Changes in growing season climate are often the foci of research exploring forest response to climate change. By contrast, little is known about tree growth response to projected declines in winter snowpack and increases in soil freezing in seasonally snow‐covered forest ecosystems, despite extensive documentation of the importance of winter climate in mediating ecological processes. We conducted a 5‐year snow‐removal experiment whereby snow was removed for the first 4–5 weeks of winter in a northern hardwood forest at the Hubbard Brook Experimental Forest in New Hampshire,
Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree‐ring and forest inventory data within a Bayesian state‐space model at a multi‐site, regional scale, focusing on
- NSF-PAR ID:
- 10368498
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 28
- Issue:
- 7
- ISSN:
- 1354-1013
- Page Range / eLocation ID:
- p. 2442-2460
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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