the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Aug 2025
A global dataset of soil organic carbon mineralization under various incubation conditions
Abstract. Microbial decomposition of soil organic carbon (SOC) is a major source of atmospheric CO2 and a key component of climate-carbon feedbacks. Understanding how SOC mineralization responds to temperature is essential for improving climate projections. Here, we compiled a global dataset of laboratory incubation experiments measuring SOC mineralization across diverse soils and temperature regimes. The dataset reveals that 84 % of samples originated from surface soils (0–30 cm), and 50 % of incubations lasted fewer than 50 days. Incubation temperatures ranged from –10 to 60 °C, with temperature intervals used to estimate temperature sensitivity (Q10) spanning 2–40 °C; notably, 81 % of Q10 estimates were based on intervals exceeding 5 °C. Moreover, in 61 % of cases, the lower incubation temperature for Q10 estimation differed from the mean annual temperature at the sampling site by more than 5 °C, indicating a mismatch with in situ conditions. Our analysis highlights critical gaps in current experimental designs, particularly the underrepresentation of subsoils (>30 cm) and the use of temperature ranges that deviate from field conditions. We further evaluated the ability of 16 temperature response functions used in 69 Earth System Models to capture SOC mineralization patterns. Most models failed to reproduce empirical temperature response, especially at higher temperatures, albeit multi-term exponential functions showed relatively better performance. By coupling our dataset with a two-pool carbon model, we found that external environmental constraints and the intrinsic temperature response (including SOC decomposability and microbial processes) similarly influence the temperature sensitivity of SOC mineralization at the global scale, with their relative importance varying across ecosystem types. Our findings underscore the need for incubation experiments that better represent field conditions—both in depth and temperature range—and call for improved model parameterizations to enhance SOC feedback projections under future climate scenarios. The dataset is archived and publicly available at https://doi.org/10.6084/m9.figshare.25808698 (Zhang et al., 2025).
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- RC1: 'Comment on essd-2025-434', Anonymous Referee #1, 30 Aug 2025
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RC2: 'Comment on essd-2025-434', Anonymous Referee #2, 17 Sep 2025
This study compiles a global dataset of 22,000 observations from 191 incubation experiments on soil organic carbon (SOC) mineralization to assess how temperature controls soil CO₂ release, a key climate–carbon feedback. The dataset, largely biased toward surface soils, short incubations, and mid-latitude regions, reveals major gaps in deep soils, extreme ecosystems, and Africa.
The author suggest that Earth System Models generally misrepresent SOC temperature sensitivity, especially under warming extremes, though multi-term exponential functions perform best. Using a two-pool carbon model, the authors find that both intrinsic factors (SOC quality, microbial traits) and extrinsic constraints (oxygen, mineral protection, moisture) contribute equally to global SOC responses, but their relative importance varies by ecosystem (e.g., croplands vs. wetlands). The work highlights the urgent need for more representative experiments and improved model formulations to reduce uncertainty in carbon–climate feedback projections.
Although I find the article interesting and the dataset very useful, I have a number of comments that could help improve the readability of the manuscript.
Firstly, the mathematical approach used to distinguish between internal and external effects is rather poorly explained in section 5.4, which makes section 5.5 more difficult to understand. I suggest that the authors provide more detail in section 5.4 and perhaps simplify the vocabulary used.
It is noted in several places that you tested the temperature response functions of 69 ESMs. This is inaccurate, as most of the models whose temperature response functions you tested are not ESMs. This approximation should therefore be corrected throughout the manuscript.
Table 2 also needs to be simplified because some of the information is incorrect. For example, for Jules, the centre is described as the UK, which is a country, whereas for the other models, the authors give a research group instead.
CENRUTY-> CENTURY
It is also unclear in this table what the difference is between ‘land carbon’ and ‘land surface models’.
Figure 4 also needs improvement as it lacks clarity, particularly as M1 and M2 have not been defined.
How eq. 3 affect eq. 2 in the model developed by the authors?
Section 2 L. 78 point 2) More details are needed here, for instance do you accept when the same samples were incubated at 2 different temperatures? Do you use equal time or equal C (Hamdi et al., 2012)?
Hamdi, S., Moyano, F., Sall, S., Bernoux, M., Chevallier, T., 2012. Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biol Biochem 58, 115–126. https://doi.org/10.1016/j.soilbio.2012.11.012
L323: “There were no significant differences of the relative importance…” how this was tested?
Citation: https://doi.org/10.5194/essd-2025-434-RC2 -
CC1: 'Comment on essd-2025-434', Yan Zhang, 18 Sep 2025
This manuscript is well structured and written, the topic and the dataset are very important to better understand and predict SOC dynamics in a warming world.
1. The dataset is a valuable resource for studying the responses of SOC to warming, and provides essential data for model optimization and parameter calibration.
2. The current experimental manipulation analyse pointing out some critical gaps could provide valuable guidance for future investigations.
3. The assessment of temperature response functions in ESMs against observed data offers valuable guidance for future model development.
4. The "Conclusions and Future Vision" section excellently synthesized current knowledge gaps and offered clear recommendations for future incubation experiments and modeling efforts.
Overall, this paper is interesting and timely and is suitable for publication.Citation: https://doi.org/10.5194/essd-2025-434-CC1
Data sets
A global dataset of soil organic carbon mineralization under various incubation conditions Shuai Zhang, Mingming Wang, Jinyang Zheng, Zhongkui Luo https://doi.org/10.6084/m9.figshare.25808698
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General comments
Nominally (see below), this manuscript describes a dataset of soil incubations focusing on carbon mineralization and temperature sensitivity (Q10) calculation. This is interesting and important for reasons well laid out in the introduction, as such incubations have been a major source of information about this process and informed models and understanding at many scales; an analysis-ready dataset of incubations is valuable. The authors’ dataset is publicly posted, has almost 22,000 rows, and seems clearly laid out (although see #2 below).
That said, there are several significant problems here. First, the ms is oddly structured. It essentially has three parts: (i) a description of the dataset; (ii) data summaries and comparison with ancillary data (in particular, incubation temperatures compared with the mean annual temperature of sampling location); and, very unexpectedly, (iii) an extended summary of earth system model approaches to decomposition and simple modeling exercise involving the dataset. From https://www.earth-system-science-data.net/about/aims_and_scope.html, the scope of ESSD is “Articles in the data section may pertain to the planning, instrumentation, and execution of experiments or collection of data. Any interpretation of data is outside the scope of regular articles.” Based on this, I think that (iii) above is clearly out of scope; it’s extremely odd to find this ESM algorithm analysis in an ESSD ms, and it should be removed. Even (ii) strikes me as marginal in terms of scope—it’s analysis, not data description!
Second, there’s no mention of SIDb (https://soilbgc-datashare.github.io/sidb/). The SIDb paper (Schädel et al. 2020) is cited but it’s bizarre not to note and discuss *at length* this pre-existing and seemingly very similar effort. How much overlap is there between the authors’ work and SIDb? Why not contribute these data to SIDb, rather than duplicate work and confuse researchers?
Finally, as already noted I have concerns about the structure of the data and how it doesn’t support easy reproducibility in terms of finding the source studies.
In summary, while I appreciate the large amount of work here, and believe this dataset will be valuable, the current ms should be rejected or subject to fundamental revisions.
Specific comments