Alaskan carbon-climate feedbacks will be weaker than inferred from short-term experiments
Abstract
Climate warming is occurring fastest at high latitudes. Based on short-term field experiments, this warming is projected to stimulate soil organic matter decomposition, and promote a positive feedback to climate change. We show here that the tightly coupled, nonlinear nature of high-latitude ecosystems implies that short-term (<10 year) warming experiments produce emergent ecosystem carbon stock temperature sensitivities inconsistent with emergent multi-decadal responses. We first demonstrate that a well-tested mechanistic ecosystem model accurately represents observed carbon cycle and active layer depth responses to short-term summer warming in four diverse Alaskan sites. We then show that short-term warming manipulations do not capture the non-linear, long-term dynamics of vegetation, and thereby soil organic matter, that occur in response to thermal, hydrological, and nutrient transformations belowground. Our results demonstrate significant spatial heterogeneity in multi-decadal Arctic carbon cycle trajectories and argue for more mechanistic models to improve predictive capabilities.
- Authors:
-
[1];
[1];
[1];
[1];
[2]
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of Alberta, Edmonton, AB (Canada)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1763682
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Carbon cycle; cryospheric science; ecological modelling
Citation Formats
Bouskill, Nicholas J., Riley, William J., Zhu, Qing, Mekonnen, Zelalem A., and Grant, Robert F. Alaskan carbon-climate feedbacks will be weaker than inferred from short-term experiments. United States: N. p., 2020.
Web. doi:10.1038/s41467-020-19574-3.
Bouskill, Nicholas J., Riley, William J., Zhu, Qing, Mekonnen, Zelalem A., & Grant, Robert F. Alaskan carbon-climate feedbacks will be weaker than inferred from short-term experiments. United States. https://doi.org/10.1038/s41467-020-19574-3
Bouskill, Nicholas J., Riley, William J., Zhu, Qing, Mekonnen, Zelalem A., and Grant, Robert F. Mon .
"Alaskan carbon-climate feedbacks will be weaker than inferred from short-term experiments". United States. https://doi.org/10.1038/s41467-020-19574-3. https://www.osti.gov/servlets/purl/1763682.
@article{osti_1763682,
title = {Alaskan carbon-climate feedbacks will be weaker than inferred from short-term experiments},
author = {Bouskill, Nicholas J. and Riley, William J. and Zhu, Qing and Mekonnen, Zelalem A. and Grant, Robert F.},
abstractNote = {Climate warming is occurring fastest at high latitudes. Based on short-term field experiments, this warming is projected to stimulate soil organic matter decomposition, and promote a positive feedback to climate change. We show here that the tightly coupled, nonlinear nature of high-latitude ecosystems implies that short-term (<10 year) warming experiments produce emergent ecosystem carbon stock temperature sensitivities inconsistent with emergent multi-decadal responses. We first demonstrate that a well-tested mechanistic ecosystem model accurately represents observed carbon cycle and active layer depth responses to short-term summer warming in four diverse Alaskan sites. We then show that short-term warming manipulations do not capture the non-linear, long-term dynamics of vegetation, and thereby soil organic matter, that occur in response to thermal, hydrological, and nutrient transformations belowground. Our results demonstrate significant spatial heterogeneity in multi-decadal Arctic carbon cycle trajectories and argue for more mechanistic models to improve predictive capabilities.},
doi = {10.1038/s41467-020-19574-3},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United States},
year = {Mon Nov 16 00:00:00 EST 2020},
month = {Mon Nov 16 00:00:00 EST 2020}
}
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