the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Jul 2026
JULES-Palm: Explicit representation of oil palm plantations in a land surface model for estimating land-use change carbon emissions in Indonesia
Abstract. Expansion of oil palm plantations is a dominant driver of land-use change in equatorial Asia and has contributed substantially to deforestation in Indonesia. Most dynamic global vegetation models (DGVMs), however, still represent oil palm plantations as generic cropland, thereby contributing to uncertainty in carbon emissions from land-use change (ELUC). We develop a new oil palm plant functional type (PFT) using the JULES land surface model, called JULES-Palm. This PFT incorporates oil palm-specific physiological, structural and plantation-management traits, including rotation-based harvest/replanting cycles, and is parameterised from literature-derived values before calibration using eddy covariance (EC) and allometric observations, including leaf area index (LAI), above-ground biomass and canopy height. This calibration improves simulated photosynthesis and the vegetation structure. We then ran JULES-Palm to quantify the contribution of oil palm plantations to Indonesian ELUC, using land-use drivers from the Mapbiomas Indonesia 2.0 land cover maps (2000–2022). We investigated three scenarios: (i) oil palm represented as a generic crop, (ii) oil palm omitted and treated as natural vegetation and (iii) explicit oil palm PFT. The mean and interannual variability of Indonesian ELUC (without peat emissions) is 0.058 ± 0.012 PgC yr-1 with the oil palm PFT and 0.052 ± 0.010 PgC yr-1 when oil palm is omitted; representing oil palm as a generic cropland gives 0.066 ± 0.011 PgC yr-1. Cumulatively, the expansion of oil palm plantations by 10.5 Mha since 2000 contributed approximately 0.14 PgC, accounting for 10.3 % of Indonesia’s total ELUC including legacy fluxes, but approximately 89 % of land-use change attributable to conversions occurring during 2000–2022. Representing oil palm as a dedicated PFT reduces the estimated oil-palm-related ELUC relative to the generic-cropland representation by capturing plantation-specific biomass accumulation and carbon turnover. Thus, using a specific PFT for oil palm is preferable to providing a more accurate, process-based representation of plantation carbon dynamics, but treating oil palm as cropland still captures a first-order estimation of ELUC when a dedicated PFT is unavailable.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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CEC1: 'Comment on egusphere-2026-3689 - No compliance with the policy of the journal', Juan Antonio Añel, 08 Aug 2026
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AC1: 'Reply on CEC1', Ida B.M. Brasika, 12 Aug 2026
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Thank you for bringing these issues to our attention.
Regarding the code, the JULES configuration used in this study is maintained in the Met Office Science Repository Service (MOSRS) as Roses suite u-ea491. We note that a similar approach was used for previous JULES developments published in GMD, such as JULES-BE, where the exact JULES version and development branch were identified within the Met Office repository. We would therefore be grateful for confirmation as to whether the fixed MOSRS suite and revision are sufficient under the current GMD policy. if an additional persistent archive is required, we would be happy to deposit all JULE-Palm material that we are legally permitted to redistribute in (for example) Zenodo and provide a DOI. Before redistributing any Met Office/JULES files, however, we need to confirm that this is permitted under the license applicable to the exact version used.
Regarding the datasets, we similarly understand the requirement for persistent archives and identifiers. We would be happy to archive the exact datasets or subsets used in this study in Zenodo where redistribution is permitted. However, the Mapbiomas, EC and allometric data originate from third-party providers or collaborators, so we first need to confirm with the respective data owners and licenses whether redistribution through Zenodo is allowed. Where redistribution is not permitted, we will identify and cite the appropriate persistent archive or identifier provided by the original data owner, where available.
We will update the Code and Data availability section and the corresponding references accordingly once these points are confirmed.
We will also revise the manuscript title to include a specific JULES-Palm model version number, as requested.
Citation: https://doi.org/10.5194/egusphere-2026-3689-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 12 Aug 2026
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Dear authors,
Thanks for your reply, and for the information regarding the version number in MORS. We can confirm that this is enough in the case of the Jules-Palm model.
