Elevation-dependent pattern of net CO_2 uptake across China

Domain: earth_science
Published: 2024-03-20
Editorial outcome: major_revision

Abstract
The elevation gradient has long been known to be vital in shaping the structure and function of terrestrial ecosystems, but little is known about the elevation-dependent pattern of net CO2 uptake, denoted by net ecosystem productivity (NEP). Here, by analyzing data from 203 eddy covariance sites across China, we report a negative linear elevation-dependent pattern of NEP, collectively shaped by varying hydrothermal factors, nutrient supply, and ecosystem types. Furthermore, the NEP shows a higher temperature sensitivity in high-elevation environments (3000–5000 m) compared with the lower-elevation environments (<3000 m). Model ensemble and satellite-based observations consistently reveal more rapid relative changes in NEP in high-elevation environments during the last four decades. Machine learning also predicts a stronger relative increase in high-elevation environments, whereas less change is expected at lower elevations. We therefore conclude a varying elevation-dependent pattern of the NEP of terrestrial ecosystems in China, although there is significant uncertainty involved.

Introduction
Terrestrial ecosystems play a fundamental role in counteracting increasing CO2 concentrations in Earth’s atmosphere1,2. The net uptake of CO2 by terrestrial ecosystems shows a geographical pattern (denoted here by net ecosystem productivity, NEP), with a peak in NEP in subtropical and temperate forests3. Similar to latitude, the elevation gradient has long been considered to exert an important physical control on vegetation, which can be traced back to Alexander von Humboldt in the nineteenth century4, or even earlier. Similar phenomena were also recorded by Andrew D. Hopkins, who observed a progressive delay in leafing out with increasing elevation5.

The elevation gradient affects temperature, precipitation and the supply of nutrients, with colder temperatures, lower precipitation and a smaller supply of nutrients at higher elevations6. This elevation-dependent pattern in the supply of resources largely determines both the structure of ecosystems (e.g., the ecosystem type, biodiversity, and soil characteristics) and their function (e.g., photosynthesis, phenology, and microbial activity)4–7. Several studies have been conducted across Earth’s high-elevation environments, e.g., the Alps8, Andes9, and Tibetan Plateau10, suggesting varying elevation-dependent patterns of photosynthesis and respiration. The strong elevation-dependent pattern of resources supply may also affect the NEP, i.e., the net balance between photosynthesis and respiration, but we do not have a broad picture of Earth’s elevation-dependent pattern of NEP due to a lack of instrumental measurements of CO2 exchange like eddy covariance observations.

High-elevation environments, i.e., high mountains (defined as >3000 m above sea-level), hold a disproportionally high proportion of Earth’s biodiversity11, despite their colder, drier climate and stronger nutrient limitation12. However, the current warming of Earth’s climate will affect the net uptake of CO2 in high-elevation environments by relieving the limitation of temperature. More importantly, these regions will experience elevation-dependent warming—that is, high-elevation environments will experience more rapid changes in temperature than lower-elevation environments6. In addition, there is less direct disturbance from humans in high-elevation environments, implying the responses of their CO2 uptake to climate change may differ from lower-elevation environments. Taken together, these factors suggest that climate warming in high-elevation environments could cause a more robust change in the uptake of CO2 than at lower elevations, although evidence is still lacking.

China is the third largest country on Earth, and the elevation of China ranges from sea level to 8844 m and includes a wide range of tropical, subtropical, temperate, boreal, and alpine ecosystems. Roughly 65% of China’s land surface is covered by mountains13. These mountains are home to 22% of China’s population, indicating much lower human pressures than at lower elevations13; plus, they also receive much stronger protection and restoration efforts. For example, most newly planted forests are located in mountain environments14—whereas non-mountainous regions are occupied by croplands, buildings and other artificial land surfaces. Most protected areas are also in mountainous regions, such as the newly built Sanjiangyuan (>4000 m) and Giant Panda (1500–3000 m) National Parks14. Existing studies have highlighted that these mountains function as a vital net CO2 sink3,15 and eddy covariance observations have been widely established in China during the last two decades16–18. China’s topography and its eddy covariance network therefore provide a unique opportunity to explore the elevation-dependent pattern of NEP under the influence of a changing climate and human activities. Therefore, in this study, we collected observations from 203 eddy covariance sites across China to determine the elevation-dependent pattern of NEP and its changes under a warming climate and changing human activities. We refer to this dataset as EddyChina2023 (Fig. 1a; more details of the dataset can be seen in Text S1, Figs. S1–S3 and Table S1). We first describe the pattern of NEP with elevation, as well as the environmental drivers of the elevation-dependent pattern of NEP. We then compare the difference between the high-elevation environments (3000–5000 m) and their lower elevation counterparts (<3000 m) in terms of the temperature sensitivity. We also explore the variation in NEP along the elevation gradient during the past four decades, based on biogeochemical models and satellite observations. Finally, we predict how the elevation-dependent pattern of NEP will be affected under various climate scenarios using machine learning, the eddy covariance dataset, and Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Taking China as an example, the objective of this study was to draw a broad picture of Earth’s elevation-dependent patten of NEP and to determine whether there will be a more robust or different change at higher elevations compared with that at lower elevations.

Results
Elevation-dependent pattern of NEP and its drivers
We first explore the elevation-dependent pattern of NEP. It shows a linear negative elevation-dependent pattern attributable to both the peak CO2 rate and the length of the growing season (Fig. 1b, c)—that is, a shorter growing season and lower peak CO2 uptake at higher elevations. There is a clear elevation-dependent pattern wherein a 100-m increase in elevation causes an NEP loss of about 4 g C m−2 yr−1. In fact, both gross primary productivity (GPP) and ecosystem respiration (RE) are significantly and negatively dependent on elevation, similar to NEP. Moreover, GPP and RE are highly correlated with each other, and RE consumes 72% of the GPP, leaving a 28% carbon use efficiency across all terrestrial ecosystem types (Fig. S4).

We find, based on >200 eddy covariance towers, that NEP and its components follow a linear negative pattern with an increase in elevation. Correlation analysis and structural equation model (SEM) analyses suggest that NEP is better correlated with GPP (Fig. 2a), rather than RE, indicating the dominant role of photosynthesis in affecting NEP19. Correlation analysis supports a dominant role for temperature, precipitation and the reactive nitrogen (N) level in shaping the spatial pattern of NEP. It is also clear that the temperature, precipitation, and reactive N level consistently show a negative elevation-dependent pattern (Fig. 2b), which contributes to a similar linear negative elevation-dependent pattern of NEP. Climate-induced land-cover and land-use types along the elevation gradient may also contribute to the varying elevation-dependent pattern of NEP. For example, there is more grassland, but less forest and cropland, at higher elevations (Fig. S6), among which forest and cropland have much higher NEP than grassland (Fig. S3). To summarize, the varying hydrothermal factors, nutrient supply, and ecosystem types may have collectively shaped the negative elevation-dependent pattern of NEP.

Temperature sensitivity difference between high- and lower elevation environments
Further analyses validate our expectation that high-elevation environments and thus colder ecosystems (3000–5000 m; mean annual temperature of −0.12°C, ranging from −6.0 to 8.7°C), such as the Tibetan Plateau, are more sensitive to climate warming than their lower-elevation counterparts (mean annual temperature of 10.9°C, ranging from −4.4 to 25.0°C). We are already aware of the higher temperature sensitivity of RE in colder areas20, which was also validated in our analysis (Fig. 3; Fig. S7), with Q10 = 2.19 in the high-elevation environments and Q10 = 1.92 at lower elevations (P<0.01). Like the RE, the temperature sensitivity of GPP shows an elevation-dependent pattern: the temperature sensitivity of GPP in high-elevation environments is higher than that at lower elevations (P<0.01). The higher temperature sensitivity of GPP in high-elevation environments thus leads to a stronger temperature sensitivity of NEP (P<0.01). The higher temperature sensitivity of GPP, RE, and NEP in high-elevation environments are also well captured by the models’ ensemble (Fig. S8).

It is notable that the temperature sensitivity of GPP in high-elevation environments is higher than that at lower elevations, while the temperature sensitivity of RE is not that significant, highlighting the role of GPP in dominating the temporal sensitivity of NEP. In terms of RE, it is well known that the carbon-rich soils accumulated in cold climates will suffer decomposition. Regarding the GPP, it is possible that the extent of temperature restriction has contributed to the significant variance in its sensitivity to temperature. Specifically, it appears that environments at lower elevations are approximately 11°C warmer than high-elevation ecosystems. A similar phenomenon has also been recorded in the Scottish mountains, where greater phenological sensitivity to temperature was found, especially for spring growth at high elevations21. Another reason for the lower temperature sensitivity in lower-elevation environments may be the strong human impact present there, such as plantation, harvesting, logging, and land use changes. These factors may have partially masked the role of temperature in relation to GPP. Additionally, in populated lower-elevation areas, the availability of photosynthetic active radiation for plant growth may also affect photosynthesis and thus the variation in GPP22. In contrast, high-elevation environments typically experience intense solar radiation due to the thin air23. Thus, our eddy covariance dataset, in conjunction with process-based models, consistently demonstrates that the NEP is more sensitive to temperature variations in high-elevation environments than at lower elevations. This may be attributed to difference in temperature constraints, human impacts, and photosynthetic active radiation.

Productivity change rates during the past four decades
NEP may have experienced varying rates of change under the varying climatic conditions along the elevation gradient—that is, it is uncertain whether high-elevation environments have become more productive during the last four decades as a result of significant changes in precipitation, as well as changes in reactive N concentrations. It is notable that reactive N deposition has increased persistently across high-elevation environments in China24. This is different from lower elevations, where N deposition increased (1980–2000) and then stabilized (after 2005), driven by socioeconomic changes and vigorous controls on N pollution25,26. We use the outputs of the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP, Table S2)27, which captures well the elevation-dependent pattern of NEP and GPP (Fig. 4a, b). Further analyses suggest that there is a consistent, more rapid relative increase in both NEP and GPP in high-elevation environments than at lower elevations, thereby validating our assumption that high-elevation environments are becoming more productive under global climate change.

Both the eddy covariance dataset and MsTMIP capture more robust changes in high-elevation environments, and this is also recorded by satellite-based GPP observations. The satellite-based GPP observations also capture the elevation-dependent pattern of average GPP (Fig. 4c), similar to the results based on both the EddyChina2023 dataset and the MsTMIP ensemble. The satellite-based GPP products further reproduce well the larger increase in GPP in the high-elevation environments, as compared with their lower elevation counterparts. Therefore, both the MsTMIP models’ ensemble and satellite-based observations consistently reveal that high-elevation environments have become more productive during the last four decades. Higher temperature sensitivity may have contributed to the varying NEP rates of change in high-elevation environments by extending the growing season. For example, global warming has led to more uniform spring phenology across elevations in the Alps, i.e., a stronger phenological advance at high elevations5. Also, stronger changes in precipitation in high-elevation environments may have contributed to their stronger rates of change in productivity, where the productivity of grasslands is strongly affected by precipitation28,29.

Projections of productivity change rates
It is uncertain whether future changes in climate, atmospheric CO2, and reactive N will alter the elevation-dependent pattern of NEP (Figs. S9–S12; Table S3). The eddy covariance dataset, CMIP6 models, and machine learning were therefore used to predict future variations in the elevation-dependent pattern of NEP under different climate scenarios (Fig. S13), including the Shared Socioeconomic Pathway (SSP) SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios. Future changes in climate and nutrient supply will cause a general increase in NEP across most elevation bands and the various climate scenarios (Fig. 5a−d). However, there are significant differences in the magnitude of their relative changes (i.e., the absolute changes divided by their mean value). Trying to validate the data-driven projections of NEP, we then compared our results with the default NEP outputs from CMIP6 (not constrained by EddyChina2023). Therefore, all scenarios, i.e., SSP1-2.6 to SSP5-8.5, consistently predict a stronger increase in NEP in the high-elevation environments (3000–5000 m), but less change in NEP at lower elevations (<3000 m), therefore affecting the slope of NEP along elevation under various climate scenarios (Fig. 5e−h).

The more robust relative change in NEP in the high-elevation environments than at lower elevations is generally consistent with the higher temperature sensitivity of colder regions. In addition, more robust changes in NEP in high-elevation environments can be attributed to changes in N deposition. There is only a slight decrease in N deposition in the high-elevation environments under SSP1-2.6 and SSP2-4.5, whereas there is an increase in N deposition under SSP3-7.0 and SSP5-8.5, which makes the more robust changes in NEP by plants possible. Recent evidence suggests that the high-elevation environments of China have experienced an increasing trend of reactive N deposition, which is different from the decreasing temporal trend from cropland/city sites at lower elevations24. Elevation-dependent warming in high-elevation environments will also lead to a robust increase in mineralization30, adding nutrients for plant growth and thus increasing NEP. We therefore predict a general increase in NEP under the various climate scenarios, with a more robust change in NEP in high-elevation environments, whereas lower elevations will be affected by future limitations in the supply of nutrients, despite increases in both CO2 concentrations and temperature31–33.

However, it is notable that the data-driven projections do have some uncertainty, as seen in the large uncertainty range in Fig. 5. The input states that statistical or “implicit” relationships were used to achieve data-driven projections between NEP and environmental factors, but it may have overlooked underlying mechanisms. Nonlinear changes and interactive effects from CO2 concentration, climate extremes, species composition, and human management could also be difficult to capture through data-driven projections. Additionally, data-driven projections tend to focus more on aboveground factors like climate and vegetation, while neglecting drastic changes in underground processes, particularly in microbial processes, which may not be accurately reflected in eddy covariance observation or data-driven projections. Furthermore, projections based on data rely heavily on the availability of data, which is not always evenly distributed across different environments. For instance, there may be more observations in lower-elevation environments and fewer in high-elevation ones, which could introduce some degree of bias.

Methods
EddyChina2023 dataset
A total of 203 eddy covariance observation sites covering all ecosystem types and climate zones in China were identified (Table S1). Data were obtained for a total of 523 site-years (i.e., 2.5 years per site). Most of the data were obtained from peer-reviewed papers from the Web of Science (for English-language papers; http://apps.webofknowledge.com/) and the China National Knowledge Infrastructure (for Chinese papers; https://www.cnki.net/). The following keywords were used during the literature search: eddy covariance; CO2 flux/exchange/sink/source; and China. Several criteria were used to guarantee the quality of the data in the studies identified in the literature review. First, the data collection must have used the eddy covariance technique to ensure the spatial and temporal coverage. Observations based on static chambers, especially manual static chambers, were excluded because of their relatively small spatial coverage (typically <1 m2) and low temporal coverage (usually conducted in the daytime at weekly intervals). Second, the observations had to cover at least a whole year because CO2 emissions outside the growing season significantly affect the annual-scale CO2 sink. Third, the paper had to include a clear description of the eddy covariance installation, data collection and processing procedures (e.g., the Webb–Pearman–Leuning correction and axis rotation). All the sites were subjected to Webb–Pearman–Leuning density correction and were determined by the coordinate axis rotation approach—that is, 2D, 3D or planar fit coordinate axis rotation. The observational data had to be clearly and correctly presented, and papers with incorrect units of measurement were not included.

