Supporting data for "Granularity of model input data impacts estimates of carbon storage in soils"

The exchange of carbon between the soil and the atmosphere is an important factor in climate change. Soil organic carbon (SOC) storage is sensitive to land management, soil properties, and climatic conditions, and these data serve as key inputs to computer models projecting SOC change. Farmland has been identified as a sink for atmospheric carbon, and we have previously estimated the potential for SOC sequestration in agricultural soils in Vermont, USA using the Rothamsted Carbon Model. However, fine spatial-scale (high granularity) input data are not always available, which can limit the skill of SOC projections. For example, climate projections are often only available at scales of 10s to 100s of km2. To overcome this, we use a climate projection dataset downscaled to <1 km2 (~18,000 cells). We compare SOC from runs forced by high granularity input data to runs forced by aggregated data averaged over the 11,690 km2 study region. We spin up and run the model individually for each cell in the fine-scale runs and for the region in the aggregated runs factorially over three agricultural land uses and four Global Climate Models. In this repository are the downscaled climate input data that drive the RothC model, as well as the model outputs for each GCM.土壤与大气之间的碳交换是气候变化的关键影响因子。土壤有机碳（Soil Organic Carbon, SOC）储量对土地管理、土壤属性及气候条件响应敏感，相关数据是预测SOC变化的计算机模型的核心输入参数。农田已被确认为大气碳汇，此前我们利用罗斯塔姆碳模型（Rothamsted Carbon Model）估算了美国佛蒙特州农业土壤的SOC固存潜力。然而，精细空间尺度（高粒度）的输入数据并非总能获取，这会限制SOC预测的预报技巧。例如，气候预测数据通常仅能获取10至100平方千米尺度的数据集。为解决这一问题，我们采用了降尺度至小于1平方千米（约18000个网格单元）的气候预测数据集。我们将高粒度输入数据驱动的模型模拟结果，与以研究区域（11690平方千米）平均后的聚合数据驱动的模拟结果进行SOC对比。针对三种农业土地利用类型与四个全球气候模式（Global Climate Models, GCM），我们在精细尺度模拟中对每个网格单元单独进行模型自旋初始化与运行，在聚合尺度模拟中则以研究区域为单位开展析因模拟。 本数据集仓库包含驱动罗斯塔姆碳模型的降尺度气候输入数据，以及各全球气候模式对应的模型模拟结果。