Proxies based on long-chain alkane-1, mid-chain diols (diol for short)
are obtaining increasing interest to reconstruct past upper ocean
temperature and productivity. Here we evaluate performance of the sea
surface temperature proxies (long-chain diol index (LDI), diol
saturation index (DSI), and diol chain length index (DCI)),
productivity and upwelling intensity proxies (two diol indices DI
The alkenone SSTs correlate best with satellite SST (
Diol proxies including 1,14 diols lag trade wind speed by 30 d. Since
wind is nearly always from the NNE to NNW and induces the upwelling, we
relate the variability in these proxies to upwelling-induced processes.
Correlation with the abundance of upwelling species and wind speed is best
for the NDI and the 1,14 diol-based DCI and DSI. The DI
At the trap site, satellite SST lags wind-speed-forced upwelling by about 4 months. The 1,13 and 1,15 diol-based LDI-derived SSTs lag satellite SSTs by
41 d but correlate poorly (
It appears thus that at the trap site the 1,14 diols primarily reflect
conditions relating to upwelling whereas the 1,15
Upper ocean temperature and productivity reconstructions are important for assessing past climate and environment. For organisms, optimal functioning of their membranes and transport in the cell are crucial, and since temperature has a large influence on the solubility and viscosity of lipids, organisms adapt their lipid composition to temperature. Common responses to a decrease in temperature are (1) reduction of the average chain length of the lipid molecules and (2) an increase in the number of double bonds or (3) the degree of cyclization (e.g. Suutari et al., 1994; Elling et al., 2015; Sollich et al., 2017). We can use this response of organisms to adapt their metabolism and metabolite composition to prevailing ocean conditions to reconstruct past ocean temperature and productivity through searching for relevant metabolites in the fossil record and taking them as environmental proxies. This is not an unequivocal enterprise since the metabolite composition of organisms is often influenced by a combination of environmental variables, and, therefore, proxies based on these metabolites bear the risk of reflecting this. Furthermore, the same metabolites may be produced by different organisms, each having its own complex response to environment. To obtain robust and reliable temperature, nutrient, and productivity reconstructions from fossil metabolites, it is essential to test the performance of these proxies in present-day conditions. In this study, we do so by investigating the long-chain mid-chain diol composition in sediment trap samples in relation to environment during a multiyear trap deployment. We performed this study in the upwelling area off Cape Blanc, one of the most productive regions in the world (Chavez and Messié, 2009). From this region detailed (daily to monthly) records of water temperature, productivity, nutrient concentrations, and upwelling dynamics in the upper ocean as well as atmospheric parameters like wind direction and wind intensity are available.
Our study focuses on several diol-based proxies that have been proposed for
temperature, productivity, and/or upper ocean nutrient concentrations. For
temperature these are (1) the long-chain diol index (LDI) (Rampen et al.,
2012), (2) the diol saturation index (DSI) (Rampen et al., 2014a), and (3) the diol chain length index (DCI) (Rampen et al., 2009, 2014a). The combined
diol index (CDI) (Rampen et al., 2014a) and two diol indices (DI), the
DI
Other diol-based proxies have been defined but these either strongly
correlate with the proxies mentioned above or are not relevant in the
Mauritanian setting such as a diol proxy for terrestrial and fresh water input
(Versteegh et al., 1997; Lattaud et al., 2017a, b). We also evaluate the diol
temperature proxies in relation to the lipid-based temperature proxy: the
long-chain-alkenone-based
The Mauritania upwelling system is part of the Canary Current (CC) Eastern Boundary Upwelling Ecosystem (CC-EBUE). The coastal upwelling off Mauritania is driven by the NNW-to-NNE trade winds and occurs where the southward CC flowing along the coast meets the northward Cap Verde Current (CVC) and Poleward Undercurrent (PUC). These currents are deflected offshore, resulting in the SW-directed Cape Verde Frontal Zone (CVFZ) (Fig. 1). The result of this deflection and the offshore water export is visible as the giant Mauritanian chlorophyll filament (Fig. 1 after Romero et al., 2020 and Lovecchio et al., 2018). As a result of the coastal, shelf, and slope topography and the ocean currents and trade winds from the north, the coastal region off Mauritania is characterized by almost permanent upwelling. Its intensity varies throughout the year, with maximal intensity and extension in boreal winter and spring (Lathuilière et al., 2008; Cropper et al., 2014; Romero et al., 2020; Fig. 2). The offshore transport by the upwelling filaments is considerable, and it has been estimated that during periods of intense upwelling 80 % of the shelf particular matter production is transported into the open ocean up to 400 km offshore, whereas remineralization and turbulence sustain production and enhanced transport of organic carbon to 2000 km westwards (e.g. Gabric et al., 1993; Lovecchio et al., 2017, 2018).
