1 / 5

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
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related