Representation of CH4 concentration and radiative forcing
The CH4 emissions from RCP2.6 and 8.5 are shown in Fig. 1. By using these emissions as input, the SCMs somewhat diverge in terms of the concentrations in 2100 – especially for the RCP8.5 scenario (see Fig. 2, upper left panel). Overall, the SCM results seem to be consistent with those projected by the expert models in ACCMIP. Note however, that the ACCMIP range is defined by only two models that recorded a long-term projection (LMDzORINCA and GISS-E2-R), and should only serve as an indication of the trend. This projection corresponds with a study of future uncertainties in methane abundance, which concludes that the methane concentration in 2100 in RCP8.5 is 3990 ± 330 ppb (Holmes et al. 2013). Compared to this range, the projections of MERGE, MERGE_ETL and MAGICC5.3 are slightly high and those of FUND slightly low. MERGE and MERGE_ETL also show high concentrations for RCP2.6. In contrast, the FUND concentration levels are relatively low for RCP8.5. This can be attributed to the use of a lower atmospheric lifetime of CH4 than the estimated perturbation lifetime of approximately 12 years (IPCC 2013)(Ch.6). For RCP2.6 in general, the difference between models is smaller than for RCP8.5 because of the much smaller relative influence of anthropogenic emissions. This leads to concentration levels closer to the more certain background concentration of CH4 (approximately 790 ppb).
All models use a very similar way to translate concentration into forcing, which leads to a very similar picture (Fig. 2, upper right panel) (see Supplementary Material for a visualisation of this relation for CH4 and N2O). For RCP8.5 this results in a difference across models of 0.25 W/m2. For RCP2.6 this value is 0.1 W/m2.
Representation N2O concentration and radiative forcing
For N2O (Fig. 2, lower left panel), the differences across models for reported concentration levels are similar for both scenarios, varying between 435 and 480 ppb in 2100 in RCP8.5 and between 344 and 387 ppb in RCP2.6. In general, N2O concentration projections for MAGICC (particularly MAGICC6.3) are relatively low compared to the other SCMs. Unlike the other models, MAGICC includes natural N2O emissions, derived from historical time series of N2O concentration profiles. In the model, atmospheric decay is applied to the complete atmospheric burden of N2O, not merely to the anthropogenic part. With the current calibration, the decay of the natural background concentration is larger than the influx by natural emissions. As such, this could lead to an overestimation of the decay of N2O, in turn resulting in lower atmospheric concentrations. This should be verified in further studies, since unfortunately, there is no information from the expert models available to compare the results to.
As with CH4, all models use a rather similar way to translate N2O concentration into forcing, and therefore both panels (Fig. 2, lower two) show comparable results. The overall spread in forcing outcomes across the models in 2100 is 0.11 W/m2 for both scenarios, much smaller than for methane in RCP8.5 and slightly larger in RCP2.6.
Representation aerosol radiative forcing
As for other physical and chemical processes, the representation of aerosols is considerably more simplified in SCMs than in the ACCMIP models. In fact, only MAGICC explicitly describes aerosol forcing other than sulphate (BC, OC and nitrate). All models except FUND and DICE make an explicit distinction between direct (scattering and absorbing) and indirect (cloud forming) effects of aerosols. FUND and MERGE (unlike MERGE_ETL) make use of scenario-independent forcing pathways. This means that they do not respond to differences in emission pathways. Although these models use only simplified approximations of aerosol RF, this is the only aerosol related input they use as a basis for further economic analysis. Therefore, it is relevant to compare these RF projections within one aggregate aerosol category.