The Mauritanian upwelling system. CC, Canary Current; NEC, North
Equatorial Current; CVC, Cap Verde Current; MC, Mauritanian Current; PUC, Poleward Under Current. The red circle in the lower left corner of the small
green square marks the location of mooring CBeu. The green square represents
the upstream 0.25
Temperature distribution off Cap Blanc for 4 d, each from a different season. The location of sediment trap CBeu is indicated by a white circle. Colder waters near the African coast result from upwelling. The westward extension and location of colder upwelled waters is highly variable as is upwelling. Days with strong (30 June 2006) or weak (2 January 2006) upwelling may occur any time of the year. From mid-July to November a temperature pattern similar to that of 21 September 2006 is more typical. Data are from ERDDAP, ID: nceiPH53sstn1day_Lon0360. NOAA Climate Data Record, AVHRR Pathfinder Version 5.3 L3-Collated. Data are courtesy of NCEI.
Particulate organic matter forming the base of this study was collected
between June 2003 and March 2008 off Cape Blanc at the eutrophic mooring
station CBeu (Fig. 1; Table 1). For the trap type and sampling performance,
see Romero et al. (2020). Classical cone-shaped traps with a surface opening
area of 0.5 m
Deployment data of sediment trap CBeu.
Composition and position of the diols in the indices.
Since all proxies are indices, all diols in the numerator (
Lipids were extracted using ultrasonic disruptor probes with successively
less polar solvent: MeOH, MeOH-DCM (
For diol analyses the polar fractions were silylated with 100
The ion source was operated at a temperature of 220
Relative amounts of alkyl diol isomers were estimated from peak areas of
specific ions resulting from
The diols of CBeu5 were analysed by GC-MS using an Agilent 6850 GC coupled
to an Agilent 5975C MSD equipped with a fused silica capillary column
(Restek Rxi-1ms; length 30 m; diameter 250
Relative amounts of alkyl diol isomers were estimated from peak areas of
specific ions as described above. Absolute amounts of diols were obtained by
comparison to the peak area of the known amount of the internal standard
androstanol (
Diol indices were calculated on the basis of relative abundances of the diol isomers.
The long-chain diol index is as follows (Rampen et al., 2012).
For this study the differences between both calibrations are negligible
(
The diol saturation index is as follows (Rampen et al., 2014a).
According to Rampen et al. (2014a) this index has a high correlation to
temperature in
The diol chain length index is as follows (Rampen et al., 2009, 2014a).
The DSI and DCI showed high correlations to culture temperature, but these correlations were absent in a global survey relating DSI and DCI derived from core tops to SST (Rampen et al., 2014a).
The diol index of Rampen et al. (2008) is as follows.
This has been suggested as a proxy for southwest monsoon upwelling in the
Arabian Sea (Rampen et al., 2008) and is also applicable to the Namibian
upwelling (see Versteegh et al., 2000).
The diol index of Wilmott et al. (2010) is as follows.
This ratio was designed as a measure of the contribution of
The combined diol index is as follows (Rampen et al., 2014a).
The nutrient diol index (NDI) is as follows (Gal et al., 2018).
As may be expected, the ratio of the slopes of these transfer functions is
Generally, fluxes recorded in sediment traps have a logarithmic nature. As a result, linear correlations between diols are subject to bias by single high values. To overcome this bias, we based our correlations on the log-transformed diol concentrations (Fig. S1 in the Supplement). Diol concentrations of CBeu1–4 are on average more than 1 order of magnitude (20.71 times) lower than for CBeu5 (Fig. S2 in the Supplement). Although this does not affect the diol proxy ratios, it does affect the direct comparison of diol fluxes. Concentrations of CBeu1–4 have been calculated with an external standard and by using the peak areas, response factors, injection volume, and sediment mass. In contrast, diol concentrations of CBeu5 have been calculated using peak areas, response factors, and an internal standard, and these fluxes are in good agreement with those reported from other sediment traps from high-productivity regions (Rampen et al., 2008, de Bar et al., 2019). For comparison of diol concentrations between both trap series, we multiplied the CBeu1–4 values by 20.71 to agree with those of the better-validated CBeu5 concentrations. Obviously, these new values do not represent exact concentrations, but they reduce the concentration error considerably, whereas the concentration dynamics between the individual samples remain intact (Fig. S2).
Lipid SST proxy data on long-chain-alkenone-based U
The alkenones in the second fraction were analysed using an Agilent 5890 gas
chromatograph equipped with a DB5-MS capillary column and a flame
ionization detector. Alkenone identification is by relative retention times
and comparison with a laboratory-internal standard sediment. The
Analytical precision based on repeated analyses of the standard sediment is
The average SST
Diatom counting has been performed as described in Romero et al. (2020).
