the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance
Alexander J. Norton
A. Anthony Bloom
Nicholas C. Parazoo
Paul A. Levine
Shuang Ma
Renato K. Braghiere
Luke T. Smallman
Abstract. Terrestrial carbon cycle models are routinely used to determine the response of the land carbon sink under expected future climate change, yet these predictions remain highly uncertain. Increasing the realism of processes in these models may help with predictive skill, but any such addition should be confronted with observations and evaluated in the context of the aggregate behavior of the carbon cycle. Here, two formulations for leaf area index (LAI) phenology are coupled to the same terrestrial biosphere model, one is climate agnostic and the other incorporates direct environmental controls on both timing and growth. Each model is calibrated simultaneously to observations of LAI, net ecosystem exchange (NEE), and biomass using the CARbon DAta-MOdel fraMework (CARDAMOM), and validated against withheld data including eddy covariance estimates of gross primary productivity (GPP) and ecosystem respiration (Re), across six ecosystems from the tropics to high-latitudes. Both model formulations show similar predictive skill for LAI and NEE. However, with the addition of direct environmental controls on LAI, the integrated model explains 22 % more variability in GPP and Re, and reduces biases in these fluxes by 58 % and 77 %, respectively, while also predicting more realistic annual litterfall rates, due to changes in carbon allocation and turnover. We extend this analysis to evaluate the inferred climate sensitivity of LAI and NEE with the new model, and show that the added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. This highlights the benefit of process complexity when inferring underlying processes from Earth observations and in representing the climate response of the terrestrial carbon cycle.
Alexander J. Norton et al.
Status: closed
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RC1: 'Comment on egusphere-2022-1265', Anonymous Referee #1, 26 Jan 2023
General comments:
This manuscript seeks to evaluate the influence of different representations of leaf phenology on modeled terrestrial carbon cycle estimates. The manuscript compares two LAI phenology formulations---one with no climate controls (CDEA, the default in DALEC), and one where timing and growth are influenced by climate (Knorr et al. 2010, with some DALEC-specific modifications). This manuscript uses the CARDAMOM terrestrial ecosystem modeling and data assimilation framework, calibrated jointly against LAI (Copernicus EO 1km product) and NEE (FLUXNET2015) measurements and validated against tower-based GPP and RE (FLUXNET, based on night-time partitioning) and in-situ biomass measurements (with site-specific allometric scaling). The analysis is performed at 6 FLUXNET sites spanning a variety of biomes. Results show that the climate-driven phenology scheme improved predictions of GPP, RE, and litterfall. The climate-driven phenology scheme also led to different NEE sensitivity to precipitation and temperature.
Overall, I found this to be a solid, well-executed study. The science topic --- representations of LAI phenology in vegetation models --- is important and relevant. The modeling approach, and the methods for calibration, validation, and sensitivity analysis, are well-explained and sound. The results are compelling and well-interpreted and contextualized in the literature. I have a few minor comments related to presentation (see detailed comments below), but I think the overall quality of this study is good.
