Abstract
Abstract. Ecosystems are important and dynamic components of the global carbon cycle, and terrestrial biospheric models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. Improving TBM skills, and quantifying and reducing their estimation uncertainties, pose significant challenges. The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal multi-scale and multi-model intercomparison effort set up to tackle these challenges. The MsTMIP protocol prescribes standardized environmental driver data that are shared among model teams to facilitate model–model and model–observation comparisons. This paper describes the global and North American environmental driver data sets prepared for the MsTMIP activity to both support their use in MsTMIP and make these data, along with the processes used in selecting/processing these data, accessible to a broader audience. Based on project needs and lessons learned from past model intercomparison activities, we compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land use and land cover change (LULCC), C3 / C4 grasses fractions, major crops, phenology and soil data into a standard format for global (0.5° × 0.5° resolution) and regional (North American: 0.25° × 0.25° resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by improving the quality, and/or changing their spatial and temporal coverage, and resolution. The resulting standardized model driver data sets are being used by over 20 different models participating in MsTMIP. The data are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov) to provide long-term data management and distribution.
Highlights
The need to understand and quantify the role of terrestrial ecosystems in the global carbon cycle and its climate change feedbacks has been driving the development of global terrestrial biogeochemistry and biogeography models since the late 1980s (Foley, 1995)
We describe the processing and analysis completed to convert the original data source into a form meeting the needs of the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) activity, and in some cases to improve the quality of the original data source
The design of the MsTMIP environmental driver data sets benefited from studying the lessons learned from these past activities and helped us to avoid pitfalls or duplicate work unnecessarily, and helped to reduce data preparation time
Summary
The need to understand and quantify the role of terrestrial ecosystems in the global carbon cycle and its climate change feedbacks has been driving the development of global terrestrial biogeochemistry and biogeography models since the late 1980s (Foley, 1995). Four types of uncertainties drive differences between predictions of terrestrial carbon flux (e.g., Enting et al, 2012): uncertainty associated with (1) the choice of driver data, (2) parameter values, (3) initial conditions as well as (4) the choice of processes to include and how these processes are represented within the model (i.e., structural uncertainty) Estimating and reducing these uncertainties are both critical to improving model performance, and to understanding the role of terrestrial ecosystems in the global carbon cycle. In response to this need, the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) was established to build on previous and ongoing MIPs to provide a consistent and unified modeling framework to interpret and address structural and parameter uncertainties. We introduce some lessons learned on data processing and management, to guide future data-intensive projects
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