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Significance Terrestrial organic-carbon reservoirs (vegetation, soils) currently consume more than a third of anthropogenic carbon emitted to the atmosphere, but the response of this “terrestrial sink” to future climate change is widely debated. Rivers export organic carbon sourced over their watersheds, offering an opportunity to assess controls on land carbon cycling on broad spatial scales. Using radiocarbon ages of biomolecular tracer compounds exported by rivers, we show that temperature and precipitation exert primary controls on biospheric-carbon turnover within river basins. These findings reveal large-scale climate control on soil carbon stocks, and they provide a framework to quantify responses of terrestrial organic-carbon reservoirs to past and future change.
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Climate control on terrestrial biospheric
carbon turnover
Timothy I. Eglinton
, Valier V. Galy
, Jordon D. Hemingway
, Xiaojuan Feng
, Hongyan Bao
Thomas M. Blattmann
, Angela F. Dickens
, Hannah Gies
, Liviu Giosan
, Negar Haghipour
, Pengfei Hou
Maarten Lupker
, Cameron P. McIntyre
, Daniel B. Montluçon
, Bernhard Peucker-Ehrenbrink
, Camilo Ponton
Enno Schefuß
, Melissa S. Schwab
, Britta M. Voss
, Lukas Wacker
, Ying Wu
, and Meixun Zhao
Department of Earth Sciences, ETH Zurich, 8092, Switzerland;
Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic
Institution, Woods Hole, MA 02543;
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138;
State Key Laboratory of
Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;
State Key Laboratory of Estuarine and
Coastal Research, East China Normal University, Shanghai 200062, China;
Department of Geology and Geophysics, Woods Hole Oceanographic Institution,
Woods Hole, MA 02543;
Laboratory for Ion Beam Physics, Department of Physics, ETH Zurich, 8093 Zurich, Switzerland;
Frontiers Science Center for Deep
Ocean Multispheres and Earth System, Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University China, Qingdao
266100, China; and
Center for Marine Environmental Sciences, University of Bremen, Bremen 28359, Germany
Edited by Susan E. Trumbore, Max Planck Institute for Biogeochemistry, Jena, Germany, and approved December 29, 2020 (received for review June 5, 2020)
Terrestrial vegetation and soils hold three times more carbon than
the atmosphere. Much debate concerns how anthropogenic activ-
ity will perturb these surface reservoirs, potentially exacerbating
ongoing changes to the climate system. Uncertainties specifically
persist in extrapolating point-source observations to ecosystem-
scale budgets and fluxes, which require consideration of vertical
and lateral processes on multiple temporal and spatial scales. To
explore controls on organic carbon (OC) turnover at the river basin
scale, we present radiocarbon (
C) ages on two groups of molec-
ular tracers of plant-derived carbonleaf-wax lipids and lignin
phenolsfrom a globally distributed suite of rivers. We find sig-
nificant negative relationships between the
C age of these bio-
markers and mean annual temperature and precipitation. Moreover,
riverine biospheric-carbon ages scale proportionally with basin-
wide soil carbon turnover times and soil
C ages, implicating OC
cycling within soils as a primary control on exported biomarker
ages and revealing a broad distribution of soil OC reactivities.
The ubiquitous occurrence of a long-lived soil OC pool suggests
soil OC is globally vulnerable to perturbations by future tempera-
ture and precipitation increase. Scaling of riverine biospheric-
carbon ages with soil OC turnover shows the former can constrain
the sensitivity of carbon dynamics to environmental controls on
broad spatial scales. Extracting this information from fluvially
dominated sedimentary sequences may inform past variations in
soil OC turnover in response to anthropogenic and/or climate per-
turbations. In turn, monitoring riverine OC composition may help
detect future climate-changeinduced perturbations of soil OC
turnover and stocks.
plant biomarkers
carbon turnover times
fluvial carbon
carbon cycle
Terrestrial biospheric carbon residing in vegetation and soils
may moderate or exacerbate ongoing buildup of atmospheric
greenhouse gases on timescales that are of direct relevance to
humankind (1). Much current debate surrounds the response
and potential contributions of terrestrial ecosystems to climate
change, with large uncertainties concerning the magnitudeand
even the signof change in response to different environmental
forcing factors such as temperature and hydrology (2, 3). Be-
cause of their large organic carbon (OC) stocks and potential to
stabilize carbon on a range of timescales, soils are thought to
regulate overall terrestrial ecosystem carbon storage (4, 5). Glob-
ally, soil carbon turnover time (τ
) (i.e., the ratio of soil carbon
stock to input flux) is estimated via remote sensing approaches
(4, 5) and Earth-system models (3) that are calibrated using
numerous observational and experimental studies investigating
controls on soil OC turnover in a range of ecosystems and soil types
(6, 7). However, findings from such studies are often relevant only
to a specific experiment, plot, or environment, thus hindering
extrapolation and regional validation of remote sensing and model
products (8, 9).
One major reason for this limitation is our lack of constraints
regarding the importance of erosion and lateral transport, de-
spite a growing realization that these processes are pervasive on
diverse landscapes (10) and link terrestrial and aquatic components
Terrestrial organic-carbon reservoirs (vegetation, soils) cur-
rently consume more than a third of anthropogenic carbon
emitted to the atmosphere, but the response of this terrestrial
sinkto future climate change is widely debated. Rivers export
organic carbon sourced over their watersheds, offering an
opportunity to assess controls on land carbon cycling on broad
spatial scales. Using radiocarbon ages of biomolecular tracer
compounds exported by rivers, we show that temperature and
precipitation exert primary controls on biospheric-carbon
turnover within river basins. These findings reveal large-scale
climate control on soil carbon stocks, and they provide a
framework to quantify responses of terrestrial organic-carbon
reservoirs to past and future change.
Author contributions: T.I.E. and V.V.G. designed research; T.I.E., V.V.G., J.D.H., and X.F.
performed research; H.B., T.M.B., A.F.D., H.G., L.G., N.H., P.H., M.L., C.P.M., D.B.M., B.P.-E.,
C.P., E.S., M.S.S., B.M.V., L.W., Y.W., and M.Z. contributed new reagents/analytic tools;
T.I.E., V.V.G., J.D.H., and X.F. analyzed data; and T.I.E., V.V.G., and J.D.H. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDeriv atives Lic ense 4.0 (CC B Y-NC-ND) .
T.I.E. and V.V.G. contributed equally to this work.
To whom correspondence may be addressed. Email: or
Present address : State Key Laborator y of Marine Environmen tal Science, Colle ge of
Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China.
Present address: Biogeochemistry Research Center, Japan Agency for Marine-Earth Sci-
ence and Technology, Yokosuka 237-0061, Japan.
Present address: Wisconsin Department of Natural Resources, Bureau of Air
Management, Madison, WI 53707.
Present address: Accelerator Mass Spectrometry Laboratory, Scottish Universities Envi-
ronmental Research Centre, East Kilbride G75 0QF, United Kingdom.
Present address : Geology Departm ent, Western Washi ngton University, Bellingham,
WA 98225.
Present address: Environmental Assessment Program, Washington State Department of
Ecology, Lacey, WA 98503.
This article contains supporting information online at
Published February 15, 2021.
PNAS 2021 Vol. 118 No. 8 e2011585118
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of the carbon cycle (11). Investigations of lateral transport of
terrestrial biospheric carbon have thus far focused on small
spatial scales (e.g., hillslopes), which do not fully encompass
heterogeneous landscape mosaics (10, 12, 13) and cannot be
readily extrapolated to assess processes relevant at the ecosystem
or biome scale. In particular, there is a paucity of information on
how carbon turnover observed at individual plots relates to
landscape- and basin-scale biospheric carbon dynamics. Depend-
ing on the mode of carbon turnover, mobilization, and transport,
lateral processes can either export freshly synthesized carbon (e.g.,
via surface runoff) or exhume carbon stocks sequestered in deeper
soils and wetlands (14, 15); the relative importance of these pro-
cesses likely exerts a strong influence on carbon stocks and dy-
namics but is currently underrepresented in large-scale models.
Given that climate change, as well as direct anthropogenic per-
turbations (e.g., land-use practices), may potentially modify and
amplify such carbon fluxes and trajectories (3), establishing the
underlying drivers and pacing of carbon cycling on appropriate
spatial and temporal scales is of key importance.
Insight into the factors that control ecosystem-scale carbon
turnover times can be determined using the
C activity (repor-
ted as age in
C years) of OC laterally exported by rivers. Rivers
integrate processes within their watersheds, thus enabling in-
vestigation of biogeochemical processes at the basin scale, fa-
cilitating observational extrapolation, and linking terrestrial and
marine realms. Fluvial systems form the major conduit that
transfers OC from the continents to the ocean, exporting a
combined 4.5 ×10
g of dissolved organic carbon (DOC) and
particulate organic carbon (POC) annually (16, 17). The majority
of this OC exported by rivers is in dissolved form, but much of
this is rapidly mineralized (16). Although POC is also subject to
extensive degradation (16) and riverine POC export is estimated
to account for less than 0.2% of net primary production (NPP,
17), the export of terrestrial biospheric POC and its subsequent
burial in marine sediments is important in modulating atmo-
spheric CO
on a range of timescales (18) and provides some of
the most continuous and long-term records of past climate and
carbon-cycle dynamics on the continents.
