the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Mar 2024
Imputation of missing IPCC AR6 data on land carbon sequestration
Abstract. The AR6 Scenario Database is a vital repository of climate change mitigation pathways used in the latest IPCC assessment cycle. In its current version, several scenarios in the database lack information about the level of gross carbon removal on land, as net and gross removals on land are not always separated and consistently reported across models. This makes scenario analyses focusing on carbon removals challenging. We test and compare the performance of different regression models to impute missing data on land carbon sequestration from available data on net CO2 emissions in agriculture, forestry, and other land use. We find that a gradient boosting regression performs best among the tested regression models and provide a publicly available imputation dataset [https://doi.org/10.5281/zenodo.10696654] (Prütz et al., 2024) on carbon removal on land for 404 incomplete scenarios in the AR6 Scenario Database. We discuss the limitations of our approach, its use cases, and how this approach compares to other recent AR6 data re-analyses.
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RC1: 'Comment on essd-2024-68', Thomas Bossy, 08 Apr 2024
Review of Prütz et al:
The manuscript presents a new dataset that imputes carbon removal on land for 404 incomplete scenarios in the AR6 Scenario Database. The authors identify a gap in the existing literature for overcoming missing data in the AR6 Scenario Database and propose a solution to fill it. Although their method is imperfect, the dataset is clearly presented and easy to use. Therefore, it deserves to be published.
General comments on the dataset:
The dataset uses the same model and scenario names as in the AR6 database, making it very easy to use as a supplement for someone willing to fill in the missing data. However, as the authors themselves acknowledge, the Gidden et al. re-analysis is "considered more useful in terms of consistency and accuracy". Therefore, I see the interest of their dataset mainly for someone who needs land carbon sequestration for as many scenarios as possible. In that case, why not provide a dataset with all scenarios, and not just those where 'Carbon sequestration|Land use' is missing? This would make it even more convenient for everyone to work with all scenarios in the same file, and it would provide an adjusted version that also corrects inconsistencies found in the original database, with net removals being greater than gross removals.
Since the Gidden et al. reanalysis seems to be of better quality, would it be possible to add a paragraph discussing the feasibility/relevance (or not) of applying the same reanalysis to the imputed scenarios?
Specific comments:
l35. “While the AR6 re-analysis dataset by Gidden et al. manages to resolve several of the data issues linked to carbon removal on land, it still combines gross and net CO2 emissions on land in their land-based CDR variable, resulting in both positive and negative CDR values, which conflicts with the concept and clean definition of gross CDR.”
Is it related to the fact that it is already inconsistent in the original Database or is it due to the re-analysis itself?
Figure 1.
Would it be possible to add one sentence explaining why do re-analysis look so different than the other 2 curves? In particular, the mismatch between the points in 2020.
l71-74.
After reading carefully, I think I understood how the metrics were calculated. However, it was quite fuzzy at first. Would it be possible to add an illustrative figure showing what the four metrics correspond to?
l107-108. “Figure 3 suggests some variance in performance across these categories – for C8 scenarios, the drop in resemblance of the actual variable is most visible”
Isn’t it because the independent variable is mostly null or near zero for C8 (and C7) scenarios that the prediction is bad?
l120-121. “our imputed dataset does not account for perceived land sequestration related data issues in the AR6 Scenario Database beyond data availability”
I find the phrasing unclear.
Citation: https://doi.org/10.5194/essd-2024-68-RC1 -
AC1: 'Reply on RC1', Ruben Prütz, 22 Apr 2024
Thanks a lot for taking the time to provide these thoughtful and constructive comments — they are much appreciated! Below, we respond to the general and specific comments point by point.
General comments:
Regarding a merged dataset of imputed and original scenarios:
We agree that our imputation dataset is especially useful for researchers who aim to include the largest possible set of scenarios in their analyses. We also agree that it would be very convenient for researchers to have all scenarios (original and imputed) together to keep the required data preprocessing as simple as possible. However, we are a bit reluctant to simply add the original scenarios to our dataset as this would simply be copy pasted from the AR6 Scenario Database (Byers et al. 2022), which is publicly accessible, but respective institutions and modeling teams hold the copyright. Nevertheless, we are eager to make our imputation dataset as easily accessible and usable as possible. Therefore, we plan to add a script to our revised manuscript that allows researchers to easily merge our imputation dataset with the AR6 Scenario Database to get the largest set of scenarios.
Regarding the reproducibility of the approach by Gidden et al.:
To our understanding, the OSCAR-based approach by Gidden et al. requires more comprehensive land use change data per scenario (compare Gidden et al. 2023) and is therefore restricted to 914 scenarios, which meet the data requirements. Our alternative approach does not have these requirements and can, therefore, be applied to a larger set of scenarios (n=783+404). Beyond the number of imputed scenarios, our approach does not allow for both positive and negative CDR values to be closely aligned with a clean conceptual definition of gross CDR - this differs from the OSCAR-based approach, as shown in Figure 1. We have already flagged this in the current version of the manuscript but aim to make it even clearer as part of the revisions.
Specific comments:
Regarding the positive and negative CDR values in Gidden et al.:
As this phenomenon is only shown by some models and scenarios in the reanalysis by Gidden et al., we suspect that this is at least partly driven by the properties of the original database. However, from the information at hand, we cannot say this with certainty. Gidden et al. would be better equipped to explain the underlying dynamics of the OSCAR model that might explain the conceptually unintuitive coexistence of both positive and negative “gross” CDR values.
Regarding Figure 1:
The mismatch in points in 2020 is due to different emission baselines, which have or have not been aligned across scenarios. This is partly already described in the manuscript (see lines 124, 35-42). We will try to make this clearer as part of the revisions.
Regarding the evaluation metrics:
Thanks for sharing this reflection. We will work on this and either provide a table or illustration to highlight what the evaluation metrics correspond to.
Regarding figure 3:
Generally, our regression approach seems to also reasonably impute values based on near zero net-negative AFOLU CO2 emissions. However, we agree that the very low levels of net-negative AFOLU in C7 and especially C8 may at least partly explain the drop in prediction performance, while emphasising that our prediction is still substantially more accurate in scale and shape resemblance than the formerly used net-negative AFOLU proxy. We will add a note on this to reflect on it in the revised manuscript.
Regarding the unclear paragraph:
L120-121 means to highlight that our approach cannot resolve underlying inconsistencies in the original dataset and mainly addresses the issue of lacking data availability. We will make sure to rephrase this as part of the revisions to make this clear and to avoid ambiguity.
Citation: https://doi.org/10.5194/essd-2024-68-AC1
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AC1: 'Reply on RC1', Ruben Prütz, 22 Apr 2024
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RC2: 'Comment on essd-2024-68', Anonymous Referee #2, 24 Jun 2024
This paper uses statistical methods to fill in missing data on land carbon sequestration in 404 scenarios of the AR6 Scenario Database. The purpose seems useful, and the concept is generally clear. However, the study remains rather superficial in several instances, for example regarding the testing of the estimated regression models, describing the methods, and placing the study within the existing scientific literature.
Major comments:
- The gradient boosting methods is suggested to be the best method. However, I miss a rigorous testing of this result, especially given that this method does not perform best in all of the analyzed metrics. I strongly advocate for using bootstrapping to estimate the variability in the analyzed metrics for the different regression methods, particularly to test whether the gradient boosting method is not just by chance the best method. Also, the selection of the best method should be clearly supported by numbers rather than by visual inspection.
- The methodological description is currently too vague to be reproducible. A few questions that came to my mind: How were the data selected/pooled? Were all scenarios and all years pooled or just scenarios or years? Are all scenarios/values global averages or do they provide spatially explicit estimates? In case of the latter, how was this considered? How was the dataset split into training and testing sets? How was the superior performance (line 67) of the four selected regression models tested? Further details should be added, such that other scientists would be able to repeat the analysis.
I also believe that a better justification for correlating gross removals with net fluxes is needed, as the two might not necessarily be correlated, for instance in cases were the temporal variability of net fluxes is dominated by emissions (and not removals).
- I feel like the introduction could and should provide more background why this study is relevant. For instance, some use cases of CDR estimates from the AR6 Scenario Database could be cited. I believe that this would also increase the visibility of this study.
Related to this, the paper mentions that use cases for the approach would be presented, but I did not really find them in the manuscript (except for one rather generic sentence in the discussion). Being more specific here would be useful.
Specific comments:
- Title: I would suggest checking if the word “imputation” is the best term in the context of this study. I am no native speaker, and when checking the translation of imputation I found that it can have several meanings, which might cause misunderstandings. Maybe “completion”, “amendment” or something similar might be clearer. Also I think that changing the title to “ [Imputation/amendment] of missing data on land carbon sequestration in the AR6 scenario database” would be a more adequate title
- I suggest being more specific in the abstract on what “gross carbon removal on land” means. Does this only refer to anthropogenic influences or does it also include the natural land sink? I suggest to check throughout the manuscript to make this clear (e.g. in line 28 as well)
- Line 12: I read this sentences several times, but it did not really become clear what this means (“net and gross removals are not separated” and “consistently reported”). I suggest being more specific here.
- Line 13: I would suggest to better link this sentence to the previous ones by making it clear that carbon removal estimates are essentially the gross anthropogenic removals.
- Line 14: Is “net CO2 emissions” the right term? It sounds surprising if one wants to deduce removals from emissions. Also, the net flux is not necessarily an emission but could also be a removal. What about “net CO2 fluxes”?
- Abstract: I’d suggest to add a sentence at the end of the abstract about potential implications or use cases of the study
- Line 29: I do not really understand the difference between the “net negative CO2 emissions in AFOLU” and the variable “Carbon Sequestration/Land Use”. As this differentiation is necessary to understand the study, I think this needs to be better explained.
- Line 33: I think that a bit more information about the filtering and exclusion of scenarios in Prütz et al. (2023) would be useful here.
- Line 36: “issues linked to carbon removals on land”: I suggest giving a few examples to make this clearer (this also relates to my major comment 3 above)
- Lines 40-41: What exactly is meant by “net-negative AFOLU CO2 proxy”? Does this refer to what is mentioned in lines 30-31? I suggest to use consistent terminology throughout the document to avoid misunderstandings.
- Lines 40-42: What are the implications of this discrepancy?
- Line 68: What is grid search?
- Line 77-78: Why is this conceptually false? Shouldn’t a removal be negative? Or is there another reason?
- Line 94-95: Where can this consistent behavior be seen? And how was this tested?
- Lines 95-96: This could be a statistical error. Bootstrapping may help to test this.
- Line 107-108: Any ideas why it does not work so good for the C8 scenario? And what exactly is this scenario “C8”?