Detailed comments:
[Line 7, "biomass"]
Based on the methods, I think the model is *validated* against biomass but only calibrated against LAI and NEE (i.e., only LAI and NEE appear in the likelihood).[Line 40]
Somewhere in here, you might also consider citing Wheeler & Dietze 2021 (DOI: 10.5194/bg-18-1971-2021).[Line 56, "Bayesian data assimilation"]
Although technically not inaccurate, I find the terms "data assimilation" and "Model data fusion" to be somewhat vague and potentially misleading in this context. Here and elsewhere, I suggest more precise terminology such as (Bayesian) "calibration", "optimization", or "parameter data assimilation", to distinguish what is done here (tuning of model *parameters* that affect the entire course of the simulation) from *state* data assimilation (a stepwise process in which model *states* at a particular time and place are tuned to better match observations, e.g., via Kalman filter, as is done in reanalysis products). (Admittedly, Macbean et al. 2016 and many others also use "data assimilation" this way, so this is not a problem unique to this study.)[Equations 3-5, 10, others]
You might consider using explicit multiplication symbols (x or dot), spacing, fonts (e.g., non-italic font for symbols like LAI), different brackets (e.g., hard brackets for indexes), or different kinds of symbols (e.g., Greek vs. Latin, capital vs. lowecase) to more clearly distinguish between multiplication, function calls, indexing, and multi-letter acronyms (e.g., in equation 2, Phi refers to the Normal CDF called on the fraction in parentheses, whereas in equation 3, the lowercase chi is presumably multiplied by the LAI difference; WLAI isn't immediately obvious as W x LAI).[Equation 7]
This probably needs the (t) index for the terms on the right?[Equation 8]
C(lab) here probably needs a time index (t-1?)[Line 476, "positive ST_LAI"]
This is slightly misleading, since the Knorr formulation predicts near-zero ST_LAI in the warmer sites (which is what one would hope!). I suggest tweaking this sentence to highlight the differences across formulations and sites.[Line 634, "available upon request"]
EGUsphere doesn't have a data use policy (or at least, I couldn't find one) so this is technically not a violation. However, I personally feel that "availability upon request" is unacceptable data sharing policy for modern scientific publications. Unless there is a clear and compelling reason (e.g., government mandate, conservation risk, etc.; if there is such a limitation, it should be explicitly stated), data should to be deposited in a publicly available repository such as Dryad, FigShare, or Open Science Framework. The importance and benefits of open data have been widely documented over the last decade; among the most recent examples is Noy and Noy 2019 (https://doi.org/10.1038/s41563-019-0539-5), and journals are increasingly requiring code and data sharing as a precondition for publication (e.g., AGU data policy -- https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Data-and-Software-for-Authors; GMD data policy -- https://www.geoscientific-model-development.net/policies/code_and_data_policy.html).Citation: https://doi.org/10.5194/egusphere-2022-1265-RC1 -
AC1: 'Reply on RC1', Alexander Norton, 27 Mar 2023
We have combined the reviewer comments and our author response into one document below. Note that the author response is in italic font while the reviewer comments are in bold font.
General comments:
This manuscript seeks to evaluate the influence of different representations of leaf phenology on modeled terrestrial carbon cycle estimates. The manuscript compares two LAI phenology formulations---one with no climate controls (CDEA, the default in DALEC), and one where timing and growth are influenced by climate (Knorr et al. 2010, with some DALEC-specific modifications). This manuscript uses the CARDAMOM terrestrial ecosystem modeling and data assimilation framework, calibrated jointly against LAI (Copernicus EO 1km product) and NEE (FLUXNET2015) measurements and validated against tower-based GPP and RE (FLUXNET, based on night-time partitioning) and in-situ biomass measurements (with site-specific allometric scaling). The analysis is performed at 6 FLUXNET sites spanning a variety of biomes. Results show that the climate-driven phenology scheme improved predictions of GPP, RE, and litterfall. The climate-driven phenology scheme also led to different NEE sensitivity to precipitation and temperature.
Overall, I found this to be a solid, well-executed study. The science topic --- representations of LAI phenology in vegetation models --- is important and relevant. The modeling approach, and the methods for calibration, validation, and sensitivity analysis, are well-explained and sound. The results are compelling and well-interpreted and contextualized in the literature. I have a few minor comments related to presentation (see detailed comments below), but I think the overall quality of this study is good.
We thank the reviewer for their thoughtful comments and encouraging feedback. We have gone through their specific comments below and addressed them point by point, while noting where we have made changes to the manuscript.
Detailed comments:
[Line 7, "biomass"]
Based on the methods, I think the model is *validated* against biomass but only calibrated against LAI and NEE (i.e., only LAI and NEE appear in the likelihood).
Thanks for pointing this out. You are correct that only the LAI and NEE appear in the likelihood. That is an error on our part. The biomass should also appear in the likelihood as it is included in the calibration step, as described in lines 103-112. We have corrected the description of the likelihood in the methods.
