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. 2021 May 10;16(1):15.
doi: 10.1186/s13021-021-00178-3.

Decadal variability in land carbon sink efficiency

Lei Zhu  1   2 , Philippe Ciais  3 , Ana Bastos  4   5 , Ashley P Ballantyne  3   6 , Frederic Chevallier  3 , Thomas Gasser  7 , Masayuki Kondo  8   9 , Julia Pongratz  4   10 , Christian Rödenbeck  11 , Wei Li  12   13
Affiliations

Decadal variability in land carbon sink efficiency

Lei Zhu et al. Carbon Balance Manag. .

Abstract

Background: The climate mitigation target of limiting the temperature increase below 2 °C above the pre-industrial levels requires the efforts from all countries. Tracking the trajectory of the land carbon sink efficiency is thus crucial to evaluate the nationally determined contributions (NDCs). Here, we define the instantaneous land sink efficiency as the ratio of natural land carbon sinks to emissions from fossil fuel and land-use and land-cover change with a value of 1 indicating carbon neutrality to track its temporal dynamics in the past decades.

Results: Land sink efficiency has been decreasing during 1957-1990 because of the increased emissions from fossil fuel. After the effect of the Mt. Pinatubo eruption diminished (after 1994), the land sink efficiency firstly increased before 2009 and then began to decrease again after 2009. This reversal around 2009 is mostly attributed to changes in land sinks in tropical regions in response to climate variations.

Conclusions: The decreasing trend of land sink efficiency in recent years reveals greater challenges in climate change mitigation, and that climate impacts on land carbon sinks must be accurately quantified to assess the effectiveness of regional scale climate mitigation policies.

Keywords: Carbon neutrality; Land carbon sink efficiency; Trend reversal.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Land sink efficiencies based on 5-yr moving averages from different datasets during 1959–2016: a annual values, b annual values (dot) with linear regression (line) from 1959 to 1988 and piecewise regression (line) from 1996–2016. B in the inversion datasets (CAMS and Jena) is calculated by BL−L where L from BLUE is used. The diamonds and error bars in (b) indicate the detected breakpoints with 95% confidence interval. The Pinatubo eruption period is marked in light yellow shade. Note that we used 5-yr moving average fluxes, and thus the analysis period is shown as 1959–2016 instead of 1957–2018 (the original annual values)
Fig. 2
Fig. 2
Regional contributions to the trend reversal after 2009. a Slope change ratio in different regions from various datasets. The short vertical black line is the mean value of the 6 datasets (colored dots). A positive (negative) value of Rsc indicates that this region strengthens (weakens) the trend reversal after the breakpoint. b Removal of pairs of regions that leads to the insignificance (p > 0.1) or disappearance of the trend reversal. Squares with the same hatch are one removal of 2-region combination
Fig. 3
Fig. 3
Global and regional trends in carbon fluxes before and after 2009 from 1996 to 2016 based on 5-year moving average. The left and right stacks of bars in each dataset represent trends before and after 2009, respectively. Note that data from GCP are only shown on the global scale. Categorical variables on the x-axis are consistent among bar plots for each region, with reference being given for Latin America. Y axes have different scales in different regions for legibility
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