Also, please, reply with the additional information when you have solved the issues regarding the storage of the data.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2026-3689-CEC2
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CEC2: 'Reply on AC1', Juan Antonio Añel, 12 Aug 2026
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AC1: 'Reply on CEC1', Ida B.M. Brasika, 12 Aug 2026
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RC1: 'Comment on egusphere-2026-3689', Anonymous Referee #1, 04 Oct 2026
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This manuscript describes a study that introduces a dedicated Plant Functional Type (PFT) for oil palm (Elaeis guineensis) into the JULES model and quantifies the contribution of oil palm expansion to carbon emissions from land-use change (ELUC) in Indonesia. Oil palm is a crucial factor in understanding land-use change and the carbon cycle in Southeast Asia. Existing land surface models commonly treat oil palm simply as a "generic crop," so the attempt to represent it as an independent PFT is inherently significant. Furthermore, the study is interesting in its approach to constructing the PFT using multiple observational datasets (such as eddy covariance measurements, LAI, AGB, and canopy height) and then scaling the analysis to calculate land-use change at a national level.
However, the current manuscript lacks much of the information needed to evaluate the study's core conclusions, including details on model structure, experimental design, calibration, independent validation, initial conditions for land-use change, carbon bookkeeping, harvesting and replanting protocols, spatial analysis, and data archiving. In particular, the "Methods" section makes it difficult for a third party to reconstruct the actual simulations performed. In contrast, the "Results" section repeatedly introduces analyses and carbon flux metrics that were not defined in the "Methods." Additionally, some key conclusions are not sufficiently supported by the analyses currently presented.
In my view, several of these issues require more than textual clarification and may require additional analyses or reconsideration of parts of the experimental design.
________ Major comments ________
(1) Insufficient explanation of the research motivation, the necessity of using JULES, and the model structureIn the Introduction, after introducing CLM-Palm and ORCHIDEE-MICT-OP, the authors state:
> "these approaches are relatively detailed and closely linked to the structural design of their respective host models"
They then argue for the necessity of the more "generic" approach adopted in this study. However, they do not explain why being "detailed" is problematic or how dependence on the host model imposes specific constraints on large-scale land-use change calculations. Furthermore, while the next paragraph mentions the development of JULES-Palm, it does not sufficiently explain why JULES is a suitable foundation for addressing this issue. Which specific features of JULES-TRIFFID make it particularly well-suited for a simplified oil palm PFT, large-scale LULCC simulations, and ELUC assessment?
Similar issues exist in the "Methods" section. The hierarchical relationships and division of roles among JULES, TRIFFID, TRIFFID-crop, JULES-BE, and the oil palm PFT are unclear. In one paragraph, TRIFFID-crop is the subject, whereas the very next paragraph abruptly shifts the subject to JULES, stating that "JULES lacks an oil palm PFT." Please explain, in a way that is understandable even to readers unfamiliar with the model, what the core JULES system calculates, what TRIFFID/TRIFFID-crop handles, where the oil palm PFT is implemented, and through which variables they interact.
As currently written, the "Methods" section makes it extremely difficult for anyone other than a reader already intimately familiar with JULES/TRIFFID to grasp where specific parameters and processes fit into the model.
(2) The land-use scenarios and experimental design are unclear, and definitions are inconsistent within the text.
Although Figure 2 is intended to be the central illustration of the experimental design, the differences between the three scenarios are not intuitively clear in its current form. A detailed reading of the text allows one to infer the following intentions:
* Scenario 1: Treat oil palm as a generic crop.
* Scenario 2: Treat the oil palm area as natural vegetation.
* Scenario 3: Treat it as an explicit oil palm PFT.However, Figure 2 alone cannot provide this information.
In particular, labels such as "No Palm" and "With Palm" are misleading. Since Scenario 1 also includes land areas corresponding to oil palm, what actually differs between scenarios is how that area is represented, not whether oil palm is present or absent. It would be far clearer to use explicit arrows or similar indicators to show how the oil palm category from MB2 is reclassified into specific land classes under each scenario. Additionally, explain what S3-S2 and S3-S1 quantify, either in Figure 2 or in its caption.