Prior studies have examined the nature and magnitude of
carbon transfer via rivers to the ocean (ref. 17 and references
therein) and have shown that soil OC represents a dominant
component of the terrestrial POC exported by many fluvial sys-
tems (19, 20). Strong contrasts in POC yield (i.e., carbon flux per
unit catchment area) and composition relate to geomorphic and
climatic factors influencing mobilization and retention of OC
within drainage basins, as well as the proportions and fluxes of
biospheric versus rock-derived (petrogenic) carbon inputs (14,
21). Bulk OC radiocarbon measurements in both particulate and
dissolved phases reveal a wide variety of
C ages (22); however,
interpretations in terms of biospheric-carbon dynamics are con-
founded by diverse OC contributions (e.g., petrogenic OC; ref.
17 and references therein) and secondary overprinting (within-river
autotrophy and heterotrophy) (22). Moreover, for most modern
river systems, anthropogenic activities influence
C ages through
the introduction of organic contaminants that may be either rel-
atively modern (e.g., domestic sewage) or fossil (e.g., petroleum or
petrochemical contamination) in age (23).
These interferences can be obviated by determining ages of
organic compounds unique to vascular land-plant biomass (24).
To explore controls on the age of terrestrial biospheric carbon
exported from river basins, here we compile previously reported
(n=95) and report additional (n=28)
C age measurements of
source-specific biomarkertracer compounds measured on 36
fluvial systems representing diverse watersheds and collectively
accounting for 42, 29, and 20% of the global riverine water,
sediment, and POC discharge, respectively (Fig. 1 and SI Ap-
pendix, Fig. S1 and Tables S1 and S2) (17). We focus on two well-
established classes of terrestrial higher plant biomarkersplant-
wax lipids (25) and lignin-derived phenols (26). Because of their
hydrophobic nature, plant waxes reside in the particulate
phaseparticularly via association with mineral surfaces (27)
and persist in soils and downstream environments (24). The
abundances, distributions, and stable isotopic compositions (
reported as δ
C) of these compounds preserved in sedimentary
and soil sequences carry information on past vegetation inputs,
plant productivity, and environmental conditions (24). We spe-
cifically analyze n-alkanoic acids (fattyacids [FAs]) since
n-alkanes can be influenced by contributions from bedrock- or
fossil-fuelderived sources that impact corresponding
C ages
(19). Lignin imparts structural support for the plant; phenolic
monomers liberated by chemical hydrolysis of this biopolymer
and its corresponding residues in soils and sediments carry in-
formation on plant and tissue type and extent of degradation
(ref. 26 and references therein). Assessments of lignin stability
and turnover in soils vary (7); however, like plant waxes, lignin
signatures are present in fluvial sediments and deposits (15). We
therefore treat measured
C ages of these two biomarker classes
60º N
60º S
60º N
60º S
180º E180º W
biomarker 14C age
absolute latitude at river mouth (º)
fatty acids
lignin phenols
14C age (yr)
0 5,000 10,000
Fig. 1. Riverine biomarker
C ages. The catchment areas of all rivers ana-
lyzed in this study are color coded by (A) plant-wax fatty-acid and (B) lignin-
C ages (SI Appendix, Table S1). Rivers with catchment areas smaller
than 30,000 km
are shown as colored circles for clarity. The legend above
(A) applies to both panels. (C) Biomarker ages as a function of the absolute
latitude at the river mouth, showing both fatty acids (black circles) and lignin
phenols (white squares).
PNAS Eglinton et al. Climate control on terrestrial biospheric carbon turnover
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as independent yet complementary estimates of the mean age of
fluvially exported biospheric OC.
Results and Discussion
Relationships with Basin Properties. We examined
C and, in se-
lected cases, δ
C variations in bulk OC, lignin phenols, and fatty
acids from fluvial sediment samples (Materials and Methods and
SI Appendix, Tables S1 and S2). Bulk OC
C ages and δ
values in the investigated rivers encompass much of the vari-
ability that has been observed in riverine POC worldwide (SI
Appendix, Fig. S2) (22), indicating that our sample set accurately
captures global trends. Bulk POC ages range from modern to
almost 11,000
C y in our sample set (n=43), whereas bio-
marker ages range from above modern (reflecting incorporation
of nuclear-bombderived
C) to more than 10,000
C y for fatty
acids (n=44) and from above modern to almost 5,000
C y for
lignin phenols (n=16) (SI Appendix, Tables S1 and S2). Ob-
served age offsets between fatty acids and lignin phenols collected
from the same river basins may reflect differences in their degree
of mineral stabilization (28) and/or pathways of mobilization (15).
To identify key parameters governing bulk OC and biomarker
C ages and δ
C values, we performed an ordinary least
squares (OLS) multivariate linear regression (MLR) using a
suite of climatic, geomorphic, and anthropogenic properties as
control variables (Materials and Methods;SI Appendix, Table S3).
This approach implicitly treats results calculated using modern
datasets as representative of control variable values over the
timescales of biomarker
C ages (i.e., centuries to millennia).
Because this assumption does not strictly hold for all control
variables (e.g., anthropogenic land use), correlations calculated
herein may deviate from steady-state results. Many control var-
iables are spatially resolved and must be averaged across each
river basin. However, it has been shown that fluvially exported
POC and, in particular, biomarker isotope signatures exhibit a
downstream bias and do not represent a uniformly integrated
basin signal (29). We therefore weight spatial values by a
factorthe e-folding distancethat decays exponentially with
upstream flow distance from each sampling location (Materials
and Methods). We choose as the optimal e-folding distance the
value that maximizes the fraction of total bulk OC and bio-
marker isotope variance that is explained by our MLR analysis
(average adjusted r
=0.72; SI Appendix, Fig. S3). This optimal
value of 500 km agrees with previous estimates of biomarker
spatial integration in large river basins (29, 30). For all statistical
analyses, spatially resolved control variables are thus weighted by
upstream flow distance with an e-folding distance of 500 km
when calculating catchment averages (SI Appendix, Table S3).
For all bulk and biomarker isotope measurements, MLR-predicted
values show no bias (measured versus predicted slopes are always
statistically identical to unity) and explain the majority of ob-
served sample variance (measured versus predicted adjusted r
always 0.35 and typically 0.64; SI Appendix, Fig. S4), indi-
cating the robustness of the chosen control variables.
Catchment-weighted geomorphic characteristics such as basin
area, relief (mean basin slope), and relative floodplain extent
showed no significant relationship with biomarker age (SI Ap-
pendix, Table S4). Instead, FA and lignin-phenol
C ages are
significantly correlated with climate variableschiefly, catchment-
weighted mean annual temperature (MAT) and precipitation
(MAP) (Fig. 2Aand SI Appendix, Table S4). Globally, biomarker
C ages decrease (become younger) with increasing MAT and
MAP; this phenomenon manifests as a relationship between
C age and latitude because of the strong covariation
of the latter with climate (Fig. 1C). A significant, albeit weaker,
positive correlation is also observed between biomarker
C ages
and the fraction of catchment area that is impacted by anthro-
pogenic land use (i.e., agriculture and urbanization; Fig. 2Aand
SI Appendix, Table S4), suggesting such perturbations might
mobilize old OC that would otherwise be stable under natural
conditions. However, directly ascribing land-usechange impacts
on biomarker
C ages is challenging since the extent of anthro-
pogenically perturbed area additionally exhibits strong covariance
with climate variables, particularly MAT and MAP. Finally,
lignin-phenol and, to a lesser extent, plant-wax fatty-acid
ages display a significant negative correlation with runoff (Fig. 2).
While other variablesfor example, soil properties such as clay
content (31, 32)may contribute to these relationships, their
strength implies that climate constitutes the dominant direct or
indirect driver of biospheric-carbon ages. Bulk OC
play systematically weaker correlations with the set of tested
control variables (e.g., MAT and MAP), as well as with latitude
(SI Appendix, Fig. S5), reflecting the influence of additional OC
sources (e.g., petrogenic carbon, in situ aquatic productivity) on
bulk OC isotopic signatures.
To further assess underlying natural variables controlling bulk
POC and biomarker isotope compositions, we additionally per-
formed a redundancy analysis (RDA) (Materials and Methods).