- Line 108: Do the regression-based estimate fill the full uncertainty range just like the original estimates? Or I their uncertainty larger or smaller than the original estimates?
- Line 124: What does this mean and to what does this sentence refer to? I cannot relate it to Figure 1 and also not to the results shown in the paper.
- Lines 129 and 130: I would suggest adding the exact numbers of scenarios (as done in other places as well)
- Line 133: I think it would be good to shortly mention the issues again to help readers remind them.
- Line 136: Where is the reduction in absolute error shown?
- Line 140: Add full reference for Gidden et al.
- Line 140-141: What does this imply? Should one skip the scenarios that do not have estimates of gross carbon sequestration in such cases?
- Figure 1: What are the categories? A description of them is missing. What exactly is meant by “conservative proxy”?
- Figure 2: I strongly suggest to not put the decision tree, random forest, and k-nearest neighbor more transparent than gradient boosting, as this might cause biased interpretations of the results (even if the black line is not the best one it might be considered the best one, just because it is less transparent). I also wonder whether the error estimates could additionally be shown as percentages of the mean flux estimates. Right now, it is difficult to say how large the errors are without knowing the mean flux estimates. Also, is this figure just for one scenario or were scenarios somehow pooled? I suggest adding that in the caption.
Citation: https://doi.org/10.5194/essd-2024-68-RC2 -
AC2: 'Reply on RC2', Ruben Prütz, 27 Jun 2024
We want to thank anonymous referee #2 for taking the time to review our manuscript. Below, we respond to the major and specific comments point by point and outline how we plan to revise our manuscript based on the feedback provided.
Major comments:
Response to major comment 1: We appreciate the suggestion to use bootstrapping to estimate the variability in performance for different subsamples of our training data. We are currently implementing bootstrapping into our analysis and plan to include this in the revised manuscript that we are compiling. We want to highlight that the selection of the best model is supported by the numbers underlying the performance evaluation plot (Figure 2). Further, we are using Grid Search and cross-validation to identify the optimal hyper-parameter combination of the tested regression models.
Response to major comment 2: In terms of reproducibility, we want to emphasise that both the input data as well as our analysis code is made available, allowing other researchers to fully trace and re-run our analysis. However, we appreciate the feedback on the level of detail in the written method section and plan to extent this description so the method can be more easily understood without having to consult the code.
Regarding the sub-questions under comment 2: As described in lines 57-60 in our manuscript, we selected all vetted scenarios from the AR6 Scenario Database for which both the independent and the dependent variable are available (n=783). Among the vetted scenarios (n=1202) in the AR6 Scenario Database, 15 scenarios from REMIND 1.6 do not report AFOLU CO2 emissions, which is why we could not include these scenarios in our imputation. For each of these 783 scenarios, we select all years between 2020-2100 in 10-year steps. The data is directly downloaded from the AR6 Scenario Database and limited to the globally aggregated data - spatially explicit (gridded) scenario data is not part of the AR6 scenario database. The scenario data of the 783 scenarios was randomly split into train and test sets using the scikit-learn library in Python. Initially, we also included linear regression and multilayer perceptron regression but excluded these models as they performed substantially worse compared to the other four models based on the same evaluation metrics presented in Figure 2. This is also described in lines 65-76.
Response to major comment 3: Regarding the net fluxes as predictor for our model - we agree that net emission fluxes are not ideal due to the mentioned variability in the variable driven by other factors than removals. However, since gross emissions are not available, we see the net emission fluxes as the best available option. We plan to address this valid comment in the discussion section by better reflecting on the implications of our imputation, which is based on statistical patterns rather than physical relationships. We are happy to highlight a few use cases of CDR estimates, which are based on the AR6 Scenario Database, to highlight its relevance and the contribution of our imputation to such analyses. The use cases of our dataset, as well as its advantages and disadvantages compared to other approaches, are already highlighted in lines 130-141.
Specific comments:
Title: We feel that imputation as a term works well in the context of our regression-based data imputation. However, we appreciate the suggestion to restructure the title for better readability and propose: “Imputation of missing land carbon sequestration data in the AR6 Scenario Database”.
Comment on gross carbon removal: We appreciate the feedback and will check the manuscript for consistency concerning the use of this term. Generally, we mean anthropogenic removal only and exclude natural fluxes. However, it is not always clear how consistently this is done across scenarios, as also described in our manuscript (see abstract – line 12).
Line 12: The mentioned sentence highlights that there is currently no consistency across scenarios in AR6 in terms of how much CO2 is stored in land sinks. While some models explicitly distinguish between gross removal (CDR via afforestation and soil carbon sequestration) and net removal (net negative emissions in the AFOLU sector), other models only report the latter. There is also a spread in baselines across gross removal across scenarios, which suggests inconsistent definitions.
Line 13: We are happy to slightly rephrase the abstract to address the comments concerning lines 12-14.
Line 14: “Net emissions” or “Net CO2 emissions” are well-established and commonly used terms. Net emissions are simply the sum of gross positive emissions and gross negative emissions. Indeed, “net emissions” can be negative if removals from the atmosphere exceed emissions to the atmosphere. “Net CO2 flux” would work as well.
Comment on use cases in the abstract: Thanks for this feedback. We will try to integrate it.
Line 29: We appreciate this feedback. The understanding of this distinction is indeed crucial for understanding our study. We will add to our existing description to make the distinction between these two variables from the AR6 database more clear. Simply put, “net negative CO2 emissions in AFOLU” refer to the net removals on land while “Carbon Sequestration|Land Use” refers to the gross removals (CDR) on land.
Line 33: In Prütz et al. (2023), an additional vetting process is applied based on a set of criteria which leads to scenario exclusion if scenarios do not meet the defined criteria. We will make sure to briefly elaborate on this in the manuscript.
Line 36: This sentence refers to the previously described issues of: lacking data availability, inconsistent baselines for land sinks, and inconsistent definitions of removals on land or the distinction between net and gross. We will make sure to better link this to previous sections in the manuscript.
Line 40-41: “Net-negative AFOLU CO2 proxy” means that the negative values in the net variable for AFOLU CO2 emissions of the AR6 database are used as substitute variable, i.e., proxy for missing data on land carbon sequestration (CDR via afforestation or soil carbon sequestration). We will double check whether consistency in terminology use can be further improved.
Line 40-42: The discrepancy highlights the caveats of the approaches to resemble carbon sequestration (CDR via afforestation and soil carbon sequestration): either large deviation from the actual values in the AR6 database or due to conflicts with the concept and clean definition of gross CDR. The implications are described in detail in lines 30-40 and later also in lines 103-105 and lines 127-136.
Line 68: Grid Search is a common machine learning hyperparameter tuning technique to find the optimal combination of model hyperparameters – in our case the setting options of our four regression models.
Line 77-78: Thank you for raising this clarification question. Removals are reported as positive values in the AR6 Scenario Database. Therefore, predicted negative values would mean emissions. We realize that this is not obvious to readers and will add a brief explanation to our manuscript.
Line 94-95: As part of our revisions and the integration of bootstrapping, we will highlight how the different models differ in terms of their performance and the variability of the performance metrics used to evaluate the models’ performances. This will be visible in the updated Figure 2.
Line 95-96: Thanks again, we will consider bootstrapping.
Line 107-108: C8 is one of the eight scenario categories used by the IPCC in AR6 to group scenarios based on their warming outcome. This category includes a total of 29 scenarios that show warming levels above 4°C (>4°C peak warming with ≥50% chance). It is the category with the lowest number of scenarios which may partly explain why the performance is lower compared to the other scenarios.
Line 108: I am trying to understand whether I fully understand this question. We use regression models and train the models based on available data to predict missing data. We test the prediction performance of the models by using train test splits to evaluate how accurately the models can predict the available data. The full performance is shown in Figure 2 based on four evaluation metrics. While imperfect, this approach allows for the reasonable resemblance of the shape and size of the target variable where missing. The shaded areas in the figures are not uncertainty ranges but show the spread on the underlying scenario sets (C1-8). In other words, the shades areas do not show the confidence interval but the full percentile range (from lowest to highest scenario).
Line 124: In Figure 1, one can see how there is a clear spread in baseline land removal levels; in other words, the level of removal in the year 2020, taken as the current condition, is not the same across scenarios. For example, for the scenarios in category C2, the baseline removal level ranges from no removals as of today up to almost 5000 MtCO2. The data by Gidden et al. does not entail such a spread as the baseline is harmonized. This spread refers to the inconsistencies in reporting that we highlight several times in the paper (e.g., line 12).
Lines 129-130: Agreed, we can include this number when revising the manuscript.
Line 133: Thanks for the suggestion. We will name them here again so readers don’t have to go back to the document.
Line 136: The reduction in absolute error is due to our statistical approach, which is more suitable to resemble the shape and size of gross removal values instead of simply using net negative emissions as a crude proxy for CDR. The reduction is clearly visible in Figure 3 (red is much more similar to blue than yellow is).
Line 140: Gidden et al. is already cited several times in previous manuscript sections and in the reference list - we don’t use the publication year when referring to papers in line. We are happy to change this if advised by the editor team.
Line 140-141: Our dataset allows users to use all scenarios. All scenarios can be included because we provide the imputation of the missing sequestration data for incomplete scenarios. This is the main contribution of our analysis to the community. We highlight that, in future analyses, where the largest possible set of scenarios and a uniform removal sign are not as relevant as a uniform baseline and direct comparability to the NGHGI, users may want to consider using the dataset by Gidden et al.. As described in our manuscript, we see different strengths and caveats of the two approaches depending on the aim of users (see lines 120-143).
Figure 1: These are the scenario categories used by the IPCC in AR6 to group scenarios based on their warming outcome (Table II.7. Classification of global pathways into warming levels using MAGICC in Guivarch et al. (2022)). We can briefly highlight this, link again to the AR6 report, or create supplementary material with this info.
“Conservative” in the context of “AR6 net negative AFOLU CO2 emissions (based on negative values in ‘Emissions|CO2|AFOLU’) as a conservative proxy for land-based CDR across AR6 scenario categories.” implies that it is a rather low approximation, underestimating the actual size of removals.
Figure 2: We are happy to use uniform linewidth and transparency if this is preferred. We will check whether percentage values for the mean error can be a suitable addition. As described in the figure caption, this is based on 79 scenarios, i.e., 79 predictions in our validation dataset, giving the overall R-squared, mean, median, and absolute error compared to the actual values of the scenarios in the validation dataset.
