Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink

Significance The future of the terrestrial carbon (C) sink has tremendous consequences for society and the rate of climate change, but is highly uncertain. The sensitivity of interannual variability in the C sink to climate drivers can help elucidate the mechanisms driving the C sink. Here, we test the statistical strength of major climate drivers of the C sink and find that nighttime tropical temperatures are most strongly associated with the global C sink from 1959–2010, likely acting through their effect on respiration. The temperature-mediated sensitivity of the global C sink to respiration highlights that tropical C stores may be vulnerable to robust projected increases in tropical nighttime temperatures. 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.

[1]  D. Baldocchi Faculty Opinions recommendation of Carbon cycle. The dominant role of semi-arid ecosystems in the trend and variability of the land CO₂ sink. , 2016 .

[2]  Atul K. Jain,et al.  The dominant role of semi-arid lands in the trend and variability of the land CO 2 sink , 2015 .

[3]  Atul K. Jain,et al.  Global Carbon Budget 2015 , 2015 .

[4]  D. Schimel,et al.  Effect of increasing CO2 on the terrestrial carbon cycle , 2014, Proceedings of the National Academy of Sciences.

[5]  A. Huete,et al.  Estimation of vegetation photosynthetic capacity from space‐based measurements of chlorophyll fluorescence for terrestrial biosphere models , 2014, Global change biology.

[6]  Mark A. Friedl,et al.  Direct human influence on atmospheric CO2 seasonality from increased cropland productivity , 2014, Nature.

[7]  Luis Guanter,et al.  Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude , 2014, Nature.

[8]  C. Tucker,et al.  Vegetation dynamics and rainfall sensitivity of the Amazon , 2014, Proceedings of the National Academy of Sciences.

[9]  R. Houghton,et al.  Audit of the global carbon budget: estimate errors and their impact on uptake uncertainty , 2014 .

[10]  Yi Y. Liu,et al.  Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle , 2014, Nature.

[11]  Keith B. Rodgers,et al.  A growing oceanic carbon uptake: Results from an inversion study of surface pCO2 data , 2014 .

[12]  P. Jones,et al.  Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .

[13]  P. Ciais,et al.  Terrestrial carbon cycle affected by non-uniform climate warming , 2014 .

[14]  Luana S. Basso,et al.  Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements , 2014, Nature.

[15]  Ranga B. Myneni,et al.  A two-fold increase of carbon cycle sensitivity to tropical temperature variations , 2014, Nature.

[16]  Pierre Friedlingstein,et al.  Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks , 2014 .

[17]  Elena Shevliakova,et al.  Historical warming reduced due to enhanced land carbon uptake , 2013, Proceedings of the National Academy of Sciences.

[18]  E. A. Kort,et al.  Enhanced Seasonal Exchange of CO2 by Northern Ecosystems Since 1960 , 2013, Science.

[19]  P. Ciais,et al.  Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation , 2013, Nature.

[20]  Atul K. Jain,et al.  CO2 emissions from land‐use change affected more by nitrogen cycle, than by the choice of land‐cover data , 2013, Global change biology.

[21]  J. Canadell,et al.  Variations in atmospheric CO2 growth rates coupled with tropical temperature , 2013, Proceedings of the National Academy of Sciences.

[22]  Piero Toscano,et al.  Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set , 2013 .

[23]  Hongmei Li,et al.  Global ocean biogeochemistry model HAMOCC: Model architecture and performance as component of the MPI‐Earth system model in different CMIP5 experimental realizations , 2013 .

[24]  D. Clark,et al.  Field‐quantified responses of tropical rainforest aboveground productivity to increasing CO2 and climatic stress, 1997–2009 , 2013 .

[25]  P. Cox,et al.  Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability , 2013, Nature.

[26]  Corinne Le Quéré,et al.  Carbon emissions from land use and land-cover change , 2012 .

[27]  Atul K. Jain,et al.  The global carbon budget 1959-2011 , 2012 .