The results show that two orthogonal axes explain a combined
-4 -2 0 42
RDA1 (35 % variance)
RDA2 (24 % variance)
12 13
FA 14C (i)
lignin 14C (ii)
POC 14C (iii)
FA δ13C (iv)
POC δ13C (v)
runoff (3)
elevation (2)
sample type (1)
log TSS yield (4)
MAT (6)
temp. CV (7)
log MAP (8)
precip. CV (9)
cont. perm. (10)
soil C stock (12)
NPP (13)
τecosystem (14)
τsoil (15)
frac. anthro. (16)
log POC yield (5)
discont. perm. (11)
15 16
correlation coefficient (r)
Fig. 2. Multivariate statistical analysis. (A) Matrix of Pearson correlation coefficients (rvalues) between environmental control variables (x-axis) and POC and
C and δ
C responses (y-axis) (SI Appendix, Table S4). Box sizes and colors correspond to the strength of the correlation (sizes: magnitude only;
colors: magnitude and sign). Correlations that are significant at the P=0.05 level are outlined with a thick, black border. Sample typerefers to the fol-
lowing: suspended sediment, bank/bedload sediment, or shelf-deposit sediment. (B) RDA triplot showing the RDA1 and RDA2 canonical axes (SI Appendix,
Table S5); labels show the percent of total sample variance explained by each axis. Environmental control variable loadings are plotted as gray arrows, POC
and biomarker
C and δ
C response variable loadings are plotted as red arrows, and individual sample scores are plotted as black circles. Environmental and
response variable loadings are scaled for visual clarity. Numbers and roman numerals correspond to control and response variables, respectively, as listed in
(A). Only control variables that are statistically significantly correlated with at least one response variable are included in the analysis (SI Appendix, Table S4).
TSS, total suspended sediment; POC, particulate organic carbon; CV, coefficient of variation; cont. perm., continuous permafrost cover; discont. perm., dis-
continuous permafrost cover; MAP, mean annual precipitation; NPP, net primary production.
Eglinton et al. PNAS
Climate control on terrestrial biospheric carbon turnover
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57% of the total sample variance (Fig. 2Band SI Appendix, Table
S5). The first axis (RDA1) loads closely with catchment-weighted
MAT, MAP, and associated highly correlated variables (e.g.,
permafrost cover, land use), whereas the second axis (RDA2)
loads closely with precipitation seasonality (coefficient of vari-
ability). All
C metrics, particularly fatty-acid
C age, load al-
most exclusively onto RDA1, indicating that they are controlled
most strongly by MAT and MAP (and/or all highly correlated
variables). In contrast, δ
C values of fatty acids and bulk POC,
broadly reflecting biome structure within the basin (25), load
almost exclusively on RDA2, revealing that they are controlled
most strongly by precipitation seasonality (and/or all highly
correlated variables). Specifically, lower δ
C values correspond
to lower seasonal variation in precipitation, as observed in other
regional studies (e.g., 33).
Relationships with τ
and Mean Age. Plant-wax fatty-acid and, to a
lesser extent, lignin-phenol
C ages display positive relationships
with catchment-weighted τ
estimated from remote-sensingderived
soil carbon stocks and NPP (Fig. 3 Aand C). These linear relation-
ships imply the age of fluvially exported biospheric OC reflects soil
carbon turnover at the basin scale, the latter being controlled by
MAT and MAP (4, 5). Still, fatty-acid and lignin-phenol
C ages
are on average 43.6- and 16.6-fold greater than the corresponding
. To further probe these relationships, we additionally con-
sidered recent spatially resolved estimates of soil
C ages (34).
C ages display similar positive relationships with
catchment-weighted soil
C ages integrated from 0 to 100 cm
depth, further demonstrating the strong imprint of soil OC aging
processes on fluvial biomarker radiocarbon ages (Fig. 3 Band D,
Materials and Methods, and SI Appendix, Table S4). Unlike τ
however, fatty-acid and lignin-phenol
C ages are on average
2.5- and 5.0-fold lower than corresponding soil
C ages, sug-
gesting that lateral transport processes do not fully capture older,
deeper soil horizons. This interpretation is supported by corre-
lations between biomarker
C ages and catchment-weighted soil
C ages integrated from 0 to 30 cm depth, in which biomarkers
display similar or slightly older
C ages than corresponding soils,
and integrated from 30 to 100 cm depth, in which soils display
significantly older
C ages than corresponding biomarkers (SI
Appendix, Fig. S6).
Nonetheless, the observed discrepancy between biomarker
ages and τ
contrasts with the much closer agreement between
C ages and soil mean carbon age, regardless of soil
integration depth (Fig. 3 and SI Appendix, Fig. S6). This offset
between both biomarker
C ages and catchment-weighted soil
C ages on one hand and τ
on the other hand likely reflects
the fundamental principles governing organic matter degrada-
tion and aging. Natural organic matter is compositionally het-
erogenous, with age heterogeneity evident even within individual
compound populations (20, 35). Complex interplay between
environmental properties and chemical composition results in
widely variable OC degradation rates (36). We therefore attribute
discrepancies between riverine biospheric-carbon age and τ
a heavy-tailed distribution of OC degradation, as has been hy-
pothesized previously (37). Assuming degradation rate constants
follow a lognormal distribution with given variance (38), the ratio
between mean age and turnover time is proportional to the
natural exponential function of the variance (39). Accepting that
mean ages can be approximated using
C ages (Materials and
0 10050
= 0.65
slope = 40.1 ± 3.9
= 0.47
slope = 24.4 ± 5.2
150 200
0 10,0005,000
= 0.61
slope = 0.62 ± 0.07
= 0.58
slope = 0.34 ± 0.06
soil mean carbon age (yr)
fatty acid
C age (yr)
C age (yr)
Fig. 3. Relationships between τ
, soil mean carbon age, and biomarker
C age. (Aand B) Plant-wax fatty-acid and (Cand D) lignin-phenol
C ages as a
function of weighted-catchment τ
(Left, ref. 5), and soil mean carbon age (0 to 100 cm, Right, ref. 34). Solid and dashed black lines are reduced major-axis
regression lines; reported values are the corresponding reduced major-axis regression slopes and r
values (Materials and Methods). Uncertainty (±1σ)is
always smaller than marker points.
PNAS Eglinton et al. Climate control on terrestrial biospheric carbon turnover
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Methods), the biomarker
C age versus τ
slopes (Fig. 3 Aand
C) provide estimates of the globally averaged ratio between
mean age and turnover time. These ratios correspond to log-
normal degradation rate-constant variances of 3.6 ±0.7 using
fatty acids (μ±1σ,n=44) and 2.7 ±0.8 using lignin phenols
(μ±1σ,n=16), consistent with experimentally determined plant
OC degradation rate-constant variances of 1.0 to 4.0 (38, 39).
The scaling of biomarker
C ages (and soil mean carbon age)
with ecosystem turnover time reveals the heterogenous nature of
organic matter and the associated wide distribution of OC deg-
radation rates. This is particularly relevant in the context of
potential soil OC destabilization upon increases in temperature
and precipitation since OC pools characterized by low decay
rates (i.e., long turnover) are proportionally more vulnerable to
destabilization. Specifically, the theory of temperature-dependent
activation energies predicts that turnover of long-lived OC pools
(characterized by low decay rates) will increase proportionally
more than that of short-lived OC pools (characterized by high
decay rates) for a given increase in temperature or precipitation
(40). As such, the prevalence of wide OC degradation rate dis-
tributions observed here implies that some fraction of the global
soil OC reservoir is characterized by low decay rates and is thus
susceptible to destabilization upon increases in temperature and
precipitationa positive climate feedback.
Interpreting Exported Carbon Ages. Lignin-phenol
C ages, fatty-
C ages, and τ
all exhibit significant negative power-law
relationships with MAT and the logarithm of MAP (Fig. 4). For both
fatty acids and lignin phenols, this implies a 0.4 order-of-magnitude
decrease in mean age for each 10-degree increase in MAT. Inter-
estingly, this power-law exponent is statistically identical for τ
both biomarker classes despite differences in their absolute
ages and the fact that they likely reflect different biospheric-
carbon provenance, transport pathways, and/or degradation
rates within the drainage basin (15). Meanwhile, sediment yield
does not appear to correlate with either τ
or the ages of
biospheric OC exported by rivers (SI Appendix, Fig. S7). This
contrasts with biospheric OC yield (i.e., the annual riverine flux
of biospheric OC normalized to catchment area), which is pri-
marily controlled by erosion rates (17), suggesting a decoupling
between biospheric OC yield and age. Soil carbon turnover is
primarily driven by respiration flux, which is linked to climate
variables (4), whereas the comparatively minor riverine bio-
spheric OC export flux is controlled by geomorphic properties
such as catchment slope and runoff (14).