Figure 3 shows the aerosol forcing projections of the different models. The SCM projections in the left panel are the combined total of direct and indirect aerosol forcing. The right panel shows effective aerosol forcing (aerosol ERF) in which MAGICC6.3 is compared with ACCMIP. For both categories, the SCMs fall within the present day ACCMIP range. For total aerosol forcing, there is a wide spread in RF projections (0.50 W/m2 in 2010 and 2100 for RCP2.6) compared to CH4 or N2O and compared to the total forcing of 2.6 W/m2. The ACCMIP range shown in both panels of Fig. 3 is the aerosol ERF projection, and is strictly speaking not an exact comparison for the SCM RF projections. Yet both are used as a comparable input for modelling temperature response in the different models. Aerosol ERF as used in ACCMIP is also slightly different from EARF as calculated by MAGICC6.3 (see Supplementary Material). Also here the comparison is relevant as both concepts are used as inputs proportional to the global temperature response. The depicted (stylized) aerosol ERF range in ACCMIP is based on the uncertainty ranges in 2000 and 2100 for RCP8.5. Because of large similarities in aerosol precursor global emissions between the scenarios, ACCMIP RCP8.5 results were also used for RCP2.6, but with a larger assumed uncertainty range. For the 2100 projection, five expert models were used of which three reached values close to −0.3 W/m2 and two were distinct, possibly questionable, outliers (0.55 W/m2 and −0.76 W/m2). The aerosol ERF mean in 2100 as used in ACCMIP is −0.12 W/m2 and was derived by using multi-model averages for the total effect in 2000 and the change in ERF towards 2100. This relatively small value can be attributed to a general decline in anthropogenic aerosol emissions, but also to a change in the relative influence of specific aerosols. As sulphate determines most of the negative indirect forcing in ACCMIP, the decreasing sulphate emissions lead to a change in total indirect cloud forcing in 2100 to a value near zero or even positive in all ACCMIP models and both RCP scenarios. Much uncertainty remains surrounding ERF values attributable to specific aerosol types, leading to a wide range forcing projections (IPCC 2013; Shindell et al. 2013; Smith and Bond 2014). In ACCMIP, for example nitrate forcing is very uncertain and potentially quite significant to total aerosol forcing. Yet, the observation that negative forcing will strongly decline driven by reduction in specific aerosol emissions seems robust. Moreover, a recent study indicates that anthropogenic aerosols are likely to be a minor contributor to RF by the end of the century (Smith and Bond 2014).
With that in mind, the SCMs seem to have too large negative forcing projections (exceptions being MAGICC5.3, PAGE and MERGE_ETL which are relatively close to the ACCMIP mean), and could likely improve their aerosol forcing representations. Notably, MAGICC6.3 shows a very large negative forcing effect, because of strong indirect forcing. For MAGICC5.3, forcing levels are less negative, as in this model indirect aerosol forcing is mainly defined by sulphate emissions. When considering ERF values, the negative effect in MAGICC6.3 is even more profound. The right panel shows that the differences between MAGICC6.3 and the mean of the ACCMIP range are 0.65 W/m2 (RCP2.6) and 0.69 W/m2 (RCP8.5) in 2100. On average, ACCMIP models project less negative aerosol ERF than direct aerosol forcing in 2100 (the latter not shown here). This means that the combination of all indirect effects and the effect of uneven global dispersion is likely to lead to positive forcing in that year. For MAGICC this is not the case, since both aerosol indirect effects and uneven aerosol distribution add to negative forcing. Improvements for MAGICC could lie in attributing different indirect forcing factors to specific aerosols (see Supplementary Material for further explanation).
Representation of tropospheric ozone radiative forcing
Figure 4 shows the ozone (O3) forcing projections in the two MAGICC versions compared to the range from 6 ACCMIP models (the other SCMs do not explicitly report O3 forcing). Both the ACCMIP models as well as MAGICC use the same data on anthropogenic precursor emissions in the RCPs (CO, NOx, VOCs and CH4), although natural emissions vary between the models. Results are within the uncertainty range of ACCMIP, but show a relatively small difference between RCP2.6 and RCP8.5, particularly MAGICC6.3. Both MAGICC versions result in O3 forcing levels that are low in the RCP8.5.
ACCMIP range. Effects not included in MAGICC that may account for this include temperature feedbacks and, particularly in RCP8.5, enhanced stratospheric-tropospheric ozone exchange (Kawase et al. 2011; Lamarque et al. 2011). The fact that other SCMs do not include the forcing effects of O3 results in higher forcing levels in MAGICC and the expert models by approximately 0.1–0.5 W/m2 in 2100.
Representation all non-CO2 forcers
Figure 5 gives an overview of the combined forcing effect of all non-CO2 gases and aerosols, using only the forcing components included in each model. In general, most IAMs (except FUND in both scenarios and DICE and MERGE in RCP8.5) are within the ACCMIP expert model range. The ACCMIP range includes all relevant forcing effects, except indirect stratospheric water vapour from CH4 (see also Table 2). This is a positive forcing effect in a 0.1 W/m2 order of magnitude (Hansen et al. 2005). As most SCMs do not include land use albedo change, a negative forcing effect of similar size as indirect stratospheric water vapour, total non-CO2 RF from ACCMIP is a good comparison for most SCMs. Only MAGICC6.3 includes both effects, implying that MAGICC6.3 results should be considered slightly lower than what is shown, in order to have a more accurate comparison with ACCMIP.