Data for
Solar insolation at 20
Variation in external variables and the total diol flux through
time for CBeu1–5. Thin blue lines connect measured values (for wind parameter 11 d moving average). Thick red lines, the most important frequency
components. Note that most parameters have a dominant annual cycle modulated
by a semiannual cycle. The total diol flux (graph J) is dominated by a 257 d cycle. For wind direction
The insolation is not a simple sinus wave. The lowest half of the insolation
amplitude (7.777–10.744 W m
Satellite-derived SST
Subsurface (0–600 m water depth) temperatures and salinities were obtained
from the World Ocean Atlas 2018 (Locarnini et al., 2018; Zweng et al., 2019)
using the statistical mean temperature on a 1
Oceanographic data from the World Ocean Atlas 2018 (WOA). The upper
three panels show temperature–salinity diagrams for each month for the
1
Wind direction and strength are based on 3-hourly observations from
Nouadhibou airport (20
The dust data (Fig. 3) represent the monthly number of dust events as
represented in the synoptic weather data recorded at Nouadhibou Airport
(20
Statistical analyses have been performed with the software package PAST4.0.4 (Hammer et al., 2001) and with R packages “grDevices”, “stats”, “EnvStats”,
“methods”, and “car”. Phase shifts reported between proxy records and
SST
Daily SST
The records of binned SST
The temperature–salinity diagram for the upper 600 m (Fig. 4) shows for
November–December a predominantly South Atlantic Central Water (SACW)
signature with relatively low salinities for a given temperature (or high
temperatures for a given salinity). From January, the contribution of North
Atlantic Central Waters (NACW) increases. From March to June the upper 80 m
shows admixture of cool, low-salinity waters (but absent in April) so that
despite increasing insolation the SST stays below 19
For the upper 40 m a decrease in PO
From 2003 to 2008 the 11 d averaged wind blows 87 % of the time from NW
to NE (36
The 11 d averaged wind speed varies between 1 and 9 m s
The dust record shows a strong seasonal cycle (
The relative abundance of upwelling species (% Upw) shows a clear seasonal
cycle and lags wind speed by 21 d (
The 1,14C
The NDI, DI
Correlations of diol concentrations and their natural logarithms for CBeu1–5.
Outliers: 1,13C
Correlations (
Correlations with
Diol proxies and their main (annual) frequency component. Note the close similarities between the diol proxies and the DCI behaving opposite to the others.
The SST
Proxy-derived SST (red, filled circles), their annual frequency
component (F1, black), and satellite-derived SST
Statistics of the SST
Cross plots of log-transformed diol fluxes (
A linear correlation of LDI to binned SST
The DCI correlates significantly with SST
or the negative correlation
The DCI lags wind strength (
The DSI correlates strongly with the NDI and DCI (Table 4). The positive
correlation with SST
The DI
Correlation of natural logarithms of 1,14 diol fluxes (
The DI
The combined diol index (CDI) (Rampen et al., 2014a) is almost identical to
the DI
The NDI shares about half its variance with the CDI, DI
The SST
During the entire record, SST
The SST
The most prominent feature of the monthly water temperature profiles is the strong annual cycle at the sea surface, which becomes smaller with increasing depth. In the upper 100 m the relatively short distances between the temperature profiles for different depths (the strength of the temperature gradient) are clearly smaller from January to June compared to the rest of the year. We attribute this to mixing and upwelling. This seems to be most intense in May and June. In May, elevated temperatures up to 400 m depth indicate this, and in June the upwelling and mixing result in SSTs that are even lower than the preceding months, despite stronger solar insolation. We also observe that the highest temperatures at depth (80–200 m) lead the SST by about 3 months (Fig. 4), which in the case of temperature proxies generated at these depths could lead to proxy-derived temperature records leading SST.
All environmental parameters investigated show a dominant annual cycle
modulated by a semi-annual component. This is also true for most of the
proxy records. This implies that a significant correlation of a proxy
parameter to an environmental variable, with a given phase shift, has a high
chance to also provide significant correlations with other environmental
parameters, albeit with different phase relations. Although this is no problem
for proxy records with a known causal relation with environment, such as the
Diol flux maxima, like total flux maxima, may occur in any season, and, therefore, the record is not dominated by an annual cycle (Fig. 10). Whereas all diol maxima occur during total flux maxima, the opposite is not true. Apparently, flux maxima may follow from different environmental configurations, whereby some are accompanied by high diol export. An obvious hypothesis in this region would be that both productivity and dust events may induce high export production and that dust events do not necessarily occur when diol concentrations are high. Unfortunately, we do not have sufficient data to support this hypothesis. Correlations between fluxes of individual diols show that the 1,14 and 1,15 diols correlate least, whereas the 1,13 diols are intermediate but correlate better to the 1,15 diols. Indeed, flux maxima of 1,14 diols are partly independent of those of 1,15 and 1,13 diols. This supports earlier work suggesting different sources for 1,15 and 1,14 diols (e.g. Rampen et al., 2007; Gal et al., 2021), whereby the 1,13 diols may be derived from both sources. The logarithmic relation between the diol flux reached and its frequency (many low fluxes, few very high) is important for the diol composition and diol proxy values integrated over timescales longer than a cup-to-cup basis. The more uneven the distribution, the more influence a few large fluxes have on the total value. In our case, the average difference between a given flux and next largest flux is about 6.5 %, so that on average the 10th largest flux still has half the influence of the largest one (see also Sect. 4.5).
