#misc_thoughts
#ecology
#global_environmental_change

I always have this doubt that a lot of large-scale studies on how the terrestrial vegetation and carbon cycle respond to climate change can be "wrong". These studies often used relatively simple statistical tools (regression, basic machine learning, structural equation modeling) to investigate a small slice (from what I see, generally < 7 variables) of a huge system. The fitted relationships inevitably miss many intermediate causal relationships and all the influences from the not-included variables. As a result, the fitted relationships may be unable to reliably project future changes. Although land surface model is always available as an alternative tool, they have their own problems because their structures are less flexible and typically lag behind current experimental understanding and the parameters are not always well-calibrated.

Wonder if there are studies that already address my doubt?