Citation: https://doi.org/10.5194/essd-2024-68-AC2
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RC3: 'Comment on essd-2024-68', Anonymous Referee #3, 24 Jun 2024
Information on the level of gross carbon dioxide removal (CDR) on land is incomplete in the AR6 Scenario Database. They are represented through the variable Carbon Sequestration/Land use, which is missing in 419 out of 1202 pathways. In this paper, the authors use the variable ‚net negative CO2 emissions in agriculture, forestry, and other land use (AFOLU)‘ as a proxy variable for land-based CDR. Therefore, they test the performance of several regression methods. They find gradient boosting regression performs best using R², the mean absolute error, the median absolute error, and the maximum absolute error as metrics.
In general, this is a very valid and essential approach to improve estimates on land-based CDR. However, the description and explanations of the respective variables and the method are much too short and not detailed enough in my point of view. The authors should further extend the discussion on limitations and use cases of their approach to underline its relevance. To fully validate the soundness of the statistical approach, I recommend an additional review by an expert on statistics.
Major Comments:
The authors should describe in detail what the relevant variables they use are (Carbon Sequestration/ Land use, net negative CO2 emissions in agriculture, forestry, and other land use (AFOLU)), e.g. do they include C uptake from natural forests and would that even point to CDR as it only involves intential interventions in land use? Does the latter include wood products and emissions from land management? Differences between the two variables and shortcomings in the approach should be discussed in-depth. Maybe a figure would help demonstrate the gross and net fluxes of both variables.
Further, the description of the statistical approach should be elaborated more (mathematically and in words). To demonstrate the robustness of the methods, cross-validation or bootstrapping should be applied. Which metrics did the authors use to show that the applied methods are superior to the other methods? Could you briefly explain the statistical methods you use? I find it also questionable if the approach is valid for all categories (Fig.1) as e.g. for C7 and C8 the net negative AFOLU fluxes are 0 for most of the time. Doesn‘t it make sense to exclude specific categories?
Smaller comments
- l. 33: scenario filtering and exclusion: please explain more in detail
- l. 33: Which limitations do they have? Name at least one for each approach.
- l. 35: OSCAR v3.2 is not an earth system model but a reduced-complexity model with an explicit treatment of the land-use sector
- Fig. 1: explain categories → helps to understand discrepancies (e.g. Why are net negative AFOLOU CO2 fluxes 0 for C7, C8?)
- l. 59: What is the fractional choice of the split in training and test sets based on? To prove the robustness, it makes sense to apply cross-validation here.
- l. 71: Why do you need three metrics to evaluate the absolute difference between the dependent and the independent variable?
- l. 80: related to a more detailed explanation of variables: why is this a conceptual error?
- Fig. 3: What does the shaded area show? Add to caption.
- l. 138: Could you add more detailed use cases here?
- l. 140: Why is the Gidden et al. Dataset perceived to be more consistent with today‘s removal?
Citation: https://doi.org/10.5194/essd-2024-68-RC3 -
AC3: 'Reply on RC3', Ruben Prütz, 28 Jun 2024
We thank referee #3 for providing reflections and feedback on our manuscript. This is much appreciated! In the following, we respond to the major and smaller comments in detail and describe how we plan to revise our manuscript based on the reviewer's feedback.
Major comments:
Major comment 1: We appreciate the feedback on the description of the two variables that we are working with. We are happy to include the variable descriptions from the AR6 database to clarify their scope further. Generally, natural C uptake should not be part of these two variables (gross and net) – only anthropogenic fluxes should be included. The AR6 database documents "Carbon Sequestration|Land Use" as "total carbon dioxide sequestered through land-based sinks (e.g., afforestation, soil carbon enhancement, biochar)". "Emissions|CO2|AFOLU" is defined as "CO2 emissions from agriculture, forestry and other land use (IPCC category 3)". However, we also highlight apparent inconsistencies across scenarios – partly shown by the range in baseline fluxes (2020) for these variables. We highlight other approaches that address the scenario data issues concerning land emissions and compare them to our data imputation approach. As part of the manuscript revisions, we aim to build on our current description to clarify the raised points further.
Major comment 2: We welcome the suggestion to include bootstrapping in our evaluation of model performances. We already use hyperparameter tuning with Grid Search and cross validation to optimize the performance of our models (details are shown in our Zenodo repository). As part of the revisions, we will include bootstrapping for evaluating the performance of the tested models. We evaluate the model performance based on four common evaluation metrics (namely, R-squared, mean absolute error, median absolute error, and maximum absolute error) and evaluate the performance for each time step in the time series (Figure 2). Adding bootstrapping, as suggested, and further elaborating on the methods used will further facilitate the reading. We also want to highlight that all required data and code are made available – thus, our approach is fully tracible and replicable.
About the models used: For all models, we use the scikit-learn library for Python. The decision tree model splits the data of the independent variable into different branches based on the features of this variable (AFOLU emissions in different years). Each node in the decision tree represents a decision based on a feature, and each leaf node represents a prediction. The tree is then built by choosing the best feature splits to maximize the accuracy of the predictions. The gradient boosting regression model sequentially combines multiple simple models, called weak learners (typically decision trees), which correct each other's predictions to minimize the overall error and improve the final model. The random forest model also combines multiple decision trees, but unlike gradient boosting, these trees run in parallel instead of sequentially. Each decision tree works independently, and their individual predictions are averaged to produce the final prediction. The k-nearest Neighbors model instead uses the proximity (similarity) of a scenario to a number (k) of neighbors (other scenarios) to make predictions. For a given scenario, the model identifies the k nearest data points in the feature space and then averages the target variable values of these neighbors to come up with a prediction. We will make sure to briefly describe the models in the method section when revising the manuscript.
Indeed, the prediction performance is lower for the scenario categories C7 and C8. However, please note that there are several scenarios in categories C7 and C8 for which there are net negative CO2 emissions towards the end of the century (see the yellow shaded area in Figure 1). More importantly, for the training of the regression models, we did not only use the net-negative values of 'Emissions|CO2|AFOLU' but all values (also positive ones). Also, for the model training, we did not distinguish between scenario categories - this was only done for the data visualization.
Smaller comments:
Line 33: In Prütz et al. (2023), an additional criteria-based scenario vetting process is applied, which leads to scenario exclusion if scenarios do not meet the defined criteria. While this allows for a more consistent scenario selection, it reduces the number of scenarios in the set. This is a limitation, as we want to be able to look at all available scenarios to consider the largest set of potential pathways. Using the net-negative emissions as a proxy does not require scenario exclusion; however, it underestimates the real level of CDR on land as residual emissions on land are not zero.
Line 35: Thanks for pointing this out. The authors refer to it as a compact earth system model, but we are happy to emphasize its reduced-complexity and explicit treatment of the land-use sector.
Figure 1: We are happy to include an explanation of the categories – we used the scenario categories by the IPCC in AR6 to group scenarios based on their warming outcome (Table II.7. Classification of global pathways into warming levels using MAGICC in Guivarch et al. (2022)). We are happy to describe the categories briefly, link again to the AR6 report, or create supplementary material to elaborate on this.
Several scenarios in C7 and C8 show net-negative CO2 emissions in AFOLU in the second half of the century. However, a larger share stays net positive throughout the century as residual emissions remain larger than removals in this sector for many scenarios. This is why the median in Figure 1 is so low (however, the yellow shade shows quite some net negative emissions towards the end of the century). For the regression analysis, this does not matter as the models are trained based on the original variable "Emissions|CO2|AFOLU", not just its negative values.
Line 59: We are using a large share (90%) of the available data for training to optimize the model and use 10% for testing. There is no fixed rule for how large the training and testing split should be, and 90:10 is perceived to be a common ratio. We already use Grid Search and cross validation to optimize the hyperparameter selection for our models. As part of the revisions, we are already working on including bootstrapping to resample the train test split many times to test the robustness of the model performance for different splits. This will help us to evaluate the models better.
Line 71: Technically, we don't need three metrics. However, these are commonly used metrics to evaluate the performance of regression models. We include all three because we feel like this gives additional information and helps us understand the model's performance. For example, the median and mean absolute error show that the models overall well manage to predict the size of removals with acceptable error (only a few megatonnes). However, in extreme cases, the accuracy of the prediction can be more substantial with gigatonne-scale error. The R-squared instead helps to understand how well the models manage to resemble the shape of the time series rather than the size of the error.
Line 80: It is a conceptual error because gross negative emissions cannot be smaller than net negative emissions, as net emissions are the sum of gross positive and gross negative emissions. Assuming that there would be no residual emissions, gross negative emissions would equal net negative emissions, but their net negative emissions cannot be larger than gross negative emissions. We will make this clearer in the manuscript.
Figure 3: It is the min-max range (full percentile range from lowest to highest scenario in the underlying scenario set), as indicated in the y-axis label. We are happy to reiterate this in the caption.
Line 138: Thanks for the suggestion; we will add more tangible examples here to highlight the different benefits and caveats of the approaches.
Line 140: The Gidden et al. dataset is perceived to be more consistent with today's removals as it is aligned with the national greenhouse gas inventories and shows harmonized removals in the year 2020, whereas in AR6, there is quite a range in removal levels for 2020 (see blue shaded areas in Figure 1).
Citation: https://doi.org/10.5194/essd-2024-68-AC3
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RC4: 'Comment on essd-2024-68', Anonymous Referee #4, 08 Jul 2024
(Comments also attached as PDF for convenience of authors and editor)
Review of –“Imputation of missing IPCC AR6 data on land carbon sequestration”
Summary- The AR6 database and its results are widely used to understand and analyze future climate mitigation and adaptation pathways. Since some Integrated Assessment Models (IAMs) used in the AR6 database model/report net land use emissions rather than gross, data on carbon sequestered on land is often missing for specific scenarios. In this paper, the authors conduct an imputation/statistical interpolation to calculate data on carbon sequestered on land for the AR6 database where not reported. Rather than developing a method to convert the net land use to gross, the authors adopt a statistical approach which directly calculates the carbon sequestration from land.
The land carbon component is indeed a key component of the database. Therefore, I find the work of the authors important. However, the paper as presented seems to be related to a specific method of interpolation applied an existing set of data rather than the creation of a generally usable dataset (which would put this paper outside the scope for a data journal like ESSD) (See Major comments 2, 4). Moreover, I had several questions and concerns regarding the general applicability of these methods outside of interpolating only global results from the current state of the AR6 database (See Major comments 1,2,3). Moreover, as a minor but important point- while the length of the paper does not automatically equate with quality (and the paper is clearly written), I found that the paper is too brief in its explanations regarding its variables, methods (As a simple example, it contains no methods section neither in the main text or the supplementary file which is usually critical for a journal like ESSD) (See Major comment 5 for details). Please see all detailed comments below. Given the comments, I recommend rejection and resubmission at a later date. However, this is subject to the editor’s discretion. All my comments are meant as constructive and in good faith to my colleagues in the field.
