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A two-fold increase of carbon cycle sensitivity to tropical temperature variations

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Abstract

Earth system models project that the tropical land carbon sink will decrease in size in response to an increase in warming and drought during this century, probably causing a positive climate feedback1,2. But available data3,4,5 are too limited at present to test the predicted changes in the tropical carbon balance in response to climate change. Long-term atmospheric carbon dioxide data provide a global record that integrates the interannual variability of the global carbon balance. Multiple lines of evidence6,7,8 demonstrate that most of this variability originates in the terrestrial biosphere. In particular, the year-to-year variations in the atmospheric carbon dioxide growth rate (CGR) are thought to be the result of fluctuations in the carbon fluxes of tropical land areas6,9,10. Recently, the response of CGR to tropical climate interannual variability was used to put a constraint on the sensitivity of tropical land carbon to climate change10. Here we use the long-term CGR record from Mauna Loa and the South Pole to show that the sensitivity of CGR to tropical temperature interannual variability has increased by a factor of 1.9 ± 0.3 in the past five decades. We find that this sensitivity was greater when tropical land regions experienced drier conditions. This suggests that the sensitivity of CGR to interannual temperature variations is regulated by moisture conditions, even though the direct correlation between CGR and tropical precipitation is weak9. We also find that present terrestrial carbon cycle models do not capture the observed enhancement in CGR sensitivity in the past five decades. More realistic model predictions of future carbon cycle and climate feedbacks require a better understanding of the processes driving the response of tropical ecosystems to drought and warming.

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Figure 1: Change in detrended anomalies in CGR and tropical MAT, in dCGR/dMAT and in over the past five decades.
Figure 2: Histograms of during the earliest period and during the most recent period, and of relative change in over the past five decades.
Figure 3: for each bin of detrended tropical moisture anomaly.

Change history

  • 12 February 2014

    Reference 31 has been moved to the main-text reference list.

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Acknowledgements

We thank S. Seneviratne and G. Zhang for comments and J. Gash for English editing. This study was supported by the National Natural Science Foundation of China (41125004), the National Basic Research Program of China (grant nos 2010CB950601 and 2013CB956303) and the National Youth Top-notch Talent Support Program in China. R.B.M. was funded by the NASA Earth Science Division.

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Contributions

S. Piao, P. Ciais and X.W. designed the research; X.W. performed the analysis; S. Piao, P. Ciais and X.W. drafted the paper; and P.F., R.B.M., P. Cox., M.H., J.M., S. Peng, T.W., H.Y. and A.C. contributed to the interpretation of the results and to the text.

Corresponding author

Correspondence to Shilong Piao.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Spatial distribution of the correlation coefficient between detrended CGR and MAT anomalies.

CGR anomalies are from Mauna Loa Observatory and local MAT anomalies were derived from the CRU data set for the period 1960–2011. The correlation coefficients 0.23 and 0.28 are the critical thresholds at significance levels of 0.10 and 0.05 (n = 52), respectively.

Extended Data Figure 2 Convergent cross-mapping for reconstruction of variations in MAT, annual precipitation and mean annual solar radiation from variations in CGR.

CGR data are from Mauna Loa Observatory. The CGR-reconstructed temperature curve gradually converges to a large positive correlation coefficient (R = 0.70), whereas the CGR-reconstructed precipitation (P) and radiation (R) curves lead to smaller correlation coefficients (R = 0.04 and R = 0.23, respectively) as time-series length increases, suggesting that CGR variations are mainly forced by temperature variations rather than by variations in precipitation and solar radiation.

Extended Data Figure 3 Change in dCGR/dMAT, , and the effects of interannual variations of precipitation and solar radiation on the estimate of dCGR/dMAT.

The changes are calculated between the latest two decades and the earliest two decades in 1960–2011. Precipitation and radiation effects are denoted f2 × dP/dMAT and f3 × dR/dMAT, respectively. dCGR/dMAT is calculated as the slope of MAT in the regression of CGR at Mauna Loa Observatory against MAT over the tropical vegetated land. , f2 and f3 are the slopes of MAT, precipitation and radiation, respectively, in the multiple regression of CGR against MAT, precipitation and radiation over the tropical vegetated land. dP/dMAT is the slope of MAT in the regression of precipitation against MAT. dR/dMAT is the slope of MAT in the regression of radiation against MAT. Error bars indicate the 95% confidence interval of the corresponding value derived from 500 bootstrap estimates.

Extended Data Figure 4 and for the first and the last time window during the study period 1960–2011.

