The world's ocean and land ecosystems act as sinks for anthropogenic
CO
The world's land ecosystems act as a major sink in the contemporary global
carbon cycle and, hence, alleviate the rise of atmospheric CO
Here, we explore if there is further independent evidence of a late-1980s regime shift in the land carbon sink through analyzing carbon fluxes from biospheric models of various complexity and observational constraints (e.g., satellite-based vegetation activity). Our emphasis is on global patterns of net primary production (NPP) since this key carbon flux is known to be a robust driver of carbon sink variability (Luyssaert et al., 2007; Zhao and Running, 2010). A particular focus is on identifying which land regions may have contributed to the potential shift and what underlying mechanisms may have caused it. Specifically, we analyze data-driven NPP data based on an established satellite-constrained biogeochemical model as well as process-based NPP data from nine terrestrial biosphere models that participated in a recent model intercomparison project “trends and drivers of the regional-scale sources and sinks of carbon dioxide (TRENDY)” (Sect. 2).
We analyze temporal patterns in various metrics of the terrestrial carbon
cycle based on three independent data sources. First, we analyze data-driven
NPP fields based on simulations with the satellite-constrained
biogeochemical Carnegie–Ames–Stanford Approach (CASA) model (van der
Werf et al., 2006) for the period of available satellite vegetation data
1982–2011 (Zhu et al., 2013). This updated and extensively validated
model runs at a 0.5
Second, we analyze a GCB for the period 1959–2011 and consisting of CO
Third, we analyze process-based NPP,
We apply a consistent change point methodology on the various metrics of the terrestrial carbon cycle to identify patterns of regime shifts (characterized as abrupt, substantial and sustained changes) and to contrast them with patterns showing either no or more gradual changes. We thus determine in a first step the statistical model that best fits the time series under investigation based on three options: (1) a constant mean, (2) a shift in the mean and (3) a linear trend. While there are numerous alternative statistical models (e.g., shifting trends as seen in satellite vegetation data at local to regional scales; Piao et al., 2011), our choice of these three models is based on our primary objective to identify large-scale patterns in global and continental carbon fluxes that would be consistent with the recently observed regime shift in the land carbon sink (Sarmiento et al., 2010; Beaulieu et al., 2012a).
In the “shift-in-the-mean” model, the shift is located through a change
point detection algorithm that includes discrimination against a
trend and the background autocorrelation (red noise) by considering all
positions in a time series as a potential change point from 5 to
The most likely model among the three statistical models fitted is
determined based on the Schwarz information criterion (SIC), which compares
their likelihoods with a penalty for the number of parameters fitted. If the
shift-in-the-mean model seems the most likely, we calculate in a second
step the direction and magnitude of the shifts (subtracting means prior to and
after the shift) and the corresponding
Spatial pattern of abrupt shifts in data-driven NPP. Maps show
Applying our change point methodology (Sect. 2.2) on data-driven global NPP fields reveals a marked spatial clustering of abrupt and sustained increases in NPP across northern Eurasia and northern Africa in the late 1980s (Fig. 1). At more regional levels, the impact of severe disturbance events such as the mountain pine beetle outbreak in the late 1990s in the temperate and boreal forests of western North America (Kurz et al., 2008) is also disclosed (via rapid and sustained decreases in NPP). A similar analysis without constraining to only statistically significant results at the grid point level implies that the coherent pattern of abrupt and sustained NPP shifts across northern Eurasia and northern Africa is spatially even more extensive (Fig. S1 in the Supplement).