Overall, our observations suggest that the age of vascular plant
biomarkers exported by rivers echo organic-matter dynamics at
the basin scale and are primarily controlled by τ
. This implies
that basin-scale information on the latter can be gleaned from
C investigations of biospheric-carbon components exported by
rivers and that past changes in ecosystem dynamics in response
to climate and anthropogenic forcing can be deduced using
sedimentary archives (41), although additional preaged carbon
sources such as permafrost or peat deposits must be carefully
considered (16). Furthermore, this study provides a global as-
sessment and mechanistic understanding of biospheric-carbon
age in modern river basins, thus contextualizing any observed
future perturbations in biospheric-carbon turnover. Ongoing
temperature increases and accompanying changes to the hydro-
logical cycle are likely to influence ecosystem turnover times and
vulnerability of previously stable carbon stocks. Potential shifts in
the balance of degradation versus lateral transport of these car-
bon stocks may influence the distribution of carbon in Earths
active reservoirs. In-depth investigations of carbon dynamics
in river basins are needed to assess the large-scale impact of
environmental change on terrestrial ecosystems and the manner
and efficiency by which rivers act as carbon conveyorsor
Materials and Methods
Sample Selection.
Rationale for selection of river systems. We focus on 36 globally distributed river
systems that collectively account for one-third of the global exorheic land
surface and for 42, 29, and 20% of the global riverine water, sediment, and
POC discharge, respectively (SI Appendix, Table S5). The majority of these
rivers have been the subject of prior in-depth biogeochemical studies. The 36
rivers included in this study nearly cover the full range of intrinsic basin
properties (e.g., catchment area, latitude, relief, physical erosion rate, dis-
charge, and POC flux) and are evenly distributed across the continuum of
geomorphic characteristics (SI Appendix, Fig. S1). This ensures that our re-
sults are not biased toward any particular set of basin properties.
Rationale for focus on POC as opposed to DOC. We focus on organic matter that is
associated with fluvial, fluvially derived, or fluvially influenced sediments
either transported as suspended particles or recently deposited near the
terminus of the river system. The translocation and sequestration of POC in
marine sediments is considered to influence atmospheric CO
over millennial
and longer timescales, whereas DOC is efficiently remineralized in coastal
waters (16, 18). This POC leaves a legacy of terrestrial carbon fixation that
can be traced in the sediment record and can be used to reconstruct conti-
nental carbon cycling over a wide range of timescales. Diagnostic bio-
markers of higher plant productivity (including lignin and plant-leaf waxes)
reside in the particulate fraction because of their physiological role (e.g.,
structural polymers) or their hydrophobic nature (e.g., lipids), enabling bio-
spheric carbon to be directly traced from plant source to sedimentary sink.
Sample Congruency. We consider potential complications and variability that
may arise from heterogeneities within the overall sample suite. Because these
measurements result from field and analytical work that span a range of
sampling dates, modes of collection, and settings, we assess potential biases
that may be introduced. Specifically, the samples have been collected over a
period spanning more than three decades, during which the atmospheric
signature, and hence the
C content of coeval produced biomass, has
changed significantly (43). Samples have also been collected in differing
locations with respect to the terminus of the river under different flow
conditions (e.g., high discharge events versus low flow conditions) and also
span a range of sample types (e.g., suspended sediments, river flood de-
posits, offshore depocenters, etc.). Our statistical analysis did not identify
any obvious biases related to the heterogenous nature of the sample set.
Still, below we discuss the influence of a set of individual parameters in the
context of the overall observed variability and trends.
Sample type. Our sample set includes suspended sediments, bed sediments,
recently deposited bank deposits (i.e., flood deposits), and floodplain sedi-
ments, as well as fluvially dominated coastal sediments collected at or close to
-20 -10 0 10 3020 1.2 1.6 2.0 2.4
MAT (ºC) log
MAP (cm yr
C age or τ
Fig. 4. Environmental controls on τ
and biomarker
C ages. Logarithmic
plant-wax fatty-acid
C ages (black circles), lignin-phenol
C ages (white
squares), and catchment τ
[gray triangles (5)] as functions of (A) MAT and
(B) logarithmic MAP (SI Appendix, Tables S1 and S3). Solid black, dashed
black, and gray lines are fatty-acid, lignin-phenol, and τ
-reduced major-
axis regression lines. Relationship slopes and r
values are as follows: (A)
C ages: slope =0.036 ±0.004, r
=0.62; lignin-phenol
C ages:
slope =0.036 ±0.006, r
=0.67; and τ
: slope =0.030 ±0.002, r
(B) Fatty-acid
C ages: slope =1.46 ±0.17, r
=0.55; lignin-phenol
C ages:
slope =1.44 ±0.30, r
=0.56; and τ
: slope =1.13 ±0.11, r
Uncertainty (±1σ) is always smaller than marker points.
Eglinton et al. PNAS
Climate control on terrestrial biospheric carbon turnover
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the river terminus. In general, biospheric OC tends to be concentrated in the
finer grain-size fractions, likely reducing potential impact from sorting
processes. Moreover, where possible, poorly sorted bed and bank/flank
sediments were presieved to concentrate finer-grained (<63 μm) material, as
well as to exclude coarse-grained detritus that may have been locally in-
troduced (e.g., from riverbank erosion) to the river. Nevertheless, given
potential differences in grain size distribution and associated hydrodynamic
characteristics, it is important to assess associated variability in
C charac-
teristics. In the Yellow River, for example, plant-wax
C ages have been
found to vary among grain-size fractions of suspended sediments (44). For
systems for which more than one sample type is available (i.e., Brahmaputra,
Danube, Ganges, and Mississippi), a comparison of
C characteristics reveals
no systematic offsets between sample type. In the Danube River, vertical
water-column profiles of plant-wax δ
C and
C ages showed no systematic
trend with depth (45), suggesting plant waxes are relatively homogenous
within the water column and rather insensitive to hydrodynamic sorting.
This likely results from the association of plant-wax compounds with mineral
surfaces (especially clay minerals) and the uniform distribution of fine-grain
clays in the water column (46, 47). We nonetheless include sample type in
the statistical analysis since it is significantly correlated with lignin
C age
(Fig. 2).
Seasonal and interannual variability. Most rivers included in this study exhibit
large seasonal variations in water and sediment discharge, which has been
linked to variations in the composition of POC (29, 48). Our sampling gen-
erally does not capture seasonal variations in the
C age of biomarkers. The
limited number of investigations of temporal variability in rivers does not
reveal marked seasonal variations in biomarker
C age [e.g., plant waxes in
the Yellow River (49); lignin phenols in the Mekong River (50)]. Furthermore,
in many cases, the samples investigated in this study were collected during
or shortly following maximum discharge, which accounts for much of the
annual sediment and POC load. As such, we argue that overall, our sampling
is not affected by a strong seasonal bias. Interannual variability of riverine
sediment load and bulk POC composition has been reported for several river
systems (51, 52). Our dataset includes samples collected over different years
for several river systems (Brahmaputra, Danube, Ganges, Yangtze, and
Yellow River). Compared to the observed overall range of variability, none
of these rivers shows large interannual variations in fatty-acid
C ages. This
suggest that interannual variations are not a significant driver of the ob-
served trends in biomarker
C ages.
Sample collection date. Samples have been collected between 1976 and 2016, a
period of time over which atmospheric
C, and hence freshly produced
terrestrial plant organic matter, has exhibited a marked decline in response
to the redistribution of bomb
C(from above-ground thermonuclear
weapons testing in the late 1950s and early 1960s) in the earths surface
carbon reservoirs (43, 53). Recently synthesized biospheric carbon then
transmits this bomb
C signal through river basins, inducing time-variable
deviations from natural
C levels. We have examined this potential influ-
ence on observed signals for selected river systems in two different ways: 1)
through comparison of data from samples from the same river system but
collected at different times over the last few decades; and 2) through
analysis of rapidly accumulating and well-dated fluvially influenced sedi-
mentary sequences with well-defined chronologies that span a time interval
encompassing the bomb spike (20, 35). Although the influence of the bomb
C spike clearly manifests itself in plant-wax fatty acids, the induced vari-
ability is small relative to the overall
C variability observed within the
global dataset presented in this study. These muted changes in plant-wax
C ages require the presence of at least two different aged
populationsone reflecting rapid (i.e., within years to decades) transfer of
these tracer molecules from biological source to sedimentary sink and a
second, substantially preaged (i.e., hundreds to several thousand years)
component that implies substantial retention prior to export and deposition.
In both of the above studies, model results suggest that the majority (49 to
83%) of the plant-wax signal derives from a preaged pool that likely cor-
responds to a mineral-bound soil OC component. Hence, sensitivity to bomb
C is relatively small, and the date of sampling does not impart a large bias
on our results.
