Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability
Abstract
Abstract Climate change will impact precipitation variability, potentially accelerating climate-terrestrial carbon feedbacks. However, the response of ecosystems to precipitation variability is difficult to constrain due to myriad physiological and abiotic variables that limit terrestrial productivity. Based on a combination of satellite imagery and a global network of daily precipitation data, we present here a statistical framework to isolate the impact of precipitation variability on the gross primary productivity of five biomes that collectively account for 50% of global land area. The productivity of mesic grasslands and forests decreases by ~28% and ~7% (respectively) in response to more irregular rain within the year, while the sensitivity is halved in response to higher year-to-year variability. Xeric grasslands are similarly impacted by intra-annual rainfall variance, but they show an increase in productivity with higher interannual rainfall variability. Conversely, the productivity of boreal forests increases under higher variability on both timescales. We conclude that projected changes in precipitation variability will have a measurable global impact on the terrestrial carbon sink.
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1783410
- Grant/Contract Number:
- SC0020285
- Resource Type:
- Published Article
- Journal Name:
- Communications Earth & Environment
- Additional Journal Information:
- Journal Name: Communications Earth & Environment Journal Volume: 1 Journal Issue: 1; Journal ID: ISSN 2662-4435
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Ritter, François, Berkelhammer, Max, and Garcia, Cynthia. Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability. United Kingdom: N. p., 2020.
Web. doi:10.1038/s43247-020-00034-1.
Ritter, François, Berkelhammer, Max, & Garcia, Cynthia. Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability. United Kingdom. https://doi.org/10.1038/s43247-020-00034-1
Ritter, François, Berkelhammer, Max, and Garcia, Cynthia. Fri .
"Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability". United Kingdom. https://doi.org/10.1038/s43247-020-00034-1.
@article{osti_1783410,
title = {Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability},
author = {Ritter, François and Berkelhammer, Max and Garcia, Cynthia},
abstractNote = {Abstract Climate change will impact precipitation variability, potentially accelerating climate-terrestrial carbon feedbacks. However, the response of ecosystems to precipitation variability is difficult to constrain due to myriad physiological and abiotic variables that limit terrestrial productivity. Based on a combination of satellite imagery and a global network of daily precipitation data, we present here a statistical framework to isolate the impact of precipitation variability on the gross primary productivity of five biomes that collectively account for 50% of global land area. The productivity of mesic grasslands and forests decreases by ~28% and ~7% (respectively) in response to more irregular rain within the year, while the sensitivity is halved in response to higher year-to-year variability. Xeric grasslands are similarly impacted by intra-annual rainfall variance, but they show an increase in productivity with higher interannual rainfall variability. Conversely, the productivity of boreal forests increases under higher variability on both timescales. We conclude that projected changes in precipitation variability will have a measurable global impact on the terrestrial carbon sink.},
doi = {10.1038/s43247-020-00034-1},
journal = {Communications Earth & Environment},
number = 1,
volume = 1,
place = {United Kingdom},
year = {Fri Oct 16 00:00:00 EDT 2020},
month = {Fri Oct 16 00:00:00 EDT 2020}
}
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