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This study uses the simplest empirical dynamical systems models\u2014two\u2010dimensional linear models\u2014and observation\u2010based data from satellites, eddy covariance towers, weather stations, and machine\u2010learning\u2010derived products to determine the dominant sub\u2010annual 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\u2010scale 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\u2014positively sloped tangent lines to a convex long\u2010term mean\u2010state curve\u2014is 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\u2010state pattern, and hypothesize that this may be due to lack of water\u2010carbon feedbacks at daily scales.<\/jats:p>","DOI":"10.1111\/gcb.17463","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T13:39:21Z","timestamp":1723210761000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Two sub\u2010annual timescales and coupling modes for terrestrial water and carbon cycles"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4891-2474","authenticated-orcid":false,"given":"Daniel J.","family":"Short\u00a0Gianotti","sequence":"first","affiliation":[{"name":"Ralph M. 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