As can be seen, MAGICC5.3 lies very well within the ACCMIP range for both scenarios. MAGICC6.3 is slightly low, particularly for RCP2.6 where it is just inside the range in 2100, especially when considering indirect CH4 effects. Reducing the negative indirect forcing from aerosols in MAGICC would lead to an overall projection that is more consistent with the ACCMIP mean. FUND shows a very low overall non-CO2 forcing for both scenarios. For FUND, similar to MAGICC6.3, strong negative aerosol forcing is also the main cause for the low projection. The exogenous, scenario-independent non-CO2 forcing time-series in DICE falls well within the RCP2.6 range, but is not suited for a baseline scenario such as RCP8.5. PAGE is within the ACCMIP model range, considering its own uncertainty range (shown with the vertical yellow bar). The high projection in RCP2.6 can be attributed to a combination of a relatively high forcing value for halogenated gases and an exogenous forcing factor of 0.13 W/m2 that compensates for missing components (see Table 2).
All SCMs show somewhat low projections for RCP8.5. Furthermore, PAGE, MERGE, and MERGE_ETL display relatively small differences in non-CO2 forcing between RCP2.6 and RCP8.5. This indicates that they are less sensitive to emission changes, which could lead to a bias towards higher projected mitigation policy costs.
Another important result is that differences in outcomes are largest in the mitigation scenario. The spread in total non-CO2 forcing in the RCP2.6 scenario is very large: 0.74 W/m2 compared to an overall forcing in the order of 2.6 W/m2. The outliers can be attributed to strong negative aerosol forcing (FUND) and a high exogenous forcing factor (PAGE). Much of this spread in model outcomes already exists in the base year (see Supplementary Material). In 2010, the spread between models in RCP2.6 is 0.36 W/m2 (range determined by MAGICC5.3 and FUND). The representation of non-CO2 in the SCMs seems to have important implications for determining the optimal mitigation strategy: a 2.6 W/m2 mitigation scenario (RCP2.6) could require CO2 forcing targets of only 1.8 W/m2 up to 2.5 W/m2 depending on the model (range determined by the outliers). This, in turn, has a very large effect on the resulting carbon budgets, which can vary between approximately 950 and 1400 MtCO2 given this range (the range is 1029 to 1177 MtCO2 when excluding the outliers PAGE and FUND) (see Supplementary Material). Obviously, this has large consequences for projected optimal mitigation strategies and policy costs.
Table 2 shows the forcing components as projected by the models in the two RCP scenarios for 2100 (only mean figures are presented for the SCMs. PAGE does have an uncertainty range for CO2 and total forcing levels). Below, the RF sum of all WMGHGs, aerosols and other forcers as well as the difference between the two scenarios are shown.
For both MAGICC model versions and FUND, the difference in total non-CO2 forcing levels between RCP2.6 and RCP8.5 is comparable to the difference between the mean values in ACCMIP, while for the other SCMs the difference is smaller. One of the reasons for this small difference is that the SCMs, with the exception of MAGICC, do not capture RF from O3. The larger difference in FUND can be attributed to taking into account the effects of stratospheric water vapour due to CH4. This compensates for excluding O3 forcing. By also including this effect, MAGICC6.3 slightly compensates for larger negative aerosol effects in RCP8.5.
The totals suggest that all SCMs have relatively high outcomes for the forcing of non-CO2 WMGHGs, but there is no basis for such a conclusion. For these parameters, ACCMIP results are based on RF in the RCPs, which was originally determined by an early MAGICC6 version (Van Vuuren et al. 2011a). Therefore, further analyses with expert models are needed. The forcing from WMGHGs is a combined effect of the different non-CO2 forcers: N2O, CH4 and halogenated gases (with a spread of 0.28–0.29 W/m2 for halogenated gases in both scenarios, shown in the Supplementary Material). Although these individual forcing effects differ considerably across models, the effect is largely cancelled out when only comparing total forcing from WMGHGs. Still, MERGE and PAGE have considerably larger non-CO2 WMGHG forcing values than the other SCMs in RCP2.6. At the same time, all models consistently show higher negative aerosol forcing levels than the ACCMIP mean. This might therefore offer an important area for improvement.
