the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 May 2025
The Atlantic Ocean's Decadal Variability in mid-Holocene Simulations using Shannon's Entropy
Abstract. Accurate simulation of mean climate and variability is crucial for numerical climate models. Traditional methods assess variability using two-dimensional standard deviation fields, like sea surface temperature (SST) and precipitation, to identify key regions. However, this approach can overlook large-scale patterns, such as ocean modes of variability, used in traditional climatology and oceanography to define climate variability. We propose a method incorporating large-scale climate patterns to evaluate and compare decadal variability in four coupled models (EC-Earth, GISS, iCESM, and CCSM-Toronto). Shannon’s Entropy compares the models’ sensitivity to different scenarios: pre-industrial period, mid-Holocene with default vegetation, and mid-Holocene with prescribed Green Sahara conditions. Results show contrasting model responses, with little consensus on the effects of Green Sahara vegetation and orbital forcing. Three models (EC-Earth, iCESM, and CCSM-Toronto) show reduced precipitation variability under Green Sahara conditions, but with differing SST responses. The GISS model shows minimal effects on variability. Additionally, reducing dust in the Green Sahara scenario significantly impacted EC-Earth’s model, increasing precipitation while decreasing SST variability. These findings highlight the diverse representations of climate variability across models and offer a new methodology for comprehensive model analysis.
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RC1: 'egusphere-2025-921 introduces an interesting new method, but could do better at explaining it and its novelty', Chris Brierley, 03 Jul 2025
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This piece of work applies a new methodology to look at the decadal variability to understand the response across an ensemble of idealised paleoclimate simulations. I do not see anything incorrect in the work. But I’m not sure that the current layout will result in many citations. This is because the manuscript jumps between trying to describe two things simultaneously: a new method and some scientific results. As the manuscript is submitted to GMD, I presume that the authors consider the new method to be the primary innovation and will address my comments and recommendations accordingly.
I see that there are three important facets of the methodology that are outside of the standard approach. Firstly, there is the fact that the EOFs are computed by looking across the whole ensemble – rather than separately within each single model. This is a nice touch and should be made clearer within the text (currently this is mentioned briefly on L123, but not stressed as key aspect of the methodology). I happen to have adopted this approach myself before (Chandler et al, 2024, https://doi.org/10.1175/JCLI-D-23-0089.1) to look at regional models. However, we were looking at the mean climate, not variability, and the primary modes were detected between the models. I was therefore surprised that your approach does not pick up any inter-model variations. I guess that this comes from the application of the decadal filtering, which is in effect removing the mean climate from the individual models. You ought to explain in more detail what kind of filter is being applied, and its implications. I suspect that you are using a band-pass filter, and that if you instead used a low-pass filter you find fundamentally different EOF patterns (more akin to the EPP, we describe in Chandler et al). The labelling of Fig 1 and 2 implies that they show EC-Earth’s EOF patterns – rather than full ensemble patterns. Only the PCs, directed graph and transition timeseries relate to the particular EC-Earth simulation.
Your second innovation is the introduction of the directed diagrams. I confess that I find these hard to interpret, but I can see that they are important. Can you please spend a bit more time describing them? Perhaps thinking of them in a reduced dimension set would help; say by using the 2 ENSO modes of Ren & Jin (2011, https://doi.org/10.1029/2010GL046031) at then you can place the phase along the x and y axes. Can you also create some possible directed graphs for a system in which all the PCs are truly independent, through building a simple statistical model ? These would then provide a suitable null-hypothesis and allow some statistical testing to be undertaken.
Your third innovation involves the use of Shannon’s entropy. You provide an equation for this and then describe some of its properties in the methods section. However, when showing the results, all the values are approximately 3. I didn’t get why that should be so. I guess it’s related to either the fact there are 3 EOFs or the fact that each PC is divided into 3 states, but I don’t know which and you didn’t explain.
I have 3 other substantive comments:
- Both the AMM and AZM/AEM are more often defined by SST indices, rather than through principal components. It would be informative to compare your new method against this style of definition.
- There is no comparison to observations contained within your analysis. Can you project the resultant EOFs onto a reanalysis dataset and look at the real world?
- You have chosen a case study (idealised green Sahara experiments during the mid-Holocene) that is not readily accessible to the general reader, and therefore requires a substantial amount of additional introduction and methods that get in the way of the methodological innovations you are trying to explain.
Other comments:
- You need to label each panel of a figure rather than collect them together (so Fig 1 has panel a-h, not just a-b).
- Can you make the system-state classification centre around the neutral state of the 3 modes? This is the most intuitive way of thinking of thinking about them, but I don’t see where that state is - presumably it is the most common mode in the directed-graph (but not necessarily).
Citation: https://doi.org/10.5194/egusphere-2025-921-RC1 -
CC1: 'Reply on RC1', Iuri Gorenstein, 04 Jul 2025
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Thank you for the detailed review, feedback and the valuable suggestions. Your review raises important points that will help improve the manuscript, and I’ll be discussing them with my co-authors to make the necessary adjustments and increments.
In the meantime, I’d like to clarify one specific point regarding the entropy values being close to 3. You are right, this is due to the structure of our phase space: with three defined phases (positive, neutral, and negative) for each of the three principal components, the system has 3³ = 27 possible states. The maximum entropy (defined by the manuscript's equation 1) of a discrete space such as this would result in ln(27) ≈ 3.296. Each simulation’s threshold is tuned to yield its maximum possible entropy, which naturally approaches ln(27); we’ll include this explanation in the revised version.
Best regards,
Iuri GorensteinCitation: https://doi.org/10.5194/egusphere-2025-921-CC1
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