Abstract
To bridge the knowledge gap between (a) our (instantaneous‐to‐seasonal‐scale) process understanding of plants and water and (b) our projections of long‐term coupled feedbacks between the terrestrial water and carbon cycles, we must uncover what the dominant dynamics are linking fluxes of water and carbon. This study uses the simplest empirical dynamical systems models—two‐dimensional linear models—and observation‐based data from satellites, eddy covariance towers, weather stations, and machine‐learning‐derived products to determine the dominant sub‐annual timescales coupling carbon uptake and (normalized) evaporation fluxes. We find two dominant modes across the Contiguous United States: (1) a negative correlation timescale on the order of a few days during which landscapes dry after precipitation and plants increase their carbon uptake through photosynthetic upregulation. (2) A slow, seasonal‐scale positive covariation through which landscape drying leads to decreased growth and carbon uptake. The slow (positively correlated) process dominates the joint distribution of local water and carbon variables, leading to similar behaviors across space, biomes, and climate regions. We propose that vegetation cover/leaf area variables link this behavior across space, leading to strong emergent spatial patterns of water/carbon coupling in the mean. The spatial pattern of local temporal dynamics—positively sloped tangent lines to a convex long‐term mean‐state curve—is surprisingly strong, and can serve as a benchmark for coupled Earth System Models. We show that many such models do not represent this emergent mean‐state pattern, and hypothesize that this may be due to lack of water‐carbon feedbacks at daily scales.