the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Feb 2025
Evaluating Dynamic Global Vegetation Models in China: Challenges in capturing trends in Leaf Area and Gross Primary Productivity, but effective seasonal variation representation
Abstract. Terrestrial ecosystems are crucial in mitigating global climate change, and Dynamic Global Vegetation Models (DGVMs) have become essential tools for simulating these ecosystems. However, uncertainties remain in DGVM simulations for China, highlighting the need to systematic evaluations of their dynamics across various time scales to enhance model performance. As such, we utilize reprocessed monthly MODIS Leaf Area Index (LAI) and Contiguous Solar-induced Fluorescence (CSIF) data as observational references to assess the long-term trends and seasonal variations of LAI and Gross Primary Production (GPP) simulated by 14 models (CABLE-POP, CLASSIC, CLM5.0, DLEM, IBIS, ISAM, ISBA-CTRIP, JULES, LPJ-GUESS, LPX, OCN, ORCHIDEEv3, SDGVM, and VISIT) in China from 2003 to 2019. Additionally, we evaluate the trends and seasonal variations of simulated LAI and GPP in response to environmental and climatic factors. Our findings indicate that: (1) While the overall trend of simulated LAI is captured, the spatial performance of simulated LAI and GPP is poor, with underestimation in forested areas, overestimation in grasslands, and misestimation in croplands; (2) The models misestimate the simulated LAI and GPP responses to changes in environmental factors, and their inaccuracy in capturing anthropogenic impacts on vegetation dynamics. We indicate that the main reason for the model's misestimation is that the model's understanding of the CO2 fertilization effect is inadequate, and thus fails to simulate the vegetation response to CO2 concentration. (3) Despite these issues, the models can effectively capture the seasonality of LAI and GPP in China, largely due to their robust representation of seasonal responses to climate factors.
- Preprint
(3871 KB) - Metadata XML
-
Supplement
(4076 KB) - BibTeX
- EndNote
Status: open (extended)
-
RC1: 'Comment on esd-2024-44', Anonymous Referee #1, 03 Mar 2025
reply
Review comments of “Evaluating Dynamic Global Vegetation Models in China: Challenges in capturing trends in Leaf Area and Gross Primary Productivity, but effective seasonal variation representation.”
Title: “Evaluating Dynamic Global Vegetation Models in China: Challenges in capturing trends in Leaf Area and Gross Primary Productivity, but effective seasonal variation representation.” The title is appropriate, but I would shorten it by removing the phrase “but effective seasonal variation representation.” The title modification is a suggestion, primarily to make the title concise. The abstract and text, in several instances throughout the manuscript, emphasize the fact that seasonal variation is well captured by the models.
General Comments:
The task taken up in the manuscript, evaluating the DGVMs, is essential for the scientific community to know the strengths and weaknesses of model performance in significant land regions, such as China. The study does a good job of comparing the model outputs with observations. It makes a good argument in assessing the errors in simulation and the drivers responsible for them. The study makes the essential hypothesis of why the observed variation in model estimates is occurring. The discussed impacts, ranging from stomatal conductance, lack of accurate N and P cycles in models, using constant temperature sensitivity curves, to single pft parameters for diverse ecosystems, are issues other studies can focus on for improving the models and their global applicability.
However, the authors should address a few concerns before the manuscript is ready for publication.
Specific Comments:
- On page 4, starting in line 98, the statement "However, it remains poorly documented what the comparison of between observations and model simulations, leading to significant uncertainty about the application of DGVMs in China,” is confusing for the readers. The authors should explain what is lacking in more detail, as this is the main gap the authors are trying to address in this manuscript.
- Overall, the introduction section makes a compelling case that DGVMs require regional scale evaluation using observations, as many studies have found significant errors in simulating regional terrestrial fluxes and vegetation growth. However, this manuscript can include a paragraph in the introduction section (preferably at the end of the section) that explains the broader impacts of the study and how this is beneficial to future regional-scale studies using DGVMs or regional studies in general. This section can elucidate studies that will benefit from assessing various models.
- Equation (1) in the manuscript uses the annual CO2, temperature, and precipitation all impacted by anthropogenic activities. What does the Xanthropogenic refer to, then? The variables investigated here, like temperature, precipitation, radiation, and vegetation parameters, have high seasonality. Using annual data for the sensitivity analysis might cause a loss of information on vegetation growth and not provide a complete picture of the impact of various drivers. In this analysis, how do you justify using annual temperature, precipitation, and radiation? Since a seasonal relation of monthly LAI and various drivers is also considered in the study, what additional information is being produced from equation (1) and the annual linear regression analysis?
- Different models are identified to be performing well in various sections of the manuscript. For example, DLEM and IBIS in the trend analysis, and CLM5 in simulating the impact of CO2 on LAI. While discussing the DGVMs challenges in accurately simulating LAI and GPP, authors provide arguments on how DGVMs are missing some processes or do not have diversity in parameters used. Two things are missing in the discussion, which readers might be looking at when referring to this manuscript. (1) What is the reason for individual models not performing well, and what are these models missing regarding processes and parameters? Of the 14 models investigated, only a few are highlighted in the manuscript, which are performing well. Adding one table to highlight the differences in processes and areas where models can improve will greatly benefit the community. (2) The discussion on which models will perform better for the studies on vegetation over China. Provide recommendations supported by the results from this study.
- The inlet bar graphs in Figures S2 and S3 show the area percentage of significant decrease, no significant change, significant change, and others. Should they add up to 100%? How is this calculated? Do readers have enough information to understand these and similar figures in the supplement material?
Technical corrections:
- On page 1, line 27, “model’s understanding of the CO2 fertilization effect...” should be “model’s representation of the CO2 fertilization effect...”. Please check and change accordingly.
- On page 3, line 68, “Medlyn et al. (2015) utilized used...” has two verbs and should be corrected.
- On page 9, line 249, “While The...” capitalization of word in the middle of the sentence should be corrected.
- On page 19, line 346, “he” should be “the”. Please check.
- On page 23, line 393, the text “This apparent accuracy...” should be “This apparent inaccuracy...”. Please check. Correct me if I am wrong.
Citation: https://doi.org/10.5194/esd-2024-44-RC1
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 164 | 27 | 7 | 198 | 21 | 7 | 6 |
- HTML: 164
- PDF: 27
- XML: 7
- Total: 198
- Supplement: 21
- BibTeX: 7
- EndNote: 6
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1