the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Better constrained climate sensitivity when accounting for dataset dependency on pattern effect estimates
Angshuman Modak
Thorsten Mauritsen
Abstract. Equilibrium climate sensitivity (ECS) constrained based on the instrumental record of the historical warming becomes coherent with other lines evidence when the dependence of radiative feedbacks on the pattern of surface temperature change (pattern effect) is incorporated. Pattern effect strength is usually estimated with atmosphereonly model simulations forced with observed historical seasurface temperature (SST) and seaice change, and constant preindustrial forcing. However, recent studies indicate that pattern effect estimates depend on the choice of SST boundary condition dataset, due to differences in the measurement sources and the techniques used to merge and construct them. Here, we systematically explore this dataset dependency by applying seven different observed SST datasets to the MPIESM1.2LR model covering 1871–2017. We find that the pattern effect ranges from 0.01 ± 0.09 Wm^{2 }K^{1} to 0.42 ± 0.10 Wm^{2 }K^{1} (standard error), whereby the commonly used AMIPII dataset produces by far the largest estimate. When accounting for the generally weaker pattern effect in MPIESM1.2LR compared to other models, as well as dataset dependency and intermodel spread, we obtain a combined pattern effect estimate of 0.30 Wm^{2 }K^{1} [0.14 to 0.74 Wm^{2 }K^{1}] (5–95 percentiles) and a resulting instrumental record ECS estimate of 3.1 K [1.7 to 9.2 K], which is slightly lower and better constrained than in previous studies.
Angshuman Modak and Thorsten Mauritsen
Status: final response (author comments only)

RC1: 'Comment on egusphere2022976', Anonymous Referee #1, 03 Nov 2022
 AC1: 'Reply on RC1', Angshuman Modak, 21 Mar 2023

RC2: 'Comment on egusphere2022976', Tim Andrews, 11 Nov 2022
Review of “Better constrained climate sensitivity when accounting for dataset dependency on pattern effect estimates” by Modak and Mauritsen.
This manuscript uses the MPIESM1.2LR model to investigate the dependence of the pattern effect on underlying SST datasets used to force AGCMs with. By forcing the AGCM with 8 different reconstructions of historical SST patterns they find a substantial spread in the diagnosed pattern effect. By combining these results with published results on the intermodel spread the authors produce a new constraint on ECS from historical observations of past climate changes.
I found the manuscript well written and presented, and applaud the authors for tackling the important question of SST dataset dependency in methods that quantify the pattern effect. I think this manuscript will make a really useful contribution to the literature.
While there are a quite a comments here, all ought to be surmountable and are intended to be constructive.
Major Comments:
1. Presentation of SST patterns: the authors present a useful analysis of the geographical patterns of SST trends across the different datasets, and show how they differ (e.g. Fig 3), but are unable to find a geographical region or principal explanation for the variation in feedback seen in their experiments (e.g. Fig 4 and Section 4.3). This is done by calculating the SST trend as a function of time (i.e. K /century). However I wonder if this is the most appropriate method to tackle the question being investigated. I think a more appropriate calculation (of more relevance to feedback and patterns effects) is the SST change per globalmean dT (analogous to how the feedback is calculated as radiative response per globalmean dT), rather than a trend in time. This normalised pattern has the additional advantage of removing any globalmean dT differences between the datasets (as shown in Figure 2).
Hence I recommend the authors repeat the analysis of Figure 3, Figure 5, Figure 6 and discussion in Section 4.3 but this time calculating the SST patterns per globalmean dT (ideally calculated like the feedback parameter, i.e. with linear regression, in this case regressing dT(lat,lon,t) against globalmean dT(t)). It might turn out to make little difference, in which that is fine – but it would be good to know and comment on if so. On the other hand, as it ought to relate better to the feedback and pattern effect it might improve the relationships in Fig 6. If so, I would recommend simply dropping the K/century trends and replacing all this with K(lat,lon)/ were <> denotes globalmean in this case.
2. Calculation of lambda_4xCO2: the authors quote the 4xCO2 lambda for MPIESM1.2LR as 1.48 + 0.03 Wm2 K1 from regression in 150yrs of abrupt4xCO2. However two other published papers (Andrews et al., 2022; https://doi.org/10.1029/2022JD036675) and Zelinka et al. (2020, GRL, updated to more models here https://zenodo.org/badge/latestdoi/259409949; see specifically https://github.com/mzelinka/cmip56_forcing_feedback_ecs/blob/master/CMIP6_ECS_ERF_fbks.txt) both report 1.39 Wm2 K1 for this model using similar linear regression on 150yrs of abrupt4xCO2. This difference of ~0.1 Wm^{2} K^{1} will feed into all estimates of the pattern effect, and is a significant % of the reported pattern effect, and could even change the sign in some cases. I wonder if the difference is because this manuscript calculates the 4xCO2 changes relative to the “mean of the last 500 years of piControl simulations”, whereas both Andrews et al. and Zelinka et al. use the corresponding section of piControl and account for control drift? The authors ought to check, and I would recommend using the corresponding section and control drift method. If this is the cause of the difference, then this sensitivity to methodological choices ought to be discussed and acknowledged as a large source of uncertainty and potential bias in the pattern effect estimate.