Major comments-
- Scale of analysis- One of the fundamental questions I had was whether this imputation exercise is conducted at a global scale or at a regional scale? The actual dataset released contains only the global results (https://zenodo.org/records/10696654). Also, from the text, I interpreted all results as global? i.e., the independent variable is the carbon sequestered on land globally? If this is the case, this seems to be a shortcoming of the approach since it ignores regional heterogeneity. All IAMs included in the AR6 database produce regional emissions results. An imputation such as this seems to consolidate all the underlying regional dynamics to a regression which I find unconvincing. This is especially true in an age when studies are focused on deriving fine resolution (pixel level) results from regional ones. Can the authors comment on this? How valid would this method be if applied to the regional scale? Also see comment no 3 below. If regional results are calculated, kindly discuss them in the manuscript (See sub-comments of comment no 5).
- General applicability of this method- Since both the dependent and independent variables are coming from the same version of the AR6 database, it seems to me that this method really just begins and ends with the current state of the AR6 database. The IAMs underlying this database are constantly evolving and several of these IAMs are now focused on developing gross emissions pathways from the land sector. If a new version of the AR6 database were to be released, would this method still be valid? If the answer is no, then the data created and presented here is not a dataset with general usability. Rather , it is just a method of extension of the current AR6 database which would put this paper outside the scope of ESSD.
- Applicability beyond AR6- Related to the above point, the Global Carbon Project (GCP) which releases its carbon budget analysis that is annually published in ESSD (https://essd.copernicus.org/articles/15/5301/2023/), includes land use emissions and carbon sequestration historically, globally. In fact, in more recent versions, there are also national emissions and sequestrations included. Would the current method produce reasonable results for a database such as the GCP’s? Since the GCP is an equally well known dataset/exercise with a richer historical dataset, I would recommend that the current method be tested on that dataset to justify general usability of this method and data. Note that the national and global land use emissions and sequestration numbers are also available here- https://www.globalcarbonproject.org/carbonbudget/archive.htm
- Interpolation of existing data or new data- Given the above points, I believe rather than a generalizable imputation, the authors have rather presented an interpolation method for existing data on the AR6 database. While this is not an issue, this would classify this paper as a methods paper rather than the development of original and novel data. In which case, this paper is not a good fit for the given journal which is generally meant for data descriptors. Finally, I would describe the methods of the authors as an interpolation rather than an imputation. That seems like a more precise description of the methods in the paper.
- Lack of description of methods (and results)- While the paper provides a summary of the methods, they are never really described in any detail in the manuscript. As a simple example- ESSD papers generally contain a detailed methods sections which describe and justify the methods underlying the data which increase usability and reproducibility. This paper does not describe the method used in much detail which I find concerning. I have added specific comments related to the same below-
- Can the authors show a scatterplot of the x and y variables underlying the R squared values shown in Figure 2? Can these be separated for the training and the testing dataset?
- What are the actual equations used for each of the methods/models? What were the final coefficients chosen for the gradient boosting method?
- I checked the AR6 database, and it seems regional results are available. Therefore, related to comment 1 above, is the regression trained on global or regional results? If it’s trained on regional results, can the regional heterogeneity be discussed in the manuscript? Does the regression fit all regions in the same way?
- How and why is the current independent variable (IV) chosen? Were other IVs tested? Does performance change when different IVs are chosen?
- The selection of the four final models for hyper parameter testing are described in a few short sentences. Can the details regarding the selection of the four models be added to the paper or supplementary information?
- I had trouble understanding Figure 3. What are the categories described in the figure? Can the authors describe what each of the categories represent? Why do C8 category emissions differ so much from observed compared to other categories? Was training and testing differentiated by category?
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AC4: 'Reply on RC4', Ruben Prütz, 09 Jul 2024
We thank the anonymous referee #4 for providing detailed feedback on our manuscript. Below, we respond to the individual comments point by point.
Major comments
Major comment 1: Indeed, our statistical approach is applied to the global outputs from the AR6 Scenario Database - therefore, our imputed variables are also global (“Imputed|Carbon Sequestration|Land Use” and “Imputed & Proxy|Carbon Sequestration|Land Use”). This is also clearly indicated in the column “Region” in the provided spreadsheet in the Zenodo repository, following the convention in the original AR6 Scenario Database. It is also specified in the manuscript (line 39). We do not see our focus on the global level as a shortcoming of our analysis as the global variable is very often used in scenario assessments and, therefore, of great value for other researchers (for example, Lamb et al. 2024 ERL, Lamb et al. 2024 Nature, Prütz et al. 2023 ERC, Schleussner et al. Commun Earth Environ 2022). We are happy to elaborate on this more in our manuscript.
While it is correct that the AR6 Scenario Database also contains regional emissions data (five regions R5, ten regions R10, or country-level data), the availability of data for the independent variable (and partly also the dependent variable for validation) differs strongly between regions, making it more challenging to predict missing data points based on the existing data per region - simply because less data is available for training and validating our regression model. Generally, the data availability problems increase the more disaggregated the data is. Also, depending on the level of disaggregation (R5, R10, or country-level), the available data does not always add up to the global numbers. However, our approach would theoretically be applicable on a sub-global level, too, while we would expect to see lower performance levels due to less available training data. We think this is an interesting suggestion, and we will try to implement it for the R10 level as part of our revisions or at least indicate why this does not yield satisfactory results if we find this to be the case.
Major comment 2: Indeed, our approach is intended to provide a complete dataset of missing data in the current version of the AR6 Scenario Database. We hope that in the future, this will not be required anymore as the reporting of variables on net and gross CO2 emissions and removals on land becomes more consistent across models. This means that our manuscript is not intended to introduce a method of general applicability. Instead, we aim to provide a more complete scenario data set for carbon sequestration on land for researchers working on scenario assessments. Of course, the imputed scenarios have a shelf life (so does the AR6 Scenario Database in general), especially due to forgone emission reductions, which are making the ambitious scenarios more and more counterfactual. We still think that our imputation dataset is of great value as long as the current AR6 Scenario Database is in use. We want to emphasize that ourcontribution to the field is the provided dataset, not the method we used to infer the data. Researchers require comprehensive scenario data on carbon sequestration on land to be able to assess the role of CDR across scenarios comprehensively. Therefore, we believe that our provided dataset and manuscript is well within the scope of ESSD as the editor’s decision to consider our manuscript for review also suggests.
Major comment 3: As highlighted in the previous section, our contribution to the field is the provision of an imputed dataset of land carbon sequestration for incomplete scenarios in the AR6 Scenario Database - not the statistical methods used to infer the data. We do not aim to provide a generalized methodology but to provide a useful and complete dataset for the research community. Theoretically, the tested machine learning models could also be used to infer missing data in pretty much any other incomplete dataset (given that there is enough data for training and validating the model) because the used method is based on statistical relationships and not physical dynamics specific to CO2 fluxes on land. Therefore, it would possibly be also applicable to infer data for historical emissions, e.g., linked to the Global Carbon Project. However, this is not the scope and purpose of our manuscript, as we infer missing data for future mitigation pathways.
Major comment 4: We are happy to use the term interpolation instead of imputation if this is perceived to be more accurate by the reviewers and editors. Thank you for this suggestion. However, we do not see how this affects the question of whether our provided dataset falls within the scope of the journal, and we want to kindly highlight that our manuscript would not be under review if the handling editor had concluded that our manuscript was not within the scope of the journal as part of the initial access review.
Major comment 5: We thank the reviewer for their suggestion to further elaborate on the methods. We want to highlight that all required data and code to replicate the study are publicly available in the Zenodo repository making our analysis fully traceable. However, we appreciate the feedback and plan to add more detail to our existing methods section to allow for a better understanding of our approach, even without consulting the code underlying our analysis. As part of the revisions, we plan to include bootstrapping in our approach to estimate the variability in performance for different subsamples of our training data. We also plan to include brief descriptions for each of the four models that we tested. We also plan to better highlight the differences between the scenario categories.
Minor comments
Minor comment 1: Please find attached a scatterplot showing the data underlying R-squared in Figure 2 (in this for the gradient boosting model). The scatterplot contains 711 data points (79 scenarios x 9 time steps). The x-axis shows the predicted values for each scenario (n=79) and year based on the gradient boosting model. The y-axis shows the actual values (the true values in the AR6 Scenario Database) for the 79 scenarios in our regression validation dataset. Color in the plot indicates the different years in the time series per scenario. The unit for both axes is MtCO2 removed via land carbon sequestration. In Figure 2 in our manuscript, we report R-squared for each time step in the time series.
Minor comment 2: All tested models are based on the scikit-learn library for machine learning in Python, where the models/algorithms are described: https://scikit-learn.org.
Gradient boosting: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
Decision tree: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html
Random forest: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
K nearest neighbours: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html
We will make sure to improve the description and referencing to facilitate the understanding.
Minor comment 3: As pointed out above, the models are trained based on the global variables because the data availability is best at the global level. This is important as we need enough data to train and test the models. However, as highlighted above, we are happy to test the model for inferring data for the sub-regions as well. We suspect that this will result in lower performance due to lower data availability for the individual regions, but we think it is worth exploring.
Minor comment 4: At an initial stage, we also tested other variables, e.g., net CO2 emissions (aggregate of AFOLU and Energy & Industry emissions). Eventually, we decided to work with the net CO2 emissions from AFOLU as this variable yielded the best results and was conceptually most closely related to carbon sequestration on land among the available variables in the database. We will make sure to better describe this when revising the manuscript.
Minor comment 5: Thank you for pointing this out. We plan to elaborate more on the methods as part of the revisions. Specifically, we will briefly describe the underlying concept of each of the four models and will also highlight why these were chosen.
Minor comment 6: Thank you for this feedback. These are the scenario categories used by the IPCC in AR6 to group scenarios based on their warming outcome (Table II.7. Classification of global pathways into warming levels using MAGICC in Guivarch et al. (2022)). We plan to explain this better as part of the revisions.
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EC1: 'Comment on essd-2024-68', Martina Stockhause, 30 Jul 2024
I have given you the opportunity to substantially revise your manuscript, taking up the reviewers' suggestions, esp.:
- provide a detailed description and critical discussion of the applied methodology, e.g. by comparing your dataset to alternative data like data of the GCB (why is this method chosen? how would the application of another method alter your dataset?)
- provide further evidence of the quality of the dataset, which is derived from scenarios with an inherent considerable uncertainty
- what is the added value of your dataset?
- who is the target user community (which will not use alternative regional datasets or the global data directly)?
