the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Jan 2026
Regional contrasts of ecosystem productivity sensitivity to atmospheric CO2 growth across East Asia
Abstract. Elevated global atmospheric CO2 intensifies the complexity of climate variability and ecosystem productivity, and its impact on terrestrial ecosystems carbon cycle remains unclear. Using twelve Dynamic Global Vegetation Models (DGVMs) in Trends in land carbon cycle datasets and Global Carbon Budget dataset, we quantified the sensitivity of ecosystem productivity-defined as the rate of change in productivity with global atmospheric CO2 growth rate-in East Asia from 1959 to 2023. Most DGVMs showed net ecosystem production sensitivity as negative, implying a weakening of terrestrial carbon absorption capacity in response to elevated atmospheric CO2. By separating East Asia into monsoon and non-monsoon regions, we examined the temporal changes in gross primary productivity sensitivity, which has been decreasing in both regions since the late 1990s. These productivity responses were primarily controlled by soil moisture sensitivity in non-monsoon region, whereas photosynthetically active radiation emerged as the key factor in monsoon region. Furthermore, the dominance of croplands and woody savannas in monsoon region contributes to regional difference in the mechanism associated with vegetation productivity to atmospheric CO2 growth. By considering regional climate systems and vegetation characteristics, this study highlights that environmental and structural differences influence the ecosystem response to atmospheric CO2 growth. Ultimately, our findings suggest that considering regionally distinct climate-vegetation feedbacks is essential for improving the accuracy of global carbon cycle projections under future climate change.
- Preprint
(1768 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-353', Anonymous Referee #1, 27 Mar 2026
-
RC2: 'Comment on egusphere-2026-353', Anonymous Referee #2, 29 Mar 2026
The study examines the sensitivity of GPP and NEP parameters to fluctuations in the rate of increase in global COâ‚‚ concentrations in the East Asian region, aiming to improve the accuracy of predictive dynamic models of atmospheric COâ‚‚ concentrations that account for carbon cycle feedbacks. While the authors acknowledge the significant influence of regional factors on carbon balance, these factors were not integrated into the methodology used to assess the sensitivity of ecosystem productivity. The methodology section requires significant expansion. Currently, there is insufficient justification for the chosen approach and insufficient detail on the specific data processing steps.
Major comments:
-
From the paper, it is unclear which detrending methods were applied to each parameter explored. It is important due to the different robustness of the existing methods and, therefore, for the accuracy of the final results.
-
Using a 20-year sliding window can introduce significant distortions in the final result due to potential cyclic fluctuations or extreme events. Therefore, it is necessary to either select a shorter window - with the length justified in advance based on additional analysis - or use other methods to calculate interannual differences.
-
The behaviour of the data presented in Fig. 4 (specifically b, c, f, and, in particular, e) suggests that the used linear approximations (likely based on linear correlation analysis) are incorrect. An exploratory data analysis is required before linear correlation can be applied. Furthermore, transitions between samples in the year-ordered sequence demonstrate different directions across various time periods. This factor also calls into question the applicability of linear regression analysis.
-
The correlation analysis between pairs of sensitivities used in Section 3.2 raises doubts about its advisability from a mathematical standpoint, since ultimately the dACGR terms drop out of the ratio, leaving only the relation of the incrementAsians of the studied parameters, for example, [dGPP/dACGR] <-> [dSoilMoisture/dACGR] => dGPP <-> dSoilMoisture (Fig. 4.a,b,e,f), i.e., it will not affect the correlation.
-
The analysis presented in this study does not account for factors related to ecosystem evolution, assuming that the ecosystem of the selected area is stable, even though ecosystems can both develop and degrade over such a long period, which in turn leads to changes in productivity characteristics.
Minor comments:
-
Table 1 lacks relevant study parameters. Consider moving it to the appendix and updating it with a detailed description of DVGM's characteristics pertinent to the study.
-
Lines 83-85: It is stated that the spatial resolution of the Land Cover data has been changed to 0.5° × 0.5°, whereas the rest of the data uses 1.0° × 1.0°. The use of a different resolution requires an explanation of why this was done and how it affects the estimation methods. It is also noted that when changing the resolution, the most frequent land cover type is used; however, no estimates or background information are provided for this concept.