3. Discussion of the limitations/assumptions in the approach: when coming up with a combined estimate of the pattern effect, I think the manuscript might be making an assumption that isn’t explicitly discussed. Specifically, if I’ve understood correctly, using the mean estimate and distribution from the observedpiForcing simulations is implicitly assuming that not only are all the various SST reconstructions independent (as the manuscript acknowledges, but clearly isn’t true), but also that they are equally plausible reconstructions of the truth. We do not know which dataset is ‘best’, right? The real world could have looked like any or none of them. If the authors have any thoughts on this, and how it could be taken forward – it might be useful to explicitly discuss. Related to this, I hesitate at statements such as line 223: “ … AMIP II dataset is an outlier suggesting that earlier studies may have overestimated the pattern effect…”. If gives the impression (perhaps not intended) that the AMIP II results are not trustworthy, whereas we have no idea if AMIP II SSTs are any less likely than any other SST reconstruction. They might be fine. For example, just as a counter argument, there are other situations where simulations with AMIPII SSTs have been shown to perform better than simulations with HadISST. As Andrews et al. (2022) discuss: “…Zhou et al. (2021; https://doi.org/10.1029/2022JD036675) showed that TOA radiative fluxes simulated by CAM5.3 correlated better with CERES observations when forced with AMIP II SSTs rather than HadISST SSTs, suggesting the results from amippiForcing may be more reliable…”. I do not think any major changes are required to address this comment, maybe just a simple change of wording or explicit acknowledgement of this point in the manuscript ought to suffice.
4. Structural (model) dependence on SST dataset sensitivity: the authors acknowledge that their results may depend on the model used (MPIESM1.2), and I appreciate the effort in trying to combine the model spread and adjusting the mean from previous intercomparisons to account for this. However the authors should update the analysis to use numbers from larger model intercomparison of Andrews et al. (2022), rather than the Andrews et al. (2018) study. For amippiForcing the pattern effect across 14 models is 0.70 + 0.47 Wm2 K1 in Andrews et al. (2022), compared to 0.64 + 0.40 Wm2 K1 in Andrews et al. (2018). So the difference in the mean to ECHAM6.3 ought to be slightly larger than the authors used here, and the spread slightly larger too. But moreover, the method applied assumes that the ECHAM/MPIESM model is similarly different to other model in ALL the datasets as it is in the amippiForcing simulation. But we know from Andrews et al. (2022) that this is not so, the ECHAM and MPIESM pattern effects under hadSSTpiForcing are much further from the mean than under amippiForcing. For example, the pattern effect for ECHAM6.3 and MPIESM2.1LL is ~0.2 Wm^{2} K^{1} in hadSSTpiForcing, whereas the model mean is 0.48 Wm2 K1 (Andrews et al. 2022; Table 2). I’m not sure exactly what the solution is here, but it seems adjusting for the mean difference to ECHAM6.3 of just 0.1 Wm2 K1 based on Andrews et al. (2018) is insufficient. It ought to be larger based on the larger ensemble of Andrews et al. (2022) and larger again given the hadSSTpiForcing results where ECHAM6.3 pattern effect is further away from the rest of the models. At a minimum this potential structural dependence of the results on the ECHAM6.3/MPIESM model ought to be explicitly discussed.
Minor comments:
* Title: “Better constrained” does not particularly read well to me, how about “Improved constraints on..” ? But I’ll let the authors decide.
* Lines 4549: I think it would be appropriate to include Andrews and Webb (2018; JCLIM, https://doi.org/10.1175/JCLID170087.1) in the discussion of the mechanisms of the pattern effect.
* Lines 110: “1.48 + 0.03 Wm2 K1”, what is the level error being presented here and throughout, 595% or something?
* Line 116: “… account for land surface warming in the fixedSST simulation to calculate the forcing..” – the literature has various ways of doing this, it would be good to briefly clarify which approach was used.
* Lines 126: “… pattern effect ranges from 0.01 + 0.09 Wm2 K1…” – please clarify how the uncertainty is calculated here. Since it is difference between lambda_4xCO2 and lambda_piForcing have you added the errors in quadrature or something?
* Line 129: “… the dataset mean pattern effect is lower than both Andrews et al. (2018) and the values considered in IPCC AR6…” – this reads as if the value is outside the range. So it is not quite right that the value is lower than those “considered” by Andrews et al. and IPCC since it is within their uncertainty range.
* Line 200: “.. pattern effect estimate in MPI… averaged over all decades…” – I’m not exactly sure what this means, please clarify.
* Line 204: “bt” typo.
* Line 21011: This sentence explains how the mean combined pattern effect estimate is arrived at, but it doesn’t explain how the uncertainty is combined between the pattern effect dataset dependence in this manuscript and the multimodel spread, i.e. how is the 0.14 to 0.74 Wm2 K1 on line 210 arrived at?
* Appendix: I found the Table summarising the datasets and Figure A1 showing the 30yr moving window lambda useful, and would recommend integrating them into the main text. One option would be to replace Figure 7 in the main text with Fig A1, since I find this figure more useful and interesting. However I do appreciate this is somewhat personal preference, so I do not demand it and leave the authors to choose.
I have signed the review.
Tim Andrews.
Citation: https://doi.org/10.5194/egusphere2022976RC2  AC2: 'Reply on RC2', Angshuman Modak, 21 Mar 2023
Angshuman Modak and Thorsten Mauritsen
Angshuman Modak and Thorsten Mauritsen
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