Climate exerts stronger control on topsoil carbon persistence than plant input in alpine grasslands

Chen et al. (2021) concluded that plant input governs topsoil carbon persistence in alpine grasslands. We demonstrated that the excluded direct effect of precipitation on topsoil Δ14C in their analysis was in fact significant and strong. Our results provide an alternative viewpoint on the drivers of soil carbon turnover.

Spatial variability of topsoil δ13C across Qinghai-Tibet Plateau

<p>Soil carbon isotopes (δ<sup>13</sup>C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ<sup>13</sup>C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ<sup>13</sup>C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ<sup>13</sup>C in QTP. In this study, we first established a database of topsoil δ<sup>13</sup>C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ<sup>13</sup>C using environmental variables. Results showed that topsoil δ<sup>13</sup>C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ<sup>13</sup>C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ<sup>13</sup>C with a model efficiency (pseudo R<sup>2</sup>) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ<sup>13</sup>C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ<sup>13</sup>C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ<sup>13</sup>C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ<sup>13</sup>C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.</p>

A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming

Carbon cycle feedbacks represent large uncertainties in climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants and especially on reductions in the turnover time of soil carbon (τs) with warming. An approximation to the latter term for the top one metre of soil (ΔCs,τ) can be diagnosed from projections made with the CMIP6 and CMIP5 Earth System Models (ESMs), and is found to span a large range even at 2 °C of global warming (−196 ± 117 PgC). Here, we present a constraint on ΔCs,τ, which makes use of current heterotrophic respiration and the spatial variability of τs inferred from observations. This spatial emergent constraint allows us to halve the uncertainty in ΔCs,τ at 2 °C to −232 ± 52 PgC.

A spatial emergent constraint on the sensitivity of soil carbon turnover time to global warming

<p>Carbon cycle feedbacks represent large uncertainties on climate change projections, and the response<br>of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil<br>carbon depend on changes in litter and root inputs from plants, and especially on reductions in the<br>turnover time of soil carbon (τ<sub>s</sub>) with warming. The latter represents the change in soil carbon<br>due to the response of soil turnover time (∆C<sub>s,τ</sub>), and can be diagnosed from projections made with<br>Earth System Models (ESMs). It is found to span a large range even at the Paris Agreement Target<br>of 2<sup>◦</sup>C global warming. We use the spatial variability of τ<sub>s</sub> inferred from observations to obtain a<br>constraint on ∆C<sub>s,τ</sub> . This spatial emergent constraint allows us to greatly reduce the uncertainty in<br>∆C<sub>s,τ</sub> at 2<sup>◦</sup>C global warming. We do likewise for other levels of global warming to derive a best<br>estimate for the effective sensitivity of τ<sub>s</sub> to global warming, and derive a q10 equivalent value for<br>heterotrophic respiration.</p>

Evaluation of CMIP5 Earth System Models for the Spatial Patterns of Biomass and Soil Carbon Turnover Times and Their Linkage with Climate

Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were diagnosed as having large discrepancies in their land carbon turnover times, which partly explains the differences in the future projections of terrestrial carbon storage from the models. Carvalhais et al. focused on evaluation of model-based ecosystem carbon turnover times τeco in relation with climate factors. In this study, τeco from models was analyzed separately for biomass and soil carbon pools, and its spatial dependency upon temperature and precipitation was evaluated using observational datasets. The results showed that 8 of 14 models slightly underestimated global biomass carbon turnover times τveg (modeled median of 8 yr vs observed 11 yr), and 11 models grossly underestimated the soil carbon turnover time τsoil (modeled median of 16 yr vs observed 26 yr). The underestimation of global carbon turnover times in ESMs was mainly due to values for τveg and τsoil being too low in the high northern latitudes and arid and semiarid regions. In addition, the models did not capture the observed spatial climate sensitivity of carbon turnover time in these regions. Modeled τveg and τsoil values were generally weakly correlated with climate variables, implying that differences between carbon cycle models primarily originated from structural differences rather than from differences in atmospheric climate models (i.e., related to temperature and precipitation). This study indicates that most models do not reproduce the underlying processes driving regional τveg and τsoil, highlighting the need for improving the model parameterization and adding key processes such as biotic disturbances and permafrost–carbon climate responses.