A more critical issue arises in Section 3.3,
> "S2 represents a no land-use change control and S3 includes observed land-use transitions"
This explanation is introduced suddenly and does not align with the definitions of Scenario 2 and 3 provided earlier (up to Figure 2). Are the S2 and S3 referred to here the TRENDY experiment identifiers? Or are they identical to Scenario 2 and 3 from Figure 2? If they are distinct, please use different notation and describe the experimental design in the Methods section.
As it stands, the relationship between Scenarios 1-3 in Figure 2 and TRENDY S2/S3 is unclear, making it impossible to trace the meaning of the ELUC differences that form the core of the paper.
Furthermore, Section 2.2.2 describes a procedure where, if the combined area of cropland (including oil palm) and pasture exceeds 1, the pasture area is capped at 1. Since the definition of total cropland area varies by scenario, please verify and discuss whether this capping procedure affects pasture allocation differently across scenarios, potentially introducing artificial differences in the ELUC comparisons themselves.
(3) Meteorological forcing, spin-up, initial conditions, and the treatment of legacy fluxes are not described
Although Section 2.2 is titled "The forcing dataset," the actual description focuses primarily on the land-use forcing from MapBiomas Indonesia. The Methods section lacks information on the meteorological forcing used to drive JULES (e.g., air temperature, precipitation, shortwave/longwave radiation, humidity, and wind speed), as well as atmospheric CO2 concentrations, dataset names, temporal and spatial resolutions, study periods, bias correction methods, and the handling of missing data.
The filename "CRUJRA2.5" appears in the data repository, but the main text does not describe this forcing dataset. This information is essential for ensuring reproducibility. Furthermore, it is unclear how the vegetation, litter, and soil carbon pools for the spin-up and the year 2000 were generated. This issue directly affects the interpretation of "legacy flux."
In the Methods section, total ELUC is described as including:
> "ongoing carbon fluxes during 2000-2022 arising from land-use changes that occurred before the analysis period"
However, the Discussion states:
> "Because the Mapbiomas Indonesia dataset used in this study begins in 2000, emissions associated with earlier plantation establishment are not explicitly represented"
and acknowledges that pre-2000 legacy flux may be underestimated.
These two explanations are inconsistent, at least in their current form. Did you provide the model with land-use history before 2000? Since the difference between the key results of 10.3% and 89% depends heavily on the definition of legacy flux, this point must be fully clarified.
Regarding the interannual variability observed in Figure 8, please explain the respective contributions of year-to-year fluctuations in land-use conversion and variations in meteorological forcing. While I do not consider sensitivity experiments with different forcings mandatory, you should at least discuss the choice of forcing and the associated uncertainties.
(4) There is no overview of the calibration workflow, and the reproducibility of parameter fitting using LHS is insufficient.
Although the Methods section explains LHS, MAE, relative error, slope/intercept penalties, asymmetric weighting, prior constraints, and composite loss sequentially, it remains difficult to track how these elements relate to one another within the overall calibration workflow.
At the beginning of Section 2.4 (or elsewhere), please first present the overall flow, reflecting the actual procedure, using text or a schematic diagram, such as:
baseline oil palm parameters
---> photosynthesis calibration
---> fixing photosynthesis parameters
---> allometric calibration
---> final PFT
---> evaluation/validationAdditionally, while some details regarding photosynthesis calibration were added in Section 2.4.1, the following points remain unclear:
* Is the +-30% reference value the JULES-BETr default, or the oil-palm baseline established in Section 2.1.2 based on literature?
* From which probability distributions were the parameters sampled within the LHS framework?
* How were the observations from Sabaju and Sebungan weighted within the MAE calculation?
* The EC periods used (while mentioned in Section 2.3.1, explicitly stating or cross-referencing them in Section 2.4.1 would improve readability).
* Gap filling, quality control, and the partitioning of NEE into GPP.
* How was it verified that a 500-member ensemble was sufficient for the 12-dimensional parameter space?
