2. Radiative Forcing – its origin, evolution and formulation
2.3 The evolution of the radiative forcing concept during the IPCC era
2.3.5 IPCC Fifth Assessment Report (AR5)
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In AR5 the effective radiative forcing (ERF) concept was introduced to allow rapid adjustment
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processes in the troposphere but avoiding changes that are associated with climate feedbacks
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(and in the conventional framework, mediated by surface temperature change – see Section
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2.1) (Boucher et al., 2013; Myhre et al., 2013). ERF is defined in Myhre et al. (2013) as
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“Change in the net top of atmosphere (TOA) downward radiative flux after allowing for
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atmospheric temperatures, water vapour and clouds to adjust, but with surface temperature or a
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portion of surface conditions unchanged”. Figure 2.3, from AR5, summarizes the progression
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from instantaneous radiative forcing, through RF and ERF, to climate response. AR5 also
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retained discussion of RF.
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No new forcing mechanisms were included in AR5, but the confidence level was raised,
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relative to earlier IPCC assessments for stratospheric water vapor, aerosol-radiation
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interactions, surface albedo due to land use, contrails, contrail-induced cirrus, solar irradiance
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changes and volcanic aerosols. The only ‘very low’ confidence level was given to rapid
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adjustment of aerosol-cloud interactions (earlier denoted as aerosol indirect effects). See the
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summary in Fig. 1.2.
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The motivation for introducing the ERF concept was that efficacies (see expression 2.2) for
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many climate drivers were different to unity when applying RF. This was particularly so for
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black carbon (Ban-Weiss et al., 2011; Hansen et al., 2005; Ming et al., 2010) and for aerosol-
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cloud interactions beyond the cloud albedo effect (Twomey effect) (e.g. Lohmann et al., 2010).
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There was also a growing understanding that rapid adjustments were important for CO2
(Andrews and Forster, 2008; Andrews et al., 2012; Doutriaux-Boucher et al., 2009).
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Furthermore, a clearer distinction between the fast changes (including instantaneous radiative
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perturbations and the rapid adjustments) and the slow climate feedback processes in terms of
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their importance for the climate response was elaborated (Andrews et al., 2010; Bala et al.,
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2010). Importantly, in single model studies, ERF was shown to provide an efficacy much
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closer to unity than the traditional RF concept (Hansen et al., 2005; Shine et al., 2003). The
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stratospheric temperature adjustment, which is included in the definition of RF, is also included
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in ERF. An additional advantage of ERF compared to RF is that a tropopause definition is
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avoided in the quantification of the forcing (e.g. Shine et al., 2003).
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Two methods have been widely adopted to calculate the ERF. One method (Gregory et al.,
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2004) regresses TOA net radiative imbalance against surface temperature change in coupled
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climate model simulations. The extrapolation of that regression line to zero surface temperature
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change then yields the ERF. The second method computes the TOA net radiative fluxes in
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fixed sea surface temperature (SST) climate model simulations (Hansen et al., 2005); while it is
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arguably more consistent to fix both land and surface temperatures (Shine et al. 2003), this is
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difficult to implement in advanced climate models. Instead Hansen et al. (2005) suggested
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adjusting the derived ERF to account for the impact of the land-surface temperature change on
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TOA radiative fluxes.
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The primary advantage of adopting ERF is that it reduces the level of approximation inherent
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in Expression 2.2 across a wide range of climate forcing mechanisms. Nevertheless, there are
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several limitations associated with its adoption. To some extent these are reflected in AR5
where the uncertainties in RF of WMGHGs were quantified as 10%, in agreement with earlier
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IPCC assessments, whereas AR5 assessed WMGHG ERF to have uncertainties of 20%.
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The necessity of climate model simulations to calculate tropospheric adjustments makes ERF
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distinct from either IRF (see Section 2.2) or RF in several ways. IRF and RF can be quantified
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using more sophisticated radiative transfer schemes than are typically available in climate
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models, and, for example, can be more easily applied to a wider range of greenhouse gases. In
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addition, the ERF technique is limited to forcing mechanisms that are of a sufficient size for
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the impact on TOA fluxes to emerge from the noise of the climate model’s own internal
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variability (see Section 2.3.6).
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Since rapid tropospheric adjustment processes are likely to be climate-model dependent this
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introduces further uncertainties beyond those involved in more traditional forcing definitions.
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For example, IRF are pure radiative transfer calculations that can be constrained reasonably
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well with detailed models and a high degree of physical understanding. The stratospheric
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temperature adjustment that is incorporated in the RF has a well-understood theoretical basis
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(resulting from the balance between changes in absorption by and emission from the
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stratosphere). By contrast, tropospheric adjustments are much more complicated. There is less
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theoretical underpinning with which to constrain these adjustments; this is particularly so for
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cloud adjustments which result from the complex interplay between different processes that
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may or may not be well-represented in individual climate models. This complicates the
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distinction between adjustments and feedbacks that are mediated by surface temperature
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change and there is no obvious way to quantify the adjustments with observations.
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One consequence of these shortcomings is a blurring of the lines between forcings and
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feedbacks. While the tropospheric adjustments are defined to have a shorter time scale than
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feedbacks, they also generally involve some coupling to the surface; e.g., land warming (in
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the fixed-SST approach to ERF calculation) or pattern of SST change (in the regression
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approach). Hence there is a need to further develop techniques that enable a robust
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separation of adjustment and feedback processes.
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A specific difficulty is that it is increasingly hard to compare different types of forcing.
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IRF, which involves purely radiative transfer calculations, has generally not been computed
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in climate model simulations (see section 2.3.6 for future efforts) due to computational
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considerations; instead, ERF has become the preferred approach to quantifying RF.
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Attempts to isolate IRF from ERF using radiative kernels have noted that most of the
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intermodel spread in ERF from CO2 forcing does not arise from differences in tropospheric
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adjustments, but rather from differences in IRF (Chung and Soden 2015). Indeed,
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intermodel differences in the calculation of IRF have been a persistent problem in GCMs
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(Cess et al. 1993; Soden et al 2018) despite the presence of accurate and observationally
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verified line-by-line calculations to constrain their counterparts in climate models (Collins
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et al. 2006).
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