9.2 Radiative Forcing and Climate Response
This section briefly summarises the understanding of radiative forcing based on the assessment in Chapter 2, and of the climate response to forcing. Uncertainties in the forcing and estimates of climate response, and their implications for understanding and attributing climate change are also discussed. The discussion of radiative forcing focuses primarily on the period since 1750, with a brief reference to periods in the more distant past that are also assessed in the chapter, such as the last millennium, the Last Glacial Maximum and the mid-Holocene.
Two basic types of calculations have been used in detection and attribution studies. The first uses best estimates of forcing together with best estimates of modelled climate processes to calculate the effects of external changes in the climate system (forcings) on the climate (the response). These ‘forward calculations’ can then be directly compared to the observed changes in the climate system. Uncertainties in these simulations result from uncertainties in the radiative forcings that are used, and from model uncertainties that affect the simulated response to the forcings. Forward calculations are explored in this chapter and compared to observed climate change.
Results from forward calculations are used for formal detection and attribution analyses. In such studies, a climate model is used to calculate response patterns (‘fingerprints’) for individual forcings or sets of forcings, which are then combined linearly to provide the best fit to the observations. This procedure assumes that the amplitude of the large-scale pattern of response scales linearly with the forcing, and that patterns from different forcings can be added to obtain the total response. This assumption may not hold for every forcing, particularly not at smaller spatial scales, and may be violated when forcings interact nonlinearly (e.g., black carbon absorption decreases cloudiness and thereby decreases the indirect effects of sulphate aerosols). Generally, however, the assumption is expected to hold for most forcings (e.g., Penner et al., 1997; Meehl et al., 2004). Errors or uncertainties in the magnitude of the forcing or the magnitude of a model’s response to the forcing should not affect detection results provided that the space-time pattern of the response is correct. However, for the linear combination of responses to be considered consistent with the observations, the scaling factors for individual response patterns should indicate that the model does not need to be rescaled to match the observations (Sections 9.1.2, 188.8.131.52 and Appendix 9.A) given uncertainty in the amplitude of forcing, model response and estimate due to internal climate variability. For detection studies, if the space-time pattern of response is incorrect, then the scaling, and hence detection and attribution results, will be affected.
In the second type of calculation, the so-called ‘inverse’ calculations, the magnitude of uncertain parameters in the forward model (including the forcing that is applied) is varied in order to provide a best fit to the observational record. In general, the greater the degree of a priori uncertainty in the parameters of the model, the more the model is allowed to adjust. Probabilistic posterior estimates for model parameters and uncertain forcings are obtained by comparing the agreement between simulations and observations, and taking into account prior uncertainties (including those in observations; see Sections 184.108.40.206, 9.6 and Supplementary Material, Appendix 9.B).