Please keep in mind that ESSD is a scientific journal. Methodology, originality of the dataset and an added value to the community are all important criteria for the acceptance of your submission.Citation: https://doi.org/10.5194/essd-2024-68-EC1
Status: closed
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RC1: 'Comment on essd-2024-68', Thomas Bossy, 08 Apr 2024
Review of Prütz et al:
The manuscript presents a new dataset that imputes carbon removal on land for 404 incomplete scenarios in the AR6 Scenario Database. The authors identify a gap in the existing literature for overcoming missing data in the AR6 Scenario Database and propose a solution to fill it. Although their method is imperfect, the dataset is clearly presented and easy to use. Therefore, it deserves to be published.
General comments on the dataset:
The dataset uses the same model and scenario names as in the AR6 database, making it very easy to use as a supplement for someone willing to fill in the missing data. However, as the authors themselves acknowledge, the Gidden et al. re-analysis is "considered more useful in terms of consistency and accuracy". Therefore, I see the interest of their dataset mainly for someone who needs land carbon sequestration for as many scenarios as possible. In that case, why not provide a dataset with all scenarios, and not just those where 'Carbon sequestration|Land use' is missing? This would make it even more convenient for everyone to work with all scenarios in the same file, and it would provide an adjusted version that also corrects inconsistencies found in the original database, with net removals being greater than gross removals.
Since the Gidden et al. reanalysis seems to be of better quality, would it be possible to add a paragraph discussing the feasibility/relevance (or not) of applying the same reanalysis to the imputed scenarios?
Specific comments:
l35. “While the AR6 re-analysis dataset by Gidden et al. manages to resolve several of the data issues linked to carbon removal on land, it still combines gross and net CO2 emissions on land in their land-based CDR variable, resulting in both positive and negative CDR values, which conflicts with the concept and clean definition of gross CDR.”
Is it related to the fact that it is already inconsistent in the original Database or is it due to the re-analysis itself?
Figure 1.
Would it be possible to add one sentence explaining why do re-analysis look so different than the other 2 curves? In particular, the mismatch between the points in 2020.
l71-74.
After reading carefully, I think I understood how the metrics were calculated. However, it was quite fuzzy at first. Would it be possible to add an illustrative figure showing what the four metrics correspond to?
l107-108. “Figure 3 suggests some variance in performance across these categories – for C8 scenarios, the drop in resemblance of the actual variable is most visible”
Isn’t it because the independent variable is mostly null or near zero for C8 (and C7) scenarios that the prediction is bad?
l120-121. “our imputed dataset does not account for perceived land sequestration related data issues in the AR6 Scenario Database beyond data availability”
I find the phrasing unclear.
Citation: https://doi.org/10.5194/essd-2024-68-RC1 -
AC1: 'Reply on RC1', Ruben Prütz, 22 Apr 2024
Thanks a lot for taking the time to provide these thoughtful and constructive comments — they are much appreciated! Below, we respond to the general and specific comments point by point.
General comments:
Regarding a merged dataset of imputed and original scenarios:
We agree that our imputation dataset is especially useful for researchers who aim to include the largest possible set of scenarios in their analyses. We also agree that it would be very convenient for researchers to have all scenarios (original and imputed) together to keep the required data preprocessing as simple as possible. However, we are a bit reluctant to simply add the original scenarios to our dataset as this would simply be copy pasted from the AR6 Scenario Database (Byers et al. 2022), which is publicly accessible, but respective institutions and modeling teams hold the copyright. Nevertheless, we are eager to make our imputation dataset as easily accessible and usable as possible. Therefore, we plan to add a script to our revised manuscript that allows researchers to easily merge our imputation dataset with the AR6 Scenario Database to get the largest set of scenarios.
Regarding the reproducibility of the approach by Gidden et al.:
To our understanding, the OSCAR-based approach by Gidden et al. requires more comprehensive land use change data per scenario (compare Gidden et al. 2023) and is therefore restricted to 914 scenarios, which meet the data requirements. Our alternative approach does not have these requirements and can, therefore, be applied to a larger set of scenarios (n=783+404). Beyond the number of imputed scenarios, our approach does not allow for both positive and negative CDR values to be closely aligned with a clean conceptual definition of gross CDR - this differs from the OSCAR-based approach, as shown in Figure 1. We have already flagged this in the current version of the manuscript but aim to make it even clearer as part of the revisions.
Specific comments:
Regarding the positive and negative CDR values in Gidden et al.:
As this phenomenon is only shown by some models and scenarios in the reanalysis by Gidden et al., we suspect that this is at least partly driven by the properties of the original database. However, from the information at hand, we cannot say this with certainty. Gidden et al. would be better equipped to explain the underlying dynamics of the OSCAR model that might explain the conceptually unintuitive coexistence of both positive and negative “gross” CDR values.
Regarding Figure 1:
The mismatch in points in 2020 is due to different emission baselines, which have or have not been aligned across scenarios. This is partly already described in the manuscript (see lines 124, 35-42). We will try to make this clearer as part of the revisions.
Regarding the evaluation metrics:
Thanks for sharing this reflection. We will work on this and either provide a table or illustration to highlight what the evaluation metrics correspond to.
Regarding figure 3:
Generally, our regression approach seems to also reasonably impute values based on near zero net-negative AFOLU CO2 emissions. However, we agree that the very low levels of net-negative AFOLU in C7 and especially C8 may at least partly explain the drop in prediction performance, while emphasising that our prediction is still substantially more accurate in scale and shape resemblance than the formerly used net-negative AFOLU proxy. We will add a note on this to reflect on it in the revised manuscript.
Regarding the unclear paragraph:
L120-121 means to highlight that our approach cannot resolve underlying inconsistencies in the original dataset and mainly addresses the issue of lacking data availability. We will make sure to rephrase this as part of the revisions to make this clear and to avoid ambiguity.
Citation: https://doi.org/10.5194/essd-2024-68-AC1
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AC1: 'Reply on RC1', Ruben Prütz, 22 Apr 2024
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RC2: 'Comment on essd-2024-68', Anonymous Referee #2, 24 Jun 2024
This paper uses statistical methods to fill in missing data on land carbon sequestration in 404 scenarios of the AR6 Scenario Database. The purpose seems useful, and the concept is generally clear. However, the study remains rather superficial in several instances, for example regarding the testing of the estimated regression models, describing the methods, and placing the study within the existing scientific literature.
Major comments:
- The gradient boosting methods is suggested to be the best method. However, I miss a rigorous testing of this result, especially given that this method does not perform best in all of the analyzed metrics. I strongly advocate for using bootstrapping to estimate the variability in the analyzed metrics for the different regression methods, particularly to test whether the gradient boosting method is not just by chance the best method. Also, the selection of the best method should be clearly supported by numbers rather than by visual inspection.
- The methodological description is currently too vague to be reproducible. A few questions that came to my mind: How were the data selected/pooled? Were all scenarios and all years pooled or just scenarios or years? Are all scenarios/values global averages or do they provide spatially explicit estimates? In case of the latter, how was this considered? How was the dataset split into training and testing sets? How was the superior performance (line 67) of the four selected regression models tested? Further details should be added, such that other scientists would be able to repeat the analysis.
I also believe that a better justification for correlating gross removals with net fluxes is needed, as the two might not necessarily be correlated, for instance in cases were the temporal variability of net fluxes is dominated by emissions (and not removals).
- I feel like the introduction could and should provide more background why this study is relevant. For instance, some use cases of CDR estimates from the AR6 Scenario Database could be cited. I believe that this would also increase the visibility of this study.
Related to this, the paper mentions that use cases for the approach would be presented, but I did not really find them in the manuscript (except for one rather generic sentence in the discussion). Being more specific here would be useful.
Specific comments:
- Title: I would suggest checking if the word “imputation” is the best term in the context of this study. I am no native speaker, and when checking the translation of imputation I found that it can have several meanings, which might cause misunderstandings. Maybe “completion”, “amendment” or something similar might be clearer. Also I think that changing the title to “ [Imputation/amendment] of missing data on land carbon sequestration in the AR6 scenario database” would be a more adequate title
- I suggest being more specific in the abstract on what “gross carbon removal on land” means. Does this only refer to anthropogenic influences or does it also include the natural land sink? I suggest to check throughout the manuscript to make this clear (e.g. in line 28 as well)
- Line 12: I read this sentences several times, but it did not really become clear what this means (“net and gross removals are not separated” and “consistently reported”). I suggest being more specific here.
- Line 13: I would suggest to better link this sentence to the previous ones by making it clear that carbon removal estimates are essentially the gross anthropogenic removals.
- Line 14: Is “net CO2 emissions” the right term? It sounds surprising if one wants to deduce removals from emissions. Also, the net flux is not necessarily an emission but could also be a removal. What about “net CO2 fluxes”?
- Abstract: I’d suggest to add a sentence at the end of the abstract about potential implications or use cases of the study
- Line 29: I do not really understand the difference between the “net negative CO2 emissions in AFOLU” and the variable “Carbon Sequestration/Land Use”. As this differentiation is necessary to understand the study, I think this needs to be better explained.
- Line 33: I think that a bit more information about the filtering and exclusion of scenarios in Prütz et al. (2023) would be useful here.
- Line 36: “issues linked to carbon removals on land”: I suggest giving a few examples to make this clearer (this also relates to my major comment 3 above)
- Lines 40-41: What exactly is meant by “net-negative AFOLU CO2 proxy”? Does this refer to what is mentioned in lines 30-31? I suggest to use consistent terminology throughout the document to avoid misunderstandings.
- Lines 40-42: What are the implications of this discrepancy?
- Line 68: What is grid search?
- Line 77-78: Why is this conceptually false? Shouldn’t a removal be negative? Or is there another reason?
- Line 94-95: Where can this consistent behavior be seen? And how was this tested?
- Lines 95-96: This could be a statistical error. Bootstrapping may help to test this.
- Line 107-108: Any ideas why it does not work so good for the C8 scenario? And what exactly is this scenario “C8”?
- Line 108: Do the regression-based estimate fill the full uncertainty range just like the original estimates? Or I their uncertainty larger or smaller than the original estimates?
- Line 124: What does this mean and to what does this sentence refer to? I cannot relate it to Figure 1 and also not to the results shown in the paper.
- Lines 129 and 130: I would suggest adding the exact numbers of scenarios (as done in other places as well)
- Line 133: I think it would be good to shortly mention the issues again to help readers remind them.
- Line 136: Where is the reduction in absolute error shown?
- Line 140: Add full reference for Gidden et al.
- Line 140-141: What does this imply? Should one skip the scenarios that do not have estimates of gross carbon sequestration in such cases?