* How was equifinality evaluated, given that only the single parameter set yielding the minimum MAE was adopted?Regarding allometric calibration, it is not explicitly stated whether the second calibration was performed with the optimal parameters from the first calibration held fixed. If you used a sequential calibration approach, please specify the order and the rationale. The manuscript should also report the values of the penalty/prior weights (e.g., lambda terms and omega_i), or explain clearly how they were determined.
In Equation 5, omega_i is set to a value greater than 1 only when the model underestimates observations, penalizing underestimation more heavily than overestimation. While avoiding underestimation of carbon stocks is understandable, this approach risks intentionally biasing the objective function toward higher carbon stock values. Please explain the scientific rationale for adopting this asymmetric loss function and describe its impact on the final AGB and ELUC estimates.
(5) Lack of substantial independent validation
In Section 2.3, EC data are described as:
> "observations to evaluate JULES simulations"
However, the GPP data from Sabaju and Sebungan were themselves used for photosynthesis calibration in Section 2.4.1. Similarly, the authors used AGB and canopy-height observations for allometric calibration.
Consequently, the agreement shown in Figures 5-7 reflects the calibration fit rather than independent validation.
If out-of-sample evaluation methods were employed (for example, a hold-out dataset, independent sites, independent time periods, cross-validation, or leave-one-site-out validation), please explicitly state this. If not, use terms such as "validated," "accurately captures," and "evaluation" with caution.
I recommend adding an independent validation step.
This issue is also evident in Figure 5. Although the authors state that the calibrated JULES-Palm model shows significant improvement for young oil palms, the calibrated model appears to overestimate GPP more strongly than the default JULES model below approximately 100 g CO2 m-2 month-1. The improvement is not uniform across the entire GPP range. Please acknowledge this point in the main text and, if necessary, present performance metrics broken down by site and GPP range.
Please also define how R2 was calculated, because the reported negative value indicates that it is not simply a squared correlation coefficient.
(6) The relationship between the structural calibration using LAI and the dynamic LAI used in the actual simulation has not been verified.
Regarding the allometric calibration, the study states:
> "We prescribe LAI using an exponential best-fit curve derived from available oil palm observations"
Here, an observation-based LAI-age curve is input to calibrate the parameters for AGB and canopy height.
However, for the subsequent national-scale simulation, the study states:
> "LAI evolves dynamically through TRIFFID-PFT processes rather than being prescribed."
In this context, the fact that AGB and canopy height values approached observational data in Figures 6-7 merely indicates an improvement in the allometry that converts realistic, prescribed LAI values into AGB and height. Because LAI varies freely in the national-scale simulation, the AGB and height results cannot be considered fully validated unless the dynamic JULES-Palm model can reproduce a realistic LAI-age trajectory.
The authors need to evaluate dynamic LAI, AGB, and canopy height consistently.
(7) The implementation of the 25-year replanting cycle and the plantation age structure within the grid cells is unclear.
The study highlights the incorporation of a 25-year replanting cycle, a key management characteristic of oil palm, as a notable feature. However, it does not explain how this 25-year cycle is implemented within the 0.5-degree grid cells.
In reality, a 0.5-degree grid cell would contain a mosaic of plantations with varying ages (e.g., 1, 2, 3 years old, etc.). Does the model represent multiple age cohorts or staggered rotations? Or does it synchronously harvest the entire oil palm fraction within the grid cell every 25 years, resetting the height to 1 m?
If the latter approach is used, it risks creating large artificial pulses in AGB, LAI, NPP, litter input, product pools, and soil respiration.
Furthermore, the analysis period (2000-2022) is shorter than the 25-year cycle. Therefore, oil palm plantations newly established after 2000 should not undergo the 25-year replanting cycle during the analysis period. How many replanting events actually occurred in the nationwide simulation?
Separately, when new oil palm areas are added through "assisted expansion," what initial values for age, LAI, AGB, canopy height, and carbon pools are assigned to them? If they share the state of existing oil palm PFTs, the newly planted areas cannot be represented as young palms.
Because this could directly affect the ELUC estimates, a detailed explanation is required.
(8) The carbon bookkeeping addressed in Figure 9 is not explained in the Methods section
In Section 3.3, carbon fluxes such as soil respiration, harvest, fire, burnt DPM/RPM, and wood products (WP) are introduced without prior definition. However, many of these are not defined in the Methods section.