Anthropogenic influences. A key consideration in any study of modern rivers is
the extent to which modern observations of fluvial-sediment flux and
composition have been impacted by anthropogenic activity, both within the
catchment area (e.g., land-use change) and on the hydrological network
(e.g., damming, channelization, etc.). As a proxy for anthropogenic influ-
ences, here we include in our statistical analysis the fraction of each river
basin that is impacted by anthropogenic land-use change (i.e., agriculture
and urbanization) (SI Appendix, Table S3). This approach implicitly assumes
the following: 1) Land use is an accurate proxy for all anthropogenic
disturbances, and 2) modern land-use change extent is representative of
perturbations over the timescale of biomarker
C ages (i.e., centuries to
millennia). Neither of these assumptions is strictly true in all studied river
basins. For example, several of the rivers included in the current study are
greatly impacted by recent human activity, particularly since the industrial
era (e.g., Yellow, Yangtze, and Danube) (49, 51). In contrast, other fluvial
systems have been subject to human modification over millennia (e.g.,
Godavari) (54). Nonetheless, our statistical correlation results indicate that
C ages are more strongly controlled by climate as opposed to
anthropogenic variables (SI Appendix, Table S4), suggesting that differences
in the type and duration of anthropogenic disturbance likely impart only a
small impact on resulting biomarker
C ages.
Sample Collection and Processing. Sample collection.
Samples include suspended sediments obtained via filtration of river
water, riverbed sediments sampled via a grab or bedload sampler, recently
deposited river flank and floodplain sediments, and fluvially influenced
coastal sediments deposited proximal to the mouth of the river. For river
flank, floodplain, and coastal sediments, emphasis was placed on sampling
deposits where fine-grained sediments accumulate, such as quiescent areas
characterized by weak river and coastal currents.
Sample processing and analysis procedures vary somewhat between field
campaigns. Detailed procedures are provided in previous publications and are
briefly summarized below. In general, exposed or slightly submerged river-
bank/flood-deposit sediments were collected using a shovel, whereas river-
bed samples used a bedload sampler (55), and suspended river samples were
obtained by large-volume filtration (typically 100 L) over either poly-
ethersulfone membrane filters or precombusted glass fiber filters (48, 55).
Samples were then stored, refrigerated, or frozen before freeze-drying in
the laboratory. For selected samples (mostly floodplain materials), the sed-
iment was wet sieved to <63 μm to remove large fragments that may have
been directly introduced (e.g., via riverbank erosion). The <63-μm fraction is
also considered to more closely resemble the riverine suspended load owing
to its overall smaller grain size compared to deposited sediments (45, 56, 57).
Sample aliquots were processed for the content, stable carbon isotopic
composition, and radiocarbon content of bulk OC. Carbonates were removed
using the acid-fumigation method (58) prior to bulk OC analysis. OC content
and stable isotope composition were measured via elemental analyzer
isotope ratio mass spectrometry (IRMS). Bulk OC radiocarbon analysis was
performed either at the National Ocean Science Accelerated Mass Spec-
trometry (NOSAMS) facility (Woods Hole Oceanographic Inst. [WHOI]) or at
the Laboratory of Ion Beam Physics (LIP) using established procedures
Plant-wax lipids were extracted from freeze-dried sample aliquots using a
mixture of dichloromethane and methanol. The lipid extract was subse-
quently treated to obtain a fatty-acid fraction, and the latter was derivat-
ized to obtain fatty-acid methyl esters (FAMEs). The FAMEs were further
purified via column chromatography and ultimately isolated by preparative
capillary gas chromatography (62). Isolated compounds were subsequently
analyzed by accelerator mass spectrometry (AMS) (
C contents) and gas
chromatography (GC)-IRMS (δ
C). Resulting
C data are corrected for any
derivative carbon as well as methodological and instrumental blanks (see
below) and reported using the fraction modern (Fm) notation and as ra-
diocarbon ages (63). We report abundance-weighted average
C composi-
tions of long-chain FA homologs in the 24 to 32 carbon number range. In
some cases, sample availability and/or technical issues led to averaging over
a subset of homologs within this carbon number range. For coastal sedi-
ments that may have additional sources of midchain FA homologs (e.g., ref.
35), we used corresponding δ
C values from GC-IRMS to select homologs
that are exclusively derived from terrestrial vascular plants.
Lignin-derived phenols were recovered by alkaline oxidative hydrolysis
(CuO oxidation) of freeze-dried or solvent-extracted sediments (64). Result-
ing hydrolysis products were then purified via high-performance liquid
chromatography (50, 64) and subsequently measured for
C content
by AMS.
C data corrections. Compound-specific radiocarbon data were
corrected using mass-balance equations for procedural blanks and, in the
case of fatty acids, for the addition of derivative carbon during methylation
with methanol with a known
C composition. Procedural blanks varied
depending on the instrumental set up used (e.g., at WHOI versus at ETH) and
the measurement date, as these methods have been continuously refined
over the course of this study. Overall, procedural blanks were in the range of
1to2μg C, with compositions intermediate between modern and dead.
Details of blank assessments and corrections can be found in Santos et al.
(65), French et al. (20), Haghipour et al. (66), and Feng et al. (64). The
PNAS Eglinton et al. Climate control on terrestrial biospheric carbon turnover
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uncertainty related to blank corrections varies from sample to sample, pri-
marily according to sample size and
C age. Overall, these uncertainties
remain below 0.05 Fm units and are therefore small compared to the vari-
ability observed across our entire dataset.
Geospatial Analysis. All geospatial analyses were performed in ArcGIS version
10 (ESRI Corporation). River basin outlines were calculated either upstream of
each sampling point (for suspended-sediment and bank-sediment samples)
or at the river mouth (for shelf-deposit samples), and environmental control
variables were calculated using the following data sources: 1) Elevation,
slope, and floodplain fractional area (here defined as area with slope less
than 1% rise) were calculated using the United States Geological Survey
(USGS) GTOPO30 digital elevation model with 30-arc-second resolution
[1 km at the equator (67)]. 2) Catchment-averaged MAT, temperature sea-
sonal coefficient of variability (TCV), MAP, and precipitation seasonal coef-
ficient of variability (PCV) were calculated from the raster data set of New
et al. (68) with 10resolution. 3) τ
and NPP were calculated from the raster
data set of Bloom et al. (5) with 1° resolution. 4) Total ecosystem carbon
turnover time (ecosystem τ) and soil carbon stocks were calculated from the
raster data set of Carvalhais et al. (4) with 0.5° resolution. 5) Anthropogenic
land-use fractional area was calculated as the sum of croplands,”“urban
and built-up,and cropland/natural vegetation mosaicpixels (Interna-
tional Geosphere Biosphere Program classification) extracted from the USGS
Global Land Cover Characterization raster data set version 2.0 with
30-arc-second resolution [1 km at the equator (69)]. 6) Soil carbon
C ages
(0 to 30 cm, 30 to 100 cm, and 0 to 100 cm depth integrated) were calculated
from the raster data set of Shi et al. (34) with 0.5° resolution.
All other environmental control variables were compiled from the liter-
ature (15, 17, 70). When comparing biomarker
C ages and soil turnover
times, we implicitly assume that the soil turnover times determined using
estimates of soil C stocks and NPP are representative of the average turnover
time over the timescales reflected in the biomarker
C ages (centuries to
millennia). While short-term small variations in NPP have likely occurred over
these timescales and regional-scale climate variability during the late Holo-
cene locally impacted soil carbon stocks (71), this relatively quiescent time
interval is unlikely to have seen large, globally coherent variations in soil
carbon stocks. As such, temporal variations in τ
are unlikely to contribute
significantly to the systematic difference observed between τ
and bio-
C ages (i.e., since plant-wax fatty-acid and lignin-phenol
on average 43.6- and 16.6-fold greater, respectively, than corresponding τ
For spatially resolved control variables (i.e., elevation, slope, floodplain
fraction, MAT, TCV, MAP, PCV, τ
, NPP, ecosystem τ, and soil C stocks),
weighting factors were calculated using hydrologically conditioned versions
of the GTOPO30 digital elevation model [i.e., Hydro1k and HydroSHEDS (72,
73)]. Hydrologic distance upstream of each sampling location was calculated
using the Flow Lengthfeature in ArcGIS and was weighted as an
exponential decay following
where wis the weighting factor (ranging from 0 to 1), lis the upstream
distance, and kis the reciprocal of the prescribed e-folding distance. All
spatially resolved control variables were taken as the weighted-catchment
Statistical Analyses.
Multiple linear regression. Regression analyses were performed using the
Numpy and Scipy packages in Python version 3.5; all analysis code is provided
in Dataset S1. To determine the strength of relationships between envi-
ronmental control variables and biomarker and POC response variables, OLS
MLR was performed following standard practices (74). In summary, all data
were first whitenedby subtracting the mean and dividing by the SD for
each variable in order to facilitate comparisons between variables with
differing units and ranges. The regression parameter matrix, B, was then
calculated as
where Xis the whitened matrix of environmental control variables, and Yis
the whitened matrix of biomarker and POC response variables. The matrix of
MLR-predicted response variable best estimates,
Y, was then calculated as
and the matrix of residuals was calculated as
Y. [4]
The matrix of correlation strengths between Xand Y,termedS
then calculated following
where nis the number of samples in the data set, and correlation Pvalues
were calculated individually for each control and response variable pair us-
ing standard OLS methods (74). Reported MLR r
values are the square of the
diagonal elements of S
. The optimal e-folding kvalue was calculated using
an inverse approach by repeating Eqs. 15and choosing the value that
maximizes the average resulting r
value (SI Appendix, Fig. S3). Because
scatterplot variables presented in this study generally contain uncertainty in
both xand yvariables (e.g., Figs. 3 and 4), predictive relationships were
calculated using reduced major-axis regression (75). Note that some envi-
ronmental control variables, and particularly biomarker response variables,
contained mis sing data entries; missing data were ignored when per-
forming statistical calculations by utilizing Numpy maskedarrays (ref-
erence Dataset S1 for details).