- Figure 1: What are the categories? A description of them is missing. What exactly is meant by “conservative proxy”?
- Figure 2: I strongly suggest to not put the decision tree, random forest, and k-nearest neighbor more transparent than gradient boosting, as this might cause biased interpretations of the results (even if the black line is not the best one it might be considered the best one, just because it is less transparent). I also wonder whether the error estimates could additionally be shown as percentages of the mean flux estimates. Right now, it is difficult to say how large the errors are without knowing the mean flux estimates. Also, is this figure just for one scenario or were scenarios somehow pooled? I suggest adding that in the caption.
Citation: https://doi.org/10.5194/essd-2024-68-RC2 -
AC2: 'Reply on RC2', Ruben Prütz, 27 Jun 2024
We want to thank anonymous referee #2 for taking the time to review our manuscript. Below, we respond to the major and specific comments point by point and outline how we plan to revise our manuscript based on the feedback provided.
Major comments:
Response to major comment 1: We appreciate the suggestion to use bootstrapping to estimate the variability in performance for different subsamples of our training data. We are currently implementing bootstrapping into our analysis and plan to include this in the revised manuscript that we are compiling. We want to highlight that the selection of the best model is supported by the numbers underlying the performance evaluation plot (Figure 2). Further, we are using Grid Search and cross-validation to identify the optimal hyper-parameter combination of the tested regression models.
Response to major comment 2: In terms of reproducibility, we want to emphasise that both the input data as well as our analysis code is made available, allowing other researchers to fully trace and re-run our analysis. However, we appreciate the feedback on the level of detail in the written method section and plan to extent this description so the method can be more easily understood without having to consult the code.
Regarding the sub-questions under comment 2: As described in lines 57-60 in our manuscript, we selected all vetted scenarios from the AR6 Scenario Database for which both the independent and the dependent variable are available (n=783). Among the vetted scenarios (n=1202) in the AR6 Scenario Database, 15 scenarios from REMIND 1.6 do not report AFOLU CO2 emissions, which is why we could not include these scenarios in our imputation. For each of these 783 scenarios, we select all years between 2020-2100 in 10-year steps. The data is directly downloaded from the AR6 Scenario Database and limited to the globally aggregated data - spatially explicit (gridded) scenario data is not part of the AR6 scenario database. The scenario data of the 783 scenarios was randomly split into train and test sets using the scikit-learn library in Python. Initially, we also included linear regression and multilayer perceptron regression but excluded these models as they performed substantially worse compared to the other four models based on the same evaluation metrics presented in Figure 2. This is also described in lines 65-76.
Response to major comment 3: Regarding the net fluxes as predictor for our model - we agree that net emission fluxes are not ideal due to the mentioned variability in the variable driven by other factors than removals. However, since gross emissions are not available, we see the net emission fluxes as the best available option. We plan to address this valid comment in the discussion section by better reflecting on the implications of our imputation, which is based on statistical patterns rather than physical relationships. We are happy to highlight a few use cases of CDR estimates, which are based on the AR6 Scenario Database, to highlight its relevance and the contribution of our imputation to such analyses. The use cases of our dataset, as well as its advantages and disadvantages compared to other approaches, are already highlighted in lines 130-141.
Specific comments:
Title: We feel that imputation as a term works well in the context of our regression-based data imputation. However, we appreciate the suggestion to restructure the title for better readability and propose: “Imputation of missing land carbon sequestration data in the AR6 Scenario Database”.
Comment on gross carbon removal: We appreciate the feedback and will check the manuscript for consistency concerning the use of this term. Generally, we mean anthropogenic removal only and exclude natural fluxes. However, it is not always clear how consistently this is done across scenarios, as also described in our manuscript (see abstract – line 12).
Line 12: The mentioned sentence highlights that there is currently no consistency across scenarios in AR6 in terms of how much CO2 is stored in land sinks. While some models explicitly distinguish between gross removal (CDR via afforestation and soil carbon sequestration) and net removal (net negative emissions in the AFOLU sector), other models only report the latter. There is also a spread in baselines across gross removal across scenarios, which suggests inconsistent definitions.
Line 13: We are happy to slightly rephrase the abstract to address the comments concerning lines 12-14.
Line 14: “Net emissions” or “Net CO2 emissions” are well-established and commonly used terms. Net emissions are simply the sum of gross positive emissions and gross negative emissions. Indeed, “net emissions” can be negative if removals from the atmosphere exceed emissions to the atmosphere. “Net CO2 flux” would work as well.
Comment on use cases in the abstract: Thanks for this feedback. We will try to integrate it.
Line 29: We appreciate this feedback. The understanding of this distinction is indeed crucial for understanding our study. We will add to our existing description to make the distinction between these two variables from the AR6 database more clear. Simply put, “net negative CO2 emissions in AFOLU” refer to the net removals on land while “Carbon Sequestration|Land Use” refers to the gross removals (CDR) on land.
Line 33: In Prütz et al. (2023), an additional vetting process is applied based on a set of criteria which leads to scenario exclusion if scenarios do not meet the defined criteria. We will make sure to briefly elaborate on this in the manuscript.
Line 36: This sentence refers to the previously described issues of: lacking data availability, inconsistent baselines for land sinks, and inconsistent definitions of removals on land or the distinction between net and gross. We will make sure to better link this to previous sections in the manuscript.
Line 40-41: “Net-negative AFOLU CO2 proxy” means that the negative values in the net variable for AFOLU CO2 emissions of the AR6 database are used as substitute variable, i.e., proxy for missing data on land carbon sequestration (CDR via afforestation or soil carbon sequestration). We will double check whether consistency in terminology use can be further improved.
Line 40-42: The discrepancy highlights the caveats of the approaches to resemble carbon sequestration (CDR via afforestation and soil carbon sequestration): either large deviation from the actual values in the AR6 database or due to conflicts with the concept and clean definition of gross CDR. The implications are described in detail in lines 30-40 and later also in lines 103-105 and lines 127-136.
Line 68: Grid Search is a common machine learning hyperparameter tuning technique to find the optimal combination of model hyperparameters – in our case the setting options of our four regression models.
Line 77-78: Thank you for raising this clarification question. Removals are reported as positive values in the AR6 Scenario Database. Therefore, predicted negative values would mean emissions. We realize that this is not obvious to readers and will add a brief explanation to our manuscript.
Line 94-95: As part of our revisions and the integration of bootstrapping, we will highlight how the different models differ in terms of their performance and the variability of the performance metrics used to evaluate the models’ performances. This will be visible in the updated Figure 2.
Line 95-96: Thanks again, we will consider bootstrapping.
Line 107-108: C8 is one of the eight scenario categories used by the IPCC in AR6 to group scenarios based on their warming outcome. This category includes a total of 29 scenarios that show warming levels above 4°C (>4°C peak warming with ≥50% chance). It is the category with the lowest number of scenarios which may partly explain why the performance is lower compared to the other scenarios.
Line 108: I am trying to understand whether I fully understand this question. We use regression models and train the models based on available data to predict missing data. We test the prediction performance of the models by using train test splits to evaluate how accurately the models can predict the available data. The full performance is shown in Figure 2 based on four evaluation metrics. While imperfect, this approach allows for the reasonable resemblance of the shape and size of the target variable where missing. The shaded areas in the figures are not uncertainty ranges but show the spread on the underlying scenario sets (C1-8). In other words, the shades areas do not show the confidence interval but the full percentile range (from lowest to highest scenario).
Line 124: In Figure 1, one can see how there is a clear spread in baseline land removal levels; in other words, the level of removal in the year 2020, taken as the current condition, is not the same across scenarios. For example, for the scenarios in category C2, the baseline removal level ranges from no removals as of today up to almost 5000 MtCO2. The data by Gidden et al. does not entail such a spread as the baseline is harmonized. This spread refers to the inconsistencies in reporting that we highlight several times in the paper (e.g., line 12).
Lines 129-130: Agreed, we can include this number when revising the manuscript.
Line 133: Thanks for the suggestion. We will name them here again so readers don’t have to go back to the document.
Line 136: The reduction in absolute error is due to our statistical approach, which is more suitable to resemble the shape and size of gross removal values instead of simply using net negative emissions as a crude proxy for CDR. The reduction is clearly visible in Figure 3 (red is much more similar to blue than yellow is).
Line 140: Gidden et al. is already cited several times in previous manuscript sections and in the reference list - we don’t use the publication year when referring to papers in line. We are happy to change this if advised by the editor team.
Line 140-141: Our dataset allows users to use all scenarios. All scenarios can be included because we provide the imputation of the missing sequestration data for incomplete scenarios. This is the main contribution of our analysis to the community. We highlight that, in future analyses, where the largest possible set of scenarios and a uniform removal sign are not as relevant as a uniform baseline and direct comparability to the NGHGI, users may want to consider using the dataset by Gidden et al.. As described in our manuscript, we see different strengths and caveats of the two approaches depending on the aim of users (see lines 120-143).
Figure 1: These are the scenario categories used by the IPCC in AR6 to group scenarios based on their warming outcome (Table II.7. Classification of global pathways into warming levels using MAGICC in Guivarch et al. (2022)). We can briefly highlight this, link again to the AR6 report, or create supplementary material with this info.
“Conservative” in the context of “AR6 net negative AFOLU CO2 emissions (based on negative values in ‘Emissions|CO2|AFOLU’) as a conservative proxy for land-based CDR across AR6 scenario categories.” implies that it is a rather low approximation, underestimating the actual size of removals.
Figure 2: We are happy to use uniform linewidth and transparency if this is preferred. We will check whether percentage values for the mean error can be a suitable addition. As described in the figure caption, this is based on 79 scenarios, i.e., 79 predictions in our validation dataset, giving the overall R-squared, mean, median, and absolute error compared to the actual values of the scenarios in the validation dataset.
Citation: https://doi.org/10.5194/essd-2024-68-AC2
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RC3: 'Comment on essd-2024-68', Anonymous Referee #3, 24 Jun 2024
Information on the level of gross carbon dioxide removal (CDR) on land is incomplete in the AR6 Scenario Database. They are represented through the variable Carbon Sequestration/Land use, which is missing in 419 out of 1202 pathways. In this paper, the authors use the variable ‚net negative CO2 emissions in agriculture, forestry, and other land use (AFOLU)‘ as a proxy variable for land-based CDR. Therefore, they test the performance of several regression methods. They find gradient boosting regression performs best using R², the mean absolute error, the median absolute error, and the maximum absolute error as metrics.