[Line 40]
Somewhere in here, you might also consider citing Wheeler & Dietze 2021 (DOI: 10.5194/bg-18-1971-2021).
Thanks for pointing out this interesting study. We have incorporated it into the introduction.
[Line 56, "Bayesian data assimilation"]
Although technically not inaccurate, I find the terms "data assimilation" and "Model data fusion" to be somewhat vague and potentially misleading in this context. Here and elsewhere, I suggest more precise terminology such as (Bayesian) "calibration", "optimization", or "parameter data assimilation", to distinguish what is done here (tuning of model *parameters* that affect the entire course of the simulation) from *state* data assimilation (a stepwise process in which model *states* at a particular time and place are tuned to better match observations, e.g., via Kalman filter, as is done in reanalysis products). (Admittedly, Macbean et al. 2016 and many others also use "data assimilation" this way, so this is not a problem unique to this study.)
We agree that the terminology in this field can be convoluted, especially to newcomers. Your suggestion is taken onboard and we have modified the paragraph to state “we use a Bayesian parameter data assimilation system…” for specificity.
[Equations 3-5, 10, others]
You might consider using explicit multiplication symbols (x or dot), spacing, fonts (e.g., non-italic font for symbols like LAI), different brackets (e.g., hard brackets for indexes), or different kinds of symbols (e.g., Greek vs. Latin, capital vs. lowecase) to more clearly distinguish between multiplication, function calls, indexing, and multi-letter acronyms (e.g., in equation 2, Phi refers to the Normal CDF called on the fraction in parentheses, whereas in equation 3, the lowercase chi is presumably multiplied by the LAI difference; WLAI isn't immediately obvious as W x LAI).
Very good suggestion. This should help with clarity of the equations. We have implemented all of the reviewers suggestions to improve readability of the mathematics.
[Equation 7]
This probably needs the (t) index for the terms on the right?
Yes, good catch, thank you. Corrected in the new version.
[Equation 8]
C(lab) here probably needs a time index (t-1?)
Yes, that is correct. Thanks for pointing that out. Corrected in the new version.
[Line 476, "positive ST_LAI"]
This is slightly misleading, since the Knorr formulation predicts near-zero ST_LAI in the warmer sites (which is what one would hope!). I suggest tweaking this sentence to highlight the differences across formulations and sites.
Yes, we understand how that sentence could be misleading and appear to conflict with the other results. The point we are trying to make is that, for none of the posterior samples is there a negative ST_LAI. We have rephrased this to say “The median $S_{LAI}^T$ on an annual timescale ranges from zero to strongly positive depending upon the model and site”. Furthermore, we have added this sentence to the end of the paragraph “In neither model does the LAI show a negative sensitivity to temperature” to highlight the point that LAI temperature sensitivities can only ever be positive from these two models.
[Line 634, "available upon request"]
EGUsphere doesn't have a data use policy (or at least, I couldn't find one) so this is technically not a violation. However, I personally feel that "availability upon request" is unacceptable data sharing policy for modern scientific publications. Unless there is a clear and compelling reason (e.g., government mandate, conservation risk, etc.; if there is such a limitation, it should be explicitly stated), data should to be deposited in a publicly available repository such as Dryad, FigShare, or Open Science Framework. The importance and benefits of open data have been widely documented over the last decade; among the most recent examples is Noy and Noy 2019 (https://doi.org/10.1038/s41563-019-0539-5), and journals are increasingly requiring code and data sharing as a precondition for publication (e.g., AGU data policy -- https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Data-and-Software-for-Authors; GMD data policy -- https://www.geoscientific-model-development.net/policies/code_and_data_policy.html).
Yes, we agree that open data sharing should be a priority. Upon publication we will provide citable repository links to the data used in the study (site level observations and forcing data), model code and post-processing analysis code. The model code will be shared as a CARDAMOM github repository similar to the approach taken in Yang et al. (2021, doi: 10.5194/gmd-15-1789-2022).
