EGU23-3457, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Evaluating Vegetation Modelling in Earth System Models with Machine Learning Approaches

Ranjini Swaminathan, Tristan Quaife, and Richard Allan
Ranjini Swaminathan et al.
  • Department of Meteorology, University of Reading, Reading, United Kingdom

The presence and amount of vegetation in any given region controls Gross Primary Production (GPP) or  the flux of carbon into the land driven by the process of photosynthesis. Earth System Models (ESMs) give us the ability to simulate GPP through modelling the various interactions between the atmosphere and biosphere including under different climate change scenarios in the future. GPP is the largest flux of the global carbon cycle and plays an important role including in carbon budget calculations.  However, GPP estimates from ESMs not only vary widely but also have much uncertainty in the underpinning attributors for this variability.  

We use data from pre-industrial Control (pi-Control) simulations to avail of the longer time period to sample data from as well as to exclude the influence of anthropogenic forcing in GPP estimation thereby leaving GPP to be largely attributable to two factor - (a) input atmospheric forcings and (b) the processes using those input climate variables to diagnose GPP. 

We explore the processes determining GPP with a physically-guided Machine Learning framework applied to a set of Earth System Models (ESMs) from the Sixth Coupled Model Intercomparison Project (CMIP6). We use this framework to examine whether differences in GPP across models are caused by differences in atmospheric state or process representations. 

Results from our analysis show that models with similar regional atmospheric forcing do not always have similar GPP distributions. While there are regions where climate models largely agree on what atmospheric variables are most relevant for GPP, there are regions such as the tropics where there is more uncertainty.  Our analysis highlights the potential of ML to identify differences in atmospheric forcing and carbon cycle process modelling across current state-of-the-art ESMs. It also allows us to extend the analysis with observational estimates of forcings as well as GPP for model improvement. 

How to cite: Swaminathan, R., Quaife, T., and Allan, R.: Evaluating Vegetation Modelling in Earth System Models with Machine Learning Approaches, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3457,, 2023.