-
Lines 88-89: For their research, the authors adopted the methodology of Li et al. (2024). It is valuable to include this work in the outlook, explain the disadvantages of the existing approach, and clarify the relevance and sense of the proposed improvements.
-
Fig. 1: Correct dAGR to dACGR.
-
Lines 110-112: It is stated that the NEP sensitivity is examined for each DGVM model over the period 1959-2023; however, the results presented (Fig. 2a) show only a single value per model rather than a time series. There is no explanation of how the presented values were obtained.
-
Lines 133-134: It is stated that spatial analysis uses average values of GPP sensitivity to increases in COâ‚‚ concentration. Given the dynamics of the characteristics under study over the selected period, using an average value for the assessment is questionable. A justification should be provided, or the possibility of using secondary parameters should be analysed.
-
Fig. 4-6: All captions include "***p<0.01; **p<0.05; *p<0.1; not significant p>0.1" part, but each figure does not include data relevant to all the probabilities.
-
Line 192: the sign of the correlation coefficient in the text does not correspond to the one in Fig. 4f.
Citation: https://doi.org/10.5194/egusphere-2026-353-RC2 -
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 142 | 49 | 19 | 210 | 19 | 27 |
- HTML: 142
- PDF: 49
- XML: 19
- Total: 210
- BibTeX: 19
- EndNote: 27
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The manuscript investigates the spatial and temporal variations of ecosystem productivity sensitivity to the atmospheric CO2 growth rate (ACGR) across East Asia, utilizing the Global Carbon Budget and a subset of TRENDY v13 dynamic global vegetation models. While evaluating the regional divergence of carbon cycle feedback is an important pursuit, the manuscript currently suffers from critical methodological flaws. Specifically, concerns regarding the statistical definition of "sensitivity," unaddressed confounding climatic variables, and structural inconsistencies in the narrative must be thoroughly addressed. Extensive revisions are required to ensure the robustness of the proposed physical mechanisms.
Major comments
Minor comments
Lines 54-59. The definition of ACGR needs clarification. The text describes ACGR as the annual change in atmospheric CO2 concentration, but the unit is mass-based unit rather than a concentration-based unit.
Lines 88-94. The exact regression framework is under-specified. Please provide the explicit regression equation used to define "sensitivity", the units of the regression slope, and how significance was assessed for each moving window.
Lines 105-106 and 245-267. PAR is analyzed using "actual PAR values instead of ACGR sensitivity", whereas the other axes in Figs. 4-6 are sensitivity metrics. This mixes variables of different statistical meaning.
Lines 110-112. The sentence over-interprets the sign of the fitted slope. A negative NEP sensitivity does not, by itself, demonstrate a weakening of ecosystem carbon uptake ability.
Lines 112-117. The model-selection criterion needs stronger justification. Retaining models based on sign consistency or significance only in the last 20-year window appears rather ad hoc, especially because the subsequent analyses cover the full 1959-2023 period.
Lines 121-127. The regional partition requires a stronger justification. The text itself acknowledges that the GPP sensitivity pattern does not perfectly align with the East Asian monsoon boundary, yet the main comparison is built on that mask. A sensitivity test with an alternative regional definition would strengthen the analysis.
Lines 140-146. Expressions such as "downward trend in negative NEP sensitivity" and "reduction in negative GPP sensitivity" are sign-ambiguous and hard to interpret. Please rewrite these passages using clearer language, for example by stating explicitly whether the values are becoming more negative or moving towards zero.
Lines 156-158. The opening sentence of Section 3.2 is grammatically incomplete and conceptually abrupt.
Lines 170-174. There are both language and presentation issues here.
Lines 190-191. There is a clear inconsistency between the text and Fig. 4f. The manuscript reports r = 0.66, but Fig. 4f shows a negative relationship and labels it as r = -0.66. Please correct either the text or the figure.
Lines 239-242. The causal language is too strong. A coefficient of determination from a bivariate relationship is not sufficient to conclude that PAR is "the primary factor" weakening GPP sensitivity.
Lines 250-264. Trend slopes for precipitation, cloud cover, and PAR are reported without units.
Lines 270-281. The land-cover discussion has a temporal mismatch. The manuscript uses mean MODIS land cover from 2001-2023 to interpret sensitivity behaviour over 1959-2023. This should be acknowledged more explicitly as a limitation.
Lines 295-306. The conclusion again goes beyond what the presented metric can directly support.