Citation: https://doi.org/10.5194/egusphere-2022-1265-AC1
-
AC1: 'Reply on RC1', Alexander Norton, 27 Mar 2023
-
RC2: 'Comment on egusphere-2022-1265', Anonymous Referee #2, 24 Feb 2023
Generally this paper is a good discussion on evaluation of the inferred climate sensitivity of LAI and NEE with the models, added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. The research showed the benefit of process complexity when inferring underlying processes from Earth observations and in representing the climate response of the terrestrial carbon cycle.
Page 6, why the LAI Phenology Models cannot be used only one model.
Page 14, Figure 2: you can highlight which graphs are the underlining ones and you've discussed.
Page 15, Figure 3: the RMSE units should be labeled clear.
Citation: https://doi.org/10.5194/egusphere-2022-1265-RC2 -
AC2: 'Reply on RC2', Alexander Norton, 27 Mar 2023
We have combined the reviewer comments and our author response into one document below. Note that the author response is in italic font while the reviewer comments are in bold font.
Generally this paper is a good discussion on evaluation of the inferred climate sensitivity of LAI and NEE with the models, added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. The research showed the benefit of process complexity when inferring underlying processes from Earth observations and in representing the climate response of the terrestrial carbon cycle.
We would like to thank this reviewer for the encouraging comments and recognition of the benefit of this research.
Page 6, why the LAI Phenology Models cannot be used only one model.
We assume there is some confusion around the implementation of the “model” and the data-fusion system. To clarify, both LAI phenology models are implemented within the same ecosystem model (DALEC). Each version of DALEC (DALEC_Knorr and DALEC_CDEA) is then used within the CARDAMOM model-data fusion system, which tunes parameters and initial conditions of DALEC to optimally fit the prior information and new observational information.
Page 14, Figure 2: you can highlight which graphs are the underlining ones and you've discussed.
We are unsure what the reviewer is requesting. All of the graphs are important to present as they represent the sites studied and model-observed comparisons. Each graph is also discussed in the main text, and we refer to the study site codes which are indicated in the title of each plot of Figure 2. We hope this clarifies the reviewer’s concern.
Page 15, Figure 3: the RMSE units should be labeled clear.
As far as we can tell, the units for RMSE in Figure 3 are clearly labeled. These are labeled in the same way as the bias metrics in that figure and align with the units used in the other figures and reported values. We believe this figure fulfills the requirements in Biogeosciences guidelines (https://www.biogeosciences.net/submission.html#figurestables). However, we will happily make modifications as per the editors request.
Citation: https://doi.org/10.5194/egusphere-2022-1265-AC2
-
AC2: 'Reply on RC2', Alexander Norton, 27 Mar 2023
Status: closed
-
RC1: 'Comment on egusphere-2022-1265', Anonymous Referee #1, 26 Jan 2023
General comments:
This manuscript seeks to evaluate the influence of different representations of leaf phenology on modeled terrestrial carbon cycle estimates. The manuscript compares two LAI phenology formulations---one with no climate controls (CDEA, the default in DALEC), and one where timing and growth are influenced by climate (Knorr et al. 2010, with some DALEC-specific modifications). This manuscript uses the CARDAMOM terrestrial ecosystem modeling and data assimilation framework, calibrated jointly against LAI (Copernicus EO 1km product) and NEE (FLUXNET2015) measurements and validated against tower-based GPP and RE (FLUXNET, based on night-time partitioning) and in-situ biomass measurements (with site-specific allometric scaling). The analysis is performed at 6 FLUXNET sites spanning a variety of biomes. Results show that the climate-driven phenology scheme improved predictions of GPP, RE, and litterfall. The climate-driven phenology scheme also led to different NEE sensitivity to precipitation and temperature.