Please clarify which biomass sources contribute to these WP pools (e.g., biomass from pre-conversion vegetation versus biomass removed during oil-palm replanting), and justify the corresponding allocation assumptions.
In Figure 9, certain fluxes for MB2_palm-as-crop exhibit extreme inter-annual variability compared to other scenarios. This could reflect differences in the bookkeeping of harvest, fire, litter, and product pools between the generic crop PFT and the dedicated oil palm PFT, rather than differences in PFT physiological traits alone.
Therefore, simply explaining the difference in ELUC between Scenario 1 and Scenario 3 by stating,
> "because generic crops have lower carbon content than oil palm trees"
is insufficient.
Please present the correspondence between annual oil palm conversion areas and the pulses shown in Figure 9, the magnitude of each carbon flux per unit of converted area, and the differences in carbon routing between generic crops and oil palms; then, discuss whether the differences between scenarios stem from physiological traits or from management/bookkeeping practices.
Please clarify which biomass sources contribute to the WP pools under each scenario, including whether they receive biomass from pre-conversion vegetation and/or oil-palm replanting, and justify the corresponding allocation rules and residence times.
(9) The impact of ignoring FFB harvest is too significant to be addressed merely in the current Discussion
The authors acknowledge that routine FFB harvesting is not explicitly represented. However, the authors' own first-order diagnostic estimates FFB-C export at 0.334-0.668 PgC. This value exceeds the cumulative ELUC of approximately 0.14 PgC attributed to oil palm expansion in this study. As the author points out, FFB harvest represents a gross product-export flux, whereas ELUC is a net carbon-balance term; therefore, the two cannot simply be subtracted from one another. However, excluding such a significant carbon pathway may alter the fate of carbon that would otherwise be exported as FFB, potentially affecting vegetation turnover, litter inputs, soil respiration, and ultimately ELUC.
Merely stating that this is "not a direct correction to ELUC" is insufficient; please provide a more careful discussion regarding the potential impact of this omission on carbon pools and ELUC.
Additionally, the Discussion section suddenly introduces the phrase:
> "Applying an age-yield ramp"
However, the equations, assumptions, and stand-age dependencies associated with this "age-yield ramp" are not explained in the Methods section. This analysis should be described in the Methods rather than being introduced for the first time in the Discussion.
Furthermore, while the carbon fraction used to convert FFB to carbon mass was set at 0.557 in the Methods section, it appears as 0.577 in the Discussion. The values of 4.33-8.65 tC ha-1 yr-1 appear consistent with the use of 0.577; please verify which figure is correct.
(10) The implications of excluding peat are substantial
JULES-Palm does not distinguish between peat soils and mineral soils. However, the Sabaju and Sebungan sites used for GPP calibration in this study are both located in peat swamp regions, and the Discussion itself notes that peat-related emissions can be comparable to, or even exceed, changes in vegetation carbon stocks.
Moreover, extensive peatlands are found in Sumatra and Kalimantan, the very regions identified in this study as ELUC hotspots.
Given this context, I believe it is an overstatement to make generalizations such as:
> "capturing both the magnitude and spatial distribution of land-use change emissions in Indonesia"
or
> "enabling a more robust carbon budget assessment"
The carbon balance evaluated in this study is limited in scope, and in its current form, excludes peat emissions. Please clearly define these limitations in the Conclusions and the Abstract.
(11) The spatial analysis in Figure 10 cannot be fully interpreted without showing the corresponding land-use-change inputs.
Figure 10 shows only the ELUC response; however, interpreting its spatial pattern requires examining the underlying land-use changes.
Please include, at a minimum, a map showing the cumulative increase in oil-palm fraction from 2000 to 2022. The authors also interpret the results by referencing pre-conversion land cover, stating that
> "Positive values dominate in areas undergoing recent conversion from natural vegetation into plantation"
Given this, it is also necessary to provide a map that distinguishes between key transition types, such as:
* natural vegetation ---> oil palm
* previously cleared/agricultural land ---> oil palmThe current ELUC map (in kgC m-2 yr-1) does not allow one to distinguish whether high ELUC values result simply from a large area of conversion to oil palm or from a high carbon impact per unit of converted area. Please also present the ELUC per unit of converted area.
