A Scaling Model for the Forced Climate Variability over the Anthropocene

The determination of the earth system climate sensitivity and associated multi-decadal climate projections is a scientific problem with important social and making. In this thesis, we develop a new approach that directly exploits the stochasticity of the internal variability and the linearity of the response to radiative forcing to make projections based on historical data. At long enough timescales, nonlinear deterministic general circulation models (GCMs) are similarly stochastic and are believed to exhibit linearity of the mean response. Our approach produces the forced temperature by convolution of a Green’s function, or Climate Response Function (CRF), with the radiative forcing. The full space-time CRF required is too general to allow for its determination either from models or from data: we therefore restrict our attention to global mean forcing and temperature response, and exploit the temporal scaling symmetry to define a scaling CRF (SCRF) characterized by the scaling exponent H and sensitivity to integrated radiative forcing λH . A key parameter is the inner scale of the linearity: we argue that it must be of the order of the ocean-atmosphere coupling time τ that we estimate, for the first time, to be ≈ 2 years. There is a large uncertainty in the forcing, particularly from aerosols and volcanoes. An aerosol linear scaling factor α and a non-linear volcanic damping exponent ν were introduced to correct the amplitude of the former and the intermittency of the latter. The result for α supports a modern aerosol forcing value in the 90 % range [−1.0,−0.3]Wm−2 with median at −0.7Wm−2. Using 5 global instrumental temperature series and the Coupled Model Intercomparison Project Phase 5 (CMIP5) standard forcing, we estimate the model and forcing parameters by Bayesian inference. This allows us to analytically calculate the climate sensitivity at different time scales: the transient climate response and the equilibrium climate sensitivity are estimated, respectively, as 1.6 −0.2K and 2.4 +1.3 −0.6K at the 90 % confidence level. This observation based model is robust and projections for the coming century are made with the forcing prescribed in the Representative Concentration Pathways scenarios 2.6, 4.5 and 8.5 : 1.5 −0.2K, 2.3K ± 0.4K and 4.0 ± 0.6K, respectively at the 90 % confidence level. Estimating the model parameters from instrumental data forces our model to converge to the real climate whereas each GCM has its own climate, leading to a large structural uncertainty for a CMIP5 multi-model ensemble which is more than twice the parametric uncertainty of our model in the year 2100 for the same RCP scenarios : 1.7± 0.8K, 2.6± 0.8K and 4.8± 1.3K respectively. This provides a result entirely independent from GCMs which adds to the overwhelming body of evidence for anthropogenic warming. According to our projections made to 2100, to avert a 1.5K warming, future emissions will be required to undergo drastic cuts similar to RCP 2.6, for which we found a 50% probability to remain under the said limit; it is virtually certain that RCP 4.5 and RCP 8.5-like futures would overshoot. Even a 2.0K warming limit would surely be surpassed by 2100 under RCP 8.5 and probably also under RCP 4.5, with only a 20% chance of remaining under the limit. The safest option remains RCP 2.6 which we project to remain under 2.0K with very high confidence. The question remains if it is realistic at all given that it relies strongly on the massive deployment of speculative negative emission technologies.