For 20-yr windows, the two windows are 1960–1979 and 1992–2011. For 25-yr windows, they are 1960–1984 and 1987–2011. a, Data from 1992 and 1993 (post-Pinatubo years) are excluded from estimates. b, Data from the record-high El Niño events of 1972–1973 and 1997–1998 are excluded from estimates. c, Interannual variations of CGR and climate variables are obtained from the frequency decomposition by SSA (Methods). d, is estimated with alternative climate data sets (tropical MAT is from the GHCN data set19, tropical annual precipitation is from the GPCC20 and solar radiation is from ref. 21). e, CGR is obtained from monthly CO2 records at the South Pole. f, Interannual temperature sensitivity of the residual land carbon sink (). The residual land carbon sink of each year is estimated from the CGR by adding the ocean sink and subtracting fossil fuel emission and emission due to land-use change22. In ad, is estimated from Mauna Loa CO2 records. Error bars indicate the 95% confidence interval of derived from 500 bootstrap estimates.

Extended Data Figure 5 Change in drought indices and 20-yr smoothed drought indices over tropical and mid-latitude regions during the past five decades.

a, Change in tropical (23° south to 23° north) annual PDSI, SPEI6 and ORCHIDEE-estimated soil moisture. b, 20-yr smoothed tropical PDSI, SPEI6 and ORCHIDEE-simulated soil moisture. c, Change in mid-latitude (23° north to 48° north) annual PDSI, SPEI6 and ORCHIDEE-simulated soil moisture. d, 20-yr smoothed mid-latitude PDSI, SPEI6 and ORCHIDEE-simulated soil moisture. Years on the x-axis indicate the central year of the 20-yr time window (for example, 1970 represents 1960–1979). All variables are normalized by their respective standard deviations. The changes in SPEI3, SPEI9 and SPEI12 are close to that in SPEI6. Note that SPEI is available till 2011, PDSI is available till 2010 and model soil moisture is available till 2009.

Extended Data Figure 6 Spatial distribution of the difference between the latest and first 20-yr periods during the past five decades in PDSI, SPEI6 and ORCHIDEE-estimated soil moisture.

a, PDSI; b, SPEI6; c, ORCHIDEE-simulated soil moisture. The changes in SPEI3, SPEI9 and SPEI12 are close to that in SPEI6.

Extended Data Figure 7 Change in and the relationship between and precipitation anomalies in the null Monte Carlo experiment.

a, in the first and last 20 yr during 1960–2011. b, for each bin of detrended tropical precipitation anomalies, which are divided into four bins (standardized departure (σ) less than −1, between −1 and 0, between 0 and 1, and greater than 1). Values of for different bins of detrended tropical precipitation anomaly are similar. c, Frequency distribution of the difference between calculated for σ > 1 (wet conditions) and calculated for σ < −1 (dry conditions). The probability of the observed difference (ranging from 5.1 to 6.0 Pg C yr−1 °C−1; Fig. 3) occurring purely by chance is very low (P < 0.01). Error bars indicate the confidence intervals of the corresponding estimates.

Extended Data Figure 8 Change in dCGR/dMAT and and variational effects on estimating dCGR/dMAT between the earlier and latest two decades during 1960–2011.

The effects of interannual variations of tropical precipitation (P), tropical short-wave solar radiation (R), mid-latitude (23° north to 48° north) temperature (MidMAT), mid-latitude precipitation (MidP) and mid-latitude short-wave solar radiation (MidR) are denoted f2 × dP/dMAT, f3 × dR/dMAT, f4 × dMidMAT/dMAT, f5 × dMidP/dMAT and f6 × dMidR/dMAT, respectively, where f2, f3, f4, f5 and f6 are respectively the slopes of P, R, MidMAT, MidP and MidR in the multiple regression of CGR against P, R, MidMAT, MidP, MidR. dy/dx represents the slope of x in the regression of y against x. Error bars indicate the 95% confidence interval of the corresponding value derived from 500 bootstrap estimates.

Extended Data Figure 9 Change in and during 1960–2008 with a 20-yr moving time window.

is the interannual temperature sensitivity of tropical net biome productivity, estimated using five carbon cycle models (HYL, LPJ, ORC, SHE and TRI). To be consistent with model estimated annual net biome productivity, CGR of a specific year is calculated as the difference between the December Mauna Loa CO2 concentration of the year and that of December the previous year. Positive value of indicates reduced anomalies of carbon sinks during warm years.

Extended Data Figure 10 Autocorrelations in CGR and MAT, and their impacts on the estimates of .

a, Autocorrelation coefficients for detrended anomalies of CGR from Mauna Loa during 1960–2011. b, Autocorrelation coefficients for detrended anomalies of MAT during 1960–2011. Dashed lines in a and b indicate 95% confidence bands. c, The comparison between calculated in the multiple regression of interannual variations of the Mauna Loa CGR record against interannual variations in temperature, precipitation and solar radiation (x axis) and calculated in the linear mixed model with same independent variables and a first-order autocorrelation function (y axis). Solid line indicates 1:1 ratio. This 1:1 relationship holds for derived for different time-window lengths.

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Wang, X., Piao, S., Ciais, P. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014). https://doi.org/10.1038/nature12915

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