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Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink
Authors:William R L Anderegg  Ashley P Ballantyne  W Kolby Smith  Joseph Majkut  Sam Rabin  Claudie Beaulieu  Richard Birdsey  John P Dunne  Richard A Houghton  Ranga B Myneni  Yude Pan  Jorge L Sarmiento  Nathan Serota  Elena Shevliakova  Pieter Tans  Stephen W Pacala
Abstract: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.Terrestrial ecosystems have been a substantial net sink of anthropogenic carbon (C) emissions since the 1960s (14), but the terrestrial C sink could switch to a C source in the 21st century, resulting in a positive C cycle-climate feedback that would accelerate global surface warming with potentially major consequences for the biosphere (57). The interannual variability of the terrestrial C sink can help constrain our understanding of C/climate feedbacks and identify regions and mechanisms of the terrestrial C cycle that are most sensitive to climate parameters, shedding light on the future of the sink and its possible transition to a source (8). Currently, several major drivers have been shown to be correlated with the interannual variability of the terrestrial C sink, including (i) tropical temperature, which is tightly coupled to interannual variability in the atmospheric growth rate (AGR) of CO2 (8, 9); (ii) tropical drought stress, including major droughts in the Amazon (1012), which has been suggested to underlie increasing sensitivity of the AGR to tropical temperature over the period from 1959–2010 (13); (iii) temperature and precipitation variability in semiarid regions (14, 15); and (iv) average minimum daily (hereafter “nighttime”) temperatures, which studies of several local field sites in the tropics have found play a major role in interannual productivity (1618).Determining the mechanism underlying the interannual variability of the terrestrial C sink, including the relative roles of precipitation vs. temperature stress and their effects on gross primary productivity (GPP) vs. total respiration (both autotrophic and heterotrophic; R), is critical to predict the sink’s future and to improve Earth system models. Here, we quantify changes in the interannual variability of the terrestrial C sink over the past half-century and then statistically evaluate four hypotheses that the variability of the terrestrial sink is most strongly influenced by (i) tropical mean temperature, (ii) tropical precipitation, (iii) precipitation and temperature in semiarid regions, and (iv) nighttime tropical temperatures. We combine multiple simulations from an atmospheric mass balance of the land C sink net ecosystem exchange (NEE)] from 1959 to 2010, remote sensing-modeled datasets of vegetation greenness and GPP from 1982 to 2010, and global gridded climate datasets to constrain globally the fundamental equation NEE = GPP − R and the relative sensitivities of each component to temperature and precipitation. We draw on a combination of model selection and partial correlation analysis to provide relative likelihood estimates of each driver and to account for covariation between predictor variables (e.g., tropical mean temperature vs. nighttime temperature).
Keywords:climate change  climate feedback  asymmetrical warming  carbon budget  inversion model
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