Finally, we test if and how averaging all samples of a given sample type
(i.e., suspended sediment, bedload sediment, or shelf/slope sediment) within
a given river basin impacts the results of our statistical analysis. We repeated
Eqs. 15using either catchment averagedor all reported samplesas the
response variable dataset (i.e., SI Appendix, Tables S1 and S2, respectively).
Resulting correlation coefficients exhibit only small differences and are
largely independent of our choice of response variable dataset (SI Appendix,
Fig. S8). We therefore use the catchment averaged dataset for all subse-
quent calculations as this avoids potential biases that could arise because of
uneven sampling density between river basins.
Redundancy analysis. To extract the canonical axes and to determine the
loadings of each sample, environmental control variable, and biomarker and
POC response variable onto each canonical axis, RDA was performed fol-
lowing Legendre and Legendre (76) using the Numpy and Scipy packages in
Python version 3.5. All analysis code is provided in Dataset S1. RDA is a ca-
nonical extension of MLR that transforms all control variables into orthog-
onal (linearly uncorrelated) axes and determines the fraction of sample
variance explained by each orthogonal axis; it is ideally suited for situations
with highly correlated control variables, as is the case here (76). Heuristically,
RDA extracts the principal components of response variables as predicted by
control variables; that is, it calculates the amount of variance within the
response variables that can be explained by the set of control variables and
maps all variables onto a set of underlying orthogonal axes. Briefly, RDA
involves independently performing principal component analyses on
Y, the
matrix of MLR-predicted response variables, and on Y
, the matrix of resid-
uals. Analogous to Eq. 5, the correlation matrix between MLR-predicted
response variables is first calculated as
Y. [6]
Then, the following eigenvalue problem is solved:
uj=0, [7]
where λ
is the jth canonical eigenvalue, u
is the jth canonical eigenvector,
and Iis the identity matrix; the response variable loadings onto the jth ca-
nonical axis (termed species scores) are thus the entries of u
. Eqs. 6and 7
are then repeated using Y
instead of
Y to calculate the noncanonical ei-
genvalues and eigenvectors. The percentage of response variable variance
explained by each axis is calculated as λ
divided by the sum of all (canonical
plus noncanonical) eigenvalues. Finally, constrained sample loadings (con-
strained site scores) onto each canonical axis, termed Z, are calculated as
and control variable loadings (constraining variable scores) onto each
canonical axis, termed C, are calculated as
where Uis the column-wise matrix of u
eigenvectors. Species scores, con-
strained site scores, and constraining variable scores for any two canonical
axes can then be plotted as in Fig. 2B; the angles between species scores and
constraining variable scores thus represent the strength of their linear cor-
relation. Reference Dataset S1 for further details and analyses.
Eglinton et al. PNAS
Climate control on terrestrial biospheric carbon turnover
Downloaded by guest on February 20, 2021
Data Availability. All study data are included in the article and/or supporting
ACKNOWLEDGMENTS. We thank R. Spencer and R.M. Holmes (Colville);
F. Filip (Danube); A. Winter, E. Tinacba, and F. Siringan (Cagayan); P. Cai and
H. Zhang (Pearl); J. Zhang (Yangtze); S.-L. Wang and L.-H. Chung (Gaoping);
K. Hughen (Unare); N. Blair (Waiapu); S. Marsh and S. Gillies (Fraser);
D. Eisma (Congo); and T. Kenna (Ob) for sample collection and fieldwork
assistance or for provision of samples. We are very grateful to A. McNichol
and other members of NOSAMS (WHOI) and to H.-A. Synal of LIP for expert
advice and technical assistance. We thank C. Johnson (WHOI) and M. Jäggi
(ETH) for measurements of bulk elemental and stable isotopic data. We
thank Z. Shi (University of California, Irvine) for soil mean age data. This
work was supported by grants from the US NSF (OCE-0928582 to T.I.E. and
V.V.G.; OCE-0851015 to B.P.-E., T.I.E., and V.V.G.; and EAR-1226818 to B.P.-E.),
Swiss National Science Foundation (200021_140850, 200020_163162, and
200020_184865 to T.I.E.), and National Natural Science Foundation of China
(41520104009 to M.Z.).
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Eglinton et al. PNAS
Climate control on terrestrial biospheric carbon turnover
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... Terrestrial ecosystem turnover is largely modulated by soils, which constitute the largest continental carbon pool [7], and evidence suggests that the majority of terrestrial biospheric carbon exported by rivers is derived from erosion of mineral soils (e.g. [8,9]). Current models increasingly place microbially mediated processes as key drivers of soil carbon turnover and stabilization (e.g. ...
... hundred grams of riverine sediment are typically needed to obtain sufficient quantities of GDGTs for 14 C measurement) and on the availability of reference data of other biomarkers, either for the same sample or close within the same catchment. Eight sediment samples were sieved to less than 63 µm and prepared for GDGT radiocarbon analysis: riverbed sediment from the Amazon [28,29], Gaoping and Kagayan [9] rivers; suspended sediment from the Koshi [30], Pearl, Earn, Dul [9]; and Arctic Red [31] rivers; and a floodplain deposit from the Danube River [32]. ...
... hundred grams of riverine sediment are typically needed to obtain sufficient quantities of GDGTs for 14 C measurement) and on the availability of reference data of other biomarkers, either for the same sample or close within the same catchment. Eight sediment samples were sieved to less than 63 µm and prepared for GDGT radiocarbon analysis: riverbed sediment from the Amazon [28,29], Gaoping and Kagayan [9] rivers; suspended sediment from the Koshi [30], Pearl, Earn, Dul [9]; and Arctic Red [31] rivers; and a floodplain deposit from the Danube River [32]. ...
Full-text available
Compound- and compound class-specific radiocarbon analysis of source-diagnostic ‘biomarker’ molecules has emerged as a powerful tool to gain insights into terrestrial carbon cycling. While most studies thus far have focused on higher plant biomarkers (i.e. plant leaf-wax n-alkanoic acids and n-alkanes, lignin-derived phenols), tracing paedogenic carbon is crucial given the pivotal role of soils in modulating ecosystem carbon turnover and organic carbon (OC) export. Here, we determine the radiocarbon (¹⁴C) ages of glycerol dialkyl glycerol tetraethers (GDGTs) in riverine sediments and compare them to those of higher plant biomarkers as well as markers of pyrogenic (fire-derived) carbon (benzene polycarboxylic acids, BPCAs) to assess their potential as tracers of soil turnover and export. GDGT Δ¹⁴C follows similar relationships with basin properties as vegetation-derived lignin phenols and leaf-wax n-alkanoic acids, suggesting that the radiocarbon ages of these compounds are significantly impacted by intermittent soil storage. Systematic radiocarbon age offsets are observable between the studied biomarkers, which are likely caused by different mobilization pathways and/or stabilization by mineral association. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'.
... Previous studies have only investigated how τ TO and τ age independently vary with temperature or across biome types (Davidson & Janssens, 2006;Koven et al., 2017;Shi et al., 2020;Varney et al., 2020;Xiao et al., 2022), and not with water availability. In fact, while previous work has revealed the significant effects of precipitation on the turnover of ecosystem C, which lumps together aboveand below-ground C (Carvalhais et al., 2014;Eglinton et al., 2021;Fan et al., 2022), its effects specifically on the turnover of soil C, the ecosystem's largest C pool, remain to be explored. A global analysis of how τ TO and τ age -and in particular their ratio-are fundamentally controlled by key climatic variables (AI and temperature) is thus needed to identify emergent climatic constraints on soil C processes. ...
... Although previous studies have shown that precipitation may be an important driver of the turnover of ecosystem C that is, the sum of both above-and below-ground C (Carvalhais et al., 2014;Eglinton et al., 2021;Fan et al., 2022), little is known about whether and to what extent water availability regulates soil C turnover specifically. Interestingly, recent work identified a self-similar scaling of global C stocks with the AI (Yin & Porporato, 2023), serving as further evidence of the primary role of hydrology. ...
Full-text available
Climate plays a critical role in altering soil carbon (C) turnover and long‐term soil C storage by regulating water availability and temperature, and in turn biological activity. However, a systematic analysis of how key climatic factors shape the global patterns of soil C turnover is still lacking. Using global observation‐based data sets and a transit time theory, here we show that—excluding croplands and cold regions—soil C turnover time (τTO) and its variability are strongly related to ecosystem aridity through a power law scaling. According to such a relation, soil C turnover is faster but also more variable in wetter regions, suggesting more complex C cycling processes. The observed scaling of τTO and its coefficient of variation with aridity underlines the fundamental controls of climate on soil C turnover and may help reconcile soil C models with empirical observations for improved projection of soil C dynamics under climate change.