Timing and magnitude of abrupt changes in the terrestrial carbon
cycle at global and continental scales. Timing of abrupt change (first data
entry) as well as corresponding direction and magnitude (second data entry in
units of PgC yr
This point is further illustrated when we apply our change point framework
on data-driven NPP time series representative of large land regions and
highlights the important role of the northern extratropics (magnitude of NPP
shift:
Temporal changes in continental data-driven NPP. Panels show
annual and seasonal (CASA-based) NPP anomalies corresponding to the
In order to unravel the mechanisms leading to the continental shifts in
data-driven NPP, we focus on the two target regions of northern Eurasia and
northern Africa that predominantly contributed to this late-1980s shift (see
Fig. 1a). A factorial analysis for specific seasons shows that the northern
Eurasian continent experienced a marked increase in spring temperatures and
spring satellite vegetation activity (fAPAR) in the late 1980s that together
produced a substantial increase in spring NPP (Fig. 2a). This relatively sudden
springtime warming was also associated with a markedly earlier spring onset
(
Over northern Africa including the dry Sahel, marked increases in data-driven NPP during wet and dry seasons that are driven both by increases in rainfall and by satellite fAPAR triggered a pronounced increase in annual NPP in the late 1980s (Fig. 2b). A closer inspection shows that in the period after this shift rainfall increased specifically during the later portion of the rainy season, which effectively lengthened the more productive growing season (Fig. S4).
Temporal changes in continental process-based NPP based on nine
terrestrial biosphere models. Panels show annual and seasonal NPP anomalies
for the
The exploited biogeochemical model (CASA) for data-driven NPP simulations
has a relatively simple structure and provides an integrated view (via
satellite fAPAR) of the many interacting factors that influence NPP
variability. Further, data-driven NPP estimates are also influenced by
observational uncertainties in both satellite (e.g., volcanic aerosols,
cloud cover, signal saturation) and key climate driver data that are only
partially accounted for in our data-driven NPP simulations (see Sect. 2.1). We thus explored if process-based terrestrial biosphere models driven
by climate and atmospheric CO
The TRENDY simulations are not restricted to the satellite period, allowing
us to assess whether the identified late-1980s NPP shifts also emerge as the
dominant pattern when the study period is extended to the last 5 decades (to
be consistent with the time frame of the GCB). Results show that the late-1980s
shift over northern Eurasia is a stable pattern. For northern Africa,
however, an even more prominent shift is identified in the late 1960s (Fig. S5 and Table S1). This may suggest that this region by
itself is not important enough to influence the global land sink (since
there is no evidence for a corresponding shift in the global residual land
sink; see Table 1). Further, in TRENDY experiments in which atmospheric
CO
The extent to which the identified climate-driven regime shifts in NPP in the late
1980s translate into a sustained carbon sink (consistent with the shift seen
in the residual land carbon sink from the GCB; Table 1) depends in part on
associated responses in key carbon loss fluxes such as
At global scale, a shift in
Our findings provide independent evidence from a biospheric modeling
perspective for the abrupt strengthening of the residual land carbon sink
in the late 1980s (Sarmiento et al., 2010) and suggest that the underlying
driver is a shift in global NPP in response to coordinated large-scale
climate shifts. However, the late-1980s climate perturbations may also
substantially influence fire regimes, but the paucity of data on burned area
and related carbon emissions extending back to the early 1980s severely limits
estimating corresponding impacts. For northern Eurasia (which is responsible
for the largest contribution to the late-1980s regime shift in data-driven
NPP), however, it is not expected that the observed profound spring
warming and greening (inferred through fAPAR) in the late 1980s may have
led to substantial changes in fire emissions since the fire activity peaks
later in the season (van der Werf et al., 2006). For northern Africa,
changes in fire regimes associated with the late-1980s shift towards wetter
conditions may have a substantial influence on net carbon balance, albeit
with uncertain direction since a shift towards wetter conditions may
increase (more fuel load) or reduce (shortening the dry season) fire
emissions (Andela and van der Werf, 2014). Models that can potentially
quantify this influence are still in their early phase of development. While
much uncertainty (specifically pertaining to magnitude) remains in
estimating the contribution of climate-driven changes in the major land
carbon fluxes to the late-1980s regime shift in the land carbon sink, our
regional NPP attributions are consistent with a reported decrease in the
interhemispheric gradient in atmospheric CO
Other factors not related to climate may have also played a role in the late-1980s
regime shift of the land carbon sink. A potentially large contribution
in this regard may be from land-use and land-cover changes across northern
Eurasia through agricultural abandonment and rapid changes in forest
management in the aftermath of the late-1980s Soviet collapse.