[28]  J. B. Miller,et al.  Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years , 2012, Nature.

[29]  P. Ciais,et al.  Archived Version from Ncdocks Institutional Repository a Synthesis of Carbon Dioxide Emissions from Fossil-fuel Combustion Title: a Synthesis of Carbon Dioxide Emissions from Fossil-fuel Combustion a Synthesis of Carbon Dioxide Emissions from Fossil-fuel Combustion , 2022 .

[30]  P. Jones,et al.  Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set , 2012 .

[31]  O. Phillips,et al.  The 2010 Amazon Drought , 2011, Science.

[32]  E. Buitenhuis,et al.  Biogeochemical fluxes through microzooplankton , 2010 .

[33]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[34]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[35]  M. Auffhammer,et al.  Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures , 2010, Proceedings of the National Academy of Sciences.

[36]  Fortunat Joos,et al.  Sensitivity of Holocene atmospheric CO 2 and the modern carbon budget to early human land use: analyses with a process-based model , 2010 .

[37]  Steven F. Oberbauer,et al.  Annual wood production in a tropical rain forest in NE Costa Rica linked to climatic variation but not to increasing CO2 , 2010 .

[38]  Corinne Le Quéré,et al.  Trends in the sources and sinks of carbon dioxide , 2009 .

[39]  A. Gnanadesikan,et al.  Regional impacts of iron-light colimitation in a global biogeochemical model , 2009 .

[40]  Christoph Heinze,et al.  An isopycnic ocean carbon cycle model , 2009 .

[41]  K. Lindsay,et al.  Mechanisms governing interannual variability in upper-ocean inorganic carbon system and air–sea CO2 fluxes: Physical climate and atmospheric dust , 2009 .

[42]  Gregg Marland,et al.  How Uncertain Are Estimates of CO2 Emissions? , 2009 .

[43]  I. C. Prentice,et al.  Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) , 2008 .

[44]  K. Davis,et al.  A Bayesian calibration of a simple carbon cycle model: The role of observations in estimating and reducing uncertainty , 2008 .

[45]  P. Ciais,et al.  Net carbon dioxide losses of northern ecosystems in response to autumn warming , 2008, Nature.

[46]  Ryotaro Kamimura,et al.  A Stepwise AIC Method for Variable Selection in Linear Regression , 2007 .

[47]  John E. Kutzbach,et al.  Projected distributions of novel and disappearing climates by 2100 AD , 2006, Proceedings of the National Academy of Sciences.

[48]  L. Bopp,et al.  Globalizing results from ocean in situ iron fertilization studies , 2006 .

[49]  Tsutomu Ikeda,et al.  Biogeochemical fluxes through mesozooplankton , 2006 .

[50]  Giovanni M. Marchetti,et al.  Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm , 2006 .

[51]  E. Davidson,et al.  On the variability of respiration in terrestrial ecosystems: moving beyond Q10 , 2006 .

[52]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[53]  Nicolas Gruber,et al.  The Oceanic Sink for Anthropogenic CO2 , 2004, Science.

[54]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[55]  Diane Vizine-Goetz,et al.  Spectrum , 2001 .

[56]  J. Canadell,et al.  Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems , 2001, Nature.

[57]  Corinne Le Quéré,et al.  Regional changes in carbon dioxide fluxes of land and oceans since 1980. , 2000, Science.

[58]  R. Betts,et al.  Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model , 2000, Nature.

[59]  Makiko Sato,et al.  GISS analysis of surface temperature change , 1999 .

[60]  Thomas C. Peterson,et al.  Global historical climatology network (GHCN) quality control of monthly temperature data , 1998 .

[61]  M. Wahlen,et al.  Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980 , 1995, Nature.

[62]  Pieter P. Tans,et al.  Evidence for interannual variability of the carbon cycle from the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory Global Air Sampling Network , 1994 .

[63]  J. Hansen,et al.  Stratospheric aerosol optical depths, 1850–1990 , 1993 .

[64]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[65]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .