the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Mar 2024
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
Abstract. The CICERO Simple Climate Model (CICERO-SCM) is a lightweight, semi-empirical model of global climate. Here we present a new open-source Python port of the model for use in climate assessment and research. The new version of CICERO-SCM has the same scientific logic and functionality as the original FORTRAN version but it is considerably more flexible and open source via Github. We describe the basic structure, improvements compared to the previous FORTRAN version, together with technical descriptions of the global thermal dynamics and carbon cycle components and the emissions module, before presenting a range of standard figures demonstrating its application. A new parameter calibration tool is demonstrated to make an example calibrated parameter set to span and fit a simple target specification. CICERO-SCM is fully open source and available through GitHub (https://github.com/ciceroOslo/ciceroscm).
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Status: open (until 02 May 2024)
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RC1: 'Comment on egusphere-2024-196', Anonymous Referee #1, 09 Apr 2024
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In this manuscript, the authors present a new version of the CICERO-SCM, an open-source simple climate model. The authors had two main objectives: (1) to describe and document the model and (2) to present some application of CICERO-SCM. The authors presented a thorough description of the model, its implementation, and a few applications using CICERO-SCM however, I think the power of CICERO-SCM could be better demonstrated by including results from future scenarios. With minor revisions, this manuscript will be a nice addition to the RCM literature.
General Comments/Questions
- Translating the model implementations between different languages is no easy task! It seems that, at times, the authors are undervaluing this effort. Implementing the model in Python greatly increases model accessibility and readability, even if the model run time increases a bit, CICERO-SCM is still operating at a fraction of the runtime of an ESM. As it is currently written, it is unclear if/what scientific changes were made between V1.1.1 and the previous Fortran implementation.
- Figures 2, A3, A4, have the y-axis start at 0, which for some variables doesn’t seem necessary, and the scale of the y-axis makes it difficult to make examine the differences between CICERO-SCM and the comparison data. One idea would be for the authors to have a free y-axis that is not always start a 0 but near the minimum.
- It looks like there were some formatting issues, such as inconsistent spacing between paragraphs (e.g., L26, L112, L225.) and several citations incorrectly formatted inline citations (L24, L63, L189). These sorts of things are easy to do and will be caught by the copy editor so minor in the grand scheme of things.
Specific Comments/Questions
L20: missing a space between eg and the citation
L45: excellent point!!
Figure 1: use subscripts in the chemical names
L96: “dictionary” is this a Python specific term?
Table 1: Sunvolc is this a boolean parameter? If is it set to 1 does that mean that inputs for sun and volcanic radiative forcing are both required? Or could a user provide only one?
L108: what is meant by tracer components here?
L120-123 : is a tad confusing, is the model only CO2-driven between 1750-1850? If so why? Or are there non-CO2 emissions during this period that are held constant? More details on which concentrations (the species and the time frame) are needed would be helpful here. Are the required concentrations user-defined?
L180 - 190: I am having a difficult time understanding equations 6-9 and I think it stems from some confusion related to f_fer and beta. What is the difference between f_fer and beta? Are they related in some way? Some more details here would be helpful. Is f_fer typically negative?
~L220: “ This implementation is appropriate for discrete input data only, where the emissions (and concentrations) are assumed constant throughout the year. For a timestep of less than one year…” what is the time step for CICERO-SCM? Can it run on a monthly time step? If it is running in with monthly inputs are all emission inputs needed to be monthly?
L244 : Equation 9, would it be possible to add a subscript to the parameters to indicate which parameters vary with the gas species?
L240: missing a space between “CH4is”
Section started at L220: Here in 2.1.3 CH4 - emissions to concentrations, the authors describe how CICERO-SCM supports three different methods for calculating tau oh. Which option does CICERO-SCM use by default? What is the motivation for supporting these three different dynamics? Is this feature common in other SCMs? It seems like this is an opportunity for the authors to highlight one of CICERO’s unique features.
Figure 3: where did the uncertainty ribbon/envelop come from? Is it from IPCC AR6 or is it from an ensemble of CICERO runs?
L476: “twelve sub-yearly timesteps” like monthly? The discussion here and then also in L220 is confusing me. It would be helpful to have a clear discussion somewhere in the manuscript that addresses CICERO’s time step addressing the following
- If/what inputs can be sub-annual? What happens to the inputs that are not sub-annual?
- If there are sub-annual inputs, what time step? Monthly? Daily?
- How does using sub-annual inputs affect outputs?
L 494: It would be helpful to have some more details about many new tunable parameters. Are there like or 5 or 10 parameters? Are these parameters listed somewhere?
L499: “automatic tests including regression test to make sure the results from the energy balance model”, is a bit unclear, so it is this regression calculating the difference between current model behavior and the previous model behavior? What sort of regression tests are good enough to pass the automatic testing?
L 510: The change in run time seems minimal to me, yes is unfortunate but adopting Python greatly improves the readability and accessibility!
Figure 7: would it be possible to do a more robust comparison between CICERO output? Either plotting a and b on the same figure? Would is be possible to compare CICERO V1.1.1 results with the CICERO results included in AR6 7.SM.4? That could provide more evidence that the results did not change very much.
L525: Where does the parameter distribution set defined in the json-file come from? Is it generated with the calibration tools included in the CICERO repository? Would running the calibration tools result in a new set of parameters? Would the calibration tool work on any tunable parameter?
L 532: the phrase “larger the calibration space” is somewhat unclear, does this refer to the uncertainty space of the parameters? The number of parameters being tuned? The number of variables being compared? The length of the time series being used in comparison data?
L 560: Just a suggestion but I think you could drop the word significantly here…
L 575: “inclusion of temperature feedbacks into the carbon uptake” is that for the ocean carbon cycle? Or the terrestrial carbon cycle?
Caption of Figure A1: missing a space in “2014and”
Figures A1, A3, A6: Why is HCFC-123 constant at 0 over the entire period of the run?
Figures A1-8: what is the plot number for? Is that the code used by the plotter functions to make the plots?
Citation: https://doi.org/10.5194/egusphere-2024-196-RC1
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