After applying these standards, the resultant EddyChina2023 dataset covered all the major ecosystem types of China—forest (61 sites), grassland (43 sites), wetland (38 sites), cropland (41 sites), desert (12 sites), and shrubland (10 sites)—with 203 sites in total. Among the data, 118 sites (58% of the dataset) covered 2+ years of observations, while 79 sites (40%) covered 3+ years. For each ecosystem type, 10+ sites were built to cover the NEP strength. In China, croplands, forests, and grasslands cover 3/4 of the land area. It is also notable that there were many eddy covariance observations in croplands (116 site-years), forests (134 site-years) and grasslands (129 site-years). Part of the dataset was retrieved from ChinaFLUX and another literature-based dataset15. For those sites whose CO2 fluxes could not be directly accessed, they were processed using GetData Graph Digitizer 2.2.6 (www.getdata-graph-digitizer.com). From the perspective of climate coverage, the eddy covariance sites were well distributed over China’s climate gradient, albeit with relatively fewer sites in cold–dry and hot–wet areas, which is consistent with the limited number of sites in northwestern China. From the perspective of the study period, most observations were conducted during 2002–2020, with a mean of 2012 (Fig. S1). The dataset presented here is therefore representative of China in terms of its ecosystems, climate, and regional contributions, albeit with a slight regional bias.

Structural equation model analysis
SEM analysis was used to estimate the relative importance of the direct and indirect impacts of geographical factors, climate factors and soil processes on the spatial pattern of CO2 fluxes. For continuous observations of more than one year, the multi-year average value was calculated for each site. All NEP data are displayed as mean ± 1 SE values unless stated otherwise. It was hypothesized that the geographical factors (altitude and latitude) would largely affect the climate factors (radiation, mean annual precipitation and temperature), whereas the climate factors would directly and indirectly regulate CO2 exchange via the ecosystem types. The site-averaged CO2 exchange, geographical factors, climate factors, and soil factors were used in the SEM analyses. For the climate factors (radiation, precipitation and temperature), the data for the nearest pixel to each site were extracted from the China Meteorological Forcing Dataset (CMFD), a high-resolution (0.1°×0.1°, 3-h timestep) climatological dataset (a simple evaluation of the CMFD dataset can be seen in Fig. S14)36. All the SEM analyses were preformed using IBM SPSS Amos (SPSS Inc., Chicago, IL, USA).

Temperature sensitivity
For GPP and NEP, their temperature sensitivities were defined as the slopes of their variations on air temperature with linear regressions. For RE, the Van’t Hoff equation was used to calculate the temperature sensitivity. i.e., y = aebt (1) and Q10 = e10b (2). During the calculation, the daily average aboveground air temperature at 2 m was obtained from the CMFD dataset, given most towers did not provide air temperature observations in the literature. We then used General Linear Model (GLM) univariate analysis to test the differences regarding the regression lines for GPP, RE and NEP. During this process, the GPP, RE and NEP were the dependent variables, the different elevation groups (higher or lower than 3000 m in elevation) were the independent variables, while air temperature was covariate. For GPP, over 40,000 observations were involved in the GLM analysis, while there were more than 50,000 observations for RE and more than 60,000 observations for NEP. Daily accumulated or averaged GPP, NEP and air temperature data were used during the GLM analyses. For RE, given exponential regression models were used to reflect its temperature sensitivities, we applied a log transformation to the RE data before the GLM analysis. Effects were considered as significant at P<0.05 (difference in temperature sensitivities between elevation bands), while the null hypothesis was accepted when P>0.05 (no difference in temperature sensitivities between elevation bands).

MsTMIP and GPP datasets
The MsTMIP dataset is a formal multiscale synthesis with prescribed environmental and meteorological drivers shared among model teams. The simulations are standardized to facilitate comparison with other model results and observations through an integrated evaluation framework (Table S2)27. The MsTMIP simulations were classified into four groups (BG1, SG1, SG2 and SG3). The BG1 group was simulated by the time-varying climate, land use, deposition of CO2, and the reactive N input (eight models). BG1 mimics the ongoing changing Earth, enabling it to be compared with EddyChina2023. Within BG1, eight models provided the NEP or net ecosystem exchange outputs, including BIOME, CLASS-CTEM-N, CLM4, CLM4-VIC, DLEM, ISAM, TEM6 and TRIPLEX-GHG. All the models were driven by Climate Research Unit–National Centers for Environmental Prediction (CRUNCEP) climate data and the same soil map, and were run at a resolution of 0.5° with monthly timesteps. For the GPP, we also used the Global Land Surface Satellite (GLASS) product. The GLASS GPP products are generated using integrated satellite GPP timeseries and Moderate Resolution Imaging Spectroradiometer surface reflectance data or Advanced Very High-Resolution Radiometer surface reflectance data37. Compared to other GPP products, the GLASS GPP product has been shown to have higher quality and accuracy38, which has facilitated its wide application in global and regional studies, e.g., to drive process-based models, to evaluate land surface models, and to investigate vegetation dynamics under the changing environment37.

Future climate scenarios and machine learning
The prediction (during 2015–2065) of the net CO2 sink of China’s terrestrial ecosystems was driven by CMIP6 model simulations under various climate scenarios (Table S3). Specifically, four scenarios were used: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. SSP1-2.6 is a combination of low societal vulnerability and a low forcing level, with a substantial change in land use (e.g., increased global forest cover). SSP2-4.5 is a combination of intermediate societal vulnerability and an intermediate forcing level. SSP3-7.0 combines relatively high societal vulnerability and a relatively high forcing level with substantial changes in land use (decreased global forest cover). SSP5-8.5 combines high societal vulnerability and a high forcing level.

The atmospheric [CO2] shows a consistent increase by 2060: 478 ppm under SSP1-2.6, 535 ppm under SSP2-4.5, 587 ppm under SSP3-3.7, and 627 ppm under SSP5-8.5. Correspondingly, temperatures in China would increase by 1.3–2.6°C by 2060 relative to the baseline period (2016–2020; annual average). All climate scenarios predict a general increase in precipitation of 48.9–86.7 mm by 2060 (annual cumulative), although there is strong spatial heterogeneity. The deposition of reactive N is projected to peak before 2030 and then decrease, consistent with the recently observed stabilization of reactive N deposition in China25. Forest cover is expected to benefit from land management and climate change39, consistent with China’s plans for forest plantations to double in size by 205040. Vegetation will experience persistent greening, albeit with the magnitude differing among the various scenarios: the LAI is projected to increase by 18.7–30.1% by 2060 relative to the baseline period.

