the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An overview of cloud-radiation denial experiments for the Energy Exascale Earth System Model version 1
Jian Lu
L. Ruby Leung
William K. M. Lau
Kyu-Myong Kim
Brian Medeiros
Brian J. Soden
Gabriel A. Vecchi
Bosong Zhang
Balwinder Singh
Abstract. The interaction of clouds and radiation is a key process within the climate system, and assessing the impacts of that interaction provides valuable insights into both the present day climate and future projections. Many modeling experiments have been designed over the years to probe the impact of the cloud radiative effect (CRE) on the climate, including those that seek to disrupt the mean CRE effect and those that only disrupt the covariance of the CRE with the circulation. Seven such experimental designs have been added into the U.S. DOE's Energy Exascale Earth System Model version 1 (E3SMv1). These experiments include both the first and second iterations of the Clouds On-Off Klimate Intercomparison Experiment (COOKIE) experimental design, as well as the cloud-locking method. This manuscript documents the code changes necessary for implement such experiments and also provides detailed instructions for how to run them. Analyses across experiment types provide valuable insights and confirm the findings of prior studies, including the role of cloud-radiative heating toward intensifying the monsoon, intensifying rain rates, and poleward expansion of the general circulation owing to cloud feedbacks.
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Bryce E. Harrop et al.
Status: open (until 01 Nov 2023)
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RC1: 'Comment on egusphere-2023-1555', Blaž Gasparini, 26 Sep 2023
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The manuscript of Harrop et al., 2023 has two goals:
- to document changes in E3SM model needed to perform several cloud-radiation denial experiments and provide instructions on how to run them
- describe and interpret results of these experiments with the aim of giving guidance on what experiments needed.
While the utility of (1) is limited to E3SM model users, (2) provides several useful insights on cloud-radiation denial experiments with a broader relevance and is therefore appropriate for GMD. The manuscript is well structured, provides several interesting findings, and can be accepted after the comments are addressed.
General comments:
- It would be great if the authors could provide a more systematic overview of what prescribing monthly mean radiative heating/CRE (Prescribed-Ht and Prescribed-CRE) methods can and what cannot do compared with Cloud-locking.
In particular, it would be interesting to get more information about convection in the Prescribed-Ht and Prescribed-CRE experiments. What causes the changes in the simulated rainfall distribution over tropical land? Why does the frequency of high rain rates decrease? How does this compare to cloud locking?
More discussion on such points would provide a lot of valuable information for researchers to decide which cloud decorrelation method to use in future studies. - Could the authors briefly comment on why the manuscript focuses on summer and winter averages, and not annual averages?
Specific comments:
Line 190 – 198, Page 9-10:
While I believe that the details of the SST prescription do not substantially change the surface temperature response, I am not sure that the plot really shows this. Qualitatively, the pattern is indeed similar. But without repeating the same experiment with E3SM, it would be hard to make a good statement. My suggestion is to either add such an E3SM experiment or to remove Figure 4 from the manuscript.Page 10:
How does surface in “surface locking” compare to ocean-covered areas? In one case, the surface fluxes are prescribed, while in the other case, sea surface temperatures are prescribed. Why did you decide to prescribe the surface energy budget and not directly temperature?
Are substantial surface temperature anomalies a result of your relaxation of surface fluxes, that was in my understanding used to avoid numerical instability?
Also, how is the surface locking method of Lau et al. 2019 that is used in this work different from the prescribed land surface temperature method by Ackerley and Dommenget, 2016 (10.5194/gmd-9-2077-2016)?
Would “surface temperature locking” be a more appropriate method to use when clouds are set to be invisible for radiation?Page 12:
Figure 5 makes a good argument for the use of Clouds-off ATM instead of Clouds-off LW, although is referenced only in 1 paragraph in the text. It may be interesting to add Surface-locking results in it. Are the anomalies further improved in Surface-locking? If not, that would give another reason for arguing that the surface-locking method is not worth the effort.Page 14, Cloud-locking:
Could storing control simulation variables less frequently, e.g., every third radiation time step, along with some interpolation for model time steps between time steps with input data, be an alternative way to reduce the storage required by cloud locking?Page 17:
Figure 8 is only mentioned in one paragraph of the text. It could probably be referenced a few more times.Page 16, line 356-360:
I guess this is done in the same way that SST is considered in AMIP simulations? (interpolation of monthly means to intermediate time steps). Mentioning this may help in understanding the implementation of Prescribed-RadHt and Prescribed-CRE.Page 18, line 381:
Why is radiative heating prescribed only within the troposphere? This seems to make the implementation a bit more complex. Would the results be substantially different if monthly averaged heating were prescribed at all model levels?Page 24, lines 499-511
More reference to Figures 13 and 14 may help get the message across. The BSISO explanation is very detailed and could be left for a follow-up publication.Page 28, lines 584-594:
Are prescribed-RadHt and prescribed-CRE really good enough to study the role of CRE on circulation?
Could you, based on your simulations, make more detailed statements/suggestions/guidelines about which method is most appropriate for a particular scientific question?Conclusions:
Your work points at Clouds-off ATM as the clear winner of the COOKIE-style experiments. If so, this should be stated even more clearly in the conclusions.Data and code:
In which E3SM branch on github can the code be found?
Could you add a branch-specific readme file with some information about the changes made (e.g. short summary, with a link to this manuscript), link to the manuscript(s) where the specific code was used?Best regards,
Blaž Gasparini with comments from other members of the Climate Dynamics and Modeling team at the University of Vienna
Citation: https://doi.org/10.5194/egusphere-2023-1555-RC1
Bryce E. Harrop et al.
Model code and software
E3SM fork Bryce E. Harrop https://github.com/beharrop/E3SM
Bryce E. Harrop et al.
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