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Spatially and temporally predict carbon storage & flux globally at 1km scales to 2300

Spatially and temporally predict carbon storage & flux globally at 1km scales to 2300. Workflow. Ecological data. Contemporary testing. Climate data. Model intercomparison and diagnosis. Existing models. Hindcast climate. Hindcast. Paleo -data. Land use projections.

porter-christensen
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Spatially and temporally predict carbon storage & flux globally at 1km scales to 2300

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  1. Spatially and temporally predict carbon storage & flux globally at 1km scales to 2300

  2. Workflow Ecological data Contemporary testing Climate data Model intercomparison and diagnosis Existing models Hindcast climate Hindcast Paleo-data Land use projections Carbon fluxes & storage Future Prediction Climate projections

  3. Data Needs • Climate & abiotic data (relatively available) • Climatologies: temperature, precipitation, irradiance, pH, nutrients • Topography/bathymetry, currents • GCM output • Ecological trait data (poorly aggregated) • Rooting depth, specific leaf area, vein density, cuticle thickness, phenological cue • Phytoplankton size, stochiometry, thermal tolerance, light requirements • Ecological comparison data • Flux measurements (particle export, flux towers) • Species composition data (forest and marine species inventories) • Cores (lake and marine sediments) • Remote sensing (e.g., color) • Projections • Land use projects • Climate projections

  4. Software Needs • Data conflation • Re-gridding & re-classification • Data quality tracking & versioning • Data cleaning • Storage & access • Tracking decisions • Workflow systems • Modeling • Data format translations • Model speed-up & high-performance computing • Model documentation & versioning • Data assimilation & parameterization • Model intercomparison • Visualization of model outputs (3 or 4D) • Standardized comparisons & skill assessment • Storage & transferring data

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