The Random Forest algorithm was used to conduct machine learning of the CO2 sink. During the training, NEP was set as the label data, whereas environmental factors, like TAS, PRE, RAD, LAI and NR, were used as the input data. Before training, items without values were dropped from the datasets. Over 70,000 items of NEP for >130 sites were used for machine learning and the remaining sites that were not used were omitted because the studies that covered them did not include daily NEP data, instead only providing an annual average NEP rate. After normalization, 90% of the data were used for training and the remaining 10% for validation. They were trained 500 times to obtain a stable performance of the model (to keep a balance between a lack of fitting and over-fitting), with the mean square error used for the evaluation of model performance. Several datasets were used to retrieve environmental factors and simulations, including air temperature, precipitation, photosynthetically active radiation, and LAI. During the prediction, all driving data—temperature, precipitation, photosynthetically active radiation, and LAI—were obtained from CMIP641. All datasets were resampled to a resolution of 0.1°×0.1° via bilinear interpolation to facilitate model simulations and analyses. Before predicting future variation in NEP with Random Forest, the results of training Random Forest based upon the EddyChina2023 dataset were compared with FLUXCOM42, a global-scale upscaling product based upon FLUXNET2015, regarding its seasonal pattern and magnitude (Fig. S15). The NEP outputs after training Random Forest were also in line with previous upscaling and remote sensing studies in China19,43.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Using collected eddy covariance measurements, Wei et al. invesfigated the elevafion-dependent of CO2 uptake. Then they aftributed the differences between the uptake of mountains and that of lower elevafion to the temperature sensifivity and human acfivifies. Furthermore, they trained machine learning algorithms to predict the future changes of CO2 uptakes and found the mountains took higher CO2 uptakes. This manuscript did some efforts in advancing our understanding on CO2 uptakes, which was the key issue of climate change, ecology, earth sciences. The topic and findings were interesfing and useful for carbon managements at mountains, while the methods were not well documented to persuade the readers believe in these resutls. Therefore, I believed that this manuscript could not be published before it suffered substainfial revisions.\n1. The authors emphasized the elevafion-dependent of CO2 uptake across China mountains, while I was not confident with this descripfion. The elevafion-dependent did exist but not at China mountains. The elevafion-dependent paftern was drawn by collecfing 204 eddy covariance measurements. However, the collected eddy covariance measurements were mostly distributed in lower elevafion regions. Only 88 sites had an elevafion higher than 1000 meters, which was deemed as mountains. From Figure 1b, the elevafion-dependent paftern of CO2 uptake was primarily sourced from their differences between lower elevafion regions and mountains. With all data from mountains, whether elevafion-dependent paftern existed was quesfionable. In addifion, the elevafion-dependent paftern of CO2 uptake was mainly aftributed to temperature sensifivity and human acfivifies, while whether there were other factors like CO2 concentrafion?\n2. The authors employed temperature sensifivity to address the differences in CO2 uptake between mountains and lower elevafion regions. However, the descripfion about calculafing this key parameter was not well documented, which made this explanafion hard to jusfify. I agreed that higher elevafions did have higher temperature sensifivity, while the authors should provide detailed methods describing the calculafions and the values.\n3. The authors used machine learning algorithms to predict the changes in CO2 uptake under future climate change. However, the data used for training the algorithm were recent data. Extrapolafing the current measurements to the future had large uncertainfies. In addifion, the collected data were mainly the yearly data, while the data used for training random forest algorithms were daily data? What was the source of those data?\n4. The terms were mixed used throughout the whole manuscript. What was the difference among net CO2 uptake, CO2 uptake, NEP?\n5. It was strange to see that R2 of GPP and ER were lower than 0.3, which were obviously lower than many previous works.\n6. The representafiveness of sites may be removed to supporfing informafion, which would make the whole manuscript more concise.\n7. Some empirical references should be cited.\n8. The dataset had some duplicafion. For example, Haibei1# and Haibei2# were the same site.", "reviewer_2": "The study compiles and analyses a large set of data from 204 eddy covariance sites ranging from 1000 up to 5000 masl across Chinese ecosystems and demonstrates a strong elevafion dependent CO2 uptake paftern. Although we know of this paftern for forests shown previously in another gradient, with a different methodology, with less experimental sites, for GPP and NPP but not for NEP in the Peruvian Andes from 0-3000 masl, Fig 3 in Malhi et al 2017, New Pythologist, hftps://nph.onlinelibrary.wiley.com/doi/epdf/10.1111/nph.14189), this paftern is demonstrated in the current study with an enormous data set across ecosystems.\nThe implicafions of the work are of relevance for global carbon cycling, global biogeochemical cycling and global environmental change.\nThe authors need to elaborate further on what is the temperature gradient across elevafions in the study sites. Importantly there is a need to explain the lack of temperature sensifivity in GPP below 3000 m, contrary to what is shown for the Andes in the reference above. The authors have not made any aftempt to explain this result.\nFew figure legends lack some details that would help to understand the figures. See coments below\nIn the methods it is unclear if the authors tested for the significance of difference in slopes on the NEP figure and Q10 values obtained from RE on correspondent to Figure 3. The method should explain how the changes in Figs 4c and 5 were calculated.\nNot 100% clear how to obtain the flux data in order to reproduce the work.\nFig 1 a\nInclude what the shades of green and blue in the background mean.\nFig S1a, Include what circle size indicate\nTable S1, are these the names of the eddy covariance sites? Should add the word sites or locafions to the lagend\nWhat does the (x2) (x3) means, two, three towers?\nFig 2a\nR2 in boxes correspond to relafionships between which variables? There are more than one arrow in most cases arriving to a single box. Unclear what the R2 corresponds to.\nDashed lines are not easy to differenfiate from solid lines\nL130 ‘indicafing the dominant role of plant growth in affecfing the net CO2 sink 4’ GPP is not plant growth, it is photosynthesis, best to use the correct terminology.\nFig 3, unclear what is the difference is between solid and confinuous lines\nL151-152\nThe lack of temperature sensifivity of GPP for ecosystems below 3000 (cite the temperature variafion) is an unexpected result, what can explain this?\nL155-157\nFig 3 NEP, are the slopes significantly different ?\nL148-149 - Are the Q10 significantly different ?\nWhat is the temperature in this elevafion gradient?\nL162, needs to briefly elaborate on what have been the changes in reacfive N in mountains in the study region.\nL163 write explicitly the GPP proxy that was used\nL173..NEP, RE, or GPP is not plant growth, eddy covariance does not measure plant growth.\nL190-191, please indicate where this is shown\nUnclear how to access the data, a link is provided, but when typing some sites names or publicafions from supplementary table nothing came up, authors should clearly explain how to access the data.", "reviewer_3": "Review comments\nThe main target of this manuscript is to invesfigate the elevafion dependent CO2 uptake paftern across China mountains, considering the effects of geographical factors, climate factors, and soil properfies. This study reported a negafive linear elevafion-dependent paftern of CO2 uptake, and this paftern has been verified by land surface model simulafions and satellite observafions. This work contributes significantly to a comprehensive understanding of the responses of mountainous ecosystem funcfioning to the ongoing and future climate change. Despite that the topic is interesfing, I have some major concerns which should be clarified before publicafion.\n1. Detailed and accurate climate data are important for the analyses. In this study, the authors used the climate data from the CMFD dataset, which is with a coarse spafial resolufion and how does this dataset consider the topography effects on climate data? Will this introduce uncertainty into your analysis?\n2. Another issue is regarding the comparison between eddy covariance data and MsTMIP simulafions. The spafial resolufion is 0.5 degree for MsTMIP models, which does not match the detailed observafions of EddyChina2023. Again, the CMIP6 model projecfions are with even much coarser resolufion for the future climate change, so what is the uncertainty for comparing the future climate change on high elevafion and low elevafion?\n3. One main finding of this study is that ecosystems on high mountains have higher temperature sensifivity and future climate change will increase the CO2 uptake in these ecosystems. However, I found weak relafionship between either GPP or RE and air temperature from the main figure 2, which is the most important figure illustrafing the relafionship between different components of NEP and mulfiple factors. There seems to be contradictory between them.\nDetailed comments\n1. line 37-42, This part is not relevant to the topic of this manuscript.\n2. Line 98-103, In which period are these results determined? Do you consider the potenfial difference in the interannual variafions in NEE among different sites in different ecosystems?\n3. Another point is that the occurrence of extreme climate events may be quite different\n4. Line 106, In this study, the largest uptake of CO2 is found in croplands, but not forests.\n5. Line 112, This sentence is not clear. The CO2 uptake in temperate grasslands of Inner Mongolia is neutral, indicafing that the carbon gain is cancelled out by the carbon loss. How can this result be verified by soil carbon inventory? Do you mean repeated inventory data?\n6. Figure 2, It is strange that the air temperature does not exert important effects on either GPP or RE across mountainous sites.\n7. Line 135-137, But from the SEM analysis, I did not find a close linkage between NEP and variafions of temperature and precipitafion and nitrogen deposifion.\n8. Figure S14, What do the different colored lines represent?"}, "author_response": "Response Letter\nReference Number: NCOMMS-23-30115T\nTitle: Elevation-dependent pattern of net CO uptake across China\nUsing collected eddy covariance measurements, Wei et al. investigated the elevation-dependent of\nCO2 uptake. Then they attributed the differences between the uptake of mountains and that of lower\nelevation to the temperature sensitivity and human activities. Furthermore, they trained machine\nlearning algorithms to predict the future changes of CO2 uptakes and found the mountains took higher\nCO2 uptakes. This manuscript did some efforts in advancing our understanding on CO2 uptakes,\nwhich was the key issue of climate change, ecology, earth sciences. The topic and findings were\ninteresting and useful for carbon managements at mountains, while the methods were not well\ndocumented to persuade the readers believe in these results. Therefore, I believed that this manuscript\ncould not be published before it suffered substantial revisions.\nDear Reviewer,\nThanks very much for your encouragement on the novelty of our work. We also appreciate your time\nand efforts to improve this manuscript. During the revision, all your concerns have been carefully\nconsidered, explained, and resolved.\n Elevations. According to your suggestion, we covered all elevation bands rather than mountains\nonly in the revised manuscript. Several figures have been redrawn and many words have been\nrevised, including the Title, i.e., the title has been changed to “Elevation-dependent pattern of net\nCO uptake across China”, by deleting the “mountainous China”.\n2\n SEM analysis. To improve the performance of SEM analysis, we updated the SEM, including\nusing ground-based climate data, adding more dependent variables, and re-organizing the model\nstructure. These did effectively improve the performance of SEM analysis. Also, we provided more\ndetails regarding the calculation and statistics of temperature sensitivity. For Fig. 3, a new section\nnamed “Temperature sensitivity” was also added to “Method and materials”.\n More concise. Following your suggestion, to make the whole manuscript more concise, we\nmoved the “Representativeness of the eddy covariance dataset” to the Supporting Materials (Text\nS1).\n Terms. Regarding the terms, we made them consistency throughout the manuscript, i.e., change\n“CO uptake”, “CO sink”, “net CO uptake” to “NEP”.\n2 2 2\n Dataset. Your concerns regarding dataset have also been resolved, by uploading the dataset to\nthe website Figureshare (https://figshare.com/s/e1f7f9c13e547e422a71).\nThanks very much for your efforts to improve our manuscript. We learned a lot from your work.\nThe authors\n1. The authors emphasized the elevation-dependent of CO2 uptake across China mountains, while I\nwas not confident with this description. The elevation-dependent did exist but not at China mountains.\nThe elevation-dependent pattern was drawn by collecting 204 eddy covariance measurements.\nHowever, the collected eddy covariance measurements were mostly distributed in lower elevation\nregions. Only 88 sites had an elevation higher than 1000 meters, which was deemed as mountains.\nFrom Figure 1b, the elevation-dependent pattern of CO2 uptake was primarily sourced from their\ndifferences between lower elevation regions and mountains. With all data from mountains, whether\nelevation-dependent pattern existed was questionable. In addition, the elevation-dependent pattern of\nCO2 uptake was mainly attributed to temperature sensitivity and human activities, while whether there\nwere other factors like CO2 concentration?\nResponse:\nThanks for your comments here.\n We agree with you that the elevation-dependent did exist but not at China mountains. Indeed,\nthe eddy covariance, MsTMIP models and remote sensing observations consistently support the\nelevation-dependent pattern of CO exchanges. Therefore, several revisions were made to reflect\n2\nthe elevation dependent pattern of net CO uptake in China, rather than China’s mountains. (1)\n2\n“mountainous China” has been revised to “China”. (2) We include the elevation of 0-1000 m in all\nfigures and redraw Figure 3, 4, 5, S8 and S14. The word “Mountains” has been deleted from all\nfigures. (3) In the main text, we shorten the descriptions regarding mountains to cover all elevation\nbands across China, rather than mountains only.\n Many environmental drivers affect the elevation-dependent pattern of NEP, including climate\nfactors, nutrient supplies, human activities, and atmospheric CO concentration (denoted by\n2\n[CO ]). In the populated lower elevation region, the higher CO concentration may benefit\n2 2\nphotosynthesis via the CO Fertilization Effect (CFE). Therefore, we added the [CO ] into the\n2 2\nSEM analyses, though it turned out that its contribution to the variation on GPP did not reach a\nsignificant level (Fig. 2a). This is also consistent to the correlation analyses, i.e., the correlation\nbetween GPP and [CO ] does not reach a significant level (Fig. S5). This indicates that the\n2\ncontribution of [CO ] to the spatial pattern of NEP may be relatively lower, compared with the\n2\nclimatic factors, ecosystem types and nitrogen deposition.\n We thank you for your efforts to improve our manuscript.\n2. The authors employed temperature sensitivity to address the differences in CO2 uptake between\nmountains and lower elevation regions. However, the description about calculating this key parameter\n2\nwas not well documented, which made this explanation hard to justify. I agreed that higher elevations\ndid have higher temperature sensitivity, while the authors should provide detailed methods describing\nthe calculations and the values.\nResponse:\n Thanks for your comment regarding the calculation of temperature sensitivity. Following your\nsuggestion, we added a new section in the Material and Method, namely “Temperature sensitivity”,\nwhich provide detailed description regarding the definition and calculation. We also made\nadditional statistical analyses, showing significant difference in temperature sensitivities between\ndifferent elevation bands. The above statistical results were then clarified in the main text (Line\n122, Line 124, and Line 127).\n To facilitate your review, we also put the descriptions here. “For GPP and NEP, their temperature\nsensitivities were defined as the slopes of their variations on air temperature with linear\nregressions. For RE, the Van't Hoff equation (y=aebt) was used to calculate the temperature\nsensitivity (Q =e10b). During the calculation, the daily average aboveground air temperature at\n10\n2 m was obtained from the CMFD dataset, given most towers did not provide air temperature\nobservations in the literature. We then used General Linear Model (GLM) univariate analysis to\ntest the differences regarding the regression lines for GPP, RE and NEP. During this process, the\nGPP, RE and NEP were the dependent variables, the different elevation groups (higher or lower\nthan 3000 m in elevation) were the independent variables, while air temperature was covariate.