PNNL

Abstract

Soil and vegetation carbon densities play a critical role in global and regional humanEarth system models such as Integrated Assessment Models (IAMs) and Multisector Dynamics models (MSD) since they affect variables such as land use change emissions and influence land use change pathways under climate mitigation scenarios where terrestrial carbon is assigned a carbon price. Recently, more spatially explicit fine resolution data have become available for both soil and vegetation carbon. However, for models to effectively use these data the fine resolution data need to be reharmonized to land types represented by these models. Without such reharmonization the carbon values may be very inaccurate for particular land types and places where the source data and the model disagree on the land type. Here we present reharmonized soil and vegetation carbon densities both at the grid cell level at 5 arcmin resolution and also aggregated to 235 water sheds for 4 different land use and 15 land cover types. These data are particularly useful as initial land carbon conditions for global MSD and IAM models. Moreover, these data include six different statistical states calculated using distinct resampling methods for each of the land use, land cover types. These statistical states are used to define a range of possible carbon values for each land classification, and any state can be used for defining initial conditions of soil and vegetation carbon for IAMs and MSD models. We make use of these statistical states to calculate spatially distinct uncertainties in the carbon densities by land type. We have implemented these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses in the model, especially when terrestrial carbon is assigned a carbon price.