... The rates of organic matter burial in deep sediment can influence its preservation and degradation, its potential to yield hydrocarbons, and the amount of CO 2 it releases to the surface waters and eventually the atmosphere. An increase in the velocity of this transport mechanism will have a direct influence on the locking conditions for carbon and its sequestration in sediments (Bianchi et al., 2018;Blair and Aller, 2012;Eglinton et al., 2021;Regnier et al., 2022) . ...
... ± 0.004; log 10 ( 14 C age) versus log 10 (MAP), (slope = −1.46; Eglinton et al., 2021). Using these regressions, we estimate the magnitude of changes in 14 C ages of long-chain FAs that could be directly linked to the changes in MAT and MAP. ...
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Human activities have increasingly changed terrestrial particulate organic carbon (POC) export to the coastal ocean since the Industrial Age (19th century). However, the influence of human perturbations on the composition and flux of terrestrial biospheric and petrogenic POC sub‐pools remains poorly constrained. Here, we examined ¹³C and ¹⁴C compositions of bulk POC and source‐specific biomarkers (fatty acids, FA) from two nearshore sediment cores collected in the Pearl River‐derived mudbelt, to determine the impacts of human perturbations of the Pearl River watershed on the burial of terrestrial POC in the coastal ocean over the last century. Our results show that although agricultural practices and deforestation during the 1930s–1950s increased C4 plant coverage in the watershed, the export fluxes of terrestrial biospheric and petrogenic POC remained rather unchanged; however, added perturbations since 1974, including increasing coal consumption, embankment and dam constructions caused massive export of both petrogenic POC and relatively fresh terrestrial biospheric POC from the river delta. Our data reveal that human activities substantially enhance the transfer of petrogenic POC and fresh biospheric POC to the coastal ocean after ca. 1974, with the latter process acting as an important sink for anthropogenic CO2.
... Thus, a clear picture has been achieved on understanding budgets of OC originating from various OC pools (Hilton and West, 2020). Researchers also attempted to estimate OC lability based on OC sources (Blattmann et al., 2019;Eglinton et al., 2021). Nonetheless, this methodology provides limited instructions on evaluating the long-term fate of OC, either preservation or degradation. ...
Full-text available
Continental shelves host 90% of annual organic carbon (OC) deposition in the global ocean and are regarded as "hot spots" of carbon burial and decomposition. Numerous studies have thus investigated OC sources, recent accumulation, long term preservation and key processes involved. Nonetheless, OC reactivity or lability, as a key property governing the fate of OC in the long term, received less attention, primarily due to a lack of proper technique of investigation. In this study, we conducted thermochemical decomposition analysis of OC using ramped-temperature pyrolysis/oxidation technique to investigate the reactivity of sedimentary OC along the Yangtze River estuary-shelf continuum. Our results reveal that sedimentary OC in the Yangtze River estuary-shelf region is relatively more stable than global average level, which is attributed to the winnowing of sediments due to frequent sedimentation-resuspension cycles. In general, OC reactivity increases gradually from the estuary to the inner shelf, which is governed by organo-mineral interactions and the progressive absorption of marine OC. Based on our results, we propose that OC reactivity is a key OC property to be considered in future organic carbon cycle frameworks.
... A variety of geochemical approaches have been employed to elucidate the composition and fate of sedimentary OC in continental margin sediments. These include the abundance and distribution of specific biomarker compounds (including plant wax n-alkanes, n-fatty acids, and phytoplankton sterols), carbon isotopic signatures (δ 13 C and Δ 14 C) of bulk OC and source-specific biomarkers (Burdige, 2005;Drenzek et al., 2007;Eglinton et al., 2021;Feng et al., 2013;Griffith et al., 2010;Hilton et al., 2015;Wei et al., 2020Wei et al., , 2021Wu et al., 2013;Xing et al., 2014;Yu et al., 2021). Such studies have been applied to river-marginal sea systems in order to characterize and quantify the sources and ages of sedimentary OC. ...
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Lateral carbon transport through the land-to-ocean-aquatic-continuum (LOAC) represents a key component of the global carbon cycle. This LOAC involves complex processes, many of which are prone to anthropogenic perturbation, yet the influence of natural and human-induced drivers remains poorly constrained. This study examines the radiocarbon (¹⁴C) signatures of particulate and dissolved organic carbon (POC, DOC) and dissolved inorganic carbon (DIC) transported by Swiss rivers to assess controls on sources and cycling of carbon within their watersheds. Twenty-one rivers were selected and sampled during high-flow conditions in summer 2021, a year of exceptionally high rainfall. Δ¹⁴C values of POC range from −446‰ to −158‰, while corresponding ranges of Δ¹⁴C values for DOC and DIC are −377‰ to −43‰ and −301‰ to −40‰, respectively, indicating the prevalence of pre-aged carbon. Region-specific agricultural practices seem to have an influential effect on all three carbon phases in rivers draining the Swiss Plateau. Based on Multivariate Regression Analysis, mean basin elevation correlated negatively with Δ¹⁴C values of all three carbon phases. These contrasts between alpine terrain and the lowlands reflect the importance of overriding ecoregional controls on riverine carbon dynamics within Switzerland, despite high spatial variability in catchment properties. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'.
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Marine sediments play a crucial role in the global carbon cycle by acting as the ultimate sink of both terrestrial and marine organic carbon. To understand the spatiotemporal variability in the content, sources, and dynamics of organic carbon in marine sediments, a curated and harmonized database of organic carbon and associated parameters is needed, which has prompted the development of the Modern Ocean Sediment Archive and Inventory of Carbon (MOSAIC) database (, last access: 26 July 2023;, Paradis, 2023;, Van der Voort et al., 2019 ). MOSAIC version 2.0 has expanded the spatiotemporal coverage of the original database by >400 % and now holds data from more than 21 000 individual sediment cores from different continental margins on a global scale. Additional variables have also been incorporated into MOSAIC v.2.0 that are crucial to interpret the quantity, origin, and age of organic carbon in marine sediments globally. Sedimentological parameters (e.g. grain size fractions and mineral surface area) help understand the effect of hydrodynamic sorting and mineral protection on the distribution of organic carbon, while molecular biomarker signatures (e.g. lignin phenols, fatty acids, and alkanes) can help constrain the specific origin of organic matter. MOSAIC v.2.0 also stores data on specific sediment and molecular fractions, which provide further insight into the processes that affect the degradation and ageing of organic carbon in marine sediments. Data included within MOSAIC are continuously expanding, and version control will allow users to benefit from updated versions while ensuring reproducibility of their findings.
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Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil ∆14C and mean age. We found that soil depth is a primary driver of ∆14C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of ∆14C in surface soil. Integrated to a depth of 1 m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate ∆14C values that imply younger carbon ages and a more rapid carbon turnover. Our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils. Soils may accumulate less carbon and with a slower turnover than Earth system models predict, according to analysis of the age distribution of global soil carbon, which finds that the mean age of soil carbon is older than that in simulated in models.
Full-text available
The storage of organic carbon in the terrestrial biosphere directly affects atmospheric concentrations of carbon dioxide over a wide range of timescales. Within the terrestrial biosphere, the magnitude of carbon storage can vary in response to environmental perturbations such as changing temperature or hydroclimate¹, potentially generating feedback on the atmospheric inventory of carbon dioxide. Although temperature controls the storage of soil organic carbon at mid and high latitudes2,3, hydroclimate may be the dominant driver of soil carbon persistence in the tropics4,5; however, the sensitivity of tropical soil carbon turnover to large-scale hydroclimate variability remains poorly understood. Here we show that changes in Indian Summer Monsoon rainfall have controlled the residence time of soil carbon in the Ganges–Brahmaputra basin over the past 18,000 years. Comparison of radiocarbon ages of bulk organic carbon and terrestrial higher-plant biomarkers with co-located palaeohydrological records⁶ reveals a negative relationship between monsoon rainfall and soil organic carbon stocks on a millennial timescale. Across the deglaciation period, a depletion of basin-wide soil carbon stocks was triggered by increasing rainfall and associated enhanced soil respiration rates. Our results suggest that future hydroclimate changes in tropical regions are likely to accelerate soil carbon destabilization, further increasing atmospheric carbon dioxide concentrations.
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Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at (Friedlingstein et al., 2019).