Additionally, the following regional process analysis appears abruptly in the Results section, yet the methodology for this analysis is missing from the Methods section:
> "Analysis of carbon fluxes in these regions shows that lower soil respiration losses drive this effect..."
Please explain how the grid cells with negative ELUC were selected, how many cells were analyzed, how the "early stages of plantation" were defined, and whether the 0.2 kgC m-2 yr-1 figure represents a maximum value, an average value, or a scenario difference. You should also present the soil respiration/NPP analysis in a figure or in the Supplementary Information.
(12) Some key quantitative conclusions are too strong given the current analysis
In particular, the result stating:
> "approximately 89% of land-use change fluxes attributable to conversion occurring during the study period"
is a quantitatively large and central result that requires substantially stronger support. The Introduction cites literature indicating that oil palm accounts for about 23% of total deforestation. While the share of land-use conversion area and the contribution rate to ELUC (Emissions from Land-Use Change) do not necessarily have to align, a decomposition analysis using factors such as pre-conversion land cover and carbon loss per unit area is required to explain why oil palm accounts for 89% of conversion-related ELUC between 2000 and 2022.
Additionally, please verify the validity of the 0.14 PgC and 89% figures by comparing them with independent, existing ELUC estimates, if possible.
Similarly, the statement in the Discussion claiming:
> "a dedicated oil palm PFT provides the most robust and scientifically defensible means"
is also too strong. This study does not include comparative validation against models like CLM-Palm or ORCHIDEE-MICT-OP, and JULES-Palm itself has significant limitations regarding peat, FFB (Fresh Fruit Bunch) harvesting, age cohorts, and independent validation. A more cautious phrasing, such as "a more process-specific representation within JULES," would be appropriate.
Furthermore, while the Discussion interprets the behavior observed "after 15 years" as the physiology of a mature plantation, the current explanation does not detail how the plantation age structure within the grid cell is handled; therefore, one cannot directly equate the simulation year with the plantation age.
(13) The description of the data repository is insufficient, resulting in low reusability and reproducibility.
The description in the Zenodo repository states:
> "Full description is in related publication"
However, the main manuscript lacks the explanation that third parties need to understand and reuse the archived data.
For instance, the meanings of filenames and variables such as:
* MB2PALMnop
* MB2PALMcap
* MB2PALMbet16
* IN_S2 / IN_S3
* CRUJRA2.5
* burnt_carbon_dpm / burnt_carbon_rpm
* WP_fast / WP_med / WP_sloware not clearly defined. Furthermore, the repository lacks details on the objects within each RData file, dimensions, units, temporal and spatial axes, masks, land fractions, scenario mappings, and NBP/ELUC calculation methods.
In particular, while the repository implies the use of TRENDY S2/S3 and CRUJRA2.5 forcing data, the manuscript's "Methods" section does not adequately explain them; this aligns with the overall lack of detail on experimental design and forcing data in the Methods.
Please add a README file or Supplementary Material and expand the documentation to a level that allows third parties to understand the dataset and the calculation methods used.
________ Minor comments ________
(1) The term "phytomer," used in the Introduction, is unfamiliar to those outside the field of oil palm research. Please briefly explain at its first mention that it refers to a repeating structural unit of the plant, consisting of a single leaf and its corresponding inflorescence (or similar structures).(2) The term "deactivation energy" in Section 2.1.2 is also not necessarily self-evident to the general readership of land-surface modeling papers. Please briefly explain that this parameter governs the deactivation (decline in activity) at high temperatures within a peaked Arrhenius-type temperature response. Additionally, since the activation energy, deactivation energy, and entropy term have been significantly modified, presenting a comparison of the temperature response curves for Vcmax(T) and Jmax(T) between BETr and oil palm would make the physiological implications of these changes much easier to understand.