\nFor GPP, over 40,000 observations were involved in the GLM analysis, while there were more\nthan 50,000 observations for RE and more than 60,000 observations for NEP. Daily accumulated\nor averaged GPP, NEP and air temperature data were used during the GLM analyses. For RE,\ngiven exponential regression models were used to reflect its temperature sensitivities, we applied\na log transformation to the RE data before the GLM analysis. Effects were considered as\nsignificant at P<0.05 (difference in temperature sensitivities between elevation bands), while the\nnull hypothesis was accepted when P>0.05 (no difference in temperature sensitivities between\nelevation bands)”. Please see Line 285 to 300.\n We hope the descriptions regarding the definition, calculation and statistical analyses would make\nthe manuscript more readable. Thanks very much for helping us with improving the manuscript.\n3. The authors used machine learning algorithms to predict the changes in CO uptake under future\n2\nclimate change. However, the data used for training the algorithm were recent data. Extrapolating the\ncurrent measurements to the future had large uncertainties. In addition, the collected data were mainly\nthe yearly data, while the data used for training random forest algorithms were daily data? What was\nthe source of those data?\nResponse:\n Yes, we used the daily data to train and validate the random forest algorithms. The algorithms\nretrieved from the random forest training was then used to predict future variation in NEP under\n3\nvarious climate scenarios. We apology that the original link to the daily data of EddyChina2023\ndid not work. To resolve this issue, we then uploaded the dataset to the website Figureshare\n(https://figshare.com/s/e1f7f9c13e547e422a71). We hope the reviewers and potential readers\nwould get access to the dataset easily.\n Before predicting future variation in NEP with random forest, the results of random forest training\nbased upon EddyChina2023 dataset is compared with the FLUXCOM regarding its seasonal\npattern and magnitude (Fig. R1) (Jung, Schwalm et al. 2020). The FLUXCOM is a global scale\nupscaling product based upon FLUXNET2015. The NEP outputs of random forest training also in\nline with previous upscaling and remote sensing studies in China (Yao, Li et al. 2018, Chen, Park\net al. 2019).\n Still, we agree that using the current dataset to predict future variation in NEP would cause\nuncertainty. That is why we combined the eddy covariance dataset and the CMIP6 outputs with\nthe help of random forest, to constrain future variation in the NEP. Also, the CMIP6 outputs in the\ncurrent study include 4 climate scenarios and each scenarios include over 20 models. We hope\nthe current method, i.e., combine of EddyChina2023 and CMIP6, would better constrain the future\nvariation in NEP under future climate changes.\n In addition, all climate scenarios suggest that higher elevation environments would experience\nmore robust changes in NEP, compared with the lower elevation environments. The random forest\npredications are generally consistent to the results based upon the GLASS LAI product, both\nhighlighting the importance of human impacts on decadal variation in net CO uptakes at lower\n2\nelevation bands, while high elevation environments would be more productive under climate\nchanges.\nReferences\nChen, C. et al. China and India lead in greening of the world through land-use management. Nat.\nSustain.2, 122-129 (2019). https://doi.org/10.1038/s41893-019-0220-7\nJung, M. et al. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation\nof the FLUXCOM approach. Biogeosciences17, 1343-1365 (2020).\nhttps://doi.org/10.5194/bg-17-1343-2020\nYao, Y. T. et al. A new estimation of China's net ecosystem productivity based on eddy covariance\nmeasurements and a model tree ensemble approach. Agricultural and Forest Meteorology\n253, 84-93 (2018). https://doi.org/10.1016/j.agrformet.2018.02.007\nbased upon the random forest training of EddyChina2023 dataset. (a) Averaged NEP of China’s\nterrestrial ecosystems during the past two decades. (b) Trend of annual NEP of China’s terrestrial\necosystems. (c) Comparison between monthly NEP of China’s terrestrial ecosystems, estimated by\nEddyChina2023 and FLUXCOM. The EddyChina2023-RF represents the results of random forest\ntraining based upon the EddyChina2023 dataset.\n4. The terms were mixed used throughout the whole manuscript. What was the difference among net\nCO2 uptake, CO2 uptake, NEP?\nResponse:\nThanks very much for your comment regarding the terms. We apologize that our terms may have\ncaused misunderstanding in a previous version of the manuscript. Following your suggestion, we\n5\ncarefully revised all terms regarding the CO uptakes throughout the whole manuscript, including the\n2\ntitle, main text, figure captions and supporting materials. Here we list how these terms were corrected\nin the revised manuscript.\n We clarify at the beginning of the Introduction that “net uptake of CO is denoted by net ecosystem\n2\nproductivity, NEP”. In the main text, most “net CO uptake” have been replaced with NEP, except\n2\nthe title.\n If necessary, we keep terms “uptake of atmosphere CO ” and “net CO sink”. For example，\n2 2\n“Existing studies have highlighted that these mountains function as a vital net CO sink ……”.\n2\n Thanks again for your comment regarding the terms, which make the manuscript more formal\nand concise.\n5. It was strange to see that R2 of GPP and ER were lower than 0.3, which were obviously lower than\nmany previous works.\nResponse:\nThanks very much for concern regarding lower R2 for either GPP or RE.\n For a specific eddy covariance site, the temporal pattern of GPP and RE are usually well\nexplained by abiotic and biotic factors like temperature, radiation, precipitation, soil moisture and\nleaf area index, which have been seen by lots of studies. For example, in one alpine wetland site\nrun by ourself, the temporal variations in GPP and RE were well explained by climate factors, with\nR2=0.70 and 0.75, respectively (Qi et al., 2021, JGR).\n We are also wondering whether we have missed some important processes that govern the\nspatial pattern of GPP and RE (Figure R2a). We questioned that the situation you mentioned\nwould be improved if more variables, like ecosystem types and atmospheric CO concentration,\n2\nwere added to the SEM. In addition, we also employed the ground-based climate data for each\nsite (from the literature of each eddy covariance tower), rather than the CMFD dataset. It seems\nthat adding more variables, using the ground-based climate data, and re-organizing the model\nstructure did improve the performance of SEM analysis (Figure R2a). The R2 for GPP has been\nenhanced to 0.39, while the R2 for RE has been enhanced to 0.29.\n Still the R2 for GPP and RE are much lower than our expectation. The situation is quite similar\nfor the spatial pattern of GPP, RE and NEP along the elevation (R range from -0.18 to -0.32). We\nconsider the relatively lower R2 or R for GPP and RE may due to the large spatial range of the\nEddyChina2023 dataset., i.e., it covers a latitude-span of 40 degree (15°N to 55°N). Therefore,\nthe spatial patten of GPP and RE (elevation-dependent pattern in this study) may include\nunrecognized contributions from other unrecognized sources, like forest species, management\n(e.g., irrigation), observations (e.g., different equipment and setting) and calculation (e.g., data\nprocessing and quality control), though their impacts can hardly be determined in the SEM.\ngeographical factors, climate factors and soil processes on ecosystem CO fluxes. GPP, gross primary\n2\nproductivity; RE, ecosystem respiration; NEP, net ecosystem productivity; PAR, photosynthetically active\nradiation; TAS, atmospheric temperature; Nr, reactive nitrogen; PREC, precipitation; CO2, atmospheric CO\n2\nconcentration. Given NEP represents the net balance between GPP and RE, we built its indirect relationship\nwith environmental factors via GPP and RE. The structural equation model analysis was calculated based\nupon the annual average CO fluxes (multi-year average used if there was more than one year of\n2\nobservations). The blue lines indicate negative impacts, while the red lines indicate positive impacts. The\nnumbers adjacent to the arrows are the standardized path coefficients. The solid lines represent a\nsignificant correlation (* P<0.05, ** P<0.01, *** P<0.001), whereas the dashed lines indicate lack of\nsignificance. The R2 values in the boxes indicate how much the dependent variables are explained by the\nindependent variable(s). (b) Variation in TAS, PREC and Nr with elevation. The dashed lines indicate the\nlinear or nonlinear fit for the environmental factors, whereas the gray shading represents the 95%\nconfidence band of the fits.\nReference: Yahui Qi, Da Wei*, Hui Zhao, Xiaodan Wang* (2021) Carbon sink of a very high marshland\non the Tibetan Plateau. Journal of Geophysical Research: Biogeosciences. 126, e2020JG006235.\n6. The representativeness of sites may be removed to supporting information, which would make the\nwhole manuscript more concise.\nResponse: Thanks for your suggestion. We have now moved this section to Supporting Materials and\nnamed it Text S1. This made the whole manuscript more concise and easier to follow for readers.\n7. Some empirical references should be cited.\nResponse:\ndependent and carbon flux. We found lots of studies have been conducted in Alps, Andes, and the\nTibetan Plateau, especially in the Alps. Therefore, these references were added to the main text:\n“Several studies have been conducted across Earth’s high mountains, i.e., Alps(Zeeman, Hiller et al.\n2010), Andes(Malhi, Girardin et al. 2017), Tibetan Plateau(Zhao, Li et al. 2017) and etc., suggesting\nvarying elevation-dependent patterns of GPP and RE.”\nReferences:\nZeeman, M. J. et al. Management and climate impacts on net CO fluxes and carbon budgets of three\n2\ngrasslands along an elevational gradient in Switzerland. Agricultural and Forest Meteorology\n150, 519-530 (2010).\nMalhi, Y. et al. The variation of productivity and its allocation along a tropical elevation gradient: a\nwhole carbon budget perspective. New Phytologist 214, 1019-1032 (2017).\nZhao, J., Li, R., Li, X. & Tian, L. Environmental controls on soil respiration in alpine meadow along a\nlarge altitudinal gradient on the central Tibetan Plateau. Catena 159, 84-92 (2017).\n8. The dataset had some duplication. For example, Haibei1# and Haibei2# were the same site.\nResponse:\n Indeed, the data collection from literatures is not an easy job. For a specific station, it may include\nseveral eddy covariance towers, to cover several ecosystem types. For example, in Haibei Station,\nat least three ecosystem types were covered by eddy covariance towers, i.e., alpine meadow,\nalpine shrub and wetland.\n When there are two or more publications regarding the eddy covariance observation for each\necosystem type and for each station, we judge whether they are the same one by checking their\nauthor list and whether they have the same instruments.\n We reviewed roughly 400 papers to obtain the EddyChina2023 dataset, and we apologize that\nwe may have made a mistake regarding the Haibei Station. We thank the reviewer’s comment\nthat the Haibei#1 and Haibei#2 may be the same site. During the revision, we rectified the above\nmistake. In addition, the number “204” has been corrected to “203” throughout the manuscript,\nincluding Main text, Figures, Supporting Materials and Datasets.\nTo Reviewer #2\nThe study compiles and analyses a large set of data from 204 eddy covariance sites ranging from\n1000 up to 5000 masl across Chinese ecosystems and demonstrates a strong elevation dependent\nCO2 uptake pattern. Although we know of this pattern for forests shown previously in another gradient,\nwith a different methodology, with less experimental sites, for GPP and NPP but not for NEP in the\nPeruvian Andes from 0-3000 masl, Fig 3 in Malhi et al 2017, New Pythologist,\nhttps://nph.onlinelibrary.wiley.com/doi/epdf/10.1111/nph.14189), this pattern is demonstrated in the\n8\ncurrent study with an enormous data set across ecosystems. The implications of the work are of\nrelevance for global carbon cycling, global biogeochemical cycling, and global environmental change.\nDear Reviewer,\nThank you very much for reviewing the paper and for providing an excellent literature. All your\nconcerns have been carefully considered and revised, and here is brief response to your comments.\n National scale study. Our research involves over 200 eddy covariance sites across China, from\nthe tropics to the cold zone (temperature span exceeds 30°C degrees), and some ecosystems\ninvolve lots of human activities (China's population density below 3,000 meters is very high). The\ncomplexity of China means that our results may be slightly different from the study in Peru. We\nhonestly present these data in the manuscript, albeit somewhat difference from the study in Peru.\nWe think the comparison between these works can deepen our understanding of the elevation\ndependent pattern of CO uptakes.\n2\n Terms. You raised questions about terminology in your paper, such as GPP not plant growth. We\nthank you for pointing out these errors, and we have corrected them based on your comments.\n Figure captions. You mentioned that there are too few descriptions in some figures, making it\nvery difficult for reviewers or potential readers to understand the results. At the same time, you\nraised some questions about statistics in your paper. We are very grateful to you for pointing out\nthese important issues, and we have added them in the revised paper, especially Figure 3, Figure\n4, Figure 5 and the supporting figures.\n Dataset. We are sorry that the link we submitted previously seems to have expired. We have re-\nuploaded the data to Figureshare (https://figshare.com/s/e1f7f9c13e547e422a71), and we hope\nthat all data can be reused easily.\n Minor comments. Your minor, details comments have been carefully considered and revised in\nthe revised manuscript.\nThanks again for your efforts to improve this manuscript.\nBest regards,\nThe authors\nThe authors need to elaborate further on what is the temperature gradient across elevations in the\nstudy sites. Importantly there is a need to explain the lack of temperature sensitivity in GPP below\n3000 m, contrary to what is shown for the Andes in the reference above. The authors have not made\nany attempt to explain this result.\nResponse:\n Thanks for your comment regarding the temperature sensitivity.\n In fact, temperature strongly affect spatial pattern of GPP (Fig. S5), which is consistent to the\nresult in Peru (i.e., temperature largely affect the spatial pattern of GPP). Regarding the\ntemperature sensitivity of GPP for below 3000 m, temperature still plays a role in regulating its\ntemporal variation, i.e., P<0.001, given more than 30000 observations were involved in the\ncalculation, though the R2 is only 0.04 (or R=0.20).\n For the reasons, we consider the higher temperature and strong human activities in the lower\nelevation environments may have contributed to lower temperature sensitivity of GPP, than that\nin high elevation environments. (1) The mean annual temperature for the ecosystems below\n3000 m is 10.9°C (–4.4 to 25.0°C), while it is –0.12°C (–6.0 to 8.7°C) for the ecosystems above\n3000 m. The high elevation environments (3000-5000 m) are 11°C colder than the ecosystems\nbelow 3000 m, which may have caused the large difference in temperature sensitivity in GPP,\nRE and NEP. (2) Our study covers a large geographic extent across China (15°N-55°N),\nincluding rubber plantation in Hainan Island, rice paddies in Yangtze River Delta, maize & corn\nin North China Plain, and soybean in Northeast China. As a highly populated country, these\nregions experienced strong human management like irrigation and fertilization. Management of\nwater and nutrient in the highly-populated region (<3000 m) may have caused a relatively\nweaker role of temperature on regulating the temporal variation in GPP. Also, the RE and NEP\nalso show lower temperature sensitivity, which is consistent to the GPP.\n Trying to resolve your concerns, we now have added the following sentences in the main text:\n“Further analyses validate our expectation that high-elevation environments and thus colder\necosystems (3000–5000 m; mean annual temperature of −0.12°C, ranging from −6.0 to 8.7°C),\nsuch as the Tibetan Plateau, are more sensitive to climate warming than their lower-elevation\ncounterparts (mean annual temperature of 10.9°C, ranging from −4.4 to 25.0°C)”. Please see\nLine 117-120. “The high elevation environments is 11°C colder than the ecosystems below\n3000 m, which may have caused the large difference in temperature sensitivity in GPP. Beside\nthe variation in temperature, management of water and nutrient in highly-populated region\n(<3000 m) may also strongly regulate the variation in plant photosynthesis”. Please see Line\n124 to 128.\n Thanks for your comments regarding the temperature sensitivity.\nFew figure legends lack some details that would help to understand the figures. See comments below.\nResponse: Thanks for your comments regarding figure legends. To resolve your concerns, we then\nadded more descriptions in each figure. More descriptions are also presented in Method and Materials.\nWe hope the additional information would make the whole manuscript more friendly to reviewer and\npotential readers.\nIn the methods it is unclear if the authors tested for the significance of difference in slopes on the NEP\nfigure and Q10 values obtained from RE on correspondent to Figure 3. The method should explain\nhow the changes in Figs 4c and 5 were calculated.\ndescription to resolve your concern, which are listed here.