The Supplementary Material also provides an analysis of what causes differences between expert models and SCMs: either missing forcing components or differently modelled forcing effects. Although the causes for large deviations differ per model and scenario, it can be stated that in RCP2.6 most of the difference is explained by differently modelled components and that in RCP8.5 most of the difference is explained by missing components (notably O3 and indirect CH4).
Effect of non-CO2 climate system representation on projected forcing of short-lived climate forcers
To further assess the climate system representation of short-lived climate forcers, the SCMs have been run with prescribed emission pathways as used in the UNEP Integrated Assessment of Black Carbon and Tropospheric Ozone study (UNEP and WMO 2011) (see Fig. 1 and the Supplementary Material). The relevance of this experiment is twofold: 1) There are large aerosol and O3 related differences between models, which are more thoroughly analysed with these scenarios, and 2) The conclusions of the UNEP study are highly policy relevant. They indicate that a large short-term radiative forcing reduction might be possible by intensifying the mitigation of short-lived forcers. It is important to assess if this can also be concluded when using commonly used SCMs.
The mitigation scenario from the UNEP report describes a situation where CH4, BC, OC and O3 precursor emissions are strongly reduced. Since GISS-PUCCINI was the only expert model in the UNEP study that included all forcing effects, it is used here as the main comparison for the SCM model outcomes. The model took part in ACCMIP in combination with an ocean-coupled climate model as GISS-E2-R. ECHAM5_HAMMOZ, the second expert model used in the study, functions as a comparison for CH4, O3 and direct forcing effects. Note that the two model projections in the UNEP report cannot fully serve as a basis for validation of the SCMs, since individual expert models differ considerably in aerosol forcing estimates (See Fig. 3). Table 3 shows the RF difference between the reference and mitigation policy scenarios as projected by the models for 2030 (a comparison of the forcing profiles until 2030 is difficult as the models show considerable differences in present day forcing levels (See Supplement)). Interestingly, the SCMs project forcing responses of less than half the expert model mean value. For models other than MAGICC this is partly the result of not including O3 and BC. Particularly for MAGICC the result is remarkable, given the RCP results presented earlier with ACCMIP as a benchmark, although an earlier analysis with MAGICC5.3 already indicated a smaller response (Smith and Mizrahi 2013). Unlike many of the other ACCMIP models, GISS-E2-R diagnoses indirect RF attributed to specific aerosols, and produces a substantial positive cloud forcing for BC. In MAGICC this is in fact a negative effect, hence causing a clear difference between the models. The reason that this difference does not occur in the RCP experiments is that there the change in BC emissions is smaller. When including ERF values from MAGICC6.3 the difference is even larger, as the negative indirect aerosol forcing is stronger. A similar effect occurs when the same efficacies are applied to MAGICC5.3 results (not shown). Note that the differences between models are relatively small for the direct effects that have been modelled by ECHAM5_HAMMOZ. Different modelling of indirect forcing effects explains a much larger part of the differences. Note that the full uncertainty range of GISS-PUCCINI can be considered larger than the 0.05 W/m2 depicted here. The reason is that the uncertainty includes only the internal variability in the model’s meteorology, and not any uncertainty in physical processes that define RF of aerosol components. The latter has a large effect on projected cloud indirect effects (Boucher et al. 2013; Shindell et al. 2013), which is in line with the large projected uncertainty range for BC in Bond et al. (Bond et al. 2013).
The representation of the short-lived forcers in the other SCMs than MAGICC show an even smaller forcing difference between the two scenarios and, thus, compared to MAGICC and the GISS-PUCCINI results underestimate the effect of reducing emissions of short-lived forcers. The main reason for this is the omission of BC in determining climate effects (this effect is 0.31 W/m2 for GISS-PUCCINI and 0.25 W/m2 for MAGICC). To a lesser extent the same is true for the exclusion of O3 with projected differences of 0.19 W/m2 and 0.09 W/m2, respectively, and a projected difference of 0.1 W/m2 by ECHAM5-HAMMOZ.
In any case, the effect of reducing short-lived forcers as assessed in the UNEP report would be much smaller if done using the SCMs discussed here.