Fluxes for individual diols in
The LDI uses the underlying assumption that the percentages of 1,13 diols
(relative to the 1,15C
Correlation is not significant between LDI and DCI (
Due to the partial absence of detected unsaturated diols, valid values of
the saturation index DSI could be calculated for only half of the samples.
Application of the culture-based transfer function to these samples provides
unrealistic water temperatures for the CBeu surface waters (range
10.1–41.0
Assuming the upwelling-related
The DCI leads the SST
The DCI follows wind speed by 27–31 d (
The sediment trap intercepts the material that would continue on its way to
the ocean floor 1500 m below the trap. Although the signal can be distorted
on its way further to the ocean floor by (selective) degradation and
transport, sediment traps still provide important insight into the evaluation
of the sedimentary signal. Time series from the sediment record mostly have
a resolution of years to millennia, much lower than the resolution of the
CBeu trap series. To get a better insight in the effect of the export
dynamics as observed for CBeu on proxy values representing longer time
periods, the values obtained from the individual trap cups have been weighed
by the respective fluxes. For the LDI, the integrated production
temperatures (IPT
The 19-sample moving-average SST
It is obvious that even if the LDI is corrected for diol flux data
(IPT
The NDI, DSI, and DCI correlate well (
A closer look at the NDI reveals that the slopes of the respective transfer
functions relate to each other according to the Redfield ratio
The DI
The DI
Other trap studies reporting diol fluxes from tropical Atlantic are upper
traps from 1150 m depth in the eastern Atlantic near the Guinea Dome and
influenced by seasonal upwelling (M1U), from 1235 m depth in the
oligotrophic Central Atlantic (M2U) and from 1130 m depth in the western
Atlantic and under seasonal influence from the Amazon outflow (M4U) (de Bar et al., 2019). It appears that for the 1,13
The 35 d phase lag of SST
The complex cubic transformation of
The variation in diol, fluxes, and relative abundances as observed in sediment trap cups off Mauritania from 2003 to 2008 have been compared with environmental conditions and alkenone and plankton composition for the same region and time period. From this comparison, the following is concluded.
Peak total mass fluxes of material to the sediment trap do not show a
statistically significant annual cycle but may occur throughout the year.
Nevertheless, total mass flux maxima are most abundant during spring. We
explain this rather unpredictable occurrence of these flux maxima by
attributing them to the passage of upwelling filaments over the
sediment trap, which occurs most often during spring, but is not limited to
this. Off Cap Blanc, upwelling variability is the major environmental variable.
It shows a strong annual cycle in response to the strength of the trade
winds. Sea surface temperature also shows a strong annual cycle, remaining
low in winter and during vernal upwelling and following insolation when
upwelling is reduced during summer. It lags upwelling by 130 d. As a
result of the predominant annual cycle in both temperature and upwelling,
correlations between parameters and/or proxy records should be interpreted
with care, and phase relations should be considered to identify the most
likely forcing mechanism. The alkenone-based The diol-derived LDI lags SST by 41 d. It correlates only weakly with
SST. On average the SST The diol-derived nutrient index NDI, the DCI, and the percentage of
upwelling species show a higher correlation to SST than the LDI. However,
they lead the SST by several months, and their variability is most likely a
response to upwelling-associated processes such as a reduction in
temperature, increased nutrient content, and modified species composition. A
rather intriguing result is the anticorrelation between the diol-derived
nutrient proxy NDI and upwelling intensity.
Data are available at
The supplement related to this article is available online at:
GJMV interpreted the data and wrote most of the paper. KAFZ discussed and edited the paper prior to submission. JH processed the lipid analyses of CBeu5 and contributed to the Material and methods section. OER contributed the diatom data and together with GM critically reviewed earlier versions of the paper. GF coordinated the sediment trap project.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank Eleonora Uliana for measuring the diol data of CBeu1–4 and Enno Schefuss for constructive comments on an earlier version of the paper. We thank both anonymous reviewers for their constructive reviews.
This research has been financially supported by the German Science Foundation (DFG) through grant GZ:EXC 2077/1. Further support was obtained from the Helmholtz Association (Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research). The article processing charges for this open-access publication were covered by the University of Bremen.
This paper was edited by Sebastian Naeher and reviewed by two anonymous referees.