\n For Figure 3, a new section named “Temperature sensitivity” was added to the Method and\nMaterials: “For GPP and NEP, their temperature sensitivities were defined as the slopes of their\nvariations on air temperature with linear regressions. For RE, the Van't Hoff equation (y=aebt)\nwas used to calculate the temperature sensitivity (Q =e10b). During the calculation, the daily\n10\naverage aboveground air temperature at 2 m was obtained from the CMFD dataset, given most\ntowers did not provide air temperature observations in the literature. We then used General Linear\nModel (GLM) univariate analysis to test the differences regarding the regression lines for GPP,\nRE and NEP. During this process, the GPP, RE and NEP were the dependent variables, the\ndifferent elevation groups (higher or lower than 3000 m in elevation) were the independent\nvariables, while air temperature was covariate. For GPP, over 40,000 observations were involved\nin the GLM analysis, while there were more than 50,000 observations for RE and more than\n60,000 observations for NEP. Daily accumulated or averaged GPP, NEP and air temperature data\nwere used during the GLM analyses. For RE, given exponential regression models were used to\nreflect its temperature sensitivities, we applied a log transformation to the RE data before the\nGLM analysis. Effects were considered as significant at P<0.05 (difference in temperature\nsensitivities between elevation bands), while the null hypothesis was accepted when P>0.05 (no\ndifference in temperature sensitivities between elevation bands)”. Please see Line 285 to 300.\n In Figure 4, we added more descriptions in figure legends to explain the calculation of the mean\nGPP, LAI and their relative changes (ΔGPP and ΔLAI): “Fig. 4. Elevation-dependent pattern of\ngross primary productivity (GPP), leaf area index (LAI) and their changes during the last\ntwo decades. (a) Elevation-dependent pattern of the annual cumulative GPP with the MsTMIP\nmodel ensemble. For the MsTMIP ensemble, all pixels of each elevation band were employed to\nderive the annual cumulative GPP for each model. Then, the results of these models were\naveraged to obtain the ensemble average GPP and the standard deviation for each elevation\nband. (b) Elevation-dependent pattern of LAI from the GLASS LAI product 36. (c) Relative changes\nin the GPP and LAI along the elevation gradient. For the V , it was used to represent the speed\nGPP\nof variation in GPP along the elevation gradient. The V was calculated by dividing the slope of\nGPP\nGPP changes by the average GPP: V =Slope(GPP)/GPP . In this equation, GPP\nGPP baseline baseline\nrepresents the annual average GPP during 2000–2004 (the first five years). The line extending\nfrom each bar represents the uncertainty in each climate scenario (±1 SD) of the MsTMIP model\ngroup (eight models: BIOME-BGC, CLASS-CTEM-N, CLM4, CLM4VIC, DLEM, ISAM, TEM6 and\nTRIPLEX-GHG). For V , a similar procedure was employed by calculating the relative changes,\nLAI\ni.e., the slope of LAI change divided by the average LAI in baseline years:\nV =Slope(LAI)/LAI , where LAI represents the annual average GPP during 2000–\nLAI baseline baseline\n2004 (the first five years)” Please see Line 571 to 585.\n In Figure 5, similar calculation procedure was used to calculate the ΔNEP and more information\n11\nwas added to the figure legends: “Fig. 5. Relative changes in net ecosystem productivity\n(ΔNEP) under various climate scenarios constrained by the CMIP6 models and the\nEddyChina2023 dataset. Relative changes in NEP under (a) SSP1-2.6, (b) SSP2-4.5, (c) SSP3-\n7.0 and (d) SSP5-8.5. Relative changes were calculated by dividing the absolute changes in NEP\nby their average for 2020–2024 (the first five years): ΔNEP=(NEP-NEP )/NEP . Here,\ni i baseline baseline\nNEP represents the NEP in year i, while NEP represents the averaged NEP during 2020–\ni baseline\n2024. During the calculation, all pixels of each elevation band were employed to derive the annual\ncumulative GPP for each model under each climate scenario. Then, the results of these models\nwere averaged to obtain the ensemble average ΔNEP and the standard deviation for each\nelevation band under each climate scenario. Solid curves are the ensemble mean of the model\nsimulations and the shading represents ±1 SD. The bar charts represent the relative changes by\nthe end of the research period (2056–2065), within which the line extending from each bar\nrepresents the uncertainty in each climate scenario (±1 SD)”. Please see Line 587 to 598.\n Thanks for your comments regarding the above figures.\nNot 100% clear how to obtain the flux data in order to reproduce the work.\nResponse:\nWe apologize that the original link to the EddyChina2023 dataset does not work. To resolve this issue,\nwe uploaded the dataset to the website of figureshare(https://figshare.com/s/e1f7f9c13e547e422a71).\nThe new address of the dataset is also added to Data Availability.\nFig 1 a\nInclude what the shades of green and blue in the background mean.\nResponse:\n In a previous version of the Figure 1a, we used the “Green and Blue” colors in the 3D map, to\nillustrate the mountainous China and location of the eddy covariance towers. The shades of green\nand blue is used after traditional Chinese painting.\n Your another comment regarding Table S1 suggest adding site name into the map, we then re-\ndrawn the map of Figure 1a (in the main text) with a 2D map, which is also shown here (Fig. R3).\nwe only list the main ecosystem type for each station, though several towers may have been\nestablished to cover multiple ecosystem types for some stations (for details, see Table S1).\nFig S1a, include what circle size indicate\nResponse: Thanks for your comments. Follow your suggestion, we have added a new legend to\nFigure S1a. The captions of the Figure S1a has also been revised to indicate “the size of the circle\nrepresents the observation years for each site”.\nTable S1, are these the names of the eddy covariance sites? Should add the word sites or locations\nto the lagend. What does the (x2) (x3) means, two, three towers?\nResponse:\n Yes, the Table S1 provide the exact site names and the tower number for each station. The x2\nand x3 indicates two and three towers for each station, respectively, given there may be several\necosystem types for a station.\n After your suggestion, we added the sites and locations into the Figure 1a. Please see Line 205\nin Supporting Materials.\n Thanks for your suggestion to help us provide a much better illustration of the flux towers.\nFig 2a. R2 in boxes correspond to relationships between which variables? There are more than one\narrow in most cases arriving to a single box. Unclear what the R2 corresponds to. Dashed lines are\n13\nnot easy to differentiate from solid lines\nResponse:\n The R2 in each box represents how much the dependent variable is explained by independent\nvariables. For example, the spatial variation of TAS (air temperature) is 97% explained by latitude\nand elevation.\n We redraw the Figure 2a to make the dash lines different from the solid lines. Other details\nregarding the Figure 2a have also been revised to make the whole figure more readable.\n Thanks very much for your comments regarding this figure.\nL130 ‘indicating the dominant role of plant growth in affecting the net CO2 sink 4’ GPP is not plant\ngrowth, it is photosynthesis, best to use the correct terminology.\nResponse: Thanks for your comment. Now the sentence has been revised to “…… indicating the\ndominant role of photosynthesis in affecting the NEP”. Please see Line 104.\nFig 3, unclear what is the difference is between solid and continuous lines\nResponse:\n We redraw the Figure 2a to make the dash lines different from the solid lines.\n The solid lines represent a significant correlation (* P<0.05, ** P<0.01, *** P<0.001), whereas the\ndashed lines indicate lack of significance.\nL151-152\nThe lack of temperature sensitivity of GPP for ecosystems below 3000 (cite the temperature variation)\nis an unexpected result, what can explain this?\nResponse: Thanks for your comment here.\n Regarding the temperature sensitivity of GPP for below 3000 m, temperature still plays a role in\nregulating its temporal variation, i.e., P<0.001, given more than 30000 observations were\ninvolved in the calculation, despite that the R2 is only 0.04 (or R=0.20). This indicates the\ntemperature may play a minor role in explaining the variation in GPP in these ecosystems.\n We consider the higher temperature and strong human activities in the lower elevation\nenvironments may have contributed to lower temperature sensitivity of GPP, than that in high\nelevation environments. (1) The mean annual temperature for the ecosystems below 3000 m is\n10.9°C (–4.4 to 25.0°C), while it is –0.12°C (–6.0 to 8.7°C) for the ecosystems above 3000 m.\nThe high elevation region (>3000 m) is 11°C colder than the ecosystems below 3000 m, which\nmay have caused the large difference in temperature sensitivity in GPP, RE and NEP. (2) It is\nnotable that the ecosystems below 3000 m cover a large geographic extent across China, from\ntropical forests in Hainan Island, rice paddies in Yangtze River Delta, maize & corn in North\nChina Plain, and soybean in Northeast China. As a highly populated country, these regions\nexperienced strong human impacts like irrigation and fertilization. Management of water and\n14\nnutrient in highly-populated region (<3000 m) may also strongly regulate the variation in plant\nphotosynthesis. Also, the RE and NEP also show lower temperature sensitivity, which is\nconsistent to the GPP.\n We now have added the following sentences in the main text. “Further analyses validate our\nexpectation that high-elevation environments and thus colder ecosystems (3000–5000 m; mean\nannual temperature of −0.12°C, ranging from −6.0 to 8.7°C), such as the Tibetan Plateau, are\nmore sensitive to climate warming than their lower-elevation counterparts (mean annual\ntemperature of 10.9°C, ranging from −4.4 to 25.0°C)”. Please see Line 117-120. “The high\nelevation environments is 11°C colder than the ecosystems below 3000 m, which may have\ncaused the large difference in temperature sensitivity in GPP. Beside the variation in\ntemperature, management of water and nutrient in highly-populated region (<3000 m) may also\nstrongly regulate the variation in plant photosynthesis”. Please see Line 124 to 128.\n Thanks for your comments regarding the temperature sensitivity of GPP.\nL155-157\nFig 3 NEP, are the slopes significantly different?\nResponse:\n Thanks for your comment. Yes, our additional analyses suggest that there is significant difference\nbetween the higher and lower elevation regions regarding their temperature sensitivities (P<0.01),\nconsistently for GPP, RE and NEP. We have added the significant level to the main text. Please\nsee Line122, 124 and 127.\n We also provided descriptions of the calculation and statistics in the “Temperature sensitivity” of\nthe Material and methods: “We then used General Linear Model (GLM) univariate analysis to test\nthe differences regarding the regression lines for GPP, RE and NEP. During this process, the GPP,\nRE and NEP were the dependent variables, the different elevation groups (higher or lower than\n3000 m in elevation) were the independent variables, while air temperature was covariate. For\nGPP, over 40,000 observations were involved in the GLM analysis, while there were more than\n50,000 observations for RE and more than 60,000 observations for NEP. Daily accumulated or\naveraged GPP, NEP and air temperature data were used during the GLM analyses. For RE, given\nexponential regression models were used to reflect its temperature sensitivities, we applied a log\ntransformation to the RE data before the GLM analysis. Effects were considered as significant at\nP<0.05 (difference in temperature sensitivities between elevation bands), while the null\nhypothesis was accepted when P>0.05 (no difference in temperature sensitivities between\nelevation bands)”. Please see Line 290 to 300.\nL148-149 - Are the Q10 significantly different?\nWhat is the temperature in this elevation gradient?\nResponse:\n15\n Thanks for your comment regarding the statistics of Q values. Yes, the Q values between\n10 10\ndifferent groups reached a significant level (P<0.01), given more than 50000 observations were\nemployed during the calculation and statistical analyses. We then added the significance level to\nthe main text (P<0.01). Please see Line 122.\n Regarding the temperature gradient, we furthered calculated the mean annual temperature of\nthese sites. It turned out that the warmer area (<3000 meters in elevation) has a mean annual\ntemperature at 10.9°C (ranged from -4.4 to 25°C), while the colder area r area (3000 meters in\nelevation) has the mean annual temperature of -0.12°C (ranged from -6 to 8.7°C). Indeed, there\nis 11.0C difference between two elevation gradient, which may have caused the significant\ndifference in GPP, RE and NEP regarding their temperature sensitivities. We also added the\nabove information regarding temperature to the main text to facilitate your review: “Further\nanalyses validate our expectation that high-elevation environments and thus colder ecosystems\n(3000–5000 m; mean annual temperature of −0.12°C, ranging from −6.0 to 8.7°C), such as the\nTibetan Plateau, are more sensitive to climate warming than their lower-elevation counterparts\n(mean annual temperature of 10.9°C, ranging from −4.4 to 25.0°C).” Please see Line 117 to 120.\n Thanks again for your comment. We hope our revisions have resolved your concern.\nL162, needs to briefly elaborate on what have been the changes in reactive N in mountains in the\nstudy region.\nResponse: Thanks for your comment. Following your suggestion, we added another sentence to\nexplain what happened to N deposition in China’s mountains −− “It is notable that reactive N deposition\nhas increased persistently across high-elevation environments in China 21. This is different from lower\nelevations, where N deposition increased (1980–2000) and then stabilized (after 2005), driven by\nsocioeconomic changes and vigorous controls on N pollution 22,23”. Please see Line 139 to 140.\nL163 write explicitly the GPP proxy that was used\nResponse: We used the leaf area index as a proxy to GPP. Now the sentence has been revised to\n“……we focused on GPP and its proxy (LAI, Leaf Area Index).” Thanks for your comment. Please see\nLine 141.\nL173..NEP, RE, or GPP is not plant growth, eddy covariance does not measure plant growth.\nResponse: Thanks for your comment. Now the sentence has been revised to “similar to the elevation-\ndependent pattern of GPP based on both the EddyChina2023 dataset and the MsTMIP ensemble”.\nPlease see Line 152-153.\nL190-191, please indicate where this is shown\nResponse: Thanks for your comment. The results are shown in Fig. 5a-d. The sentence has now\nbeen revised to “Future changes in climate and nutrient supply will cause a general increase in NEP\n16\nacross most elevation bands and the various climate scenarios (Fig. 5a-d)”. Please see Line 167-169.\nUnclear how to access the data, a link is provided, but when typing some sites names or publications\nfrom supplementary table nothing came up, authors should clearly explain how to access the data.\nResponse: We apology that the original link to the EddyChina2023 dataset does not work. To resolve\nthis issue, we uploaded the dataset to FigureShare (https://figshare.com/s/e1f7f9c13e547e422a71).\nWe spent roughly 6 months to create the EddyChina2023 dataset, and hope the dataset could be\nreused by the scientific community, especially for machine and deep learning. The new address of the\ndataset has also been added to thesection of Data Availability. Please see Line 372.\nTo Reviewer #3\nThe main target of this manuscript is to investigate the elevation dependent CO2 uptake pattern across\nChina mountains, considering the effects of geographical factors, climate factors, and soil properties.\nThis study reported a negative linear elevation-dependent pattern of CO2 uptake, and this pattern has\nbeen verified by land surface model simulations and satellite observations. This work contributes\nsignificantly to a comprehensive understanding of the responses of mountainous ecosystem\nfunctioning to the ongoing and future climate change. Despite that the topic is interesting, I have some\nmajor concerns which should be clarified before publication.\nDear Reviewer,\nThanks very much for your time and thoughtful comments regarding the manuscript. We are happy to\nsee that our work contributes significantly to a comprehensive understanding of the responses of\nmountainous ecosystem functioning to the ongoing and future climate change. During the revision, we\ncarefully considered all your comments and made lots of revisions, which are summarized here.\n Climate Dataset. To resolve your concern regarding climate dataset, we then retrieved ground-\nbased climate data for each site (from the literatures), and compare them with the CMFD dataset\n(0.1°x0.1°). We also employed a finer climate dataset, namely WorldClim (0.0083°x0.0083°), to\nexplore whether an increase in spatial resolution would reduce its uncertainty. The results\ngenerally show that the CMFD is good enough to represent the spatial variation in climate factors.\n SEM analyses. Your another concern is about the SEM analyses. In the revision, we updated\nthe SEM by employing the ground-based climate data, rather than the CMFD data. We also added\necosystem types and atmosphere CO concentration to the SEM. The updated SEM analyses\n2\nshow general consistent result to Pearson correlation analyses, i.e., both verified the importance\nof air temperature in affecting the spatial pattern of GPP. Also, the updated SEM also performs\nbetter in explaining the spatial variation in GPP and RE.\n Uncertainty. We admit that there is still uncertainty regarding the drivers of the spatial pattern of\nNEP. Given our study represent a national scale study, which means that other factors may also\nplay a significant role in affecting the apparent CO exchange recorded by eddy covariance\n2\ntowers, such as equipment setting, data processing, human activities, species and etc. However,\nthese unrecognized factors and their contribution can hardly be determined by the present study.\nAlso, the description regarding the uncertainty were also added to the Implications of the main\ntext.\n Minor comments. All your specific comments regarding the manuscript have been rectified or\nexplained in the revised manuscript. Several figures have also been redrawn to resolve your\nconcern. We hope our revisions have met your expectations.