In general, this is a very valid and essential approach to improve estimates on land-based CDR. However, the description and explanations of the respective variables and the method are much too short and not detailed enough in my point of view. The authors should further extend the discussion on limitations and use cases of their approach to underline its relevance. To fully validate the soundness of the statistical approach, I recommend an additional review by an expert on statistics.
Major Comments:
The authors should describe in detail what the relevant variables they use are (Carbon Sequestration/ Land use, net negative CO2 emissions in agriculture, forestry, and other land use (AFOLU)), e.g. do they include C uptake from natural forests and would that even point to CDR as it only involves intential interventions in land use? Does the latter include wood products and emissions from land management? Differences between the two variables and shortcomings in the approach should be discussed in-depth. Maybe a figure would help demonstrate the gross and net fluxes of both variables.
Further, the description of the statistical approach should be elaborated more (mathematically and in words). To demonstrate the robustness of the methods, cross-validation or bootstrapping should be applied. Which metrics did the authors use to show that the applied methods are superior to the other methods? Could you briefly explain the statistical methods you use? I find it also questionable if the approach is valid for all categories (Fig.1) as e.g. for C7 and C8 the net negative AFOLU fluxes are 0 for most of the time. Doesn‘t it make sense to exclude specific categories?
Smaller comments
- l. 33: scenario filtering and exclusion: please explain more in detail
- l. 33: Which limitations do they have? Name at least one for each approach.
- l. 35: OSCAR v3.2 is not an earth system model but a reduced-complexity model with an explicit treatment of the land-use sector
- Fig. 1: explain categories → helps to understand discrepancies (e.g. Why are net negative AFOLOU CO2 fluxes 0 for C7, C8?)
- l. 59: What is the fractional choice of the split in training and test sets based on? To prove the robustness, it makes sense to apply cross-validation here.
- l. 71: Why do you need three metrics to evaluate the absolute difference between the dependent and the independent variable?
- l. 80: related to a more detailed explanation of variables: why is this a conceptual error?
- Fig. 3: What does the shaded area show? Add to caption.
- l. 138: Could you add more detailed use cases here?
- l. 140: Why is the Gidden et al. Dataset perceived to be more consistent with today‘s removal?
Citation: https://doi.org/10.5194/essd-2024-68-RC3 -
AC3: 'Reply on RC3', Ruben Prütz, 28 Jun 2024
We thank referee #3 for providing reflections and feedback on our manuscript. This is much appreciated! In the following, we respond to the major and smaller comments in detail and describe how we plan to revise our manuscript based on the reviewer's feedback.
Major comments:
Major comment 1: We appreciate the feedback on the description of the two variables that we are working with. We are happy to include the variable descriptions from the AR6 database to clarify their scope further. Generally, natural C uptake should not be part of these two variables (gross and net) – only anthropogenic fluxes should be included. The AR6 database documents "Carbon Sequestration|Land Use" as "total carbon dioxide sequestered through land-based sinks (e.g., afforestation, soil carbon enhancement, biochar)". "Emissions|CO2|AFOLU" is defined as "CO2 emissions from agriculture, forestry and other land use (IPCC category 3)". However, we also highlight apparent inconsistencies across scenarios – partly shown by the range in baseline fluxes (2020) for these variables. We highlight other approaches that address the scenario data issues concerning land emissions and compare them to our data imputation approach. As part of the manuscript revisions, we aim to build on our current description to clarify the raised points further.
Major comment 2: We welcome the suggestion to include bootstrapping in our evaluation of model performances. We already use hyperparameter tuning with Grid Search and cross validation to optimize the performance of our models (details are shown in our Zenodo repository). As part of the revisions, we will include bootstrapping for evaluating the performance of the tested models. We evaluate the model performance based on four common evaluation metrics (namely, R-squared, mean absolute error, median absolute error, and maximum absolute error) and evaluate the performance for each time step in the time series (Figure 2). Adding bootstrapping, as suggested, and further elaborating on the methods used will further facilitate the reading. We also want to highlight that all required data and code are made available – thus, our approach is fully tracible and replicable.
About the models used: For all models, we use the scikit-learn library for Python. The decision tree model splits the data of the independent variable into different branches based on the features of this variable (AFOLU emissions in different years). Each node in the decision tree represents a decision based on a feature, and each leaf node represents a prediction. The tree is then built by choosing the best feature splits to maximize the accuracy of the predictions. The gradient boosting regression model sequentially combines multiple simple models, called weak learners (typically decision trees), which correct each other's predictions to minimize the overall error and improve the final model. The random forest model also combines multiple decision trees, but unlike gradient boosting, these trees run in parallel instead of sequentially. Each decision tree works independently, and their individual predictions are averaged to produce the final prediction. The k-nearest Neighbors model instead uses the proximity (similarity) of a scenario to a number (k) of neighbors (other scenarios) to make predictions. For a given scenario, the model identifies the k nearest data points in the feature space and then averages the target variable values of these neighbors to come up with a prediction. We will make sure to briefly describe the models in the method section when revising the manuscript.
Indeed, the prediction performance is lower for the scenario categories C7 and C8. However, please note that there are several scenarios in categories C7 and C8 for which there are net negative CO2 emissions towards the end of the century (see the yellow shaded area in Figure 1). More importantly, for the training of the regression models, we did not only use the net-negative values of 'Emissions|CO2|AFOLU' but all values (also positive ones). Also, for the model training, we did not distinguish between scenario categories - this was only done for the data visualization.
Smaller comments:
Line 33: In Prütz et al. (2023), an additional criteria-based scenario vetting process is applied, which leads to scenario exclusion if scenarios do not meet the defined criteria. While this allows for a more consistent scenario selection, it reduces the number of scenarios in the set. This is a limitation, as we want to be able to look at all available scenarios to consider the largest set of potential pathways. Using the net-negative emissions as a proxy does not require scenario exclusion; however, it underestimates the real level of CDR on land as residual emissions on land are not zero.
Line 35: Thanks for pointing this out. The authors refer to it as a compact earth system model, but we are happy to emphasize its reduced-complexity and explicit treatment of the land-use sector.
Figure 1: We are happy to include an explanation of the categories – we used the scenario categories by the IPCC in AR6 to group scenarios based on their warming outcome (Table II.7. Classification of global pathways into warming levels using MAGICC in Guivarch et al. (2022)). We are happy to describe the categories briefly, link again to the AR6 report, or create supplementary material to elaborate on this.
Several scenarios in C7 and C8 show net-negative CO2 emissions in AFOLU in the second half of the century. However, a larger share stays net positive throughout the century as residual emissions remain larger than removals in this sector for many scenarios. This is why the median in Figure 1 is so low (however, the yellow shade shows quite some net negative emissions towards the end of the century). For the regression analysis, this does not matter as the models are trained based on the original variable "Emissions|CO2|AFOLU", not just its negative values.
Line 59: We are using a large share (90%) of the available data for training to optimize the model and use 10% for testing. There is no fixed rule for how large the training and testing split should be, and 90:10 is perceived to be a common ratio. We already use Grid Search and cross validation to optimize the hyperparameter selection for our models. As part of the revisions, we are already working on including bootstrapping to resample the train test split many times to test the robustness of the model performance for different splits. This will help us to evaluate the models better.
Line 71: Technically, we don't need three metrics. However, these are commonly used metrics to evaluate the performance of regression models. We include all three because we feel like this gives additional information and helps us understand the model's performance. For example, the median and mean absolute error show that the models overall well manage to predict the size of removals with acceptable error (only a few megatonnes). However, in extreme cases, the accuracy of the prediction can be more substantial with gigatonne-scale error. The R-squared instead helps to understand how well the models manage to resemble the shape of the time series rather than the size of the error.
Line 80: It is a conceptual error because gross negative emissions cannot be smaller than net negative emissions, as net emissions are the sum of gross positive and gross negative emissions. Assuming that there would be no residual emissions, gross negative emissions would equal net negative emissions, but their net negative emissions cannot be larger than gross negative emissions. We will make this clearer in the manuscript.
Figure 3: It is the min-max range (full percentile range from lowest to highest scenario in the underlying scenario set), as indicated in the y-axis label. We are happy to reiterate this in the caption.
Line 138: Thanks for the suggestion; we will add more tangible examples here to highlight the different benefits and caveats of the approaches.
Line 140: The Gidden et al. dataset is perceived to be more consistent with today's removals as it is aligned with the national greenhouse gas inventories and shows harmonized removals in the year 2020, whereas in AR6, there is quite a range in removal levels for 2020 (see blue shaded areas in Figure 1).
Citation: https://doi.org/10.5194/essd-2024-68-AC3
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RC4: 'Comment on essd-2024-68', Anonymous Referee #4, 08 Jul 2024
(Comments also attached as PDF for convenience of authors and editor)
Review of –“Imputation of missing IPCC AR6 data on land carbon sequestration”
Summary- The AR6 database and its results are widely used to understand and analyze future climate mitigation and adaptation pathways. Since some Integrated Assessment Models (IAMs) used in the AR6 database model/report net land use emissions rather than gross, data on carbon sequestered on land is often missing for specific scenarios. In this paper, the authors conduct an imputation/statistical interpolation to calculate data on carbon sequestered on land for the AR6 database where not reported. Rather than developing a method to convert the net land use to gross, the authors adopt a statistical approach which directly calculates the carbon sequestration from land.
The land carbon component is indeed a key component of the database. Therefore, I find the work of the authors important. However, the paper as presented seems to be related to a specific method of interpolation applied an existing set of data rather than the creation of a generally usable dataset (which would put this paper outside the scope for a data journal like ESSD) (See Major comments 2, 4). Moreover, I had several questions and concerns regarding the general applicability of these methods outside of interpolating only global results from the current state of the AR6 database (See Major comments 1,2,3). Moreover, as a minor but important point- while the length of the paper does not automatically equate with quality (and the paper is clearly written), I found that the paper is too brief in its explanations regarding its variables, methods (As a simple example, it contains no methods section neither in the main text or the supplementary file which is usually critical for a journal like ESSD) (See Major comment 5 for details). Please see all detailed comments below. Given the comments, I recommend rejection and resubmission at a later date. However, this is subject to the editor’s discretion. All my comments are meant as constructive and in good faith to my colleagues in the field.
Major comments-
- Scale of analysis- One of the fundamental questions I had was whether this imputation exercise is conducted at a global scale or at a regional scale? The actual dataset released contains only the global results (https://zenodo.org/records/10696654). Also, from the text, I interpreted all results as global? i.e., the independent variable is the carbon sequestered on land globally? If this is the case, this seems to be a shortcoming of the approach since it ignores regional heterogeneity. All IAMs included in the AR6 database produce regional emissions results. An imputation such as this seems to consolidate all the underlying regional dynamics to a regression which I find unconvincing. This is especially true in an age when studies are focused on deriving fine resolution (pixel level) results from regional ones. Can the authors comment on this? How valid would this method be if applied to the regional scale? Also see comment no 3 below. If regional results are calculated, kindly discuss them in the manuscript (See sub-comments of comment no 5).
