The loss of carbon from soils to the atmosphere resulting from climate change is projected to be large, but these projections exhibit significant uncertainty, largely due to insufficient knowledge of the patterns and controls of the temperature sensitivity of soil microbial respiration. Here we synthesized data from 52 soil incubation studies across the Northern Hemisphere to assess the spatial patterns of Q₁₀ and its key drivers in different soil layers and geographic zones. The mean Q₁₀ was 2.51 ± 1.13 across the northern ecosystems, but it exhibited significant variability. After averaged by ecosystem types, the highest mean Q₁₀ value was observed in the northern permafrost soils, where the Q₁₀ values were nearly 18% higher than those in nonpermafrost regions. The temperature sensitivity was larger in subsoil than in topsoil layers, particularly in permafrost subsoils. Besides, the dominant factors that correlate with Q₁₀ values are the carbon input, described by satellite‐derived net primary productivity (NPP) in the topsoil and the soil C:N ratio in the subsoil. Based on the main factors affecting Q₁₀, we provide a gridded Q₁₀ data set for the midhigh‐latitude areas, which further indicates that northern permafrost regions are more sensitive to climate warming than others. These results highlight the key role played by the permafrost in the temperature sensitivity of soil C release, and the necessity of including depth‐specific soil C release processes in models, if we are to make better predictions of the soil C dynamics in future climate change scenarios.

中文翻译：

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荟萃分析表明，北方多年冻土区土壤碳释放到大气中的温度敏感性较高

预计由于气候变化导致土壤向大气中的碳损失会很大，但是这些预测存在很大的不确定性，这主要是由于对土壤微生物呼吸的温度敏感性的模式和控制缺乏了解。在这里，我们综合了北半球52个土壤培养研究的数据，以评估Q ₁₀及其在不同土壤层和地理区域中的关键驱动因素的空间格局。北部生态系统的平均Q ₁₀为2.51±1.13，但表现出显着的变异性。在按生态系统类型进行平均后，在北部多年冻土中观测到最高的平均Q ₁₀值，其中Q ₁₀值比非多年冻土地区高近18％。与下层土壤相比，地下土壤对温度的敏感性更大，尤其是在永冻土层下。此外，与Q ₁₀值相关的主要因素是碳输入，其通过表层土壤中卫星衍生的净初级生产力（NPP）和下层土壤中的C：N比来描述。基于影响Q ₁₀的主要因素，我们提供了网格Q ₁₀中高纬度地区的数据集进一步表明，北部多年冻土地区对气候变暖的敏感性高于其他地区。这些结果突显了多年冻土在土壤碳释放温度敏感性中的关键作用，以及如果我们要对未来气候变化中土壤碳的动态做出更好的预测，则必须在模型中包括深度特定的土壤碳释放过程。场景。