Overall, I found this to be a solid, well-executed study. The science topic --- representations of LAI phenology in vegetation models --- is important and relevant. The modeling approach, and the methods for calibration, validation, and sensitivity analysis, are well-explained and sound. The results are compelling and well-interpreted and contextualized in the literature. I have a few minor comments related to presentation (see detailed comments below), but I think the overall quality of this study is good.
Detailed comments:
[Line 7, "biomass"]
Based on the methods, I think the model is *validated* against biomass but only calibrated against LAI and NEE (i.e., only LAI and NEE appear in the likelihood).[Line 40]
Somewhere in here, you might also consider citing Wheeler & Dietze 2021 (DOI: 10.5194/bg-18-1971-2021).[Line 56, "Bayesian data assimilation"]
Although technically not inaccurate, I find the terms "data assimilation" and "Model data fusion" to be somewhat vague and potentially misleading in this context. Here and elsewhere, I suggest more precise terminology such as (Bayesian) "calibration", "optimization", or "parameter data assimilation", to distinguish what is done here (tuning of model *parameters* that affect the entire course of the simulation) from *state* data assimilation (a stepwise process in which model *states* at a particular time and place are tuned to better match observations, e.g., via Kalman filter, as is done in reanalysis products). (Admittedly, Macbean et al. 2016 and many others also use "data assimilation" this way, so this is not a problem unique to this study.)[Equations 3-5, 10, others]
You might consider using explicit multiplication symbols (x or dot), spacing, fonts (e.g., non-italic font for symbols like LAI), different brackets (e.g., hard brackets for indexes), or different kinds of symbols (e.g., Greek vs. Latin, capital vs. lowecase) to more clearly distinguish between multiplication, function calls, indexing, and multi-letter acronyms (e.g., in equation 2, Phi refers to the Normal CDF called on the fraction in parentheses, whereas in equation 3, the lowercase chi is presumably multiplied by the LAI difference; WLAI isn't immediately obvious as W x LAI).[Equation 7]
This probably needs the (t) index for the terms on the right?[Equation 8]
C(lab) here probably needs a time index (t-1?)[Line 476, "positive ST_LAI"]
This is slightly misleading, since the Knorr formulation predicts near-zero ST_LAI in the warmer sites (which is what one would hope!). I suggest tweaking this sentence to highlight the differences across formulations and sites.[Line 634, "available upon request"]
EGUsphere doesn't have a data use policy (or at least, I couldn't find one) so this is technically not a violation. However, I personally feel that "availability upon request" is unacceptable data sharing policy for modern scientific publications. Unless there is a clear and compelling reason (e.g., government mandate, conservation risk, etc.; if there is such a limitation, it should be explicitly stated), data should to be deposited in a publicly available repository such as Dryad, FigShare, or Open Science Framework. The importance and benefits of open data have been widely documented over the last decade; among the most recent examples is Noy and Noy 2019 (https://doi.org/10.1038/s41563-019-0539-5), and journals are increasingly requiring code and data sharing as a precondition for publication (e.g., AGU data policy -- https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Data-and-Software-for-Authors; GMD data policy -- https://www.geoscientific-model-development.net/policies/code_and_data_policy.html).Citation: https://doi.org/10.5194/egusphere-2022-1265-RC1 -
AC1: 'Reply on RC1', Alexander Norton, 27 Mar 2023
We have combined the reviewer comments and our author response into one document below. Note that the author response is in italic font while the reviewer comments are in bold font.
General comments:
This manuscript seeks to evaluate the influence of different representations of leaf phenology on modeled terrestrial carbon cycle estimates. The manuscript compares two LAI phenology formulations---one with no climate controls (CDEA, the default in DALEC), and one where timing and growth are influenced by climate (Knorr et al. 2010, with some DALEC-specific modifications). This manuscript uses the CARDAMOM terrestrial ecosystem modeling and data assimilation framework, calibrated jointly against LAI (Copernicus EO 1km product) and NEE (FLUXNET2015) measurements and validated against tower-based GPP and RE (FLUXNET, based on night-time partitioning) and in-situ biomass measurements (with site-specific allometric scaling). The analysis is performed at 6 FLUXNET sites spanning a variety of biomes. Results show that the climate-driven phenology scheme improved predictions of GPP, RE, and litterfall. The climate-driven phenology scheme also led to different NEE sensitivity to precipitation and temperature.
