem here on this blog.