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Soil organic carbon (SOC) in the subsoil below 0.3 m accounts for the majority of total SOC and may be as sensitive to climate change as topsoil SOC. Here we map global SOC turnover times (τ) in the subsoil layer at 1 km resolution using observational databases. Global mean τ is estimated to be [Formula: see text] yr (mean with 95% confidence interval), and deserts and tundra show the shortest ([Formula: see text] yr) and longest ([Formula: see text] yr) τ respectively. Across the globe, mean τ ranges from 9 (the 5% quantile) to 6332 years (the 95% quantile). Temperature is the most important factor negatively affecting τ, but the overall effect of climate (including temperature and precipitation) is secondary compared with the overall effect of assessed soil properties (e.g., soil texture and pH). The high-resolution mapping of τ and the quantification of its controls provide a benchmark for diagnosing subsoil SOC dynamics under climate change.
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The balance between photosynthetic organic carbon production and respiration controls atmospheric composition and climate1,2. The majority of organic carbon is respired back to carbon dioxide in the biosphere, but a small fraction escapes remineralization and is preserved over geological timescales³. By removing reduced carbon from Earth’s surface, this sequestration process promotes atmospheric oxygen accumulation² and carbon dioxide removal¹. Two major mechanisms have been proposed to explain organic carbon preservation: selective preservation of biochemically unreactive compounds4,5 and protection resulting from interactions with a mineral matrix6,7. Although both mechanisms can operate across a range of environments and timescales, their global relative importance on 1,000-year to 100,000-year timescales remains uncertain⁴. Here we present a global dataset of the distributions of organic carbon activation energy and corresponding radiocarbon ages in soils, sediments and dissolved organic carbon. We find that activation energy distributions broaden over time in all mineral-containing samples. This result requires increasing bond-strength diversity, consistent with the formation of organo-mineral bonds⁸ but inconsistent with selective preservation. Radiocarbon ages further reveal that high-energy, mineral-bound organic carbon persists for millennia relative to low-energy, unbound organic carbon. Our results provide globally coherent evidence for the proposed⁷ importance of mineral protection in promoting organic carbon preservation. We suggest that similar studies of bond-strength diversity in ancient sediments may reveal how and why organic carbon preservation—and thus atmospheric composition and climate—has varied over geological time.
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Developing and testing decadal‐scale predictions of soil response to climate change is difficult because there are few long‐term warming experiments or other direct observations of temperature response. As a result, spatial variation in temperature is often used to characterize the influence of temperature on soil organic carbon (SOC) stocks under current and warmer temperatures. This approach assumes that the decadal‐scale response of SOC to warming is similar to the relationship between temperature and SOC stocks across sites that are at quasi steady state; however, this assumption is poorly tested. We developed four variants of a Reaction‐network‐based model of soil organic matter and microbes using measured SOC stocks from a 4,000‐km latitudinal transect. Each variant reflects different assumptions about the temperature sensitivities of microbial activity and mineral sorption. All four model variants predicted the same response of SOC to temperature at steady state, but different projections of transient warming responses. The relative importance of Qmax, mean annual temperature, and net primary production, assessed using a machine‐learning algorithm, changed depending on warming duration. When mineral sorption was temperature sensitive, the predicted average change in SOC after 100 years of 5 °C warming was −18% if warming decreased sorption or +9% if warming increased sorption. When microbial activity was temperature sensitive but mineral sorption was not, average site‐level SOC loss was 5%. We conclude that spatial climate gradients of SOC stocks are insufficient to constrain the transient response; measurements that distinguish process controls and/or observations from long‐term warming experiments, especially mineral fractions, are needed.
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Soils contain more carbon than plants or the atmosphere, and sensitivities of soil organic carbon (SOC) stocks to changing climate and plant productivity are a major uncertainty in global carbon cycle projections. Despite a consensus that microbial degradation and mineral stabilization processes control SOC cycling, no systematic synthesis of long-term warming and litter addition experiments has been used to test process-based microbe-mineral SOC models. We explored SOC responses to warming and increased carbon inputs using a synthesis of 147 field manipulation experiments and five SOC models with different representations of microbial and mineral processes. Model projections diverged but encompassed a similar range of variability as the experimental results. Experimental measurements were insufficient to eliminate or validate individual model outcomes. While all models projected that CO2 efflux would increase and SOC stocks would decline under warming, nearly one-third of experiments observed decreases in CO2 flux and nearly half of experiments observed increases in SOC stocks under warming. Long-term measurements of C inputs to soil and their changes under warming are needed to reconcile modeled and observed patterns. Measurements separating the responses of mineral-protected and unprotected SOC fractions in manipulation experiments are needed to address key uncertainties in microbial degradation and mineral stabilization mechanisms. Integrating models with experimental design will allow targeting of these uncertainties and help to reconcile divergence among models to produce more confident projections of SOC responses to global changes.
Hydrodynamic sorting has been shown to strongly influence the composition and age of organic carbon (OC) during sediment transport and burial in the marine environment, yet sorting effects on terrestrial OC (OCterr) in fluvial systems remain poorly understood. We conducted size fractionation of suspended particle samples from the lower Yellow River, China, and examined variations in mass distribution and carbon isotopic (δ¹³C and Δ¹⁴C) composition of bulk OC and specific biomarkers among grain size fractions in order to investigate the influence of hydrodynamic sorting and selective transport on organic matter export. In general, the 16–32 μm and 32–63 μm fractions contributed the most of sediment mass while the majority of the OC resided in the 16–32 μm fraction. Over 80% of OC and n-fatty acids (FAs) were concentrated in <32 μm fractions. Significant differences in OC%, surface area (SA), Δ¹⁴COC, n-FAs contents, and compound-specific ¹³C and ¹⁴C compositions were found among size fractions. Of particular note was a progressive decrease of Δ¹⁴C values (i.e., increase in ¹⁴C age) of long-chain (C26+28+30) FAs with decreasing grain size. Taken together, the bulk and molecular characteristics imply two distinct types of selective OCterr transport in the Yellow River. Coarser particles (>32 μm), characterized by relatively low SA, OC%, and Δ¹⁴COC values, but higher Δ¹⁴C values of C26+28+30 FAs, are inferred to reflect a combination of bedrock-derived detrital sediment and fresh vascular-plant material (e.g., plant fragments). In contrast, finer particles (<32 μm), exhibiting higher SA, OC%, and lower Δ¹⁴C26+28+30FAs values, reflect preferential transport of pre-aged, mineral soil-derived OC that is susceptible to repeated mobilization, as well as widespread dispersal in marginal seas. The latter, once buried in marine sediments, could account for the high burial efficiency of OCterr in the adjacent Bohai Sea and Yellow Sea. Thus, hydrodynamic sorting processes induce heterogeneity of composition and selective transport of OC. Bulk and molecular ¹⁴C measurements of size-fractionated particles facilitate both elucidation of these processes and assessment of their impact on OC cycling in (and export from) rivers.
Natural and human-induced hydrological changes can influence OC composition in fluvial systems, with biogeochemical consequences in both terrestrial and marine environments. Here, we use bulk and molecular carbon isotopes (¹³C and ¹⁴C) to examine spatiotemporal variations in particulate OC (POC) composition and age from two locations along the course of the Yellow River during 2015–2016. Dual carbon isotopes enable deconvolution of modern, pre-aged (millennial age) soil and fossil inputs, revealing heterogeneous OC sources at both sites. Pre-aged OC predominated at the upstream site (Huayuankou) throughout the study period, mostly reflecting the upper riverine OC. Strong downstream (Kenli) intra-annual variations in modern and pre-aged OC were caused by increased contributions from modern aquatic OC production under the drier and less turbid conditions during this El Niño year. The month of July, which included the human-induced water and sediment regulation (WSR) event at Kenli, accounted for 82% of annual POC flux, with lower modern OC contribution compared with periods of natural seasonal variability. Both natural and human-induced hydrological events clearly exert strong influence on both fluxes and composition of Yellow River POC which, in turn, affect the balance between OC remineralization and burial for this major fluvial system.
We examine instrumental and methodological capabilities for microscale (10-50 µgC) radiocarbon analysis of individual compounds in the context of paleoclimate and paleoceanography applications for which relatively high precision measurements are required. An extensive suite of data for 14C-free and modern reference materials processed using different methods and acquired using an elemental analyzer-accelerator mass spectrometry (EA-AMS) instrumental setup at ETH-Zurich was compiled to assess the reproducibility of specific isolation procedures. In order to determine the precision, accuracy and reproducibility of measurements on processed compounds, we explore results of both reference materials and of three classes of compounds (fatty acids, alkenones and amino acids) extracted from sediment samples. We utilize a MATLAB code developed to systematically evaluate constant contamination model parameters, which in turn can be applied to measurements of unknown process samples. This approach is computationally reliable and can be used for any blank assessment of small-size radiocarbon samples. Our results show that a conservative lower estimate of the sample sizes required to produce relatively high-precision 14C data (i.e., with acceptable errors of <5% on final 14C ages) and high reproducibility in “old” samples (i.e., F14C ≈ 0.1) using current isolation methods are 50 µgC and 30 µgC for alkenones and fatty acids, respectively. Moreover, when the F14C is > 0.5, a precision of 2% can be achieved for alkenone and fatty acid samples containing ≥15 µgC and10 µgC, respectively.