Overall, I found this to be a solid, well-executed study. The science topic --- representations of LAI phenology in vegetation models --- is important and relevant. The modeling approach, and the methods for calibration, validation, and sensitivity analysis, are well-explained and sound. The results are compelling and well-interpreted and contextualized in the literature. I have a few minor comments related to presentation (see detailed comments below), but I think the overall quality of this study is good.
We thank the reviewer for their thoughtful comments and encouraging feedback. We have gone through their specific comments below and addressed them point by point, while noting where we have made changes to the manuscript.
Detailed comments:
[Line 7, "biomass"]
Based on the methods, I think the model is *validated* against biomass but only calibrated against LAI and NEE (i.e., only LAI and NEE appear in the likelihood).
Thanks for pointing this out. You are correct that only the LAI and NEE appear in the likelihood. That is an error on our part. The biomass should also appear in the likelihood as it is included in the calibration step, as described in lines 103-112. We have corrected the description of the likelihood in the methods.
[Line 40]
Somewhere in here, you might also consider citing Wheeler & Dietze 2021 (DOI: 10.5194/bg-18-1971-2021).
Thanks for pointing out this interesting study. We have incorporated it into the introduction.
[Line 56, "Bayesian data assimilation"]
Although technically not inaccurate, I find the terms "data assimilation" and "Model data fusion" to be somewhat vague and potentially misleading in this context. Here and elsewhere, I suggest more precise terminology such as (Bayesian) "calibration", "optimization", or "parameter data assimilation", to distinguish what is done here (tuning of model *parameters* that affect the entire course of the simulation) from *state* data assimilation (a stepwise process in which model *states* at a particular time and place are tuned to better match observations, e.g., via Kalman filter, as is done in reanalysis products). (Admittedly, Macbean et al. 2016 and many others also use "data assimilation" this way, so this is not a problem unique to this study.)
We agree that the terminology in this field can be convoluted, especially to newcomers. Your suggestion is taken onboard and we have modified the paragraph to state “we use a Bayesian parameter data assimilation system…” for specificity.
[Equations 3-5, 10, others]
You might consider using explicit multiplication symbols (x or dot), spacing, fonts (e.g., non-italic font for symbols like LAI), different brackets (e.g., hard brackets for indexes), or different kinds of symbols (e.g., Greek vs. Latin, capital vs. lowecase) to more clearly distinguish between multiplication, function calls, indexing, and multi-letter acronyms (e.g., in equation 2, Phi refers to the Normal CDF called on the fraction in parentheses, whereas in equation 3, the lowercase chi is presumably multiplied by the LAI difference; WLAI isn't immediately obvious as W x LAI).
Very good suggestion. This should help with clarity of the equations. We have implemented all of the reviewers suggestions to improve readability of the mathematics.
[Equation 7]
This probably needs the (t) index for the terms on the right?
Yes, good catch, thank you. Corrected in the new version.
[Equation 8]
C(lab) here probably needs a time index (t-1?)
Yes, that is correct. Thanks for pointing that out. Corrected in the new version.
[Line 476, "positive ST_LAI"]
This is slightly misleading, since the Knorr formulation predicts near-zero ST_LAI in the warmer sites (which is what one would hope!). I suggest tweaking this sentence to highlight the differences across formulations and sites.
Yes, we understand how that sentence could be misleading and appear to conflict with the other results. The point we are trying to make is that, for none of the posterior samples is there a negative ST_LAI. We have rephrased this to say “The median $S_{LAI}^T$ on an annual timescale ranges from zero to strongly positive depending upon the model and site”. Furthermore, we have added this sentence to the end of the paragraph “In neither model does the LAI show a negative sensitivity to temperature” to highlight the point that LAI temperature sensitivities can only ever be positive from these two models.