(3) In Section 2.1.2, numerous internal model parameters (such as jv25_ratio, alpha_io, neff_io, kext_io, lma_io, and r_grow_io) are listed without explanation. Please clearly state, either in the main text or in a table, which process each parameter belongs to, what it controls, and how model behavior changes when its value increases or decreases.
(4) Section 2.1.3 references Figure 3, yet there are no references to Figure 1 or Figure 2 in the preceding text. Please check the order, numbering, and placement of the figures.
(5) The term "FFB" is used in Section 2.1.5 without explanation. Please write it as "fresh fruit bunch (FFB)" upon its first appearance.
(6) In Section 2.4.2, the text reads:
> "five key PFT parameters - a_wl, b_wl, lma_io, a_ws and -"
The name of the fifth parameter is missing.
(7) The caption for Figure 2 lacks sufficient information to understand the experimental design. Please explain within the caption itself which category the oil palm land fraction is assigned to in each scenario.
(8) Regarding Figure 9, please ensure that the variables in each panel, the meaning of the symbols, and the definitions of S2 and S3 can be understood from the caption alone.
(9) Regarding Figure 10, please clearly define the meaning of the differences (Scenario 3 minus Scenario 2, and Scenario 1 minus Scenario 3) within the caption.
(10) Please standardize the notation for "Mapbiomas," "MapBiomas," "Mapbiomas Indonesia," etc. Similarly, please clearly distinguish between the notations Scenario 1/2/3 and S1/S2/S3 if they refer to different experiments.
(11) The "last access" date for the MapBiomas Indonesia dataset differs between the Data Availability section and the list of references (e.g., December 2025 in the text versus December 2022 in the references). Please verify which date is correct and standardize the notation.
________ Overall recommendation ________
The research topic itself is important, and there is potential value in introducing an oil palm-specific PFT into JULES. However, the current manuscript suffers from a combination of issues: insufficient explanation of model structure and experimental design; inconsistencies in the definitions of Scenario/TRENDY experiments; missing details on forcing and initialization; a lack of independent validation; unclear representation of plantation age and harvesting; no description of carbon bookkeeping; significant limitations regarding peat and FFB; and overly strong interpretations of certain results.Addressing these issues requires not only substantial restructuring of the Methods section but also independent validation, clearer age/harvest representation, process-level diagnostics, additional spatial analysis, and re-evaluation of some key conclusions.
Citation: https://doi.org/10.5194/egusphere-2026-3689-RC1
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
First, although we are aware of the licensing issues with the MetOffice models, we need that you provide additional information on the measures taken to ensure the adequate storage and internal preservation of the Jules-Palm code, with specific information about how to locate and obtain it on the long-term. Additionally, it is necessary that you indicate in the title of your manuscript a model version number.
Also, the sites that you link in your Code and Data Policy statement to access the Mapbiomas, EC and allometric data are not valid as they do not fulfil GMD’s requirements for a persistent data archive because:
- They do not appear to have a published policy for data preservation over many years or decades (some flexibility exists over the precise length of preservation, but the policy must exist).
- They do not appear to have a published mechanism for preventing authors from unilaterally removing material. Archives must have a policy which makes removal of materials only possible in exceptional circumstances and subject to an independent curatorial decision,
- They do not appear to issue a persistent identifier such as a DOI or Handle for each precise dataset.
If we have missed a published policy which does in fact address this matter satisfactorily, please post a response linking to it. If you have any questions about this issue, please post them in a reply.
The GMD review and publication process depends on reviewers and community commentators being able to access, during the discussion phase, the code and data on which a manuscript depends, and on ensuring the provenance of replicability of the published papers for years after their publication. Please, therefore, provide the necessary information about the code and publish the data in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible. We cannot have manuscripts under discussion that do not comply with our policy.
Later, if the Topical Editor decides to continue with the review or publication process of your manuscript and you are requested to upload a new version of it, then The 'Code and Data Availability’ section of your manuscript must also be modified to cite the new information and repository locations, and corresponding references added to the bibliography.
I must note that if you do not fix this problem, we cannot continue with the peer-review process or accept your manuscript for publication in GMD.
Juan A. Añel
Geosci. Model Dev. Executive Editor