Review of AR6 updates to RF by GHGs and aerosols lowers the probability of accomplishing the Paris Agreement compared to AR5 formulations by Farago et al.

Summary

The authors apply an existing multiple linear regression model to decompose the relative contribution of internal and external forcing factors to global mean surface temperature (GMST) change over the 20th and 21st century. They compare the influence of different assumptions about effective radiative forcing from various constituents (but primarily tropospheric aerosols) between two generations of the CMIP protocol (AR5, referred to as Baseline, and AR6). The authors show that their MLR model reproduces the majority of features of the GMST response, and the effective climate sensitivity, simulated by a range of previous simplified and comprehensive modelling efforts. They use this information to provide probabilistic estimates of GMST remaining below Paris targets (1.5C and 2.0C).

Major comments

The paper is well researched, and well written; the figures are clear and communicate the main findings of the analysis. I believe that the conclusions reached are appropriate based on the methods and evidence presented. However, I cannot recommend publication of this manuscript for the following reasons.

First, this reviewer found that the authors have not adequately communicated what are the primary novel contributions of the research. On the contrary, in virtually all cases in the results sections, the authors highlight that their results are consistent with previous studies. This holds true for previous studies using simplified or intermediate-complexity models, and comprehensive modelling like CMIP6. This is a very well-studied field over the past decade, and the authors must articulate clearly how this research advances the discipline beyond what the myriad of previous studies has done.

Second, while the authors must be commended on their attention to detail and the depth of the research undertaken, the manuscript is much too long considering the paucity of new results being presented. At times it felt like a PhD thesis; for example, Section 2.3 provides many textbook-level definitions of ERF for various atmospheric constituents, and Section 2.5 describes every assumption for the inputs of the regression model in intricate detail. This raised the question of who exactly is the target audience for this work? Since the aim is to publish in ESD, there should be an assumption that interested readers will have sufficient background knowledge in climate change science to trust that emissions inventories, sources of natural/internal climate variability etc. are properly referenced and incorporated without the need for such a detailed assessment here. Potentially important and relevant previous literature was also excluded; for example, see: https://iopscience.iop.org/article/10.1088/1748-9326/6/4/044022/meta.

Third, the linear modelling framework itself does not appear to be a new contribution (e.g., McBride et al. 2021). Therefore, it is somewhat surprising that potentially important limitations of the linear model approach are not investigated or advanced in this research. For example, on L513 the authors describe needing to include a third constraint (consistency with recent observed temperature trends) in order to yield solutions that match the GMST time series over the recent past. Given the importance of ERF_CO2 and ERF_AER on the GMST predictions from the EM-GC model, this suggests a probable role for nonlinear interactions between aerosol and CO2 forcing that the current model cannot capture. It would have been interesting to see this commented on, if not addressed, in the research.

Fourth, one of the major findings of the research highlighted by the authors is the apparent increase in warming rate under the AR6 assumptions compared to pre-AR6 (baseline). However, on L502 the authors state that the 6\% higher climate sensitivity in AR6 comes from applying the published formula for ERF_CO2 from AR6 that is larger than the pre-AR6 formula provided by Myhre et al. (1998). Therefore, it appears that the increased warming rate is "baked-in" to the EM-GC model, rather than an emergent property, making the findings of more warming and a lower probability of remaining below the Paris targets largely unsurprising. 

Minor comments:

-L23: The overlap of the baseline and AR6 confidence intervals suggests that the statistical evidence for an increase in the mean is rather weak.

-L119 and L122: do the authors mean to say adopted, rather than adapted?

-L152: Can the authors provide the proportions for the different effects in this attribution? Are CO2 concentrations the dominant effect?

-L205: Can the authors comment on why the ERF_AER value changes by so much (15% larger) when the time period is shortened by only 5 years (ending in 2014)?

-L235: This paragraph is very unclear. What is the single best estimate ERF_AER time series? What portion of the difference is highlighted?

-L690: Why did the authors elect to not examine a business-as-usual/ high emission scenario like SSP5-8.5?

-L855: This conclusion is challenging, because the agreement between EM-GC outputs and the observed GMST timeseries is explicitly built in to the EM-GC model, whereas for the majority of CMIP6 models they are freely running through the 20th Century. Whether this reduces the value of those simulations is a matter for debate; perhaps a more nuanced view is that it affects the types of questions that one should ask of the CMIP-class models.

In this manuscript, the authors use the Empirical Model of Global Change (EM-GC) to explore how updates to effective radiative forcing, as reported in the IPCC Sixth Assessment Report (AR6), influence the following climate metrics: effective climate sensitivity, the rate of attributable anthropogenic warming, and projections of future warming. The manuscript concludes with an assessment of how these updates affect the likelihood of meeting the climate policy goals set by the 2015 Paris Agreement.

I was genuinely excited to see the EM-GC used in this context, and I believe this study has the potential to make a meaningful contribution to the literature. The research question is both timely and important, and I encourage the authors to continue developing this work. However, in its current form, the manuscript faces some structural challenges and clarity issues that make it difficult to fully appreciate the significance of the results. I would strongly encourage the authors to revise and resubmit, as I believe that with improvements, this paper could become a valuable addition to the field.

While I did not feel that a detailed, line-by-line review was appropriate at this stage, I would like to share a few broader comments that I hope will be helpful in guiding the revision.

At times, the manuscript reads like a blend of two distinct papers — part literature review, part research article. I recognize and appreciate the substantial effort the authors have put into the background material, and the breadth of the literature covered is impressive. That said, I feel that the extensive background somewhat overshadows the more novel and exciting aspects of the authors' analysis. I would recommend streamlining the background, particularly in the methods and data sections, and relying more heavily on citations to established work, which would allow the new contributions to stand out more clearly. 

I think referencing prior EM-GC literature more explicitly could help improve clarity in the model description. For example, Section 2.4 closely resembles McBride et al. (2021), and equations (1)–(4) appear to be the same as those in that manuscript. Could the authors clarify whether these equations are indeed unchanged, or if they have been modified in this study? Providing that clarification will help situate the current work within the existing EM-GC framework and highlight any new developments more effectively.

Another question that arose when reading this manuscript was how do the authors deal with the issue with the ERF of aerosols in the AR6 analysis (see Zelinka 2023). Did the authors account for or correct for this basis their analysis? A comment of if/how the authors address this should be included in the manuscript or discussed as a potential limitation of the study. 

I appreciate the authors’ effort and I am confident that with thoughtful revisions, this work can make a valuable impact. I look forward to seeing a future version of this manuscript and the contributions it will bring to our field.