[Line 634, "available upon request"]
EGUsphere doesn't have a data use policy (or at least, I couldn't find one) so this is technically not a violation. However, I personally feel that "availability upon request" is unacceptable data sharing policy for modern scientific publications. Unless there is a clear and compelling reason (e.g., government mandate, conservation risk, etc.; if there is such a limitation, it should be explicitly stated), data should to be deposited in a publicly available repository such as Dryad, FigShare, or Open Science Framework. The importance and benefits of open data have been widely documented over the last decade; among the most recent examples is Noy and Noy 2019 (https://doi.org/10.1038/s41563-019-0539-5), and journals are increasingly requiring code and data sharing as a precondition for publication (e.g., AGU data policy -- https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Data-and-Software-for-Authors; GMD data policy -- https://www.geoscientific-model-development.net/policies/code_and_data_policy.html).
Yes, we agree that open data sharing should be a priority. Upon publication we will provide citable repository links to the data used in the study (site level observations and forcing data), model code and post-processing analysis code. The model code will be shared as a CARDAMOM github repository similar to the approach taken in Yang et al. (2021, doi: 10.5194/gmd-15-1789-2022).
Citation: https://doi.org/10.5194/egusphere-2022-1265-AC1
-
AC1: 'Reply on RC1', Alexander Norton, 27 Mar 2023
-
RC2: 'Comment on egusphere-2022-1265', Anonymous Referee #2, 24 Feb 2023
Generally this paper is a good discussion on evaluation of the inferred climate sensitivity of LAI and NEE with the models, added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. The research showed the benefit of process complexity when inferring underlying processes from Earth observations and in representing the climate response of the terrestrial carbon cycle.
Page 6, why the LAI Phenology Models cannot be used only one model.
Page 14, Figure 2: you can highlight which graphs are the underlining ones and you've discussed.
Page 15, Figure 3: the RMSE units should be labeled clear.
Citation: https://doi.org/10.5194/egusphere-2022-1265-RC2 -
AC2: 'Reply on RC2', Alexander Norton, 27 Mar 2023
We have combined the reviewer comments and our author response into one document below. Note that the author response is in italic font while the reviewer comments are in bold font.
Generally this paper is a good discussion on evaluation of the inferred climate sensitivity of LAI and NEE with the models, added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. The research showed the benefit of process complexity when inferring underlying processes from Earth observations and in representing the climate response of the terrestrial carbon cycle.
We would like to thank this reviewer for the encouraging comments and recognition of the benefit of this research.
Page 6, why the LAI Phenology Models cannot be used only one model.
We assume there is some confusion around the implementation of the “model” and the data-fusion system. To clarify, both LAI phenology models are implemented within the same ecosystem model (DALEC). Each version of DALEC (DALEC_Knorr and DALEC_CDEA) is then used within the CARDAMOM model-data fusion system, which tunes parameters and initial conditions of DALEC to optimally fit the prior information and new observational information.
Page 14, Figure 2: you can highlight which graphs are the underlining ones and you've discussed.
We are unsure what the reviewer is requesting. All of the graphs are important to present as they represent the sites studied and model-observed comparisons. Each graph is also discussed in the main text, and we refer to the study site codes which are indicated in the title of each plot of Figure 2. We hope this clarifies the reviewer’s concern.
Page 15, Figure 3: the RMSE units should be labeled clear.
As far as we can tell, the units for RMSE in Figure 3 are clearly labeled. These are labeled in the same way as the bias metrics in that figure and align with the units used in the other figures and reported values. We believe this figure fulfills the requirements in Biogeosciences guidelines (https://www.biogeosciences.net/submission.html#figurestables). However, we will happily make modifications as per the editors request.
Citation: https://doi.org/10.5194/egusphere-2022-1265-AC2
-
AC2: 'Reply on RC2', Alexander Norton, 27 Mar 2023
Alexander J. Norton et al.
Alexander J. Norton et al.
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