Functionally Assembled Terrestrial Ecosystem Simulator (FATES) - TeSS (Training eSupport System)

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e-learning

Functionally Assembled Terrestrial Ecosystem Simulator (FATES)

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Abstract

Terrestrial ecosystem models have been widely used to study the impact of climate changes on vegetation and terrestrial biogeochemical cycles in climate modelling community. They are also more and more applied in ecological studies to help ecologists to better understand the processes. But the technical challenges are still too high for most of the ecologists to use them. This practical aims at familiarizing you (especially ecologists) with running a terrestrial ecosystem model (i.e., CLM-FATES) at site-level in Galaxy and analyzing the model results.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

How to run CLM-FATES with the CLM-FATES Galaxy tool?
How to upload input data for running CLM-FATES?
How to customize your runs?
How to analyze your model outputs?
How to create a workflow?
How to share your workflow?

Learning Objectives

Setting up a CLM-FATES case.
Customizing your run.
Interactive visualization with Panoply.
Automating your analyzes and visualisations of your CLM-FATES case.
Creating multi-case scenarios.
Composing, executing and publishing CML-FATES workflow.

Licence: Creative Commons Attribution 4.0 International

Keywords: Climate, interactive-tools

Target audience: Students

Resource type: e-learning

Version: 17

Status: Active

Prerequisites:

Extracting Workflows from Histories
Introduction to Galaxy Analyses
Visualize Climate data with Panoply netCDF viewer

Learning objectives:

Setting up a CLM-FATES case.
Customizing your run.
Interactive visualization with Panoply.
Automating your analyzes and visualisations of your CLM-FATES case.
Creating multi-case scenarios.
Composing, executing and publishing CML-FATES workflow.

Date modified: 2024-06-14

Date published: 2020-10-25

Authors: Anne Fouilloux, Hui Tang

Contributors: Anne Fouilloux, Hui Tang

External resources:

Associated Workflows

Associated Training Datasets