\nSincerely, we sincerely appreciate your comments to our manuscript, which pushed us to better\ninterpret our findings. The revisions and explanations to your comments have largely improved the\nquality of the manuscript.\nThe authors\n1. Detailed and accurate climate data are important for the analyses. In this study, the authors used\nthe climate data from the CMFD dataset, which is with a coarse spatial resolution and how does this\ndataset consider the topography effects on climate data? Will this introduce uncertainty into your\nanalysis?\nResponse:\nThanks for your comment regarding the climate dataset. We agree with you that detailed and accurate\nclimate data is important for our study, given the importance of climate factors in regulating the\ntemporal and spatial variations in CO exchange. To resolve your concerns, we did the following work.\n2\n During the revision, we collect the observed climate data for each eddy covariance tower from\nliteratures, i.e., usually mean annual temperature or precipitation, and then compared them with\nthe CMFD dataset (0.1° in spatial resolution). It turns out that the CMFD is highly correlated with\nthe observed climate data (R2=0.94 for air temperature; R2=0.93 for precipitation; Fig. R4a-b).\nThis suggests that the retrieved climate information from the CMFD dataset is good enough to\nrepresent the climate background for the eddy covariance tower during analyses and random\nforest training.\n Still, we are wondering whether a finer climate dataset would better represent local climate\ninformation. To verify above issue, we then used the highest climate dataset public available\n(0.0083° in spatial resolution), the WorldClim dataset, i.e., it is 120 times better than the CMFD\nin spatial resolution. Indeed, a finer climate dataset would better represent observed the climate\ninformation of the eddy covariance towers (R2=0.95 for air temperature; R2=0.95 for precipitation;\nFig. R4c-d), though there is no significant improvement compared with the CMFD dataset.\n In addition, it is notable that the CMFD dataset even performs better than the WorldClim dataset,\ngiven the CMFD estimation is closer to the 1:1 line (CMFD: y=0.98x for temperature, y=0.95x for\nprecipitation; WorldClim: y=0.95x for temperature, y=0.90x for precipitation). Based upon the\nfacts, we consider the performance of CMFD and WorldClim are comparable, and they both could\nrepresent the observed climate background of the eddy covariance towers.\n We expect the observed climate background of the eddy covariance towers would improve the\ncontribution in explaining the elevation dependent pattern of NEP. In the revised manuscript, we\nthen used the ground-based temperature and precipitation in SEM and correlation analyses.\nIndeed, it turned out that the observed climate dataset did perform better than before (Fig. R2 in\nresponse letter, or Fig. 2a in main text).\nFig. R4. Comparison among temperature/precipitation from ground-based observations, CMFD,\nWorldClim, CMIP6 datasets and MsTMIP climate datasets. (a-b) Correlation between ground-\nbased observations and CMFD; (c-d) Correlation between ground-based observations and WorldClim;\n(e-f) Correlation between ground-based observations and CMIP6; (g-h) Correlation between ground-\nbased observations and MsTMIP forcing data (CRUNCEP). CMFD=China Meteorological Forcing\nData; WC=WorldClim; MsTMIP=Multi-Scale Synthesis and Terrestrial Model Intercomparison Project;\nCMIP6= Coupled Model Intercomparison Project Phase 6; TAS=Air temperature; PREC=Precipitation.\n During the random forest training, we need to obtain the time series dataset for each climate\nfactors, for each day and for each eddy covariance tower. However, most studies did not provide\n19\ncomplete climate information in the literature. That is why we used the CMFD dataset to build a\ncomplete dataset for NEP variation at daily scale (like temperature, precipitation). By contrast,\nthe WorldClim dataset only covers the period of 1970-2000, while the range of the\nEddyChina2023 is 2002-2022, i.e., the WorldClim cannot be used in random forest training.\n Finally, we admit that the lack of high-quality ground-based observed climate dataset may have\ninduced uncertainty to the random forest training, though we have no choice, before more detailed,\nhigh-quality datasets were released by our colleagues in China. The comparison between\nobserved climate factors CMFD and similar datasets were then added to the “Materials and\nmethods” as Fig. S15. The uncertainty arising from climate dataset is also mentioned in the\n“Limitations”, which could be seen in Line 229 to 235.\n2. Another issue is regarding the comparison between eddy covariance data and MsTMIP simulations.\nThe spatial resolution is 0.5 degree for MsTMIP models, which does not match the detailed\nobservations of EddyChina2023. Again, the CMIP6 model projections are with even much coarser\nresolution for the future climate change, so what is the uncertainty for comparing the future climate\nchange on high elevation and low elevation?\nResponse:\nThanks for your concern regarding the MsTMIP dataset. Indeed, we agree with you that both the\nMsTMIP and CMIP6 have relatively coarse spatial resolution, which may induce uncertainty due to\ntopographic effect in mountain areas. To resolve your concern, we conduct additional work and list it\nhere.\n Regarding the CMIP6, we also compare the outputs of temperature and precipitation from the\nCMIP6 models with the ground-based observations. The comparison shows that the CMIP6\nensemble agrees well with the ground-based observations (Figure R3 e-f), which is very much\nlike the performance of CMFD and WorldClim, ensuring them to be used as forcing data for the\nrandom forest predictions in the future. In addition, over 20 models for each scenario are used\nacross 4 climate scenarios in the current study, which may also provide a strong constrain to\nfuture variation in NEP.\n Regarding the MsTMIP ensemble, we also compare the ground-based observations of\ntemperature and precipitation of each eddy covariance tower with the forcing data of MsTMIP\nensemble (CRUNCEP climate forcing data). The MsTMIP ensemble generally captured the\nvarying spatial pattern of temperature and precipitation across China (Figure R4 g-h). Also, the\nFigure 4a (in main text) show similar elevation dependent pattern of GPP, which is consistent to\nthe result from ground-based GPP of the EddyChina2023.\n Sincerely, thanks a lot for pushing us to think deeper. Your comments did help us improve the\nquality of the manuscript.\n3. One main finding of this study is that ecosystems on high mountains have higher temperature\n20\nsensitivity and future climate change will increase the CO2 uptake in these ecosystems. However, I\nfound weak relationship between either GPP or RE and air temperature from the main figure 2, which\nis the most important figure illustrating the relationship between different components of NEP and\nmultiple factors. There seems to be contradictory between them.\nResponse:\nThanks for your comments.\n We employed the Pearson correlation (Figure R5, in supporting materials) and SEM (Figure 2a,\nin main text) analyses to explain the spatial pattern of NEP, rather than its temporal variation.\nThe Pearson correlation analyses did find significant correlations between NEP, GPP and RE with\ntemperature variations, including air temperature and land surface temperature. In the updated\nSEM analyses (Fig. 2a), we used the observed climate factors for each eddy covariance tower.\nIt turned out the ground-based observations perform better than previous version, showing a clear\nrelationship between GPP and air temperature.\n The analyses regarding the temperature sensitivity focused on the temporal variation of CO\n2\nexchange. Temperature is widely witnessed as an important factor in controlling the temporal\nvariation of CO exchange. In addition, the higher temperature sensitives, and future increase in\nvariation in CO exchange. Therefore, we suggest that temperature play a role in both the spatial\n2\npattern and temporal variation of CO exchange.\nrespiration (RE) and net ecosystem productivity (NEP) and climate, soil and permafrost factors.\nLAT, latitude; LON, longitude; ELV, elevation; TAS, atmospheric temperature; PREC, precipitation; NR,\nreactive N; LST, land surface temperature; PAR, photosynthetic active radiation; SWC, soil water\ncontent; LAI, leaf area index; [CO ], atmospheric CO concentration.\n2 2\nDetailed comments\n1. line 37-42, This part is not relevant to the topic of this manuscript.\nResponse: Thanks for your comment. This sentence has been removed from this paragraph.\n2. Line 98-103, In which period are these results determined? Do you consider the potential difference\n21\nin the interannual variations in NEE among different sites in different ecosystems? Another point is\nthat the occurrence of extreme climate events may be quite different\nResponse:\n These sentences aimed to provide a contemporary estimation of the NEP strength across China’s\nvarious ecosystem types. The observations of eddy covariances range from 2002 to 2020, with a\nmean of 2012. Now this sentence has been revised to “Among the terrestrial ecosystem types\nduring 2002 to 2020 (with a mean of 2012) ……”. Please see Line 100 in Text S1.·\n Indeed, the net CO sink usually show strong interannual variability, which can be captured by\n2\nmultiyear eddy covariance observations. We consider these eddy covariance observations (>200\nsites and >500 site-year) as random sampling of the status of net CO sink of each ecosystem\n2\ntype across China. In addition, it is notable that 118 sites (58% of the dataset) covered 2+ years’\nobservation, while 79 sites (40%) provide covered 3+ years’ observation. This may have reduced\nthe uncertainty due to interannual variability, especially due to climate extremes.\n The Line 98-103 intended to verify whether the net CO sink is consistent to repeated soil\n2\nsampling across each ecosystem. We notice that, for each ecosystem type, 10+ sites were\navailable to cover the net CO sink strength. In China, the croplands, forests, and grasslands\n2\ncover 3/4 of China’s land area. It is also notable that there were lots of eddy covariance\nobservations in croplands (40 sites and 116 site-year), forests (58 sites and 134 site-year) and\ngrasslands (41 sites and 129 site-year). Therefore, multiple observations for each ecosystem type\nmay have also largely reduced the uncertainty from interannual variability.\n The sentences have been added to the descriptions to the EddyChina2023 dataset in “Materials\nand methods”. Please see Line 260 to 261. It is also notable that the “Representativeness of the\neddy covariance dataset” has been moved to Text S1.\n4. Line 106, In this study, the largest uptake of CO2 is found in croplands, but not forests.\nResponse: Yes, we understand your concerns. We think that C loss via grain consumption in\ncroplands should be considered when we compare the eddy covariance CO sink with changes in soil\n2\nsink is by forests (note most C in croplands is consumed by humans, resulting almost a neutral CO\n2\nsink in croplands)”. Thanks for your comment to help us correct the error. Please see Line 109-110 in\nText S1 of Supporting Materials.\n5. Line 112, This sentence is not clear. The CO2 uptake in temperate grasslands of Inner Mongolia is\nneutral, indicating that the carbon gain is cancelled out by the carbon loss. How can this result be\nverified by soil carbon inventory? Do you mean repeated inventory data?\nResponse: Yes, the result is verified by repeated soil carbon inventory. We have revised the sentence\nto “The NEP by the different terrestrial ecosystems is generally consistent with the variation in soil\norganic carbon during the last two decades, as shown by national-scale repeated soil carbon\n22\nsurveys”. According to the suggestion to Reviewer #1, this part has been moved to Supporting\nMaterials. Please see Line 106-108 in Supporting Materials Text S1.\n6. Figure 2, It is strange that the air temperature does not exert important effects on either GPP or RE\nacross mountainous sites.\nResponse:\n Indeed, we agree with you that air temperature play a significant role in regulating temporal\nvariation in GPP and RE, verified by lots of studies. We apologize that we did not emphasize\nthat the Figure 2a (in main text) is intended to explore the controls of biotic and abiotic factors on\nthe spatial pattern of GPP and RE. Furthermore, in a previous version of this manuscript, we\nused the CMFD-retrieved climate dataset, which may have caused a worse performance of air\ntemperature in explaining the spatial pattern of CO exchanges. During the revision, we extracted\n2\nthe ground-based climate factors for each site, and it did improve the performance of air\ntemperature in explaining the spatial variation in GPP.\n In fact, air temperature is still quite important in controlling the spatial pattern of GPP and RE,\nillustrated in Figure S5 (in supporting materials), the Pearson correlation analyses. The land\nsurface temperature (LST) also plays a significant role in affecting the spatial pattern of GPP and\nRE. Temperature (air temperature and land surface temperature), water factors (precipitation and\nsoil water content) and nutrient supply (N) all strongly affect the spatial pattern of GPP and RE\n(Figure S5).\n Based upon the Pearson correlation (in supporting materials) and the updated SEM analyses (in\nmain text), we therefore conclude the temperature as a player in affecting the spatial pattern of\ncarbon exchange.\n7. Line 135-137, But from the SEM analysis, I did not find a close linkage between NEP and variations\nof temperature and precipitation and nitrogen deposition.\nResponse:\nThanks for your comments here.\n We apologize that we did not clarify that we tried to build its indirect relationship between NEP\nand environmental factors via GPP and RE, since the NEP represents the net balance between\nGPP and RE. This does not mean that there is none of a close linkage between NEP and\nvariations in temperature, precipitation, and nitrogen deposition. The above descriptions have\nbeen added to the captions of Figure 2a (in main text). Please see Line 548-550.\n In addition, in a previous version of this manuscript, we used the CMFD-retrieved climate dataset,\nwhich may have caused a worse performance of climate factors in explaining the spatial pattern\nof GPP and RE. During the revision, we extracted the observed climate factors for each site, and\nit did improve the performance of environmental factors in explaining the spatial variation in GPP\nand RE, i.e., the R2 have increase from 0.28 to 0.39 for GGP, and 0.22 to 0.29. Please see Figure\n8. Figure S14, what do the different colored lines represent?\nResponse: We apologize for lack of legend in the Figure S14. We added legends in this figure by\nredrawing the Figure S14. Thanks for your help with correcting this error.\n24"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Using collected eddy covariance data, Wei et al. revealed the elevafion related paftern of net ecosystem producfivity (NEP) over Chinese terrestrial ecosystems. The geographical paftern of NEP was aftributed into the difference in temperature sensifivity, indicafing the high elevafion regions had a higher increase in NEP in future. This work had some improvements and gave an implicafion of NEP spafial paftern and its future trend in China, but it sfill had many shortcomings, which prohibited its publicafion in Nature Communicafion.\n1. Based on eddy covariance data, the authors found a negafive elevafion-NEP relafion over Chinese terrestrial ecosystems, which resulted from the differences in the varying hydrothermal factors, nutrient supply and ecosystem types (L. 113- L.114). However, the authors also implied that temperature sensifivity and human acfivifies would account for the negafive elevafion-NEP paftern. These results made readers confusing: which factor or factors affected the elevafion-NEP paftern? Many works addressed the factors shaping NEP spafial pafterns like mean climafic factors, the changing climafic factors. What were the relafionships among those factors and temperature sensifivity as author menfioned?\n2. The descripfions about temperature sensifivity calculafion were confused. It seems that they calculated temperature sensifivity with all daily data over the specific region, which may cover the temporal variafions and spafial variafions of daily NEP. The numbers and distribufions of sites used for calculafing temperature sensifivity may introduce the biases in calculated values, though I did agree with them that the high elevafion regions took higher temperature sensifivity. In addifion, the dataset of daily NEP was not available, which further confused the understanding of temperature sensifivity calculafion.\n3. After calculafing the temperature sensifivity, the authors validated the differences in the temperature sensifivity with values of modeled GPP and LAI. This validafion had many gaps between NEP temperature sensifivity and GPP or LAI values, though they gave the changing rates of GPP and LAI. Higher sensifivity did not ensure more producfive. NEP differed from GPP and LAI.\n4. When predicfing the future trend of NEP with machine learning and models, the variafions of temperature sensifivity were not considered, which should introduce uncertainfies in modeled NEP. This shortcoming did exist in many works, but it should be discussed at least.\n5. Some sentences like L. 40- L. 42 seem unprofessional as this was a scienfific arficle.", "reviewer_3": "The revised manuscript has well addressed my former comments. The manuscript is now much clearer. I suggest acceptance after addressing the minor comments.\nMinor comments\n1. Line 38, alfitude? Or lafitude?\n2. line 106-107, for all EC sites? I did not find any support for this point."