Atmospheric deposition, CO2, and change in the land carbon sink

Abstract

Concentrations of atmospheric carbon dioxide (CO₂) have continued to increase whereas atmospheric deposition of sulphur and nitrogen has declined in Europe and the USA during recent decades. Using time series of flux observations from 23 forests distributed throughout Europe and the USA, and generalised mixed models, we found that forest-level net ecosystem production and gross primary production have increased by 1% annually from 1995 to 2011. Statistical models indicated that increasing atmospheric CO₂ was the most important factor driving the increasing strength of carbon sinks in these forests. We also found that the reduction of sulphur deposition in Europe and the USA lead to higher recovery in ecosystem respiration than in gross primary production, thus limiting the increase of carbon sequestration. By contrast, trends in climate and nitrogen deposition did not significantly contribute to changing carbon fluxes during the studied period. Our findings support the hypothesis of a general CO₂-fertilization effect on vegetation growth and suggest that, so far unknown, sulphur deposition plays a significant role in the carbon balance of forests in industrialized regions. Our results show the need to include the effects of changing atmospheric composition, beyond CO₂, to assess future dynamics of carbon-climate feedbacks not currently considered in earth system/climate modelling.

Figure 1

Long-term trends in C fluxes for 23 forests (1992–2013). Most of the forests presented increasing trends in (a) NEP and (b) GPP, whereas (c) respiration remained fairly constant. The percentage of forests with increasing NEP was statistically higher (P = 0.001) than the percentage of forests with decreasing NEP, but the percentage of forests where GPP tended to increase was not statistically different (P = 0.28) from those with decreasing GPP. Red and blue lines indicate forests with increasing and decreasing trends, respectively, and black lines indicate the average trends. The shaded area indicates the standard error of the average trend. Grey dots indicate forest-year observations, and all values were adjusted to the same mean to remove forest-specific variability. The inset shows the modelled distribution of the trends using kernel-density estimation, indicating the percentage of forests with increasing and decreasing trends. See Methods for further information on the methodology used to calculate the trends. All data came from eddy-covariance towers.

Figure 2

Trends in forest maximum LAI. Red and blue lines indicate forests with increasing and decreasing trends, respectively, and the thick black line indicates the average trend. The shaded area indicates the standard error of the average trend. Grey dots indicate forest-year observations, and all values were adjusted to the same mean to remove forest-specific variability. The inset shows the modelled distribution of the trends using kernel-density estimation.

Figure 3

Temporal evolution and trends in N and S deposition, mean annual temperature (MAT), SPEI for the 23 forest sites (1995–2011). Trends were calculated using GLMMs with random slopes, with the forest as a random effect and year as a fixed effect. Models also used an ARMA (1,0) autocorrelation structure. Shading indicates the 95% confidence intervals of the means (calculated as 1.96 times the standard error of the mean). See Methods for further details.

Figure 4

Partial residual plots showing significant relationships found between predictors and C-flux trends in the 23 forests (ΔNEP/Δt, ΔGPP/Δt and ΔRe/Δt) and ΔLAI/Δt. Model summaries can be found in Supplementary Information section 1. Corrected maturity age (MatAge) was calculated by dividing the mean stand age by the logging maturity tree age as described by Stokland et al. for average productivity classes. See Methods for more information on the calculation of the corrected logging maturity age.

Figure 5

Temporal contribution of the predictor variables on NEP, GPP and Re, for the period 1995–2011. Models (see Supplementary Information, section 2.1.1–2.1.3) suggest that increasing CO₂ is the main contributor to the observed increases in NEP and GPP. The difference between the modelled contributions and the observed trends (yellow shaded) has been considered as an unknown contribution to the temporal variation in C fluxes. The temporal variations of the predictors are shown in Fig. 3. Error bars indicate standard errors. Units are ppm for CO₂, kg ha⁻¹ yr⁻¹ for S and N deposition, °C for temperature and standard deviation for SPEI. Data for forest C fluxes came from eddy-covariance towers. Error bars indicate standard errors. See Methods for information about the methodology used to calculate the contributions. Significance levels: ⁽*⁾ P < 0.1; *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 6

Temporal contribution of the predictor variables. The model (Supplementary Information, section 2.1.4) suggested that increasing CO₂ is the main contributor to the observed increases in LAI. The difference between the modelled contributions and the observed trends has been considered as an unknown contribution to the temporal variation LAI. The temporal variations of the predictors are shown in square brackets. Error bars indicate standard errors. Units are ppm for CO₂, kg ha⁻¹ yr⁻¹ for S and N deposition, °C for temperature and standard deviations for SPEI. Error bars indicate standard errors. See Methods for information about the methodology used to calculate the contributions. Significance levels: ⁽*⁾ P < 0.1; *P < 0.05; **P < 0.01; ***P < 0.001.