The terrestrial biosphere is currently a strong carbon (C) sink but may switch to a source in the 21st century as climate-driven losses exceed CO2-driven C gains, thereby accelerating global warming. Although it has long been recognized that tropical climate plays a critical role in regulating interannual climate variability, the causal link between changes in temperature and precipitation and terrestrial processes remains uncertain. Here, we combine atmospheric mass balance, remote sensing-modeled datasets of vegetation C uptake, and climate datasets to characterize the temporal variability of the terrestrial C sink and determine the dominant climate drivers of this variability. We show that the interannual variability of global land C sink has grown by 50-100% over the past 50 y. We further find that interannual land C sink variability is most strongly linked to tropical nighttime warming, likely through respiration. This apparent sensitivity of respiration to nighttime temperatures, which are projected to increase faster than global average temperatures, suggests that C stored in tropical forests may be vulnerable to future warming.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Dec 22 2015|
Bibliographical noteFunding Information:
We thank Pekka Kauppi and the Finnish Society of Sciences and Letters, and the Carbon Mitigation Initiative at Princeton University for sponsoring the workshop that led to this paper. W.R.L.A. was supported, in part, by US National Science Foundation (NSF) MacroSystems Biology Grant Division of Environmental Biology EF-1340270, NSF RAPID Grant DEB-1249256, NSF Early Concept Grants for Exploratory Research (EAGER) Grant 1550932, and a National Oceanic and Atmospheric Administration (NOAA) Climate and Global Change postdoctoral fellowship, administered by the University Corporation of Atmospheric Research.
- Asymmetrical warming
- Carbon budget
- Climate change
- Climate feedback
- Inversion model