- General applicability of this method- Since both the dependent and independent variables are coming from the same version of the AR6 database, it seems to me that this method really just begins and ends with the current state of the AR6 database. The IAMs underlying this database are constantly evolving and several of these IAMs are now focused on developing gross emissions pathways from the land sector. If a new version of the AR6 database were to be released, would this method still be valid? If the answer is no, then the data created and presented here is not a dataset with general usability. Rather , it is just a method of extension of the current AR6 database which would put this paper outside the scope of ESSD.
- Applicability beyond AR6- Related to the above point, the Global Carbon Project (GCP) which releases its carbon budget analysis that is annually published in ESSD (https://essd.copernicus.org/articles/15/5301/2023/), includes land use emissions and carbon sequestration historically, globally. In fact, in more recent versions, there are also national emissions and sequestrations included. Would the current method produce reasonable results for a database such as the GCP’s? Since the GCP is an equally well known dataset/exercise with a richer historical dataset, I would recommend that the current method be tested on that dataset to justify general usability of this method and data. Note that the national and global land use emissions and sequestration numbers are also available here- https://www.globalcarbonproject.org/carbonbudget/archive.htm
- Interpolation of existing data or new data- Given the above points, I believe rather than a generalizable imputation, the authors have rather presented an interpolation method for existing data on the AR6 database. While this is not an issue, this would classify this paper as a methods paper rather than the development of original and novel data. In which case, this paper is not a good fit for the given journal which is generally meant for data descriptors. Finally, I would describe the methods of the authors as an interpolation rather than an imputation. That seems like a more precise description of the methods in the paper.
- Lack of description of methods (and results)- While the paper provides a summary of the methods, they are never really described in any detail in the manuscript. As a simple example- ESSD papers generally contain a detailed methods sections which describe and justify the methods underlying the data which increase usability and reproducibility. This paper does not describe the method used in much detail which I find concerning. I have added specific comments related to the same below-
- Can the authors show a scatterplot of the x and y variables underlying the R squared values shown in Figure 2? Can these be separated for the training and the testing dataset?
- What are the actual equations used for each of the methods/models? What were the final coefficients chosen for the gradient boosting method?
- I checked the AR6 database, and it seems regional results are available. Therefore, related to comment 1 above, is the regression trained on global or regional results? If it’s trained on regional results, can the regional heterogeneity be discussed in the manuscript? Does the regression fit all regions in the same way?
- How and why is the current independent variable (IV) chosen? Were other IVs tested? Does performance change when different IVs are chosen?
- The selection of the four final models for hyper parameter testing are described in a few short sentences. Can the details regarding the selection of the four models be added to the paper or supplementary information?
- I had trouble understanding Figure 3. What are the categories described in the figure? Can the authors describe what each of the categories represent? Why do C8 category emissions differ so much from observed compared to other categories? Was training and testing differentiated by category?
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AC4: 'Reply on RC4', Ruben Prütz, 09 Jul 2024
We thank the anonymous referee #4 for providing detailed feedback on our manuscript. Below, we respond to the individual comments point by point.
Major comments
Major comment 1: Indeed, our statistical approach is applied to the global outputs from the AR6 Scenario Database - therefore, our imputed variables are also global (“Imputed|Carbon Sequestration|Land Use” and “Imputed & Proxy|Carbon Sequestration|Land Use”). This is also clearly indicated in the column “Region” in the provided spreadsheet in the Zenodo repository, following the convention in the original AR6 Scenario Database. It is also specified in the manuscript (line 39). We do not see our focus on the global level as a shortcoming of our analysis as the global variable is very often used in scenario assessments and, therefore, of great value for other researchers (for example, Lamb et al. 2024 ERL, Lamb et al. 2024 Nature, Prütz et al. 2023 ERC, Schleussner et al. Commun Earth Environ 2022). We are happy to elaborate on this more in our manuscript.
While it is correct that the AR6 Scenario Database also contains regional emissions data (five regions R5, ten regions R10, or country-level data), the availability of data for the independent variable (and partly also the dependent variable for validation) differs strongly between regions, making it more challenging to predict missing data points based on the existing data per region - simply because less data is available for training and validating our regression model. Generally, the data availability problems increase the more disaggregated the data is. Also, depending on the level of disaggregation (R5, R10, or country-level), the available data does not always add up to the global numbers. However, our approach would theoretically be applicable on a sub-global level, too, while we would expect to see lower performance levels due to less available training data. We think this is an interesting suggestion, and we will try to implement it for the R10 level as part of our revisions or at least indicate why this does not yield satisfactory results if we find this to be the case.
Major comment 2: Indeed, our approach is intended to provide a complete dataset of missing data in the current version of the AR6 Scenario Database. We hope that in the future, this will not be required anymore as the reporting of variables on net and gross CO2 emissions and removals on land becomes more consistent across models. This means that our manuscript is not intended to introduce a method of general applicability. Instead, we aim to provide a more complete scenario data set for carbon sequestration on land for researchers working on scenario assessments. Of course, the imputed scenarios have a shelf life (so does the AR6 Scenario Database in general), especially due to forgone emission reductions, which are making the ambitious scenarios more and more counterfactual. We still think that our imputation dataset is of great value as long as the current AR6 Scenario Database is in use. We want to emphasize that ourcontribution to the field is the provided dataset, not the method we used to infer the data. Researchers require comprehensive scenario data on carbon sequestration on land to be able to assess the role of CDR across scenarios comprehensively. Therefore, we believe that our provided dataset and manuscript is well within the scope of ESSD as the editor’s decision to consider our manuscript for review also suggests.
Major comment 3: As highlighted in the previous section, our contribution to the field is the provision of an imputed dataset of land carbon sequestration for incomplete scenarios in the AR6 Scenario Database - not the statistical methods used to infer the data. We do not aim to provide a generalized methodology but to provide a useful and complete dataset for the research community. Theoretically, the tested machine learning models could also be used to infer missing data in pretty much any other incomplete dataset (given that there is enough data for training and validating the model) because the used method is based on statistical relationships and not physical dynamics specific to CO2 fluxes on land. Therefore, it would possibly be also applicable to infer data for historical emissions, e.g., linked to the Global Carbon Project. However, this is not the scope and purpose of our manuscript, as we infer missing data for future mitigation pathways.
Major comment 4: We are happy to use the term interpolation instead of imputation if this is perceived to be more accurate by the reviewers and editors. Thank you for this suggestion. However, we do not see how this affects the question of whether our provided dataset falls within the scope of the journal, and we want to kindly highlight that our manuscript would not be under review if the handling editor had concluded that our manuscript was not within the scope of the journal as part of the initial access review.
Major comment 5: We thank the reviewer for their suggestion to further elaborate on the methods. We want to highlight that all required data and code to replicate the study are publicly available in the Zenodo repository making our analysis fully traceable. However, we appreciate the feedback and plan to add more detail to our existing methods section to allow for a better understanding of our approach, even without consulting the code underlying our analysis. As part of the revisions, we plan to include bootstrapping in our approach to estimate the variability in performance for different subsamples of our training data. We also plan to include brief descriptions for each of the four models that we tested. We also plan to better highlight the differences between the scenario categories.
Minor comments
Minor comment 1: Please find attached a scatterplot showing the data underlying R-squared in Figure 2 (in this for the gradient boosting model). The scatterplot contains 711 data points (79 scenarios x 9 time steps). The x-axis shows the predicted values for each scenario (n=79) and year based on the gradient boosting model. The y-axis shows the actual values (the true values in the AR6 Scenario Database) for the 79 scenarios in our regression validation dataset. Color in the plot indicates the different years in the time series per scenario. The unit for both axes is MtCO2 removed via land carbon sequestration. In Figure 2 in our manuscript, we report R-squared for each time step in the time series.
Minor comment 2: All tested models are based on the scikit-learn library for machine learning in Python, where the models/algorithms are described: https://scikit-learn.org.
Gradient boosting: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
Decision tree: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html
Random forest: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
K nearest neighbours: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html
We will make sure to improve the description and referencing to facilitate the understanding.
Minor comment 3: As pointed out above, the models are trained based on the global variables because the data availability is best at the global level. This is important as we need enough data to train and test the models. However, as highlighted above, we are happy to test the model for inferring data for the sub-regions as well. We suspect that this will result in lower performance due to lower data availability for the individual regions, but we think it is worth exploring.
Minor comment 4: At an initial stage, we also tested other variables, e.g., net CO2 emissions (aggregate of AFOLU and Energy & Industry emissions). Eventually, we decided to work with the net CO2 emissions from AFOLU as this variable yielded the best results and was conceptually most closely related to carbon sequestration on land among the available variables in the database. We will make sure to better describe this when revising the manuscript.
Minor comment 5: Thank you for pointing this out. We plan to elaborate more on the methods as part of the revisions. Specifically, we will briefly describe the underlying concept of each of the four models and will also highlight why these were chosen.
Minor comment 6: Thank you for this feedback. These are the scenario categories used by the IPCC in AR6 to group scenarios based on their warming outcome (Table II.7. Classification of global pathways into warming levels using MAGICC in Guivarch et al. (2022)). We plan to explain this better as part of the revisions.
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EC1: 'Comment on essd-2024-68', Martina Stockhause, 30 Jul 2024
I have given you the opportunity to substantially revise your manuscript, taking up the reviewers' suggestions, esp.:
- provide a detailed description and critical discussion of the applied methodology, e.g. by comparing your dataset to alternative data like data of the GCB (why is this method chosen? how would the application of another method alter your dataset?)
- provide further evidence of the quality of the dataset, which is derived from scenarios with an inherent considerable uncertainty
- what is the added value of your dataset?
- who is the target user community (which will not use alternative regional datasets or the global data directly)?
Please keep in mind that ESSD is a scientific journal. Methodology, originality of the dataset and an added value to the community are all important criteria for the acceptance of your submission.Citation: https://doi.org/10.5194/essd-2024-68-EC1
Data sets
Imputation of missing IPCC AR6 data on land carbon sequestration Ruben Prütz, Sabine Fuss, and Joeri Rogelj https://zenodo.org/doi/10.5281/zenodo.10696653
Model code and software
Imputation of missing IPCC AR6 data on land carbon sequestration Ruben Prütz, Sabine Fuss, and Joeri Rogelj https://zenodo.org/doi/10.5281/zenodo.10696653
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