}, "author_response": "Response Letter\nReference Number:NCOMMS-23-30115B\nTitle: Elevation-dependent pattern of net CO uptake across China\nUsing collected eddy covariance data, Wei et al. revealed the elevation related pattern of net\necosystem productivity (NEP) over Chinese terrestrial ecosystems. The geographical pattern of NEP\nwas attributed into the difference in temperature sensitivity, indicating the high elevation regions had\na higher increase in NEP in future. This work had some improvements and gave an implication of NEP\nspatial pattern and its future trend in China, but it still had many shortcomings, which prohibited its\npublication in Nature Communication.\nDear Reviewer,\nThanks very much for your efforts to review our manuscript for two rounds. We appreciate your new,\nthoughtful comments. All your comments have been carefully considered and revised. Here is a\nsummary of our revisions.\n Temperature sensitivity. We validate our results with two new approaches, rather than using the\nchanging rates. (1) We calculated each site’s temperature sensitivity of NEP. The results validate\nthat there is higher temperature sensitivity in high-elevation environments. (2) The MsTMIP\nensemble also confirms the higher temperature sensitivity of NEP at high-elevation environments.\n(3) We consider the strong temperature constrain and less human impacts may have caused the\nhigher temperature sensitivity in high-elevation environments.\n Changing rates of NEP. (1) Regarding the “NEP differed from GPP and LAI”, we then analyzed\nthe changing rates of NEP based upon the MsTMIP ensemble. The results of NEP are quite like\nthat of the GPP. (2) Regarding the “Higher sensitivity did not ensure more productive”, we\nseparate the changing rates of NEP into a new section. (3) We then analyzed the changing rates\nof GPP of models and satellite observations, both confirming the stronger relative changes in\nproductivity in high-elevation environments.\n NEP projection. All daily NEP data of EddyChina2023 have been used to train/validate Random\nForest to build an implicit model between NEP and independent factors. This means that the\ntemperature sensitivity has been considered in NEP projections.Trying to validate our finding, we\nthen analyzed the default NEP of CMIP6 (without constrain from the EddyChina2023). The CMIP6\ndefault NEP outputs generally support our findings that there would be a more robust relative\nchange in high-elevation environments.\n Other revisions. We slightly revised the structure of the manuscript. Other necessary revisions\nhave also been made to the main text, figures and supporting materials.\nBy responding each your comment and conducting more analyses, we believe that the revised\nmanuscript has strengthened our findings substantially. We also hope the revised manuscript have\nmeet your expectation. Again, thanks very much for your contribution!\nBest regards,\nProf. Da Wei\nOn behalf of all co-authors\n1\n1. Based on eddy covariance data, the authors found a negative elevation-NEP relation over Chinese\nterrestrial ecosystems, which resulted from the differences in the varying hydrothermal factors, nutrient\nsupply, and ecosystem types (L. 113- L.114). However, the authors also implied that temperature\nsensitivity and human activities would account for the negative elevation-NEP pattern. These results\nmade readers confusing: which factor or factors affected the elevation-NEP pattern? Many works\naddressed the factors shaping NEP spatial patterns like mean climatic factors, the changing climatic\nfactors. What were the relationships among those factors and temperature sensitivity as author\nmentioned?\nResponse:\n Manuscript structure. We apologize that our manuscript and descriptions may have caused\nsome misunderstanding to the reviewer. Here we clarify the structure of the manuscript. Now, our\nmanuscript included the following sections. (1) Elevation-dependent pattern of NEP. Section 1\naims to recognize the elevation dependent pattern of NEP and to figure out its driving factors. (2)\nHigher temperature sensitivity in high-elevation environments. Section 2 aims to compare the\ndifference between high- and lower-elevation environments regarding their temperature sensitivity.\n(3) Changing rates of productivity during the past four decades. Section 3 aims to explore\nretrospective variation in NEP along elevation gradient. (4) More robust changes in high-elevation\nenvironments in future. Section 4 aims to predict future variation in NEP.\n Drivers of the spatial pattern and variation. (1) Spatial pattern of NEP, our analyses suggest\nthat climate factors, ecosystem types and reactive nitrogen level largely determines the spatial\npattern of GPP, RE and thus NEP. (2) On the temporal variations, the NEP and photosynthesis\nshowed a higher temperature sensitivity in high-elevation environments (3000–5000 m), compared\nwith the lower-elevation environments (<3000 m). (3) During the past decades, high-elevation\nenvironments experience more rapid relative changes in productivity, due to higher temperature\nsensitivity and stronger increase in precipitation, compared with the lower-elevation environments.\n(4) In the future, Random Forest based upon EddyChina2023 and CMIP6 predicted a stronger\nrelative change in NEP in high-elevation environments, whereas less change is expected at lower\nelevations, due to a reduced supply of nitrogen.\n Regarding the “relationships among those factors and temperature sensitivity”, we believe\nthat the higher temperature in high-elevation environments is an intrinsic feature, validated by both\neddy covariance datasets and process-based models, largely due to strong temperature limitation\nin these environments. Also, we think the varying environmental factors may also affect the NEP\n(like the precipitation in section 3 and reactive nitrogen in section 4), besides the temperature\nsensitivity.\n To summarize, the spatial pattern of NEP is affected by temperature, precipitation, ecosystem\ntypes and reactive nitrogen. For the decadal variation in NEP, the changing environmental factors\nand temperature sensitivities collectively affected the past and future variation in NEP along the\nelevation gradient. We also revised the structure of the manuscript, and we hope our explanations\nand revisions have well addressed your concerns.\n2. The descriptions about temperature sensitivity calculation were confused. It seems that they\ncalculated temperature sensitivity with all daily data over the specific region, which may cover the\ntemporal variations and spatial variations of daily NEP. The numbers and distributions of sites used\n2\nfor calculating temperature sensitivity may introduce the biases in calculated values, though I did agree\nwith them that the high elevation regions took higher temperature sensitivity. In addition, the dataset\nof daily NEP was not available, which further confused the understanding of temperature sensitivity\ncalculation.\nResponse:\n Thanks for your comment regarding the calculation of temperature sensitivity. We adopted this\nmethod following a previous study in the Arctic (Natali et al., 2019, Nature Climate Change: 852-\n857). Indeed, we agree with you that the numbers and distributions of sites used for calculating\ntemperature sensitivity may have introduced some biases. For example, high-elevation\nenvironments have less observation sites, largely because they receive less research attentions\nand lack of traffic accessibility. The EddyChina2023 dataset is not a well-designed network, and\nthis would make the high-elevation environments under-represented.\n Trying to resolve your concern, additional work has been done to validate our previous findings\nin Figure 3. We then test whether the high-elevation environments have higher temperature\nsensitivities, by calculating each site’s temperature sensitivity of NEP and then conducting\nstatistical analyses. The results further validate there is significant difference between high-\nelevation and lower elevation environments (Figure S7. This is a new figure). Please see Line\n122 in the main text.\n Regarding the data availability of daily NEP. We already uploaded the whole dataset to the\nsubmission system as Data Availability. The daily NEP dataset was also uploaded in FigureShare.\nA link to the FigureShare is provided here (https://figshare.com/s/e1f7f9c13e547e422a71), which\nwould facilitate your review.\nFig. S7. Comparison of the temperature sensitivities of net ecosystem productivity between\nhigh-elevation environments and their lower elevation counterparts. High-elevation\nenvironments are defined as >3000 meter in elevation, while lower elevation environments as <3000\nmeters. The NEP sensitivity to temperature is defined as slope between NEP and temperature\nvariation for each site (Slope ; NEP, net ecosystem productivity; TAS=Atmospheric Temperature).\nNEP/TAS\nsensitivities between two groups, i.e., high- and lower elevation environments, were tested with\nIndependent-Samples T test. The mean temperature sensitivity for high- and lower-elevation\nenvironments are 0.08±0.01 (n=33 sites) and 0.07±0.01 g C ºC-1 (n=78 sites). The “*” indicates\nsignificant difference between two groups (P<0.05).\nReference:\nNatali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nature\nClimate Change 9, 852-857 (2019). https://doi.org/10.1038/s41558-019-0592-8\n3. After calculating the temperature sensitivity, the authors validated the differences in the temperature\nsensitivity with values of modeled GPP and LAI. This validation had many gaps between NEP\ntemperature sensitivity and GPP or LAI values, though they gave the changing rates of GPP and LAI.\nHigher sensitivity did not ensure more productive. NEP differed from GPP and LAI.\nResponse:\n Thanks very much for your comment. When we analyzed the EddyChina2023 data and noticed\nthat there is higher temperature sensitivity of NEP in high-elevation environments, we expected\nthat this may benefit productivity in these environments. That is why we then analyzed the\nvariation in NEP during the past decades. Indeed, we agree with you that higher temperature\nsensitivity would not ensure higher productivity. To resolve your concern, we conducted the\nfollowing work.\n More validation of temperature sensitivity. After the calculation of temperature sensitivity for\neach site with a new approach (Fig. S7, This is a new figure), we then explored the temperature\nsensitivity of NEP from the MsTMIP ensemble. It turns out that the MsTMIP ensemble also\nvalidated our finding that the NEP at high-elevation environments is more sensitive to temperature\nvariation than that in lower elevation environments (Fig. S8. This is a new figure). The result of Fig.\nS8 is consistent to both Fig. 3 and Fig. S7, therefore further validating the result based upon\nEddyChina2023 dataset. The above additional work has been added to the main text. Please see\nLine 129-131.\n Analyses of changing rates of NEP and GPP. In this part, we conducted the following work. (1)\nIt is true that “NEP differed from GPP and LAI.” Therefore, during the revision, we analyzed the\nchanging rates of NEP based upon the MsTMIP ensemble, besides the GPP. The results of NEP\n(Fig. 4a. This is a new figure), including averaged and changing rates of NEP across various\nelevation bands, are quite similar to the results based upon GPP (Fig. 4b. This is a redrawn figure),\ni.e., both show higher relative changing rates in high-elevation environments. (2) We also analyzed\nthe changing rates of GPP based upon the GLASS product, given satellite observations provide\nanother independent evidence. The GLASS GPP show very similar results to the findings of\nMsTMIP GPP and NEP (Fig. 4c. This is a new figure). Based upon the above evidences (i.e.,\nMsTMIP NEP, MsTMIP GPP and GLASS GPP), we therefore conclude that higher-elevation\nenvironments experience more rapid relative changes in productivity.\n Reasons to include GPP. Besides the NEP, we also keep the analyses regarding the GPP,\nlargely based upon the following reasons. (1) Satellite-based GPP observations can provide a direct and\nindependent observation of ecosystem productivity, which would further validate the results of\nprocess-based models. (2) Satellite-based observations can hardly estimate NEP, while GPP\n4\nestimation is easier, like the GLASS GPP product used in the current study. (3) The spatial pattern\nand changing rates of GPP and NEP are quite similar to each other (Fig. 4a and 4b), which means\nthat the GPP can be employed as a “proxy” to NEP. (4) The spatial pattern and temporal variation\nof NEP is better correlated with GPP, compared with the RE (Fig. 2a). In Fig. 3a-c, it also shows\nthat the temperature sensitivity of GPP of high-elevation environments is 5 times of that in the\nlower elevations, highlighting the role of GPP in dominating the temporal variation of NEP along\nelevation gradient. Therefore, by employing the GPP, especially satellite-based GPP obversions,\nwe believe it could better understand the variation in NEP along the elevation gradient.\n Potential causes for the changing rates of productivity. We agree with you that “Higher\nsensitivity did not ensure more productive”. During the revision, the changing rates of NEP or GPP\nwas not used to validate the difference in temperature sensitivities. Instead, we separate the\nchanging rates of NEP and GPP into a new section, named “Changing rates of productivity during\nthe past four decades.”. We also added a new figure (Fig. S9. This is a new figure), trying to answer\nwhy the high-elevation environments experience more rapid changes. We examine the changes\nin atmospheric temperature, precipitation, reactive nitrogen, atmospheric CO concentration and\n2\ntemperature sensitivity along elevation gradient (atmospheric CO concentration map not\n2\navailable). We consider stronger changes in precipitation and higher temperature sensitivity in\nhigh-elevation environments may have contributed to its stronger relative increase in NEP, while\nthe opposite is true for the lower elevation environments. These revisions have been added to the\nmain text. Please see Line 155-167.\n Finally, we thank you very much for your comment here. By responding each your comment\nand conducing the above additional analyses, we largely strengthen our findings. Thanks\nvery much for your contribution to this manuscript.\nFig. S8 Temperature sensitivity of gross primary productivity, ecosystem respiration and net\necosystem productivity based upon the MsTMIP ensemble 2. GPP, gross primary productivity; RE,\necosystem respiration; NEP, net ecosystem productivity. The temperature sensitivity of GPP, RE and\nNEP of each pixel was calculated following the procedure descripted in Figure 3. Linear slopes were\nused for GPP and NEP, while the slope of RE were calculated after logarithm transformation. During\nthe calculation, all pixels of each elevation band were employed to derive the slopes. Then, the results\nof these models were averaged to obtain the ensemble average and the standard deviation for each\nelevation band. The mean, median and variations (e.g., 25~75%) indicates the difference among\n5\nvarious models of the MsTMIP BG1 group (driven by climate change, nitrogen deposition, atmospheric\nCO enrich and land use change). The MsTMIP BG1 group includes 8 models, i.e., BIOME-BGC,\nFig. S9. Trend of atmospheric temperature, precipitation, and reactive nitrogen level during the\npast four decades. TAS, atmospheric temperature; PREC, precipitation; Nr, Reactive nitrogen. The\nvariations of TAS and PREC were retrieved from the CMFD dataset 3. The Nr is based on the Multi-\nscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) product 2. During the\ncalculation, all pixels of each elevation band were employed to derive the variations.\n4. When predicting the future trend of NEP with machine learning and models, the variations of\ntemperature sensitivity were not considered, which should introduce uncertainties in modeled NEP.\nThis shortcoming did exist in many works, but it should be discussed at least.\nResponse:\n Thanks for your concern regarding temperature sensitivity during NEP projection. (1) In fact, all\ndaily NEP data have been used to train and validate Random Forest model to build an implicit\nmodel between NEP and its dependent factors. This means that the difference of temperature\nsensitivity between high- and lower-elevation environments has already been considered in the\nRandom Forest model and subsequent NEP projections. (2) Trying to further validate our findings,\nwe then analyzed the variations in NEP along elevation gradient, based upon the NEP outputs\nfrom the CMIP6 themselves (without constrain from the EddyChina2023 dataset). The CMIP6 NEP\noutputs also generally captured the more robust changes in high-elevation environments in future\n(Fig. S14. This is a new figure), though not that significant like the results based upon the fusion\nresults of Random Forest projections (constrained by EddyChina2023). Therefore, the CMIP6\ndefault NEP outputs also validate our findings that there would be a more robust relative changes\nin high-elevation environments in future, consistent to their variation during the past four decades.\nAll these evidences make us to believe that the higher temperature in high-elevation environments\nis an intrinsic feature, captured by eddy covariances, biogeochemical models (the MsTMIP),\nearth system models (CMIP6) and Random Forest projections. Also, it could be seen that incorporation\nthe temperature sensitivity into the Random Forest training, represented by training with\nEddyChina2023 dataset, better characterized the NEP variation along elevation gradient.\nFig. S14. Changes in net ecosystem productivity (ΔNEP) and reactive nitrogen (ΔNr) under\nvarious climate scenarios of CMIP6 models. The bar charts represent the relative changes by the\nend of the research period (2056–2065), within which the line extending from each bar represents the\nuncertainty in each climate scenario (±1 SE). For ΔNr, their calculations used the following algorithm:\nΔNr=Nr-Nr . For NEP, their relative changes were calculated by dividing the absolute changes in\ni i baseline\nNEP by their average for 2020–2024 (the first five years): ΔNEP=(NEP-NEP )/NEP . Here,\ni i baseline baseline\nNEP represents the NEP in year i, while NEP represents the averaged NEP during 2020–2024.