While such processes are accounted for in net LUC emission estimates
compiled in the GCB (and therefore included in our analysis; see Table 1),
corresponding effects may not be fully captured due to a lack of robust data
especially in the period prior to the Soviet collapse (Achard et al.,
2006). However, at least in the case of agricultural abandonment newly
available estimates (Schierhorn et al., 2013) of associated carbon sinks for
the post-Soviet period 1990–2009 suggest a minor contribution
(
Synchronous continental shifts in climate and satellite vegetation
data and links to North Atlantic climate variability. In panel
A remarkable finding is that two key climatic constraints on plant growth
(temperature and precipitation) shifted in the late 1980s in such a way as
to facilitate an abrupt and sustained increase in continental-scale
terrestrial NPP. This bears the question of whether there is an underlying link that
would explain why these large-scale climate patterns varied nearly
synchronously. The Arctic Oscillation (AO) is the most important climate
mode in the northern extratropics (Thompson and Wallace, 1998) and
also a prominent mode in coupled global (Los et al., 2001) and
hemispheric (Buermann et al., 2003) climate and satellite vegetation
greenness data. Consistent with these results, we find that over the
satellite period 1982–2011 the winter AO is tightly correlated with northern
Eurasian spring temperatures (
Northern African wet-season rainfall patterns are strongly influenced by Atlantic sea surface temperature (SST) variability (Hoerling et al., 2006). In this regard, the warming of the North Atlantic relative to the South Atlantic that resumed in the late 1980s to mid-1990s caused a northward displacement of the Atlantic intertropical convergence zone (ITCZ) and increased rainfall rates across northern Africa, which led to a recovery from earlier severe drought conditions (Hoerling et al., 2006). This increased moisture supply also led to rapid increases in satellite fAPAR (Fig. 4b). An open question is to what extent AO/NAO and Atlantic SST forcings may have interacted (Xie and Carton, 2004) in the wake of the apparent coordinated regional climate shifts over northern Eurasia and northern Africa in the late 1980s. It is well established that ENSO (van der Werf et al., 2004) and volcanic eruptions (Lucht et al., 2002) have a dominant influence on the terrestrial carbon cycle at interannual timescales, and much of recent research has focused on associated links (Cox et al., 2013; Wang et al., 2014). Our findings here may suggest that North Atlantic climate variability and corresponding impacts on adjacent vast land masses may be more important in regards to abrupt, substantial and more sustained shifts in the terrestrial carbon cycle.
Our results point to a mechanism whereby North Atlantic climate variability modulates the global terrestrial carbon cycle. New research suggests that a large portion of the variability in the North Atlantic may be externally forced by anthropogenic aerosols (Booth et al., 2012) and the pronounced warming trend in the Arctic regions, known as Arctic amplification (Cohen et al., 2014). Arctic amplification specifically is thought to intensify under climate change (Deser et al., 2010), and this may drive the AO/NAO more into their respective negative phases (Cohen et al., 2014), which, based on our results, would substantially reduce carbon uptake by terrestrial plants and weaken the land carbon sink. This illustrates the pressing need for improved knowledge of North Atlantic climate variability and associated forcing mechanisms in order to more credibly project the evolution of the land carbon sink and carbon cycle climate feedbacks under climate change.
W. Buermann, C. Beaulieu, B. Parida and G. J. Collatz designed the analyses. W. Buermann, C. Beaulieu and B. Parida conducted the analyses. All authors contributed to the writing of the manuscript.
We gratefully acknowledge support for this study from the National Aeronautics and Space Administration Carbon Cycle Science Program (grant NNX11AD45G).
We also thank Emanuel Gloor, Stephen Sitch, John Chiang and Chris Jones as well as two anonymous reviewers for constructive comments that improved the manuscript substantially. Finally, we thank the TRENDY modeling group for making their data available. Edited by: S. Zaehle