 functionally assembled Terrestrial Ecosystem Simulator, FATES. Before diving into this slide deck, we recommend you to have a look at the following. What is FATES? How can I use FATES? Have an understanding of what FATES is and how it works. Land surface models have significantly evolved through time. For instance, representations of numerous processes that are known to impact the dynamics of systems have been incrementally added to land surface models. They have been expanded from their initial simple biophysical configurations. This includes representations of soil moisture dynamics, stomatal functioning, land surface heterogeneity and surface hydrological processes, and plant and soil carbon cycling, dynamic vegetation distributions, fire, urban environments, land cover and management, nitrogen cycling and crops, and latterly plant demographic processes, phosphorus cycling, and plant hydraulics. The global carbon budget averaged over the last half century is shown on this figure. For this time period, 82% of the total emissions were caused by fossil CO2 emissions and 18% by land use change. The total emissions were partitioned among the atmosphere, 45% ocean, 24% and land, 29% with an unattributed budget imbalance, who percent all components except land use change emissions have significantly grown since 1959. The processes that govern carbon cycle feedbacks are highly affected by both biophysical feedbacks in the earth system and by land use decisions that are in turn affected by climate impacts on human societies. Surface processes play a key role in extreme events, such as heat waves, fires, crop failures, floods, droughts. Climate change impacts such as drought and fire are mediated by plant biophysical responses to elevated CO2, which are themselves impacted by limitations imposed by nutrient limitations on growth. Developing land surface models by adding complexity in already complex codes is not manageable anymore. To face this perpetual increase in complexity, the community needs to better organize code developments. One major issue is that there are few land surface models code developers and land surface modeling teams are typically not large enough to be able to tackle all of the interacting processes in a modern land surface model. A wider collaborative approach is required. So to summarize, we have three major problems to solve. First, the perpetual increase in complexity. Second, the subcritical development efforts. Third, the oversimplified representation of ecosystems. On this slide, we show three different ways to represent ecological processes in land surface models. On the left, we have the big leaf model which is the kind of default land surface model used in climate models. Big leaf models are oversimplified and the different types of vegetation do not compete with each other for light or resources. The forestry community has been typically using a complete opposite approach with stochastic individual models where you model individual trees with specific location and space. Stochastic aspect comes from the fact that the location of the trees are random and where the plants die is semi-random. These models are great but very expensive. So the cohort model is a compromise between the two approaches. To develop these kind of models and achieve our final goal which is to improve climate models and better assess the impact of climate changes on our societies, we need to find a way to manage the complexity of the codes by using a modular approach, make all our development open source and available to all the community to make compromise and balance ecological realism. FATES stands for the Functionally Assembled Terrestrial Ecosystem Simulator. It is fully open source. It is available on GitHub. It is designed to operate with a host land surface model. FATES simulates plant physiology, competitive processes, ecosystem assembly and vegetation distribution. With FATES, trees have a height and compete for light. The following advances are enabled with a demographic model, a better representation of light competition and ecosystem assembly, the prediction of the distribution of plants based on their functional traits, the lags between climate and vegetation change and growth after disturbances, the better representation of physiological processes such as hydraulics, fire and nutrients. On this slide, we show how the code developments are taking place. The approach is modular and each component can be developed separately. This approach significantly speed up developments. Thanks to this modular approach, new groups joined our community and contribute to develop new modules. Many new developments have been added to FATES and we see an increasing number of developers contributing to the code. There are about 39 funded researchers working on the FATES code. These developers are spread in about six different countries and more than 26 different institutions. We had more than 130 participants at the recent planning meeting in 2020. About three Earth Systemo modeling centers are using FATES and more than 11 proposal funded in 2020 for further developing FATES. The code is fully modular and easy to update. To help code developers, a number of reduced complexity modes were made available. However, we know that more needs to be done for simplifying the usage of FATES and reduce the learning curve. Running FATES remains technically challenging. The scripting environment is complicated. Porting the model to new platforms can be incredibly hard. Running at single sites or regions is more cumbersome than running the code globally and workflows are complicated to put in place. Interesting science is at the interface of the input parameters, model processes and emergent outcomes. We know it takes a very long time for a new researcher to run successfully a simple FATES test simulation. In general it takes several months and it is very frustrating. However, once researchers manage to run FATES, they usually can contribute and improve the model. FATES in Galaxy aims at reducing the learning curve and help researchers to quickly produce scientific results. There are two ways to run SLAM FATES in Galaxy and we suggest new users to start with the FATES Galaxy tool. For more advanced users and if you are willing to develop the FATES model, use the Galaxy Climate Jupiter Lab where you can develop new features, set up simple test simulations and visualize your results. The Galaxy FATES tool can then be used to run operational and long simulations. FATES is a numerical terrestrial ecosystem model used in climate models. Thank you for watching.