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.

土壤与大气间的碳交换是气候变化的重要影响因子。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变化的计算机数值模型的核心输入参数。农田已被证实为大气碳汇，此前我们已借助罗斯塔姆碳模型（Rothamsted Carbon Model）估算了美国佛蒙特州农业土壤的SOC固存潜力。然而，高空间分辨率（细粒度）的输入数据往往难以获取，这会限制SOC预测的精度。例如，气候预测数据的空间分辨率通常仅为数十至数百平方千米。为解决这一问题，我们采用了降尺度至<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下的模型SOC模拟结果，与以研究区11690平方千米范围内平均后的聚合数据驱动的模拟结果进行对比。在细粒度模拟中，我们针对每个网格单元单独完成模式自旋初始化与运行；在聚合模拟中，则针对整个研究区，结合3种农业土地利用类型与4种全球气候模式（Global Climate Models, GCM）开展全因子模拟。
本仓库包含驱动罗斯塔姆碳模型的降尺度气候输入数据，以及对应各全球气候模式的模型输出结果。