\ni baseline\nDuring the calculation, all pixels of each elevation band were employed to derive the annual cumulative\nNEP for each model under each climate scenario. Then, the results of these models were averaged\nto obtain the ensemble average ΔNEP for each elevation band under each climate scenario.\n5. Some sentences like L. 40- L. 42 seem unprofessional as this was a scientific article.\nResponse: Thanks for your comment. We have now removed the sentence from the text.\nThe revised manuscript has well addressed my former comments. The manuscript is now much clearer.\nI suggest acceptance after addressing the minor comments.\nDear Reviewer,\nWe are glad to see your suggestion of acceptance. We appreciate your time, efforts, and expertise\nto improve our manuscript. We add a sentence regarding your contribution in Acknowledgements.\nThanks again for your help to improve the manuscript.\nBest regards,\nProf. Da Wei\nOn behalf of all co-authors\nMinor comments\n1. Line 38, altitude? Or latitude?\nResponse: Thanks for your comment. The sentence has been revised to “Similar to latitude, the\nelevation gradient has long been considered……”.\n2. line 106-107, for all EC sites? I did not find any support for this point.\nResponse: Thanks for your correction. Now the sentence has been revised to “Structural equation\nmodel analysis further validates the importance of temperature, precipitation and reactive N level to\nthe spatial pattern in NEP”. Please see Line 107-108.\n8"}, {"round": 3, "reviewer_comments": {"reviewer_1": "This manuscript focusing on the elevafion-NEP paftern improved some from its previous version and answered part of my concerns, while it sfill had some confusing points, which should be documented clearly before its publicafion.\nFirst, the relafionship between elevafion-dependent paftern of NEP and temperature sensifivity should be clearly addressed. I agreed with you that higher temperature sensifivity appeared at high elevafion regions. But the elevafion paftern of NEP resulted from the varying hydrothermal factors, nutrient supply, and ecosystem types. Therefore, elevafion-dependent paftern of NEP resulted in not from the difference in temperature sensifivity. You may tell the story with the order of elevafion-NEP paftern and its drivers, the induced temperature sensifivity difference, the producfivity change rates, and future projecfions.\nSecond, the implicafion that future changes in climate and human acfivifies may affect this elevafion-NEP paftern was too arbitrary. From this work, we can find that future changes may enlarge the difference in NEP among regions, while whether this change in NEP altered the elevafion-NEP paftern needed further analysis.\nThird, with data-driven results to project future changes may be careful as the data-driven results may have shortcomes with projecfions, which should be discussed at least.", "reviewer_2": "My previous comment was regardign the lack of explanafion in the text of the nearly flat temperature sensifivity of GPP at low elevafions.\nThe authors have not deatl much with that but I only adressed the editor on my comment.\nAuthors have done a bit more work trying to explain the difference in obtained temperature sensifivityies with elevafion /geographical locafion.\nThey esfimated the equivalent temperature sensifivifies from the MsTMIP ensemble and show it in Fig S8. the units of these figures are not comparable to the units shown in Fig3 of the manuscript.\nI do wondere if the flat GPP Temperature sensifivity is obtained with the models as well.\nAuthors should change the units of Fig S8 to make them comparable to Fig 3."}, "author_response": "Response Letter\nReference Number: NCOMMS-23-30115C\nTitle: Elevation-dependent pattern of net CO uptake across China\nThis manuscript focusing on the elevation-NEP pattern improved some from its previous version and\nanswered part of my concerns, while it still had some confusing points, which should be documented\nclearly before its publication.\nDear Reviewer,\nThank you very much for your comments. As you can see, all your comments have been carefully\nconsidered during the revision, including the following aspects:\n Structure. We are pleased that the structure you proposed is basically consistent with the\nstructure of the paper. Trying to make the manuscript easier for potential readers to read the\npaper clearly, we have simplified the titles of the four sections. These sections are now titled:\n\"Elevation-dependent pattern of NEP and its drivers\", \"Temperature sensitivity difference\nbetween high- and lower elevation environments\", \"Productivity change rates during the past\nfour decades\", and \"Projections of productivity change rates\".\n Projections. You mentioned that \"... whether this change in NEP altered the elevation-NEP\npattern needed further analysis\". We then analyzed the change in the NEP elevation pattern\nand determined that the slope of NEP would be slightly smoothed. However, this will not reverse\nthe elevation-dependent pattern of NEP. Similar phenomena occur with temperature and\nphenology along elevations. For instance, even though temperatures are rising more rapidly at\nhigher elevations, this will not reverse the negative elevation-dependent pattern of temperature.\n Uncertainty. Predictions based on observed data can effectively constrain the future changes\nof NEP, but machine learning does have a lot of uncertainty. In our case, data-based predictions\nmay overlook underlying mechanisms. Predictions focus more on aboveground factors, while\nneglecting drastic changes in underground processes. Predictions rely on the availability of data,\nwhich is not always evenly distributed across different environments. These can indeed bring\nuncertainty to predictions. We have specifically added a section to discuss its uncertainty.\nFinally, thank you very much for providing a lot of value comments during the three rounds\nof review. Your comments greatly changed the structure of the manuscript. At the same time,\nyour comments also prompted us to conduct a more in-depth analysis of the results, which\ngreatly strengthened the conclusion of this article. We greatly appreciate your time, expertise,\nand suggestions!\nBest regards,\nThe authors\nshould be clearly addressed. I agreed with you that higher temperature sensitivity appeared at high\nelevation regions. But the elevation pattern of NEP resulted from the varying hydrothermal factors,\nnutrient supply, and ecosystem types. Therefore, elevation-dependent pattern of NEP resulted in not\nfrom the difference in temperature sensitivity. You may tell the story with the order of elevation-NEP\npattern and its drivers, the induced temperature sensitivity difference, the productivity change rates,\nand future projections.\nResponse: We are pleased with your consistent agreement regarding the concept of higher\ntemperature sensitivity in high elevation regions. In response to your concern, we have made\nstructural revisions to the paper, and we are glad to know that the revised structure meets your\nexpectations. Furthermore, we have modified the titles of four sections in the paper to enhance its\noverall readability. These sections are now titled: \"Elevation-dependent pattern of NEP and its\ndrivers\", \"Temperature sensitivity difference between high- and lower elevation environments\",\n\"Productivity change rates during the past four decades\", and \"Projections of productivity change\nrates\". We appreciate the your suggestions, which have significantly improved the clarity and reader-\nfriendliness of the results at every stage.\nSecond, the implication that future changes in climate and human activities may affect this elevation-\nNEP pattern was too arbitrary. From this work, we can find that future changes may enlarge the\ndifference in NEP among regions, while whether this change in NEP altered the elevation-NEP\npattern needed further analysis.\nResponse: Thank you very much for your comment here. Trying to resolve your concerns, we have\nconducted the following work:\n We have toned down the conclusion in the abstract: “We therefore conclude an elevation-\ndependent pattern of the NEP of terrestrial ecosystems in China and highlight that future\nchanges in Earth’s climate and human activities may slightly affect this pattern, although there\nis significant uncertainty involved”. Please see Line 31-33.\n We then analyzed the elevation pattern of NEP and its changes under various climate scenarios.\nOur results confirm that the more rapid changes in productivity in higher elevation environments\nmay slightly smooth the elevation-dependent pattern of NEP in the future, although there is still\na large amount of uncertainty that exists. We have included the new analysis in the main text\n(Line 185-188) and have also added it as Fig. S16 in the supplementary materials.\n The phenomenon of changing NEP slope along elevation is similar to the more rapid\ntemperature change along elevation, known as elevation dependent warming. Another similar\nphenomenon is the changing phenology along elevation, meaning that higher elevation\nenvironments experience stronger greening advancement than lower elevation environments.\nDespite more rapid changes in temperature, phenology, and NEP in higher elevation\nenvironments (i.e. smoothing slopes of temperature, phenology, and NEP), the overall negative\npattern of temperature, phenology, and NEP along elevation would never be reversed.\nclimate scenarios constrained by the CMIP6 models and the EddyChina2023 dataset. (a) SSP1-\n2.6, (b) SSP2-4.5, (c) SSP3-7.0 and (d) SSP5-8.5. For the left part of each figure, the NEP slope (SL )\nNEP\nrepresents the slope between NEP with elevation. The bar represents the 1.5 times interquartile range.\nFor the right part of each figure, the NEP slope change was the difference between 2020–2024 and 2056–\n2065: SL Change = SL - SL .\nNEP NEP-2020–2024 NEP-2056–2065\nThird, with data-driven results to project future changes may be careful as the data-driven results\nmay have shortcomes with projections, which should be discussed at least.\nResponse: We agree that the data-driven projections may contain some uncertainty. (1) The input\nhighlights the use of statistical or implicit relationships to achieve data-driven projections between\nNEP and environmental factors, but it may have overlooked underlying mechanisms. Nonlinear\nchanges and interactive effects from CO concentration, climate extremes, species composition, and\n2\nhuman management could also be challenging to capture through data-driven projections. (2)\nvegetation, while neglecting significant changes in underground processes, particularly in microbial\nprocesses, which may not be accurately reflected in eddy covariance observation or data-driven\nprojections. (3) In addition, projections based on data rely heavily on the availability of data, which is\nnot always evenly distributed across different environments. For example, there may be more\nobservations in lower-elevation environments. The above discussions have been added to the main\ntext. Please see Line 202-213.\nTo Reviewer #2\nMy previous comment was regarding the lack of explanation in the text of the nearly flat temperature\nsensitivity of GPP at low elevations. The authors have not dealt much with that but I only addressed\nthe editor on my comment.\nDear Reviewer,\nThank you for your interest in GPP's temperature sensitivity in lower-elevation environments. We\nwould like to provide an explanation regarding your concern. We believe that there could be five\npossible factors that influence GPP, which include temperature, radiation, moisture, nutrients,\nand human management.\n Temperature. Strong temperature limitations may be the main reason for the difference in\ntemperature sensitivity between high- and lower-elevation environments. High-elevation\nenvironments are 11°C colder than ecosystems below 3000 m. However, the temperature\nlimitation to GPP is not as significant in lower-elevation environments.\n Radiation. Solar radiation is abundant in high-elevation areas due to the thinner air, while it is\nrelatively weak in low-altitude areas, as noted by Tang et al. (2017). Meanwhile, air pollution is\nmore severe in low-elevation areas, resulting in a higher concentration of aerosols that\nsignificantly affect radiation, as reported by Wang et al. (2022). The role of solar radiation in\ninfluencing GPP in lower-elevation environments cannot be ignored.\n Nutrient or water limitation. Lower-elevation areas usually have more nutrient and water\nsupply, under irrigation, fertilization, and nitrogen deposition. Therefore, it is possible that\nnutrient and water limitations may not be the primary factors affecting the lower GPP-\ntemperature sensitivities in lower-elevation environments.\n Management. Human management is influential in low-elevation areas, which can result in a\nlower apparent temperature sensitivity of GPP, as illustrated in the comparison between Zoige\nStation (at an elevation of 3500 m) and Gucheng Station (at an elevation of 15 m). At Zoige\nmeadow, GPP closely follows the temperature variation, resulting in a higher temperature\nsensitivity of GPP (Figure R2 a-b). In contrast, at Gucheng Station, there is a significant\ndecrease in GPP during the summer (wheat-corn rotation period), which leads to a relatively\nlow apparent temperature sensitivity of GPP (Figure R2 c-d). In addition to the above\nphenomenon, human activities such as plantation, harvesting, logging, and land use changes\nin the lower-elevation environments may also contribute to the lower apparent temperature\nsensitivity of GPP.\n Therefore, we consider that temperature, radiation, and management may be responsible for\nthe lower temperature sensitivity of GPP in lower-elevation environments. However, it must be\nacknowledged that there are still knowledge gaps in this study. In fact, research regarding the\nvariation of CO exchange along the elevation gradient can be quite challenging, especially at\n2\na daily scale, because it is difficult to build eddy covariance stations in mountainous areas. In\nthis study, we adopted a unique method to tackle the above issue and did obtain some new\nfindings. Given the fact that there are still knowledge gaps regarding the CO exchange along\n2\nelevation, such as the lower temperature sensitivity of GPP, our team will continue to conduct\nmore observations to address the above issue. The above discussions have been added to the\nmain text. Please see Line 132 to 146.\nFinally, we would like to express our sincere appreciation to you for providing us with lots of\nvaluable comments during the three rounds of review. This greatly strengthened our\nconclusions and improved the quality of the manuscript. Personally, I also learn a lot from\nyour comments. Thank you very much!\nBest regards,\nThe authors\n---------\nThe following is a figure to illustrate the human disturbance to GPP-temperature relationship.\nFig. R2. Variations in temperature and GPP in natural ecosystems and human-managed\necosystems. (a-b) alpine meadow in Zoige; (c-d) winter wheat-summer corn in Gucheng. TAS refers\nto air temperature, and GPP refers to gross primary productivity. DOY, Day of Year. Datasource:\nhttps://doi.org/10.57760/sciencedb.o00119.00052; ttps://doi.org/10.57760/sciencedb.o00119.00071.\nReference: (1) Tang WJ. Qin K, Yang X et al. (2017) An efficient algorithm for calculating\nphotosynthetically active radiation with MODIS products, Remote Sens. Environ., 194, 146-154. (2)\nWang Z, Wang C, Wang X. et al. (2022) Aerosol pollution alters the diurnal dynamics of sun and\n5\nshade leaf photosynthesis through different mechanisms. Plant, Cell & Environment, 45(10), 2943-\n2953.\nAuthors have done a bit more work trying to explain the difference in obtained temperature\nsensitivities with elevation /geographical location. They estimated the equivalent temperature\nsensitivities from the MsTMIP ensemble and show it in Fig S8. the units of these figures are not\ncomparable to the units shown in Fig3 of the manuscript. I do wonder if the flat GPP Temperature\nsensitivity is obtained with the models as well. Authors should change the units of Fig S8 to make\nthem comparable to Fig 3.\nResponse:\n We apologize for using different units in Fig. 3 and Fig. S8, where they were respectively\nrepresented as g C m-2 day-1 °C-1 and g C m-2 yr-1 °C-1. Following your suggestion, we have\nmodified the units in Fig. S8. Please note that the values in Fig. S8 are equivalent to the slope\nin Fig. 3. For instance, the slope at high and low elevations in Fig. 3 are 0.090 and 0.016 g C\nm-2 day-1 °C-1 for GPP, 0.07 and 0.05 g C m-2 day-1 °C-1 for NEP, respectively. The temperature\nsensitivity of RE is 0.070 and 0.048 (unitless) at high and low elevations, respectively.\n The ground-based temperature sensitivity of GPP, RE and NEP are generally higher than the\nresults based upon MsTMIP models, though they both supported generally higher temperature\nsensitivity at higher elevation environments.\n Regarding your comments “I do wonder if the flat GPP Temperature sensitivity is obtained with\nthe models as well”, the results of MsTMIP models shows that the slope of GPP in low-altitude\nareas is indeed lower than in high-altitude areas (Fig. S8), which is consistent to Figure 3.\nFig. R3 Temperature sensitivity of gross primary productivity, ecosystem respiration and net\necosystem productivity based upon the MsTMIP ensemble 2. High, high-elevation environments;\nLower, lower elevation environments; GPP, gross primary productivity; RE, ecosystem respiration;\nNEP, net ecosystem productivity. The temperature sensitivity of GPP, RE and NEP of each pixel\nwas calculated following the procedure descripted in Figure 3. Linear slopes were used for GPP and\nNEP, while the slope of RE were calculated after logarithm transformation. The mean, median and\nvariations (e.g., 25~75%) indicates the difference among various models of the MsTMIP models.\n6"}]

Decision letter