The draft manuscript titled “Predicting Sea Surface Temperatures with Coupled Reservoir Computers” is an excellent effort in using coupled reservoir computers for predicting global sea surface temperatures. The manuscript may be accepted after minor revision after the following comments have been addressed:

1. Line 125 says that the actual values of SST were used for training. Can the authors comment on why normalization was not used as it has been shown to be necessary to train machine learning models?
2. The authors train on a daily SST dataset, now oceans are known to operate on long temporal scales having a memory of at least a month. Can the authors comment on the utility of these forecasts? For example, Nino3, or Nino3.4 are considered based on monthly datasets precisely for the reason that the oceanic processes are slow.
3. Was any hyperparameter tuning performed?
4. From figure 4, it can be seen that the model performs well where there is an established trend. For example, Fig 4a-f have a clear trend and the model is performing very well in all of them. There are some deviations in Fig 4g whereas Fig 4h is showing good performance. The intent of using such as coupled reservoir computer is to simulate the chaoticity of the system, whereas out of the 8 subplots in Figure 4, 6 have a clear linear trend where the model performs well, whereas in Fig 4g where there are some deviations, the model is not performing good relative to the previous subplots. Fig 4h is satisfactory. Can the authors maybe provide some more examples or describe these features from the results?

A domain-decomposed set of coupled reservoirs

that share the same set of hyperparameters are trained over multiple

years of reanalysis daily sea surface temperature (SST) data and used to

predict SST at lead times of between one day and six weeks.

The application of RC to the specific problem the authors consider,

the analysis of the results and the write-up, all seem to be of a

somewhat preliminary nature. As such it is not clear what a potential

reader is expected to take away from this article. This issue needs to

be addressed in a substantive fashion to be further considered for

publication. A few other issues are noted below.

The authors state: To observe the effect of

the randomly selected input and middle weights on the performance of

the RCs, the model was ran 15 times all with the same metaparameters

as described in Table 1, to collect data for the examination of the

error.

Please state how the ensemble of predictions that use random

variations of input and middle weights was analyzed. Meaning, what

error is being shown is say figures 4-13

In the context of Figs. 6 and 7, while the authors chose to

spinup/warmup the reservoirs over a period of a week, the results

suggest that a longer spinup of the reservoirs (of about 4 weeks) is

called for. Please comment on the a priori choice of one week and the

longer timescale that is required as indicated by the results. How

does the longer timescale vary with changes in the hyperparameters?

What is the relevance of the leakage parameter in the context of a

leaky reservoir update in this context? (and which the authors do not consider)

Please comment on possible reasons for 4-5 day timescale seen in

Figure 8, particularly since the data itself, e.g., as seen in fig. 4, seems

to display variations on a broader range of timescales.

Given the results presented, it may seem  somewhat of an

over-statement when the authors state that "The results are

demonstrated to replicate the actual dynamics of the system over a

forecasting period of several weeks."